diff --git a/articles/clustering.html b/articles/clustering.html index d1c8ec1..e0b439e 100644 --- a/articles/clustering.html +++ b/articles/clustering.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/clustering.Rmd
clustering.Rmd
diff --git a/articles/differential-discovery-analysis.html b/articles/differential-discovery-analysis.html index c82f0b8..9b91bfa 100644 --- a/articles/differential-discovery-analysis.html +++ b/articles/differential-discovery-analysis.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/differential-discovery-analysis.Rmd
differential-discovery-analysis.Rmd
diff --git a/articles/differential-discovery-analysis_files/figure-html/unnamed-chunk-9-1.png b/articles/differential-discovery-analysis_files/figure-html/unnamed-chunk-9-1.png index 895a356..bd55772 100644 Binary files a/articles/differential-discovery-analysis_files/figure-html/unnamed-chunk-9-1.png and b/articles/differential-discovery-analysis_files/figure-html/unnamed-chunk-9-1.png differ diff --git a/articles/dimensionality-reduction.html b/articles/dimensionality-reduction.html index bf6dbc1..e0e3f39 100644 --- a/articles/dimensionality-reduction.html +++ b/articles/dimensionality-reduction.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/dimensionality-reduction.Rmd
dimensionality-reduction.Rmd
@@ -141,12 +141,12 @@

Dimensionality red #> # A tibble: 6 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 -13.8 -8.35 -#> 2 -2.20 -13.5 -#> 3 -19.7 -29.0 -#> 4 -8.65 -20.0 -#> 5 -14.5 -12.1 -#> 6 -3.06 -26.1 +#> 1 5.75 -16.0 +#> 2 -1.53 -7.52 +#> 3 -23.2 -15.1 +#> 4 -8.86 -16.9 +#> 5 2.17 -17.9 +#> 6 -8.98 -24.1

By default, tof_reduce_dimensions will add reduced-dimension feature embeddings to the input tof_tbl and return the augmented tof_tbl (that is, a @@ -159,18 +159,18 @@

Dimensionality red tof_preprocess() |> tof_reduce_dimensions(method = "tsne", augment = FALSE) #> # A tibble: 3,000 × 2 -#> .tsne1 .tsne2 -#> <dbl> <dbl> -#> 1 9.32 -14.4 -#> 2 3.31 -14.6 -#> 3 -8.80 -34.1 -#> 4 -1.49 -21.9 -#> 5 8.69 -17.5 -#> 6 -9.96 -19.1 -#> 7 -0.924 -13.3 -#> 8 -11.2 -20.4 -#> 9 -6.23 -18.6 -#> 10 5.46 -8.96 +#> .tsne1 .tsne2 +#> <dbl> <dbl> +#> 1 -13.1 -12.2 +#> 2 -10.9 -0.332 +#> 3 -32.5 10.2 +#> 4 -18.5 0.134 +#> 5 -17.9 -9.29 +#> 6 -21.1 7.89 +#> 7 -11.2 1.36 +#> 8 -25.7 8.52 +#> 9 -18.5 3.61 +#> 10 -6.88 -7.10 #> # ℹ 2,990 more rows

Changing the method argument results in different low-dimensional embeddings:

@@ -178,18 +178,18 @@

Dimensionality red phenograph_data |> tof_reduce_dimensions(method = "umap", augment = FALSE) #> # A tibble: 3,000 × 2 -#> .umap1 .umap2 -#> <dbl> <dbl> -#> 1 -10.4 4.32 -#> 2 -9.51 3.42 -#> 3 -8.27 0.695 -#> 4 -6.87 -0.268 -#> 5 -10.6 4.13 -#> 6 0.129 -4.19 -#> 7 -10.7 3.77 -#> 8 -8.40 -0.172 -#> 9 -6.22 0.175 -#> 10 -9.22 5.39 +#> .umap1 .umap2 +#> <dbl> <dbl> +#> 1 -9.77 4.05 +#> 2 -8.71 3.49 +#> 3 -7.10 2.62 +#> 4 -2.22 -2.59 +#> 5 -9.86 3.80 +#> 6 0.174 -3.20 +#> 7 -9.85 3.23 +#> 8 -1.38 -1.78 +#> 9 -6.17 1.24 +#> 10 -8.62 5.78 #> # ℹ 2,990 more rows phenograph_data |> diff --git a/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-1.png b/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-1.png index c924abd..7d2be6f 100644 Binary files a/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-1.png and b/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-2.png b/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-2.png index 9d0f212..c122719 100644 Binary files a/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-2.png and b/articles/dimensionality-reduction_files/figure-html/unnamed-chunk-7-2.png differ diff --git a/articles/downsampling.html b/articles/downsampling.html index 284e7bd..5fbd53f 100644 --- a/articles/downsampling.html +++ b/articles/downsampling.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/downsampling.Rmd
downsampling.Rmd
diff --git a/articles/feature-extraction.html b/articles/feature-extraction.html index 92683dd..e638b77 100644 --- a/articles/feature-extraction.html +++ b/articles/feature-extraction.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/feature-extraction.Rmd
feature-extraction.Rmd
diff --git a/articles/modeling.html b/articles/modeling.html index 9b715cf..e924067 100644 --- a/articles/modeling.html +++ b/articles/modeling.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/modeling.Rmd
modeling.Rmd
diff --git a/articles/preprocessing.html b/articles/preprocessing.html index 0e83b46..25bf14f 100644 --- a/articles/preprocessing.html +++ b/articles/preprocessing.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/preprocessing.Rmd
preprocessing.Rmd
@@ -227,10 +227,10 @@

Postprocessing with tof_postprocess #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> <dbl> <dbl> <dbl> -#> 1 131. 2.36 0.619 -#> 2 229. -1.13 11.2 -#> 3 292. 4.32 1.17 -#> 4 431. -0.635 12.7 +#> 1 130. 2.62 0.535 +#> 2 230. -0.918 11.0 +#> 3 293. 4.91 1.10 +#> 4 430. 0.318 13.3 #> # ℹ 2 more rows
diff --git a/articles/quality-control.html b/articles/quality-control.html index 4ae7455..f0c23fe 100644 --- a/articles/quality-control.html +++ b/articles/quality-control.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/quality-control.Rmd
quality-control.Rmd
diff --git a/articles/reading-and-writing-data.html b/articles/reading-and-writing-data.html index 96bdc73..fce0402 100644 --- a/articles/reading-and-writing-data.html +++ b/articles/reading-and-writing-data.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/reading-and-writing-data.Rmd
reading-and-writing-data.Rmd
diff --git a/articles/tidytof.html b/articles/tidytof.html index 2e9a388..be2c905 100644 --- a/articles/tidytof.html +++ b/articles/tidytof.html @@ -96,7 +96,7 @@
Sample-level operations

Timothy Keyes

-

2024-03-25

+

2024-03-26

Source: vignettes/tidytof.Rmd
tidytof.Rmd
diff --git a/pkgdown.yml b/pkgdown.yml index 190fdd1..bd7c91e 100644 --- a/pkgdown.yml +++ b/pkgdown.yml @@ -13,7 +13,7 @@ articles: quality-control: quality-control.html reading-and-writing-data: reading-and-writing-data.html tidytof: tidytof.html -last_built: 2024-03-25T21:33Z +last_built: 2024-03-26T04:07Z urls: reference: https://keyes-timothy.github.io/tidytof/reference article: https://keyes-timothy.github.io/tidytof/articles diff --git a/reference/tof_assess_model.html b/reference/tof_assess_model.html index e4f31e0..a1334c4 100644 --- a/reference/tof_assess_model.html +++ b/reference/tof_assess_model.html @@ -115,18 +115,18 @@

Value

deviance.glmnet), +deviance.glmnet), "mse" (the mean squared error of the logit function), and "mae" (the mean absolute error of the logit function). For multiclass models, "roc_auc" (the area under the Receiver-Operating Curve for the classification using the Hand-Till generalization of the ROC AUC for multiclass models in roc_auc), "misclassification error" (the proportion of misclassified observations), -"multinomial_deviance" (see deviance.glmnet), +"multinomial_deviance" (see deviance.glmnet), and "mse" and "mae" as above. For survival models, "concordance_index" (Harrel's C index; -see deviance.glmnet) and "partial_likelihood_deviance" -(see deviance.glmnet).

+see deviance.glmnet) and "partial_likelihood_deviance" +(see deviance.glmnet).

"roc_curve"

Reported only for "two-class" and "multiclass" models. For both, diff --git a/reference/tof_cluster_kmeans.html b/reference/tof_cluster_kmeans.html index 4267bab..26114f1 100644 --- a/reference/tof_cluster_kmeans.html +++ b/reference/tof_cluster_kmeans.html @@ -142,31 +142,31 @@

Examples#> # A tibble: 1,000 × 1 #> .kmeans_cluster #> <chr> -#> 1 8 -#> 2 4 -#> 3 8 -#> 4 1 -#> 5 2 -#> 6 15 -#> 7 11 -#> 8 18 -#> 9 19 -#> 10 20 +#> 1 14 +#> 2 11 +#> 3 4 +#> 4 6 +#> 5 20 +#> 6 6 +#> 7 16 +#> 8 9 +#> 9 3 +#> 10 7 #> # ℹ 990 more rows tof_cluster_kmeans(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> <chr> -#> 1 2 -#> 2 1 -#> 3 3 -#> 4 4 -#> 5 19 -#> 6 4 -#> 7 5 -#> 8 11 -#> 9 2 -#> 10 20 +#> 1 3 +#> 2 9 +#> 3 6 +#> 4 12 +#> 5 13 +#> 6 15 +#> 7 4 +#> 8 18 +#> 9 6 +#> 10 18 #> # ℹ 990 more rows

diff --git a/reference/tof_cluster_phenograph.html b/reference/tof_cluster_phenograph.html index aa13caa..14d95ec 100644 --- a/reference/tof_cluster_phenograph.html +++ b/reference/tof_cluster_phenograph.html @@ -153,31 +153,31 @@

Examples#> # A tibble: 1,000 × 1 #> .phenograph_cluster #> <chr> -#> 1 8 +#> 1 6 #> 2 3 -#> 3 6 -#> 4 4 -#> 5 4 -#> 6 2 -#> 7 2 -#> 8 9 -#> 9 1 -#> 10 2 +#> 3 4 +#> 4 6 +#> 5 5 +#> 6 5 +#> 7 1 +#> 8 10 +#> 9 10 +#> 10 7 #> # ℹ 990 more rows tof_cluster_phenograph(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> <chr> -#> 1 4 -#> 2 6 -#> 3 4 -#> 4 11 -#> 5 4 -#> 6 9 -#> 7 3 -#> 8 1 -#> 9 10 -#> 10 9 +#> 1 11 +#> 2 7 +#> 3 5 +#> 4 5 +#> 5 5 +#> 6 1 +#> 7 9 +#> 8 6 +#> 9 6 +#> 10 2 #> # ℹ 990 more rows diff --git a/reference/tof_downsample.html b/reference/tof_downsample.html index 669b619..b3541fc 100644 --- a/reference/tof_downsample.html +++ b/reference/tof_downsample.html @@ -152,18 +152,18 @@

Examples method = "constant" ) #> # A tibble: 200 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -0.976 1.90 -0.238 0.441 p -#> 2 -0.195 -0.474 -2.22 -0.600 p -#> 3 -0.212 -0.464 -1.20 -0.891 z -#> 4 1.88 0.643 1.52 -0.869 b -#> 5 -0.888 -0.231 0.321 0.0759 v -#> 6 0.768 -0.0448 -1.85 0.525 x -#> 7 0.0491 -1.82 -0.690 -0.507 b -#> 8 -0.937 0.751 -1.21 0.252 l -#> 9 0.473 0.261 0.298 1.00 f -#> 10 0.457 -0.547 1.71 0.00101 x +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 1.26 0.998 0.444 -0.591 h +#> 2 0.341 1.50 -0.0357 1.11 a +#> 3 1.39 0.0528 0.900 0.851 o +#> 4 -0.542 -1.44 0.510 0.435 a +#> 5 1.14 1.77 -2.72 2.04 p +#> 6 -0.271 -0.450 0.541 0.325 k +#> 7 -1.19 -0.680 0.459 0.557 b +#> 8 -0.492 0.726 0.215 -1.06 g +#> 9 -0.926 1.10 0.989 0.741 s +#> 10 -1.31 0.203 1.83 -0.0195 c #> # ℹ 190 more rows # sample 10% of all cells from the input data @@ -173,18 +173,18 @@

Examples method = "prop" ) #> # A tibble: 100 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 0.0933 -0.218 0.840 0.164 k -#> 2 0.0421 1.32 -1.24 0.163 e -#> 3 1.27 -0.522 -0.135 1.48 u -#> 4 -0.231 -0.223 -0.301 0.239 q -#> 5 -0.721 0.722 -0.739 1.18 y -#> 6 -0.0458 0.785 0.697 0.319 z -#> 7 0.701 -0.605 1.84 0.861 r -#> 8 -0.905 -0.783 -1.42 -0.385 s -#> 9 0.867 -0.180 1.90 0.273 w -#> 10 1.41 2.16 2.27 0.974 q +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.271 -0.450 0.541 0.325 k +#> 2 1.49 -0.0481 1.25 0.344 q +#> 3 -0.190 -1.77 -0.665 0.240 n +#> 4 -1.25 -0.0155 0.439 -1.53 r +#> 5 -1.05 0.616 -0.159 0.989 m +#> 6 -0.236 1.32 -0.388 0.134 i +#> 7 -1.22 1.36 0.209 0.293 m +#> 8 -1.50 -0.976 -2.46 0.688 r +#> 9 0.287 0.0766 -0.0885 0.133 l +#> 10 -1.34 -0.197 -1.06 1.45 f #> # ℹ 90 more rows # sample ~10% of cells from the input data using density dependence @@ -193,20 +193,20 @@

Examples target_prop_cells = 0.1, method = "density" ) -#> # A tibble: 96 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 1.88 0.643 1.52 -0.869 b -#> 2 -0.146 0.828 1.10 -0.0467 t -#> 3 -1.18 -0.259 -0.383 -0.588 e -#> 4 -1.86 0.0770 1.31 0.0992 l -#> 5 -0.340 0.269 -0.969 1.63 m -#> 6 0.979 0.370 -1.24 1.00 d -#> 7 -0.241 -0.523 0.492 1.34 n -#> 8 -1.90 -1.80 -1.81 -0.999 q -#> 9 -0.448 -0.294 1.46 0.830 p -#> 10 -0.191 0.737 1.10 0.0106 a -#> # ℹ 86 more rows +#> # A tibble: 93 × 5 +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.754 -1.28 0.537 0.824 z +#> 2 -1.52 0.146 0.895 -1.11 v +#> 3 -0.952 0.705 -0.481 -1.59 p +#> 4 0.668 0.911 -0.406 1.03 e +#> 5 -1.58 0.504 1.99 -0.764 r +#> 6 1.22 2.31 -0.486 0.649 a +#> 7 0.417 -2.14 -1.70 0.713 y +#> 8 1.36 0.199 -0.00351 1.11 b +#> 9 -0.546 0.574 1.65 0.230 r +#> 10 0.658 -0.0507 1.25 0.855 c +#> # ℹ 83 more rows diff --git a/reference/tof_downsample_constant.html b/reference/tof_downsample_constant.html index 002c3a8..e8e64db 100644 --- a/reference/tof_downsample_constant.html +++ b/reference/tof_downsample_constant.html @@ -134,18 +134,18 @@

Examples num_cells = 500L ) #> # A tibble: 500 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -1.37 0.119 -0.452 0.150 e -#> 2 -0.806 -1.36 1.44 0.803 e -#> 3 -0.167 0.366 -0.512 -0.806 c -#> 4 2.13 1.18 -0.405 0.343 w -#> 5 1.31 1.04 0.628 0.0154 z -#> 6 1.88 -0.707 0.798 0.0972 i -#> 7 -1.85 1.01 0.381 -0.256 i -#> 8 -0.702 0.919 0.800 0.537 y -#> 9 0.0713 1.98 -0.735 -0.220 h -#> 10 -1.03 -0.344 -1.15 0.698 y +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -1.15 -0.746 -1.73 -0.625 y +#> 2 1.50 -1.49 0.641 1.54 i +#> 3 -0.0911 -1.43 -1.09 0.407 n +#> 4 1.28 2.04 -0.208 0.289 q +#> 5 0.508 -0.314 -0.516 2.53 g +#> 6 -1.28 -0.686 -1.75 -0.873 n +#> 7 0.284 -1.12 -0.919 0.171 c +#> 8 -0.456 1.70 -1.30 -0.718 w +#> 9 0.602 -0.255 1.05 0.507 e +#> 10 0.876 -0.737 1.68 2.06 g #> # ℹ 490 more rows # sample 20 cells per cluster from the input data @@ -155,18 +155,18 @@

Examples num_cells = 20L ) #> # A tibble: 520 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -0.544 0.561 0.141 0.899 c -#> 2 -0.167 0.366 -0.512 -0.806 c -#> 3 2.13 1.18 -0.405 0.343 w -#> 4 -0.881 -1.28 -1.08 -0.725 s -#> 5 -0.299 0.119 -1.84 -0.0465 d -#> 6 -1.09 -1.17 -0.0514 1.09 w -#> 7 1.88 -0.707 0.798 0.0972 i -#> 8 -1.85 1.01 0.381 -0.256 i -#> 9 -0.420 -0.0579 1.19 -0.571 d -#> 10 -0.702 0.919 0.800 0.537 y +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 0.545 0.0489 -0.702 1.63 x +#> 2 -0.0911 -1.43 -1.09 0.407 n +#> 3 -0.322 1.61 -1.02 0.315 t +#> 4 0.508 -0.314 -0.516 2.53 g +#> 5 -1.28 -0.686 -1.75 -0.873 n +#> 6 -0.456 1.70 -1.30 -0.718 w +#> 7 -0.682 -0.121 0.689 0.220 b +#> 8 -0.366 0.197 -0.802 0.0356 m +#> 9 0.544 0.490 -0.973 0.898 i +#> 10 0.876 -0.737 1.68 2.06 g #> # ℹ 510 more rows diff --git a/reference/tof_downsample_density.html b/reference/tof_downsample_density.html index 24e6006..c2cf4b3 100644 --- a/reference/tof_downsample_density.html +++ b/reference/tof_downsample_density.html @@ -198,20 +198,20 @@

Examples target_prop_cells = 0.5, density_estimation_method = "spade" ) -#> # A tibble: 469 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 0.157 -0.657 -0.422 -0.171 -#> 2 0.247 -2.28 -0.769 -0.640 -#> 3 2.00 -0.484 -1.32 -0.375 -#> 4 -1.99 -0.522 0.941 -0.574 -#> 5 -0.0392 0.372 0.179 -0.188 -#> 6 -1.29 0.165 0.400 -1.63 -#> 7 -0.728 -0.993 1.72 0.745 -#> 8 0.603 1.11 0.543 -0.284 -#> 9 1.29 -1.87 -2.40 1.36 -#> 10 0.233 0.323 -2.28 0.883 -#> # ℹ 459 more rows +#> # A tibble: 496 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 0.860 0.781 0.646 -0.0890 +#> 2 -1.04 0.117 -0.565 0.114 +#> 3 0.489 -1.27 -0.524 -0.438 +#> 4 -1.01 -2.04 1.44 -0.187 +#> 5 1.48 -0.0556 -0.164 -1.29 +#> 6 -1.50 0.663 -0.140 -0.379 +#> 7 -2.10 -0.486 0.189 -0.477 +#> 8 -0.465 -1.13 0.908 -0.674 +#> 9 -0.895 -0.984 -0.459 -0.00729 +#> 10 0.678 2.00 0.224 -0.963 +#> # ℹ 486 more rows tof_downsample_density( tof_tibble = sim_data, @@ -219,20 +219,20 @@

Examples target_num_cells = 200L, density_estimation_method = "spade" ) -#> # A tibble: 208 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.149 -0.134 0.965 -0.224 -#> 2 1.32 1.34 0.951 -0.763 -#> 3 0.104 -0.470 1.35 0.362 -#> 4 1.29 -1.87 -2.40 1.36 -#> 5 0.679 0.766 0.515 -0.0614 -#> 6 -0.542 0.444 0.949 0.199 -#> 7 0.516 1.11 0.402 0.216 -#> 8 1.03 -0.134 -0.271 -0.0218 -#> 9 -0.308 -0.00761 0.740 -0.209 -#> 10 -0.581 0.417 -0.846 -1.68 -#> # ℹ 198 more rows +#> # A tibble: 187 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 0.985 -2.11 -0.162 -0.342 +#> 2 0.489 -1.27 -0.524 -0.438 +#> 3 -0.895 -0.984 -0.459 -0.00729 +#> 4 0.678 2.00 0.224 -0.963 +#> 5 -0.536 -0.362 -0.705 -0.0699 +#> 6 0.102 -1.93 -1.39 -1.03 +#> 7 2.04 -0.528 0.177 0.406 +#> 8 1.58 -0.313 0.0864 1.35 +#> 9 1.62 1.23 -1.12 -0.366 +#> 10 0.568 1.67 0.386 -2.56 +#> # ℹ 177 more rows tof_downsample_density( tof_tibble = sim_data, @@ -240,20 +240,20 @@

Examples target_num_cells = 200L, density_estimation_method = "mean_distance" ) -#> # A tibble: 197 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 1.05 1.17 0.775 1.36 -#> 2 -0.149 -0.134 0.965 -0.224 -#> 3 0.104 -0.470 1.35 0.362 -#> 4 -0.224 0.117 -0.945 -0.916 -#> 5 1.50 1.71 2.40 -0.500 -#> 6 -0.275 -0.375 1.50 -0.163 -#> 7 0.351 0.768 -0.963 -1.20 -#> 8 -0.608 -0.849 -0.496 -1.39 -#> 9 1.25 0.457 0.0341 1.83 -#> 10 1.71 -0.0492 0.276 -0.864 -#> # ℹ 187 more rows +#> # A tibble: 179 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 0.262 1.34 -1.33 -0.422 +#> 2 -1.50 0.663 -0.140 -0.379 +#> 3 -0.654 1.56 0.730 -0.603 +#> 4 -2.48 -0.611 0.623 0.906 +#> 5 -0.767 -0.744 0.648 -0.214 +#> 6 -1.33 0.690 0.796 -0.804 +#> 7 -0.706 0.537 -0.125 -0.746 +#> 8 0.795 -1.04 -1.66 -0.695 +#> 9 -1.90 -0.139 -1.34 -1.92 +#> 10 -1.37 -1.01 -0.892 0.426 +#> # ℹ 169 more rows diff --git a/reference/tof_downsample_prop.html b/reference/tof_downsample_prop.html index 9b78efa..ce56382 100644 --- a/reference/tof_downsample_prop.html +++ b/reference/tof_downsample_prop.html @@ -135,18 +135,18 @@

Examples prop_cells = 0.1 ) #> # A tibble: 100 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -1.86 0.177 -1.61 0.915 w -#> 2 0.648 -0.110 0.861 -0.257 p -#> 3 -0.736 1.22 -0.351 -1.49 l -#> 4 -2.39 -1.59 0.813 0.325 y -#> 5 2.05 -0.591 -0.727 -0.335 p -#> 6 -0.244 -0.691 -2.46 -0.0472 w -#> 7 -0.336 0.539 -0.613 -1.42 s -#> 8 -0.356 -1.06 0.896 0.494 g -#> 9 -1.13 2.39 0.335 -1.20 e -#> 10 -0.273 -0.403 2.13 0.596 d +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 1.44 0.286 0.409 -0.554 i +#> 2 -0.944 -2.00 -1.11 2.09 i +#> 3 -1.30 0.893 0.643 0.217 c +#> 4 0.167 -1.23 -1.91 0.895 o +#> 5 -0.955 -0.00646 0.221 -0.748 r +#> 6 -0.326 1.23 1.26 -0.237 w +#> 7 -0.862 -0.826 -0.232 0.859 y +#> 8 0.664 0.570 1.23 0.738 j +#> 9 1.73 0.625 -0.695 -0.216 d +#> 10 -0.613 1.07 -0.387 0.133 g #> # ℹ 90 more rows # sample 10% of all cells from each cluster in the input data @@ -155,20 +155,20 @@

Examples group_cols = cluster_id, prop_cells = 0.1 ) -#> # A tibble: 87 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 1.91 0.329 0.458 0.0340 a -#> 2 -1.25 0.769 0.0705 0.923 a -#> 3 -0.663 -1.04 1.23 -0.0216 a -#> 4 1.33 -0.764 -1.86 -0.348 b -#> 5 0.638 -2.31 -1.48 -0.371 b -#> 6 -1.14 0.0955 -0.543 0.566 b -#> 7 1.35 0.575 -0.346 -1.01 b -#> 8 -2.01 0.769 -2.01 -0.988 c -#> 9 1.88 0.748 -0.0520 0.348 c -#> 10 1.06 -0.926 1.44 -1.18 c -#> # ℹ 77 more rows +#> # A tibble: 90 × 5 +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.573 -2.37 1.00 0.239 a +#> 2 -1.77 1.23 1.44 0.309 a +#> 3 0.667 1.45 0.701 -0.234 a +#> 4 -0.770 1.36 -1.04 0.806 a +#> 5 0.178 0.427 1.85 -0.188 b +#> 6 0.409 1.10 1.57 -0.626 b +#> 7 1.02 0.316 -0.220 -0.622 b +#> 8 -1.16 1.74 -0.435 0.814 c +#> 9 0.478 2.03 0.692 -0.541 c +#> 10 -0.418 -0.0122 0.0897 0.516 c +#> # ℹ 80 more rows diff --git a/reference/tof_estimate_density.html b/reference/tof_estimate_density.html index 11e038e..0531d2e 100644 --- a/reference/tof_estimate_density.html +++ b/reference/tof_estimate_density.html @@ -161,18 +161,18 @@

Examples# perform the density estimation tof_estimate_density(tof_tibble = sim_data, method = "spade") #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .spade_density -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.252 0.331 -2.74 -1.08 1 -#> 2 0.759 0.857 -0.277 0.966 1 -#> 3 -0.241 1.15 -2.36 -2.13 1 -#> 4 0.189 3.05 0.923 -1.41 1 -#> 5 0.0278 0.197 1.06 0.220 1 -#> 6 0.838 -1.28 1.55 -0.758 1 -#> 7 0.923 -0.981 0.291 -0.286 1 -#> 8 0.893 -0.143 1.51 1.54 1 -#> 9 0.661 0.102 0.632 0.805 1 -#> 10 0.347 0.708 -0.122 -2.55 1 +#> cd45 cd38 cd34 cd19 .spade_density +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.414 -1.35 1.28 -0.00732 1 +#> 2 1.63 1.31 -0.726 -0.672 1 +#> 3 -1.36 -0.108 -0.191 -0.884 1 +#> 4 -0.975 -0.826 -0.0932 -0.586 1 +#> 5 -1.03 0.359 0.379 -0.390 1 +#> 6 0.649 -0.805 -0.368 0.411 1 +#> 7 2.17 0.861 1.02 -0.199 1 +#> 8 -1.16 1.94 -1.16 0.205 1 +#> 9 1.76 -0.453 0.680 0.357 1 +#> 10 -0.719 -1.65 0.947 -0.0462 1 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around @@ -183,18 +183,18 @@

Examples method = "spade" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .spade_density -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.252 0.331 -2.74 -1.08 0.1 -#> 2 0.759 0.857 -0.277 0.966 1 -#> 3 -0.241 1.15 -2.36 -2.13 0 -#> 4 0.189 3.05 0.923 -1.41 0 -#> 5 0.0278 0.197 1.06 0.220 1 -#> 6 0.838 -1.28 1.55 -0.758 0.1 -#> 7 0.923 -0.981 0.291 -0.286 1 -#> 8 0.893 -0.143 1.51 1.54 0.7 -#> 9 0.661 0.102 0.632 0.805 1 -#> 10 0.347 0.708 -0.122 -2.55 0.4 +#> cd45 cd38 cd34 cd19 .spade_density +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 0.414 -1.35 1.28 -0.00732 0.9 +#> 2 1.63 1.31 -0.726 -0.672 0.4 +#> 3 -1.36 -0.108 -0.191 -0.884 1 +#> 4 -0.975 -0.826 -0.0932 -0.586 1 +#> 5 -1.03 0.359 0.379 -0.390 1 +#> 6 0.649 -0.805 -0.368 0.411 1 +#> 7 2.17 0.861 1.02 -0.199 0.1 +#> 8 -1.16 1.94 -1.16 0.205 0.2 +#> 9 1.76 -0.453 0.680 0.357 1 +#> 10 -0.719 -1.65 0.947 -0.0462 1 #> # ℹ 990 more rows diff --git a/reference/tof_extract_central_tendency.html b/reference/tof_extract_central_tendency.html index a137523..9552a68 100644 --- a/reference/tof_extract_central_tendency.html +++ b/reference/tof_extract_central_tendency.html @@ -198,8 +198,8 @@

Examples#> # A tibble: 2 × 105 #> patient `cd45@a_ct` `cd38@a_ct` `cd34@a_ct` `cd19@a_ct` `cd45@b_ct` #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 kirby -0.216 -0.0538 -0.0502 0.429 0.175 -#> 2 mario 0.350 -0.278 -0.0983 -0.289 -0.0923 +#> 1 kirby -0.239 -0.135 0.128 -0.489 0.0664 +#> 2 mario 0.485 0.232 -0.313 -0.563 0.264 #> # ℹ 99 more variables: `cd38@b_ct` <dbl>, `cd34@b_ct` <dbl>, `cd19@b_ct` <dbl>, #> # `cd45@c_ct` <dbl>, `cd38@c_ct` <dbl>, `cd34@c_ct` <dbl>, `cd19@c_ct` <dbl>, #> # `cd45@d_ct` <dbl>, `cd38@d_ct` <dbl>, `cd34@d_ct` <dbl>, `cd19@d_ct` <dbl>, @@ -218,16 +218,16 @@

Examples#> # A tibble: 208 × 4 #> patient cluster_id channel values #> <chr> <chr> <chr> <dbl> -#> 1 kirby a cd45 -0.216 -#> 2 kirby a cd38 -0.0538 -#> 3 kirby a cd34 -0.0502 -#> 4 kirby a cd19 0.429 -#> 5 kirby b cd45 0.175 -#> 6 kirby b cd38 0.144 -#> 7 kirby b cd34 0.416 -#> 8 kirby b cd19 0.0494 -#> 9 kirby c cd45 0.113 -#> 10 kirby c cd38 0.567 +#> 1 kirby a cd45 -0.239 +#> 2 kirby a cd38 -0.135 +#> 3 kirby a cd34 0.128 +#> 4 kirby a cd19 -0.489 +#> 5 kirby b cd45 0.0664 +#> 6 kirby b cd38 -0.428 +#> 7 kirby b cd34 0.125 +#> 8 kirby b cd19 0.140 +#> 9 kirby c cd45 0.0619 +#> 10 kirby c cd38 -0.540 #> # ℹ 198 more rows diff --git a/reference/tof_extract_emd.html b/reference/tof_extract_emd.html index dd6a65c..6f93312 100644 --- a/reference/tof_extract_emd.html +++ b/reference/tof_extract_emd.html @@ -215,17 +215,17 @@

Examples reference_level = "basal" ) #> # A tibble: 2 × 105 -#> patient `stim_cd45@x_emd` `stim_cd38@x_emd` `stim_cd34@x_emd` +#> patient `stim_cd45@p_emd` `stim_cd38@p_emd` `stim_cd34@p_emd` #> <chr> <dbl> <dbl> <dbl> -#> 1 mario NA NA NA -#> 2 kirby NA NA NA -#> # ℹ 101 more variables: `stim_cd19@x_emd` <dbl>, `stim_cd45@d_emd` <dbl>, -#> # `stim_cd38@d_emd` <dbl>, `stim_cd34@d_emd` <dbl>, `stim_cd19@d_emd` <dbl>, -#> # `stim_cd45@i_emd` <dbl>, `stim_cd38@i_emd` <dbl>, `stim_cd34@i_emd` <dbl>, -#> # `stim_cd19@i_emd` <dbl>, `stim_cd45@j_emd` <dbl>, `stim_cd38@j_emd` <dbl>, -#> # `stim_cd34@j_emd` <dbl>, `stim_cd19@j_emd` <dbl>, `stim_cd45@p_emd` <dbl>, -#> # `stim_cd38@p_emd` <dbl>, `stim_cd34@p_emd` <dbl>, `stim_cd19@p_emd` <dbl>, -#> # `stim_cd45@m_emd` <dbl>, `stim_cd38@m_emd` <dbl>, … +#> 1 kirby NA NA NA +#> 2 mario NA NA NA +#> # ℹ 101 more variables: `stim_cd19@p_emd` <dbl>, `stim_cd45@y_emd` <dbl>, +#> # `stim_cd38@y_emd` <dbl>, `stim_cd34@y_emd` <dbl>, `stim_cd19@y_emd` <dbl>, +#> # `stim_cd45@c_emd` <dbl>, `stim_cd38@c_emd` <dbl>, `stim_cd34@c_emd` <dbl>, +#> # `stim_cd19@c_emd` <dbl>, `stim_cd45@r_emd` <dbl>, `stim_cd38@r_emd` <dbl>, +#> # `stim_cd34@r_emd` <dbl>, `stim_cd19@r_emd` <dbl>, `stim_cd45@b_emd` <dbl>, +#> # `stim_cd38@b_emd` <dbl>, `stim_cd34@b_emd` <dbl>, `stim_cd19@b_emd` <dbl>, +#> # `stim_cd45@v_emd` <dbl>, `stim_cd38@v_emd` <dbl>, … # extract emd of each cluster (using the "basal" stim # condition as a reference) in long format @@ -239,16 +239,16 @@

Examples#> # A tibble: 104 × 4 #> cluster_id marker stimulation emd #> <chr> <chr> <chr> <dbl> -#> 1 x cd45 stim 9.75 -#> 2 x cd38 stim 5.06 -#> 3 x cd34 stim 7.06 -#> 4 x cd19 stim 14.9 -#> 5 d cd45 stim 6.70 -#> 6 d cd38 stim 8.55 -#> 7 d cd34 stim 6.65 -#> 8 d cd19 stim 7.10 -#> 9 i cd45 stim 6.66 -#> 10 i cd38 stim 6.59 +#> 1 p cd45 stim 9.59 +#> 2 p cd38 stim 12.4 +#> 3 p cd34 stim 10.5 +#> 4 p cd19 stim 11.4 +#> 5 y cd45 stim 5.34 +#> 6 y cd38 stim 4.73 +#> 7 y cd34 stim 4.07 +#> 8 y cd19 stim 10.3 +#> 9 c cd45 stim 7.95 +#> 10 c cd38 stim 9.91 #> # ℹ 94 more rows diff --git a/reference/tof_extract_features.html b/reference/tof_extract_features.html index e47808e..6e1b73f 100644 --- a/reference/tof_extract_features.html +++ b/reference/tof_extract_features.html @@ -238,8 +238,8 @@

Examples#> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 kirby 0.0375 0.0208 0.0458 0.0292 0.0417 0.0604 0.0208 -#> 2 mario 0.0558 0.0519 0.0308 0.0442 0.0442 0.0558 0.05 +#> 1 kirby 0.0476 0.0290 0.0373 0.0476 0.0393 0.0414 0.0331 +#> 2 mario 0.0484 0.0387 0.0542 0.0309 0.0445 0.0426 0.0445 #> # ℹ 123 more variables: `prop@h` <dbl>, `prop@i` <dbl>, `prop@j` <dbl>, #> # `prop@k` <dbl>, `prop@l` <dbl>, `prop@m` <dbl>, `prop@n` <dbl>, #> # `prop@o` <dbl>, `prop@p` <dbl>, `prop@q` <dbl>, `prop@r` <dbl>, @@ -268,8 +268,8 @@

Examples#> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 kirby 0.0375 0.0208 0.0458 0.0292 0.0417 0.0604 0.0208 -#> 2 mario 0.0558 0.0519 0.0308 0.0442 0.0442 0.0558 0.05 +#> 1 kirby 0.0476 0.0290 0.0373 0.0476 0.0393 0.0414 0.0331 +#> 2 mario 0.0484 0.0387 0.0542 0.0309 0.0445 0.0426 0.0445 #> # ℹ 123 more variables: `prop@h` <dbl>, `prop@i` <dbl>, `prop@j` <dbl>, #> # `prop@k` <dbl>, `prop@l` <dbl>, `prop@m` <dbl>, `prop@n` <dbl>, #> # `prop@o` <dbl>, `prop@p` <dbl>, `prop@q` <dbl>, `prop@r` <dbl>, diff --git a/reference/tof_extract_jsd.html b/reference/tof_extract_jsd.html index db64273..02853d8 100644 --- a/reference/tof_extract_jsd.html +++ b/reference/tof_extract_jsd.html @@ -208,16 +208,16 @@

Examples reference_level = "basal" ) #> # A tibble: 2 × 105 -#> patient `stim_cd45@q_jsd` `stim_cd38@q_jsd` `stim_cd34@q_jsd` +#> patient `stim_cd45@e_jsd` `stim_cd38@e_jsd` `stim_cd34@e_jsd` #> <chr> <dbl> <dbl> <dbl> -#> 1 kirby 0.844 1 0.766 -#> 2 mario NA NA NA -#> # ℹ 101 more variables: `stim_cd19@q_jsd` <dbl>, `stim_cd45@y_jsd` <dbl>, -#> # `stim_cd38@y_jsd` <dbl>, `stim_cd34@y_jsd` <dbl>, `stim_cd19@y_jsd` <dbl>, -#> # `stim_cd45@x_jsd` <dbl>, `stim_cd38@x_jsd` <dbl>, `stim_cd34@x_jsd` <dbl>, -#> # `stim_cd19@x_jsd` <dbl>, `stim_cd45@p_jsd` <dbl>, `stim_cd38@p_jsd` <dbl>, -#> # `stim_cd34@p_jsd` <dbl>, `stim_cd19@p_jsd` <dbl>, `stim_cd45@f_jsd` <dbl>, -#> # `stim_cd38@f_jsd` <dbl>, `stim_cd34@f_jsd` <dbl>, `stim_cd19@f_jsd` <dbl>, +#> 1 mario 0.9 0.8 0.9 +#> 2 kirby NA NA NA +#> # ℹ 101 more variables: `stim_cd19@e_jsd` <dbl>, `stim_cd45@p_jsd` <dbl>, +#> # `stim_cd38@p_jsd` <dbl>, `stim_cd34@p_jsd` <dbl>, `stim_cd19@p_jsd` <dbl>, +#> # `stim_cd45@s_jsd` <dbl>, `stim_cd38@s_jsd` <dbl>, `stim_cd34@s_jsd` <dbl>, +#> # `stim_cd19@s_jsd` <dbl>, `stim_cd45@r_jsd` <dbl>, `stim_cd38@r_jsd` <dbl>, +#> # `stim_cd34@r_jsd` <dbl>, `stim_cd19@r_jsd` <dbl>, `stim_cd45@q_jsd` <dbl>, +#> # `stim_cd38@q_jsd` <dbl>, `stim_cd34@q_jsd` <dbl>, `stim_cd19@q_jsd` <dbl>, #> # `stim_cd45@c_jsd` <dbl>, `stim_cd38@c_jsd` <dbl>, … # extract jsd of each cluster (using the "basal" stim @@ -232,16 +232,16 @@

Examples#> # A tibble: 104 × 4 #> cluster_id marker stimulation jsd #> <chr> <chr> <chr> <dbl> -#> 1 q cd45 stim 0.646 -#> 2 q cd38 stim 0.842 -#> 3 q cd34 stim 0.897 -#> 4 q cd19 stim 0.646 -#> 5 y cd45 stim 0.765 -#> 6 y cd38 stim 0.836 -#> 7 y cd34 stim 0.804 -#> 8 y cd19 stim 0.784 -#> 9 x cd45 stim 0.757 -#> 10 x cd38 stim 0.687 +#> 1 e cd45 stim 0.749 +#> 2 e cd38 stim 0.785 +#> 3 e cd34 stim 0.829 +#> 4 e cd19 stim 0.771 +#> 5 p cd45 stim 0.625 +#> 6 p cd38 stim 0.823 +#> 7 p cd34 stim 0.601 +#> 8 p cd19 stim 0.800 +#> 9 s cd45 stim 0.647 +#> 10 s cd38 stim 0.858 #> # ℹ 94 more rows diff --git a/reference/tof_extract_proportion.html b/reference/tof_extract_proportion.html index 96af791..5ffdca4 100644 --- a/reference/tof_extract_proportion.html +++ b/reference/tof_extract_proportion.html @@ -166,8 +166,8 @@

Examples#> # A tibble: 2 × 27 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 kirby 0.0532 0.0389 0.0327 0.0327 0.0511 0.0450 0.0511 -#> 2 mario 0.0528 0.0313 0.0372 0.0274 0.0411 0.0391 0.0333 +#> 1 kirby 0.036 0.028 0.042 0.022 0.05 0.044 0.046 +#> 2 mario 0.046 0.046 0.028 0.04 0.028 0.06 0.02 #> # ℹ 19 more variables: `prop@h` <dbl>, `prop@i` <dbl>, `prop@j` <dbl>, #> # `prop@k` <dbl>, `prop@l` <dbl>, `prop@m` <dbl>, `prop@n` <dbl>, #> # `prop@o` <dbl>, `prop@p` <dbl>, `prop@q` <dbl>, `prop@r` <dbl>, @@ -182,18 +182,18 @@

Examples format = "long" ) #> # A tibble: 52 × 3 -#> patient cluster_id prop -#> <chr> <chr> <dbl> -#> 1 kirby a 0.0532 -#> 2 kirby b 0.0389 -#> 3 kirby c 0.0327 -#> 4 kirby d 0.0327 -#> 5 kirby e 0.0511 -#> 6 kirby f 0.0450 -#> 7 kirby g 0.0511 -#> 8 kirby h 0.0204 -#> 9 kirby i 0.0225 -#> 10 kirby j 0.0184 +#> patient cluster_id prop +#> <chr> <chr> <dbl> +#> 1 kirby a 0.036 +#> 2 kirby b 0.028 +#> 3 kirby c 0.042 +#> 4 kirby d 0.022 +#> 5 kirby e 0.05 +#> 6 kirby f 0.044 +#> 7 kirby g 0.046 +#> 8 kirby h 0.04 +#> 9 kirby i 0.026 +#> 10 kirby j 0.046 #> # ℹ 42 more rows diff --git a/reference/tof_extract_threshold.html b/reference/tof_extract_threshold.html index 0296bf8..4f6ef49 100644 --- a/reference/tof_extract_threshold.html +++ b/reference/tof_extract_threshold.html @@ -195,8 +195,8 @@

Examples#> # A tibble: 2 × 105 #> patient `cd45@a_threshold` `cd38@a_threshold` `cd34@a_threshold` #> <chr> <dbl> <dbl> <dbl> -#> 1 kirby 0.0526 0.105 0 -#> 2 mario 0.0294 0.0882 0.0294 +#> 1 kirby 0.0556 0.0556 0.111 +#> 2 mario 0.125 0 0 #> # ℹ 101 more variables: `cd19@a_threshold` <dbl>, `cd45@b_threshold` <dbl>, #> # `cd38@b_threshold` <dbl>, `cd34@b_threshold` <dbl>, #> # `cd19@b_threshold` <dbl>, `cd45@c_threshold` <dbl>, @@ -215,16 +215,16 @@

Examples#> # A tibble: 208 × 4 #> patient cluster_id channel values #> <chr> <chr> <chr> <dbl> -#> 1 kirby a cd45 0.0526 -#> 2 kirby a cd38 0.105 -#> 3 kirby a cd34 0 -#> 4 kirby a cd19 0.105 -#> 5 kirby b cd45 0.143 -#> 6 kirby b cd38 0.143 -#> 7 kirby b cd34 0.0476 -#> 8 kirby b cd19 0.0476 -#> 9 kirby c cd45 0.0455 -#> 10 kirby c cd38 0 +#> 1 kirby a cd45 0.0556 +#> 2 kirby a cd38 0.0556 +#> 3 kirby a cd34 0.111 +#> 4 kirby a cd19 0.111 +#> 5 kirby b cd45 0.0455 +#> 6 kirby b cd38 0.0909 +#> 7 kirby b cd34 0.0455 +#> 8 kirby b cd19 0 +#> 9 kirby c cd45 0.0526 +#> 10 kirby c cd38 0.158 #> # ℹ 198 more rows diff --git a/reference/tof_find_knn.html b/reference/tof_find_knn.html index 509457e..5d7b4a3 100644 --- a/reference/tof_find_knn.html +++ b/reference/tof_find_knn.html @@ -143,3010 +143,3010 @@

Examples) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] -#> [1,] 519 950 311 110 409 192 884 753 426 133 -#> [2,] 782 386 902 938 407 955 247 667 756 284 -#> [3,] 789 309 180 707 670 569 900 796 587 554 -#> [4,] 891 845 413 535 37 322 298 269 195 664 -#> [5,] 112 743 962 404 553 957 741 545 155 535 -#> [6,] 10 232 22 765 948 522 820 724 759 368 -#> [7,] 513 486 697 388 650 144 105 374 333 769 -#> [8,] 577 212 951 835 525 116 115 794 210 595 -#> [9,] 193 495 323 430 94 596 784 69 378 791 -#> [10,] 6 232 522 22 765 820 479 27 674 368 -#> [11,] 737 850 448 580 132 804 173 226 65 50 -#> [12,] 602 445 162 348 610 49 375 873 571 83 -#> [13,] 7 513 767 105 244 109 800 544 246 249 -#> [14,] 245 100 848 600 710 883 590 754 711 920 -#> [15,] 686 351 25 265 433 839 863 953 170 263 -#> [16,] 870 793 578 582 872 559 812 762 918 276 -#> [17,] 558 337 520 696 713 510 402 129 841 509 -#> [18,] 668 742 697 95 228 551 68 474 202 892 -#> [19,] 343 856 117 750 796 328 63 422 340 546 -#> [20,] 496 908 127 281 749 575 892 194 318 828 -#> [21,] 937 914 616 184 751 644 909 689 270 875 -#> [22,] 123 332 581 820 759 948 435 227 992 319 -#> [23,] 188 821 157 90 905 882 43 95 274 79 -#> [24,] 615 388 161 344 545 962 745 269 340 333 -#> [25,] 686 15 351 863 265 433 756 170 211 208 -#> [26,] 829 182 646 598 589 249 752 668 819 716 -#> [27,] 556 151 401 555 655 534 465 91 57 820 -#> [28,] 586 358 470 382 32 379 299 645 572 54 -#> [29,] 826 929 530 470 292 110 379 287 54 515 -#> [30,] 161 700 817 739 205 957 5 545 77 388 -#> [31,] 544 675 391 109 327 277 387 251 960 224 -#> [32,] 470 837 367 294 299 54 28 922 887 412 -#> [33,] 216 786 124 334 213 894 72 665 664 180 -#> [34,] 978 694 252 612 488 949 220 720 766 304 -#> [35,] 456 291 272 906 418 500 805 677 64 416 -#> [36,] 541 827 75 178 933 472 152 726 502 380 -#> [37,] 891 664 4 413 322 461 213 298 62 101 -#> [38,] 979 492 965 314 571 302 629 476 533 966 -#> [39,] 149 411 390 104 930 402 337 841 520 185 -#> [40,] 861 458 505 840 594 565 404 155 403 845 -#> [41,] 514 102 857 423 125 936 917 859 911 107 -#> [42,] 774 648 809 851 828 720 821 598 892 643 -#> [43,] 821 918 90 188 604 578 23 762 870 79 -#> [44,] 81 176 695 141 941 818 89 421 768 78 -#> [45,] 319 680 952 756 251 547 948 389 117 608 -#> [46,] 739 957 269 597 307 592 161 436 506 404 -#> [47,] 566 974 391 259 201 224 31 385 109 968 -#> [48,] 127 121 815 261 370 641 676 191 289 194 -#> [49,] 445 425 873 602 12 814 897 662 219 83 -#> [50,] 804 132 138 737 313 324 124 11 945 703 -#> [51,] 847 167 630 979 842 533 314 298 880 38 -#> [52,] 874 911 893 612 949 171 304 736 766 803 -#> [53,] 896 785 684 950 869 99 969 628 352 338 -#> [54,] 470 922 186 292 294 477 808 32 113 758 -#> [55,] 523 537 449 720 813 803 488 477 198 980 -#> [56,] 116 276 951 642 241 928 525 8 490 794 -#> [57,] 507 106 293 342 654 399 750 227 966 532 -#> [58,] 261 191 97 960 48 675 127 575 142 815 -#> [59,] 626 795 755 441 195 866 176 70 836 768 -#> [60,] 830 218 304 102 864 93 190 859 776 606 -#> [61,] 285 397 341 682 372 264 599 126 998 86 -#> [62,] 253 461 474 664 181 685 359 383 988 882 -#> [63,] 136 557 454 224 19 387 856 532 261 142 -#> [64,] 209 594 492 840 629 861 418 272 373 494 -#> [65,] 145 737 850 11 448 173 226 704 324 816 -#> [66,] 161 788 374 858 24 615 30 961 817 246 -#> [67,] 137 373 728 611 898 893 129 278 459 301 -#> [68,] 960 228 392 333 551 18 315 144 202 388 -#> [69,] 871 356 430 184 9 453 937 747 561 914 -#> [70,] 306 836 745 891 37 755 269 615 664 4 -#> [71,] 268 577 320 78 885 941 814 83 872 248 -#> [72,] 508 428 213 334 880 160 780 33 630 939 -#> [73,] 591 166 444 156 721 906 352 596 378 609 -#> [74,] 283 326 150 296 339 920 963 860 656 723 -#> [75,] 964 899 380 718 502 85 196 805 152 416 -#> [76,] 439 366 579 490 234 138 854 313 410 943 -#> [77,] 315 635 144 731 246 242 388 30 700 923 -#> [78,] 268 71 83 941 212 577 934 885 610 794 -#> [79,] 812 23 43 821 188 578 622 237 740 353 -#> [80,] 501 643 776 540 986 606 864 190 120 130 -#> [81,] 695 44 421 176 818 429 89 941 844 872 -#> [82,] 368 232 483 760 522 974 546 999 674 994 -#> [83,] 602 814 794 268 610 78 897 885 445 49 -#> [84,] 419 115 394 910 640 806 317 262 305 8 -#> [85,] 677 416 718 380 209 418 964 805 75 543 -#> [86,] 163 682 371 397 223 998 264 285 466 61 -#> [87,] 692 748 875 751 757 270 165 637 873 572 -#> [88,] 197 984 992 773 698 919 255 916 460 721 -#> [89,] 481 995 359 695 844 177 941 44 81 253 -#> [90,] 188 43 821 23 605 274 95 762 882 604 -#> [91,] 555 497 310 719 986 700 968 288 817 549 -#> [92,] 307 655 46 597 96 506 436 763 917 739 -#> [93,] 373 354 120 209 830 920 797 64 975 190 -#> [94,] 791 495 561 365 193 378 947 9 596 323 -#> [95,] 882 188 90 274 668 905 23 605 18 892 -#> [96,] 498 739 46 857 866 92 506 597 307 269 -#> [97,] 575 552 395 191 712 58 335 641 589 48 -#> [98,] 310 493 242 788 961 731 824 635 91 817 -#> [99,] 558 684 969 717 521 970 53 967 705 877 -#> [100,] 848 883 809 14 711 920 643 963 619 459 -#> [101,] 325 229 216 461 665 328 664 383 284 386 -#> [102,] 41 917 859 423 830 60 857 155 775 40 -#> [103,] 159 295 282 394 300 621 904 114 946 651 -#> [104,] 149 147 583 590 997 39 663 930 312 588 -#> [105,] 716 366 769 528 742 215 7 244 943 513 -#> [106,] 507 342 966 57 654 399 293 386 148 807 -#> [107,] 766 125 911 60 102 41 859 612 830 949 -#> [108,] 460 473 239 733 88 992 647 984 131 981 -#> [109,] 544 31 620 277 68 800 960 224 982 646 -#> [110,] 519 409 292 303 929 753 346 133 29 200 -#> [111,] 881 274 302 516 810 548 685 882 476 965 -#> [112,] 962 5 743 535 344 807 741 845 404 553 -#> [113,] 375 393 808 922 348 292 627 477 659 406 -#> [114,] 300 203 914 324 282 689 450 665 21 704 -#> [115,] 640 317 910 419 262 806 381 210 219 8 -#> [116,] 8 56 276 559 577 835 951 928 212 794 -#> [117,] 328 856 796 19 454 136 319 750 422 399 -#> [118,] 240 260 890 589 646 829 575 649 238 876 -#> [119,] 450 981 180 356 203 216 300 935 324 946 -#> [120,] 975 540 593 797 93 920 290 279 706 245 -#> [121,] 48 370 641 452 437 199 754 127 710 676 -#> [122,] 294 808 922 867 777 361 715 953 360 837 -#> [123,] 332 22 435 759 581 227 871 948 319 747 -#> [124,] 786 350 33 989 313 50 527 945 62 334 -#> [125,] 766 911 995 107 171 853 936 140 844 41 -#> [126,] 192 792 753 158 341 723 61 770 285 230 -#> [127,] 676 48 194 638 20 815 777 261 641 496 -#> [128,] 714 621 831 331 356 946 608 783 184 69 -#> [129,] 137 520 185 728 67 459 611 696 278 713 -#> [130,] 290 986 540 502 553 120 349 80 917 152 -#> [131,] 609 721 832 916 485 88 460 214 239 156 -#> [132,] 138 945 662 50 425 804 11 320 737 798 -#> [133,] 625 139 165 627 530 175 393 637 692 753 -#> [134,] 833 811 699 257 506 773 197 959 916 701 -#> [135,] 744 658 898 416 243 338 283 718 456 500 -#> [136,] 63 454 532 557 261 750 117 856 955 19 -#> [137,] 67 611 129 459 728 373 893 301 304 278 -#> [138,] 132 804 234 313 76 320 50 579 662 703 -#> [139,] 625 993 175 133 299 529 379 637 530 607 -#> [140,] 582 853 872 171 16 766 559 812 125 421 -#> [141,] 443 44 176 768 195 880 842 441 941 524 -#> [142,] 233 202 815 551 454 289 261 392 988 191 -#> [143,] 826 311 884 346 1 29 254 862 929 950 -#> [144,] 315 388 77 486 697 976 545 392 333 68 -#> [145,] 65 850 737 11 324 576 50 804 173 704 -#> [146,] 832 562 396 916 543 134 131 721 487 699 -#> [147,] 104 583 590 149 549 933 639 541 337 574 -#> [148,] 966 979 654 106 791 38 507 322 467 342 -#> [149,] 104 583 185 39 147 520 997 390 312 129 -#> [150,] 230 632 723 339 74 393 420 204 283 753 -#> [151,] 556 27 888 464 899 984 534 655 197 364 -#> [152,] 380 502 472 130 917 290 986 102 540 775 -#> [153,] 990 802 398 412 367 512 837 438 890 973 -#> [154,] 273 855 956 297 681 971 562 959 441 958 -#> [155,] 404 859 423 40 957 157 5 359 995 845 -#> [156,] 444 378 214 591 596 609 193 73 352 495 -#> [157,] 905 190 23 403 353 155 987 882 881 274 -#> [158,] 723 770 792 970 296 509 150 230 921 339 -#> [159,] 295 103 946 300 114 576 282 128 450 324 -#> [160,] 428 780 722 942 919 72 255 778 70 213 -#> [161,] 30 817 24 739 957 46 700 388 615 5 -#> [162,] 980 12 375 610 659 494 348 406 602 330 -#> [163,] 86 223 466 998 407 285 671 568 235 453 -#> [164,] 779 439 636 865 169 491 438 772 528 489 -#> [165,] 572 627 637 692 393 87 211 113 175 217 -#> [166,] 591 200 275 784 73 236 947 434 347 634 -#> [167,] 533 51 972 842 468 571 314 979 377 630 -#> [168,] 802 607 735 990 529 299 379 993 924 887 -#> [169,] 489 258 579 761 604 854 462 897 491 918 -#> [170,] 265 208 839 955 686 676 261 15 247 127 -#> [171,] 799 231 353 889 582 52 995 494 403 766 -#> [172,] 633 389 442 801 680 531 201 391 251 547 -#> [173,] 865 850 226 838 580 11 652 448 725 424 -#> [174,] 679 983 345 369 199 121 327 446 437 712 -#> [175,] 637 299 572 139 863 625 165 993 211 607 -#> [176,] 44 818 440 81 141 695 939 795 941 89 -#> [177,] 253 934 685 320 941 882 359 268 814 89 -#> [178,] 541 521 623 183 933 384 558 99 744 705 -#> [179,] 258 488 978 752 622 694 598 605 578 42 -#> [180,] 119 3 216 33 229 101 450 707 422 124 -#> [181,] 383 461 187 284 62 247 988 318 142 474 -#> [182,] 926 552 663 589 26 819 504 315 829 336 -#> [183,] 414 451 705 178 967 417 877 717 684 536 -#> [184,] 937 783 21 914 430 644 909 902 616 751 -#> [185,] 129 520 459 390 198 402 696 137 611 848 -#> [186,] 296 758 54 841 294 509 922 402 339 808 -#> [187,] 284 383 318 181 194 247 798 548 667 749 -#> [188,] 90 23 821 605 43 762 95 578 668 79 -#> [189,] 446 235 532 293 706 279 996 288 370 593 -#> [190,] 157 774 987 905 834 643 218 80 354 859 -#> [191,] 641 202 551 48 834 97 289 815 58 261 -#> [192,] 126 753 792 950 409 723 628 158 61 230 -#> [193,] 495 378 9 94 940 323 352 156 596 784 -#> [194,] 318 815 127 749 908 667 187 225 289 676 -#> [195,] 443 880 4 626 891 141 298 768 257 842 -#> [196,] 467 805 701 790 888 895 418 699 846 484 -#> [197,] 773 88 919 255 257 722 701 984 811 916 -#> [198,] 459 390 809 930 185 312 523 720 449 851 -#> [199,] 679 983 121 345 710 174 446 437 452 907 -#> [200,] 634 275 886 166 347 784 434 475 519 601 -#> [201,] 172 391 442 369 405 787 174 327 633 801 -#> [202,] 551 233 142 289 191 834 545 504 815 392 -#> [203,] 665 914 689 616 937 935 334 325 847 213 -#> [204,] 628 518 283 869 150 684 785 409 74 338 -#> [205,] 859 864 976 30 237 155 190 739 957 486 -#> [206,] 550 670 587 3 309 554 271 789 569 666 -#> [207,] 524 231 889 799 560 141 768 468 879 594 -#> [208,] 170 265 955 247 839 251 686 454 675 2 -#> [209,] 64 594 93 418 373 861 840 629 492 272 -#> [210,] 525 794 709 781 885 381 115 212 8 317 -#> [211,] 526 863 637 953 448 165 217 175 572 652 -#> [212,] 794 78 977 8 83 610 210 595 577 262 -#> [213,] 216 942 778 325 37 334 665 101 203 891 -#> [214,] 444 698 156 721 378 984 596 940 495 846 -#> [215,] 740 613 769 486 697 976 812 481 622 237 -#> [216,] 213 101 325 229 665 33 37 664 334 942 -#> [217,] 568 627 263 953 777 225 715 671 938 667 -#> [218,] 776 60 830 864 606 501 190 304 643 80 -#> [219,] 757 471 425 640 381 49 445 362 910 873 -#> [220,] 34 252 312 694 198 488 978 185 611 55 -#> [221,] 672 500 321 434 236 660 906 166 657 879 -#> [222,] 904 570 764 475 200 884 875 683 644 1000 -#> [223,] 163 682 998 921 285 230 466 671 715 86 -#> [224,] 63 557 345 391 58 387 907 136 960 566 -#> [225,] 667 671 782 568 777 915 194 217 407 676 -#> [226,] 652 448 580 173 850 526 211 953 265 11 -#> [227,] 435 581 791 654 57 293 747 106 871 123 -#> [228,] 960 68 982 18 424 551 142 908 392 767 -#> [229,] 101 328 216 325 856 664 796 117 461 37 -#> [230,] 150 723 339 627 393 753 420 223 632 263 -#> [231,] 889 799 171 207 594 840 494 403 844 353 -#> [232,] 820 368 10 674 546 944 760 465 82 22 -#> [233,] 142 202 815 289 551 284 962 191 318 261 -#> [234,] 527 313 76 410 742 138 177 320 943 854 -#> [235,] 189 293 446 706 503 996 466 326 288 279 -#> [236,] 500 434 347 456 338 291 303 518 377 601 -#> [237,] 905 976 697 23 668 188 157 95 79 504 -#> [238,] 447 890 638 281 575 412 395 438 265 584 -#> [239,] 460 473 647 108 485 609 131 9 88 954 -#> [240,] 829 589 118 575 649 335 20 496 598 648 -#> [241,] 490 951 931 410 525 729 56 943 517 76 -#> [242,] 731 493 700 310 907 635 77 574 817 315 -#> [243,] 338 135 409 744 250 236 346 969 950 518 -#> [244,] 336 752 716 105 976 513 923 280 26 215 -#> [245,] 600 754 593 14 710 706 975 920 120 279 -#> [246,] 77 635 455 144 315 923 731 388 486 800 -#> [247,] 955 383 284 454 187 2 181 328 208 142 -#> [248,] 440 931 989 517 320 350 71 939 738 818 -#> [249,] 26 646 260 624 620 819 280 829 767 336 -#> [250,] 243 346 932 708 672 969 338 221 898 135 -#> [251,] 389 387 801 675 208 680 454 170 45 955 -#> [252,] 928 34 220 55 803 612 949 736 813 720 -#> [253,] 685 62 359 177 882 474 934 548 413 461 -#> [254,] 311 1 143 884 570 426 519 950 826 222 -#> [255,] 919 778 942 322 654 197 364 722 992 891 -#> [256,] 795 440 939 248 176 818 508 350 363 989 -#> [257,] 811 773 722 919 701 316 197 880 195 443 -#> [258,] 179 605 489 169 598 604 488 918 578 762 -#> [259,] 566 345 907 679 391 199 224 974 310 557 -#> [260,] 646 118 876 829 649 620 589 249 240 819 -#> [261,] 815 454 48 955 58 127 136 839 142 191 -#> [262,] 595 317 538 115 419 212 524 910 806 8 -#> [263,] 217 372 599 627 568 953 863 625 715 230 -#> [264,] 397 86 371 61 267 285 669 372 825 998 -#> [265,] 170 686 15 208 839 638 676 25 955 226 -#> [266,] 511 932 736 611 515 252 977 803 250 893 -#> [267,] 264 585 831 397 372 608 599 783 25 263 -#> [268,] 71 814 78 320 885 83 941 934 662 445 -#> [269,] 597 615 535 306 506 891 4 592 46 845 -#> [270,] 875 637 87 704 683 21 751 757 748 211 -#> [271,] 994 206 550 569 66 554 13 999 858 900 -#> [272,] 565 594 35 805 418 840 64 505 291 699 -#> [273,] 855 956 681 154 959 833 971 441 768 562 -#> [274,] 882 881 111 90 95 23 318 539 548 749 -#> [275,] 634 200 415 660 538 434 347 947 166 167 -#> [276,] 928 16 793 559 116 56 870 794 872 410 -#> [277,] 927 31 544 309 387 109 670 707 760 666 -#> [278,] 623 469 521 384 728 963 67 373 933 137 -#> [279,] 975 370 593 797 706 656 326 754 120 920 -#> [280,] 336 244 923 249 455 26 182 819 588 752 -#> [281,] 649 575 20 496 908 238 838 638 127 829 -#> [282,] 651 683 806 394 689 21 764 114 914 475 -#> [283,] 204 74 456 658 150 518 758 406 901 236 -#> [284,] 187 383 247 810 318 181 194 815 461 233 -#> [285,] 998 913 223 61 825 86 163 466 682 230 -#> [286,] 288 996 536 877 466 967 717 921 719 503 -#> [287,] 709 929 477 427 586 977 515 358 659 29 -#> [288,] 286 235 189 996 465 466 503 401 446 706 -#> [289,] 834 987 815 233 202 191 641 318 194 48 -#> [290,] 540 130 741 797 120 553 986 790 349 502 -#> [291,] 377 972 468 35 347 434 456 314 272 571 -#> [292,] 901 113 515 929 54 758 110 303 659 922 -#> [293,] 235 57 507 106 189 342 227 532 401 407 -#> [294,] 122 922 808 54 837 867 186 715 32 627 -#> [295,] 159 946 300 103 450 618 114 647 282 128 -#> [296,] 758 339 186 963 74 158 901 970 883 723 -#> [297,] 564 855 971 59 818 154 936 661 429 795 -#> [298,] 413 630 685 4 847 842 880 516 934 37 -#> [299,] 607 379 175 529 863 953 599 625 32 139 -#> [300,] 114 450 295 914 946 203 689 119 282 618 -#> [301,] 851 720 304 813 893 449 803 459 980 373 -#> [302,] 476 965 111 810 629 881 492 516 38 539 -#> [303,] 601 330 348 406 659 347 901 434 292 110 -#> [304,] 851 301 60 830 373 893 218 52 611 720 -#> [305,] 542 816 362 703 640 781 355 910 683 115 -#> [306,] 70 836 745 615 269 891 37 755 664 4 -#> [307,] 92 655 46 436 597 592 506 349 957 917 -#> [308,] 632 601 347 393 634 886 692 420 87 571 -#> [309,] 670 927 707 3 789 587 387 952 796 422 -#> [310,] 493 242 700 91 907 731 574 817 555 259 -#> [311,] 254 1 884 143 826 530 29 519 110 993 -#> [312,] 588 997 978 663 198 694 488 34 42 851 -#> [313,] 527 234 742 138 76 50 124 132 181 804 -#> [314,] 842 979 972 167 468 38 533 51 571 965 -#> [315,] 144 77 388 68 731 926 976 635 545 392 -#> [316,] 773 257 722 916 441 811 478 880 443 197 -#> [317,] 115 381 640 262 806 910 219 538 757 471 -#> [318,] 194 908 749 187 289 274 284 815 496 881 -#> [319,] 944 117 435 45 693 820 407 532 546 136 -#> [320,] 268 71 177 941 945 662 934 814 248 234 -#> [321,] 396 221 660 672 906 524 500 166 478 434 -#> [322,] 891 4 37 535 778 845 413 654 255 966 -#> [323,] 784 886 9 634 644 909 430 94 193 947 -#> [324,] 704 737 114 50 616 119 783 145 665 203 -#> [325,] 101 386 935 665 216 747 213 229 203 37 -#> [326,] 593 706 754 921 975 74 279 996 600 915 -#> [327,] 395 58 391 712 97 174 584 675 679 575 -#> [328,] 117 796 856 229 101 454 247 19 422 399 -#> [329,] 771 852 843 657 250 221 672 681 396 321 -#> [330,] 659 901 406 303 348 162 375 601 879 515 -#> [331,] 631 128 946 576 831 608 159 295 356 621 -#> [332,] 123 22 435 581 654 747 227 935 820 992 -#> [333,] 392 388 988 68 545 144 24 474 18 960 -#> [334,] 665 630 213 945 203 298 33 847 216 72 -#> [335,] 711 648 641 452 589 97 552 712 867 643 -#> [336,] 280 923 244 182 455 26 752 663 925 315 -#> [337,] 17 520 558 614 713 521 639 970 841 770 -#> [338,] 243 518 409 236 204 519 950 135 869 283 -#> [339,] 808 296 150 230 393 922 723 74 627 758 -#> [340,] 343 344 615 19 856 999 962 24 750 399 -#> [341,] 991 61 599 868 372 682 126 367 770 792 -#> [342,] 507 106 399 807 57 966 750 743 322 293 -#> [343,] 19 856 750 340 999 117 546 368 63 399 -#> [344,] 962 112 535 399 807 340 988 743 664 233 -#> [345,] 174 679 199 557 983 259 391 224 446 136 -#> [346,] 110 519 409 929 243 515 932 338 250 236 -#> [347,] 601 377 308 291 434 632 236 303 634 365 -#> [348,] 659 601 12 375 406 113 303 162 330 571 -#> [349,] 553 436 743 592 741 790 290 807 5 540 -#> [350,] 989 124 786 248 363 320 939 440 138 256 -#> [351,] 15 433 863 25 953 686 372 599 263 211 -#> [352,] 378 785 869 591 896 193 156 518 444 357 -#> [353,] 403 171 157 23 799 821 79 43 840 494 -#> [354,] 93 797 656 539 476 920 629 302 861 492 -#> [355,] 816 542 305 781 362 703 65 764 683 640 -#> [356,] 69 871 759 608 831 756 184 119 783 935 -#> [357,] 869 985 913 503 365 94 825 628 785 518 -#> [358,] 427 586 28 362 471 572 381 287 477 781 -#> [359,] 253 905 882 89 685 62 155 177 474 23 -#> [360,] 867 712 361 395 122 930 638 452 575 890 -#> [361,] 991 715 638 360 122 867 395 823 712 294 -#> [362,] 471 572 358 219 875 381 640 683 757 270 -#> [363,] 989 350 440 248 786 650 256 795 124 818 -#> [364,] 255 919 597 942 778 506 592 992 197 556 -#> [365,] 947 94 347 518 632 791 357 596 886 291 -#> [366,] 76 943 528 678 234 105 410 716 439 490 -#> [367,] 398 837 32 991 412 294 361 122 341 868 -#> [368,] 483 343 999 820 19 546 340 760 232 422 -#> [369,] 787 983 174 679 442 345 371 682 199 201 -#> [370,] 279 48 121 671 915 815 641 754 777 225 -#> [371,] 397 86 573 682 264 442 61 369 998 163 -#> [372,] 599 263 433 351 61 341 953 863 15 715 -#> [373,] 93 67 209 893 64 137 304 301 830 728 -#> [374,] 858 613 486 513 7 24 215 564 739 66 -#> [375,] 477 113 659 348 162 980 12 901 406 330 -#> [376,] 498 506 597 364 134 92 755 307 197 655 -#> [377,] 291 468 972 571 434 601 347 533 167 314 -#> [378,] 193 495 156 596 352 94 444 214 940 947 -#> [379,] 529 607 299 530 993 175 28 470 139 887 -#> [380,] 502 152 718 130 290 472 85 917 75 384 -#> [381,] 471 317 219 757 49 348 445 692 640 362 -#> [382,] 586 28 645 636 837 54 477 358 470 427 -#> [383,] 181 461 187 284 247 318 988 101 62 142 -#> [384,] 469 623 718 417 933 521 278 860 658 380 -#> [385,] 968 674 719 259 974 91 549 465 555 566 -#> [386,] 902 966 325 2 935 747 782 106 616 284 -#> [387,] 251 796 63 960 454 136 675 707 224 117 -#> [388,] 144 333 545 392 24 486 315 77 697 161 -#> [389,] 801 251 680 172 45 675 531 547 442 686 -#> [390,] 930 402 411 198 841 848 185 696 883 520 -#> [391,] 345 224 327 566 31 201 974 174 259 172 -#> [392,] 333 545 551 68 388 202 142 988 233 960 -#> [393,] 627 113 632 339 165 308 808 692 230 922 -#> [394,] 282 651 806 910 419 764 683 640 103 727 -#> [395,] 712 575 97 638 360 452 361 437 121 552 -#> [396,] 321 832 956 681 478 524 906 487 221 543 -#> [397,] 264 371 86 61 285 267 682 372 573 998 -#> [398,] 367 991 837 361 412 868 153 360 341 433 -#> [399,] 342 750 507 106 807 57 856 344 962 654 -#> [400,] 465 693 401 944 479 319 435 820 163 466 -#> [401,] 465 400 293 57 944 820 27 532 693 435 -#> [402,] 696 841 390 510 520 186 185 411 713 509 -#> [403,] 840 353 516 458 861 157 965 799 40 629 -#> [404,] 155 957 741 5 40 845 112 535 553 743 -#> [405,] 482 201 573 369 442 787 371 868 584 398 -#> [406,] 901 330 348 601 659 303 162 113 375 393 -#> [407,] 782 2 671 225 839 568 938 955 454 915 -#> [408,] 748 757 538 533 806 87 167 634 875 692 -#> [409,] 519 950 110 338 753 204 283 628 243 346 -#> [410,] 943 234 490 559 872 762 579 76 480 578 -#> [411,] 390 402 930 849 198 510 696 841 185 186 -#> [412,] 837 890 367 652 438 122 953 294 398 238 -#> [413,] 298 4 37 685 810 891 322 845 516 253 -#> [414,] 451 705 183 967 717 536 286 877 567 417 -#> [415,] 954 947 485 596 275 51 660 167 784 634 -#> [416,] 677 718 85 658 898 744 35 135 418 456 -#> [417,] 877 384 860 432 469 658 718 623 204 74 -#> [418,] 805 272 209 35 64 196 467 658 456 492 -#> [419,] 115 910 262 640 806 317 595 84 8 394 -#> [420,] 632 913 561 230 150 308 568 393 938 217 -#> [421,] 81 695 853 44 140 559 872 429 582 835 -#> [422,] 952 707 796 19 117 328 856 229 343 760 -#> [423,] 155 859 857 995 102 844 40 404 936 41 -#> [424,] 838 982 228 725 767 649 675 281 173 865 -#> [425,] 49 662 445 873 798 219 814 897 945 132 -#> [426,] 352 570 732 323 784 1 193 785 378 591 -#> [427,] 358 586 287 645 781 28 709 210 382 362 -#> [428,] 780 160 673 894 72 942 70 722 755 457 -#> [429,] 695 872 734 559 480 818 81 481 661 140 -#> [430,] 909 644 937 184 689 914 21 886 9 475 -#> [431,] 725 772 865 876 173 666 424 838 512 447 -#> [432,] 567 417 503 467 196 888 706 617 235 985 -#> [433,] 351 372 15 599 686 25 953 263 863 299 -#> [434,] 377 660 347 236 291 468 601 972 303 275 -#> [435,] 227 123 581 871 319 332 293 57 747 453 -#> [436,] 349 592 790 553 741 290 535 655 404 743 -#> [437,] 452 710 823 121 754 712 983 600 867 395 -#> [438,] 890 412 652 238 226 772 837 281 779 173 -#> [439,] 490 579 76 729 779 169 854 164 865 489 -#> [440,] 818 248 176 256 795 939 44 931 695 989 -#> [441,] 768 316 195 959 141 443 833 560 855 626 -#> [442,] 172 680 371 801 389 633 369 201 174 86 -#> [443,] 195 880 842 141 630 298 768 4 257 314 -#> [444,] 156 214 378 73 591 352 721 596 617 495 -#> [445,] 49 12 425 873 602 814 83 662 219 348 -#> [446,] 189 235 370 279 532 996 199 121 706 293 -#> [447,] 238 265 584 675 638 15 686 801 327 170 -#> [448,] 580 526 226 211 11 652 737 850 270 637 -#> [449,] 537 720 813 301 980 523 803 55 851 918 -#> [450,] 119 300 946 981 114 618 203 295 914 180 -#> [451,] 705 414 717 967 183 536 877 286 684 99 -#> [452,] 437 710 711 712 121 335 14 754 867 823 -#> [453,] 871 938 561 756 902 407 2 782 420 163 -#> [454,] 955 136 261 247 815 117 63 142 328 233 -#> [455,] 923 246 336 244 635 77 925 280 315 144 -#> [456,] 35 658 283 291 236 64 418 500 492 406 -#> [457,] 900 569 745 70 894 780 306 836 554 428 -#> [458,] 861 840 40 629 403 965 505 845 516 594 -#> [459,] 851 809 137 301 198 848 100 185 720 611 -#> [460,] 239 473 108 9 88 485 647 609 954 992 -#> [461,] 664 62 181 383 988 37 101 284 474 253 -#> [462,] 489 854 761 749 798 897 605 90 604 908 -#> [463,] 549 968 259 385 787 566 719 47 98 147 -#> [464,] 899 151 534 888 567 556 432 846 985 75 -#> [465,] 401 400 693 944 288 820 293 532 189 435 -#> [466,] 998 996 163 921 235 223 503 86 285 682 -#> [467,] 196 805 701 888 895 790 418 148 484 846 -#> [468,] 972 377 571 167 533 291 314 660 434 842 -#> [469,] 384 860 278 623 417 933 120 593 521 963 -#> [470,] 54 32 530 28 29 292 379 922 299 929 -#> [471,] 381 572 219 362 757 692 49 87 873 875 -#> [472,] 152 380 933 775 502 278 830 60 384 653 -#> [473,] 647 460 981 239 108 992 69 9 450 88 -#> [474,] 62 988 253 461 527 359 181 664 95 882 -#> [475,] 644 909 886 634 21 430 222 748 875 764 -#> [476,] 302 810 629 656 492 881 111 965 484 354 -#> [477,] 375 113 586 659 980 537 54 761 162 348 -#> [478,] 485 316 727 415 954 508 321 660 524 687 -#> [479,] 400 435 581 465 401 227 123 693 669 820 -#> [480,] 943 769 429 734 410 481 234 818 661 872 -#> [481,] 89 613 359 177 429 215 769 995 695 480 -#> [482,] 405 398 584 868 153 447 367 573 991 433 -#> [483,] 368 343 961 340 999 19 674 555 655 750 -#> [484,] 790 741 797 656 629 807 476 861 492 743 -#> [485,] 954 415 478 727 596 460 51 275 947 239 -#> [486,] 697 976 144 388 237 215 7 613 205 315 -#> [487,] 560 543 699 272 565 677 681 811 85 207 -#> [488,] 978 694 179 720 258 813 449 42 523 34 -#> [489,] 462 169 605 258 761 854 649 604 20 496 -#> [490,] 439 729 579 76 410 241 169 525 854 943 -#> [491,] 169 258 488 179 523 694 489 870 164 16 -#> [492,] 629 656 476 302 965 64 38 861 840 484 -#> [493,] 242 731 310 907 635 700 574 77 817 98 -#> [494,] 610 889 799 162 231 12 879 64 840 353 -#> [495,] 193 94 378 940 9 791 596 214 323 947 -#> [496,] 20 908 892 281 318 575 828 127 191 749 -#> [497,] 555 91 986 130 502 763 827 655 817 349 -#> [498,] 96 755 506 822 376 866 626 597 59 833 -#> [499,] 618 673 894 730 256 690 363 795 350 428 -#> [500,] 906 236 35 672 456 221 434 291 879 338 -#> [501,] 80 776 606 864 643 218 926 190 574 986 -#> [502,] 380 152 130 290 540 986 917 718 120 384 -#> [503,] 235 432 466 357 985 293 996 536 288 706 -#> [504,] 926 834 987 892 551 668 202 648 828 191 -#> [505,] 565 40 458 861 845 840 272 594 404 155 -#> [506,] 597 269 46 722 306 257 919 755 592 255 -#> [507,] 342 106 57 399 807 293 966 654 484 750 -#> [508,] 72 687 939 880 256 141 630 478 795 443 -#> [509,] 713 970 758 296 158 792 186 841 728 770 -#> [510,] 696 402 841 411 186 509 390 54 520 17 -#> [511,] 736 266 893 874 803 611 879 932 330 67 -#> [512,] 924 990 802 153 168 772 438 412 735 607 -#> [513,] 7 769 215 374 486 244 105 613 697 650 -#> [514,] 41 857 936 423 102 866 125 971 844 995 -#> [515,] 929 292 901 330 758 659 54 303 346 110 -#> [516,] 965 111 403 685 934 302 881 548 458 810 -#> [517,] 931 577 248 951 71 958 738 8 440 835 -#> [518,] 869 204 628 338 785 365 283 236 347 357 -#> [519,] 110 409 1 950 338 346 753 200 236 518 -#> [520,] 129 185 713 402 696 728 841 963 390 848 -#> [521,] 623 860 384 278 713 469 970 728 963 933 -#> [522,] 82 368 483 232 10 674 6 27 961 820 -#> [523,] 55 537 449 488 720 198 813 258 477 491 -#> [524,] 207 660 595 141 468 262 321 972 538 231 -#> [525,] 885 210 729 794 781 8 577 490 951 71 -#> [526,] 211 448 863 953 580 217 637 226 667 652 -#> [527,] 234 313 742 474 62 177 253 786 95 989 -#> [528,] 366 624 716 767 105 76 439 742 164 649 -#> [529,] 607 379 993 299 168 139 175 530 735 887 -#> [530,] 379 29 470 133 625 826 299 572 529 139 -#> [531,] 547 172 389 952 760 801 251 45 633 680 -#> [532,] 750 136 189 557 944 446 399 57 293 63 -#> [533,] 167 571 468 972 51 377 408 979 314 842 -#> [534,] 464 27 151 556 655 567 899 555 307 92 -#> [535,] 845 112 4 592 404 741 322 962 891 269 -#> [536,] 967 877 503 286 466 998 684 357 825 985 -#> [537,] 449 55 523 720 803 813 980 477 301 375 -#> [538,] 408 660 275 167 533 262 806 317 468 972 -#> [539,] 603 980 354 619 828 274 302 821 111 749 -#> [540,] 290 120 130 797 741 986 975 553 80 743 -#> [541,] 933 178 36 827 384 472 469 521 705 623 -#> [542,] 816 781 305 362 355 358 427 640 525 729 -#> [543,] 487 906 416 677 699 85 35 75 805 500 -#> [544,] 31 982 109 675 424 960 228 277 68 767 -#> [545,] 202 392 551 388 962 5 233 112 905 504 -#> [546,] 557 760 944 19 820 343 63 136 319 974 -#> [547,] 531 952 45 389 251 760 801 172 948 387 -#> [548,] 685 111 945 662 798 516 934 881 187 274 -#> [549,] 719 286 968 91 288 463 259 385 590 310 -#> [550,] 206 587 670 3 309 271 927 666 569 554 -#> [551,] 202 142 191 233 504 392 545 834 289 68 -#> [552,] 97 589 335 191 182 712 575 395 926 711 -#> [553,] 349 743 741 5 436 112 404 290 807 592 -#> [554,] 900 569 745 457 786 836 650 124 789 3 -#> [555,] 91 497 655 310 719 27 349 986 817 700 -#> [556,] 151 27 888 401 581 364 57 655 227 984 -#> [557,] 546 944 136 63 532 345 224 19 750 693 -#> [558,] 17 713 99 521 337 970 509 520 623 129 -#> [559,] 872 577 16 429 582 410 794 870 695 71 -#> [560,] 487 768 811 699 565 272 441 207 505 833 -#> [561,] 791 453 420 94 913 871 163 227 430 909 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[582,] 140 872 171 16 870 853 559 79 812 43 -#> [583,] 590 278 933 14 848 100 104 520 147 149 -#> [584,] 238 327 447 260 395 118 712 646 890 876 -#> [585,] 783 184 453 267 714 644 69 561 139 909 -#> [586,] 358 477 28 382 761 427 287 471 375 54 -#> [587,] 670 309 3 180 666 576 707 145 927 725 -#> [588,] 997 312 663 978 752 218 776 694 606 336 -#> [589,] 240 829 819 552 335 575 97 646 182 648 -#> [590,] 14 710 639 245 583 600 452 848 711 100 -#> [591,] 73 166 156 352 784 378 444 596 947 609 -#> [592,] 436 349 535 553 845 269 597 790 741 807 -#> [593,] 975 706 245 120 279 754 326 600 920 14 -#> [594,] 840 64 209 40 861 272 565 458 889 231 -#> [595,] 262 78 524 212 8 419 317 538 115 141 -#> [596,] 947 698 378 954 415 495 94 214 9 365 -#> [597,] 506 269 592 46 307 364 306 436 655 615 -#> [598,] 668 892 605 188 828 829 622 258 648 179 -#> [599,] 372 263 433 299 351 341 953 625 863 61 -#> [600,] 245 754 14 593 639 963 326 710 823 920 -#> [601,] 303 347 348 377 308 406 571 434 632 533 -#> [602,] 12 83 49 445 604 814 918 610 897 162 -#> [603,] 619 539 980 867 354 749 777 641 828 920 -#> [604,] 918 43 602 897 761 90 870 462 821 605 -#> [605,] 188 762 90 598 489 668 258 578 892 462 -#> [606,] 864 501 776 218 80 925 60 830 643 205 -#> [607,] 529 379 299 993 168 175 802 28 139 530 -#> [608,] 831 356 759 45 948 871 756 267 69 128 -#> [609,] 156 131 721 444 460 596 591 214 378 73 -#> [610,] 494 12 162 799 83 602 889 803 78 659 -#> [611,] 137 67 893 736 304 301 459 612 129 52 -#> [612,] 949 911 52 736 611 874 304 766 34 893 -#> [613,] 481 215 769 89 486 374 740 697 359 564 -#> [614,] 770 639 792 868 841 158 337 970 991 341 -#> [615,] 24 269 306 745 836 340 70 344 161 597 -#> [616,] 751 937 21 902 914 665 203 386 184 704 -#> [617,] 846 895 365 357 985 869 467 698 888 518 -#> [618,] 647 450 300 295 673 894 946 981 119 473 -#> [619,] 603 867 539 641 980 809 828 648 920 883 -#> [620,] 646 260 249 819 109 584 589 552 327 26 -#> [621,] 714 912 128 904 69 585 184 783 356 103 -#> [622,] 752 740 812 179 79 598 578 188 237 762 -#> [623,] 521 860 384 278 469 728 963 713 970 417 -#> [624,] 528 249 716 767 26 649 260 164 876 179 -#> [625,] 133 139 175 627 165 263 637 299 530 599 -#> [626,] 866 59 195 441 269 768 443 70 755 836 -#> [627,] 165 922 393 808 217 230 113 263 953 625 -#> [628,] 204 869 785 684 518 913 150 357 409 753 -#> [629,] 492 861 458 840 965 476 302 64 484 38 -#> [630,] 298 880 51 842 847 334 167 413 443 314 -#> [631,] 331 128 946 608 759 356 948 119 450 831 -#> [632,] 308 150 393 420 347 601 230 365 406 886 -#> [633,] 172 442 693 974 680 531 389 400 944 760 -#> [634,] 886 909 275 200 644 308 347 748 475 323 -#> [635,] 77 731 246 800 242 493 315 144 923 700 -#> [636,] 382 779 164 849 438 491 586 890 772 169 -#> [637,] 175 165 572 211 270 863 87 875 692 526 -#> [638,] 676 127 395 361 575 712 777 265 238 48 -#> [639,] 600 590 245 963 14 614 754 770 710 921 -#> [640,] 910 115 806 219 317 381 419 738 757 362 -#> [641,] 191 834 335 48 711 289 648 121 619 987 -#> [642,] 56 241 490 729 781 439 276 779 491 525 -#> [643,] 776 80 711 648 774 100 501 190 14 809 -#> [644,] 909 430 475 886 21 634 184 937 751 323 -#> [645,] 887 28 382 470 427 379 358 29 32 826 -#> [646,] 260 829 589 26 819 118 240 575 649 620 -#> [647,] 473 618 239 981 460 450 295 300 946 108 -#> [648,] 774 828 42 335 987 892 834 641 643 711 -#> [649,] 281 829 489 838 240 496 20 575 982 462 -#> [650,] 7 363 554 769 527 480 513 989 678 786 -#> [651,] 806 282 687 910 727 394 738 408 538 689 -#> [652,] 226 580 448 953 526 412 211 438 761 281 -#> [653,] 775 472 60 606 830 218 107 152 864 997 -#> [654,] 106 57 778 966 747 322 507 148 227 255 -#> [655,] 307 92 436 592 46 349 597 555 497 27 -#> [656,] 975 797 476 492 915 484 354 629 279 920 -#> [657,] 672 879 677 221 874 500 207 898 736 681 -#> [658,] 456 744 718 416 283 418 135 417 898 35 -#> [659,] 330 375 348 901 162 406 303 113 477 12 -#> [660,] 972 434 468 538 275 377 524 167 291 533 -#> [661,] 734 429 818 958 695 440 559 421 480 81 -#> [662,] 798 814 425 897 548 945 49 268 445 132 -#> [663,] 776 588 182 42 926 997 648 589 312 752 -#> [664,] 461 37 62 988 101 229 344 836 891 253 -#> [665,] 203 334 325 101 616 216 213 847 386 945 -#> [666,] 670 309 587 277 725 544 431 927 31 547 -#> [667,] 225 782 194 568 938 671 2 217 187 284 -#> [668,] 598 892 95 188 605 18 504 237 828 987 -#> [669,] 825 264 732 397 479 285 86 998 61 466 -#> [670,] 309 666 587 3 927 789 707 277 544 725 -#> [671,] 225 568 777 915 370 407 782 217 667 715 -#> [672,] 221 500 434 236 321 657 879 906 660 524 -#> [673,] 894 428 780 72 33 457 160 618 213 942 -#> [674,] 385 968 555 483 91 232 27 719 310 974 -#> [675,] 58 170 251 31 982 960 208 265 387 389 -#> [676,] 127 777 638 839 48 194 225 715 671 261 -#> [677,] 416 85 35 898 209 418 64 456 658 272 -#> [678,] 366 76 313 943 234 527 989 105 650 528 -#> [679,] 983 199 174 345 369 121 437 710 259 452 -#> [680,] 389 45 686 442 839 251 801 319 172 693 -#> [681,] 487 207 273 956 560 657 396 524 768 855 -#> [682,] 223 86 998 61 285 371 921 163 466 341 -#> [683,] 875 764 270 757 282 21 806 748 362 87 -#> [684,] 628 967 785 204 869 877 536 518 357 417 -#> [685,] 548 253 934 516 882 413 111 881 298 177 -#> [686,] 15 25 265 170 351 839 680 433 208 863 -#> [687,] 651 508 738 939 595 141 910 727 806 419 -#> [688,] 793 734 769 740 215 812 480 105 244 622 -#> [689,] 914 203 21 937 430 616 184 909 51 847 -#> [690,] 730 738 248 517 350 931 256 989 687 703 -#> [691,] 394 727 687 651 1000 419 478 282 103 84 -#> [692,] 87 748 165 757 572 875 471 393 308 873 -#> [693,] 400 944 465 319 557 532 401 446 345 680 -#> [694,] 978 488 179 34 312 752 258 622 720 491 -#> [695,] 81 44 429 818 421 89 872 176 559 941 -#> [696,] 402 510 841 520 185 390 129 186 509 713 -#> [697,] 486 976 237 18 144 905 668 95 388 545 -#> [698,] 596 895 846 214 947 701 721 888 773 148 -#> [699,] 811 701 895 805 565 773 272 196 846 560 -#> [700,] 817 30 242 731 310 161 986 493 788 574 -#> [701,] 811 699 895 196 467 773 888 846 805 698 -#> [702,] 61 126 563 573 614 682 998 285 371 792 -#> [703,] 804 138 305 11 737 132 730 50 738 145 -#> [704,] 324 270 616 783 737 21 184 751 937 526 -#> [705,] 451 414 717 967 183 877 286 536 684 417 -#> [706,] 593 975 279 326 235 245 120 189 996 754 -#> [707,] 927 422 309 796 952 387 999 3 789 19 -#> [708,] 243 338 250 969 221 346 73 672 591 166 -#> [709,] 287 210 977 794 427 928 212 525 55 781 -#> [710,] 452 437 245 14 754 711 590 199 600 121 -#> [711,] 335 452 643 641 14 648 100 710 883 848 -#> [712,] 395 97 452 575 360 437 638 335 121 361 -#> [713,] 970 509 728 963 520 860 296 623 521 758 -#> [714,] 621 128 783 585 831 184 912 475 267 904 -#> [715,] 777 361 217 568 676 823 671 808 263 953 -#> [716,] 105 528 622 179 767 605 742 752 598 26 -#> [717,] 705 967 451 877 414 286 536 639 860 684 -#> [718,] 416 384 658 380 418 85 417 469 744 75 -#> [719,] 549 91 555 288 567 968 286 497 827 385 -#> [720,] 813 449 301 851 537 488 42 803 980 304 -#> [721,] 916 214 698 832 131 596 846 156 444 773 -#> [722,] 257 919 160 255 316 197 773 880 506 891 -#> [723,] 158 230 150 339 753 792 921 770 296 74 -#> [724,] 765 479 940 581 759 22 473 123 69 495 -#> [725,] 424 838 173 865 850 982 675 208 431 265 -#> [726,] 36 178 541 75 472 85 543 964 718 416 -#> [727,] 1000 485 651 478 415 954 538 275 687 282 -#> [728,] 713 67 963 137 129 970 278 623 758 373 -#> [729,] 490 579 525 439 885 169 897 854 781 210 -#> [730,] 690 703 350 114 804 738 50 989 124 138 -#> [731,] 242 493 635 77 700 315 907 574 310 144 -#> [732,] 669 825 426 785 352 285 913 628 53 264 -#> [733,] 984 992 108 88 581 197 364 556 22 151 -#> [734,] 429 661 480 559 943 793 872 410 276 140 -#> [735,] 168 529 607 993 802 379 990 924 887 299 -#> [736,] 511 874 611 893 612 52 803 949 879 67 -#> [737,] 11 704 448 270 132 324 50 65 804 850 -#> [738,] 910 640 71 320 248 219 687 939 517 268 -#> [739,] 957 46 161 30 857 155 423 404 859 205 -#> [740,] 812 622 215 79 237 578 793 976 752 762 -#> [741,] 553 743 404 484 807 797 790 112 290 349 -#> [742,] 527 18 95 313 234 605 854 474 668 762 -#> [743,] 807 553 741 112 5 962 349 797 484 535 -#> [744,] 135 658 898 416 283 718 243 623 456 338 -#> [745,] 836 70 306 900 615 569 457 664 24 554 -#> [746,] 1000 904 570 222 884 394 84 727 764 691 -#> [747,] 935 871 386 654 325 902 966 106 227 937 -#> [748,] 757 87 408 692 875 751 909 21 634 308 -#> [749,] 318 194 462 798 908 20 274 539 761 187 -#> [750,] 399 532 856 343 19 136 57 117 342 507 -#> [751,] 616 87 937 21 909 748 902 644 270 692 -#> [752,] 622 179 978 598 740 663 668 694 976 26 -#> [753,] 230 192 723 150 409 110 158 292 126 792 -#> [754,] 245 600 593 710 823 326 14 437 279 121 -#> [755,] 70 306 836 506 745 626 428 59 269 160 -#> [756,] 938 453 902 2 783 782 871 184 25 704 -#> [757,] 748 875 408 87 692 219 471 381 683 270 -#> [758,] 296 901 186 509 339 292 728 406 515 74 -#> [759,] 948 123 356 608 22 871 332 435 69 581 -#> [760,] 546 952 422 387 557 974 547 944 531 19 -#> [761,] 462 604 489 749 897 169 918 854 580 602 -#> [762,] 578 605 188 90 43 870 918 604 812 79 -#> [763,] 497 92 307 827 961 655 555 817 91 788 -#> [764,] 683 875 222 806 475 757 748 270 282 408 -#> [765,] 724 479 581 22 759 940 123 733 473 435 -#> [766,] 107 125 911 171 79 52 949 140 612 304 -#> [767,] 982 424 528 228 716 838 649 742 18 26 -#> [768,] 441 560 844 971 443 141 195 44 833 231 -#> [769,] 215 613 480 481 105 513 740 429 7 650 -#> [770,] 158 792 614 723 970 841 639 713 509 963 -#> [771,] 329 852 681 843 657 956 524 672 221 207 -#> [772,] 876 438 890 164 512 238 636 118 779 153 -#> [773,] 811 916 257 701 197 699 316 698 722 88 -#> [774,] 42 828 648 821 987 190 851 643 892 834 -#> [775,] 653 102 472 60 41 830 152 107 864 606 -#> [776,] 218 643 501 80 606 864 663 190 774 926 -#> [777,] 715 676 671 225 568 217 127 808 370 867 -#> [778,] 942 255 919 213 654 322 992 37 891 325 -#> [779,] 164 439 865 636 729 438 169 173 490 579 -#> [780,] 428 942 160 894 673 213 778 72 457 70 -#> [781,] 542 525 210 427 729 305 358 362 115 640 -#> [782,] 2 407 225 667 938 568 671 386 955 839 -#> [783,] 184 704 756 937 21 585 831 270 902 938 -#> [784,] 323 886 634 947 200 166 596 9 275 378 -#> [785,] 869 628 684 518 352 896 204 357 338 53 -#> [786,] 124 33 350 989 62 527 939 334 253 664 -#> [787,] 369 983 679 174 201 259 563 345 199 371 -#> [788,] 817 961 700 161 30 242 66 77 310 635 -#> [789,] 3 333 988 707 309 228 796 960 68 900 -#> [790,] 484 436 741 467 196 290 349 553 592 807 -#> [791,] 94 561 227 495 148 365 966 9 947 357 -#> [792,] 158 126 770 723 192 509 753 970 614 341 -#> [793,] 16 812 578 740 870 762 622 276 872 79 -#> [794,] 212 83 885 210 525 78 870 559 872 577 -#> [795,] 256 440 176 818 939 59 363 248 508 350 -#> [796,] 328 117 856 19 387 422 707 229 454 247 -#> [797,] 975 540 656 279 120 484 741 354 743 290 -#> [798,] 662 425 749 897 462 814 548 187 854 49 -#> [799,] 889 231 494 171 610 403 840 353 594 516 -#> [800,] 635 246 77 731 493 994 242 315 109 455 -#> [801,] 389 172 251 680 675 442 686 531 170 265 -#> [802,] 990 168 153 607 512 299 529 412 924 379 -#> [803,] 813 893 301 537 449 720 162 610 494 980 -#> [804,] 138 50 703 132 11 737 313 850 76 145 -#> [805,] 418 196 467 272 895 35 565 699 701 209 -#> [806,] 910 640 408 651 317 757 538 115 683 282 -#> [807,] 743 342 507 112 741 962 399 484 553 535 -#> [808,] 922 294 627 339 122 113 393 777 217 715 -#> [809,] 100 883 848 42 851 459 619 711 643 774 -#> [810,] 881 476 111 302 413 284 516 807 548 274 -#> [811,] 699 773 257 701 565 916 560 505 272 316 -#> [812,] 740 79 578 793 622 762 16 582 43 215 -#> [813,] 720 449 301 803 918 851 537 821 43 980 -#> [814,] 897 662 268 83 602 798 49 445 425 885 -#> [815,] 261 48 233 289 194 142 127 454 955 318 -#> [816,] 542 355 305 362 781 65 358 703 11 737 -#> [817,] 700 788 30 161 961 310 242 46 731 349 -#> [818,] 440 176 695 44 81 429 661 248 89 795 -#> [819,] 589 182 646 552 829 663 26 240 260 118 -#> [820,] 944 319 546 401 465 22 123 332 343 435 -#> [821,] 43 188 23 90 828 774 79 918 539 604 -#> [822,] 498 858 96 755 59 374 66 297 866 626 -#> [823,] 754 437 715 867 600 777 452 121 339 361 -#> [824,] 98 827 763 788 310 961 463 493 497 91 -#> [825,] 913 285 998 357 466 669 561 536 163 985 -#> [826,] 29 929 530 470 110 884 292 143 515 346 -#> [827,] 497 719 763 91 541 567 555 502 933 36 -#> [828,] 987 892 774 648 834 821 539 42 188 598 -#> [829,] 240 589 26 649 598 575 646 496 892 668 -#> [830,] 60 218 93 304 102 373 190 864 859 776 -#> [831,] 608 783 356 756 128 267 25 714 585 184 -#> [832,] 916 721 396 773 699 131 811 316 846 906 -#> [833,] 959 441 768 134 560 866 626 971 257 811 -#> [834,] 987 289 504 828 641 892 202 648 191 551 -#> [835,] 958 8 577 116 421 517 595 951 212 661 -#> [836,] 70 306 745 615 664 891 37 269 4 900 -#> [837,] 367 412 32 294 122 398 890 360 991 382 -#> [838,] 424 982 649 173 865 281 725 228 908 767 -#> [839,] 170 676 955 261 265 782 127 407 225 686 -#> [840,] 458 594 861 403 629 965 40 64 799 889 -#> [841,] 402 186 696 390 520 770 713 509 970 296 -#> [842,] 314 167 630 880 51 972 443 298 468 979 -#> [843,] 266 977 771 928 709 511 329 116 212 862 -#> [844,] 995 423 768 89 971 936 231 171 866 155 -#> [845,] 535 4 404 112 458 741 322 891 413 505 -#> [846,] 895 888 698 617 701 467 196 699 899 805 -#> [847,] 51 979 630 298 665 842 167 203 334 314 -#> [848,] 100 883 809 14 711 963 459 920 643 390 -#> [849,] 411 973 636 382 837 890 118 523 438 930 -#> [850,] 173 865 11 226 580 448 65 737 145 804 -#> [851,] 301 304 720 809 42 459 774 813 449 373 -#> [852,] 771 329 84 321 221 262 419 396 862 672 -#> [853,] 140 421 582 949 125 171 874 81 911 695 -#> [854,] 579 462 897 489 798 662 169 605 761 814 -#> [855,] 273 971 441 959 956 768 833 176 44 81 -#> [856,] 19 117 328 750 343 399 796 229 136 340 -#> [857,] 423 514 866 41 936 739 102 917 859 96 -#> [858,] 374 66 513 613 564 7 650 822 24 486 -#> [859,] 155 423 205 102 404 190 957 157 864 995 -#> [860,] 623 877 963 970 521 469 417 74 384 713 -#> [861,] 458 629 40 840 403 594 492 64 484 476 -#> [862,] 143 884 826 346 843 29 932 311 929 84 -#> [863,] 211 953 351 637 526 175 263 217 299 25 -#> [864,] 606 501 218 205 776 80 60 190 859 830 -#> [865,] 173 850 838 226 779 439 580 11 725 424 -#> [866,] 626 857 936 423 844 971 833 995 768 96 -#> [867,] 122 360 619 823 777 603 452 294 361 883 -#> [868,] 991 361 398 341 437 367 360 614 823 770 -#> [869,] 785 518 628 204 357 684 352 365 913 617 -#> [870,] 16 918 578 43 762 604 83 872 582 813 -#> [871,] 453 747 69 935 435 902 356 227 561 123 -#> [872,] 559 582 16 870 429 695 140 78 83 71 -#> [873,] 445 425 49 12 692 87 667 798 757 471 -#> [874,] 52 736 949 893 911 612 879 171 803 511 -#> [875,] 270 87 757 748 683 692 764 637 572 21 -#> [876,] 260 772 118 646 649 238 584 890 438 982 -#> [877,] 860 417 967 921 536 684 717 996 469 204 -#> [878,] 951 517 931 8 835 958 241 577 56 116 -#> [879,] 494 330 893 889 874 456 434 610 64 35 -#> [880,] 443 630 195 842 298 51 847 314 891 257 -#> [881,] 111 274 810 882 302 476 516 685 548 318 -#> [882,] 274 95 881 23 685 905 111 253 90 359 -#> [883,] 848 100 809 14 920 619 963 711 867 296 -#> [884,] 826 29 110 1 311 519 222 530 200 929 -#> [885,] 268 525 71 794 83 814 897 49 729 78 -#> [886,] 634 909 644 323 308 200 784 430 475 275 -#> [887,] 645 32 379 470 529 607 530 299 28 168 -#> [888,] 467 846 196 895 701 899 698 151 432 617 -#> [889,] 799 231 494 171 840 610 594 403 353 207 -#> [890,] 438 238 412 118 837 360 652 281 122 398 -#> [891,] 4 37 322 413 535 845 306 269 298 70 -#> [892,] 828 668 987 598 496 648 834 504 605 188 -#> [893,] 301 803 52 373 611 304 67 736 494 137 -#> [894,] 673 780 428 457 33 72 216 160 213 942 -#> [895,] 846 701 467 698 888 196 805 699 617 418 -#> [896,] 785 352 869 53 444 617 684 518 628 591 -#> [897,] 814 662 604 798 854 462 579 602 49 83 -#> [898,] 67 744 677 416 135 658 456 728 373 879 -#> [899,] 464 888 846 75 196 895 432 151 467 617 -#> [900,] 569 554 745 457 836 664 70 306 229 988 -#> [901,] 406 758 330 659 292 375 113 515 303 162 -#> [902,] 386 938 937 2 616 935 453 756 751 747 -#> [903,] 431 624 876 164 772 528 779 767 249 865 -#> [904,] 222 570 475 764 1000 621 784 714 644 282 -#> [905,] 157 237 23 882 95 359 987 274 190 188 -#> [906,] 500 35 291 660 272 895 236 321 434 543 -#> [907,] 574 242 493 199 310 731 259 552 224 345 -#> [908,] 318 496 194 20 749 127 462 281 187 815 -#> [909,] 644 430 886 634 475 751 21 937 184 748 -#> [910,] 640 806 115 738 419 317 219 651 683 757 -#> [911,] 52 949 612 125 766 874 107 171 853 304 -#> [912,] 621 714 904 128 585 426 69 570 222 475 -#> [913,] 420 285 357 561 825 998 150 628 230 223 -#> [914,] 689 937 21 184 203 616 430 909 644 902 -#> [915,] 671 656 225 370 476 279 326 667 568 407 -#> [916,] 773 832 721 811 699 316 698 701 846 895 -#> [917,] 102 957 130 404 152 859 290 423 380 502 -#> [918,] 604 43 870 602 90 821 578 813 762 897 -#> [919,] 255 778 722 942 197 322 257 891 160 992 -#> [920,] 975 593 120 354 100 14 883 279 754 656 -#> [921,] 996 326 998 466 223 723 877 600 754 158 -#> [922,] 808 294 627 113 122 339 54 393 165 953 -#> [923,] 925 336 455 246 976 77 315 144 182 635 -#> [924,] 512 990 802 168 153 735 607 529 772 433 -#> [925,] 923 606 864 501 205 336 455 218 776 30 -#> [926,] 504 182 648 834 501 551 776 987 552 892 -#> [927,] 707 309 952 422 387 277 670 760 796 999 -#> [928,] 276 252 116 16 794 709 212 870 582 559 -#> [929,] 515 29 292 826 110 287 303 659 470 54 -#> [930,] 390 360 809 198 848 883 335 411 867 100 -#> [931,] 517 248 577 440 951 71 241 958 989 320 -#> [932,] 266 515 346 250 511 929 287 509 696 826 -#> [933,] 384 469 278 472 541 623 521 583 152 502 -#> [934,] 941 685 516 548 177 268 253 298 320 814 -#> [935,] 747 325 386 902 871 203 937 654 966 665 -#> [936,] 564 995 844 971 866 857 423 514 125 41 -#> [937,] 184 616 914 21 902 430 751 909 689 644 -#> [938,] 782 902 568 2 756 453 667 217 225 407 -#> [939,] 941 334 176 248 320 630 934 508 440 141 -#> [940,] 495 193 214 378 94 984 9 791 581 985 -#> [941,] 934 268 177 78 320 71 44 685 939 516 -#> [942,] 778 780 213 919 255 992 160 216 654 322 -#> [943,] 410 480 366 234 76 734 490 559 762 241 -#> [944,] 693 557 546 319 820 532 465 400 136 401 -#> [945,] 548 132 662 685 320 425 334 798 187 665 -#> [946,] 295 450 300 159 119 128 114 618 576 647 -#> [947,] 596 365 415 954 698 275 94 784 634 886 -#> [948,] 759 123 45 22 952 332 319 422 820 608 -#> [949,] 612 911 52 874 766 853 736 140 125 582 -#> [950,] 409 519 1 338 192 628 243 785 753 110 -#> [951,] 878 8 517 241 525 577 931 116 56 835 -#> [952,] 422 547 760 707 45 948 927 796 387 251 -#> [953,] 863 211 217 351 627 263 526 715 652 122 -#> [954,] 415 485 947 596 275 51 698 784 167 9 -#> [955,] 247 454 261 839 170 208 815 2 782 194 -#> [956,] 855 273 681 524 396 441 141 478 207 560 -#> [957,] 404 739 5 46 155 553 112 741 535 859 -#> [958,] 835 517 440 661 931 818 577 248 176 256 -#> [959,] 833 441 768 855 866 971 560 134 273 626 -#> [960,] 68 228 58 392 142 387 551 675 63 982 -#> [961,] 788 817 483 655 555 161 700 763 310 66 -#> 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338 277 +#> [2,] 873 936 304 397 677 479 667 680 230 856 +#> [3,] 480 369 471 186 486 911 569 826 178 10 +#> [4,] 288 481 746 93 802 252 120 849 17 291 +#> [5,] 295 33 212 289 484 556 466 851 776 804 +#> [6,] 999 575 125 386 36 211 695 651 20 1000 +#> [7,] 510 644 30 933 275 407 990 189 483 190 +#> [8,] 953 943 55 278 966 576 730 168 342 870 +#> [9,] 657 493 899 38 71 404 268 401 227 307 +#> [10,] 468 930 569 710 486 238 615 186 981 664 +#> [11,] 456 884 872 375 752 423 218 909 738 430 +#> [12,] 314 64 603 790 421 359 800 478 757 740 +#> [13,] 264 889 46 394 63 131 64 314 972 88 +#> [14,] 720 412 780 352 585 226 320 526 945 537 +#> [15,] 475 215 83 793 521 982 217 411 900 946 +#> [16,] 107 465 86 819 560 101 355 523 592 728 +#> [17,] 252 481 220 675 120 672 446 19 612 302 +#> [18,] 613 32 77 606 121 810 944 12 757 314 +#> [19,] 446 843 836 821 189 931 269 164 988 252 +#> [20,] 651 692 125 386 36 575 1000 901 6 370 +#> [21,] 188 914 627 213 101 629 272 417 777 136 +#> [22,] 283 906 426 256 321 992 537 945 595 501 +#> [23,] 421 518 869 575 562 591 566 47 36 999 +#> [24,] 492 682 787 550 912 517 463 679 522 572 +#> [25,] 798 942 932 157 405 640 638 348 561 86 +#> [26,] 597 704 222 191 940 40 895 90 413 735 +#> [27,] 960 126 592 417 213 913 346 523 152 766 +#> [28,] 588 646 169 114 512 669 750 789 768 932 +#> [29,] 673 790 767 901 881 921 406 307 501 963 +#> [30,] 644 933 969 7 173 483 189 510 407 275 +#> [31,] 812 87 648 917 187 165 554 958 571 858 +#> [32,] 18 77 613 314 944 448 606 64 12 871 +#> [33,] 295 5 289 776 212 556 926 484 180 513 +#> [34,] 639 109 763 498 891 702 712 827 119 519 +#> [35,] 815 690 182 92 831 607 382 200 803 715 +#> [36,] 386 695 651 6 165 439 20 575 999 858 +#> [37,] 711 323 113 973 351 726 450 665 353 393 +#> [38,] 71 53 307 657 352 9 310 827 493 956 +#> [39,] 659 400 613 810 32 643 800 64 606 121 +#> [40,] 357 90 702 498 735 863 222 624 75 763 +#> [41,] 954 976 623 829 468 270 155 10 84 348 +#> [42,] 556 289 263 225 851 337 762 466 212 5 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394 192 889 800 64 191 13 413 46 +#> [64,] 800 314 12 131 192 264 63 394 643 32 +#> [65,] 701 398 957 930 829 473 238 59 886 84 +#> [66,] 487 452 112 347 238 473 118 833 826 534 +#> [67,] 500 807 442 408 770 608 277 136 584 929 +#> [68,] 373 462 594 193 71 542 657 741 975 341 +#> [69,] 315 416 58 398 764 94 914 174 708 941 +#> [70,] 445 684 627 416 447 80 708 204 914 764 +#> [71,] 657 310 38 103 373 504 9 519 542 827 +#> [72,] 623 842 41 560 296 829 241 954 819 528 +#> [73,] 864 134 617 45 564 219 266 458 884 115 +#> [74,] 60 817 718 358 530 820 258 325 489 162 +#> [75,] 624 823 712 735 863 754 876 702 979 498 +#> [76,] 643 400 63 435 413 46 800 26 264 895 +#> [77,] 32 18 871 613 376 314 12 64 183 800 +#> [78,] 781 137 621 122 736 709 958 983 924 322 +#> [79,] 383 250 655 125 152 728 200 668 575 355 +#> [80,] 447 761 204 445 123 684 347 253 70 59 +#> [81,] 844 240 535 849 93 865 882 380 199 743 +#> [82,] 197 636 545 661 492 269 923 24 371 133 +#> [83,] 620 521 411 349 678 215 593 475 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535 543 73 266 865 198 883 +#> [125,] 1000 6 575 386 20 651 523 36 152 79 +#> [126,] 27 960 565 592 801 152 417 188 913 766 +#> [127,] 385 703 422 631 727 816 759 625 963 950 +#> [128,] 290 116 137 994 958 284 709 781 183 143 +#> [129,] 402 255 297 784 144 243 305 276 414 229 +#> [130,] 364 532 768 344 821 725 928 169 460 931 +#> [131,] 394 921 673 998 64 264 192 390 314 29 +#> [132,] 176 837 286 830 786 726 660 495 144 474 +#> [133,] 339 923 580 163 714 82 676 517 568 24 +#> [134,] 266 115 564 541 198 864 964 159 327 263 +#> [135,] 419 119 443 432 451 859 166 822 896 410 +#> [136,] 629 21 627 914 188 353 941 807 777 428 +#> [137,] 709 78 128 958 781 290 143 917 752 122 +#> [138,] 230 757 667 566 977 401 421 899 869 304 +#> [139,] 220 476 120 866 214 252 716 189 991 371 +#> [140,] 282 707 429 955 671 968 978 691 720 719 +#> [141,] 691 276 978 522 488 589 671 968 707 996 +#> [142,] 526 818 780 616 412 719 760 196 370 49 +#> [143,] 917 122 709 87 290 128 137 31 850 781 +#> [144,] 402 660 129 176 235 582 474 755 276 600 +#> [145,] 484 43 180 943 54 538 384 995 924 736 +#> [146,] 934 579 239 48 232 690 681 509 257 599 +#> [147,] 895 313 76 540 769 26 330 400 88 961 +#> [148,] 929 408 708 324 729 834 867 761 524 762 +#> [149,] 847 803 899 231 208 232 934 959 936 401 +#> [150,] 851 42 354 696 556 337 225 212 466 148 +#> [151,] 272 174 706 391 807 353 217 136 748 21 +#> [152,] 592 383 523 960 801 125 27 1000 417 985 +#> [153,] 582 660 868 637 144 297 235 175 474 129 +#> [154,] 797 930 84 886 723 10 710 562 954 591 +#> [155,] 751 270 710 954 810 369 10 41 51 468 +#> [156,] 795 826 455 859 480 737 569 3 626 119 +#> [157,] 798 942 25 312 405 932 561 640 348 332 +#> [158,] 172 666 441 669 542 626 796 789 822 341 +#> [159,] 852 568 263 198 95 266 778 180 99 289 +#> [160,] 897 577 860 935 766 813 306 194 331 253 +#> [161,] 507 546 366 766 734 346 49 335 533 635 +#> [162,] 668 630 699 748 747 221 489 999 6 575 +#> [163,] 676 834 337 705 912 517 24 682 225 568 +#> [164,] 910 344 581 19 197 931 189 661 988 821 +#> [165,] 648 695 439 36 880 31 903 386 187 812 +#> [166,] 451 298 419 552 443 280 460 181 618 364 +#> [167,] 596 531 50 406 767 881 998 970 284 656 +#> [168,] 356 794 635 576 8 236 278 848 836 546 +#> [169,] 588 768 618 669 280 181 808 28 460 130 +#> [170,] 820 184 195 853 228 461 234 359 62 448 +#> [171,] 430 784 255 501 399 752 759 878 596 972 +#> [172,] 158 789 441 626 669 666 822 410 472 796 +#> [173,] 969 649 855 30 716 760 644 203 689 915 +#> [174,] 272 151 391 706 605 217 215 914 411 946 +#> [175,] 474 235 663 209 950 496 377 582 857 939 +#> [176,] 132 837 144 726 286 91 660 235 600 474 +#> [177,] 937 286 923 600 132 837 339 786 714 328 +#> [178,] 3 480 615 186 486 223 112 471 919 553 +#> [179,] 623 41 842 348 829 65 976 72 441 666 +#> [180,] 145 289 543 563 115 513 159 538 549 484 +#> [181,] 280 618 460 378 169 588 746 821 768 887 +#> [182,] 815 831 965 505 381 231 585 690 35 607 +#> [183,] 116 871 376 128 290 687 242 994 77 143 +#> [184,] 170 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735 863 641 498 979 995 +#> [625,] 727 509 665 422 901 62 747 816 127 759 +#> [626,] 455 172 158 441 569 666 639 34 664 472 +#> [627,] 914 445 629 21 764 70 447 331 213 813 +#> [628,] 825 984 92 520 205 100 815 990 477 557 +#> [629,] 136 627 428 21 941 331 914 764 813 565 +#> [630,] 699 436 162 748 999 916 23 575 518 6 +#> [631,] 948 362 907 996 692 91 249 858 385 978 +#> [632,] 593 853 982 518 376 562 475 234 854 111 +#> [633,] 619 792 281 786 545 496 923 830 529 637 +#> [634,] 918 529 685 371 267 637 220 300 229 573 +#> [635,] 576 245 840 259 168 756 546 210 260 356 +#> [636,] 371 82 269 550 685 492 522 772 24 197 +#> [637,] 918 529 660 634 685 786 496 300 414 402 +#> [638,] 942 798 193 25 640 594 405 932 975 68 +#> [639,] 34 109 472 298 702 604 534 808 119 712 +#> [640,] 561 819 348 798 942 25 157 638 193 405 +#> [641,] 938 372 924 624 995 75 735 736 712 437 +#> [642,] 975 356 594 967 405 811 507 533 546 969 +#> [643,] 400 800 76 64 435 63 413 39 659 301 +#> [644,] 189 30 7 510 716 969 875 173 933 275 +#> [645,] 512 114 483 646 514 379 581 28 434 164 +#> [646,] 28 588 114 169 181 110 460 645 512 254 +#> [647,] 52 224 788 392 528 296 505 831 68 993 +#> [648,] 439 165 31 812 858 187 695 577 903 782 +#> [649,] 969 760 383 173 49 780 965 616 526 142 +#> [650,] 600 905 986 144 385 755 176 396 91 962 +#> [651,] 20 386 692 36 1000 858 125 439 6 907 +#> [652,] 993 392 717 915 224 622 200 655 250 381 +#> [653,] 271 884 709 752 287 905 909 686 137 589 +#> [654,] 221 655 777 44 188 152 368 79 108 113 +#> [655,] 79 654 728 383 250 336 152 200 993 16 +#> [656,] 322 237 167 430 805 423 970 752 998 968 +#> [657,] 9 71 38 310 268 373 493 51 96 227 +#> [658,] 806 926 244 563 219 33 556 437 295 851 +#> [659,] 39 400 810 51 590 800 613 64 497 201 +#> [660,] 786 144 637 918 582 176 132 685 874 474 +#> [661,] 453 344 82 197 95 931 492 269 679 503 +#> [662,] 893 927 481 849 104 697 17 499 220 862 +#> [663,] 175 377 474 235 209 877 950 857 257 939 +#> [664,] 981 827 227 468 96 210 840 758 876 754 +#> [665,] 747 668 625 692 727 902 904 216 450 948 +#> [666,] 441 158 976 172 626 542 348 341 796 640 +#> [667,] 977 304 230 138 590 936 680 397 757 677 +#> [668,] 747 162 665 113 108 625 973 699 79 6 +#> [669,] 822 280 618 789 158 172 169 796 588 542 +#> [670,] 662 893 783 927 861 849 697 291 104 288 +#> [671,] 691 970 968 531 805 980 978 140 282 955 +#> [672,] 612 688 449 316 506 832 586 117 499 17 +#> [673,] 29 307 921 790 131 767 406 901 934 881 +#> [674,] 863 735 357 971 90 876 53 454 559 956 +#> [675,] 612 431 547 832 302 846 574 862 559 17 +#> [676,] 163 834 337 568 852 762 263 225 705 133 +#> [677,] 936 803 667 839 2 579 149 590 831 304 +#> [678,] 615 620 486 570 935 238 742 186 102 521 +#> [679,] 95 492 568 953 99 463 24 538 730 269 +#> [680,] 57 667 799 304 977 241 397 230 2 839 +#> [681,] 718 146 625 257 817 509 108 668 62 727 +#> [682,] 787 24 912 492 517 550 194 420 463 989 +#> [683,] 243 440 229 541 602 688 745 414 548 909 +#> [684,] 445 70 447 204 80 701 627 273 86 27 +#> [685,] 634 371 918 529 636 522 786 573 637 402 +#> [686,] 458 564 322 909 621 141 884 78 691 327 +#> [687,] 116 301 423 128 183 290 284 871 972 994 +#> [688,] 229 745 573 683 243 602 672 414 305 541 +#> [689,] 261 409 203 855 360 173 403 716 969 915 +#> [690,] 803 579 831 847 35 182 146 815 149 48 +#> [691,] 141 671 978 707 233 522 996 968 282 955 +#> [692,] 694 651 907 20 948 631 333 727 858 1000 +#> [693,] 106 97 343 326 925 363 504 477 908 788 +#> [694,] 61 370 692 333 727 719 216 20 651 616 +#> [695,] 36 165 386 6 211 999 439 648 858 31 +#> [696,] 337 225 912 989 163 340 442 517 444 354 +#> [697,] 927 291 515 120 662 990 502 226 893 431 +#> [698,] 434 379 791 203 765 483 855 114 338 105 +#> [699,] 630 436 162 575 62 566 916 668 23 728 +#> [700,] 459 765 622 774 750 842 296 652 791 638 +#> [701,] 59 86 684 445 398 433 447 101 70 80 +#> [702,] 624 432 40 498 712 75 34 357 639 763 +#> [703,] 385 127 422 904 631 457 902 389 948 587 +#> [704,] 222 26 597 237 334 940 265 449 40 888 +#> [705,] 834 163 929 408 676 337 696 148 545 912 +#> [706,] 272 748 151 221 391 174 21 217 188 353 +#> [707,] 978 955 996 140 719 522 196 691 282 550 +#> [708,] 70 416 148 941 445 627 929 136 684 761 +#> [709,] 137 128 143 958 78 290 917 781 983 87 +#> [710,] 486 186 10 723 468 47 911 591 841 562 +#> [711,] 973 113 37 108 450 323 985 374 353 665 +#> [712,] 75 823 624 498 702 432 34 763 754 876 +#> [713,] 919 395 311 303 484 43 739 742 145 833 +#> [714,] 580 874 339 133 964 905 986 755 555 923 +#> [715,] 965 61 939 200 250 370 857 649 607 232 +#> [716,] 476 875 189 855 644 360 173 772 969 760 +#> [717,] 993 250 915 381 200 392 652 224 965 649 +#> [718,] 817 60 681 108 323 973 113 668 747 625 +#> [719,] 707 196 978 333 142 694 631 140 319 996 +#> [720,] 320 50 531 14 429 185 196 140 671 881 +#> [721,] 598 551 72 797 560 465 829 107 418 84 +#> [722,] 609 279 261 403 360 830 409 338 855 689 +#> [723,] 591 562 421 23 47 518 710 603 954 84 +#> [724,] 885 396 428 56 340 539 989 274 362 890 +#> [725,] 532 364 446 55 931 794 836 778 821 953 +#> [726,] 216 837 362 176 333 495 450 91 948 885 +#> [727,] 625 901 694 692 509 963 20 127 759 665 +#> [728,] 251 959 79 436 381 566 107 208 655 699 +#> [729,] 324 204 867 148 105 317 952 761 949 684 +#> [730,] 966 55 99 953 278 8 955 233 679 836 +#> [731,] 330 199 499 992 262 888 334 738 218 769 +#> [732,] 309 85 824 344 931 814 852 661 164 988 +#> [733,] 527 788 647 382 815 52 477 677 831 35 +#> [734,] 335 49 161 196 955 320 185 507 780 782 +#> [735,] 863 75 624 674 995 971 40 823 265 191 +#> [736,] 924 621 437 43 983 78 122 781 372 995 +#> [737,] 553 795 369 3 471 826 480 156 911 178 +#> [738,] 218 892 98 548 334 456 731 992 11 330 +#> [739,] 833 54 756 943 604 123 536 713 534 293 +#> [740,] 102 828 841 911 570 454 971 754 478 186 +#> [741,] 190 462 407 275 373 378 887 990 594 933 +#> [742,] 315 395 521 620 678 935 860 473 713 303 +#> [743,] 117 882 506 267 380 578 844 988 586 93 +#> [744,] 318 606 944 121 18 32 77 613 39 871 +#> [745,] 688 229 243 683 305 548 499 602 414 968 +#> [746,] 431 802 502 181 821 4 291 510 378 288 +#> [747,] 665 668 809 162 625 904 113 902 489 727 +#> [748,] 706 999 162 489 630 793 6 221 211 272 +#> [749,] 308 482 962 511 457 358 294 872 703 491 +#> [750,] 932 25 798 765 638 774 28 254 942 842 +#> [751,] 270 155 51 268 947 954 810 227 977 723 +#> [752,] 589 490 656 909 958 884 137 423 11 709 +#> [753,] 418 174 900 65 215 151 398 69 154 797 +#> [754,] 876 454 823 971 956 828 827 96 981 75 +#> [755,] 905 874 144 909 246 440 129 243 683 276 +#> [756,] 576 604 245 635 739 534 424 840 210 342 +#> [757,] 138 613 421 12 667 590 723 810 359 230 +#> [758,] 210 840 259 880 493 207 869 433 260 227 +#> [759,] 399 816 257 422 509 415 727 950 127 625 +#> [760,] 616 649 772 818 383 49 142 173 801 969 +#> [761,] 123 80 416 536 524 204 347 452 447 867 +#> [762,] 804 852 524 676 212 42 453 289 263 568 +#> [763,] 498 34 89 519 357 119 712 90 702 432 +#> [764,] 914 860 331 605 627 813 59 416 897 941 +#> [765,] 791 774 622 700 273 254 459 557 932 750 +#> [766,] 161 417 202 572 577 160 213 801 346 523 +#> [767,] 881 596 963 29 901 878 501 406 167 50 +#> [768,] 169 588 808 821 618 356 794 236 130 928 +#> [769,] 330 256 888 63 46 704 413 906 940 237 +#> [770,] 277 136 708 67 1 777 70 807 44 408 +#> [771,] 835 66 33 295 776 487 867 5 212 452 +#> [772,] 616 760 818 838 716 319 142 467 476 734 +#> [773,] 22 283 611 321 992 893 861 426 663 906 +#> [774,] 622 791 273 765 932 913 25 942 811 798 +#> [775,] 894 839 48 527 677 599 936 579 2 88 +#> [776,] 833 54 295 33 549 145 293 5 484 739 +#> [777,] 188 101 21 654 44 592 152 27 221 126 +#> [778,] 549 95 99 814 578 725 159 384 55 532 +#> [779,] 967 887 103 848 794 302 275 891 642 843 +#> [780,] 412 526 335 142 49 734 14 196 320 649 +#> [781,] 78 122 137 621 128 958 709 143 736 290 +#> [782,] 185 955 996 439 202 278 233 572 707 320 +#> [783,] 262 862 670 908 662 291 802 431 574 697 +#> [784,] 255 129 171 297 305 402 430 992 548 785 +#> [785,] 784 868 255 297 171 129 872 153 399 516 +#> [786,] 660 918 132 371 685 637 792 496 830 923 +#> [787,] 682 912 24 194 517 420 550 890 233 492 +#> [788,] 647 505 343 831 677 52 528 803 224 68 +#> [789,] 172 669 410 158 822 441 472 169 808 796 +#> [790,] 29 673 406 359 603 307 421 901 36 12 +#> [791,] 774 765 622 273 913 336 698 960 173 915 +#> [792,] 371 786 476 636 82 545 875 139 923 918 +#> [793,] 982 111 748 217 489 632 15 809 879 475 +#> [794,] 168 848 302 843 779 356 725 836 891 342 +#> [795,] 156 826 737 455 859 480 3 432 553 471 +#> [796,] 109 260 967 441 669 891 618 808 405 542 +#> [797,] 154 84 886 723 930 954 562 560 591 900 +#> [798,] 25 942 157 932 405 640 348 638 561 86 +#> [799,] 680 839 57 606 184 613 944 757 667 810 +#> [800,] 64 192 643 314 63 12 400 191 387 497 +#> [801,] 333 319 616 985 202 417 1000 565 766 152 +#> [802,] 746 4 288 431 93 574 291 552 783 502 +#> [803,] 831 690 149 579 847 936 505 677 231 899 +#> [804,] 762 852 524 814 212 289 453 5 293 295 +#> [805,] 322 265 980 671 316 458 237 970 656 559 +#> [806,] 658 926 544 223 244 851 437 563 219 983 +#> [807,] 136 151 353 500 941 629 428 391 272 67 +#> [808,] 472 298 768 604 109 618 796 410 639 169 +#> [809,] 457 904 902 587 747 482 111 974 665 962 +#> [810,] 121 613 18 757 659 39 751 270 606 12 +#> [811,] 405 642 356 913 546 932 942 508 975 366 +#> [812,] 31 389 858 554 87 648 571 958 439 974 +#> [813,] 331 860 897 605 914 485 160 764 391 417 +#> [814,] 549 778 532 364 931 804 852 95 293 309 +#> [815,] 182 831 520 35 690 505 607 477 92 462 +#> [816,] 950 759 422 399 257 127 625 727 509 385 +#> [817,] 718 358 60 681 747 625 464 820 668 816 +#> [818,] 616 142 772 760 319 333 866 991 719 61 +#> [819,] 640 829 561 348 86 16 798 25 107 942 +#> [820,] 170 228 358 62 195 817 461 234 509 587 +#> [821,] 768 19 446 794 510 843 725 746 181 130 +#> [822,] 669 280 419 618 172 789 158 181 169 542 +#> [823,] 75 754 712 876 624 454 827 96 498 735 +#> [824,] 309 198 85 732 743 578 266 159 852 117 +#> [825,] 628 203 557 392 689 915 92 254 652 717 +#> [826,] 156 795 480 455 859 3 569 471 737 178 +#> [827,] 876 96 424 754 956 664 891 454 981 823 +#> [828,] 971 454 903 754 740 387 876 192 191 841 +#> [829,] 819 84 640 348 561 86 954 107 465 16 +#> [830,] 279 495 132 609 837 866 991 786 286 722 +#> [831,] 505 803 847 182 690 231 404 815 585 149 +#> [832,] 846 612 316 675 547 862 449 672 586 559 +#> [833,] 54 739 776 293 943 604 342 549 979 145 +#> [834,] 163 705 676 929 337 408 682 912 762 24 +#> [835,] 771 66 867 487 452 112 952 347 212 427 +#> [836,] 467 269 463 168 843 19 953 838 794 236 +#> [837,] 286 132 176 726 495 830 319 216 786 818 +#> [838,] 467 463 772 572 161 546 836 766 503 734 +#> [839,] 590 667 799 677 936 448 680 613 757 606 +#> [840,] 210 259 758 635 260 207 245 433 576 880 +#> [841,] 911 740 497 96 454 754 828 471 51 823 +#> [842,] 348 819 638 640 623 829 666 72 798 750 +#> [843,] 510 836 794 19 302 189 168 335 848 779 +#> [844,] 849 882 743 199 380 300 104 93 662 267 +#> [845,] 338 601 44 609 277 279 1 126 336 698 +#> [846,] 832 612 547 862 316 675 559 302 674 888 +#> [847,] 831 585 149 231 803 404 505 232 934 352 +#> [848,] 335 302 794 559 583 278 779 168 320 533 +#> [849,] 481 844 93 662 104 214 4 882 252 17 +#> [850,] 917 271 143 879 974 87 554 571 709 294 +#> [851,] 354 466 556 212 303 5 42 484 289 295 +#> [852,] 159 568 263 804 198 95 762 289 676 814 +#> [853,] 632 234 376 359 170 184 518 982 593 77 +#> [854,] 234 994 999 111 587 904 359 695 518 790 +#> [855,] 360 875 716 173 476 261 203 689 434 644 +#> [856,] 916 479 397 230 699 436 304 325 2 251 +#> [857,] 939 209 61 991 877 415 257 950 866 694 +#> [858,] 812 439 907 648 389 948 651 963 996 249 +#> [859,] 432 826 119 712 702 795 624 639 156 75 +#> [860,] 897 331 813 605 764 160 914 306 485 935 +#> [861,] 611 299 520 927 607 670 893 697 205 945 +#> [862,] 846 832 888 262 547 675 922 612 431 662 +#> [863,] 735 674 75 624 995 971 40 357 876 979 +#> [864,] 73 134 266 45 564 898 541 115 180 964 +#> [865,] 586 506 449 365 222 367 704 266 574 578 +#> [866,] 991 818 142 857 939 474 877 139 476 719 +#> [867,] 761 212 123 452 729 347 148 524 204 487 +#> [868,] 582 153 785 144 235 308 950 175 129 755 +#> [869,] 603 421 23 401 566 493 880 575 478 36 +#> [870,] 536 943 420 538 194 8 484 311 953 466 +#> [871,] 183 116 77 376 32 128 318 687 242 18 +#> [872,] 11 171 375 456 218 255 884 784 752 972 +#> [873,] 2 325 397 304 479 856 936 251 230 579 +#> [874,] 714 755 660 637 580 144 964 600 246 918 +#> [875,] 360 716 855 476 189 644 173 434 197 30 +#> [876,] 754 454 956 827 424 823 971 53 96 583 +#> [877,] 920 415 939 857 537 866 991 945 474 429 +#> [878,] 881 415 963 767 429 50 596 501 537 490 +#> [879,] 974 274 56 571 902 962 850 111 554 809 +#> [880,] 210 758 478 165 603 53 869 956 840 227 +#> [881,] 767 963 596 878 50 901 29 406 167 490 +#> [882,] 743 844 267 380 849 117 988 85 506 93 +#> [883,] 451 443 543 513 364 166 309 814 778 367 +#> [884,] 11 909 752 375 653 686 548 458 456 322 +#> [885,] 724 396 362 340 989 274 558 890 56 428 +#> [886,] 614 84 930 591 211 485 47 107 465 433 +#> [887,] 967 103 779 378 373 504 618 741 542 71 +#> [888,] 262 330 862 922 769 846 832 704 731 499 +#> [889,] 264 13 394 63 88 46 131 256 64 906 +#> [890,] 558 444 287 787 249 488 306 555 420 274 +#> [891,] 109 424 827 260 34 103 848 794 779 604 +#> [892,] 98 218 738 997 548 456 992 872 731 11 +#> [893,] 662 927 920 945 697 499 862 670 283 611 +#> [894,] 88 106 693 839 677 343 775 590 39 97 +#> [895,] 540 26 940 704 769 248 597 330 617 222 +#> [896,] 222 365 40 432 26 367 451 702 119 704 +#> [897,] 860 331 306 813 160 292 187 605 935 485 +#> [898,] 45 964 864 535 440 134 541 73 380 683 +#> [899,] 404 231 401 959 208 505 9 355 149 493 +#> [900,] 632 982 215 593 154 15 793 797 361 886 +#> [901,] 767 29 881 963 509 727 20 406 62 790 +#> [902,] 974 571 904 948 809 274 554 362 389 457 +#> [903,] 828 971 53 165 531 454 648 406 880 970 +#> [904,] 587 902 809 571 703 294 854 974 554 111 +#> [905,] 986 909 276 555 755 650 488 653 600 589 +#> [906,] 283 22 426 256 394 537 501 430 595 889 +#> [907,] 202 948 858 577 692 439 362 631 996 249 +#> [908,] 922 363 321 925 862 888 783 262 313 97 +#> [909,] 276 884 905 686 683 243 752 589 141 458 +#> [910,] 164 197 581 344 19 875 661 189 434 82 +#> [911,] 471 186 841 369 740 102 3 486 710 570 +#> [912,] 517 682 787 989 24 340 225 555 890 444 +#> [913,] 960 811 366 346 27 546 273 774 592 507 +#> [914,] 764 627 21 860 813 605 331 59 629 897 +#> [915,] 622 717 392 993 173 652 969 649 774 203 +#> [916,] 436 856 630 230 699 397 757 138 304 23 +#> [917,] 87 554 143 571 31 974 290 812 958 850 +#> [918,] 634 529 685 637 371 786 267 660 300 220 +#> [919,] 713 395 311 43 145 484 742 437 122 303 +#> [920,] 945 877 537 893 226 429 412 305 283 526 +#> [921,] 131 673 29 50 307 394 352 167 501 767 +#> [922,] 363 908 862 888 925 547 846 262 90 832 +#> [923,] 133 339 82 545 786 636 371 177 580 286 +#> [924,] 736 437 641 621 938 995 43 372 145 78 +#> [925,] 363 504 103 595 390 97 310 922 352 373 +#> [926,] 563 437 145 33 484 180 295 5 289 556 +#> [927,] 697 291 893 515 662 945 299 226 120 920 +#> [928,] 236 508 332 293 949 532 808 453 524 768 +#> [929,] 408 148 834 277 705 324 708 163 503 453 +#> [930,] 10 468 84 886 591 154 615 238 678 614 +#> [931,] 344 446 532 725 95 19 836 661 814 778 +#> [932,] 25 798 942 157 811 774 405 273 638 750 +#> [933,] 407 190 275 594 30 7 642 969 975 990 +#> [934,] 232 239 146 847 231 149 208 673 537 29 +#> [935,] 253 485 160 187 860 897 678 570 742 395 +#> [936,] 677 579 803 149 479 667 2 48 839 590 +#> [937,] 177 600 328 286 393 340 584 132 176 650 +#> [938,] 641 372 924 437 995 624 736 75 145 919 +#> [939,] 857 61 209 991 877 415 257 715 866 694 +#> [940,] 597 248 237 617 413 26 704 265 456 191 +#> [941,] 428 629 764 331 94 914 136 306 627 605 +#> [942,] 798 25 405 157 932 640 638 561 811 366 +#> [943,] 8 953 870 54 55 739 342 979 549 536 +#> [944,] 606 32 613 18 39 799 448 744 77 184 +#> [945,] 920 537 226 283 14 595 426 893 927 515 +#> [946,] 94 605 521 411 217 215 742 391 292 860 +#> [947,] 285 268 751 326 270 51 657 977 343 106 +#> [948,] 362 631 907 902 858 692 389 558 249 216 +#> [949,] 332 312 508 928 347 204 472 157 487 808 +#> [950,] 816 235 257 759 857 399 377 175 939 127 +#> [951,] 308 516 464 195 817 358 820 749 377 816 +#> [952,] 949 729 317 204 347 312 332 410 487 867 +#> [953,] 8 55 730 943 966 679 278 463 99 836 +#> [954,] 84 268 468 591 723 10 41 561 227 930 +#> [955,] 782 278 707 185 966 734 282 320 572 140 +#> [956,] 53 424 245 583 876 754 827 210 454 260 +#> [957,] 112 238 118 480 569 615 10 65 468 473 +#> [958,] 812 290 137 284 389 31 128 554 917 87 +#> [959,] 208 401 231 566 899 381 232 355 404 728 +#> [960,] 27 592 913 126 346 152 366 161 546 766 +#> [961,] 997 321 88 147 98 330 773 256 908 769 +#> [962,] 457 482 879 809 56 974 902 571 703 274 +#> [963,] 881 767 596 878 490 901 858 631 406 996 +#> [964,] 541 440 683 134 714 380 602 898 580 573 +#> [965,] 715 232 231 250 182 649 585 381 780 717 +#> [966,] 730 278 55 955 8 953 282 559 99 233 +#> [967,] 887 779 594 642 975 618 190 933 796 407 +#> [968,] 305 429 671 691 140 141 978 50 656 745 +#> [969,] 173 649 30 642 644 933 975 760 716 526 +#> [970,] 671 531 980 167 996 782 185 490 805 903 +#> [971,] 828 454 754 903 876 387 192 674 735 191 +#> [972,] 46 301 13 264 687 889 423 171 256 998 +#> [973,] 108 711 113 374 323 37 665 351 718 668 +#> [974,] 571 554 274 902 389 87 917 879 812 111 +#> [975,] 642 594 193 967 533 405 207 933 969 507 +#> [976,] 666 41 626 441 158 268 172 947 751 569 +#> [977,] 667 304 138 241 680 230 528 397 268 270 +#> [978,] 996 707 691 141 490 719 671 589 488 249 +#> [979,] 995 342 583 54 424 75 245 943 876 863 +#> [980,] 805 970 671 995 265 322 278 979 966 583 +#> [981,] 664 570 227 754 96 827 478 876 210 468 +#> [982,] 793 632 111 475 748 593 853 489 15 854 +#> [983,] 736 544 122 78 437 621 709 781 43 924 +#> [984,] 100 92 403 409 628 205 689 299 520 261 +#> [985,] 450 801 216 152 417 333 692 188 665 565 +#> [986,] 555 905 488 91 444 276 327 287 517 890 +#> [987,] 722 409 567 261 403 609 279 689 338 360 +#> [988,] 252 446 19 267 17 85 117 931 743 481 +#> [989,] 340 912 885 517 724 428 682 787 396 890 +#> [990,] 275 407 515 7 510 933 190 741 502 644 +#> [991,] 866 857 939 818 142 209 877 474 476 61 +#> [992,] 499 218 256 731 784 22 906 430 738 330 +#> [993,] 717 652 381 250 200 392 224 915 355 182 +#> [994,] 284 290 128 406 854 958 234 587 294 790 +#> [995,] 979 980 735 863 641 924 75 621 624 265 +#> [996,] 978 249 707 782 185 490 907 970 631 691 +#> [997,] 98 256 892 972 46 992 769 218 330 738 +#> [998,] 387 167 394 423 131 430 921 656 284 413 +#> [999,] 6 695 854 211 36 111 575 748 23 386 +#> [1000,] 125 386 651 523 49 20 801 417 692 577 #> #> $neighbor_distances -#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] -#> [1,] 0.7048266 0.8083694 0.8882599 0.9421757 1.0040249 1.0144722 1.0248596 -#> [2,] 0.2646638 0.4447534 0.4508229 0.4509399 0.4834092 0.5247719 0.5497563 -#> [3,] 0.5625080 0.7485735 0.8228400 0.8580177 0.9447221 1.0220488 1.0268545 -#> [4,] 0.2337535 0.3869610 0.3987572 0.4311430 0.4322313 0.5076585 0.5243604 -#> [5,] 0.3071985 0.4135715 0.4187607 0.4275021 0.4449949 0.4480393 0.4862072 -#> [6,] 0.8162260 1.3583798 1.5773534 1.6751377 1.8243273 1.8572698 1.8800572 -#> [7,] 0.6913829 0.7541327 0.8888739 0.9243094 0.9559779 0.9618115 0.9714105 -#> [8,] 0.5987923 0.6929355 0.7099335 0.7234910 0.7436081 0.7586708 0.7788853 -#> [9,] 0.5972976 0.6019840 0.6145234 0.6544568 0.6621461 0.7077951 0.7314612 -#> [10,] 0.8162260 1.1388998 1.5691586 1.6517703 1.7160733 1.8003366 1.8312074 -#> [11,] 0.4387941 0.4894021 0.5311648 0.5452097 0.6617801 0.6809391 0.7264780 -#> [12,] 0.3486888 0.3676536 0.4109260 0.4335793 0.4539022 0.4627766 0.5623945 -#> [13,] 1.6469234 1.7292788 1.8007623 1.8432932 1.8877758 1.9391614 1.9416047 -#> [14,] 0.3999155 0.4020145 0.4489713 0.4504919 0.4615817 0.4779987 0.5029892 -#> [15,] 0.2436403 0.3935127 0.4089076 0.5083646 0.5488945 0.6392918 0.6604720 -#> [16,] 0.3293306 0.4893257 0.5458239 0.5741937 0.5897778 0.6447046 0.6659995 -#> [17,] 0.6977377 0.8617257 1.0572980 1.1038723 1.1918617 1.2138401 1.2184243 -#> [18,] 0.5040802 0.5627145 0.5672224 0.5718031 0.6170351 0.6306272 0.6928692 -#> [19,] 0.2857866 0.2940478 0.5034045 0.5351798 0.5760647 0.5784438 0.5962825 -#> [20,] 0.2286886 0.4625997 0.4797133 0.4798772 0.5166952 0.5390328 0.5771252 -#> [21,] 0.3915093 0.4433784 0.4566071 0.4575790 0.4861910 0.5110320 0.5232558 -#> [22,] 0.4862339 0.5522699 0.7315827 0.7898797 0.8151923 0.8296489 0.8669828 -#> [23,] 0.3284029 0.3912126 0.4004372 0.4007780 0.4112151 0.4410145 0.4468764 -#> [24,] 0.4052950 0.5732842 0.6229928 0.6652722 0.6817741 0.7026462 0.7338538 -#> [25,] 0.3843435 0.4089076 0.4893418 0.6487378 0.6911578 0.7590991 0.7672499 -#> [26,] 0.5589624 0.6996202 0.7212007 0.7595346 0.7693359 0.7742780 0.8258053 -#> [27,] 0.5874171 0.7553784 0.8066190 0.9306648 0.9755979 1.0063360 1.0732831 -#> [28,] 0.5737651 0.5889834 0.6094010 0.6560304 0.7157484 0.7175131 0.7469774 -#> [29,] 0.3036663 0.5015118 0.5242928 0.6236399 0.7883728 0.8257304 0.9218299 -#> [30,] 0.4241206 0.5605815 0.6033279 0.6619944 0.6658839 0.6841600 0.7411967 -#> [31,] 0.7023488 0.7160217 0.8238013 0.8848219 0.9756494 0.9797639 0.9837719 -#> [32,] 0.5225561 0.5611636 0.5643889 0.6709421 0.6924779 0.7095881 0.7157484 -#> [33,] 0.5907024 0.5912506 0.6060134 0.6538817 0.7120533 0.8081701 0.8266657 -#> [34,] 0.7235467 0.7685739 0.7745123 0.8247635 0.8285483 0.9214560 0.9243558 -#> [35,] 0.4614993 0.4723733 0.4747357 0.5278493 0.5339777 0.5495823 0.6000009 -#> [36,] 1.0280044 1.3326072 1.4438542 1.4950672 1.5100762 1.5186078 1.5462172 -#> [37,] 0.3299522 0.3549475 0.4322313 0.4429551 0.5124888 0.5439571 0.5654315 -#> [38,] 0.2847934 0.4855697 0.4862419 0.4930848 0.4956664 0.5022562 0.5031505 -#> [39,] 1.2646007 1.2969284 1.3367225 1.3488551 1.4737490 1.4945992 1.5523904 -#> [40,] 0.3035479 0.3186834 0.3551038 0.4236751 0.4554516 0.4722940 0.4805071 -#> [41,] 0.4353166 0.5238137 0.6346238 0.7027129 0.8082479 0.8549699 0.8945612 -#> [42,] 0.3158758 0.4122767 0.5134259 0.5156644 0.5397715 0.5698328 0.5908687 -#> [43,] 0.3112028 0.3118351 0.3385731 0.3728544 0.4352734 0.4380835 0.4468764 -#> [44,] 0.3042617 0.4165118 0.4180805 0.5900513 0.6286326 0.6854924 0.7085806 -#> [45,] 0.6570087 0.6574265 0.7661296 0.7933378 0.7996498 0.8144727 0.8169656 -#> [46,] 0.4739766 0.5013117 0.6193471 0.6287011 0.6384725 0.7145823 0.7185547 -#> [47,] 0.8716038 1.1291670 1.2601548 1.2731740 1.4309672 1.5484044 1.6242022 -#> [48,] 0.3418835 0.3449666 0.3816936 0.3847305 0.3990696 0.4634436 0.4700327 -#> [49,] 0.1960542 0.3481627 0.4300678 0.4569880 0.4627766 0.5043400 0.5348194 -#> [50,] 0.5048296 0.6151931 0.7414556 0.7697675 0.7865517 0.7933014 0.8135147 -#> [51,] 0.3107841 0.3815946 0.4300301 0.4810555 0.4820274 0.4976498 0.5715760 -#> [52,] 0.4401777 0.4851220 0.5566957 0.5749747 0.5953573 0.6111659 0.6666715 -#> [53,] 0.8284443 0.9455623 1.0445572 1.1584753 1.2246511 1.2271954 1.2533244 -#> [54,] 0.5165410 0.5483363 0.5577906 0.5610397 0.6063109 0.6596769 0.6916249 -#> [55,] 0.3401918 0.3740531 0.5727056 0.7549157 0.8370006 0.8412854 0.8601071 -#> [56,] 0.7918233 0.8732220 0.9036908 0.9597329 0.9924884 1.1504494 1.1632732 -#> [57,] 0.3175616 0.3724500 0.4292543 0.4828516 0.4994282 0.4997180 0.5676278 -#> [58,] 0.4791532 0.5493708 0.5503179 0.6035151 0.6610069 0.6694261 0.7053166 -#> [59,] 0.6290930 0.9289136 0.9689169 0.9733285 1.0257024 1.0359829 1.0698120 -#> [60,] 0.1793892 0.4433549 0.5704134 0.6528788 0.6883512 0.7304316 0.7677203 -#> [61,] 0.6116325 0.6763739 0.6778514 0.6978583 0.7198049 0.7402864 0.7830806 -#> [62,] 0.3478599 0.3490601 0.3556601 0.4149381 0.5310006 0.5597738 0.6224384 -#> [63,] 0.3444005 0.5210476 0.5378630 0.5725214 0.5962825 0.6565383 0.6822445 -#> [64,] 0.2266562 0.3956464 0.4746320 0.4763757 0.4905728 0.5245846 0.5899013 -#> [65,] 0.5080052 0.7771783 0.8011727 0.8205875 0.9891684 1.0671992 1.0835386 -#> [66,] 0.8743386 1.0018150 1.1016418 1.1232248 1.1248432 1.1419690 1.1658660 -#> [67,] 0.3534918 0.5119499 0.5529916 0.5719831 0.5959716 0.6538127 0.7144411 -#> [68,] 0.4588987 0.5010224 0.5109920 0.5944829 0.6216318 0.6928692 0.6936148 -#> [69,] 0.5602409 0.6469960 0.7002396 0.7106937 0.7325301 0.8068203 0.8211363 -#> [70,] 0.2020652 0.2218200 0.4595513 0.6095924 0.6650239 0.6690871 0.6998387 -#> [71,] 0.3062630 0.4370194 0.4648428 0.4746678 0.5015561 0.6170349 0.6515960 -#> [72,] 0.6308904 0.7111541 0.7114052 0.7174661 0.7458210 0.7520065 0.7886171 -#> [73,] 0.4637712 0.7955241 0.8066526 0.8093809 1.0033759 1.0384308 1.0415756 -#> [74,] 0.4819553 0.5052221 0.5255958 0.5520613 0.5833905 0.6326745 0.6415663 -#> [75,] 0.7557878 0.7656519 0.7914277 0.8048466 0.8943719 0.9215540 0.9592574 -#> [76,] 0.5677734 0.6241592 0.6257073 0.6401146 0.6488231 0.6922880 0.7164694 -#> [77,] 0.4402712 0.4445392 0.4796866 0.5374811 0.5765453 0.7042095 0.7100582 -#> [78,] 0.4369430 0.4746678 0.5246621 0.5705223 0.5949956 0.6440177 0.6726832 -#> [79,] 0.4590312 0.4811924 0.4847473 0.4859729 0.4906804 0.5147732 0.5902184 -#> [80,] 0.2734565 0.4826327 0.5053076 0.5955601 0.5985745 0.6196470 0.6214573 -#> [81,] 0.2202836 0.3042617 0.5492871 0.6267163 0.6952081 0.7114169 0.7539298 -#> [82,] 1.1691834 1.3014405 1.3326076 1.3625383 1.4254770 1.5510179 1.5517087 -#> [83,] 0.3844678 0.4266016 0.4934404 0.5010035 0.5011315 0.5246621 0.5409899 -#> [84,] 0.8915097 1.1654770 1.1700043 1.3160452 1.3369395 1.4012029 1.4224582 -#> [85,] 0.4650003 0.5372201 0.7173710 0.7750321 0.8502619 0.8524977 0.8613864 -#> [86,] 0.4552099 0.5632050 0.6270920 0.6319328 0.6451453 0.6526862 0.6801134 -#> [87,] 0.2697039 0.2989971 0.3722417 0.3864791 0.3979419 0.5044755 0.5115880 -#> [88,] 0.5890583 0.6303962 0.6972553 0.8012599 0.8484478 0.8800725 0.8999855 -#> [89,] 0.4085959 0.6081224 0.6106471 0.6470521 0.6488359 0.6704771 0.7069878 -#> [90,] 0.2682098 0.3385731 0.3977697 0.4007780 0.4088687 0.4110661 0.4334255 -#> [91,] 0.2751204 0.5993097 0.7266816 0.7926226 0.9236902 1.0381235 1.0600730 -#> [92,] 0.3414077 0.6613483 0.7949358 0.9309210 0.9628605 1.0047288 1.0271264 -#> [93,] 0.4188806 0.4482251 0.4985003 0.5105230 0.5590794 0.6109957 0.6160324 -#> [94,] 0.2915407 0.4006096 0.5662374 0.5703396 0.6104983 0.6254522 0.6571560 -#> [95,] 0.3570338 0.4294050 0.4334255 0.4494573 0.4615580 0.4654187 0.4721043 -#> [96,] 0.8064250 0.8357599 0.8473472 0.8582234 0.8847025 0.9628605 0.9966873 -#> [97,] 0.3610341 0.3774352 0.4789762 0.4862033 0.5030681 0.5503179 0.6050880 -#> [98,] 1.0499795 1.0643079 1.2747939 1.2884228 1.3809873 1.4183431 1.4441306 -#> [99,] 0.8425571 1.0012882 1.0613208 1.1160400 1.2090369 1.2196290 1.2271954 -#> [100,] 0.1719823 0.2005349 0.3995663 0.4020145 0.5419647 0.5490831 0.5606193 -#> [101,] 0.2832737 0.4051340 0.4424304 0.5553900 0.5557919 0.5561115 0.6074579 -#> [102,] 0.5238137 0.5270508 0.5384865 0.5802341 0.6482590 0.6528788 0.7393022 -#> [103,] 0.7780846 0.9160502 1.0251646 1.0758883 1.2273985 1.3250280 1.3650345 -#> [104,] 0.8402520 0.9372123 0.9846061 1.1511613 1.2117447 1.3488551 1.3855672 -#> [105,] 0.6289333 0.8665598 0.8765106 0.9286206 0.9619865 0.9700839 0.9714105 -#> [106,] 0.2661266 0.2988244 0.3085853 0.3724500 0.4407825 0.4594065 0.5107381 -#> [107,] 0.4815630 0.7025629 0.7512546 0.8192894 0.8972547 0.9296012 0.9443613 -#> [108,] 0.9098251 0.9994496 1.0043093 1.0074580 1.0819849 1.2459773 1.3049420 -#> [109,] 0.8127954 0.8848219 1.0958495 1.1919957 1.2672147 1.2879393 1.3004464 -#> [110,] 0.3765864 0.5735751 0.6231267 0.6317110 0.6976519 0.7089059 0.7372494 -#> [111,] 0.2312386 0.3439918 0.3618488 0.3632050 0.3725628 0.3938001 0.4741794 -#> [112,] 0.2479778 0.3071985 0.3768641 0.3914624 0.4080576 0.4565760 0.4807659 -#> [113,] 0.3536279 0.4134636 0.4428247 0.4719349 0.4721957 0.5400492 0.5448039 -#> [114,] 0.4448283 0.7417602 0.7560302 0.7727866 0.8194439 0.8326412 0.8550672 -#> [115,] 0.3670521 0.4335027 0.4463284 0.5121596 0.6746002 0.6782024 0.7550491 -#> [116,] 0.7586708 0.7918233 0.8495530 0.8584823 0.8656548 0.8673209 0.8829771 -#> [117,] 0.2824080 0.3407325 0.5028377 0.5034045 0.5364504 0.5724725 0.6111820 -#> [118,] 0.6011015 0.6568896 0.6970957 0.7760345 0.7873279 0.8389859 0.8612169 -#> [119,] 0.4645052 0.7627281 0.7664906 0.8466935 0.8795198 0.9012175 0.9062407 -#> [120,] 0.3015907 0.3781865 0.4005184 0.4426711 0.4985003 0.5100151 0.5129489 -#> [121,] 0.3449666 0.4175508 0.5115338 0.5185349 0.5413713 0.5454344 0.5565225 -#> [122,] 0.2218137 0.4422711 0.4834617 0.4870908 0.6026000 0.6130869 0.6203350 -#> [123,] 0.3457117 0.4862339 0.4869867 0.5838292 0.5907311 0.6927150 0.7224404 -#> [124,] 0.3859147 0.5860999 0.6060134 0.6827474 0.8100991 0.8135147 0.8984934 -#> [125,] 0.5688090 0.5920986 0.6839222 0.7025629 0.7132284 0.7472718 0.7557377 -#> [126,] 0.3214490 0.4776311 0.7167084 0.7890833 0.8072259 0.8133698 0.8167129 -#> [127,] 0.2652173 0.3418835 0.4172473 0.4641082 0.4797133 0.4834878 0.5094085 -#> [128,] 0.8132454 0.8566013 0.8912662 0.9567154 0.9995281 1.0529038 1.0892656 -#> [129,] 0.5173034 0.5385999 0.5909574 0.6495746 0.7144411 0.7530588 0.7793916 -#> [130,] 0.3016603 0.3589144 0.3798359 0.5123829 0.6620639 0.6644755 0.6849573 -#> [131,] 0.7945027 0.8052348 1.0094416 1.0380981 1.1226155 1.1483449 1.1911871 -#> [132,] 0.4773687 0.4896124 0.5876542 0.6151931 0.6152917 0.6525114 0.6617801 -#> [133,] 0.2957885 0.5998256 0.6250829 0.6819637 0.6955587 0.7119675 0.7181709 -#> [134,] 0.8466075 0.9559963 0.9653752 0.9709938 0.9928338 1.0115360 1.0325245 -#> [135,] 0.1418194 0.6279938 0.6904804 0.7189922 0.7214698 0.8149124 0.8672728 -#> [136,] 0.3444005 0.3513501 0.4608625 0.5105701 0.5202147 0.5617529 0.5724725 -#> [137,] 0.3534918 0.3892923 0.5173034 0.5489988 0.5815853 0.6398496 0.6930631 -#> [138,] 0.4773687 0.4978547 0.6620687 0.6726346 0.6922880 0.7011757 0.7414556 -#> [139,] 0.4446937 0.4528535 0.5435197 0.5998256 0.6946994 0.7683952 0.7739046 -#> [140,] 0.3638288 0.4680880 0.6973555 0.7250861 0.7494283 0.7619575 0.7656392 -#> [141,] 0.5383358 0.5900513 0.6787596 0.7081918 0.7108781 0.7493679 0.7631740 -#> [142,] 0.2109421 0.3381355 0.4371760 0.4410435 0.5464723 0.5576133 0.5636682 -#> [143,] 0.9889649 1.0560097 1.1533725 1.1601190 1.2254556 1.2703172 1.3124793 -#> [144,] 0.2954659 0.4437187 0.4796866 0.5504881 0.6164471 0.6256895 0.6496666 -#> [145,] 0.5080052 0.8341716 0.8432496 0.9130185 0.9294930 0.9832351 0.9880707 -#> [146,] 1.4565224 1.7843633 1.7996840 1.8705359 1.8760104 1.8799524 1.9060669 -#> [147,] 0.9372123 1.0059526 1.1388661 1.3188446 1.3606501 1.3640721 1.5037697 -#> [148,] 0.4468204 0.5485547 0.5751650 0.5758143 0.6294308 0.6353628 0.6354754 -#> [149,] 0.8402520 1.0644100 1.1333596 1.2646007 1.3188446 1.3367809 1.4007968 -#> [150,] 0.3891018 0.4304763 0.4883660 0.4925399 0.5255958 0.6052847 0.6073665 -#> [151,] 0.3124576 0.7553784 0.7786041 0.9153356 0.9816910 0.9895036 1.0228704 -#> [152,] 0.4181309 0.4396069 0.5606495 0.7003802 0.7263227 0.8679645 0.8940518 -#> [153,] 0.5745431 0.6589509 0.8750195 0.8975972 0.9072083 0.9513614 0.9557934 -#> [154,] 1.0687995 1.2359980 1.5919794 1.6551029 1.8572032 1.8726503 1.9143099 -#> [155,] 0.3525775 0.4188517 0.4430698 0.5028352 0.5053819 0.5655088 0.5838935 -#> [156,] 0.3304958 0.4964543 0.6085589 0.7224517 0.7508667 0.7620922 0.7791763 -#> [157,] 0.3737895 0.3898532 0.4004372 0.4822703 0.5067166 0.5655088 0.5766819 -#> [158,] 0.2298626 0.4305420 0.4738325 0.5797168 0.6287894 0.6676141 0.6826555 -#> [159,] 0.5807290 0.7780846 0.8875493 1.0127100 1.1450179 1.1916313 1.2690950 -#> [160,] 0.4289125 0.5011922 0.5540639 0.6948436 0.7208311 0.7520065 0.8818430 -#> [161,] 0.4241206 0.6107870 0.6229928 0.6529928 0.7129272 0.7185547 0.7243970 -#> [162,] 0.3859954 0.4109260 0.4555023 0.4626248 0.4633659 0.4918698 0.5110051 -#> [163,] 0.4552099 0.4855611 0.5240031 0.6194519 0.7022538 0.7035018 0.7373904 -#> [164,] 0.5278035 0.7871674 0.9289076 0.9783975 1.0084421 1.0511804 1.0655496 -#> [165,] 0.3799644 0.3806210 0.3934462 0.4135310 0.4970086 0.5115880 0.5358688 -#> [166,] 0.5511508 0.6390218 0.6504903 0.7225953 0.7955241 0.8164118 0.8319241 -#> [167,] 0.2105175 0.3815946 0.3900844 0.4083693 0.4314051 0.4369799 0.4537252 -#> [168,] 0.5598617 0.6075112 0.6221261 0.6616265 0.6686554 0.9067763 0.9191234 -#> [169,] 0.4829863 0.5799477 0.5957137 0.5966500 0.6040105 0.6383378 0.6721866 -#> [170,] 0.2656860 0.3399969 0.4277456 0.4720053 0.5953922 0.6416989 0.6518913 -#> [171,] 0.4612447 0.4740072 0.4972074 0.5100298 0.5698245 0.6111659 0.6466797 -#> [172,] 0.6097647 0.6132011 0.6296337 0.6743795 0.8499388 0.8502969 0.8613284 -#> [173,] 0.2503394 0.2994409 0.5889596 0.6348820 0.7158948 0.7264780 0.8250275 -#> [174,] 0.3672958 0.4160685 0.4725977 0.5723332 0.5830259 0.7428737 0.7919458 -#> [175,] 0.3196900 0.5194182 0.5298376 0.5435197 0.5572317 0.5750432 0.5840255 -#> [176,] 0.4165118 0.5049022 0.5466477 0.6267163 0.6787596 0.6830429 0.7427972 -#> [177,] 0.4497791 0.5191472 0.5219601 0.5395790 0.5416235 0.6160995 0.6362140 -#> [178,] 0.9989923 1.0082055 1.1357055 1.1672237 1.1891288 1.2180572 1.2978906 -#> [179,] 0.4670087 0.5287582 0.5334193 0.5478272 0.5721634 0.5743282 0.6294766 -#> [180,] 0.7664906 0.8228400 0.8585636 0.8969156 0.9115949 1.0017615 1.0335018 -#> [181,] 0.1362916 0.3588431 0.4794574 0.5286231 0.5310006 0.5579764 0.5622382 -#> [182,] 0.4579561 0.6701687 0.6773683 0.6883186 0.6996202 0.7164687 0.7429378 -#> [183,] 0.7755421 0.8804326 0.9078180 1.1672237 1.1684649 1.1729379 1.2657212 -#> [184,] 0.3119060 0.4224714 0.4575790 0.4683933 0.5423035 0.5515163 0.5919193 -#> [185,] 0.5909574 0.6332117 0.6853056 0.7348973 0.7398255 0.7435528 0.7496748 -#> [186,] 0.5069319 0.5264586 0.5577906 0.5580674 0.6443203 0.7141846 0.7309459 -#> [187,] 0.3943873 0.3955992 0.4359817 0.4794574 0.4972336 0.5284220 0.5321714 -#> [188,] 0.2682098 0.3284029 0.3530090 0.3678781 0.3728544 0.4075196 0.4294050 -#> [189,] 0.2991949 0.3692605 0.5451733 0.5594507 0.5624723 0.6126309 0.6801919 -#> [190,] 0.3898532 0.5403542 0.5727275 0.6038711 0.6124425 0.6162748 0.6253421 -#> [191,] 0.4279815 0.4480237 0.4493396 0.4829012 0.4842416 0.4862033 0.4921528 -#> [192,] 0.3214490 0.5980038 0.6570703 0.8534005 0.9127974 0.9277686 0.9352843 -#> [193,] 0.3394844 0.3853540 0.5972976 0.6104983 0.6413421 0.6960078 0.7603939 -#> [194,] 0.2752557 0.4161253 0.4172473 0.4176578 0.4396160 0.4940575 0.4972336 -#> [195,] 0.2578455 0.4328642 0.6321894 0.6537618 0.6839005 0.7108781 0.7116276 -#> [196,] 0.2791779 0.4024938 0.4860003 0.5316131 0.5517135 0.5688071 0.5936018 -#> [197,] 0.5488358 0.5890583 0.5960984 0.6170807 0.6979232 0.7174303 0.7531728 -#> [198,] 0.6311555 0.6573906 0.7012529 0.7290197 0.7398255 0.7983180 0.8183816 -#> [199,] 0.3642713 0.5072499 0.5454344 0.5506138 0.5703296 0.5830259 0.6540970 -#> [200,] 0.4726632 0.5489127 0.5812676 0.6390218 0.6806105 0.6894002 0.7998378 -#> [201,] 0.8613284 0.8640089 0.8871901 1.0109677 1.0149250 1.0495105 1.1598583 -#> [202,] 0.2164501 0.2860089 0.3381355 0.4283662 0.4480237 0.4736562 0.4737440 -#> [203,] 0.3527183 0.4759184 0.5130482 0.5664733 0.5982752 0.6008386 0.6296641 -#> [204,] 0.4037595 0.4106354 0.4734001 0.5075357 0.6279512 0.6381214 0.6766374 -#> [205,] 0.4613472 0.5733389 0.6536408 0.6658839 0.7161500 0.7405805 0.7691944 -#> [206,] 0.7557748 1.9032298 1.9909608 2.0085635 2.1575094 2.1825889 2.2086869 -#> [207,] 0.5352316 0.5703028 0.6775417 0.8012966 0.8110856 0.8860435 0.8907570 -#> [208,] 0.3399969 0.5087585 0.5108792 0.5812280 0.6684315 0.7307516 0.7418706 -#> [209,] 0.2266562 0.4415222 0.5105230 0.5281845 0.5290100 0.5508943 0.5960563 -#> [210,] 0.5144181 0.6123625 0.6639630 0.6989697 0.7102417 0.7557781 0.7588572 -#> [211,] 0.2442281 0.3479301 0.4439951 0.4642895 0.4872823 0.5358688 0.5773974 -#> [212,] 0.4894648 0.5949956 0.6911686 0.6929355 0.6989893 0.7356213 0.7939766 -#> [213,] 0.3559836 0.4992742 0.5002217 0.5591558 0.5654315 0.5836867 0.5955450 -#> [214,] 0.5745717 0.5981669 0.6085589 0.6228606 0.6447953 0.6589045 0.6828360 -#> [215,] 0.4768436 0.5065259 0.5627565 0.7367339 0.7530721 0.7609732 0.7615598 -#> [216,] 0.3559836 0.4424304 0.4907152 0.4921030 0.5727925 0.5907024 0.6593339 -#> [217,] 0.3052188 0.4374722 0.4523524 0.4868032 0.5092464 0.5101514 0.5210982 -#> [218,] 0.3974524 0.4433549 0.4991711 0.5081086 0.5470574 0.6239051 0.6253421 -#> [219,] 0.4592319 0.4923259 0.4934713 0.5198035 0.5287760 0.5481299 0.5616279 -#> [220,] 0.9243558 0.9301088 1.1803583 1.1868249 1.2316803 1.3025945 1.3337114 -#> [221,] 0.1765483 0.6991221 0.7436371 0.8061793 0.8457712 0.8822349 0.9095427 -#> [222,] 0.4114465 0.6579970 0.7081140 0.7223757 1.0008298 1.0602373 1.0841085 -#> [223,] 0.4855611 0.5070293 0.5399607 0.5737582 0.5895435 0.6067046 0.6136829 -#> [224,] 0.5725214 0.6602495 0.7616623 0.7667874 0.8101183 0.8179785 0.8180067 -#> [225,] 0.2694920 0.2950353 0.3692134 0.3719505 0.4592028 0.4992582 0.4999803 -#> [226,] 0.3343100 0.4750387 0.4832977 0.5889596 0.6089304 0.6234424 0.6648360 -#> [227,] 0.4123463 0.4757520 0.5777199 0.5832221 0.6133775 0.6400521 0.6722586 -#> [228,] 0.4657427 0.5010224 0.5418577 0.6170351 0.6605389 0.6997621 0.7465732 -#> [229,] 0.4051340 0.4735834 0.4921030 0.5809545 0.5859734 0.6244618 0.6464568 -#> [230,] 0.3891018 0.4681615 0.4966708 0.5392035 0.5647906 0.5702987 0.5720533 -#> [231,] 0.2547535 0.3056966 0.4740072 0.5703028 0.5714908 0.5925200 0.5940281 -#> [232,] 0.9555092 1.1303360 1.1388998 1.1594726 1.2539825 1.2605820 1.2764892 -#> [233,] 0.2109421 0.2860089 0.3835569 0.4145382 0.4747144 0.5700085 0.5719874 -#> [234,] 0.5355423 0.5872310 0.6488231 0.6511711 0.6514708 0.6620687 0.6808537 -#> [235,] 0.3692605 0.4257540 0.5174856 0.5175952 0.5428824 0.5457228 0.6078203 -#> [236,] 0.5011913 0.5154095 0.5271494 0.6122418 0.6528631 0.6848110 0.7000411 -#> [237,] 0.3951226 0.4605785 0.4844073 0.4856967 0.5214682 0.5475801 0.5896545 -#> [238,] 0.5555567 0.6041729 0.6486946 0.6611702 0.7187924 0.7520435 0.7555620 -#> [239,] 0.4374822 0.9087339 0.9675360 1.0043093 1.0416752 1.1352041 1.2382200 -#> [240,] 0.3848214 0.4431134 0.6011015 0.6048017 0.6541363 0.7072750 0.7132638 -#> [241,] 0.7002668 0.8351929 0.9067863 0.9174519 0.9210116 0.9361946 0.9924884 -#> [242,] 0.2230059 0.3106024 0.5786764 0.6139568 0.6573238 0.6714148 0.7042095 -#> [243,] 0.3631836 0.7214698 0.7647506 0.7948385 0.8413211 0.8450892 0.8465010 -#> [244,] 0.8610764 0.9358077 0.9801249 0.9993196 1.0108796 1.0252545 1.0280008 -#> [245,] 0.3080751 0.3107541 0.3160396 0.3999155 0.4615110 0.5349535 0.5481820 -#> [246,] 0.5765453 0.6558690 0.7005177 0.8138565 0.8658315 0.8670046 1.0029064 -#> [247,] 0.3342053 0.4390372 0.4789193 0.4895538 0.5284220 0.5497563 0.5579764 -#> [248,] 0.5187787 0.5655398 0.5926421 0.6029241 0.6503403 0.6525829 0.7330386 -#> [249,] 0.7742780 0.8390463 0.8879565 0.8886585 0.9933613 1.0113779 1.1279124 -#> [250,] 0.8413211 0.9289766 0.9943680 1.0636714 1.1219632 1.1385667 1.1523223 -#> [251,] 0.4423658 0.4739314 0.6919364 0.7125685 0.7307516 0.7743503 0.7797978 -#> [252,] 0.7704966 0.7745123 0.9301088 0.9765920 0.9806921 1.0033158 1.0061996 -#> [253,] 0.3183395 0.3478599 0.4142914 0.4497791 0.4867866 0.5707005 0.5740675 -#> [254,] 0.6927369 1.1663722 1.3124793 1.3924713 1.6727395 1.6976233 1.7778336 -#> [255,] 0.1963591 0.4167413 0.5624760 0.5832461 0.6152891 0.6170807 0.6645390 -#> [256,] 0.3642874 0.6713886 0.8404900 0.8498604 0.8597038 0.9465393 0.9807787 -#> [257,] 0.4127776 0.4727399 0.4924958 0.6556979 0.6588958 0.6865332 0.6979232 -#> [258,] 0.4670087 0.4929666 0.5014880 0.5799477 0.6056041 0.6182922 0.6302956 -#> [259,] 0.4788930 0.7232206 0.7920760 0.8184894 0.8846523 0.8963857 0.9163078 -#> [260,] 0.3298885 0.6568896 0.6602341 0.8319435 0.8572144 0.8684830 0.8820444 -#> [261,] 0.3461297 0.3779047 0.3847305 0.4344955 0.4791532 0.5155695 0.5202147 -#> [262,] 0.4019718 0.5540441 0.6682981 0.6746002 0.7254377 0.8308921 0.8470057 -#> [263,] 0.4523524 0.4550712 0.4623505 0.5549466 0.5637004 0.5863843 0.5943596 -#> [264,] 0.2353301 0.6801134 0.7381024 0.7402864 0.7923956 0.8268060 0.8741396 -#> [265,] 0.2656860 0.4940036 0.5083646 0.5087585 0.5427427 0.6460751 0.6637709 -#> [266,] 0.6714131 0.8008904 0.9622620 1.2575237 1.2897530 1.2925865 1.3800389 -#> [267,] 0.7923956 0.8258942 0.8966985 0.9029819 1.0015818 1.0553813 1.1269123 -#> [268,] 0.3062630 0.3782222 0.4369430 0.4465721 0.4803380 0.5010035 0.5324977 -#> [269,] 0.5009687 0.5250248 0.5428234 0.5473297 0.5942332 0.5955329 0.6020509 -#> [270,] 0.3694694 0.4990745 0.5044755 0.5073828 0.5221797 0.5618657 0.6075119 -#> [271,] 2.1686806 2.2086869 2.2810268 2.3181527 2.3606029 2.4186803 2.4244825 -#> [272,] 0.3530585 0.4734049 0.4747357 0.4937261 0.5221003 0.5710723 0.6007936 -#> [273,] 0.7566811 0.9849265 1.0073145 1.0687995 1.0916150 1.2922580 1.3093768 -#> [274,] 0.2474450 0.2828653 0.3439918 0.4110661 0.4494573 0.4789884 0.5098917 -#> [275,] 0.4648660 0.5489127 0.5699855 0.5739238 0.5982141 0.6347572 0.6426404 -#> [276,] 0.6293818 0.6993759 0.8308841 0.8464172 0.8495530 0.8732220 0.9309170 -#> [277,] 0.9493566 0.9797639 1.1373486 1.1400396 1.1867739 1.1919957 1.1947125 -#> [278,] 0.5676543 0.5906277 0.6551925 0.6584415 0.6663706 0.7028327 0.7198973 -#> [279,] 0.3376254 0.3556077 0.4071768 0.4304381 0.4513943 0.5140644 0.5481116 -#> [280,] 0.4443316 1.0295641 1.0404369 1.1279124 1.1370093 1.1961986 1.2268191 -#> [281,] 0.4701398 0.4753663 0.4798772 0.5016437 0.5879416 0.6611702 0.6612903 -#> [282,] 0.5963667 0.6405775 0.7198823 0.7262374 0.7851440 0.7967534 0.8175944 -#> [283,] 0.4734001 0.4819553 0.5610950 0.6125552 0.6348908 0.6632266 0.7207668 -#> [284,] 0.3943873 0.4328513 0.4789193 0.4850523 0.5263223 0.5286231 0.5301923 -#> [285,] 0.3747992 0.5221909 0.5895435 0.6116325 0.6512875 0.6895740 0.7035018 -#> [286,] 0.6183229 0.7769803 0.7859109 0.8773568 0.9343448 0.9549166 0.9764120 -#> [287,] 0.6583572 0.7055782 0.7864954 0.8128896 0.8280572 0.8424534 0.8690105 -#> [288,] 0.6183229 0.6824711 0.6825961 0.6918185 0.7775237 0.7884203 0.7974064 -#> [289,] 0.3075964 0.3862536 0.4024244 0.4145382 0.4283662 0.4921528 0.5025113 -#> [290,] 0.1998153 0.3016603 0.4811913 0.5119983 0.5129489 0.5203712 0.5852687 -#> [291,] 0.3000201 0.4148677 0.4633867 0.4723733 0.4938870 0.5325850 0.5666957 -#> [292,] 0.5232328 0.5400492 0.5425748 0.5460978 0.5610397 0.6117036 0.6231267 -#> [293,] 0.4257540 0.4292543 0.4966009 0.5107381 0.5594507 0.6267861 0.6400521 -#> [294,] 0.2218137 0.3576403 0.4037885 0.6063109 0.6104833 0.6138500 0.6443203 -#> [295,] 0.5807290 0.6296239 0.7645480 0.9160502 0.9723325 1.0319325 1.1000059 -#> [296,] 0.3246618 0.4604986 0.5069319 0.5312717 0.5520613 0.6287894 0.6300649 -#> [297,] 1.3482078 1.4732361 1.4893027 1.5836625 1.6339643 1.6551029 1.6707667 -#> [298,] 0.2296156 0.3813579 0.5184814 0.5243604 0.5431865 0.5595356 0.5634057 -#> [299,] 0.3972822 0.4965400 0.5194182 0.6076082 0.6290822 0.6760092 0.6789478 -#> [300,] 0.4448283 0.5734645 0.7645480 0.7941773 0.8390236 0.8422752 0.8519435 -#> [301,] 0.3365770 0.4171835 0.4481301 0.4496889 0.4506818 0.4891075 0.5171006 -#> [302,] 0.2569293 0.3341905 0.3618488 0.4208733 0.4286491 0.4562478 0.4609700 -#> [303,] 0.3582576 0.4393601 0.4750677 0.4935474 0.5200496 0.6106381 0.6153815 -#> [304,] 0.3985065 0.4481301 0.5704134 0.5758475 0.6468491 0.6484340 0.6493102 -#> [305,] 0.5517353 0.6800489 0.8270838 0.8313950 0.8533623 0.8767628 0.8883322 -#> [306,] 0.2020652 0.2753051 0.4636687 0.5449661 0.5473297 0.5852984 0.6668215 -#> [307,] 0.3414077 0.3471195 0.6384725 0.7448607 0.7551961 0.8762055 0.9248971 -#> [308,] 0.3537939 0.4440057 0.4910911 0.5114391 0.5476241 0.5530247 0.5760182 -#> [309,] 0.4916895 0.5995342 0.6447800 0.7485735 0.8892563 0.9886686 0.9982018 -#> [310,] 0.5312083 0.6139568 0.6994112 0.7266816 0.7379023 0.8330002 0.8425017 -#> [311,] 0.6927369 0.8882599 1.0306228 1.0560097 1.2125442 1.3396713 1.3635505 -#> [312,] 0.4636346 0.5320051 0.7262585 0.7761523 0.7983180 0.8087218 0.9527683 -#> [313,] 0.5474971 0.5872310 0.6326847 0.6726346 0.7840795 0.7865517 0.8100991 -#> [314,] 0.3160586 0.3882206 0.4023933 0.4537252 0.4826127 0.4930848 0.5560572 -#> [315,] 0.2954659 0.4402712 0.6798214 0.6936148 0.7349776 0.7459878 0.7593610 -#> [316,] 0.6696794 0.6865332 0.6996609 0.7618664 0.7626236 0.7792351 0.8628118 -#> [317,] 0.4335027 0.4585807 0.5434027 0.5540441 0.6385622 0.6481007 0.6811298 -#> [318,] 0.2752557 0.2994951 0.3710504 0.4359817 0.5036908 0.5098917 0.5263223 -#> [319,] 0.5254022 0.6111820 0.6481640 0.6570087 0.6724047 0.6794556 0.7683324 -#> [320,] 0.4465721 0.4648428 0.5395790 0.5984565 0.6111190 0.6136581 0.6233926 -#> [321,] 0.7127578 0.7436371 0.7673176 0.8291398 0.8413863 0.8558235 1.0156060 -#> [322,] 0.4470890 0.5076585 0.5124888 0.5231660 0.5373169 0.5393927 0.5440362 -#> [323,] 0.4066972 0.4586935 0.6145234 0.6310264 0.6404431 0.6568517 0.6626158 -#> [324,] 0.4874757 0.7017439 0.7727866 0.7933014 0.8831344 0.9190963 0.9253171 -#> [325,] 0.2832737 0.4406743 0.4510282 0.4699401 0.4907152 0.5575169 0.5591558 -#> [326,] 0.4557063 0.4871143 0.4931461 0.5019206 0.5051712 0.5052221 0.5481116 -#> [327,] 0.7143952 0.7606851 0.7677387 0.7730754 0.7746293 0.7919458 0.8392739 -#> [328,] 0.2824080 0.3459307 0.3787681 0.4735834 0.5561115 0.5666532 0.5784076 -#> [329,] 1.0676613 1.5983391 1.7461672 1.8718903 1.9509921 1.9752659 1.9934865 -#> [330,] 0.2656600 0.4197421 0.4314120 0.4393601 0.5153055 0.5641456 0.6239847 -#> [331,] 0.8832363 0.9567154 1.2394640 1.4999657 1.5093249 1.5724634 1.6109561 -#> [332,] 0.3457117 0.5522699 0.6965300 0.7437559 0.7467575 0.7628491 0.7762495 -#> [333,] 0.3895432 0.4687791 0.5209139 0.5944829 0.6893626 0.7648775 0.7656143 -#> [334,] 0.4374755 0.5166567 0.5836867 0.6258101 0.6296641 0.6343839 0.6538817 -#> [335,] 0.2724458 0.4483847 0.4595417 0.5615556 0.5907221 0.6050880 0.6080600 -#> [336,] 0.4443316 0.6424847 0.8610764 0.8815996 0.9384542 0.9568739 1.0199679 -#> [337,] 0.8617257 0.8710661 0.9524282 1.0021075 1.0310961 1.0416989 1.0630547 -#> [338,] 0.3631836 0.5953177 0.6319675 0.6528631 0.7359611 0.7627102 0.8105955 -#> [339,] 0.4309298 0.4604986 0.4925399 0.4966708 0.4968930 0.5229099 0.5622740 -#> [340,] 0.5696837 0.5945664 0.6140291 0.6607465 0.6679704 0.6915614 0.7433810 -#> [341,] 0.5259341 0.6778514 0.7219848 0.7570375 0.7698148 0.8017146 0.8072259 -#> [342,] 0.2217617 0.2988244 0.3202706 0.3326814 0.4828516 0.4954094 0.6143176 -#> [343,] 0.2857866 0.4578845 0.5097455 0.5696837 0.7037830 0.7221809 0.7342913 -#> [344,] 0.2499468 0.4080576 0.5500536 0.5505802 0.5763671 0.5945664 0.6108302 -#> [345,] 0.4725977 0.5025481 0.5506138 0.6319652 0.7194182 0.7232206 0.7233274 -#> [346,] 0.7372494 0.7678418 0.7828068 0.8265162 0.8465010 0.8489286 0.8980172 -#> [347,] 0.4087473 0.4442985 0.4910911 0.4938870 0.4999916 0.5135788 0.5271494 -#> [348,] 0.3932012 0.4194612 0.4335793 0.4401427 0.4459262 0.4721957 0.4750677 -#> [349,] 0.2273583 0.3806511 0.4652016 0.4704623 0.4829322 0.5894456 0.5898104 -#> [350,] 0.1983044 0.5860999 0.5923463 0.6525829 0.6622607 0.8927730 0.9317428 -#> [351,] 0.3935127 0.4116965 0.4475573 0.4893418 0.5434911 0.5978571 0.6817049 -#> [352,] 0.6198571 0.6364684 0.6631117 0.7236107 0.7350833 0.7603939 0.8215576 -#> [353,] 0.3747890 0.4972074 0.5067166 0.5456494 0.5765121 0.5823562 0.6178335 -#> [354,] 0.4482251 0.4768116 0.4777298 0.4975465 0.5205927 0.5351490 0.5767044 -#> [355,] 0.5921002 0.8509328 0.8883322 1.3493029 1.4194159 1.5123797 1.5465838 -#> [356,] 0.6469960 0.6535593 0.6651903 0.6765484 0.7467857 0.8138460 0.8155243 -#> [357,] 0.5297576 0.5460581 0.5752158 0.6501874 0.6887962 0.6977100 0.7395676 -#> [358,] 0.4777599 0.5337423 0.5889834 0.6460212 0.6903412 0.7210871 0.8240193 -#> [359,] 0.4142914 0.5176881 0.5277014 0.6106471 0.6123415 0.6224384 0.6269097 -#> [360,] 0.4919594 0.5453030 0.5484186 0.5550756 0.6550249 0.6896709 0.7104783 -#> [361,] 0.4577316 0.5203527 0.5270669 0.5484186 0.6130869 0.6325047 0.6629479 -#> [362,] 0.5048736 0.6315611 0.6460212 0.6517485 0.7300337 0.7449540 0.7578651 -#> [363,] 0.6151594 0.6622607 0.9363069 0.9611949 0.9692649 0.9902827 1.0039163 -#> [364,] 0.6645390 0.7595542 0.7690297 0.8238071 0.8250804 0.9323085 0.9492159 -#> [365,] 0.4801832 0.5703396 0.6265655 0.6522410 0.6744898 0.6847840 0.6887962 -#> [366,] 0.6241592 0.6385163 0.6849981 0.7652407 0.8636321 0.8665598 0.8747543 -#> [367,] 0.4036686 0.4074138 0.5643889 0.5962516 0.6315293 0.7345245 0.7536984 -#> [368,] 0.5929305 0.7351369 0.8083417 0.9341815 0.9427737 1.0242711 1.0624381 -#> [369,] 0.4695051 0.4996085 0.5723332 0.6187645 0.8859597 0.8961061 0.9085786 -#> [370,] 0.3556077 0.3990696 0.4175508 0.4701749 0.5415097 0.5704060 0.5748674 -#> [371,] 0.5189913 0.6270920 0.7058054 0.7151584 0.7381024 0.7822497 0.8412011 -#> [372,] 0.1821476 0.4550712 0.5155402 0.6817049 0.7198049 0.7698148 0.7935792 -#> [373,] 0.4188806 0.5119499 0.5290100 0.5673679 0.6157251 0.6398496 0.6468491 -#> [374,] 0.6158580 0.7952401 0.8465456 0.9759081 0.9771859 1.0338753 1.0490758 -#> [375,] 0.3524337 0.3536279 0.3891551 0.4401427 0.4555023 0.4699200 0.5623945 -#> [376,] 1.2005248 1.2138753 1.3149943 1.3170209 1.3290479 1.3774349 1.4527436 -#> [377,] 0.3000201 0.3066120 0.3272235 0.3809927 0.4125014 0.4249279 0.4442985 -#> [378,] 0.3853540 0.4068972 0.4964543 0.5422557 0.6198571 0.6254522 0.6384770 -#> [379,] 0.3454563 0.3713510 0.4965400 0.5224370 0.6194128 0.6682066 0.7175131 -#> [380,] 0.3392109 0.4181309 0.6375285 0.7202229 0.7429733 0.7588076 0.7750321 -#> [381,] 0.3905628 0.4585807 0.5287760 0.6011118 0.6414148 0.6638869 0.6706265 -#> [382,] 0.6376604 0.6560304 0.7972184 0.8490657 0.8542834 0.9027296 0.9437868 -#> [383,] 0.1362916 0.3912933 0.3955992 0.4328513 0.4390372 0.5727553 0.6178691 -#> [384,] 0.3091575 0.5322739 0.5451491 0.5872667 0.6306143 0.6480375 0.6584415 -#> [385,] 0.1628608 0.7103740 1.0996304 1.1373904 1.1416821 1.1607203 1.2325702 -#> [386,] 0.3784988 0.4051373 0.4406743 0.4447534 0.4783833 0.4970822 0.5196511 -#> [387,] 0.4739314 0.5851950 0.6565383 0.6904380 0.7537516 0.8005908 0.8106037 -#> [388,] 0.4437187 0.4687791 0.5177161 0.5509537 0.5732842 0.6291464 0.6798214 -#> [389,] 0.3113292 0.4423658 0.6108399 0.6132011 0.8419828 0.8532589 0.8539395 -#> [390,] 0.5042632 0.5047298 0.6332989 0.6573906 0.6640788 0.7308810 0.7348973 -#> [391,] 0.7233274 0.7667874 0.7677387 0.8072730 0.8238013 0.8640089 0.8686242 -#> [392,] 0.3895432 0.4814243 0.5017388 0.5109920 0.5509537 0.5559009 0.5684539 -#> [393,] 0.3934241 0.4134636 0.4875948 0.4968930 0.4970086 0.5114391 0.5413453 -#> [394,] 0.7262374 0.7883433 0.8249225 0.8961636 0.9529310 1.0486522 1.0517503 -#> [395,] 0.1257555 0.4669204 0.4789762 0.5074462 0.5550756 0.6272511 0.6629479 -#> [396,] 0.7127578 0.9761487 1.0934914 1.1792269 1.2529735 1.2647336 1.2691116 -#> [397,] 0.2353301 0.5189913 0.6319328 0.6763739 0.8770099 0.9029819 0.9047004 -#> [398,] 0.4036686 0.6553439 0.7098716 0.7248484 0.7313914 0.7385971 0.8750195 -#> [399,] 0.3202706 0.3913705 0.4149331 0.4594065 0.4824973 0.4997180 0.5302900 -#> [400,] 0.3830567 0.4335794 0.6856147 0.7246882 0.7279501 0.8605461 0.8679605 -#> [401,] 0.3458029 0.6856147 0.6893942 0.7525894 0.7639661 0.7671241 0.8066190 -#> [402,] 0.3184559 0.4518358 0.5047298 0.6410868 0.6615776 0.7332994 0.7435528 -#> [403,] 0.3710223 0.3747890 0.3890036 0.4005514 0.4566556 0.4822703 0.4933322 -#> [404,] 0.3525775 0.3633023 0.3969505 0.4275021 0.4805071 0.4826465 0.5098671 -#> [405,] 0.7915908 1.0149250 1.1095494 1.1976677 1.2020657 1.2915119 1.4095128 -#> [406,] 0.3285568 0.4314120 0.4459262 0.4767470 0.4802200 0.4935474 0.5290869 -#> [407,] 0.3213510 0.4834092 0.5131636 0.5381790 0.5893552 0.5904987 0.6029693 -#> [408,] 0.3178739 0.3953197 0.5304649 0.5385771 0.5808212 0.6046218 0.6353393 -#> [409,] 0.4980218 0.5544410 0.5735751 0.6319675 0.6597138 0.7081038 0.7502911 -#> [410,] 0.4111658 0.6511711 0.6553934 0.7264462 0.7492739 0.7716554 0.8001210 -#> [411,] 0.6332989 0.7615951 0.8218535 0.8311973 0.9326075 0.9754550 0.9913331 -#> [412,] 0.5250252 0.6174626 0.6315293 0.6413620 0.6457151 0.6603378 0.6631487 -#> [413,] 0.2296156 0.3987572 0.4429551 0.4651612 0.4720911 0.5008382 0.5440362 -#> [414,] 0.4051202 0.4769180 0.7755421 0.9211892 0.9332052 0.9669258 1.0150922 -#> [415,] 0.1457126 0.5002871 0.5610436 0.5681428 0.5699855 0.7477874 0.7981500 -#> [416,] 0.4056079 0.5185333 0.5372201 0.5717254 0.6669696 0.6682361 0.6959216 -#> [417,] 0.5261482 0.5872667 0.5905954 0.6268633 0.6326371 0.6944146 0.7490721 -#> [418,] 0.2546616 0.5221003 0.5281845 0.5339777 0.5899013 0.5936018 0.6222072 -#> [419,] 0.5121596 0.6132402 0.7254377 0.7297429 0.7689811 0.7979053 0.8296740 -#> [420,] 0.5019129 0.5067543 0.5348742 0.5720533 0.6073665 0.6227840 0.6371878 -#> [421,] 0.5492871 0.6242689 0.6266138 0.7856933 0.8007903 0.8282300 0.8867929 -#> [422,] 0.4955922 0.5857594 0.5938233 0.6401902 0.6480952 0.6741597 0.6915875 -#> [423,] 0.4430698 0.4549399 0.4942273 0.5141040 0.5802341 0.5968755 0.6428334 -#> [424,] 0.3802711 0.3884973 0.6605389 0.7177136 0.7344810 0.8601750 0.8970716 -#> [425,] 0.3481627 0.3920226 0.3926587 0.4059614 0.4381897 0.4934713 0.5628062 -#> [426,] 0.9883830 1.0004716 1.0509800 1.0558282 1.1139603 1.1253654 1.1421931 -#> [427,] 0.4777599 0.7755872 0.8128896 0.8477208 0.8490090 0.8849198 0.9162768 -#> [428,] 0.3918320 0.4289125 0.6084791 0.6263253 0.7111541 0.8128610 0.8505465 -#> [429,] 0.5138186 0.6642021 0.6690111 0.6806061 0.6947922 0.7058257 0.7114169 -#> [430,] 0.3688427 0.3948392 0.4609358 0.5423035 0.5549392 0.5716828 0.6080963 -#> [431,] 1.0730223 1.4578942 1.4752809 1.5188541 1.5335735 1.5639044 1.5855170 -#> [432,] 0.6152174 0.6268633 0.6339550 0.7537199 0.8068748 0.8384083 0.8432481 -#> [433,] 0.4116965 0.5155402 0.5488945 0.5653569 0.7218092 0.7590991 0.7820999 -#> [434,] 0.4125014 0.4983567 0.4999916 0.5154095 0.5325850 0.5391964 0.5422872 -#> [435,] 0.4123463 0.4869867 0.5505204 0.5944104 0.6481640 0.6965300 0.7600829 -#> [436,] 0.3806511 0.3838156 0.4368219 0.4897840 0.5797199 0.6907963 0.6946080 -#> [437,] 0.2618901 0.4174240 0.5035822 0.5413713 0.5488100 0.5831975 0.5879309 -#> [438,] 0.4959007 0.6457151 0.7176706 0.7920279 0.8368992 0.8614404 0.9128021 -#> [439,] 0.4485787 0.5459737 0.5677734 0.6356550 0.6956782 0.7170209 0.7448506 -#> [440,] 0.3514631 0.5187787 0.5466477 0.6713886 0.6968107 0.8028419 0.8264479 -#> [441,] 0.5425146 0.7626236 0.7633689 0.7675038 0.7677695 0.7694295 0.7838109 -#> [442,] 0.6296337 0.7434690 0.7822497 0.8595808 0.8613229 0.8841422 0.8859597 -#> [443,] 0.2578455 0.3417088 0.5196555 0.5383358 0.6234864 0.6611541 0.6897736 -#> [444,] 0.3304958 0.5745717 0.6384770 0.8066526 0.8386210 0.8411821 0.8508133 -#> [445,] 0.1960542 0.3676536 0.3926587 0.3980096 0.4613971 0.5085484 0.5577556 -#> [446,] 0.2991949 0.5174856 0.5902651 0.6158555 0.6207376 0.6400436 0.6540970 -#> [447,] 0.5555567 0.8119474 0.9274681 0.9510667 0.9553364 0.9636199 0.9896622 -#> [448,] 0.3136491 0.4379508 0.4750387 0.4872823 0.5311648 0.6108173 0.6707253 -#> [449,] 0.2922924 0.2928507 0.3874939 0.4891075 0.4980564 0.5681757 0.5693549 -#> [450,] 0.4645052 0.5734645 0.6892862 0.7327970 0.8550672 0.9059494 0.9226508 -#> [451,] 0.3776611 0.4051202 0.7571150 0.7891617 0.8804326 0.9623114 1.1344319 -#> [452,] 0.2618901 0.3390154 0.4804254 0.5135363 0.5185349 0.5615556 0.5796006 -#> [453,] 0.4366422 0.5072311 0.5238620 0.5409284 0.5454542 0.7176306 0.7218152 -#> [454,] 0.3352511 0.3513501 0.3779047 0.4895538 0.5026705 0.5364504 0.5378630 -#> [455,] 0.6907413 0.7005177 0.9384542 1.0699758 1.0746198 1.0962976 1.1049130 -#> [456,] 0.4614993 0.5081797 0.5610950 0.5666957 0.6122418 0.6170191 0.6381189 -#> [457,] 0.5955406 0.6458231 0.7036961 0.7863549 0.8026338 0.8140232 0.8283215 -#> [458,] 0.2289517 0.3067851 0.3186834 0.3819500 0.4005514 0.4741237 0.4938784 -#> [459,] 0.5252485 0.5339298 0.5489988 0.5691434 0.6311555 0.6720264 0.6812624 -#> [460,] 0.4374822 0.7195262 0.9098251 0.9343713 0.9505368 0.9764738 1.0424884 -#> [461,] 0.2996762 0.3490601 0.3588431 0.3912933 0.5119789 0.5439571 0.5553900 -#> [462,] 0.3660383 0.3848525 0.4423240 0.4423984 0.4764335 0.5007229 0.5412566 -#> [463,] 1.1788565 1.3423411 1.4133923 1.4198957 1.4895292 1.5667001 1.6822037 -#> [464,] 0.6225775 0.9153356 1.0056298 1.0156434 1.0508046 1.1443681 1.1523387 -#> [465,] 0.3458029 0.3830567 0.5383580 0.6264371 0.7775237 0.7781291 0.8263970 -#> [466,] 0.3757904 0.4253779 0.5240031 0.5697414 0.6078203 0.6136829 0.6402618 -#> [467,] 0.2791779 0.4828984 0.4913224 0.5192799 0.5194278 0.5242562 0.6222072 -#> [468,] 0.1412353 0.3066120 0.3913374 0.4314051 0.4435253 0.4633867 0.4826127 -#> [469,] 0.3091575 0.5814689 0.5906277 0.5992562 0.6326371 0.6469669 0.6984444 -#> [470,] 0.5165410 0.5225561 0.5504840 0.6094010 0.6236399 0.7190391 0.7255185 -#> [471,] 0.3905628 0.4570374 0.4923259 0.5048736 0.5072869 0.5268461 0.5910757 -#> [472,] 0.5606495 0.7588076 0.7928859 0.8473664 0.9024861 0.9034418 0.9504198 -#> [473,] 0.6265314 0.7195262 0.8195750 0.9087339 0.9994496 1.0561105 1.1280151 -#> [474,] 0.3556601 0.5642782 0.5707005 0.5888508 0.6033136 0.6389466 0.6528188 -#> [475,] 0.4024483 0.5136618 0.6266999 0.6293683 0.6331598 0.6568448 0.7223757 -#> [476,] 0.2569293 0.3657331 0.4026997 0.4276793 0.4572070 0.4812868 0.5029748 -#> [477,] 0.3524337 0.5567038 0.5635622 0.5871811 0.6359616 0.6450181 0.6596769 -#> [478,] 0.6504698 0.8628118 0.8934793 0.9492043 0.9711269 1.0379094 1.0430789 -#> [479,] 0.7279501 0.8232968 0.8815718 0.8875119 0.9731088 1.0534073 1.0644012 -#> [480,] 0.5943301 0.6619937 0.6947922 0.7085971 0.8332180 0.8358105 0.9264773 -#> [481,] 0.4085959 0.4556442 0.6605592 0.6979482 0.7189606 0.7891139 0.7960983 -#> [482,] 0.7915908 1.0648047 1.2543197 1.3099819 1.4000270 1.4309635 1.4379298 -#> [483,] 0.5929305 0.8490390 0.9457200 1.0348564 1.0396577 1.1022982 1.1396698 -#> [484,] 0.3525252 0.4063249 0.4582774 0.4732585 0.4942109 0.5168748 0.5171654 -#> [485,] 0.5038661 0.5610436 0.6504698 0.7018391 0.9611765 0.9764738 1.0006022 -#> [486,] 0.3358029 0.4505269 0.5504881 0.6291464 0.7264237 0.7367339 0.7541327 -#> [487,] 0.5383990 0.6707741 0.7384176 0.8436659 0.9150972 0.9363727 0.9406192 -#> [488,] 0.4432166 0.4470028 0.5287582 0.5528294 0.6302956 0.6820235 0.7083550 -#> [489,] 0.3660383 0.4829863 0.4848390 0.5014880 0.5230184 0.5234596 0.5705091 -#> [490,] 0.4485787 0.4863853 0.5623340 0.6401146 0.6553934 0.7002668 0.8068167 -#> [491,] 0.6932394 0.7742510 0.8789513 0.8817063 0.9108048 0.9883042 1.0338475 -#> [492,] 0.2094617 0.4414033 0.4572070 0.4609700 0.4661846 0.4746320 0.4855697 -#> [493,] 0.3106024 0.4460617 0.5312083 0.6673814 0.7340519 0.8070216 0.8581231 -#> [494,] 0.3301119 0.3437702 0.3669123 0.4918698 0.5940281 0.6070454 0.6111134 -#> [495,] 0.3394844 0.4006096 0.4068972 0.4172339 0.6019840 0.6138800 0.6667747 -#> [496,] 0.2286886 0.3686147 0.4272458 0.5016437 0.5448599 0.5484320 0.5511875 -#> [497,] 0.5624785 0.5993097 0.7017688 0.7706361 0.8810112 0.9182541 0.9193467 -#> [498,] 0.8064250 1.0526432 1.1023809 1.1811835 1.2005248 1.2230364 1.2752681 -#> [499,] 2.4787351 2.5249703 2.6359338 2.6802160 2.6815841 2.6952508 2.7314304 -#> [500,] 0.4542098 0.5011913 0.5495823 0.6193803 0.6722716 0.6991221 0.7031971 -#> [501,] 0.2734565 0.4199832 0.4287305 0.4473554 0.5979192 0.6239051 0.6380812 -#> [502,] 0.3392109 0.4396069 0.5123829 0.6164692 0.7387613 0.7750119 0.7871300 -#> [503,] 0.5428824 0.6339550 0.6402618 0.6501874 0.6507848 0.7304828 0.7361710 -#> [504,] 0.3386233 0.3930878 0.4120583 0.4747824 0.4762068 0.5085280 0.5215113 -#> [505,] 0.2612405 0.3551038 0.4938784 0.5573365 0.5755479 0.5987590 0.6044852 -#> [506,] 0.3083919 0.5942332 0.7722180 0.7991157 0.8329750 0.8516392 0.8547082 -#> [507,] 0.2217617 0.2661266 0.3175616 0.4149331 0.4537911 0.4966009 0.5105782 -#> [508,] 0.6308904 0.7466674 0.7903432 0.9064238 0.9807787 1.0042939 1.0348121 -#> [509,] 0.4242651 0.4347131 0.5480419 0.6643007 0.6676141 0.7039359 0.7141846 -#> [510,] 0.5904378 0.6410868 0.8222237 0.9754550 1.0094423 1.0567393 1.0831111 -#> [511,] 0.4332651 0.6714131 0.7998238 0.8317753 0.8457471 0.8535646 0.8628611 -#> [512,] 0.5671601 0.7282968 0.8362417 0.9513614 1.1567506 1.1991442 1.3318198 -#> [513,] 0.6913829 0.8888571 0.9334695 0.9759081 0.9892210 1.0252545 1.0327843 -#> [514,] 0.4353166 0.5455117 0.7431773 0.8553774 0.8819996 0.9236206 0.9959342 -#> [515,] 0.4870615 0.5425748 0.6153536 0.6784835 0.6855743 0.7353062 0.7886938 -#> [516,] 0.3604918 0.3632050 0.3890036 0.4383867 0.4599237 0.4645308 0.4826099 -#> [517,] 0.3329671 0.5828192 0.6029241 0.7347236 0.7795816 0.8302082 0.8375110 -#> [518,] 0.3944994 0.4106354 0.5612706 0.5953177 0.6034767 0.6522410 0.6632266 -#> [519,] 0.3765864 0.4980218 0.7048266 0.7224488 0.7627102 0.7678418 0.8104953 -#> [520,] 0.5385999 0.6332117 0.6527146 0.6615776 0.6865330 0.7093842 0.7124826 -#> [521,] 0.1923351 0.5812571 0.6480375 0.6551925 0.7185370 0.7214546 0.7555407 -#> [522,] 1.4254770 1.4553427 1.5319170 1.5682789 1.5691586 1.6937886 1.8572698 -#> [523,] 0.3401918 0.4893371 0.5681757 0.7157430 0.7289997 0.8183816 0.8595664 -#> [524,] 0.5352316 0.6435275 0.7486387 0.7818217 0.8367021 0.8470057 0.8558235 -#> [525,] 0.4876600 0.5144181 0.5706278 0.6483658 0.6916804 0.7436081 0.8408944 -#> [526,] 0.2442281 0.4379508 0.5443972 0.5917659 0.5943638 0.6069509 0.6195639 -#> [527,] 0.5355423 0.5474971 0.5550847 0.6033136 0.6927700 0.6934277 0.7839221 -#> [528,] 0.6849981 0.7236067 0.7239378 0.7559130 0.9286206 0.9476253 1.0313606 -#> [529,] 0.3109425 0.3454563 0.4489389 0.6076082 0.6686554 0.7683952 0.7746529 -#> [530,] 0.5224370 0.5242928 0.5504840 0.6955587 0.7028009 0.7331899 0.7756818 -#> [531,] 0.2686635 0.8502969 0.8539395 0.8875681 0.9449080 0.9607605 0.9725843 -#> [532,] 0.3968053 0.4608625 0.5451733 0.5693529 0.5913540 0.6207376 0.6556169 -#> [533,] 0.2105175 0.2849864 0.4435253 0.4451457 0.4976498 0.5039508 0.5385771 -#> [534,] 1.0056298 1.0063360 1.0228704 1.1758045 1.3061648 1.3131092 1.3539577 -#> [535,] 0.1795017 0.3914624 0.4311430 0.4808685 0.5177915 0.5188129 0.5231660 -#> [536,] 0.3886122 0.6940858 0.7367848 0.7859109 0.7948439 0.8092575 0.8410188 -#> [537,] 0.2922924 0.3740531 0.4893371 0.5506097 0.5691912 0.5700471 0.5963322 -#> [538,] 0.5304649 0.5727852 0.5982141 0.5984345 0.6064560 0.6682981 0.6768179 -#> [539,] 0.3723138 0.4811191 0.4975465 0.5152524 0.5242217 0.5337690 0.5374942 -#> [540,] 0.1998153 0.3781865 0.3798359 0.4114479 0.5380592 0.5418090 0.5437751 -#> [541,] 0.8346431 0.9989923 1.0280044 1.2451451 1.2641855 1.3222055 1.3797486 -#> [542,] 0.5150732 0.5405132 0.5517353 0.8327506 0.8509328 1.0145762 1.0188366 -#> [543,] 0.6707741 0.8546885 0.8712047 0.9233843 0.9337107 0.9400282 1.0088752 -#> [544,] 0.7023488 0.7678536 0.8127954 0.8866871 0.9579694 1.0152249 1.0761013 -#> [545,] 0.4737440 0.4814243 0.5159818 0.5177161 0.5302001 0.5374756 0.6089900 -#> [546,] 0.4570762 0.4936120 0.5217608 0.6644822 0.7165986 0.7342913 0.7596182 -#> [547,] 0.2686635 0.6729536 0.8144727 0.8584639 0.8972973 0.9212723 0.9984089 -#> [548,] 0.2965186 0.3938001 0.4227960 0.4627563 0.5003201 0.5122100 0.5155835 -#> [549,] 0.7471292 1.0120172 1.0738716 1.0850179 1.1114957 1.1788565 1.2134379 -#> [550,] 0.7557748 1.9640711 2.0158992 2.1958536 2.2025514 2.2810268 2.3287320 -#> [551,] 0.2164501 0.4410435 0.4493396 0.4747144 0.4762068 0.5017388 0.5159818 -#> [552,] 0.3774352 0.5874570 0.6080600 0.6632698 0.6701687 0.6766880 0.6805240 -#> [553,] 0.2273583 0.3103047 0.3158317 0.4449949 0.4897840 0.5127951 0.5189196 -#> [554,] 0.4215890 0.4280685 0.7515559 0.8982119 0.9799687 0.9824180 1.0017711 -#> [555,] 0.2751204 0.5624785 0.8821678 0.8904358 0.9163542 0.9306648 0.9887995 -#> [556,] 0.3124576 0.5874171 0.9109125 0.9132635 0.9570289 0.9756815 0.9871541 -#> [557,] 0.4570762 0.4896084 0.5105701 0.5210476 0.5693529 0.6319652 0.6602495 -#> [558,] 0.6977377 0.8379934 0.8425571 0.9253071 0.9524282 0.9583905 1.0047693 -#> [559,] 0.2618401 0.6252074 0.6447046 0.6806061 0.6927381 0.7264462 0.7378000 -#> [560,] 0.5383990 0.5511466 0.6680109 0.7092152 0.7109173 0.7745257 0.8069808 -#> [561,] 0.4650941 0.5238620 0.5348742 0.5662374 0.6147478 0.6925079 0.7901603 -#> [562,] 1.4131752 1.4267515 1.5172372 1.6144240 1.7843633 1.9143099 2.0157166 -#> [563,] 1.0995921 1.1604199 1.2860329 1.3602894 1.4155908 1.4740372 1.5191169 -#> [564,] 0.5700973 0.9030833 1.0028225 1.0354115 1.0786533 1.0800418 1.0995374 -#> [565,] 0.2612405 0.3530585 0.4722940 0.5048796 0.5476488 0.5617994 0.5865857 -#> [566,] 0.4788930 0.8072730 0.8456541 0.8498218 0.8716038 0.9956315 1.0053389 -#> [567,] 0.6152174 0.9260792 0.9534972 0.9594619 1.0066028 1.0099486 1.0219929 -#> [568,] 0.3052188 0.3113680 0.3719505 0.4422055 0.4989853 0.5074590 0.5246449 -#> [569,] 0.2343413 0.4280685 0.6458231 0.6796532 0.9604054 0.9616049 1.0077803 -#> [570,] 0.6254268 0.6579970 0.8277229 0.9013307 0.9356341 0.9553533 1.0004716 -#> [571,] 0.2849864 0.3809927 0.3913374 0.4213421 0.4369799 0.4488936 0.4956664 -#> [572,] 0.3799644 0.3981264 0.4570374 0.5037029 0.5298376 0.5905216 0.5940546 -#> [573,] 0.7058054 0.9816568 1.1095494 1.1333906 1.1793584 1.2026618 1.2271717 -#> [574,] 0.4938878 0.7164475 0.7231428 0.7274085 0.7840491 0.8155527 0.8425017 -#> [575,] 0.3610341 0.4669204 0.4753663 0.5390328 0.5429686 0.5484320 0.5824740 -#> [576,] 0.9832351 1.1916313 1.2309731 1.2885887 1.3348366 1.4022129 1.4999657 -#> [577,] 0.4370194 0.5828192 0.5987923 0.6252074 0.6440177 0.7103820 0.7120553 -#> [578,] 0.1907358 0.4380835 0.4398420 0.4453320 0.4968742 0.5043623 0.5147732 -#> [579,] 0.3394250 0.5017704 0.5073744 0.5459737 0.5623340 0.5957137 0.6257073 -#> [580,] 0.3136491 0.4832977 0.5452097 0.5469488 0.5943638 0.6684409 0.6788909 -#> [581,] 0.4757520 0.5505204 0.5907311 0.7315827 0.7437559 0.8815718 0.8903385 -#> [582,] 0.3638288 0.5512129 0.5698245 0.5741937 0.6482118 0.6834861 0.6927381 -#> [583,] 0.7201322 0.8014768 0.9182950 0.9211684 0.9495949 0.9767439 0.9846061 -#> [584,] 0.8286330 0.8392739 0.9274681 0.9415304 0.9662324 0.9756150 1.0326113 -#> [585,] 0.7150264 0.8001651 0.8241293 0.8258942 0.8378235 0.8571228 0.8893262 -#> [586,] 0.5337423 0.5635622 0.5737651 0.6376604 0.7493295 0.7755872 0.8280572 -#> [587,] 0.9260981 0.9886686 1.0634161 1.2119385 1.3173416 1.4022129 1.4536936 -#> [588,] 0.2770382 0.4636346 0.6602455 0.8192408 0.9824257 0.9941006 1.0064931 -#> [589,] 0.4431134 0.4585248 0.4858187 0.5874570 0.5907221 0.6573963 0.6631667 -#> [590,] 0.5029892 0.5650864 0.6707272 0.6804693 0.7201322 0.7361470 0.7548057 -#> [591,] 0.4637712 0.5511508 0.7224517 0.7236107 0.7981595 0.8298472 0.8386210 -#> [592,] 0.3838156 0.4704623 0.4808685 0.5406582 0.6070318 0.6076115 0.6214845 -#> [593,] 0.2780944 0.2938909 0.3160396 0.4005184 0.4071768 0.4305712 0.4557063 -#> [594,] 0.3080777 0.3956464 0.4415222 0.4554516 0.4652289 0.4734049 0.5048796 -#> [595,] 0.4019718 0.7367865 0.7486387 0.7984211 0.8065179 0.8296740 0.8335389 -#> [596,] 0.3458876 0.5025590 0.5422557 0.5644321 0.5681428 0.6667747 0.6730841 -#> [597,] 0.3083919 0.5009687 0.6214845 0.6287011 0.7551961 0.7690297 0.7985806 -#> [598,] 0.3176937 0.3592673 0.4412460 0.5300126 0.5439430 0.5769023 0.6025091 -#> [599,] 0.1821476 0.4623505 0.5653569 0.6789478 0.6922921 0.7219848 0.7308414 -#> [600,] 0.3080751 0.3136977 0.4504919 0.4725017 0.4993532 0.5012870 0.5669852 -#> [601,] 0.3582576 0.4087473 0.4194612 0.4249279 0.4440057 0.4767470 0.4961666 -#> [602,] 0.3486888 0.3844678 0.4569880 0.4613971 0.4621962 0.4693899 0.4812505 -#> [603,] 0.2676504 0.3723138 0.5245181 0.5998886 0.6102405 0.6116010 0.6204448 -#> [604,] 0.1880397 0.4352734 0.4621962 0.4660285 0.4711256 0.4978754 0.5305005 -#> [605,] 0.3678781 0.4005174 0.4088687 0.4412460 0.4848390 0.4860993 0.4929666 -#> [606,] 0.3445994 0.4287305 0.5211516 0.5470574 0.6196470 0.7423847 0.8015919 -#> [607,] 0.3109425 0.3713510 0.3972822 0.5843780 0.6075112 0.6639107 0.7612510 -#> [608,] 0.6211108 0.6765484 0.7801991 0.9374454 0.9723720 1.0410421 1.0413188 -#> [609,] 0.7620922 0.7945027 0.9671000 1.0241726 1.0526528 1.0645023 1.0721897 -#> [610,] 0.3301119 0.4539022 0.4626248 0.4929649 0.5011315 0.5038229 0.5371010 -#> [611,] 0.3892923 0.5719831 0.6193540 0.6307219 0.6754799 0.6939600 0.7055080 -#> [612,] 0.3590217 0.5622607 0.5749747 0.7050365 0.7092287 0.7328549 0.7642209 -#> [613,] 0.4556442 0.5065259 0.6415368 0.7681048 0.7847803 0.7952401 0.7995223 -#> [614,] 0.4841278 0.7330356 0.8625143 0.8627456 0.8871369 0.9040311 1.0021075 -#> [615,] 0.4052950 0.5250248 0.5449661 0.5487680 0.5836566 0.6140291 0.7035311 -#> [616,] 0.3342382 0.3528047 0.4566071 0.4588823 0.5595899 0.5650627 0.5664733 -#> [617,] 0.5910692 0.6109909 0.7725971 0.7880406 0.8080487 0.8162246 0.8352604 -#> [618,] 0.8995747 0.9059494 1.0124701 1.0319325 1.0337591 1.1374333 1.1429259 -#> [619,] 0.2676504 0.4989698 0.5152524 0.5376920 0.5667576 0.5704259 0.5736706 -#> [620,] 0.8284279 0.8684830 0.9933613 0.9954375 1.0958495 1.1693827 1.2511751 -#> [621,] 0.7137572 0.7962522 0.8566013 1.1097381 1.1318377 1.1977082 1.2191116 -#> [622,] 0.3788236 0.4559011 0.5616415 0.5721634 0.5902184 0.6025091 0.6110487 -#> [623,] 0.1923351 0.4792305 0.5322739 0.5676543 0.5992562 0.6682089 0.7108051 -#> [624,] 0.7236067 0.8886585 0.9266796 0.9953299 1.1276024 1.2146687 1.2330272 -#> [625,] 0.2957885 0.4446937 0.5750432 0.5765267 0.5925585 0.6037244 0.6568023 -#> [626,] 0.5509826 0.6290930 0.6537618 0.8620734 0.8626699 0.8700212 0.8840395 -#> [627,] 0.3806210 0.3815088 0.3934241 0.4213650 0.4374722 0.5392035 0.5448039 -#> [628,] 0.4037595 0.4367022 0.4729371 0.5086459 0.5612706 0.7159270 0.7336171 -#> [629,] 0.2094617 0.3032933 0.3819500 0.3945596 0.3978667 0.4026997 0.4286491 -#> [630,] 0.3813579 0.4197812 0.4300301 0.4399549 0.4519320 0.5166567 0.5815696 -#> [631,] 0.8832363 1.3477283 1.3545155 1.4381920 1.4890247 1.5271940 1.5304553 -#> [632,] 0.3537939 0.4304763 0.4875948 0.5019129 0.5135788 0.5934187 0.6370304 -#> [633,] 0.6097647 0.8841422 0.9312233 0.9659309 0.9901034 1.0455495 1.0524537 -#> [634,] 0.2411622 0.4635766 0.4648660 0.4726632 0.5298530 0.5476241 0.6141360 -#> [635,] 0.4445392 0.5229273 0.6558690 0.6608258 0.6714148 0.7340519 0.7681936 -#> [636,] 0.8490657 0.9090991 0.9289076 0.9717083 1.0127865 1.0738554 1.1702953 -#> [637,] 0.3196900 0.3934462 0.3981264 0.4439951 0.4990745 0.5239454 0.5315435 -#> [638,] 0.3856247 0.4641082 0.5074462 0.5270669 0.5824740 0.5874906 0.6256009 -#> [639,] 0.4993532 0.6707272 0.6866497 0.7076178 0.7314473 0.7330356 0.7394303 -#> [640,] 0.2615611 0.3670521 0.5169540 0.5198035 0.5434027 0.7077364 0.7297429 -#> [641,] 0.4279815 0.4513086 0.4595417 0.4634436 0.4923078 0.5025113 0.5047042 -#> [642,] 0.9597329 1.1546740 1.1975968 1.3252038 1.3591368 1.3595380 1.3768743 -#> [643,] 0.4094364 0.4826327 0.4858038 0.5331247 0.5526499 0.5606193 0.5979192 -#> [644,] 0.1502014 0.3948392 0.4024483 0.4363727 0.5110320 0.5298530 0.5515163 -#> [645,] 0.7483794 0.7660779 0.7972184 0.8423217 0.8477208 0.9264165 0.9535310 -#> [646,] 0.3298885 0.6257554 0.6793500 0.7212007 0.7591843 0.7873279 0.7969979 -#> [647,] 0.6265314 0.8995747 0.9675360 1.0166948 1.0424884 1.0450260 1.1418658 -#> [648,] 0.3706053 0.3744725 0.4122767 0.4483847 0.4539152 0.4645888 0.4827478 -#> [649,] 0.4701398 0.5616011 0.5705091 0.6293527 0.6541363 0.6560097 0.7152830 -#> [650,] 0.9559779 0.9902827 1.0017711 1.0408619 1.0470346 1.2111807 1.2288811 -#> [651,] 0.5931816 0.5963667 0.6109270 0.7269927 0.7596997 0.7883433 0.8504473 -#> [652,] 0.3343100 0.5469488 0.6108173 0.6170841 0.6329588 0.6413620 0.6597046 -#> [653,] 0.8118327 1.0229776 1.2321380 1.3246079 1.3556170 1.3676437 1.4039970 -#> [654,] 0.4407825 0.4994282 0.5063334 0.5119253 0.5206700 0.5721931 0.5740050 -#> [655,] 0.3471195 0.6613483 0.7012632 0.7894252 0.7945076 0.8148472 0.8189127 -#> [656,] 0.4028254 0.4151472 0.4276793 0.4414033 0.4663869 0.4732585 0.4777298 -#> [657,] 0.8341872 0.8865120 0.9124553 0.9776484 0.9869563 0.9919096 1.0184556 -#> [658,] 0.5081797 0.5225944 0.5464530 0.5717254 0.6125552 0.6259600 0.6279938 -#> [659,] 0.2656600 0.3891551 0.3932012 0.4618651 0.4633659 0.4802200 0.5200496 -#> [660,] 0.4583481 0.4983567 0.5065117 0.5727852 0.5739238 0.6249346 0.6435275 -#> [661,] 0.7064498 0.7354697 0.7449229 0.8980629 0.9261128 0.9273094 0.9797472 -#> [662,] 0.2599636 0.3310614 0.3920226 0.4318124 0.4627563 0.5120180 0.5462467 -#> [663,] 0.6329682 0.6602455 0.6773683 0.7154039 0.7537751 0.7591667 0.7627073 -#> [664,] 0.2996762 0.3549475 0.4149381 0.6040388 0.6074579 0.6244618 0.6274167 -#> [665,] 0.3527183 0.4374755 0.4699401 0.5557919 0.5650627 0.5727925 0.5955450 -#> [666,] 0.8355623 1.1958227 1.3173416 1.3620397 1.3930836 1.4061571 1.5639044 -#> [667,] 0.2694920 0.4093251 0.4940575 0.5246449 0.5301315 0.5522417 0.5525923 -#> [668,] 0.3176937 0.3298639 0.4615580 0.4781089 0.4860993 0.5040802 0.5085280 -#> [669,] 0.8076283 0.8741396 0.9529027 1.0189253 1.1386040 1.1676503 1.2302778 -#> [670,] 0.4916895 0.8355623 0.9260981 0.9447221 0.9853316 1.0694053 1.1113730 -#> [671,] 0.2950353 0.3113680 0.4127848 0.4388591 0.4701749 0.5131636 0.5181448 -#> [672,] 0.1765483 0.6193803 0.7609701 0.7924176 0.8291398 0.8341872 0.8631459 -#> [673,] 0.1882941 0.6084791 0.6591222 0.8985321 0.9114107 0.9670695 0.9727846 -#> [674,] 0.7103740 0.7996086 1.1310247 1.1396698 1.1415995 1.1594726 1.2106312 -#> [675,] 0.6694261 0.7091689 0.7125685 0.7160217 0.7290825 0.7359664 0.7543159 -#> [676,] 0.2652173 0.3591586 0.3856247 0.4302680 0.4700327 0.5284957 0.5455016 -#> [677,] 0.4056079 0.4650003 0.6410389 0.6644238 0.7346686 0.7799487 0.7987587 -#> [678,] 0.7652407 1.0127818 1.1077200 1.1214226 1.1279028 1.2002891 1.2068199 -#> [679,] 0.3086478 0.3642713 0.3672958 0.5025481 0.6187645 0.7493980 0.7529638 -#> [680,] 0.6108399 0.6574265 0.6846185 0.7434690 0.7667026 0.7743503 0.8051438 -#> [681,] 0.9406192 0.9900879 1.0073145 1.0223147 1.0868001 1.1268332 1.1792269 -#> [682,] 0.5070293 0.5632050 0.6182612 0.6978583 0.7107505 0.7151584 0.7312178 -#> [683,] 0.4334864 0.4357644 0.5221797 0.6035912 0.6405775 0.6628211 0.6794570 -#> [684,] 0.5086459 0.5793263 0.5985444 0.6381214 0.6499291 0.7309148 0.8410188 -#> [685,] 0.2965186 0.3183395 0.3937665 0.4383867 0.4429527 0.4651612 0.4741794 -#> [686,] 0.2436403 0.3843435 0.4940036 0.5953922 0.5978571 0.6161363 0.6846185 -#> [687,] 0.6109270 0.7466674 0.8328029 0.8611830 0.9437925 0.9840271 0.9916199 -#> [688,] 1.1609350 1.1780794 1.2971786 1.2993309 1.3046307 1.3752090 1.4155252 -#> [689,] 0.3227764 0.5130482 0.5373405 0.5434537 0.5549392 0.6665069 0.7139751 -#> [690,] 0.6298396 0.9606412 0.9806925 1.0191859 1.0786642 1.0977456 1.1067650 -#> [691,] 1.1912198 1.2343525 1.3290869 1.3660693 1.3753267 1.5181986 1.6123363 -#> [692,] 0.2697039 0.3892588 0.4135310 0.4528358 0.5037029 0.5203917 0.5268461 -#> [693,] 0.4335794 0.4556055 0.5383580 0.6724047 0.7737363 0.7917263 0.8150843 -#> [694,] 0.3496784 0.4470028 0.5743282 0.7685739 0.8087218 0.8201508 0.9122154 -#> [695,] 0.2202836 0.4180805 0.5138186 0.6213372 0.6242689 0.6470521 0.6813480 -#> [696,] 0.3184559 0.5904378 0.6486461 0.6865330 0.7496748 0.7809224 0.8325244 -#> [697,] 0.3358029 0.3893338 0.4844073 0.5672224 0.6164471 0.6320225 0.6561801 -#> [698,] 0.5025590 0.5278623 0.5865902 0.5981669 0.6182001 0.6365512 0.6947839 -#> [699,] 0.3784456 0.4625917 0.6006478 0.6319259 0.6326365 0.6393936 0.6485100 -#> [700,] 0.4097627 0.5605815 0.5786764 0.6970361 0.6994112 0.7243970 0.7910807 -#> [701,] 0.4532875 0.4625917 0.4769697 0.4860003 0.4913224 0.5144246 0.5899088 -#> [702,] 1.0514347 1.1351614 1.1604199 1.2314421 1.2675142 1.2719898 1.2757552 -#> [703,] 0.6039762 0.8073952 0.8313950 0.8410883 0.8428926 0.8934076 0.8941275 -#> [704,] 0.4874757 0.5073828 0.6075559 0.6104783 0.6204127 0.6582852 0.7073790 -#> [705,] 0.3776611 0.4769180 0.5101209 0.8239215 0.9078180 0.9702416 1.0187160 -#> [706,] 0.2938909 0.4308291 0.4513943 0.4871143 0.5175952 0.5349535 0.5568170 -#> [707,] 0.4360400 0.5857594 0.6447800 0.6451698 0.7482271 0.8122301 0.8444847 -#> [708,] 0.9514395 1.0606779 1.0636714 1.1426683 1.2643485 1.2781764 1.2947481 -#> [709,] 0.6583572 0.6639630 0.7268477 0.9004944 0.9162768 0.9609299 1.0435224 -#> [710,] 0.3390154 0.4174240 0.4615110 0.4615817 0.4617750 0.5644427 0.5650864 -#> [711,] 0.2724458 0.4804254 0.4858038 0.4923078 0.5294087 0.5417924 0.5419647 -#> [712,] 0.1257555 0.5030681 0.5135363 0.5429686 0.5453030 0.5831975 0.5874906 -#> [713,] 0.2316066 0.4242651 0.5503053 0.5668125 0.6527146 0.6826380 0.7104982 -#> [714,] 0.7137572 0.8132454 0.8288236 0.8378235 0.9917530 1.0033330 1.1027254 -#> [715,] 0.3445357 0.5203527 0.5210982 0.5302128 0.5474013 0.5504232 0.5537372 -#> [716,] 0.6289333 0.7239378 0.7605889 0.8364011 0.8404026 0.8441677 0.8458550 -#> [717,] 0.5101209 0.7502501 0.7571150 0.7914278 0.9332052 0.9764120 1.0077118 -#> [718,] 0.5185333 0.5451491 0.5464530 0.6375285 0.7150237 0.7173710 0.7490721 -#> [719,] 0.7471292 0.7926226 0.9163542 0.9244381 0.9594619 0.9813975 0.9902617 -#> [720,] 0.2798545 0.2928507 0.4171835 0.4241446 0.5506097 0.5528294 0.5698328 -#> [721,] 0.5810662 0.6228606 0.6947839 0.6994219 0.8052348 0.8134730 0.8163599 -#> [722,] 0.4924958 0.4981826 0.5540639 0.6878055 0.6996609 0.7174303 0.7634109 -#> [723,] 0.2298626 0.4681615 0.4883660 0.5622740 0.6198420 0.6236002 0.6291796 -#> [724,] 0.2985586 1.2498559 1.3073808 1.3259615 1.4278049 1.4933361 1.5392211 -#> [725,] 0.7177136 0.7769373 0.8442932 0.9119135 0.9680577 0.9692823 1.0137121 -#> [726,] 1.6054625 2.0848020 2.2737335 2.3440355 2.4777165 2.4800381 2.5488145 -#> [727,] 0.6776881 0.7018391 0.7596997 0.8934793 0.8953289 0.9017456 0.9556454 -#> [728,] 0.5503053 0.5529916 0.5644517 0.5815853 0.6495746 0.6587434 0.6663706 -#> [729,] 0.4863853 0.5017704 0.5706278 0.6356550 0.6714550 0.7973914 0.8065675 -#> [730,] 0.6298396 0.8941275 1.0586703 1.1020911 1.1238132 1.1308614 1.1605411 -#> [731,] 0.2230059 0.4460617 0.5229273 0.5374811 0.6970361 0.7349776 0.7393258 -#> [732,] 0.9529027 1.0492829 1.0509800 1.2951662 1.3222785 1.3498134 1.4098043 -#> [733,] 0.9483649 0.9880734 1.0074580 1.0216102 1.1741660 1.2103866 1.2133149 -#> [734,] 0.6690111 0.7064498 0.7085971 0.8331035 0.8896609 0.9334063 0.9730671 -#> [735,] 0.6221261 0.8075866 0.9425628 1.0028363 1.0798067 1.1136847 1.1192310 -#> [736,] 0.4332651 0.5409303 0.6307219 0.6624071 0.7050365 0.7191833 0.7874377 -#> [737,] 0.4387941 0.6204127 0.6707253 0.6965939 0.6972482 0.7017439 0.7697675 -#> [738,] 0.5950760 0.7354020 0.7369217 0.7641021 0.8088560 0.8308253 0.8328029 -#> [739,] 0.4441841 0.4739766 0.6529928 0.6619944 0.7024932 0.7527359 0.7571572 -#> [740,] 0.3433086 0.4559011 0.4768436 0.6148437 0.6968936 0.7092597 0.7328150 -#> [741,] 0.3158317 0.3165580 0.3969505 0.4063249 0.4579166 0.4631483 0.4714148 -#> [742,] 0.5550847 0.5627145 0.6103244 0.6326847 0.6514708 0.7510046 0.7562212 -#> [743,] 0.3056034 0.3103047 0.3165580 0.3768641 0.4135715 0.4179349 0.4652016 -#> [744,] 0.1418194 0.5225944 0.6063061 0.6682361 0.7811779 0.7920934 0.7948385 -#> [745,] 0.3113118 0.4595513 0.4636687 0.5151912 0.5487680 0.6796532 0.7036961 -#> [746,] 1.2094194 1.2693452 1.2838527 1.2862101 1.5435401 1.6377179 1.6807627 -#> [747,] 0.1921174 0.4963909 0.4970822 0.5206700 0.5575169 0.5603175 0.5684175 -#> [748,] 0.2709125 0.2989971 0.3178739 0.3892588 0.3984968 0.5257014 0.6096382 -#> [749,] 0.3710504 0.4176578 0.4423984 0.4646938 0.4921990 0.5166952 0.5442631 -#> [750,] 0.3913705 0.3968053 0.4414737 0.5097455 0.5351798 0.5617529 0.5676278 -#> [751,] 0.3342382 0.3864791 0.4802595 0.4861910 0.5138721 0.5257014 0.5507399 -#> [752,] 0.3788236 0.5478272 0.5989136 0.6381861 0.7703288 0.8092654 0.8158994 -#> [753,] 0.5702987 0.5980038 0.6198420 0.6559528 0.6597138 0.7089059 0.7139787 -#> [754,] 0.3107541 0.3136977 0.4305712 0.4617750 0.4785309 0.4931461 0.5282038 -#> [755,] 0.6690871 0.6860968 0.7725749 0.8556933 0.8632565 0.9030162 0.9208716 -#> [756,] 0.4915624 0.5409284 0.5495761 0.5752191 0.6137225 0.7168790 0.7279401 -#> [757,] 0.2709125 0.3940243 0.3953197 0.3979419 0.4528358 0.4592319 0.5072869 -#> [758,] 0.3246618 0.4099349 0.5264586 0.5480419 0.5910737 0.6117036 0.6689205 -#> [759,] 0.5578534 0.5838292 0.6651903 0.7801991 0.8151923 0.8256253 0.8408133 -#> [760,] 0.4936120 0.7140313 0.8417973 0.8946692 0.9161463 0.9171797 0.9212723 -#> [761,] 0.4423240 0.4711256 0.5230184 0.5773746 0.5822046 0.5966500 0.6572810 -#> [762,] 0.1907358 0.4005174 0.4075196 0.4639866 0.4674898 0.5261269 0.5539596 -#> [763,] 0.9182541 1.0350864 1.1025945 1.1219863 1.1424055 1.1723399 1.1851861 -#> [764,] 0.4357644 0.5396707 0.7081140 0.7458473 0.7559599 0.7662393 0.7817104 -#> [765,] 0.2985586 1.2673941 1.3162858 1.3490841 1.3812083 1.4572480 1.4857447 -#> [766,] 0.4815630 0.5688090 0.6190446 0.7048870 0.7183197 0.7194659 0.7259945 -#> [767,] 0.7216168 0.7344810 0.7559130 0.8101955 0.8404026 0.8766603 0.8847948 -#> [768,] 0.5425146 0.5511466 0.6194563 0.6747291 0.6897736 0.7081918 0.7287542 -#> [769,] 0.5627565 0.6415368 0.6619937 0.7960983 0.8765106 0.8888571 0.8970740 -#> [770,] 0.4305420 0.4811124 0.4841278 0.6360874 0.6567670 0.7184861 0.7732483 -#> [771,] 1.0676613 1.4296416 1.4939067 1.5614253 1.5694705 1.7011895 1.7708172 -#> [772,] 0.7779108 0.8614404 1.0133924 1.0792046 1.1991442 1.2035680 1.2053993 -#> [773,] 0.3852675 0.4621747 0.4727399 0.5144246 0.5488358 0.6393936 0.6696794 -#> [774,] 0.3158758 0.3585868 0.3706053 0.4252485 0.4941148 0.5403542 0.5470744 -#> [775,] 0.8118327 0.8399439 0.8473664 0.8689506 0.9741627 0.9763009 0.9807302 -#> [776,] 0.3974524 0.4094364 0.4199832 0.5053076 0.5211516 0.6099352 0.6329682 -#> [777,] 0.3445357 0.3591586 0.4127848 0.4592028 0.4989853 0.5092464 0.5094085 -#> [778,] 0.3027772 0.4167413 0.4696109 0.5002217 0.5063334 0.5373169 0.6704545 -#> [779,] 0.5278035 0.6956782 0.8427137 0.9090991 0.9683068 0.9759108 0.9917875 -#> [780,] 0.3918320 0.4896400 0.5011922 0.6041616 0.6591222 0.7215146 0.7815183 -#> [781,] 0.5405132 0.6916804 0.6989697 0.8490090 0.8606617 0.8767628 0.9370817 -#> [782,] 0.2646638 0.3213510 0.3692134 0.4093251 0.4185317 0.5074590 0.5181448 -#> [783,] 0.4224714 0.6104783 0.6137225 0.6729815 0.7095745 0.7150264 0.7208141 -#> [784,] 0.4066972 0.6110123 0.6401066 0.6787280 0.6894002 0.7225953 0.7293708 -#> [785,] 0.2995764 0.4729371 0.5985444 0.6034767 0.6364684 0.6724590 0.6766374 -#> [786,] 0.3859147 0.5912506 0.5923463 0.6539348 0.7856543 0.8075600 0.8499710 -#> [787,] 0.4695051 0.8063933 0.8487997 0.9466795 1.0495105 1.0972245 1.0995921 -#> [788,] 0.5394996 0.7051774 0.8265935 0.8760955 0.8869731 0.9950556 1.0018150 -#> [789,] 0.5625080 0.8329584 0.8397169 0.8863304 0.8892563 0.8977614 0.9131567 -#> [790,] 0.3525252 0.4368219 0.4714148 0.5242562 0.5316131 0.5881955 0.5894456 -#> [791,] 0.2915407 0.4650941 0.5777199 0.6138800 0.6294308 0.6847840 0.7266085 -#> [792,] 0.4738325 0.4776311 0.4811124 0.6236002 0.6570703 0.7039359 0.7526110 -#> [793,] 0.4893257 0.5173880 0.6514329 0.7328150 0.7466487 0.7937440 0.7984116 -#> [794,] 0.4894648 0.4934404 0.5353879 0.6123625 0.6483658 0.6927912 0.7029612 -#> [795,] 0.3642874 0.6968107 0.7434605 0.8517166 0.9269132 0.9289136 1.0205662 -#> [796,] 0.3459307 0.5028377 0.5434625 0.5760647 0.5851950 0.5938233 0.6451698 -#> [797,] 0.3974026 0.4114479 0.4151472 0.4304381 0.4426711 0.4582774 0.4631483 -#> [798,] 0.2599636 0.4381897 0.4646938 0.4704474 0.4764335 0.4929968 0.5003201 -#> [799,] 0.1603362 0.3056966 0.3669123 0.4612447 0.4929649 0.5254921 0.5291550 -#> [800,] 0.6608258 1.0429812 1.0439467 1.1099186 1.1286047 1.2167245 1.2207269 -#> [801,] 0.3113292 0.6743795 0.6919364 0.8051438 0.8565539 0.8595808 0.8784011 -#> [802,] 0.3474395 0.5598617 0.6589509 0.7612510 0.8362417 0.9492731 0.9708233 -#> [803,] 0.4604155 0.4981237 0.5171006 0.5691912 0.5693549 0.6212383 0.6379603 -#> [804,] 0.4978547 0.5048296 0.6039762 0.6525114 0.6809391 0.8173460 0.8338392 -#> [805,] 0.2546616 0.4024938 0.4828984 0.4937261 0.5813304 0.6000009 0.6202705 -#> [806,] 0.4080902 0.5169540 0.5808212 0.5931816 0.6385622 0.6709518 0.6768179 -#> [807,] 0.3056034 0.3326814 0.4537911 0.4565760 0.4579166 0.4781016 0.4824973 -#> [808,] 0.2136091 0.4037885 0.4213650 0.4309298 0.4422711 0.4428247 0.5413453 -#> [809,] 0.3995663 0.4340247 0.4436905 0.5134259 0.5156347 0.5339298 0.5704259 -#> [810,] 0.3092599 0.3657331 0.3725628 0.4208733 0.4720911 0.4850523 0.5555859 -#> [811,] 0.3784456 0.3852675 0.4127776 0.4532875 0.6210650 0.6525644 0.6680109 -#> [812,] 0.3433086 0.4590312 0.4968742 0.5173880 0.5616415 0.6270975 0.6659995 -#> [813,] 0.2798545 0.3874939 0.4496889 0.4604155 0.5485144 0.5682557 0.5700471 -#> [814,] 0.3082097 0.3310614 0.3782222 0.4266016 0.4693899 0.4929968 0.5043400 -#> [815,] 0.3461297 0.3816936 0.3835569 0.4024244 0.4161253 0.4371760 0.4834878 -#> [816,] 0.5150732 0.5921002 0.6800489 0.9387221 1.0449819 1.1255987 1.2031884 -#> [817,] 0.4097627 0.5394996 0.6033279 0.6107870 0.7756786 0.8485907 0.8577339 -#> [818,] 0.3514631 0.5049022 0.6213372 0.6854924 0.6952081 0.7058257 0.7449229 -#> [819,] 0.4858187 0.7164687 0.7591843 0.8094750 0.8311792 0.8322227 0.8456569 -#> [820,] 0.5556390 0.6794556 0.7165986 0.7671241 0.7781291 0.7898797 0.8203556 -#> [821,] 0.3112028 0.3530090 0.3912126 0.3977697 0.4197831 0.4252485 0.4859729 -#> [822,] 1.1811835 1.4155497 1.6946680 1.7408778 1.7963002 1.9419126 2.0003276 -#> [823,] 0.4785309 0.5035822 0.5504232 0.5581019 0.5920239 0.6022480 0.6114413 -#> [824,] 1.4441306 1.9944252 2.0704969 2.2442786 2.2650094 2.3348776 2.3617711 -#> [825,] 0.6218947 0.6512875 0.6732712 0.7395676 0.7976003 0.8076283 0.8764037 -#> [826,] 0.3036663 0.6271310 0.7331899 0.8259215 0.9237299 0.9609905 0.9817945 -#> [827,] 0.9193467 1.0772629 1.1219863 1.2334469 1.2451451 1.2538902 1.3005034 -#> [828,] 0.2696111 0.2853528 0.3585868 0.3744725 0.4054881 0.4197831 0.5242217 -#> [829,] 0.3848214 0.4585248 0.5589624 0.5616011 0.5769023 0.6223452 0.6257554 -#> [830,] 0.1793892 0.4991711 0.5590794 0.5758475 0.6482590 0.6809989 0.6897370 -#> [831,] 0.6211108 0.7208141 0.7467857 0.7814715 0.8912662 0.8966985 0.9665416 -#> [832,] 0.5439116 0.6994219 0.9761487 0.9915919 1.0092772 1.0094416 1.0673060 -#> [833,] 0.3097309 0.7838109 0.8271271 0.8466075 0.8590657 0.8687430 1.0163926 -#> [834,] 0.1657857 0.3075964 0.3930878 0.4054881 0.4513086 0.4665484 0.4736562 -#> [835,] 0.6569257 0.7234910 0.8336439 0.8673209 0.9341469 0.9458068 0.9533946 -#> [836,] 0.2218200 0.2753051 0.3113118 0.5836566 0.6286956 0.6558379 0.6649380 -#> [837,] 0.4074138 0.5250252 0.5611636 0.6104833 0.6594435 0.7098716 0.7220400 -#> [838,] 0.3802711 0.5821115 0.6293527 0.6348820 0.6427768 0.6612903 0.7769373 -#> [839,] 0.4277456 0.4302680 0.4351358 0.5361542 0.5427427 0.5441774 0.5844532 -#> [840,] 0.3067851 0.3080777 0.3261829 0.3710223 0.3945596 0.4150834 0.4236751 -#> [841,] 0.4518358 0.5580674 0.6486461 0.6640788 0.7124826 0.7184861 0.7256895 -#> [842,] 0.3160586 0.4083693 0.4399549 0.4689018 0.4820274 0.5174262 0.5196555 -#> [843,] 1.5311992 1.5536753 1.5614253 1.6258930 1.7115210 1.7434298 1.7461672 -#> [844,] 0.2972695 0.5968755 0.6194563 0.6488359 0.6492446 0.6546223 0.7091232 -#> [845,] 0.1795017 0.3869610 0.4826465 0.4837839 0.4985066 0.5214377 0.5393927 -#> [846,] 0.2536980 0.5370576 0.5865902 0.5910692 0.6302841 0.6917589 0.7002640 -#> [847,] 0.3107841 0.4453761 0.4519320 0.5431865 0.6136417 0.6244927 0.6254025 -#> [848,] 0.1719823 0.1765733 0.4436905 0.4489713 0.6157921 0.6186984 0.6720264 -#> [849,] 0.8311973 0.8747926 0.9717083 1.0013779 1.0122960 1.0163732 1.2066618 -#> [850,] 0.2994409 0.4768582 0.4894021 0.6089304 0.6684409 0.7042698 0.8011727 -#> [851,] 0.3365770 0.3985065 0.4241446 0.5156347 0.5156644 0.5252485 0.5470744 -#> [852,] 1.4296416 1.5983391 1.9137894 2.0242065 2.0456072 2.0512273 2.0535124 -#> [853,] 0.4680880 0.6266138 0.6834861 0.7391465 0.7472718 0.8071015 0.8707201 -#> [854,] 0.3394250 0.3848525 0.4798373 0.5234596 0.5509819 0.6162140 0.6383378 -#> [855,] 0.7566811 0.8401697 0.8518542 0.9411251 0.9457062 0.9563972 1.1295818 -#> [856,] 0.2940478 0.3407325 0.3787681 0.4414737 0.4578845 0.5302900 0.5434625 -#> [857,] 0.4942273 0.5455117 0.6028087 0.6346238 0.6826033 0.7024932 0.7393022 -#> [858,] 0.6158580 1.1232248 1.2848476 1.3117549 1.3278298 1.3405774 1.3982930 -#> [859,] 0.4188517 0.4549399 0.4613472 0.5384865 0.6145878 0.6387264 0.6612431 -#> [860,] 0.4792305 0.5141196 0.5410854 0.5430704 0.5812571 0.5814689 0.5905954 -#> [861,] 0.2289517 0.3032933 0.3035479 0.3261829 0.4566556 0.4652289 0.4926226 -#> [862,] 1.3375979 1.5159084 1.7651675 1.7951556 1.8873251 1.9282501 1.9311304 -#> [863,] 0.3479301 0.4158467 0.4475573 0.5239454 0.5443972 0.5572317 0.5943596 -#> [864,] 0.3445994 0.4473554 0.5081086 0.5733389 0.6099352 0.6214573 0.6883512 -#> [865,] 0.2503394 0.4768582 0.6427768 0.7949227 0.8427137 0.8543760 0.8600206 -#> [866,] 0.5509826 0.6028087 0.6743603 0.7374324 0.7572010 0.8214049 0.8687430 -#> [867,] 0.4870908 0.4919594 0.4989698 0.5581019 0.5980778 0.5998886 0.6074706 -#> [868,] 0.4299956 0.6966550 0.7385971 0.7570375 0.8153590 0.8393366 0.8576094 -#> [869,] 0.2995764 0.3944994 0.4367022 0.5075357 0.5297576 0.6499291 0.6631117 -#> [870,] 0.3293306 0.3850889 0.4398420 0.4766956 0.5261269 0.5305005 0.6059146 -#> [871,] 0.4366422 0.4963909 0.5602409 0.5644773 0.5944104 0.6285445 0.6535593 -#> [872,] 0.2618401 0.5512129 0.5897778 0.6207546 0.6642021 0.6813480 0.6973555 -#> [873,] 0.3980096 0.4059614 0.4300678 0.5654316 0.5859741 0.5926003 0.6126095 -#> [874,] 0.4401777 0.5409303 0.6310690 0.6985039 0.7151776 0.7328549 0.7661509 -#> [875,] 0.3694694 0.3722417 0.3940243 0.3984968 0.4334864 0.5203917 0.5396707 -#> [876,] 0.6602341 0.7779108 0.8882337 0.9693348 1.0649957 1.0676211 1.1084016 -#> [877,] 0.5141196 0.5261482 0.5798880 0.6404703 0.6940858 0.7309148 0.7914278 -#> [878,] 0.6389136 0.9553313 1.0718025 1.1883763 1.2044469 1.2161923 1.2191719 -#> [879,] 0.6111134 0.6330021 0.7264062 0.7466766 0.7661509 0.7662488 0.7706794 -#> [880,] 0.3417088 0.4197812 0.4328642 0.4689018 0.5634057 0.6811357 0.6952935 -#> [881,] 0.2312386 0.2828653 0.3092599 0.3927840 0.4562478 0.4812868 0.4826099 -#> [882,] 0.2474450 0.3570338 0.3927840 0.4410145 0.4429527 0.4597591 0.4845243 -#> [883,] 0.1765733 0.2005349 0.4340247 0.4779987 0.5640750 0.5933102 0.5941373 -#> [884,] 0.9609905 0.9755831 1.0158348 1.0248596 1.0306228 1.0421030 1.0602373 -#> [885,] 0.4803380 0.4876600 0.5015561 0.5353879 0.5473466 0.5765421 0.6334912 -#> [886,] 0.2411622 0.3859884 0.4363727 0.4586935 0.5530247 0.5812676 0.6110123 -#> [887,] 0.7483794 0.7858414 0.7975968 0.8328092 0.8531193 0.9108383 0.9761211 -#> [888,] 0.5192799 0.5370576 0.5517135 0.5649657 0.5899088 0.6580907 0.6966668 -#> [889,] 0.1603362 0.2547535 0.3437702 0.5100298 0.5368799 0.5371010 0.5595756 -#> [890,] 0.4959007 0.6041729 0.6174626 0.6970957 0.7220400 0.7628530 0.9100871 -#> [891,] 0.2337535 0.3299522 0.4470890 0.5008382 0.5391656 0.5486938 0.5852984 -#> [892,] 0.2853528 0.3298639 0.3561649 0.3592673 0.4272458 0.4645888 0.4665484 -#> [893,] 0.4506818 0.4981237 0.5566957 0.5673679 0.6193540 0.6484340 0.6538127 -#> [894,] 0.1882941 0.6041616 0.6263253 0.8026338 0.8081701 0.9258872 0.9768949 -#> [895,] 0.2536980 0.4769697 0.5194278 0.5278623 0.5649657 0.5688071 0.5813304 -#> [896,] 0.6724590 0.7350833 0.8214040 0.8284443 0.9983396 1.0588523 1.0601857 -#> [897,] 0.3082097 0.4318124 0.4660285 0.4704474 0.4798373 0.5007229 0.5073744 -#> [898,] 0.5959716 0.6063061 0.6644238 0.6669696 0.6904804 0.7117599 0.7358067 -#> [899,] 0.6225775 0.6580907 0.7458233 0.7656519 0.8636642 0.9055562 0.9134872 -#> [900,] 0.2343413 0.4215890 0.5151912 0.5955406 0.7681511 0.8515844 0.8669462 -#> [901,] 0.3285568 0.4099349 0.4197421 0.4618651 0.5232328 0.5740169 0.6015343 -#> [902,] 0.3784988 0.4220740 0.4358854 0.4508229 0.4588823 0.5052489 0.5454542 -#> [903,] 2.0556048 2.1108382 2.1577122 2.1925334 2.2083299 2.2801831 2.5363151 -#> [904,] 0.4114465 0.6254268 0.8252143 1.0101165 1.0390967 1.1097381 1.1741516 -#> [905,] 0.3737895 0.3951226 0.4112151 0.4597591 0.4654187 0.5176881 0.5691951 -#> [906,] 0.4542098 0.5278493 0.6860703 0.7970234 0.8091093 0.8225765 0.8265693 -#> [907,] 0.4938878 0.6573238 0.6673814 0.7260920 0.7379023 0.7393258 0.7920760 -#> [908,] 0.2994951 0.3686147 0.4396160 0.4625997 0.4921990 0.5790150 0.5847977 -#> [909,] 0.1502014 0.3688427 0.3859884 0.4635766 0.5136618 0.5138721 0.5232558 -#> [910,] 0.2615611 0.4080902 0.4463284 0.5950760 0.6132402 0.6481007 0.6598114 -#> [911,] 0.4851220 0.5057987 0.5622607 0.5920986 0.6190446 0.7151776 0.7512546 -#> [912,] 0.7962522 1.1027254 1.3672287 1.3807148 1.4204162 1.4331734 1.6037038 -#> [913,] 0.5067543 0.5221909 0.5752158 0.6147478 0.6218947 0.6348818 0.6790847 -#> [914,] 0.3227764 0.3627995 0.4433784 0.4683933 0.4759184 0.5595899 0.5716828 -#> [915,] 0.4388591 0.4663869 0.4992582 0.5415097 0.5598902 0.5853602 0.5982363 -#> [916,] 0.4621747 0.5439116 0.5810662 0.6525644 0.7261151 0.7618664 0.7742697 -#> [917,] 0.5270508 0.6896589 0.6949101 0.6962610 0.7263227 0.7282629 0.7321677 -#> [918,] 0.1880397 0.3118351 0.3850889 0.4812505 0.4999104 0.5074146 0.5335917 -#> [919,] 0.1963591 0.4696109 0.4981826 0.5449325 0.5960984 0.6329302 0.6556979 -#> [920,] 0.3811083 0.4901664 0.5100151 0.5351490 0.5490831 0.5580313 0.5640750 -#> [921,] 0.3875109 0.5019206 0.5601844 0.5697414 0.5737582 0.6291796 0.6404703 -#> [922,] 0.2136091 0.3576403 0.3815088 0.4719349 0.4834617 0.5229099 0.5483363 -#> [923,] 0.6154598 0.6424847 0.6907413 0.8670046 0.8818672 0.8885824 0.8975248 -#> [924,] 0.5671601 0.8629524 1.0250429 1.0556881 1.2399047 1.2926398 1.5970988 -#> [925,] 0.6154598 0.7423847 0.8346140 1.0411140 1.0660808 1.0823524 1.1049130 -#> [926,] 0.3386233 0.4579561 0.6172867 0.6309674 0.6380812 0.6801436 0.6812344 -#> [927,] 0.4360400 0.5995342 0.8487750 0.8763325 0.9250326 0.9493566 0.9853316 -#> [928,] 0.6293818 0.7704966 0.8845181 0.9375603 0.9499213 0.9609299 1.0233418 -#> [929,] 0.4870615 0.5015118 0.5460978 0.6271310 0.6976519 0.7055782 0.8089936 -#> [930,] 0.5042632 0.6896709 0.7113964 0.7290197 0.7380223 0.7713337 0.7809407 -#> [931,] 0.3329671 0.5655398 0.7402701 0.8392612 0.8733825 0.8951593 0.9067863 -#> [932,] 0.8008904 0.8786278 0.8980172 0.9943680 1.0081010 1.0912510 1.3251528 -#> [933,] 0.6306143 0.6469669 0.7280660 0.7928859 0.8346431 0.8725721 0.8731730 -#> [934,] 0.2743603 0.3937665 0.4599237 0.5155835 0.5191472 0.5414595 0.5740675 -#> [935,] 0.1921174 0.4510282 0.4783833 0.5052489 0.5644773 0.6008386 0.6455745 -#> [936,] 0.5700973 0.6075683 0.6546223 0.6574239 0.6743603 0.6826033 0.6853944 -#> [937,] 0.3119060 0.3528047 0.3627995 0.3915093 0.4358854 0.4609358 0.4802595 -#> [938,] 0.4185317 0.4220740 0.4422055 0.4509399 0.4915624 0.5072311 0.5301315 -#> [939,] 0.6726428 0.7354445 0.7427972 0.7515865 0.7576142 0.7620362 0.7784925 -#> [940,] 0.4172339 0.6413421 0.6954323 0.7064193 0.7385654 0.8145048 0.8370775 -#> [941,] 0.2743603 0.5324977 0.5416235 0.5705223 0.5984565 0.6170349 0.6286326 -#> [942,] 0.3027772 0.4896400 0.4992742 0.5449325 0.5624760 0.6325971 0.6948436 -#> [943,] 0.4111658 0.5943301 0.6385163 0.6979967 0.8446133 0.8896609 0.9001202 -#> [944,] 0.4556055 0.4896084 0.5217608 0.5254022 0.5556390 0.5913540 0.6264371 -#> [945,] 0.4227960 0.4896124 0.5120180 0.5913042 0.6111190 0.6146042 0.6258101 -#> [946,] 0.6296239 0.6892862 0.8390236 0.8875493 0.9461592 1.0529038 1.1175567 -#> [947,] 0.3458876 0.4801832 0.5002871 0.5415791 0.6182001 0.6493688 0.6571560 -#> [948,] 0.5578534 0.7378963 0.8169656 0.8296489 0.8388325 0.8670486 0.8909451 -#> [949,] 0.3590217 0.5057987 0.5953573 0.6310690 0.7259945 0.7391465 0.8038318 -#> [950,] 0.5544410 0.7224488 0.8083694 0.8105955 0.8534005 0.8792767 0.8823584 -#> [951,] 0.6389136 0.7099335 0.7347236 0.8351929 0.8554912 0.8708675 0.8733825 -#> [952,] 0.4955922 0.6729536 0.7140313 0.7482271 0.7661296 0.8388325 0.8487750 -#> [953,] 0.4158467 0.4642895 0.4868032 0.5434911 0.5755035 0.5863843 0.5917659 -#> [954,] 0.1457126 0.5038661 0.5415791 0.5644321 0.6778771 0.7278628 0.8170445 -#> [955,] 0.3342053 0.3352511 0.4344955 0.4351358 0.4720053 0.5108792 0.5189029 -#> [956,] 0.9457062 0.9849265 1.0223147 1.0483017 1.0934914 1.0951222 1.1603080 -#> [957,] 0.3633023 0.4441841 0.4480393 0.5013117 0.5053819 0.5627093 0.6093778 -#> [958,] 0.6569257 0.8302082 0.8922407 0.8980629 0.9490880 0.9769237 1.0046860 -#> [959,] 0.3097309 0.7675038 0.9328969 0.9411251 1.0235728 1.0326252 1.0389430 -#> [960,] 0.4588987 0.4657427 0.6035151 0.6461901 0.6718601 0.6904380 0.7021603 -#> [961,] 0.7051774 0.7756786 0.9457200 1.0963949 1.1187557 1.1253954 1.1275522 -#> [962,] 0.2479778 0.2499468 0.4179349 0.4187607 0.4781016 0.5302001 0.5350829 -#> [963,] 0.5012870 0.5258843 0.5312717 0.5410854 0.5644517 0.5668125 0.5941373 -#> [964,] 0.7557878 0.8613864 0.9285815 0.9622023 1.0018294 1.0047035 1.0277976 -#> [965,] 0.3341905 0.3604918 0.3978667 0.4150834 0.4213421 0.4661846 0.4741237 -#> [966,] 0.3085853 0.4051373 0.4468204 0.4954094 0.5105782 0.5119253 0.5684175 -#> [967,] 0.3886122 0.5793263 0.5798880 0.7502501 0.7891617 0.8239215 0.9211892 -#> [968,] 0.1628608 0.7996086 0.9813975 1.0457239 1.0600730 1.0738716 1.1739681 -#> [969,] 0.8815454 0.9994666 1.0613208 1.1361912 1.1385667 1.1426683 1.2533244 -#> [970,] 0.2316066 0.4347131 0.5258843 0.5430704 0.5797168 0.6455426 0.6567670 -#> [971,] 0.6492446 0.6574239 0.6747291 0.8214049 0.8362544 0.8401697 0.8707394 -#> [972,] 0.1412353 0.3272235 0.3900844 0.4023933 0.4148677 0.4451457 0.4488936 -#> [973,] 0.8747926 1.0726503 1.1068952 1.1648686 1.2204814 1.2386833 1.2633442 -#> [974,] 0.8193688 0.8456541 0.8527007 0.8686242 0.9171797 0.9520110 0.9659309 -#> [975,] 0.2780944 0.3015907 0.3376254 0.3811083 0.3974026 0.4028254 0.4308291 -#> [976,] 0.3893338 0.4505269 0.4605785 0.6256895 0.6536408 0.7331622 0.7586668 -#> [977,] 0.6911686 0.7268477 0.8231290 0.8424534 0.9000961 0.9033437 0.9233767 -#> [978,] 0.3496784 0.4432166 0.5334193 0.5989136 0.7096008 0.7235467 0.7262585 -#> [979,] 0.2847934 0.3882206 0.4453761 0.4810555 0.5301634 0.5475485 0.5485547 -#> [980,] 0.3859954 0.4699200 0.4811191 0.4980564 0.5245181 0.5667576 0.5963322 -#> [981,] 0.7278976 0.7327970 0.7627281 0.7788747 0.7951149 0.8195750 0.8378108 -#> [982,] 0.3884973 0.5418577 0.5821115 0.7216168 0.7290825 0.7671997 0.7678536 -#> [983,] 0.3086478 0.4160685 0.4996085 0.5072499 0.5879309 0.7115002 0.7117846 -#> [984,] 0.6303962 0.6589045 0.8001376 0.8145048 0.8338672 0.9112486 0.9150192 -#> [985,] 0.5460581 0.6507848 0.8080487 0.8499740 0.8614721 0.8856987 0.8991177 -#> [986,] 0.3589144 0.5418090 0.5852687 0.5985745 0.7017688 0.7516601 0.7699749 -#> [987,] 0.1657857 0.2696111 0.3561649 0.3862536 0.4120583 0.4539152 0.4941148 -#> [988,] 0.5119789 0.5209139 0.5622382 0.5642782 0.5787497 0.5996913 0.6040388 -#> [989,] 0.1983044 0.5926421 0.6151594 0.6539348 0.6827474 0.8271119 0.8582468 -#> [990,] 0.3474395 0.5745431 0.6616265 0.7282968 0.8629524 1.0278892 1.1192310 -#> [991,] 0.4299956 0.4577316 0.5259341 0.5962516 0.6553439 0.7172299 0.7221793 -#> [992,] 0.6325971 0.6704545 0.6972553 0.7056987 0.7278976 0.7571621 0.7676339 -#> [993,] 0.4489389 0.4528535 0.5843780 0.6116490 0.6194128 0.7036732 0.8328092 -#> [994,] 1.2167245 1.4022221 1.4315399 1.4493745 1.4979496 1.5439557 1.6053293 -#> [995,] 0.2972695 0.5141040 0.6075683 0.6081224 0.6466797 0.6477152 0.6839222 -#> [996,] 0.3875109 0.4253779 0.5457228 0.5545367 0.5935204 0.6400436 0.6516956 -#> [997,] 0.2770382 0.5320051 0.7591667 1.0625637 1.1118740 1.1502454 1.1741015 -#> [998,] 0.3747992 0.3757904 0.5399607 0.5601844 0.6182612 0.6194519 0.6348818 -#> [999,] 0.6915614 0.7037830 0.7320299 0.8083417 0.8444847 0.8632591 0.9026419 -#> [1000,] 0.6776881 0.9553533 1.0390967 1.1113306 1.1381286 1.1394759 1.2094194 +#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] +#> [1,] 1.21046951 1.2814328 1.3088671 1.3903896 1.4093499 1.5123118 1.5177298 +#> [2,] 0.55047599 0.7540306 0.7634534 0.7722017 0.8163211 0.8700257 0.9226647 +#> [3,] 0.35784179 0.4869218 0.5242352 0.5781613 0.6461551 0.6900840 0.7158303 +#> [4,] 0.53401809 0.5887559 0.7787472 0.7808913 0.7860686 0.8376048 0.9398310 +#> [5,] 0.34914673 0.4117812 0.4147237 0.5550794 0.5681887 0.5832709 0.6670932 +#> [6,] 0.34558386 0.3718587 0.3866957 0.4058981 0.4168423 0.4366775 0.4563907 +#> [7,] 0.46565599 0.4989607 0.5344266 0.5990700 0.6116902 0.6150030 0.6260920 +#> [8,] 0.24524756 0.3766672 0.4190034 0.4619930 0.4910142 0.4985297 0.5080894 +#> [9,] 0.26592251 0.5272305 0.5303869 0.5855830 0.5917343 0.5936955 0.6659433 +#> [10,] 0.20624216 0.4890596 0.6274324 0.6552598 0.6766045 0.6837926 0.6886020 +#> [11,] 0.57189888 0.5912276 0.6606174 0.6676028 0.7789707 0.9019471 0.9936128 +#> [12,] 0.59154586 0.5943194 0.6043368 0.6477592 0.6792485 0.6849131 0.6892158 +#> [13,] 0.19424836 0.2125101 0.6348915 0.6565775 0.7102408 0.7548653 0.7556356 +#> [14,] 0.50366019 0.5213843 0.5306987 0.5441858 0.5766456 0.6146114 0.6332020 +#> [15,] 0.66621597 0.7634713 0.9313510 0.9380089 0.9422618 0.9714016 1.0029979 +#> [16,] 0.42796979 0.5702875 0.6811840 0.7296882 0.7650716 0.8027896 0.8264574 +#> [17,] 0.43980417 0.5712633 0.6833969 0.7022311 0.7234893 0.7255583 0.7870018 +#> [18,] 0.32998788 0.3715182 0.4313687 0.7057181 0.7156661 0.7459431 0.7606691 +#> [19,] 0.53175341 0.5804025 0.6025482 0.6220356 0.6549309 0.6895867 0.7023902 +#> [20,] 0.25537990 0.4888065 0.5012188 0.5132578 0.5201907 0.5244126 0.5297184 +#> [21,] 0.44869504 0.5176139 0.5201538 0.5333403 0.5528347 0.5637833 0.6056317 +#> [22,] 0.24270294 0.4181342 0.4994289 0.7301984 0.7687906 0.8701522 0.8722797 +#> [23,] 0.27916247 0.4182686 0.4627760 0.5187923 0.5625505 0.5745288 0.5912516 +#> [24,] 0.27801534 0.2808169 0.4150215 0.4842020 0.5161527 0.5207735 0.5940268 +#> [25,] 0.07900148 0.2370228 0.3602234 0.3902051 0.5426235 0.6006307 0.6474137 +#> [26,] 0.61071508 0.6573590 0.7340550 0.7405824 0.7735499 0.7830390 0.8050931 +#> [27,] 0.34159301 0.3748690 0.3932452 0.5942108 0.6043088 0.6143347 0.6237749 +#> [28,] 0.62085681 0.6916244 0.7386633 0.9016987 1.0270393 1.0317743 1.1095360 +#> [29,] 0.22512765 0.3803994 0.3970135 0.4499811 0.4840932 0.4850417 0.4858446 +#> [30,] 0.39126729 0.4931540 0.5295156 0.5344266 0.5987257 0.6567391 0.6665549 +#> [31,] 0.30912486 0.3716844 0.4069067 0.4383080 0.4538033 0.4771445 0.4805272 +#> [32,] 0.37151820 0.4253336 0.4780872 0.6146927 0.6674207 0.7188448 0.7326479 +#> [33,] 0.09113993 0.4117812 0.6715584 0.7239671 0.7447867 0.7682577 0.8076352 +#> [34,] 0.37734773 0.4000469 0.4212806 0.5560209 0.5659865 0.5809123 0.6173314 +#> [35,] 0.69441613 0.7249473 0.7624164 0.8119654 0.9839406 1.0092612 1.0173604 +#> [36,] 0.24808700 0.2723521 0.4092183 0.4168423 0.4383710 0.5023097 0.5201907 +#> [37,] 0.52469938 0.6230936 0.7173692 0.7371090 0.7860783 0.9153443 0.9223275 +#> [38,] 0.45160491 0.5060944 0.5127088 0.5314992 0.5812371 0.5855830 0.6128921 +#> [39,] 0.33992786 0.6105890 0.7697521 0.8120088 0.8502975 0.8543398 0.8718331 +#> [40,] 0.45826699 0.4952567 0.5293771 0.5759559 0.5790945 0.5833047 0.5974994 +#> [41,] 0.78237982 0.8547462 0.9179658 0.9656688 1.0256551 1.0375951 1.0403373 +#> [42,] 0.34658311 0.5914036 0.6843618 0.7177654 0.8091027 0.8208785 0.8380186 +#> [43,] 0.36424210 0.4869968 0.5056829 0.5323747 0.5782066 0.5927128 0.6488748 +#> [44,] 0.45940455 0.6715617 0.6978360 0.7154144 0.7471025 0.7953747 0.8342867 +#> [45,] 0.66295655 1.0285802 1.1348424 1.1780126 1.5407360 1.5456936 1.5925654 +#> [46,] 0.59517291 0.6055320 0.6175850 0.6348915 0.6934208 0.7472936 0.7604855 +#> [47,] 0.26748597 0.4514422 0.5305996 0.5355726 0.5653137 0.5940433 0.6347029 +#> [48,] 0.44182012 0.4790345 0.7311678 0.7590097 0.7912512 0.8369607 0.8826947 +#> [49,] 0.39492438 0.4265521 0.4639095 0.4944305 0.4999843 0.5031262 0.5226127 +#> [50,] 0.36671734 0.3998036 0.4010056 0.4295132 0.4577139 0.4684129 0.4841514 +#> [51,] 0.48536186 0.5327137 0.6370667 0.6729774 0.6874784 0.6910342 0.6948833 +#> [52,] 0.58197751 0.8388852 0.9412836 1.0082361 1.0723705 1.1975280 1.2467210 +#> [53,] 0.28197372 0.4617600 0.4898238 0.5057847 0.5060944 0.5117953 0.5443672 +#> [54,] 0.31425204 0.4628165 0.4717548 0.4836014 0.4837092 0.5745749 0.6417850 +#> [55,] 0.33077154 0.3722042 0.3859907 0.4190034 0.4615879 0.5161005 0.5482435 +#> [56,] 0.35792720 0.5561728 0.6250605 0.6409314 0.7018025 0.7086441 0.7429670 +#> [57,] 0.47956545 0.7789857 0.9671439 1.1418139 1.1795548 1.2010407 1.2126780 +#> [58,] 0.63049552 0.9036267 0.9673234 0.9727745 1.0094522 1.0096174 1.0156655 +#> [59,] 0.46314350 0.5494480 0.5521143 0.5776104 0.6092869 0.6229224 0.7165581 +#> [60,] 0.66813811 0.7942737 0.8143436 0.9562376 0.9588842 0.9783526 1.0074788 +#> [61,] 0.30665876 0.3083442 0.5477442 0.5914676 0.6283700 0.6488153 0.6576743 +#> [62,] 0.48137672 0.5174087 0.5922966 0.6447556 0.6930791 0.6942257 0.7407218 +#> [63,] 0.53136017 0.5715258 0.6273539 0.6438647 0.6653796 0.6855298 0.6951885 +#> [64,] 0.29071253 0.3427768 0.5943194 0.6082906 0.6168765 0.6543068 0.6855298 +#> [65,] 0.90797943 1.0011695 1.0073150 1.0568285 1.1074157 1.1380730 1.1655519 +#> [66,] 0.51356621 0.6649191 0.7882149 0.8889239 0.9799349 1.0152143 1.0274019 +#> [67,] 0.91009161 1.1452958 1.1939267 1.2397482 1.2778259 1.3142367 1.3691782 +#> [68,] 0.43896058 0.6626579 0.7063940 0.7192270 0.7217734 0.7289212 0.7324372 +#> [69,] 0.89069032 0.9313217 0.9673234 0.9976411 0.9996854 1.0331601 1.0587833 +#> [70,] 0.33989198 0.4327614 0.5350378 0.6258618 0.6469255 0.7164036 0.7242920 +#> [71,] 0.38397973 0.3862299 0.4516049 0.4913964 0.4920131 0.5414338 0.5917343 +#> [72,] 1.00848047 1.0768269 1.0943501 1.1057459 1.1238643 1.1548307 1.2246481 +#> [73,] 0.49674503 1.1517222 1.1530540 1.1780126 1.2000227 1.2599610 1.2891031 +#> [74,] 0.95888418 1.4328568 1.4894015 1.6543722 1.6595918 1.6635901 1.6692800 +#> [75,] 0.20286252 0.2775229 0.3058515 0.4774863 0.4874333 0.5379454 0.5445072 +#> [76,] 0.56327896 0.6449463 0.8223549 0.8228872 0.9294344 0.9357540 1.0041295 +#> [77,] 0.42533361 0.4313687 0.5218097 0.6681459 0.6745087 0.7003972 0.7146743 +#> [78,] 0.31406018 0.4260126 0.4445334 0.5684864 0.5926834 0.6159768 0.6696777 +#> [79,] 0.58282320 0.5919036 0.6241585 0.6445785 0.6560100 0.7319469 0.7938257 +#> [80,] 0.19979753 0.5161356 0.5565107 0.5836934 0.5945889 0.6472249 0.6842989 +#> [81,] 1.81951854 1.9922656 2.0205986 2.0790480 2.0946907 2.1277494 2.1302952 +#> [82,] 0.55400715 0.5834456 0.7181371 0.7294277 0.7365553 0.7525134 0.7698621 +#> [83,] 0.38538472 0.4618722 0.6916650 0.7051790 0.7148987 0.7293677 0.7540948 +#> [84,] 0.47027993 0.5917161 0.6165220 0.6246316 0.6297133 0.7374528 0.7390052 +#> [85,] 0.72936937 0.8543112 0.9746944 1.0084146 1.1090694 1.1174333 1.1207087 +#> [86,] 0.59078288 0.6462234 0.6689212 0.6699648 0.6811840 0.6858087 0.6860345 +#> [87,] 0.27254531 0.3651141 0.3716844 0.3921957 0.4445022 0.4630358 0.4686448 +#> [88,] 0.66562908 0.8177474 0.8235249 0.9084179 0.9557719 1.0141622 1.0408833 +#> [89,] 0.31747977 0.4114420 0.4268198 0.6100400 0.6136000 0.6282401 0.6387592 +#> [90,] 0.43156060 0.4952567 0.5908792 0.6033091 0.6173670 0.6258699 0.6387592 +#> [91,] 0.31421513 0.5338823 0.5412133 0.5900256 0.6272943 0.6422183 0.6592550 +#> [92,] 0.81196536 0.9115486 0.9346957 0.9929918 1.0456172 1.0754124 1.0787575 +#> [93,] 0.64736887 0.7271914 0.7541340 0.7808913 0.7979371 0.8244759 0.8497188 +#> [94,] 0.49533234 0.6598227 0.7076012 0.7186929 0.7863789 0.8153246 0.8174131 +#> [95,] 0.34692452 0.5627264 0.6081151 0.6135268 0.6495568 0.6515160 0.6583607 +#> [96,] 0.39060104 0.4790116 0.4927501 0.4989591 0.5117631 0.5192327 0.5327137 +#> [97,] 0.55207656 0.5697016 0.6301777 0.6495728 0.6696607 0.7135412 0.8605164 +#> [98,] 0.63783218 0.7148571 0.7776584 0.8507334 0.9494797 1.0558888 1.0583920 +#> [99,] 0.39020552 0.4962837 0.5675716 0.5861157 0.5976980 0.6081151 0.6092434 +#> [100,] 0.89978391 0.9308147 0.9408754 0.9696101 0.9858472 0.9923786 1.0627488 +#> [101,] 0.55283471 0.5907829 0.5922631 0.6141458 0.6261074 0.6392327 0.6421244 +#> [102,] 0.35502072 0.4501754 0.6099109 0.6260703 0.6280210 0.6670661 0.6866346 +#> [103,] 0.37801086 0.4721603 0.4913964 0.5124604 0.5450710 0.6028122 0.6238292 +#> [104,] 0.34134019 0.5929541 0.6372357 0.7820931 0.7897595 0.8339584 0.8447999 +#> [105,] 0.54679682 0.9584708 0.9619867 0.9668903 1.1132133 1.1147389 1.1797309 +#> [106,] 0.42115831 0.5175675 0.5697016 0.7871844 1.0125592 1.0360698 1.0951413 +#> [107,] 0.37049110 0.4279698 0.7430611 0.7789587 0.7795453 0.7839516 0.7865491 +#> [108,] 0.37950826 0.5067155 0.6800699 0.6945929 0.7168134 0.7420902 0.7524010 +#> [109,] 0.31009228 0.4000469 0.4590026 0.5164836 0.5549018 0.6026765 0.6227651 +#> [110,] 1.01791160 1.0815005 1.0840109 1.2249167 1.2279099 1.2384120 1.2896179 +#> [111,] 0.51360556 0.5314622 0.5624893 0.5767651 0.5827102 0.6198561 0.6221631 +#> [112,] 0.70836509 0.7513724 0.7882149 0.8676796 0.8703300 0.9034353 0.9156993 +#> [113,] 0.49869553 0.5067155 0.5460506 0.5801585 0.6696494 0.7173692 0.7608483 +#> [114,] 0.40062803 0.7368107 0.8528844 0.8633145 0.8855950 0.9016987 0.9022142 +#> [115,] 0.39939033 0.5879220 0.6793512 0.6805757 0.7058957 0.7344912 0.7391128 +#> [116,] 0.31704666 0.3983268 0.5037551 0.5216523 0.5296416 0.6908122 0.7452978 +#> [117,] 0.37326479 0.5340094 0.5787218 0.5794222 0.7135245 0.7212985 0.7981541 +#> [118,] 0.58711013 0.6636078 0.6993385 0.7835613 0.7958064 0.8146638 0.8318024 +#> [119,] 0.38781056 0.6292556 0.6395271 0.6717917 0.6905921 0.7365392 0.7809893 +#> [120,] 0.51972264 0.5565621 0.5588898 0.6499334 0.6994073 0.7234893 0.7733899 +#> [121,] 0.52007377 0.7156661 0.7769445 0.8729608 0.9187279 0.9307765 0.9801876 +#> [122,] 0.37462522 0.5608964 0.5684864 0.5867999 0.6280210 0.6488748 0.6532210 +#> [123,] 0.28637481 0.5232306 0.5870548 0.5945889 0.6033274 0.6372778 0.6648775 +#> [124,] 1.46107996 1.6845896 1.6912515 1.6925196 1.7416590 1.7471112 1.8158605 +#> [125,] 0.34397845 0.3866957 0.3924534 0.4412515 0.5012188 0.5058887 0.5131453 +#> [126,] 0.37486905 0.4751257 0.5087351 0.5887325 0.7051331 0.7298008 0.7491495 +#> [127,] 0.29550828 0.3681418 0.5015297 0.6060122 0.6288299 0.6308819 0.6992010 +#> [128,] 0.21279658 0.3983268 0.4456823 0.4821855 0.5067848 0.5608770 0.5763736 +#> [129,] 0.33082271 0.3812432 0.4656621 0.5812384 0.6536513 0.7041940 0.7333086 +#> [130,] 0.59124589 0.7267236 0.7863414 0.8405639 0.8473076 0.8762039 0.8769079 +#> [131,] 0.29341112 0.3539795 0.5754289 0.5817286 0.6082906 0.6362094 0.6390304 +#> [132,] 0.38097849 0.4882768 0.5360774 0.7430517 0.7626059 0.8503862 0.8728612 +#> [133,] 0.34767639 0.5731660 0.6695476 0.8528346 0.8894494 0.8955452 0.9042629 +#> [134,] 0.67153583 0.7344912 0.7583382 0.7931788 0.8128573 0.8623701 0.9081630 +#> [135,] 1.14665624 1.2219094 1.2304212 1.3831278 1.4038045 1.4243506 1.5006148 +#> [136,] 0.44825377 0.6461841 0.7169382 0.7503173 0.7732450 0.7867631 0.8033412 +#> [137,] 0.26411020 0.4260126 0.4456823 0.4673949 0.4828882 0.5533188 0.6529094 +#> [138,] 0.52191310 0.5914450 0.5995905 0.6128027 0.6217932 0.6322603 0.6677923 +#> [139,] 0.58668618 0.6357454 0.6994073 0.7920784 0.8237250 0.8393177 0.8492497 +#> [140,] 0.29261567 0.4439449 0.5403259 0.5471188 0.5475349 0.5604020 0.5725399 +#> [141,] 0.25265749 0.4204692 0.4459950 0.4995554 0.5339018 0.5646986 0.5659258 +#> [142,] 0.44731276 0.4693085 0.4891228 0.4957363 0.5366771 0.5708846 0.5962150 +#> [143,] 0.43630431 0.5608964 0.5913644 0.6018143 0.6031855 0.6383166 0.6529094 +#> [144,] 0.55029717 0.6149651 0.6536513 0.6960493 0.7244467 0.7711151 0.8378862 +#> [145,] 0.46758775 0.5323747 0.6548214 0.6687391 0.6954821 0.6959315 0.6987302 +#> [146,] 0.56358593 0.6714041 0.7109351 0.7311678 0.7458959 0.7665936 0.7959432 +#> [147,] 1.27924469 1.3343597 1.4185485 1.4773048 1.7597464 1.7606057 1.7904207 +#> [148,] 0.69682063 0.8924923 0.9030081 0.9054818 1.0016268 1.0311475 1.0401675 +#> [149,] 0.47105967 0.5015533 0.5732198 0.5992964 0.6070402 0.6333816 0.6404007 +#> [150,] 1.08881693 1.1088618 1.1437963 1.2449947 1.2620592 1.2672230 1.3379752 +#> [151,] 0.60289777 0.6267536 0.6512268 0.8387839 0.8905984 0.9767534 1.0555368 +#> [152,] 0.38434510 0.5474227 0.5580321 0.6047128 0.6159771 0.6368901 0.6379932 +#> [153,] 0.76091799 1.0115355 1.0132570 1.0881376 1.0951688 1.1263791 1.1440797 +#> [154,] 0.42601353 0.7362994 0.7374528 0.9115949 0.9399216 0.9703215 0.9797734 +#> [155,] 0.55380071 0.5868136 0.8971739 0.9859653 0.9871506 1.0210517 1.0258789 +#> [156,] 0.38074638 0.5154705 0.8954715 0.9021617 0.9227504 1.0985167 1.1493428 +#> [157,] 0.33667180 0.3653168 0.3902051 0.4978214 0.5171986 0.6065168 0.6329947 +#> [158,] 0.35433111 0.4546433 0.5162549 0.5985664 0.6256440 0.6345276 0.7123310 +#> [159,] 0.46309918 0.4648272 0.4978386 0.5025315 0.6583607 0.6659381 0.7088686 +#> [160,] 0.43700057 0.4744331 0.5245809 0.5412125 0.5517345 0.5612849 0.5615328 +#> [161,] 0.26497463 0.3009462 0.3889990 0.4135342 0.4214572 0.4266499 0.4639095 +#> [162,] 0.42839864 0.5531608 0.5698067 0.6107278 0.6536423 0.7160076 0.7929313 +#> [163,] 0.35057975 0.4309376 0.5965018 0.7168258 0.7637036 0.7787982 0.7869576 +#> [164,] 0.37272716 0.6543357 0.6840445 0.7475963 0.8614908 0.9399279 0.9457978 +#> [165,] 0.28816770 0.3682785 0.3965755 0.4383710 0.4658402 0.4771445 0.5262443 +#> [166,] 0.49137345 0.6205306 0.6278612 0.7186285 0.7435339 0.7440504 0.7887943 +#> [167,] 0.32875221 0.4161305 0.4295132 0.4324372 0.4674339 0.4922360 0.5073235 +#> [168,] 0.34534255 0.4533731 0.4648813 0.4786173 0.5276847 0.5344793 0.5385578 +#> [169,] 0.23154039 0.4996064 0.5751151 0.6726457 0.6773536 0.7041913 0.7237772 +#> [170,] 0.52243421 0.7852364 0.8055764 0.8948322 0.9694110 1.0355170 1.0355223 +#> [171,] 0.57831635 0.6068026 0.7323271 0.8061708 0.9309761 0.9451110 0.9462643 +#> [172,] 0.35433111 0.4766277 0.5867573 0.5916953 0.6389778 0.6756414 0.7301892 +#> [173,] 0.39210704 0.5408687 0.5812758 0.5987257 0.6104140 0.6381541 0.7037034 +#> [174,] 0.60447661 0.6267536 0.7537042 0.7770416 0.7910509 0.7976067 0.8122335 +#> [175,] 0.37131688 0.4732668 0.6357584 0.7240616 0.7558192 0.8210686 0.8438541 +#> [176,] 0.38097849 0.6842423 0.6960493 0.7390536 0.7940312 0.8275767 0.8500671 +#> [177,] 0.61987271 0.9503676 0.9604526 1.1101986 1.1372493 1.1962238 1.1983213 +#> [178,] 0.86356366 0.8696071 0.8979664 0.9135622 0.9136056 0.9146832 0.9156993 +#> [179,] 0.90251624 1.2391223 1.2551132 1.3247592 1.3892696 1.4173955 1.5960577 +#> [180,] 0.65482141 0.6582334 0.6583989 0.6872535 0.7058957 0.7233368 0.7298838 +#> [181,] 0.47581826 0.5598178 0.6172547 0.6436547 0.7041913 0.7288602 0.7499769 +#> [182,] 0.46401044 0.5627313 0.5956964 0.6124989 0.6997715 0.7332486 0.7392277 +#> [183,] 0.31704666 0.3587281 0.6026883 0.6088547 0.6555160 0.7286017 0.7831765 +#> [184,] 0.78523641 0.9052024 0.9599744 1.0731588 1.0797704 1.1046482 1.1092652 +#> [185,] 0.26858368 0.3132308 0.3914943 0.4122333 0.4825606 0.4826539 0.4865361 +#> [186,] 0.42992809 0.4314484 0.4412957 0.5306193 0.5781613 0.6099109 0.6317479 +#> [187,] 0.32558630 0.4538033 0.4867114 0.5004484 0.5212093 0.5499718 0.5767945 +#> [188,] 0.27986585 0.4486950 0.6261074 0.6478857 0.6791720 0.6830734 0.7098652 +#> [189,] 0.35281360 0.4997944 0.5622268 0.6197868 0.6285987 0.6537978 0.6549309 +#> [190,] 0.27383529 0.3569462 0.3955108 0.4536409 0.4845361 0.5086319 0.6688656 +#> [191,] 0.43967830 0.4634355 0.5661557 0.6033091 0.6095929 0.6202265 0.6597499 +#> [192,] 0.44065919 0.4634355 0.5367478 0.5504818 0.5631576 0.5679693 0.6168765 +#> [193,] 0.35144341 0.4494418 0.4993466 0.5087696 0.5155247 0.5654223 0.5736881 +#> [194,] 0.22112194 0.3996389 0.5086507 0.5654880 0.5935245 0.5983421 0.6027282 +#> [195,] 0.80557643 0.9636475 0.9959560 1.0036229 1.2343316 1.2907756 1.2967553 +#> [196,] 0.39149426 0.4101772 0.4281875 0.4773816 0.4779779 0.4999843 0.5505670 +#> [197,] 0.55400715 0.7314181 0.7574815 0.7799084 0.7873526 0.8264940 0.8332343 +#> [198,] 0.50253147 0.6811196 0.6893140 0.8128573 0.8158326 0.8572884 0.9836412 +#> [199,] 0.72804450 0.8080975 0.8230186 0.8388021 0.9040104 0.9090553 0.9122911 +#> [200,] 0.36816787 0.5370509 0.5996214 0.6130515 0.7065292 0.7703608 0.7938257 +#> [201,] 0.63311158 0.7397344 0.7983179 0.8409059 0.8487797 0.8629053 0.8852884 +#> [202,] 0.35553709 0.3656315 0.3725824 0.4895812 0.4997822 0.5088901 0.5389288 +#> [203,] 0.65886490 0.7329002 0.7347903 0.7411261 0.7580537 0.8624063 0.8720974 +#> [204,] 0.55651070 0.5587885 0.5830566 0.5955075 0.6618140 0.7156208 0.7315282 +#> [205,] 1.12746773 1.1447031 1.1611430 1.2061711 1.2973666 1.2998117 1.4372318 +#> [206,] 1.04008113 1.0741286 1.0838163 1.0959222 1.1050789 1.1226859 1.1568236 +#> [207,] 0.21900050 0.2742296 0.3514434 0.4414084 0.4509631 0.4979605 0.4985219 +#> [208,] 0.21563769 0.3590978 0.3669434 0.3987107 0.4993616 0.5127696 0.5669487 +#> [209,] 0.55611113 0.5794918 0.7240616 0.7361423 0.8079934 0.8221198 0.8290688 +#> [210,] 0.20603309 0.2903457 0.3807584 0.3999190 0.4606817 0.4924360 0.5132561 +#> [211,] 0.35146422 0.4366775 0.4686757 0.4812793 0.5317383 0.5949025 0.6197411 +#> [212,] 0.41472370 0.5742801 0.6313599 0.6807774 0.6951039 0.7447867 0.7760244 +#> [213,] 0.24306608 0.2691089 0.4780553 0.5333403 0.5817011 0.6043088 0.6178096 +#> [214,] 0.34134019 0.5565621 0.5584969 0.6664560 0.6867161 0.8019273 0.8237250 +#> [215,] 0.46304774 0.7293677 0.7351532 0.7634713 0.8081023 0.8122335 0.8499778 +#> [216,] 0.40235927 0.4250036 0.5136656 0.5626172 0.5915929 0.5997113 0.6013005 +#> [217,] 0.36827211 0.4581167 0.5627529 0.6870920 0.6881646 0.7117143 0.7378429 +#> [218,] 0.33982358 0.5976765 0.7207886 0.7449855 0.8507334 0.9094096 0.9321405 +#> [219,] 0.73585058 0.9232594 0.9565458 1.0190818 1.0440702 1.0562080 1.0697942 +#> [220,] 0.55849685 0.5866862 0.6150630 0.6372357 0.6499334 0.6833969 0.6999140 +#> [221,] 0.63004997 0.6805731 0.7160076 0.7317062 0.7545404 0.7969842 0.7992506 +#> [222,] 0.44727767 0.5974994 0.7193678 0.7340550 0.7608659 0.7996507 0.7997487 +#> [223,] 0.90362672 0.9146832 0.9180192 0.9874210 0.9927248 1.0419432 1.1020623 +#> [224,] 0.46826716 0.6439832 0.7046757 0.7119260 0.7290976 0.7391057 0.7963469 +#> [225,] 0.49106116 0.5244854 0.6788071 0.7176650 0.7177654 0.7749013 0.7870105 +#> [226,] 0.32322310 0.3357362 0.4338062 0.6146114 0.6173491 0.6359929 0.6747795 +#> [227,] 0.28556909 0.4419782 0.4688051 0.4989591 0.5661041 0.5827281 0.6393244 +#> [228,] 0.43303596 0.5174087 0.5792943 0.5882164 0.6120067 0.6697537 0.6772856 +#> [229,] 0.24611185 0.3842782 0.3983348 0.4293245 0.4612341 0.4862838 0.5753730 +#> [230,] 0.39388994 0.4755652 0.5219131 0.5983490 0.6630029 0.6829525 0.6841384 +#> [231,] 0.30036760 0.3198414 0.3590978 0.4168866 0.4785825 0.4997144 0.5040527 +#> [232,] 0.30036760 0.3669434 0.3815305 0.5653624 0.5656248 0.5965183 0.5996629 +#> [233,] 0.50544474 0.5182948 0.5418878 0.5623380 0.5669282 0.5773719 0.5893375 +#> [234,] 0.31343717 0.4221985 0.5792943 0.6308612 0.6487788 0.6565327 0.6942257 +#> [235,] 0.32559725 0.4732668 0.6090162 0.6755251 0.7244467 0.7776300 0.8144492 +#> [236,] 0.42319471 0.4568666 0.5344793 0.5792409 0.5955864 0.6276450 0.6599847 +#> [237,] 0.46543336 0.4792014 0.5336176 0.5733939 0.5851822 0.6046285 0.6290967 +#> [238,] 0.42144588 0.5031239 0.6132328 0.6837926 0.7049319 0.7312486 0.7506026 +#> [239,] 0.46484263 0.5704845 0.7109351 0.7118330 0.7417987 0.7526987 0.7569339 +#> [240,] 1.09317617 1.1040206 1.1193103 1.2529562 1.2863927 1.2939069 1.3269674 +#> [241,] 0.69303540 0.8205277 0.9671439 0.9847831 1.0134838 1.0473081 1.1663187 +#> [242,] 0.78317649 0.8697816 0.8849601 0.9668492 1.0168150 1.0595519 1.1677669 +#> [243,] 0.35963054 0.3806475 0.3983348 0.4068962 0.4617965 0.4657141 0.5526758 +#> [244,] 1.26864772 1.6713056 1.7214903 1.7557255 1.7616370 1.7898021 1.8336938 +#> [245,] 0.23444596 0.2765470 0.2852333 0.2970063 0.4048737 0.4606817 0.4664689 +#> [246,] 0.47238131 0.4935583 0.5652610 0.6414176 0.6576266 0.6836239 0.8132373 +#> [247,] 0.78307298 0.8013535 0.8650611 0.8930465 0.9029668 0.9399767 0.9486970 +#> [248,] 0.51792634 0.6692384 0.7227889 0.7777086 0.7952843 0.8018866 0.9766410 +#> [249,] 0.31072384 0.3755687 0.4014202 0.4074870 0.4573402 0.5314506 0.5385851 +#> [250,] 0.36816787 0.4060399 0.5202421 0.5714498 0.5858707 0.5880933 0.5919036 +#> [251,] 0.60180716 0.6018285 0.9662172 0.9774597 0.9816729 1.0145107 1.0180882 +#> [252,] 0.43980417 0.4874431 0.5197226 0.6150630 0.6634790 0.6664560 0.7864254 +#> [253,] 0.44190042 0.4631435 0.5505767 0.5886008 0.6377367 0.6873164 0.7012542 +#> [254,] 0.39100186 0.7409369 0.7806299 0.8388192 0.9217313 0.9222943 0.9345085 +#> [255,] 0.34632790 0.3812432 0.5636713 0.6431622 0.6769138 0.6824575 0.6911725 +#> [256,] 0.54436133 0.5948165 0.7289802 0.7301984 0.7438620 0.7875841 0.7967487 +#> [257,] 0.57458790 0.5926552 0.6283424 0.6438393 0.6785323 0.6825467 0.7500568 +#> [258,] 1.27783404 1.2900982 1.2950464 1.4985037 1.5286311 1.5460156 1.5713194 +#> [259,] 0.21900050 0.3075903 0.3096341 0.3999190 0.4035350 0.4148944 0.4527564 +#> [260,] 0.40353499 0.4664689 0.4852165 0.4871731 0.4873610 0.4924360 0.5023658 +#> [261,] 0.41794591 0.5664209 0.5781964 0.5864097 0.6685038 0.6909388 0.8368906 +#> [262,] 0.48463135 0.6728008 0.8050962 0.8370144 0.8376331 0.8393648 0.8598953 +#> [263,] 0.32319175 0.4870720 0.4978386 0.5671789 0.6692245 0.6793512 0.6843618 +#> [264,] 0.19424836 0.2040277 0.4992867 0.5313602 0.6055320 0.6362094 0.6543068 +#> [265,] 0.36833863 0.4654334 0.5017085 0.5656454 0.6003747 0.6170498 0.6251389 +#> [266,] 0.58386724 0.6659381 0.6715358 0.6811196 0.6829526 0.7913083 0.8226197 +#> [267,] 0.53151133 0.5787218 0.6020524 0.6985332 0.7286656 0.7835030 0.7867163 +#> [268,] 0.64847362 0.6573676 0.6659433 0.7604574 0.7968490 0.8643554 0.9069869 +#> [269,] 0.49223996 0.5229711 0.5686139 0.6100679 0.6674642 0.6830877 0.6999881 +#> [270,] 0.47282196 0.5868136 0.8988946 0.9045820 0.9142488 0.9619491 0.9636785 +#> [271,] 0.56905828 0.7277196 0.7705092 0.8684019 0.8710113 0.8973821 0.8976672 +#> [272,] 0.32987731 0.5085852 0.6028978 0.6044766 0.6056317 0.7368727 0.7378429 +#> [273,] 0.57827386 0.6399215 0.6848409 0.7001828 0.7300695 0.7999589 0.8096686 +#> [274,] 0.38353125 0.4820388 0.5002889 0.5103997 0.5439551 0.5473826 0.5618381 +#> [275,] 0.31634825 0.3453730 0.4488479 0.4783741 0.5086319 0.5134158 0.6014621 +#> [276,] 0.42046922 0.4730321 0.6023254 0.6381665 0.6473181 0.6619429 0.6708163 +#> [277,] 0.75559906 0.8434938 0.9864432 1.0654337 1.0883128 1.1004546 1.1442323 +#> [278,] 0.23177341 0.3876206 0.4300517 0.4489872 0.4615879 0.4619930 0.5083644 +#> [279,] 0.17043615 0.6115139 0.6144349 0.7220758 0.7441820 0.9432558 0.9551188 +#> [280,] 0.31136211 0.4758183 0.5105971 0.5345327 0.6773536 0.7440504 0.7614294 +#> [281,] 1.28735206 1.4495158 1.5862658 1.5876154 1.6128527 1.6299672 1.6443932 +#> [282,] 0.29261567 0.4436452 0.5262098 0.5505027 0.5601840 0.5625149 0.5846818 +#> [283,] 0.24270294 0.3663440 0.3688916 0.6605804 0.6611236 0.8047881 0.8244194 +#> [284,] 0.34324427 0.3852723 0.4807027 0.5292069 0.5543009 0.5608770 0.5657912 +#> [285,] 0.62918180 1.0125592 1.1663187 1.2016624 1.2094716 1.2117365 1.2418795 +#> [286,] 0.29686347 0.5360774 0.7940312 0.8762371 0.9201335 0.9503676 0.9619168 +#> [287,] 0.40142020 0.4634532 0.4824160 0.5110722 0.5282545 0.6008158 0.6378858 +#> [288,] 0.53401809 0.6609695 0.7889064 0.7917791 0.8890348 0.8939098 0.9408754 +#> [289,] 0.38448622 0.4870720 0.5550794 0.5776140 0.5852819 0.5914036 0.6358499 +#> [290,] 0.21279658 0.3871573 0.4590166 0.5166758 0.5296416 0.5533188 0.5543009 +#> [291,] 0.35604642 0.5907578 0.6481719 0.6609695 0.7840776 0.8362565 0.8475800 +#> [292,] 0.32558630 0.4674089 0.5277063 0.5832308 0.6203573 0.6430868 0.6531550 +#> [293,] 0.58301167 0.6375903 0.6964726 0.7071698 0.7245649 0.7315685 0.7625511 +#> [294,] 0.27397800 0.4990195 0.5213306 0.5736358 0.5985232 0.6072090 0.6459071 +#> [295,] 0.09113993 0.3491467 0.6358499 0.6807774 0.6979795 0.7459699 0.7863417 +#> [296,] 0.64120027 0.8388852 0.8525389 0.9818075 1.0069490 1.0293006 1.0614266 +#> [297,] 0.46566207 0.5201454 0.5636713 0.5652610 0.6131437 0.6701657 0.6707414 +#> [298,] 0.57944873 0.5921952 0.6205306 0.6440024 0.6743616 0.6780611 0.7077063 +#> [299,] 0.35056102 0.4323315 0.5741677 0.6964146 0.7629855 0.7754890 0.8597179 +#> [300,] 0.59295405 0.6744941 0.6985332 0.6999140 0.7287384 0.7367904 0.7500358 +#> [301,] 0.58706661 0.5951729 0.6874442 0.7326062 0.7522365 0.8230275 0.8272819 +#> [302,] 0.36175024 0.4209899 0.4813064 0.5223277 0.5475791 0.6160059 0.6233547 +#> [303,] 0.63812120 0.6595335 0.6641357 0.7310347 0.7366293 0.7377899 0.7552492 +#> [304,] 0.28967890 0.4755652 0.5373530 0.6050027 0.7367644 0.7569945 0.7634534 +#> [305,] 0.39860107 0.5109188 0.5967728 0.6581444 0.6616765 0.6637005 0.6784733 +#> [306,] 0.35967592 0.3691845 0.3996389 0.4213417 0.5454917 0.5615328 0.5832308 +#> [307,] 0.39541408 0.5084299 0.5127088 0.5249234 0.5292019 0.5643657 0.5927760 +#> [308,] 0.72699233 1.0108327 1.0447182 1.0627107 1.1576053 1.1857721 1.2353420 +#> [309,] 0.72936937 0.7679370 0.8357712 0.9106449 0.9236965 1.0219507 1.0300689 +#> [310,] 0.38622987 0.4114420 0.4536031 0.5582578 0.6083764 0.6128921 0.6449446 +#> [311,] 0.22968159 0.3896564 0.4692602 0.5056829 0.6336309 0.6535046 0.6674393 +#> [312,] 0.49782138 0.6603331 0.6654162 0.6993385 0.7263683 0.7397532 0.7719971 +#> [313,] 0.94691536 1.0304489 1.2154122 1.2366640 1.2861856 1.3044798 1.3060265 +#> [314,] 0.34277683 0.5761198 0.5915459 0.5993767 0.6146927 0.6753620 0.7003972 +#> [315,] 0.50998616 0.5479445 0.6304955 0.7377899 0.7726233 0.8401201 0.8418575 +#> [316,] 0.35402695 0.4413732 0.4589638 0.4667636 0.5178635 0.5726849 0.6361679 +#> [317,] 0.54679682 1.1183533 1.1625312 1.2716053 1.2726941 1.2864367 1.3436808 +#> [318,] 0.78307298 0.8124752 0.8480663 0.8515825 0.8729608 0.9198239 1.0690410 +#> [319,] 0.30066148 0.4654537 0.4985146 0.5389288 0.5498350 0.5507061 0.6302704 +#> [320,] 0.31021064 0.3132308 0.4101772 0.4694301 0.4810949 0.5101946 0.5164189 +#> [321,] 0.75567952 0.7687906 0.8486365 0.8781762 0.8926911 0.9084179 0.9230808 +#> [322,] 0.34928645 0.3637145 0.3923042 0.4792014 0.5017085 0.5937359 0.6005207 +#> [323,] 0.58015850 0.6230936 0.6470833 0.7195649 0.7420902 0.7616721 0.7977826 +#> [324,] 0.79251236 0.9054818 0.9668903 1.0041571 1.0512183 1.1058004 1.1671591 +#> [325,] 0.60182850 0.9306545 0.9786673 1.0296871 1.0457754 1.1541700 1.2631363 +#> [326,] 0.64957283 0.7590596 0.7749350 0.7850495 0.7871844 0.7927290 0.8156598 +#> [327,] 0.42672090 0.5879220 0.6760600 0.6836464 0.6876713 0.7225715 0.7573887 +#> [328,] 0.91433588 0.9158306 0.9784031 1.2583021 1.2764300 1.3413194 1.5356199 +#> [329,] 1.01843729 1.2450534 1.4353399 1.4878778 1.4932276 1.5408669 1.5720413 +#> [330,] 0.53075473 0.5583400 0.6635461 0.8393648 0.8629734 0.9292874 0.9293687 +#> [331,] 0.26707323 0.2953271 0.3320452 0.4213417 0.4737726 0.4925101 0.5484479 +#> [332,] 0.28263796 0.4708422 0.5357388 0.5955864 0.6937404 0.7394286 0.7719971 +#> [333,] 0.30066148 0.4250036 0.4539085 0.4947684 0.4997310 0.5221296 0.5411184 +#> [334,] 0.60462854 0.6625574 0.6892968 0.7561637 0.7664300 0.7800571 0.7937415 +#> [335,] 0.35753455 0.3898205 0.4122781 0.4397189 0.4454174 0.4694301 0.5457032 +#> [336,] 0.71541443 0.7496531 0.7674448 0.7797565 0.8114114 0.8249625 0.8688823 +#> [337,] 0.44798338 0.4910612 0.5965018 0.6305607 0.8125750 0.8208785 0.8604156 +#> [338,] 0.59733251 0.6119211 0.7441820 0.7640671 0.8495275 0.9484934 0.9795572 +#> [339,] 0.34767639 0.4231106 0.7018844 0.7598041 1.1616720 1.1800716 1.1930159 +#> [340,] 0.25283902 0.5982736 0.6251264 0.6512282 0.6701734 0.6992505 0.8306882 +#> [341,] 0.62170404 0.7770250 0.8326278 0.8458111 0.9135953 1.0200667 1.0227937 +#> [342,] 0.33306395 0.4476540 0.4836014 0.5161005 0.5396148 0.5397691 0.5607260 +#> [343,] 0.51756753 0.5550128 0.6301777 0.7850495 0.7903039 0.8444186 0.8738890 +#> [344,] 0.46604194 0.6543357 0.6882759 0.7544034 0.8082659 0.8203685 0.8405639 +#> [345,] 2.08627568 2.2164901 2.3605552 2.4335841 2.4893736 2.7627295 2.8010679 +#> [346,] 0.13034995 0.4238135 0.4250519 0.4266499 0.5022303 0.5382494 0.5479820 +#> [347,] 0.34099743 0.4887354 0.5830566 0.6372778 0.6603331 0.6619228 0.6842989 +#> [348,] 0.51517039 0.5962294 0.6121996 0.6451439 0.6529798 0.6540016 0.6602214 +#> [349,] 0.26748597 0.4683940 0.5793739 0.6053595 0.6260703 0.6309714 0.6394856 +#> [350,] 0.76858466 1.1761635 1.2582593 1.2940643 1.5105286 1.6101623 1.6421377 +#> [351,] 0.76916011 0.7860783 0.7977826 0.8974579 0.9256936 0.9290629 0.9301239 +#> [352,] 0.52825894 0.5441858 0.5634698 0.5812371 0.6239770 0.6274126 0.6737094 +#> [353,] 0.58578655 0.7566644 0.7721850 0.7867631 0.7960317 0.8069427 0.8407096 +#> [354,] 0.58458911 0.6064475 0.7186929 0.7366293 0.8120694 0.8515900 0.9056615 +#> [355,] 0.52335784 0.5589289 0.5656257 0.5669487 0.5721419 0.5722827 0.6008707 +#> [356,] 0.34534255 0.4616561 0.5112414 0.5470238 0.5596469 0.5750012 0.5792409 +#> [357,] 0.43156060 0.4582670 0.5476464 0.5485833 0.5877672 0.6220212 0.6434446 +#> [358,] 0.73220521 0.8099764 0.8445454 0.9186037 0.9566207 0.9656704 1.0108327 +#> [359,] 0.31343717 0.5510211 0.6036509 0.6120067 0.6209275 0.6326844 0.6540326 +#> [360,] 0.36655786 0.3690922 0.5734929 0.5834994 0.5864097 0.6540622 0.7491659 +#> [361,] 0.76858466 1.0874815 1.1149484 1.1430548 1.2046681 1.2715879 1.2920916 +#> [362,] 0.19062462 0.3839844 0.4583266 0.4943451 0.5136656 0.5699897 0.5736296 +#> [363,] 0.28951401 0.4910873 0.6248821 0.6611893 0.6696607 0.7183489 0.7233776 +#> [364,] 0.42181553 0.4877192 0.5912459 0.6743616 0.7056267 0.7461754 0.8017895 +#> [365,] 0.76086589 0.7760302 0.8728537 0.9342568 0.9361687 0.9381583 0.9653260 +#> [366,] 0.13034995 0.3167348 0.3422170 0.3889990 0.4509631 0.4680016 0.4929429 +#> [367,] 0.57134342 0.7193678 0.8413470 0.8535926 0.8536220 0.8554087 0.8668236 +#> [368,] 0.63441274 0.6715617 0.8162489 1.0910088 1.1201771 1.1260417 1.1408357 +#> [369,] 0.48692180 0.5494740 0.5578253 0.7312511 0.8092917 0.8365102 0.8545020 +#> [370,] 0.30834417 0.3717870 0.5505670 0.5663475 0.5913376 0.6025436 0.6040373 +#> [371,] 0.37423009 0.4064087 0.5369217 0.5752935 0.6668281 0.7664228 0.7809228 +#> [372,] 0.54015491 0.5936938 0.6481829 0.6765533 0.7104502 0.8064179 0.8720588 +#> [373,] 0.43896058 0.4437918 0.4920131 0.5450710 0.5919320 0.6239316 0.6245348 +#> [374,] 0.44397251 0.5654979 0.6834941 0.6972669 0.7510266 0.7524010 0.7836004 +#> [375,] 0.66760280 0.7469794 0.9887768 1.0101594 1.1013004 1.1914199 1.2538680 +#> [376,] 0.59437982 0.6026883 0.6497441 0.6697146 0.6745087 0.7748304 0.7891744 +#> [377,] 0.66407082 0.7500568 0.7535016 0.8290688 0.8438541 0.9890276 1.0188957 +#> [378,] 0.41627299 0.5488501 0.6116729 0.6436547 0.6610865 0.6855123 0.7180888 +#> [379,] 0.69345760 0.7210034 0.8104446 0.8915143 0.9844797 1.0110316 1.0172064 +#> [380,] 0.76087780 0.8230186 0.9407532 0.9450609 0.9547859 0.9661827 0.9709767 +#> [381,] 0.40603994 0.5180424 0.5222549 0.5370509 0.5608112 0.5768237 0.5923872 +#> [382,] 0.73832735 1.0173604 1.1611430 1.1846141 1.1984804 1.2295837 1.2735942 +#> [383,] 0.53100533 0.5449393 0.5474227 0.5828232 0.5858707 0.6840361 0.6868059 +#> [384,] 0.57134342 0.5851683 0.6375299 0.6651148 0.6688456 0.6987302 0.6993011 +#> [385,] 0.27373125 0.2955083 0.5659957 0.6637059 0.6880188 0.6971506 0.7213557 +#> [386,] 0.24808700 0.3510083 0.3794034 0.3877919 0.4058981 0.4412515 0.4755460 +#> [387,] 0.43967830 0.4726402 0.4932683 0.5055582 0.5679693 0.5897617 0.6561252 +#> [388,] 0.84819084 1.1595079 1.1723107 1.1948092 1.2608081 1.2782794 1.2918580 +#> [389,] 0.32217673 0.3829084 0.4074870 0.4187954 0.4443748 0.4634532 0.4686448 +#> [390,] 0.52825894 0.5631576 0.6271125 0.6557014 0.6611893 0.6657764 0.6932175 +#> [391,] 0.45811669 0.5054670 0.5085852 0.6096429 0.6646026 0.7051882 0.7080762 +#> [392,] 0.46826716 0.6150778 0.6268764 0.6573377 0.7076093 0.8563626 0.9239508 +#> [393,] 0.95746914 1.0086629 1.0406724 1.0532450 1.1052959 1.1057283 1.1518480 +#> [394,] 0.29341112 0.4992867 0.5268116 0.5280320 0.5652156 0.5715258 0.6271125 +#> [395,] 0.22968159 0.3101792 0.3454661 0.5909986 0.5927128 0.6689339 0.6976758 +#> [396,] 0.36120649 0.4103122 0.6250605 0.6992505 0.7602595 0.8301865 0.8850193 +#> [397,] 0.28967890 0.3938899 0.7029221 0.7187057 0.7722017 0.8253453 0.8307571 +#> [398,] 0.75147404 0.7849116 0.9170330 0.9208913 0.9440306 0.9802764 0.9832832 +#> [399,] 0.24282195 0.5745879 0.6237231 0.7167928 0.7501435 0.7539793 0.7585124 +#> [400,] 0.44085544 0.6105890 0.6449463 0.6787343 0.7155752 0.7958527 0.8565443 +#> [401,] 0.33143558 0.3987107 0.4398471 0.4584524 0.4812428 0.5219024 0.5589289 +#> [402,] 0.33082271 0.5502972 0.6431622 0.6637005 0.6701657 0.6755251 0.7168742 +#> [403,] 0.56642090 0.6540622 0.8223332 0.8576855 0.8731733 0.8882479 0.9480153 +#> [404,] 0.26714363 0.3198414 0.4024143 0.4422043 0.5714739 0.5722827 0.5828092 +#> [405,] 0.30823539 0.4929429 0.5151064 0.5155247 0.5171986 0.5267013 0.5308653 +#> [406,] 0.38527232 0.4324372 0.4494279 0.4531303 0.4787794 0.4858446 0.4919623 +#> [407,] 0.27383529 0.2797752 0.3163482 0.4943825 0.5064923 0.5986820 0.6116729 +#> [408,] 0.38240555 0.8434938 0.8584295 0.8924923 0.9832103 1.1613203 1.2121301 +#> [409,] 0.57819639 0.6505936 0.8731733 0.9346957 0.9573444 1.0097077 1.0714018 +#> [410,] 0.52591639 0.6012587 0.7080196 0.7624489 0.7958698 0.8552217 0.8827765 +#> [411,] 0.36827211 0.4711137 0.4719894 0.4929756 0.6203573 0.6707708 0.6902118 +#> [412,] 0.23216678 0.3232231 0.3311819 0.5213843 0.5366771 0.5760775 0.5889128 +#> [413,] 0.58976169 0.6202265 0.6450097 0.6692384 0.6843983 0.7223482 0.7326062 +#> [414,] 0.24263568 0.4612341 0.4657141 0.4723813 0.5201454 0.6181234 0.6518934 +#> [415,] 0.36476374 0.5452436 0.5489041 0.6107487 0.6231284 0.6486865 0.6606610 +#> [416,] 0.60728448 0.6157924 0.6258618 0.7511654 0.7687882 0.7726233 0.7889009 +#> [417,] 0.26910893 0.3252515 0.4800824 0.5574661 0.5587419 0.5779394 0.5942108 +#> [418,] 1.12512471 1.3314374 1.4135245 1.4562553 1.5906817 1.6334235 1.6924701 +#> [419,] 0.62786122 0.6749549 0.6909721 0.7614294 0.7672396 0.8301462 0.8979768 +#> [420,] 0.22112194 0.3691845 0.5145285 0.5252724 0.5844169 0.6151094 0.6728585 +#> [421,] 0.27916247 0.4592070 0.4632790 0.4938952 0.5738974 0.5998774 0.6008497 +#> [422,] 0.50152971 0.5310501 0.5585375 0.6059301 0.6115119 0.6137666 0.6637059 +#> [423,] 0.53080944 0.5963091 0.6491302 0.6666116 0.6843983 0.6985799 0.7032316 +#> [424,] 0.27654704 0.2951427 0.3070175 0.3842054 0.4358474 0.4817677 0.4873610 +#> [425,] 0.97840314 1.7536070 1.8131297 2.0017496 2.1434535 2.1819352 2.2393017 +#> [426,] 0.36634397 0.4353989 0.4994289 0.5350266 0.7159151 0.7472615 0.7556795 +#> [427,] 0.70836509 1.0437403 1.1130162 1.2179101 1.2229333 1.3177732 1.3912272 +#> [428,] 0.55813831 0.5829462 0.5906029 0.6822739 0.7171960 0.8027568 0.8069427 +#> [429,] 0.40100564 0.4380599 0.4580945 0.4843531 0.5109188 0.5388405 0.5403259 +#> [430,] 0.57831635 0.5799145 0.6269897 0.6892983 0.6985799 0.7360508 0.7376631 +#> [431,] 0.44682494 0.4837806 0.5786943 0.6870117 0.7904464 0.8035448 0.8086243 +#> [432,] 0.38781056 0.4518521 0.5390001 0.5638302 0.6139850 0.6920587 0.7199420 +#> [433,] 0.47805534 0.5401356 0.5505767 0.5521143 0.5617291 0.6032702 0.6186321 +#> [434,] 0.64271917 0.7210034 0.7781986 0.8294768 0.8347201 0.9185371 0.9272091 +#> [435,] 0.78213116 0.8018866 0.8228872 0.8803566 1.0575084 1.0734745 1.0943855 +#> [436,] 0.42797926 0.5164301 0.5924125 0.6018320 0.6691727 0.6717369 0.6841384 +#> [437,] 0.46247887 0.5067058 0.6335023 0.6481829 0.6948617 0.7102101 0.7358506 +#> [438,] 0.21124475 0.8079009 0.8139015 0.8850193 0.9155204 0.9589698 1.0491241 +#> [439,] 0.25940823 0.3564569 0.3725663 0.3965755 0.4547488 0.4755460 0.4823437 +#> [440,] 0.40146034 0.5058961 0.5291942 0.5949664 0.6414176 0.6517776 0.6678141 +#> [441,] 0.43381139 0.5162549 0.5867573 0.6451439 0.6698491 0.6805422 0.7958064 +#> [442,] 0.27226466 0.7965124 0.8139015 0.9680754 1.0481851 1.0729468 1.1314613 +#> [443,] 0.67495487 0.6964098 0.7077063 0.7435339 0.8941679 0.9820487 0.9932132 +#> [444,] 0.42672090 0.4742585 0.6577665 0.6621273 0.7176650 0.7227302 0.7306703 +#> [445,] 0.27374113 0.3398920 0.4336246 0.4878851 0.5836934 0.7156208 0.7161151 +#> [446,] 0.49775036 0.5057079 0.5317534 0.6670638 0.6812482 0.7011247 0.7810805 +#> [447,] 0.19979753 0.4336246 0.5156453 0.5955075 0.6469255 0.6540243 0.6858037 +#> [448,] 0.57611984 0.6540326 0.6697537 0.7188448 0.7964828 0.8270086 0.8270891 +#> [449,] 0.35402695 0.5317214 0.5661899 0.6237384 0.6483689 0.6932376 0.7014995 +#> [450,] 0.37853720 0.5857865 0.6541418 0.6932700 0.7015655 0.7664324 0.7808781 +#> [451,] 0.49137345 0.6440024 0.6964098 0.7731717 0.8979768 0.9132668 0.9222713 +#> [452,] 0.34099743 0.3787914 0.6033274 0.6649191 0.6806684 0.7186290 0.8443421 +#> [453,] 0.32777468 0.4084296 0.5964598 0.6985484 0.7299506 0.7334949 0.7418415 +#> [454,] 0.24444572 0.2539434 0.3194772 0.3515583 0.4927501 0.5041885 0.5241539 +#> [455,] 0.55947715 0.6226706 0.7406254 0.7418054 0.8031751 0.8122835 0.8346231 +#> [456,] 0.57189888 0.7630114 0.7777086 0.8392729 0.8726374 0.9481810 0.9678224 +#> [457,] 0.41927666 0.4611625 0.6163551 0.6427449 0.6779266 0.6880188 0.7513486 +#> [458,] 0.39230424 0.5280235 0.5472313 0.5493735 0.6251389 0.6625574 0.6653248 +#> [459,] 0.77560656 0.9673221 1.3283529 1.4040438 1.4118502 1.4425507 1.4774432 +#> [460,] 0.61725469 0.7662297 0.7672396 0.7733261 0.7887943 0.8812575 0.9060264 +#> [461,] 0.43303596 0.5888637 0.7407218 0.8279811 0.8655723 0.8826947 0.9146768 +#> [462,] 0.42912632 0.4845361 0.6626579 0.6728326 0.6761037 0.6790748 0.7104630 +#> [463,] 0.41043967 0.4491331 0.4698574 0.5527091 0.5605946 0.5686139 0.5844173 +#> [464,] 0.72699233 0.8099764 0.9777157 1.2406322 1.2518960 1.3226513 1.3457507 +#> [465,] 0.57028752 0.5922631 0.7839516 0.7948862 0.7992506 0.8687603 0.9287249 +#> [466,] 0.52735350 0.5742801 0.5845891 0.6067881 0.6068605 0.6641357 0.6670932 +#> [467,] 0.28297282 0.4491331 0.4689097 0.6411230 0.6674642 0.6797679 0.6916542 +#> [468,] 0.20624216 0.5091761 0.5980774 0.6159974 0.6453148 0.6593648 0.7085769 +#> [469,] 1.19141986 1.5441863 1.5456936 1.7348901 1.8111149 1.8559001 1.9191438 +#> [470,] 1.41774477 1.4487873 1.4903795 1.6992258 1.7577959 1.7602376 1.8522810 +#> [471,] 0.29797572 0.4299281 0.5242352 0.5578253 0.6776225 0.7056283 0.7425035 +#> [472,] 0.38302962 0.5127490 0.5259164 0.5390705 0.6286051 0.6704237 0.7531162 +#> [473,] 0.50312387 0.5099862 0.6608039 0.7022452 0.7096227 0.7186290 0.7293509 +#> [474,] 0.32559725 0.3713169 0.7130241 0.7659304 0.7700191 0.7789670 0.7808682 +#> [475,] 0.66621597 0.7830272 0.7831751 0.8182106 0.8966966 0.9351781 0.9509069 +#> [476,] 0.31706208 0.4752431 0.5834994 0.6357454 0.6537978 0.6696843 0.7443812 +#> [477,] 0.63307436 0.8746795 0.9048494 1.0334246 1.0734693 1.0859972 1.1214003 +#> [478,] 0.24835875 0.2855691 0.4485579 0.5506574 0.5675909 0.5897147 0.5921158 +#> [479,] 0.67299483 0.6867017 0.6929659 0.7637374 0.8307571 0.8561672 0.8700257 +#> [480,] 0.35784179 0.5513518 0.5670087 0.7056283 0.7706672 0.8031751 0.8092917 +#> [481,] 0.48744310 0.5588898 0.5712633 0.5887559 0.6020757 0.6867161 0.6954605 +#> [482,] 0.64034442 0.7109808 0.7513486 0.7552150 0.7938263 0.8022374 0.9097428 +#> [483,] 0.65673910 0.6934576 0.7347903 0.7368107 0.7609579 0.8347201 0.8816674 +#> [484,] 0.46758775 0.5681887 0.5852819 0.6067881 0.6260452 0.6605836 0.6614731 +#> [485,] 0.47111371 0.4812793 0.4850204 0.5201222 0.5477646 0.5577346 0.5793739 +#> [486,] 0.43144845 0.4391736 0.5432727 0.5923682 0.6461551 0.6766045 0.7690241 +#> [487,] 0.37879138 0.4887354 0.5135662 0.7270004 0.7552587 0.7578103 0.8245740 +#> [488,] 0.31421513 0.4441723 0.4573402 0.4961898 0.5060788 0.5282545 0.5339018 +#> [489,] 0.65656076 0.7463260 0.7929313 0.8194621 0.9020570 0.9177525 0.9422374 +#> [490,] 0.30104083 0.3744047 0.4592138 0.5041361 0.5161984 0.5169359 0.5456778 +#> [491,] 0.96364754 1.1528099 1.1904007 1.2277327 1.2608628 1.3413665 1.3659466 +#> [492,] 0.27801534 0.4372083 0.4698574 0.5177640 0.5229711 0.5237595 0.6135268 +#> [493,] 0.49796048 0.5036863 0.5219024 0.5233578 0.5272305 0.5284553 0.5654223 +#> [494,] 0.98742099 1.0437403 1.2940643 1.3715098 1.3899578 1.3997916 1.4121160 +#> [495,] 0.44397251 0.7141122 0.7220758 0.7276547 0.7367931 0.7933537 0.7945902 +#> [496,] 0.78865536 0.8210686 0.8651220 0.8884697 0.9370270 1.0128090 1.0499836 +#> [497,] 0.44065919 0.4790116 0.4853619 0.5303881 0.5448443 0.6220982 0.6282401 +#> [498,] 0.34765312 0.4310536 0.5417388 0.5560209 0.5638302 0.5759559 0.5821031 +#> [499,] 0.62476464 0.6853526 0.7231728 0.7364530 0.7754434 0.7937415 0.8122482 +#> [500,] 0.86382960 0.9100916 0.9574691 0.9589698 0.9872226 1.0080292 1.0085337 +#> [501,] 0.44876414 0.5102163 0.5347131 0.5379716 0.5645399 0.5704845 0.6053305 +#> [502,] 0.41627299 0.6481719 0.6935705 0.6963693 0.6982418 0.7340551 0.7619814 +#> [503,] 0.32777468 0.4800625 0.6171153 0.6411230 0.6927322 0.7000504 0.7321665 +#> [504,] 0.35071989 0.3780109 0.4437918 0.5414338 0.6048693 0.6083764 0.6248821 +#> [505,] 0.33721916 0.4024143 0.4997144 0.5299611 0.5741004 0.6124989 0.6187411 +#> [506,] 0.29866995 0.5794222 0.6637734 0.6932376 0.7236006 0.7750488 0.7979371 +#> [507,] 0.21629621 0.2649746 0.3422170 0.3558626 0.4238135 0.4414084 0.4454174 +#> [508,] 0.28263796 0.4568666 0.4619486 0.7048069 0.7134353 0.7177440 0.7321665 +#> [509,] 0.48137672 0.4824022 0.5419171 0.5632433 0.5882164 0.5888637 0.6137666 +#> [510,] 0.46565599 0.4783741 0.5334498 0.5335707 0.6197868 0.6302965 0.6626189 +#> [511,] 1.08972495 1.4503325 1.4872049 1.6333087 1.6377813 1.6615663 1.6656201 +#> [512,] 0.40062803 0.7811736 0.9422343 0.9619867 0.9771794 0.9813070 1.0157656 +#> [513,] 0.24363560 0.7233368 0.9136292 0.9197246 0.9389733 0.9935159 1.0471151 +#> [514,] 0.57508362 0.8877721 0.9844797 1.0681754 1.0911114 1.1350714 1.1527929 +#> [515,] 0.33573616 0.5134158 0.5348730 0.5760775 0.6140883 0.6615503 0.6626189 +#> [516,] 1.34136645 1.3838227 1.4342775 1.4943989 1.5133858 1.5837403 1.6842036 +#> [517,] 0.31680663 0.5132193 0.5207735 0.5239781 0.5288084 0.6530337 0.6701734 +#> [518,] 0.33083441 0.3806689 0.4182686 0.4632790 0.5653137 0.6036509 0.6394856 +#> [519,] 0.31747977 0.4444787 0.4536031 0.6238292 0.6440949 0.6693203 0.6872239 +#> [520,] 0.63307436 0.6750776 0.6964146 0.7806849 0.7888541 0.9482153 0.9678648 +#> [521,] 0.43546948 0.4618722 0.4630477 0.4719894 0.6224500 0.6884615 0.7096870 +#> [522,] 0.27135405 0.4652345 0.4995554 0.5058957 0.5669282 0.6064614 0.6081116 +#> [523,] 0.24306608 0.3252515 0.4779854 0.5131453 0.5564435 0.5580321 0.5617291 +#> [524,] 0.40842964 0.4800625 0.5511625 0.5870548 0.6589856 0.6864307 0.7071698 +#> [525,] 0.86978163 1.0184373 1.2608628 1.2638072 1.3526222 1.4484502 1.4599987 +#> [526,] 0.23216678 0.3463831 0.4338062 0.4473128 0.6140883 0.6430809 0.6863041 +#> [527,] 0.73832735 1.0898115 1.1625550 1.2302076 1.2953288 1.3461254 1.3687894 +#> [528,] 0.64120027 0.7090600 0.7290976 0.8012898 0.8137912 0.8145357 0.8163358 +#> [529,] 0.31341527 0.3253701 0.5315113 0.5945311 0.6297303 0.6668281 0.7500358 +#> [530,] 1.14549023 1.2158055 1.3127295 1.4186153 1.4811360 1.5310853 1.5420888 +#> [531,] 0.39980361 0.4032872 0.4148328 0.4161305 0.4579549 0.5164189 0.5330906 +#> [532,] 0.37766750 0.4218155 0.5093474 0.6618992 0.6812482 0.6834770 0.6870574 +#> [533,] 0.27422963 0.3075903 0.3558626 0.3898205 0.4871731 0.5087696 0.5242883 +#> [534,] 0.38302962 0.5250651 0.5477875 0.6636078 0.7090112 0.7146490 0.7397532 +#> [535,] 1.12806137 1.1348424 1.1938812 1.4175963 1.5498325 1.5511832 1.6104801 +#> [536,] 0.41300116 0.5232306 0.5273535 0.5511625 0.5844169 0.6403226 0.6632456 +#> [537,] 0.48435314 0.5178139 0.5347131 0.5489041 0.5685398 0.5879447 0.6548786 +#> [538,] 0.49628373 0.5831077 0.6269285 0.6550708 0.6605836 0.6656679 0.6692245 +#> [539,] 0.35792720 0.6560213 0.7569771 0.7602595 0.7759812 0.7785397 0.8970102 +#> [540,] 0.76904320 1.4661106 1.4702632 1.4773048 1.5262415 1.5297211 1.5432717 +#> [541,] 0.44133972 0.5291942 0.6068363 0.6208701 0.7134594 0.7263323 0.7303278 +#> [542,] 0.62170404 0.6256440 0.6446191 0.7070516 0.7112517 0.7289212 0.7560268 +#> [543,] 0.24363560 0.6583989 0.8760443 0.8771099 0.8894776 0.8955944 0.9222511 +#> [544,] 0.65932573 0.8223836 0.8684019 0.8693571 0.8740993 0.8921200 0.9172862 +#> [545,] 0.71813707 0.8264940 0.9067110 1.0099780 1.1022822 1.2036360 1.2095566 +#> [546,] 0.21629621 0.3009462 0.3167348 0.4250519 0.4616561 0.5050785 0.5242883 +#> [547,] 0.45699431 0.4590581 0.5355108 0.5476464 0.5826571 0.6701486 0.6719392 +#> [548,] 0.35963054 0.5976765 0.6206061 0.6336101 0.6707414 0.6790821 0.6824575 +#> [549,] 0.58301167 0.5851683 0.6196863 0.6228291 0.6402223 0.6417850 0.6535752 +#> [550,] 0.27135405 0.4842020 0.4882325 0.5237595 0.5327186 0.5418878 0.5643148 +#> [551,] 0.80281248 0.8087226 0.9071681 0.9365881 0.9549767 0.9721787 1.0265847 +#> [552,] 0.71862846 0.7597058 0.8234266 0.8728537 0.9222713 0.9789055 1.0440381 +#> [553,] 0.45094915 0.8421501 0.8582952 0.8886290 0.9141369 0.9763113 0.9821884 +#> [554,] 0.13934368 0.2428452 0.3342411 0.3651141 0.3679166 0.3829084 0.4805272 +#> [555,] 0.27237342 0.4441723 0.5132193 0.5338823 0.6621273 0.6778590 0.6797562 +#> [556,] 0.34658311 0.3844862 0.5832709 0.6088823 0.6749278 0.6793633 0.6951039 +#> [557,] 0.39100186 0.8816674 0.9568694 0.9716891 1.0118846 1.0173781 1.0519565 +#> [558,] 0.31072384 0.4064494 0.4824160 0.4943451 0.4961898 0.5423066 0.5552995 +#> [559,] 0.36175024 0.4205963 0.4667636 0.5322349 0.5562671 0.5765295 0.5846818 +#> [560,] 0.37049110 0.7604475 0.7650716 0.8009941 0.8012898 0.8748209 0.8785264 +#> [561,] 0.21399890 0.5146832 0.5962294 0.6301110 0.6329947 0.6412104 0.6431744 +#> [562,] 0.33083441 0.4327513 0.4589491 0.5305996 0.5625505 0.6008497 0.6177383 +#> [563,] 0.39939033 0.5776140 0.6556661 0.6749278 0.6872535 0.7165905 0.7329333 +#> [564,] 0.54723126 0.5513452 0.6829526 0.7028998 0.7263323 0.7384764 0.7391128 +#> [565,] 0.50873514 0.5717532 0.6554691 0.6807494 0.6955825 0.7046757 0.7062653 +#> [566,] 0.33143558 0.4451621 0.4826764 0.4883416 0.5127696 0.5912516 0.5924125 +#> [567,] 1.41926620 1.4453761 1.5806618 1.6063033 1.6172418 1.6276178 1.6809096 +#> [568,] 0.32319175 0.4648272 0.5217926 0.5503828 0.5627264 0.6550708 0.7109863 +#> [569,] 0.56700865 0.5871101 0.6226706 0.6274324 0.6453148 0.7142274 0.7158303 +#> [570,] 0.43535072 0.4501754 0.5306193 0.5830972 0.5962874 0.6341743 0.6489536 +#> [571,] 0.13267935 0.1393437 0.4187954 0.4336526 0.4364860 0.4371461 0.4445022 +#> [572,] 0.36563150 0.4882325 0.5004472 0.5279354 0.5432098 0.5498350 0.5527091 +#> [573,] 0.50642499 0.5753730 0.6208701 0.6528180 0.6671471 0.6804542 0.6851323 +#> [574,] 0.48378059 0.6244787 0.6473689 0.6603144 0.7580379 0.7597058 0.7760302 +#> [575,] 0.37185869 0.3924534 0.4451621 0.5187923 0.5219579 0.5244126 0.5347960 +#> [576,] 0.23445424 0.2852333 0.4086348 0.4174691 0.4786173 0.4985297 0.5030537 +#> [577,] 0.37256625 0.3725824 0.4192504 0.4744331 0.5042462 0.5308093 0.5538165 +#> [578,] 0.37326479 0.5838672 0.6528180 0.6782440 0.7643309 0.7686223 0.7750488 +#> [579,] 0.47903449 0.5110472 0.5121367 0.5338226 0.6714041 0.6867017 0.7523310 +#> [580,] 0.42311058 0.6695476 0.7311306 0.9776841 1.0525541 1.0632890 1.0634033 +#> [581,] 0.57508362 0.6840445 0.8620530 0.9108403 0.9978395 1.0064777 1.0194699 +#> [582,] 0.76091799 0.7711151 0.8032878 0.8050413 0.8144492 0.8472938 0.9161961 +#> [583,] 0.23444596 0.3015421 0.3070175 0.4174691 0.4489872 0.4617600 0.4706032 +#> [584,] 0.27226466 0.8079009 0.8499023 1.0784853 1.0893290 1.1458741 1.1687427 +#> [585,] 0.44220434 0.4669321 0.5040527 0.5766456 0.6187411 0.6245348 0.6274126 +#> [586,] 0.29866995 0.5317214 0.6603144 0.7035090 0.7042937 0.7435667 0.8003562 +#> [587,] 0.27397800 0.3872981 0.5268575 0.5447722 0.5489092 0.6181197 0.6198561 +#> [588,] 0.23154039 0.5680358 0.6208568 0.6608245 0.7288602 0.7413602 0.7439166 +#> [589,] 0.37440467 0.5032150 0.5277032 0.5510784 0.5646986 0.5658666 0.5828211 +#> [590,] 0.64620569 0.6604637 0.7526997 0.7527220 0.8270891 0.8307374 0.8406601 +#> [591,] 0.40406787 0.4514422 0.5745288 0.5820708 0.6177383 0.6246316 0.6402169 +#> [592,] 0.38434510 0.3900757 0.3932452 0.5887325 0.6267533 0.6392327 0.6516217 +#> [593,] 0.38066893 0.4327513 0.4683940 0.4974019 0.5355726 0.6497441 0.7012273 +#> [594,] 0.38085583 0.4536409 0.4904569 0.5187908 0.5235230 0.5986820 0.6696586 +#> [595,] 0.53502656 0.6239770 0.6597689 0.6757738 0.7233776 0.7877170 0.8047881 +#> [596,] 0.27831665 0.3010408 0.3120374 0.3287522 0.3432095 0.4494279 0.4723983 +#> [597,] 0.37616048 0.6107151 0.6290967 0.6931054 0.6972586 0.7213988 0.7268775 +#> [598,] 0.76376970 0.8028125 1.4043754 1.4072762 1.4190972 1.4209938 1.4947212 +#> [599,] 0.44182012 0.7566974 0.8450309 0.8655723 0.9357569 1.0665545 1.0948709 +#> [600,] 0.55397237 0.7960216 0.8821900 0.9143359 0.9184174 0.9304678 0.9681963 +#> [601,] 0.45940455 0.6344127 0.7640671 0.8751113 0.9069941 0.9339558 0.9425497 +#> [602,] 0.24263568 0.4617965 0.4862838 0.4935583 0.5058961 0.5164612 0.6160175 +#> [603,] 0.24835875 0.3899307 0.4419782 0.4938952 0.5140316 0.5292019 0.6025951 +#> [604,] 0.33306395 0.4201660 0.5164836 0.5178701 0.5361367 0.5477875 0.5745749 +#> [605,] 0.40116324 0.4737726 0.4747191 0.4929756 0.5054670 0.5098325 0.5183205 +#> [606,] 0.26497430 0.6516321 0.7057181 0.7326479 0.8690916 0.9142811 0.9759745 +#> [607,] 0.43233153 0.6705407 0.7719599 0.7760562 0.7888541 0.8285201 0.8285825 +#> [608,] 0.21124475 0.7965124 0.8499023 0.8638296 0.9862154 1.0242589 1.0566198 +#> [609,] 0.17043615 0.5791031 0.6119211 0.7568780 0.7933537 0.9401932 0.9784879 +#> [610,] 1.27251818 1.4007409 1.5872242 1.6502714 1.6759689 1.7931794 1.7962503 +#> [611,] 0.35056102 0.5386376 0.6705407 0.8652738 0.8902867 0.9165879 0.9442007 +#> [612,] 0.36081713 0.4053927 0.4144134 0.4205963 0.4209899 0.4413732 0.5342375 +#> [613,] 0.32998788 0.4780872 0.5829748 0.6451118 0.6516321 0.6681459 0.7481556 +#> [614,] 0.35146422 0.3814274 0.5201222 0.7057621 0.7107186 0.7137567 0.7610594 +#> [615,] 0.36291605 0.4214459 0.4391736 0.6600433 0.6886020 0.7072703 0.7096227 +#> [616,] 0.34646260 0.4265521 0.4487823 0.4702363 0.4947684 0.4957363 0.4985146 +#> [617,] 0.62805720 0.6972586 0.7227889 1.0564521 1.0855136 1.0942662 1.1260716 +#> [618,] 0.31136211 0.5369502 0.5598178 0.5751151 0.6118213 0.6281894 0.6608245 +#> [619,] 1.11726240 1.3848146 1.6063874 1.6113179 1.6482571 1.8290001 1.8811300 +#> [620,] 0.38538472 0.4354695 0.4614661 0.6278003 0.7072703 0.7150355 0.7591022 +#> [621,] 0.36424210 0.4117843 0.4445334 0.5619969 0.5867999 0.5987444 0.6254579 +#> [622,] 0.42107740 0.5226775 0.6905425 0.8072431 0.8114114 0.8218081 0.8353734 +#> [623,] 0.90251624 0.9179658 1.0056713 1.0084805 1.1554840 1.3880295 1.4135907 +#> [624,] 0.20286252 0.3610021 0.4372544 0.4740533 0.4997761 0.5182454 0.5524235 +#> [625,] 0.25229778 0.4824022 0.5822296 0.6059301 0.6334506 0.6447556 0.6600611 +#> [626,] 0.55947715 0.5916953 0.6345276 0.6698491 0.7170007 0.7471335 0.7557454 +#> [627,] 0.46573585 0.4878851 0.5195055 0.5201538 0.5315147 0.5350378 0.6540243 +#> [628,] 0.93623884 1.0977995 1.1947872 1.2876165 1.2998117 1.4436202 1.4755800 +#> [629,] 0.44825377 0.5195055 0.5581383 0.5637833 0.5975474 0.5977062 0.6021255 +#> [630,] 0.37536730 0.5164301 0.5531608 0.6666422 0.7220469 0.7574397 0.7691004 +#> [631,] 0.32903489 0.3839844 0.4796330 0.5315577 0.5368963 0.5412133 0.5453723 +#> [632,] 0.49740194 0.5447744 0.6280775 0.6527705 0.6697146 0.7372376 0.7830272 +#> [633,] 1.11726240 1.2423258 1.5862658 1.6544705 1.6930723 1.7550223 1.8218150 +#> [634,] 0.27820372 0.3253701 0.3660151 0.5369217 0.6020524 0.7003384 0.7089577 +#> [635,] 0.23445424 0.4048737 0.4322466 0.4608005 0.4648813 0.4977342 0.5050785 +#> [636,] 0.40640874 0.5834456 0.6100679 0.6851942 0.7146722 0.7241467 0.7301356 +#> [637,] 0.46252459 0.6297303 0.6331314 0.7003384 0.8112534 0.8124345 0.8651220 +#> [638,] 0.60088987 0.6339919 0.6388741 0.6474137 0.6474838 0.6696586 0.6988707 +#> [639,] 0.37734773 0.4590026 0.5390705 0.5921952 0.6671026 0.6974902 0.7090112 +#> [640,] 0.21399890 0.4031256 0.5151704 0.5494136 0.5664058 0.6006307 0.6424917 +#> [641,] 0.29207978 0.5401549 0.5474909 0.5524235 0.5796832 0.6059761 0.6585259 +#> [642,] 0.29137886 0.5112414 0.5187908 0.5307719 0.5494953 0.5495123 0.5756728 +#> [643,] 0.44085544 0.5514176 0.5632790 0.7421846 0.7821312 0.7895657 0.7912623 +#> [644,] 0.35281360 0.3912673 0.4989607 0.5335707 0.5414298 0.6156885 0.6527913 +#> [645,] 0.78117363 0.8633145 1.0734705 1.0875151 1.0911114 1.1794586 1.2188109 +#> [646,] 0.69162439 0.7439166 0.9022142 0.9147525 0.9972726 1.0179116 1.0717781 +#> [647,] 0.58197751 0.7391057 0.8008340 0.9239508 0.9941564 1.0069490 1.0591957 +#> [648,] 0.25940823 0.2881677 0.4069067 0.4167995 0.4490281 0.5004484 0.5369063 +#> [649,] 0.43361530 0.4576543 0.5310053 0.5408687 0.5907444 0.6274188 0.6276467 +#> [650,] 0.55397237 0.7664153 0.9252118 0.9803168 1.0258493 1.0333822 1.0508506 +#> [651,] 0.25537990 0.3510083 0.3896725 0.4092183 0.4114427 0.5009519 0.5058887 +#> [652,] 0.50856221 0.6573377 0.6668035 0.7638306 0.8564216 0.8851545 0.9487532 +#> [653,] 0.72771956 0.7701128 0.8085942 0.8565908 0.8612755 0.8616950 0.9033424 +#> [654,] 0.63004997 0.6790883 0.6930265 0.7471025 0.7796087 0.8001579 0.8162489 +#> [655,] 0.62415853 0.6790883 0.8149223 0.8164451 0.8245908 0.8249625 0.8542619 +#> [656,] 0.36371447 0.5336176 0.5416509 0.5799145 0.5927017 0.5963091 0.5968608 +#> [657,] 0.26592251 0.3839797 0.5314992 0.5582578 0.6573676 0.6859789 0.6869186 +#> [658,] 0.96860515 1.5596684 1.7214903 1.8390047 1.8732701 2.0095196 2.0340435 +#> [659,] 0.33992786 0.6787343 0.7953610 0.7995270 0.8406601 0.8451771 0.8946463 +#> [660,] 0.57850160 0.6149651 0.6331314 0.7958120 0.8032878 0.8500671 0.8728612 +#> [661,] 0.59645976 0.6882759 0.7294277 0.7314181 0.7398650 0.7451284 0.7905992 +#> [662,] 0.51562884 0.6948035 0.6954605 0.7612766 0.7820931 0.7821108 0.8110738 +#> [663,] 0.63575836 0.6640708 0.7659304 0.7776300 0.8079934 0.8763540 0.9216423 +#> [664,] 0.32642149 0.5204109 0.5661041 0.5980774 0.6062228 0.6408244 0.6936385 +#> [665,] 0.34583666 0.5094248 0.5822296 0.6019879 0.6508259 0.6516076 0.6744814 +#> [666,] 0.43381139 0.4546433 0.5788702 0.6756414 0.7471335 0.8011819 0.8455020 +#> [667,] 0.53467357 0.5373530 0.5983490 0.5995905 0.6462057 0.7008084 0.7061636 +#> [668,] 0.37654869 0.4283986 0.5094248 0.6696494 0.7168134 0.7303654 0.7825482 +#> [669,] 0.45033714 0.5345327 0.5369502 0.5948893 0.5985664 0.6389778 0.6726457 +#> [670,] 0.83931843 0.8628560 0.9748641 0.9761721 1.0343402 1.0928670 1.0943788 +#> [671,] 0.36481889 0.3875701 0.4533006 0.4579549 0.5029217 0.5244214 0.5263868 +#> [672,] 0.53423748 0.6205033 0.6237384 0.6597708 0.6637734 0.6859404 0.7042937 +#> [673,] 0.22512765 0.3954141 0.4057245 0.4066714 0.5754289 0.5981497 0.6459042 +#> [674,] 0.41796157 0.5450184 0.5485833 0.5590485 0.5908792 0.5971899 0.6076136 +#> [675,] 0.41441336 0.4468249 0.4590581 0.4647662 0.5223277 0.5907237 0.6244787 +#> [676,] 0.35057975 0.6053708 0.6305607 0.7109863 0.7912119 0.8047230 0.8227136 +#> [677,] 0.47357398 0.6408472 0.7471467 0.7822333 0.8163211 0.8184313 0.8571742 +#> [678,] 0.36291605 0.4614661 0.5432727 0.5830972 0.5858808 0.6132328 0.6351667 +#> [679,] 0.34692452 0.4372083 0.5217926 0.5248431 0.5675716 0.5873715 0.6131153 +#> [680,] 0.47956545 0.7061636 0.7550567 0.7569945 0.8113778 0.8205277 0.8486483 +#> [681,] 0.69219525 0.7959432 0.7989554 0.8713739 0.8858275 0.8900467 0.9009966 +#> [682,] 0.23817426 0.2808169 0.3592144 0.5177640 0.5288084 0.5930796 0.5983421 +#> [683,] 0.38064751 0.4014603 0.4293245 0.4413397 0.5164612 0.5171589 0.5566918 +#> [684,] 0.27374113 0.4327614 0.5156453 0.5587885 0.6472249 0.7160519 0.7567857 +#> [685,] 0.36601515 0.3742301 0.4121055 0.5945311 0.7146722 0.7312741 0.7746565 +#> [686,] 0.52802346 0.5513452 0.6314889 0.6345006 0.7183474 0.7496084 0.7939503 +#> [687,] 0.50375515 0.5870666 0.6666116 0.7169169 0.7286017 0.8465621 0.8663543 +#> [688,] 0.24611185 0.3370508 0.5064250 0.5171589 0.5526758 0.6160175 0.6205033 +#> [689,] 0.41794591 0.6505936 0.6588649 0.7485899 0.7491659 0.7548487 0.8576855 +#> [690,] 0.48897230 0.5338226 0.5631145 0.6876064 0.7249473 0.7610390 0.7665936 +#> [691,] 0.25265749 0.3648189 0.4231851 0.4983350 0.5054447 0.5058957 0.5379967 +#> [692,] 0.37194528 0.3896725 0.4518190 0.4888065 0.5286499 0.5368963 0.5411184 +#> [693,] 0.42115831 0.5520766 0.5550128 0.9107576 0.9213139 0.9311899 1.0733480 +#> [694,] 0.30665876 0.3717870 0.3719453 0.4997310 0.5480230 0.6036892 0.6316852 +#> [695,] 0.27235215 0.3682785 0.3877919 0.4563907 0.4686757 0.4790775 0.5102164 +#> [696,] 0.44798338 0.5244854 0.8689665 0.9423840 0.9442252 0.9472707 0.9680754 +#> [697,] 0.27562530 0.3560464 0.6615503 0.7733899 0.7821108 0.7931469 0.8066735 +#> [698,] 0.64271917 0.8104446 0.8518589 0.9819177 1.0527555 1.0704130 1.1284419 +#> [699,] 0.37536730 0.4279793 0.5698067 0.6805403 0.6930791 0.7037863 0.7775509 +#> [700,] 0.77560656 0.8556852 1.1021752 1.1159335 1.1821154 1.2422865 1.2640127 +#> [701,] 0.54944796 0.6858087 0.7160519 0.7161151 0.7514740 0.8011942 0.8040033 +#> [702,] 0.43725443 0.4518521 0.5293771 0.5417388 0.5617081 0.5693952 0.5809123 +#> [703,] 0.27373125 0.3681418 0.5585375 0.5629239 0.6276305 0.6427449 0.6776797 +#> [704,] 0.44727767 0.6573590 0.7268775 0.7276097 0.7664300 0.7956606 0.8151043 +#> [705,] 0.48215564 0.7168258 0.7797592 0.8584295 0.8881306 0.9258788 1.1747701 +#> [706,] 0.32987731 0.5539253 0.6512268 0.6805731 0.7051882 0.7770416 0.8028310 +#> [707,] 0.34290313 0.3916555 0.4384343 0.4439449 0.4451151 0.4652345 0.4779779 +#> [708,] 0.72429204 0.8485679 0.9030081 1.0299906 1.0379055 1.0509786 1.0511232 +#> [709,] 0.26411020 0.5763736 0.5913644 0.6009726 0.6159768 0.6332756 0.6387827 +#> [710,] 0.59236817 0.6317479 0.6552598 0.6905037 0.7620866 0.7751092 0.7835180 +#> [711,] 0.49777424 0.4986955 0.5246994 0.6800699 0.6932700 0.7195649 0.7310794 +#> [712,] 0.30585146 0.3569595 0.3610021 0.4310536 0.5617081 0.6139850 0.6173314 +#> [713,] 0.22730905 0.3101792 0.3896564 0.6595335 0.6614731 0.6616534 0.7026276 +#> [714,] 0.73113062 0.7404447 0.7598041 0.8894494 0.9218340 0.9772646 1.0149214 +#> [715,] 0.53546582 0.6488153 0.6827288 0.7065292 0.7176423 0.7196598 0.7858885 +#> [716,] 0.31706208 0.3824672 0.4997944 0.5211668 0.5414298 0.5734929 0.6104140 +#> [717,] 0.29573626 0.5202421 0.5274417 0.5923872 0.6130515 0.6268764 0.6668035 +#> [718,] 0.51787090 0.6681381 0.6921952 0.6945929 0.7616721 0.7728200 0.7767504 +#> [719,] 0.44511515 0.4773816 0.5208903 0.5221296 0.5708846 0.6036892 0.6071663 +#> [720,] 0.31021064 0.3667173 0.4148328 0.5036602 0.5388405 0.5443826 0.5743608 +#> [721,] 0.76376970 1.4945173 1.5618344 1.6583313 1.7226438 1.7337409 1.8270387 +#> [722,] 0.57910311 0.6115139 0.6685038 0.8223332 0.8940572 0.9323082 0.9573444 +#> [723,] 0.40406787 0.4589491 0.5738974 0.6282063 0.6353663 0.6645386 0.6905037 +#> [724,] 0.26361352 0.3612065 0.5829462 0.6409314 0.6512282 0.6560213 0.6666938 +#> [725,] 0.37766750 0.4877192 0.4977504 0.6141899 0.6374305 0.6463524 0.7035879 +#> [726,] 0.40235927 0.7095820 0.7211537 0.7390536 0.7565762 0.8014326 0.8093141 +#> [727,] 0.25229778 0.5443461 0.5480230 0.5517974 0.5632433 0.6230362 0.6285004 +#> [728,] 0.60180716 0.6345307 0.7319469 0.7498497 0.7510102 0.7522254 0.7795453 +#> [729,] 0.79251236 0.9169129 0.9624720 1.0016268 1.1132133 1.1183533 1.1703586 +#> [730,] 0.21651187 0.3307715 0.3902055 0.4048998 0.4300517 0.5080894 0.5970754 +#> [731,] 0.66354613 0.8080975 0.8122482 0.8448919 0.8598953 0.8703998 0.8736572 +#> [732,] 0.76793703 0.9746944 1.0603844 1.1809858 1.3134783 1.3312986 1.4184369 +#> [733,] 1.41806022 1.4979673 1.5334406 1.6314486 1.6429539 1.7030105 1.7172150 +#> [734,] 0.35753455 0.3949244 0.4214572 0.4281875 0.4408832 0.4810949 0.4826539 +#> [735,] 0.16898732 0.4774863 0.4997761 0.5450184 0.5577090 0.5654247 0.5790945 +#> [736,] 0.27779408 0.4117843 0.4624789 0.4869968 0.5152503 0.5926834 0.6532210 +#> [737,] 0.45094915 0.7870609 0.8365102 0.9197372 0.9669580 0.9771254 1.0148486 +#> [738,] 0.33982358 0.7319139 0.7776584 0.7905012 0.8014663 0.8392729 0.9160105 +#> [739,] 0.39752836 0.4628165 0.5139286 0.5557389 0.6299496 0.6648775 0.6892268 +#> [740,] 0.35502072 0.5015861 0.5160277 0.6083196 0.6489536 0.6874109 0.6959152 +#> [741,] 0.35694620 0.4291263 0.5064923 0.6014621 0.6239316 0.6610865 0.6857948 +#> [742,] 0.54794447 0.5909986 0.6224500 0.6278003 0.6351667 0.6394549 0.6607352 +#> [743,] 0.53400936 0.5574238 0.7236006 0.7286656 0.7608778 0.7813525 0.8909473 +#> [744,] 0.81247524 1.0111774 1.0421754 1.0457865 1.1062559 1.1693766 1.2517755 +#> [745,] 0.33705076 0.3842782 0.4068962 0.5566918 0.5967728 0.6206061 0.6247646 +#> [746,] 0.57869430 0.6643098 0.6963693 0.7499769 0.7532504 0.7787472 0.7840776 +#> [747,] 0.34583666 0.3765487 0.6485353 0.6536423 0.6600611 0.6950010 0.7608483 +#> [748,] 0.55392527 0.6011789 0.6107278 0.6565608 0.6666422 0.7067511 0.7295667 +#> [749,] 1.28096940 1.3035623 1.4027108 1.4503325 1.5398309 1.6228690 1.6251855 +#> [750,] 0.75682393 0.9759578 1.0086455 1.0171610 1.0781083 1.0961189 1.1095360 +#> [751,] 0.47282196 0.5538007 0.6370667 0.7604574 0.8299728 0.8469233 0.8905465 +#> [752,] 0.50321505 0.6012756 0.6440738 0.6763688 0.6811249 0.6878932 0.7024877 +#> [753,] 1.12512471 1.5002459 1.6186691 1.6746142 1.6971242 1.7244525 1.7529364 +#> [754,] 0.19126137 0.2539434 0.3530391 0.4211737 0.4610017 0.4735912 0.4812976 +#> [755,] 0.74738137 0.8490869 0.8598790 0.8819419 0.8834692 0.8956083 0.9020044 +#> [756,] 0.40863482 0.4201660 0.4875000 0.4977342 0.5139286 0.5250651 0.5548998 +#> [757,] 0.59144499 0.6451118 0.6556036 0.6960641 0.7246572 0.7526997 0.7562335 +#> [758,] 0.29034567 0.3153831 0.4148944 0.4420203 0.5036863 0.5100027 0.5967067 +#> [759,] 0.24282195 0.5158832 0.5926552 0.6115119 0.6196056 0.6231284 0.6441286 +#> [760,] 0.34646260 0.4576543 0.5116226 0.5403305 0.5449393 0.5790349 0.5962150 +#> [761,] 0.28637481 0.5161356 0.6072845 0.6403226 0.6589856 0.6618140 0.6619228 +#> [762,] 0.22663217 0.7094030 0.7527483 0.8047230 0.8173328 0.8380186 0.8391967 +#> [763,] 0.34765312 0.4212806 0.4268198 0.4444787 0.6220212 0.6292556 0.6796196 +#> [764,] 0.25529604 0.4244684 0.4925101 0.5098325 0.5315147 0.5866931 0.6092869 +#> [765,] 0.63149423 0.6344471 0.8072431 0.8556852 0.9310300 0.9539625 0.9673221 +#> [766,] 0.41353422 0.4800824 0.4997822 0.5004472 0.5308093 0.5517345 0.5817011 +#> [767,] 0.17192229 0.3120374 0.3274886 0.3970135 0.4186993 0.4204261 0.4487641 +#> [768,] 0.49960642 0.5680358 0.5989883 0.6187675 0.6281894 0.6901499 0.7043782 +#> [769,] 0.53075473 0.5948165 0.7371089 0.7845360 0.8615314 0.8746930 0.9299736 +#> [770,] 1.08831280 1.1290978 1.2708480 1.2778259 1.2814328 1.3589799 1.4092152 +#> [771,] 0.84050232 1.2081018 1.3537660 1.3676314 1.3998444 1.4217575 1.4250399 +#> [772,] 0.49874972 0.5116226 0.5348619 0.5606675 0.6284367 0.6302704 0.6997016 +#> [773,] 1.01823009 1.1625109 1.1724161 1.2807257 1.3346802 1.3372022 1.3953495 +#> [774,] 0.42107740 0.5228137 0.5782739 0.6344471 0.6408634 0.6552209 0.7278126 +#> [775,] 1.44425777 1.5024005 1.5148908 1.5275841 1.5392507 1.5788080 1.6184525 +#> [776,] 0.51100732 0.6797352 0.6979795 0.7239671 0.7271320 0.7607239 0.7625511 +#> [777,] 0.27986585 0.6141458 0.6186548 0.6930265 0.6978360 0.7064858 0.7565171 +#> [778,] 0.61968631 0.6495568 0.6692727 0.6764916 0.6782440 0.7079180 0.7088686 +#> [779,] 0.45979711 0.4943986 0.5124604 0.5573640 0.5791735 0.6387119 0.6584954 +#> [780,] 0.33118191 0.3463831 0.4397189 0.4891228 0.4944305 0.5297014 0.5306987 +#> [781,] 0.31406018 0.3746252 0.4828882 0.5987444 0.6003784 0.6580683 0.6789879 +#> [782,] 0.26858368 0.3672683 0.4606300 0.4823437 0.4895812 0.5083644 0.5182948 +#> [783,] 0.80509624 0.9642723 0.9748641 0.9932183 1.0175262 1.0440972 1.0506605 +#> [784,] 0.34632790 0.5812384 0.6068026 0.6817921 0.7782817 0.7818776 0.8135992 +#> [785,] 0.95031389 1.1390276 1.1488276 1.1717946 1.1885282 1.2093484 1.2547582 +#> [786,] 0.57850160 0.7428819 0.7626059 0.7664228 0.7746565 0.8124345 0.8786955 +#> [787,] 0.23817426 0.3740172 0.4150215 0.5086507 0.5239781 0.5252724 0.5643148 +#> [788,] 0.80083402 0.8688661 0.8952005 0.9025712 1.0016799 1.0082361 1.0213946 +#> [789,] 0.47662769 0.5948893 0.6012587 0.7134555 0.7582208 0.8186225 0.8845991 +#> [790,] 0.38039938 0.4066714 0.4787794 0.5510211 0.6025951 0.6032960 0.6046658 +#> [791,] 0.52281365 0.6314942 0.6905425 0.7001828 0.7370280 0.7496531 0.8518589 +#> [792,] 0.87344003 0.8786955 0.9130445 0.9356086 1.0061377 1.0099780 1.0406190 +#> [793,] 0.39295645 0.6883166 0.7067511 0.7409552 0.7463260 0.9268664 0.9380089 +#> [794,] 0.45337305 0.5127075 0.5475791 0.5705612 0.5791735 0.5867750 0.6463524 +#> [795,] 0.38074638 0.5428344 0.7870609 0.8122835 0.8285331 0.9000570 1.0503715 +#> [796,] 0.65741762 0.6595386 0.6786018 0.6805422 0.6911190 0.6945954 0.6964581 +#> [797,] 0.42601353 0.6297133 0.8849188 0.9089493 0.9556250 0.9724911 1.0142959 +#> [798,] 0.07900148 0.2134423 0.3366718 0.3990870 0.5151064 0.5494136 0.6121996 +#> [799,] 0.75505674 0.7705614 0.7789857 0.8690916 0.9599744 0.9880098 1.0048817 +#> [800,] 0.29071253 0.5367478 0.5514176 0.5993767 0.6653796 0.6892158 0.7155752 +#> [801,] 0.45390851 0.4654537 0.4702363 0.4998753 0.5088901 0.5587419 0.5677993 +#> [802,] 0.66430980 0.7860686 0.7917791 0.8035448 0.8512671 0.8943658 0.9688910 +#> [803,] 0.43339983 0.4889723 0.5015533 0.5121367 0.5496062 0.5623404 0.6369267 +#> [804,] 0.22663217 0.6737981 0.6864307 0.7741433 0.7760244 0.7914695 0.8054832 +#> [805,] 0.34928645 0.3683386 0.3883146 0.5029217 0.5178635 0.5493735 0.5851822 +#> [806,] 0.96860515 1.5474225 1.6916781 1.7508751 1.8336938 1.8432269 1.8601614 +#> [807,] 0.81823719 0.8905984 0.9877874 1.0080292 1.0574307 1.0694985 1.0751693 +#> [808,] 0.51274896 0.5794487 0.5989883 0.6016442 0.6227651 0.6675875 0.7007486 +#> [809,] 0.41927666 0.4758018 0.5229936 0.6353158 0.6485353 0.7109808 0.7184761 +#> [810,] 0.52007377 0.5829748 0.7459431 0.7847037 0.7953610 0.8120088 0.8905465 +#> [811,] 0.52670130 0.5495123 0.5750012 0.5960382 0.6278278 0.6401071 0.6482104 +#> [812,] 0.30912486 0.3221767 0.3445838 0.3679166 0.3921957 0.4167995 0.4364860 +#> [813,] 0.26707323 0.3675232 0.3676174 0.4747191 0.5430170 0.5577346 0.5612849 +#> [814,] 0.66246718 0.6764916 0.6870574 0.7056267 0.7653108 0.7741433 0.7975241 +#> [815,] 0.46401044 0.6718409 0.6750776 0.6944161 0.8141279 0.8415446 0.8674535 +#> [816,] 0.40585333 0.5158832 0.5310501 0.6237231 0.6283424 0.6308819 0.6735509 +#> [817,] 0.51787090 0.7322052 0.7942737 0.8858275 0.8992017 0.9529758 0.9777157 +#> [818,] 0.44878229 0.4693085 0.5348619 0.5403305 0.5507061 0.5780262 0.5890797 +#> [819,] 0.40312556 0.4724889 0.5146832 0.6540016 0.6699648 0.7296882 0.7548694 +#> [820,] 0.52243421 0.9528990 0.9566207 0.9690615 0.9959560 1.0041419 1.0048973 +#> [821,] 0.61876751 0.6220356 0.6670638 0.7302815 0.7320587 0.7336890 0.7419142 +#> [822,] 0.45033714 0.5105971 0.6909721 0.7200852 0.7301892 0.7582208 0.7617684 +#> [823,] 0.27752292 0.3530391 0.3569595 0.4105468 0.4740533 0.5041885 0.5822294 +#> [824,] 0.92369646 0.9935013 1.0084146 1.0603844 1.1688281 1.2238993 1.2536617 +#> [825,] 0.93623884 0.9670659 0.9716891 1.0213059 1.0791829 1.0908452 1.2003763 +#> [826,] 0.51547045 0.5428344 0.5513518 0.7418054 0.7596351 0.8207332 0.8464236 +#> [827,] 0.36357445 0.3906010 0.4358474 0.4812976 0.4851370 0.5204109 0.5300283 +#> [828,] 0.21918209 0.3515583 0.4460121 0.4735912 0.5015861 0.5055582 0.5928341 +#> [829,] 0.47248888 0.7390052 0.7862303 0.7934345 0.8240780 0.8774079 0.8816378 +#> [830,] 0.61443490 0.7276547 0.7430517 0.7568780 0.8004466 0.8797075 0.8800598 +#> [831,] 0.33721916 0.4333998 0.4494589 0.5627313 0.5631145 0.6392648 0.6425977 +#> [832,] 0.35584388 0.3608171 0.4589638 0.4647662 0.5355108 0.5409202 0.5661899 +#> [833,] 0.31425204 0.3975284 0.5110073 0.6375903 0.6674449 0.7254397 0.7415100 +#> [834,] 0.43093765 0.4821556 0.6053708 0.7107931 0.8125750 0.9832103 1.0043076 +#> [835,] 0.84050232 1.4143479 1.5009154 1.6691936 1.6755054 1.6854697 1.8534532 +#> [836,] 0.46890975 0.4922400 0.5605946 0.5643164 0.5666706 0.6025482 0.6248032 +#> [837,] 0.29686347 0.4882768 0.6842423 0.7095820 0.7367931 0.8004466 0.8982905 +#> [838,] 0.28297282 0.4104397 0.5606675 0.5922417 0.5930066 0.6435596 0.6506995 +#> [839,] 0.66046375 0.7587216 0.7705614 0.7822333 0.8118788 0.9794113 0.9822419 +#> [840,] 0.20603309 0.3096341 0.3153831 0.4322466 0.4852165 0.4985219 0.5298338 +#> [841,] 0.51426500 0.5160277 0.5448443 0.6115975 0.6209217 0.6602422 0.6673354 +#> [842,] 0.87682996 0.8871343 0.9657971 0.9972174 1.0056713 1.0089594 1.0226326 +#> [843,] 0.53344981 0.5666706 0.5705612 0.5804025 0.6160059 0.6285987 0.6888110 +#> [844,] 0.63329928 0.7035094 0.8909473 0.9090553 0.9450609 1.0034860 1.0322017 +#> [845,] 0.59733251 0.9069941 1.0315972 1.0651302 1.1004546 1.1819033 1.2104695 +#> [846,] 0.35584388 0.4053927 0.4569943 0.4801926 0.5726849 0.5907237 0.6562850 +#> [847,] 0.44945895 0.4669321 0.4710597 0.5335359 0.5496062 0.5714739 0.5741004 +#> [848,] 0.41227811 0.4813064 0.5127075 0.5322349 0.5396463 0.5524752 0.5573640 +#> [849,] 0.60207570 0.6332993 0.7541340 0.7612766 0.8339584 0.8695335 0.9403258 +#> [850,] 0.56667337 0.5690583 0.6806436 0.6841255 0.7057482 0.7196784 0.7266933 +#> [851,] 0.60644755 0.6068605 0.6088823 0.6313599 0.6381212 0.7043648 0.8091027 +#> [852,] 0.46309918 0.5503828 0.5671789 0.6737981 0.6893140 0.6897462 0.7094030 +#> [853,] 0.54477441 0.7328934 0.7748304 0.7982394 0.8948322 0.9052024 0.9099494 +#> [854,] 0.42219845 0.5288742 0.5313286 0.5314622 0.5489092 0.6208746 0.6326844 +#> [855,] 0.36655786 0.4248826 0.5211668 0.5812758 0.6696843 0.6909388 0.7411261 +#> [856,] 0.66906906 0.6929659 0.7029221 0.7363367 0.8576981 0.9298216 0.9509733 +#> [857,] 0.10961117 0.5561111 0.5914676 0.6253939 0.6720140 0.6791187 0.6825467 +#> [858,] 0.34458382 0.3564569 0.4068168 0.4490281 0.4817999 0.5008421 0.5009519 +#> [859,] 0.53900013 0.7596351 0.7809893 0.7977962 0.8110831 0.8285331 0.8717266 +#> [860,] 0.26056421 0.3320452 0.3675232 0.4011632 0.4244684 0.5245809 0.5277923 +#> [861,] 0.53863757 0.5741677 0.7806849 0.9257462 0.9832158 1.0343402 1.0652415 +#> [862,] 0.48019263 0.5409202 0.6188566 0.6728008 0.6845853 0.6976790 0.6983050 +#> [863,] 0.16898732 0.4179616 0.4874333 0.5182454 0.5581737 0.5727718 0.5833047 +#> [864,] 0.49674503 0.8623701 0.9361598 1.0285802 1.0919945 1.1454716 1.1467348 +#> [865,] 0.74356670 0.8506358 0.8988180 0.9653260 0.9658914 1.0456813 1.1495489 +#> [866,] 0.19274415 0.5890797 0.6656190 0.6929399 0.7092455 0.7130241 0.7300549 +#> [867,] 0.82780332 0.8682733 0.8819994 0.9382707 0.9624720 1.0012975 1.0401675 +#> [868,] 0.80504130 1.0132570 1.1390276 1.2647887 1.2893205 1.3519468 1.4066961 +#> [869,] 0.38993068 0.4592070 0.4627760 0.4812428 0.4883416 0.5284553 0.5554485 +#> [870,] 0.41300116 0.4726289 0.5145285 0.5831077 0.6027282 0.6196082 0.6260452 +#> [871,] 0.35872808 0.5216523 0.5218097 0.5943798 0.8396827 0.8405865 0.8480663 +#> [872,] 0.66061744 0.9751220 0.9887768 1.0250606 1.1462059 1.1488878 1.1533324 +#> [873,] 0.55047599 0.9306545 0.9634239 1.0486355 1.0540035 1.1314857 1.1924546 +#> [874,] 0.74044467 0.8490869 0.8839707 1.0353342 1.0634033 1.0914634 1.1081549 +#> [875,] 0.36909222 0.3824672 0.4248826 0.4752431 0.5622268 0.6527913 0.7932064 +#> [876,] 0.19126137 0.3194772 0.3331275 0.3635745 0.3842054 0.4105468 0.4783768 +#> [877,] 0.52330958 0.5452436 0.6420042 0.6720140 0.7052971 0.7300549 0.7423220 +#> [878,] 0.35754567 0.3647637 0.3856706 0.4204261 0.4580945 0.4684129 0.4723983 +#> [879,] 0.48698634 0.5103997 0.5561728 0.5943264 0.6441364 0.6522988 0.6841255 +#> [880,] 0.38075836 0.4420203 0.4485579 0.4658402 0.5140316 0.5446332 0.5554485 +#> [881,] 0.17192229 0.2007145 0.2783166 0.3575457 0.4577139 0.4804332 0.4840932 +#> [882,] 0.55742381 0.7035094 0.8815920 0.9407532 0.9665835 1.0070158 1.0256505 +#> [883,] 1.06671323 1.1098776 1.1939526 1.2708891 1.3019662 1.3272026 1.3346704 +#> [884,] 0.59122759 0.5924289 0.6878932 0.7469794 0.7701128 0.7939503 0.9124692 +#> [885,] 0.26361352 0.4103122 0.5966234 0.6251264 0.6412155 0.6638039 0.6959499 +#> [886,] 0.38142744 0.4702799 0.6183745 0.6692462 0.7132491 0.7992174 0.8603296 +#> [887,] 0.41103442 0.4721603 0.4943986 0.5488501 0.5919320 0.6048693 0.6696642 +#> [888,] 0.48463135 0.5583400 0.6188566 0.7207993 0.7371089 0.7768013 0.7927239 +#> [889,] 0.20402766 0.2125101 0.5652156 0.6438647 0.6656291 0.6934208 0.7234980 +#> [890,] 0.40644938 0.4742585 0.5110722 0.5993060 0.6140650 0.6602692 0.6758489 +#> [891,] 0.31009228 0.5020218 0.5300283 0.5325700 0.5659865 0.6462933 0.6560148 +#> [892,] 0.63783218 0.7207886 0.7319139 1.1488815 1.2363910 1.2394352 1.2466568 +#> [893,] 0.51562884 0.6047001 0.6736678 0.7180052 0.8424851 0.8446939 0.8627030 +#> [894,] 1.16058388 1.2652724 1.3307926 1.3720424 1.4087473 1.4230225 1.4442578 +#> [895,] 0.76904320 0.8050931 1.0306609 1.0424817 1.0576121 1.1172876 1.1341513 +#> [896,] 1.03405675 1.0807985 1.1629530 1.1726818 1.2371444 1.2431209 1.2989847 +#> [897,] 0.26056421 0.2953271 0.3596759 0.3676174 0.4370006 0.4674089 0.4867114 +#> [898,] 0.66295655 1.0524327 1.1454716 1.1938812 1.2348503 1.2659794 1.3541057 +#> [899,] 0.26714363 0.4168866 0.4398471 0.4827427 0.4993616 0.5299611 0.5303869 +#> [900,] 0.99383792 1.0207571 1.0337819 1.0365731 1.0417322 1.0553590 1.0669284 +#> [901,] 0.41869934 0.4499811 0.4804332 0.4892405 0.5419171 0.5443461 0.5630225 +#> [902,] 0.41001145 0.4336526 0.4356013 0.4651105 0.5229936 0.5439551 0.5553379 +#> [903,] 0.44601213 0.4739712 0.4898238 0.5262443 0.5330906 0.5404534 0.5571109 +#> [904,] 0.38729811 0.4356013 0.4758018 0.5605504 0.5629239 0.6072090 0.6208746 +#> [905,] 0.43376457 0.6284165 0.6473181 0.6926234 0.7473814 0.7664153 0.8196974 +#> [906,] 0.36889156 0.4181342 0.4353989 0.5443613 0.6925852 0.7820596 0.8175952 +#> [907,] 0.35553709 0.3639759 0.4068168 0.4192504 0.4518190 0.4547488 0.4583266 +#> [908,] 0.62373689 0.7183489 0.8926911 0.9374606 0.9533518 0.9709443 0.9932183 +#> [909,] 0.47303215 0.5924289 0.6284165 0.6345006 0.6550931 0.6738575 0.6763688 +#> [910,] 0.37272716 0.7574815 0.8620530 0.9235493 1.0293627 1.0346818 1.0506003 +#> [911,] 0.29797572 0.4412957 0.5142650 0.5494740 0.6083196 0.6670661 0.6900840 +#> [912,] 0.31680663 0.3592144 0.3740172 0.4725692 0.5161527 0.5982736 0.6788071 +#> [913,] 0.40154240 0.5960382 0.6015735 0.6039505 0.6143347 0.6173411 0.6399215 +#> [914,] 0.25529604 0.4657359 0.5176139 0.5277923 0.5430170 0.5472789 0.5484479 +#> [915,] 0.52267751 0.5274417 0.7076093 0.7294261 0.7614309 0.7638306 0.7763127 +#> [916,] 0.60183197 0.6690691 0.7574397 0.7667885 0.7775509 0.8601285 0.9643448 +#> [917,] 0.27254531 0.3342411 0.4363043 0.4371461 0.4383080 0.4857113 0.5166758 +#> [918,] 0.27820372 0.3134153 0.4121055 0.4625246 0.5752935 0.7428819 0.7835030 +#> [919,] 0.22730905 0.3454661 0.4692602 0.5782066 0.7374615 0.7477163 0.7979180 +#> [920,] 0.38219942 0.5233096 0.6548786 0.6736678 0.6747795 0.7224611 0.7872064 +#> [921,] 0.35397953 0.4057245 0.4850417 0.5075179 0.5084299 0.5268116 0.5634698 +#> [922,] 0.49108733 0.6237369 0.6983050 0.7207993 0.7317220 0.7329495 0.8043957 +#> [923,] 0.57316599 0.7018844 0.7698621 0.9067110 0.9072002 0.9167711 0.9358035 +#> [924,] 0.27779408 0.5067058 0.5474909 0.5619969 0.5990116 0.6007823 0.6651803 +#> [925,] 0.28951401 0.3507199 0.6028122 0.6597689 0.6932175 0.7135412 0.7213954 +#> [926,] 0.65566614 0.7996484 0.8013142 0.8076352 0.8094535 0.8484571 0.8515109 +#> [927,] 0.27562530 0.5907578 0.6047001 0.6923461 0.6948035 0.7315810 0.7754890 +#> [928,] 0.42319471 0.4619486 0.5357388 0.6964726 0.7374605 0.7408393 0.7504980 +#> [929,] 0.38240555 0.6968206 0.7107931 0.7555991 0.7797592 1.0041571 1.0511232 +#> [930,] 0.48905962 0.5091761 0.6165220 0.6183745 0.6802482 0.7362994 0.7480086 +#> [931,] 0.46604194 0.5057079 0.5093474 0.6374305 0.6515160 0.6895867 0.7152979 +#> [932,] 0.36022339 0.3990870 0.4527244 0.6065168 0.6401071 0.6408634 0.6579255 +#> [933,] 0.27977522 0.3955108 0.4488479 0.4904569 0.4931540 0.5990700 0.6173134 +#> [934,] 0.38153048 0.4648426 0.5635859 0.6371110 0.6375488 0.6404007 0.6428293 +#> [935,] 0.44190042 0.4850204 0.5412125 0.5499718 0.5658402 0.5743258 0.5858808 +#> [936,] 0.47357398 0.5110472 0.5623404 0.6583454 0.6729948 0.7008084 0.7540306 +#> [937,] 0.61987271 0.7960216 0.9158306 1.0644285 1.1518480 1.1549978 1.1687427 +#> [938,] 0.29207978 0.5936938 0.5990116 0.6335023 0.6814437 0.6869994 0.7562474 +#> [939,] 0.10961117 0.5477442 0.5794918 0.6271646 0.6420042 0.6697815 0.6785323 +#> [940,] 0.37616048 0.5179263 0.5733939 0.6280572 0.6450097 0.7735499 0.7956606 +#> [941,] 0.59060294 0.5975474 0.6558207 0.6976132 0.7076012 0.7717883 0.8033412 +#> [942,] 0.21344226 0.2370228 0.3082354 0.3653168 0.4527244 0.5664058 0.6008899 +#> [943,] 0.37666715 0.4403305 0.4726289 0.4837092 0.5482435 0.5557389 0.5831863 +#> [944,] 0.26497430 0.6674207 0.7481556 0.7606691 0.9812453 1.0048817 1.0092092 +#> [945,] 0.38219942 0.5879447 0.6173491 0.6611236 0.6726969 0.6757738 0.7159151 +#> [946,] 0.49533234 0.6346750 0.6884615 0.7041224 0.7872879 0.8081023 0.8233290 +#> [947,] 0.62918180 0.7968490 0.8299728 0.8488415 0.9636785 0.9671748 0.9727649 +#> [948,] 0.19062462 0.3290349 0.3639759 0.4651105 0.5008421 0.5286499 0.5410208 +#> [949,] 0.47084221 0.6654162 0.7048069 0.7374605 0.7551687 0.8144466 0.8210441 +#> [950,] 0.40585333 0.6090162 0.6438393 0.6645667 0.6893189 0.7167928 0.7535016 +#> [951,] 1.71535420 1.7710182 1.8012323 1.9644858 2.0306185 2.1379828 2.1948562 +#> [952,] 0.94719990 1.1703586 1.2716053 1.2792180 1.2884615 1.3356232 1.3512644 +#> [953,] 0.24524756 0.3722042 0.4048998 0.4403305 0.4920482 0.5248431 0.5715971 +#> [954,] 0.59171612 0.6484736 0.6593648 0.7002319 0.7342629 0.7738593 0.7823798 +#> [955,] 0.36726831 0.3876206 0.3916555 0.4122333 0.4146105 0.4408832 0.4436452 +#> [956,] 0.28197372 0.2951427 0.2970063 0.3015421 0.3331275 0.4610017 0.4851370 +#> [957,] 0.75137243 0.7668919 0.8383914 0.8551747 0.8874871 0.8942799 0.9154840 +#> [958,] 0.43970503 0.4590166 0.4673949 0.4807027 0.4878093 0.4942333 0.5067848 +#> [959,] 0.21563769 0.4584524 0.4785825 0.4826764 0.4827427 0.5608112 0.5656248 +#> [960,] 0.34159301 0.3900757 0.4015424 0.4751257 0.5479820 0.6047128 0.6139697 +#> [961,] 1.64730746 1.7606569 1.8298994 1.8558945 1.8647541 1.9069203 2.0772323 +#> [962,] 0.46116246 0.6403444 0.6522988 0.7392183 0.7429670 0.7526534 0.8085424 +#> [963,] 0.20071453 0.3274886 0.3432095 0.3856706 0.4592138 0.4892405 0.5513524 +#> [964,] 0.60683629 0.6517776 0.8600679 0.9081630 0.9218340 0.9547859 0.9884917 +#> [965,] 0.53546582 0.5653624 0.5850334 0.5880933 0.5956964 0.6276467 0.6442174 +#> [966,] 0.21651187 0.2317734 0.3859907 0.4146105 0.4910142 0.4920482 0.5625149 +#> [967,] 0.41103442 0.4597971 0.5235230 0.5307719 0.5510616 0.6118213 0.6688656 +#> [968,] 0.39860107 0.4380599 0.4533006 0.5529875 0.5604020 0.5783303 0.5932555 +#> [969,] 0.39210704 0.4336153 0.5295156 0.6056370 0.6156885 0.6515893 0.6693918 +#> [970,] 0.38757011 0.4032872 0.4681296 0.5233900 0.5238755 0.5789343 0.5793320 +#> [971,] 0.21918209 0.2444457 0.4211737 0.4739712 0.4783768 0.4932683 0.5504818 +#> [972,] 0.61758497 0.6874442 0.8036485 0.8796937 0.8933293 0.9167823 0.9520005 +#> [973,] 0.37950826 0.4977742 0.5460506 0.5654979 0.6470833 0.7371090 0.7557208 +#> [974,] 0.13267935 0.2428452 0.3835312 0.4100114 0.4443748 0.4630358 0.4857113 +#> [975,] 0.29137886 0.3808558 0.4494418 0.5510616 0.5705460 0.5788722 0.5844677 +#> [976,] 0.57887022 0.8547462 0.8845617 0.8948754 0.9142141 0.9572253 1.0449902 +#> [977,] 0.53467357 0.6050027 0.6217932 0.6930354 0.8113778 0.8167004 0.8336929 +#> [978,] 0.24855464 0.3429031 0.4231851 0.4459950 0.5169359 0.5208903 0.5263868 +#> [979,] 0.38428962 0.4476540 0.4706032 0.4717548 0.4817677 0.5750238 0.5753789 +#> [980,] 0.38831457 0.4681296 0.5244214 0.5359329 0.5656454 0.6005207 0.6054502 +#> [981,] 0.32642149 0.4353507 0.4688051 0.5189561 0.5746850 0.5750395 0.5897147 +#> [982,] 0.39295645 0.6280775 0.7546048 0.8182106 0.8942453 0.9120783 0.9266390 +#> [983,] 0.51525033 0.6593257 0.6665253 0.6819764 0.6948617 0.7044144 0.7242251 +#> [984,] 0.93081475 0.9929918 1.0686850 1.0714018 1.0977995 1.1447031 1.1805190 +#> [985,] 0.37853720 0.4998753 0.6013005 0.6443655 0.6587918 0.6663823 0.6978056 +#> [986,] 0.27237342 0.4337646 0.5060788 0.6272943 0.6577665 0.6708163 0.6836464 +#> [987,] 1.38856912 1.6361155 1.7030627 1.7118549 1.7578300 1.7924477 1.8974918 +#> [988,] 0.66347900 0.7011247 0.7657464 0.7867163 0.8439553 0.8543112 0.8612993 +#> [989,] 0.25283902 0.4725692 0.6412155 0.6530337 0.6666938 0.6822739 0.6852734 +#> [990,] 0.34537301 0.4943825 0.5348730 0.6260920 0.6302965 0.6686640 0.6707097 +#> [991,] 0.19274415 0.6253939 0.6271646 0.6384520 0.7050826 0.7361423 0.7423220 +#> [992,] 0.68535263 0.7449855 0.8332449 0.8448919 0.8546296 0.8701522 0.9151551 +#> [993,] 0.29573626 0.5085622 0.5222549 0.5714498 0.5996214 0.6150778 0.6439832 +#> [994,] 0.34324427 0.3871573 0.4821855 0.4984314 0.5288742 0.5620869 0.6308612 +#> [995,] 0.38428962 0.5359329 0.5577090 0.5581737 0.5796832 0.6007823 0.6205757 +#> [996,] 0.24855464 0.3755687 0.4384343 0.4606300 0.4865361 0.5041361 0.5170806 +#> [997,] 0.71485709 1.0349787 1.1488815 1.1793684 1.1988625 1.2671123 1.2780407 +#> [998,] 0.47264023 0.5073235 0.5280320 0.5308094 0.5817286 0.6269897 0.6459467 +#> [999,] 0.34558386 0.4790775 0.5313286 0.5317383 0.5337046 0.5827102 0.5840487 +#> [1000,] 0.34397845 0.3794034 0.4114427 0.4779854 0.5226127 0.5297184 0.5677993 #> [,8] [,9] [,10] -#> [1,] 1.0948143 1.1253654 1.1294703 -#> [2,] 0.5525923 0.5752191 0.5948832 -#> [3,] 1.0433555 1.0634161 1.0639414 -#> [4,] 0.6020509 0.6321894 0.6900302 -#> [5,] 0.5374756 0.5838935 0.6076137 -#> [6,] 1.9607194 1.9707421 1.9894195 -#> [7,] 0.9771859 1.0105006 1.0358824 -#> [8,] 0.7905344 0.7948463 0.8065179 -#> [9,] 0.7325301 0.7529760 0.7644297 -#> [10,] 1.8648328 1.8954785 1.9155196 -#> [11,] 0.7809546 0.8205875 0.8244164 -#> [12,] 0.5654316 0.5838902 0.5863865 -#> [13,] 1.9906122 2.0133325 2.0347324 -#> [14,] 0.5282038 0.5294087 0.5580313 -#> [15,] 0.6695476 0.6720961 0.7860751 -#> [16,] 0.6848587 0.6942770 0.6993759 -#> [17,] 1.2454930 1.2909212 1.3323128 -#> [18,] 0.7167810 0.7366055 0.7377562 -#> [19,] 0.6401902 0.6607465 0.6644822 -#> [20,] 0.5855031 0.5910505 0.6080686 -#> [21,] 0.5373405 0.5618657 0.5968875 -#> [22,] 1.0337314 1.1039228 1.1077311 -#> [23,] 0.4721043 0.4789884 0.4811924 -#> [24,] 0.7415321 0.7484844 0.7656143 -#> [25,] 0.8201046 0.8592889 0.8610294 -#> [26,] 0.8449646 0.8456569 0.8807989 -#> [27,] 1.1437594 1.1803415 1.2018161 -#> [28,] 0.7660779 0.7776805 0.8061198 -#> [29,] 0.9298050 0.9307238 0.9379767 -#> [30,] 0.7981114 0.8339729 0.8374160 -#> [31,] 0.9943953 1.0235233 1.0283907 -#> [32,] 0.7459984 0.7858414 0.7963569 -#> [33,] 0.8277493 0.8512057 0.8969156 -#> [34,] 0.9271726 0.9673539 0.9850058 -#> [35,] 0.6410389 0.6600794 0.6959216 -#> [36,] 1.6054625 1.6865838 1.6911640 -#> [37,] 0.5849180 0.6452064 0.6531977 -#> [38,] 0.5227291 0.5905755 0.6106738 -#> [39,] 1.5553362 1.6244928 1.6247010 -#> [40,] 0.5028352 0.5363079 0.5814556 -#> [41,] 0.9051968 0.9132779 0.9296012 -#> [42,] 0.6478459 0.6729798 0.6803337 -#> [43,] 0.4674898 0.4766956 0.4847473 -#> [44,] 0.7856933 0.8096945 0.8112277 -#> [45,] 0.8419828 0.8642496 0.9374454 -#> [46,] 0.7466119 0.7722180 0.7869417 -#> [47,] 1.6690312 1.6896383 1.6958277 -#> [48,] 0.4829012 0.5538597 0.5852644 -#> [49,] 0.5462467 0.5481299 0.5585343 -#> [50,] 0.8244164 0.8805266 0.9358046 -#> [51,] 0.6613715 0.6811357 0.7124117 -#> [52,] 0.7191833 0.7194659 0.7649574 -#> [53,] 1.2564305 1.3031879 1.3709338 -#> [54,] 0.7095881 0.7101002 0.7457393 -#> [55,] 0.8632450 0.9452734 0.9582362 -#> [56,] 1.2330285 1.2860053 1.2878207 -#> [57,] 0.6133775 0.6570875 0.6647266 -#> [58,] 0.7066226 0.7161633 0.7200286 -#> [59,] 1.1455786 1.1821034 1.2087668 -#> [60,] 0.7898192 0.7939243 0.8015919 -#> [61,] 0.8167129 0.8387293 0.8398155 -#> [62,] 0.6275538 0.6390093 0.6403610 -#> [63,] 0.6823798 0.6855014 0.6864298 -#> [64,] 0.6007936 0.6157251 0.6158978 -#> [65,] 1.0931949 1.1069983 1.1255987 -#> [66,] 1.1747662 1.1762794 1.1793043 -#> [67,] 0.7198973 0.8448278 0.8528679 -#> [68,] 0.7744335 0.8008949 0.8113809 -#> [69,] 0.8337477 0.8546016 0.8666153 -#> [70,] 0.7035311 0.7185677 0.7657909 -#> [71,] 0.6959320 0.7141490 0.7330386 -#> [72,] 0.8266657 0.8555373 0.8824980 -#> [73,] 1.0689941 1.0765964 1.0970276 -#> [74,] 0.6422858 0.6753351 0.6794117 -#> [75,] 0.9881594 1.0049256 1.0092698 -#> [76,] 0.7840795 0.8029412 0.8446133 -#> [77,] 0.8339729 0.8852561 0.8885824 -#> [78,] 0.6776343 0.6783473 0.6927912 -#> [79,] 0.5990214 0.6148437 0.6178335 -#> [80,] 0.6261561 0.6705603 0.6917049 -#> [81,] 0.8183042 0.8248413 0.8452508 -#> [82,] 1.5711966 1.5921858 1.6799638 -#> [83,] 0.5473466 0.5577556 0.5585343 -#> [84,] 1.4575733 1.4748398 1.5450134 -#> [85,] 0.9101200 0.9215540 0.9400282 -#> [86,] 0.6895740 0.6964980 0.8398155 -#> [87,] 0.5315435 0.5926003 0.5940546 -#> [88,] 0.9466164 0.9505368 0.9819191 -#> [89,] 0.7085806 0.7539298 0.7576612 -#> [90,] 0.4639866 0.4886340 0.4978754 -#> [91,] 1.0661671 1.0703207 1.0850179 -#> [92,] 1.0350864 1.1246410 1.1410353 -#> [93,] 0.6273846 0.6396846 0.6729936 -#> [94,] 0.6621461 0.6730841 0.6755372 -#> [95,] 0.5589076 0.5718031 0.5726976 -#> [96,] 1.0967401 1.1063678 1.1619855 -#> [97,] 0.6227062 0.6631667 0.7028310 -#> [98,] 1.4545619 1.4804709 1.4940935 -#> [99,] 1.2369132 1.2409701 1.2629280 -#> [100,] 0.6414267 0.6420026 0.6812624 -#> [101,] 0.6256541 0.6270762 0.6340315 -#> [102,] 0.8030102 0.8399439 0.8470966 -#> [103,] 1.3906688 1.4321209 1.4531103 -#> [104,] 1.4258069 1.4359298 1.4368440 -#> [105,] 0.9993196 1.0286461 1.0327843 -#> [106,] 0.5507339 0.5758143 0.6218453 -#> [107,] 0.9608068 0.9634866 0.9682306 -#> [108,] 1.3341929 1.3603603 1.4866068 -#> [109,] 1.3107763 1.3393935 1.3667934 -#> [110,] 0.8175726 0.8257304 0.8447845 -#> [111,] 0.4845243 0.5029748 0.5270942 -#> [112,] 0.4837839 0.5098671 0.5127951 -#> [113,] 0.5567038 0.5651490 0.5693620 -#> [114,] 0.8781318 0.9038817 0.9218376 -#> [115,] 0.7588572 0.7636821 0.7788853 -#> [116,] 0.8845181 0.9186031 0.9341500 -#> [117,] 0.6133688 0.6480952 0.6680145 -#> [118,] 0.8748226 0.8857262 0.8882337 -#> [119,] 0.9168692 0.9190963 0.9461592 -#> [120,] 0.5529531 0.5568170 0.5956290 -#> [121,] 0.5807087 0.5853281 0.6225125 -#> [122,] 0.6371422 0.6550249 0.6594435 -#> [123,] 0.7378963 0.8014425 0.8158279 -#> [124,] 0.9562657 0.9765916 0.9771331 -#> [125,] 0.7866932 0.7870167 0.8082479 -#> [126,] 0.8753202 0.9776262 0.9876411 -#> [127,] 0.5155695 0.5517497 0.5578328 -#> [128,] 1.1372116 1.2638671 1.2847125 -#> [129,] 0.8325244 0.8735306 0.8902842 -#> [130,] 0.6917049 0.6949101 0.7003802 -#> [131,] 1.2206230 1.2382200 1.2576681 -#> [132,] 0.6918563 0.6972482 0.6981607 -#> [133,] 0.7293444 0.7451747 0.7648917 -#> [134,] 1.0767381 1.1098807 1.1120624 -#> [135,] 0.8793499 0.9069460 0.9134570 -#> [136,] 0.6395725 0.6667419 0.6688439 -#> [137,] 0.7184914 0.7327227 0.7652532 -#> [138,] 0.7553037 0.8062697 0.8073952 -#> [139,] 0.7791502 0.8055212 0.8210332 -#> [140,] 0.7756952 0.7866932 0.8007903 -#> [141,] 0.7677695 0.7754018 0.7818217 -#> [142,] 0.5684539 0.5996913 0.6007714 -#> [143,] 1.3375979 1.4037835 1.4188600 -#> [144,] 0.7343907 0.7648775 0.7744335 -#> [145,] 1.0005149 1.0839008 1.1359603 -#> [146,] 1.9247666 2.0528754 2.1450529 -#> [147,] 1.5396802 1.5606996 1.5799396 -#> [148,] 0.6387329 0.6418468 0.7399409 -#> [149,] 1.4024336 1.4556944 1.4722021 -#> [150,] 0.6279512 0.6348908 0.6559528 -#> [151,] 1.0326327 1.0389256 1.0914895 -#> [152,] 0.9378915 0.9794508 0.9807302 -#> [153,] 1.0243505 1.0493163 1.0726503 -#> [154,] 1.9435902 2.0558332 2.0685179 -#> [155,] 0.6269097 0.6477152 0.6494768 -#> [156,] 0.8093809 0.8215576 0.8361150 -#> [157,] 0.5778372 0.5838077 0.5843167 -#> [158,] 0.6856160 0.6878116 0.6889679 -#> [159,] 1.3000231 1.3299072 1.4954100 -#> [160,] 0.9060245 0.9248323 0.9391637 -#> [161,] 0.7344118 0.7827488 0.8158820 -#> [162,] 0.5290869 0.5333304 0.5641456 -#> [163,] 0.7405819 0.7453283 0.7493836 -#> [164,] 1.0792046 1.1108133 1.1561070 -#> [165,] 0.5784775 0.5840255 0.5874667 -#> [166,] 0.8497432 0.8526097 0.8793478 -#> [167,] 0.5301634 0.5773118 0.5815696 -#> [168,] 0.9205566 1.0556881 1.0748362 -#> [169,] 0.6847138 0.6932394 0.7051132 -#> [170,] 0.6720961 0.6875287 0.7077823 -#> [171,] 0.6625082 0.6636391 0.7048870 -#> [172,] 0.9430009 0.9596114 1.0043056 -#> [173,] 0.8368947 0.8442932 0.9303547 -#> [174,] 0.8347588 0.8516636 0.8723330 -#> [175,] 0.6116490 0.6363949 0.6639107 -#> [176,] 0.7434605 0.7671118 0.8219937 -#> [177,] 0.6475037 0.6693719 0.6704771 -#> [178,] 1.3141811 1.3641529 1.3654928 -#> [179,] 0.7288562 0.7513864 0.8175451 -#> [180,] 1.0607921 1.0778866 1.0892902 -#> [181,] 0.5993173 0.6505006 0.6528188 -#> [182,] 0.7856517 0.8236180 0.8815996 -#> [183,] 1.2932255 1.2943257 1.3099257 -#> [184,] 0.5943086 0.6065965 0.6508316 -#> [185,] 0.8558937 1.0087461 1.0173097 -#> [186,] 0.7332994 0.7524957 0.7620211 -#> [187,] 0.5388128 0.5724169 0.5871660 -#> [188,] 0.4453320 0.4781089 0.4906804 -#> [189,] 0.6825961 0.7315208 0.7662661 -#> [190,] 0.6261561 0.6322562 0.6387264 -#> [191,] 0.5431800 0.5493708 0.5842524 -#> [192,] 0.9366650 1.0115112 1.0139018 -#> [193,] 0.7791763 0.7877686 0.8267271 -#> [194,] 0.4999803 0.5224957 0.5284957 -#> [195,] 0.7287542 0.7338708 0.7382701 -#> [196,] 0.6793757 0.7002640 0.7527591 -#> [197,] 0.8001376 0.8348291 0.8683633 -#> [198,] 0.8281828 0.8604421 0.8701024 -#> [199,] 0.7081871 0.7107376 0.7260920 -#> [200,] 0.8064651 0.8132218 0.8245745 -#> [201,] 1.1696744 1.1757446 1.1955097 -#> [202,] 0.5215113 0.5506487 0.5559009 -#> [203,] 0.6368902 0.6431490 0.6779259 -#> [204,] 0.7081038 0.7217274 0.7359611 -#> [205,] 0.7783967 0.7897575 0.8099471 -#> [206,] 2.2261040 2.2775297 2.3224374 -#> [207,] 0.9126229 0.9135974 0.9346452 -#> [208,] 0.7500319 0.7543159 0.8307144 -#> [209,] 0.5990616 0.6094973 0.6518840 -#> [210,] 0.7939766 0.7948463 0.8174748 -#> [211,] 0.6363949 0.6399279 0.6597046 -#> [212,] 0.7984211 0.8140872 0.8308921 -#> [213,] 0.6526012 0.6779259 0.7076093 -#> [214,] 0.6954323 0.7328516 0.8023101 -#> [215,] 0.7891139 0.8550059 0.8793124 -#> [216,] 0.6767596 0.6819420 0.7056023 -#> [217,] 0.5446488 0.5520749 0.5598840 -#> [218,] 0.6493102 0.6569956 0.7001344 -#> [219,] 0.6517485 0.6598114 0.6637741 -#> [220,] 1.3616232 1.3623973 1.3651631 -#> [221,] 0.9490913 0.9776484 1.0237415 -#> [222,] 1.0844905 1.0989884 1.1113306 -#> [223,] 0.6221887 0.6253999 0.6451453 -#> [224,] 0.8194787 0.8278785 0.8498218 -#> [225,] 0.5101514 0.5381790 0.5455016 -#> [226,] 0.7723712 0.7773210 0.7809546 -#> [227,] 0.6798027 0.6873260 0.6927150 -#> [228,] 0.7488809 0.7905154 0.8101955 -#> [229,] 0.6734346 0.6924004 0.7396594 -#> [230,] 0.6067046 0.6370304 0.6404394 -#> [231,] 0.6918033 0.7091232 0.7606984 -#> [232,] 1.2896599 1.3014405 1.3156883 -#> [233,] 0.5900934 0.6004548 0.6081852 -#> [234,] 0.6822182 0.6979967 0.7666046 -#> [235,] 0.6520138 0.6824711 0.6960982 -#> [236,] 0.7053574 0.7241683 0.7403172 -#> [237,] 0.5986689 0.5990214 0.6377405 -#> [238,] 0.7920279 0.8213955 0.8286330 -#> [239,] 1.2819247 1.2855510 1.3117287 -#> [240,] 0.7161184 0.7414333 0.7443708 -#> [241,] 1.0238656 1.0239953 1.0652112 -#> [242,] 0.7274085 0.8577339 0.9286956 -#> [243,] 0.8815454 0.8823584 0.9279361 -#> [244,] 1.0295641 1.0425783 1.0451755 -#> [245,] 0.5792627 0.5956290 0.5963923 -#> [246,] 1.0161725 1.0426072 1.0429812 -#> [247,] 0.5784076 0.5812280 0.6161878 -#> [248,] 0.7515865 0.8088560 0.8094516 -#> [249,] 1.1293264 1.1461501 1.1804159 -#> [250,] 1.1818696 1.1882331 1.1978149 -#> [251,] 0.7825243 0.7996498 0.8301877 -#> [252,] 1.0085870 1.0653633 1.0755396 -#> [253,] 0.5808555 0.5864900 0.6044754 -#> [254,] 1.7971424 1.8032660 1.8215755 -#> [255,] 0.6878055 0.7056987 0.7316238 -#> [256,] 0.9891129 1.0039163 1.0513837 -#> [257,] 0.7224331 0.7338708 0.7404499 -#> [258,] 0.6437494 0.6840457 0.6865839 -#> [259,] 0.9520110 1.0119328 1.0333963 -#> [260,] 0.8879565 0.8959714 0.9304029 -#> [261,] 0.5361542 0.5636682 0.5842524 -#> [262,] 0.8899253 0.8908305 0.9057293 -#> [263,] 0.6037244 0.6114538 0.6404394 -#> [264,] 1.0037048 1.0046564 1.0069419 -#> [265,] 0.6911578 0.7127932 0.7773210 -#> [266,] 1.4057024 1.4076882 1.4225578 -#> [267,] 1.1279720 1.1550765 1.1581750 -#> [268,] 0.5414595 0.5564536 0.6357178 -#> [269,] 0.6076115 0.6193471 0.6276841 -#> [270,] 0.6143492 0.6556173 0.6836048 -#> [271,] 2.4496041 2.5344793 2.5391836 -#> [272,] 0.6044852 0.6194560 0.6485100 -#> [273,] 1.3404538 1.4055332 1.4131752 -#> [274,] 0.5337690 0.5394863 0.5442631 -#> [275,] 0.6493688 0.6504903 0.6757886 -#> [276,] 0.9664330 1.0029752 1.0502675 -#> [277,] 1.2916295 1.3270275 1.3620397 -#> [278,] 0.7264431 0.7280660 0.7652532 -#> [279,] 0.5512399 0.5529531 0.5653469 -#> [280,] 1.2674576 1.2947856 1.3007113 -#> [281,] 0.6846347 0.6856328 0.7447192 -#> [282,] 0.8194439 0.8902137 0.9103053 -#> [283,] 0.7289972 0.7363352 0.7464540 -#> [284,] 0.5464866 0.5698526 0.5700085 -#> [285,] 0.7070283 0.7107505 0.7586351 -#> [286,] 0.9786823 0.9902617 1.0043523 -#> [287,] 0.8835670 0.9166792 0.9298050 -#> [288,] 0.8211376 0.8313677 0.8479914 -#> [289,] 0.5036908 0.5224957 0.5538597 -#> [290,] 0.5881955 0.5898104 0.6164692 -#> [291,] 0.5928864 0.6194560 0.6233224 -#> [292,] 0.6303293 0.6526242 0.6651987 -#> [293,] 0.6756108 0.6893942 0.7012269 -#> [294,] 0.6567613 0.6709421 0.6779583 -#> [295,] 1.1418658 1.3219935 1.3361117 -#> [296,] 0.6455426 0.6471754 0.6570015 -#> [297,] 1.6822661 1.7729263 1.7731140 -#> [298,] 0.5762717 0.5778650 0.5849180 -#> [299,] 0.6902308 0.6924779 0.6946994 -#> [300,] 0.9062407 0.9221555 1.0124701 -#> [301,] 0.5691434 0.6206144 0.6520764 -#> [302,] 0.4645308 0.5022562 0.5374942 -#> [303,] 0.6195858 0.6303293 0.6317110 -#> [304,] 0.6666715 0.6754799 0.6776307 -#> [305,] 0.9657700 1.0268108 1.0743191 -#> [306,] 0.6860968 0.7290304 0.7363749 -#> [307,] 0.9330724 1.0323207 1.0533723 -#> [308,] 0.6227840 0.6391521 0.6412936 -#> [309,] 1.0081923 1.0499037 1.1157758 -#> [310,] 0.8485907 0.8904358 1.0119328 -#> [311,] 1.4585826 1.5491043 1.5699872 -#> [312,] 1.0084942 1.0480537 1.1049122 -#> [313,] 0.8173120 0.8294177 0.8338392 -#> [314,] 0.5715760 0.5716476 0.5847812 -#> [315,] 0.7681936 0.7818949 0.7836927 -#> [316,] 0.8939884 0.9396375 0.9799018 -#> [317,] 0.6936951 0.7488810 0.7905965 -#> [318,] 0.5284969 0.5448599 0.5563110 -#> [319,] 0.7708202 0.7891957 0.7910154 -#> [320,] 0.6448727 0.6503403 0.6822182 -#> [321,] 1.0162726 1.0430789 1.0781098 -#> [322,] 0.5721931 0.5832461 0.6130888 -#> [323,] 0.6755372 0.6960078 0.7344727 -#> [324,] 0.9294930 0.9347328 0.9469777 -#> [325,] 0.5809545 0.6368902 0.6649969 -#> [326,] 0.5545367 0.5669852 0.5982363 -#> [327,] 0.8570095 0.8741171 0.9018141 -#> [328,] 0.5784438 0.6741597 0.6947247 -#> [329,] 2.1946260 2.1992391 2.2446966 -#> [330,] 0.6299160 0.6330021 0.6784835 -#> [331,] 1.6457078 1.6651054 1.7061774 -#> [332,] 0.8167660 0.8280890 0.8350104 -#> [333,] 0.7697932 0.7825019 0.8039611 -#> [334,] 0.6594180 0.6819420 0.7174661 -#> [335,] 0.6489326 0.6538819 0.6575023 -#> [336,] 1.0346965 1.0823524 1.1103062 -#> [337,] 1.1231594 1.1492845 1.1676271 -#> [338,] 0.8149124 0.8261038 0.8263901 -#> [339,] 0.5833905 0.5875335 0.5910737 -#> [340,] 0.7484844 0.7489488 0.8072147 -#> [341,] 0.8174948 0.8685083 0.8690115 -#> [342,] 0.6255115 0.6255987 0.6267861 -#> [343,] 0.7351369 0.7598897 0.7924194 -#> [344,] 0.6258388 0.6274167 0.6378654 -#> [345,] 0.7616623 0.7817998 0.7882697 -#> [346,] 0.9191435 0.9289766 0.9628716 -#> [347,] 0.6106381 0.6141360 0.6265655 -#> [348,] 0.5110051 0.5153055 0.5965174 -#> [349,] 0.6150592 0.6544731 0.6558452 -#> [350,] 0.9379206 0.9586946 0.9891129 -#> [351,] 0.6922921 0.7232509 0.7310245 -#> [352,] 0.8284646 0.8411821 0.8562097 -#> [353,] 0.6232940 0.6251972 0.6336534 -#> [354,] 0.5802153 0.5900908 0.5905871 -#> [355,] 1.6016927 1.6183683 1.6376652 -#> [356,] 0.8466935 0.8662803 0.8833108 -#> [357,] 0.7460089 0.7497731 0.7529493 -#> [358,] 0.8835670 0.9017609 0.9370817 -#> [359,] 0.6362140 0.6389466 0.6555353 -#> [360,] 0.7257879 0.7571024 0.7628530 -#> [361,] 0.6661770 0.6735804 0.6826631 -#> [362,] 0.7604367 0.7631046 0.7719649 -#> [363,] 1.0205662 1.0823203 1.1001316 -#> [364,] 0.9601521 0.9658617 0.9756815 -#> [365,] 0.7093827 0.7349957 0.7540449 -#> [366,] 0.9733015 0.9780837 0.9795397 -#> [367,] 0.8142208 0.8174948 0.8393366 -#> [368,] 1.1288409 1.1303360 1.1309967 -#> [369,] 0.9086849 0.9438448 1.0109677 -#> [370,] 0.5769253 0.5848354 0.5854538 -#> [371,] 0.9085786 1.0613554 1.0737194 -#> [372,] 0.8003550 0.8014943 0.8155339 -#> [373,] 0.6520764 0.6809989 0.6925582 -#> [374,] 1.0800418 1.0875153 1.1016418 -#> [375,] 0.5740169 0.5790913 0.6239847 -#> [376,] 1.4694834 1.4839979 1.5690349 -#> [377,] 0.5039508 0.5773118 0.6093329 -#> [378,] 0.6447953 0.7064193 0.7456880 -#> [379,] 0.7255185 0.7739046 0.7975968 -#> [380,] 0.7766619 0.7914277 0.7957228 -#> [381,] 0.6988580 0.7077364 0.7449540 -#> [382,] 0.9485530 0.9536875 0.9786161 -#> [383,] 0.6256541 0.6275538 0.6362574 -#> [384,] 0.6720682 0.7787870 0.7957228 -#> [385,] 1.2687172 1.2802805 1.3047140 -#> [386,] 0.5507339 0.6064328 0.6108049 -#> [387,] 0.8122301 0.8179785 0.8191682 -#> [388,] 0.7100582 0.7123172 0.7344118 -#> [389,] 0.8584639 0.8613229 0.8715004 -#> [390,] 0.7809224 0.7943283 0.8036352 -#> [391,] 0.8789989 0.8846523 0.9430009 -#> [392,] 0.5787497 0.6434306 0.6461901 -#> [393,] 0.5508534 0.5647906 0.5816181 -#> [394,] 1.0615136 1.0758883 1.1293451 -#> [395,] 0.6867323 0.7006231 0.7072118 -#> [396,] 1.3040997 1.3157568 1.3392170 -#> [397,] 0.9589776 0.9816568 1.0123912 -#> [398,] 0.8932279 0.9187887 0.9317281 -#> [399,] 0.5505802 0.6370801 0.6504363 -#> [400,] 0.9190036 0.9236847 0.9552591 -#> [401,] 0.8119278 0.8150843 0.8183023 -#> [402,] 0.7615951 0.9273662 0.9407729 -#> [403,] 0.5254921 0.5363079 0.5819930 -#> [404,] 0.5177915 0.5189196 0.5980605 -#> [405,] 1.4713607 1.4878362 1.5052571 -#> [406,] 0.5693620 0.5790913 0.6271961 -#> [407,] 0.6229257 0.6454792 0.6617013 -#> [408,] 0.6365288 0.6380717 0.6629856 -#> [409,] 0.7639416 0.7647506 0.7828068 -#> [410,] 0.8029412 0.8332180 0.8366698 -#> [411,] 1.0092043 1.1255428 1.1476501 -#> [412,] 0.7288516 0.7313914 0.7520435 -#> [413,] 0.5735514 0.5819185 0.5864900 -#> [414,] 1.1381939 1.2571642 1.2603551 -#> [415,] 0.8042587 0.8070106 0.8208756 -#> [416,] 0.7189922 0.7228040 0.7890711 -#> [417,] 0.8023364 0.8728391 0.8812638 -#> [418,] 0.6259600 0.6381189 0.6573023 -#> [419,] 0.8915097 0.9321882 0.9529310 -#> [420,] 0.6882820 0.7178746 0.7298191 -#> [421,] 0.8980328 0.9007722 0.9341469 -#> [422,] 0.7858526 0.8211451 0.8417973 -#> [423,] 0.6611121 0.6853944 0.7027129 -#> [424,] 0.9034129 0.9303547 0.9338346 -#> [425,] 0.6103830 0.6146042 0.6152917 -#> [426,] 1.2741930 1.3048889 1.3166447 -#> [427,] 0.9660454 0.9786161 1.0160631 -#> [428,] 0.9027740 0.9208716 0.9272575 -#> [429,] 0.7189606 0.7354697 0.8051510 -#> [430,] 0.6149044 0.6544568 0.6568448 -#> [431,] 1.6014659 1.6124795 1.6464965 -#> [432,] 0.8645507 0.8864328 0.8991177 -#> [433,] 0.7941475 0.7968045 0.8960429 -#> [434,] 0.5584686 0.6195858 0.6347572 -#> [435,] 0.7867618 0.7902651 0.8077762 -#> [436,] 0.7012632 0.7241992 0.7251975 -#> [437,] 0.6840500 0.6843970 0.6867323 -#> [438,] 0.9685332 0.9759108 0.9967674 -#> [439,] 0.7871674 0.8543760 0.9412158 -#> [440,] 0.8392612 0.8716437 0.8721196 -#> [441,] 0.8069808 0.8518542 0.8620734 -#> [442,] 0.8871901 0.9332700 1.0400032 -#> [443,] 0.7402080 0.7404499 0.7449671 -#> [444,] 0.9053295 0.9441010 0.9513810 -#> [445,] 0.5589335 0.5616279 0.6127602 -#> [446,] 0.6576721 0.6804033 0.7033662 -#> [447,] 0.9927727 0.9967219 1.0073378 -#> [448,] 0.7042698 0.7459097 0.7554355 -#> [449,] 0.5727056 0.5983884 0.7005419 -#> [450,] 0.9723325 0.9768080 1.0335018 -#> [451,] 1.1559923 1.2082916 1.3320827 -#> [452,] 0.5845112 0.6074706 0.6114413 -#> [453,] 0.7398085 0.7490561 0.7493836 -#> [454,] 0.5464723 0.5666532 0.6183374 -#> [455,] 1.1370093 1.2190428 1.2428198 -#> [456,] 0.6722716 0.6847392 0.6861309 -#> [457,] 0.8434437 0.8982119 0.9272575 -#> [458,] 0.4985066 0.5338072 0.5382786 -#> [459,] 0.6853056 0.6895883 0.7055080 -#> [460,] 1.0526528 1.0920031 1.1344009 -#> [461,] 0.5698526 0.5888508 0.6044754 -#> [462,] 0.5562461 0.5621462 0.5847977 -#> [463,] 1.7375101 1.8041639 1.8503639 -#> [464,] 1.2224570 1.2429012 1.2841455 -#> [465,] 0.8482913 0.8697166 0.8788324 -#> [466,] 0.6964980 0.7070283 0.7537934 -#> [467,] 0.6418468 0.6671150 0.6917589 -#> [468,] 0.5065117 0.5391964 0.5705462 -#> [469,] 0.7010090 0.7214546 0.7667716 -#> [470,] 0.7275787 0.8099892 0.8246746 -#> [471,] 0.6374177 0.6419992 0.6515068 -#> [472,] 0.9687647 0.9824862 1.0229776 -#> [473,] 1.1807701 1.2286957 1.2921542 -#> [474,] 0.6557559 0.6844510 0.7066562 -#> [475,] 0.7262108 0.7448291 0.7559599 -#> [476,] 0.5164314 0.5171654 0.5205927 -#> [477,] 0.6980515 0.7317562 0.7372731 -#> [478,] 1.0509450 1.0538652 1.0635850 -#> [479,] 1.1200334 1.1386040 1.1417755 -#> [480,] 0.9911805 1.0084563 1.0218342 -#> [481,] 0.7985012 0.8332130 0.8358105 -#> [482,] 1.4960626 1.5213869 1.5779222 -#> [483,] 1.1499770 1.1650330 1.1797567 -#> [484,] 0.5254051 0.5810606 0.5872490 -#> [485,] 1.0248897 1.0299032 1.0416752 -#> [486,] 0.7847803 0.8099471 0.8112365 -#> [487,] 0.9444033 0.9586442 0.9673000 -#> [488,] 0.7103499 0.7157430 0.8285483 -#> [489,] 0.6194508 0.6614908 0.6766100 -#> [490,] 0.8434020 0.8588308 0.9001202 -#> [491,] 1.0468715 1.0511804 1.0698476 -#> [492,] 0.4926226 0.5391086 0.5810606 -#> [493,] 0.9021870 1.0212893 1.0643079 -#> [494,] 0.6158978 0.6229634 0.6336534 -#> [495,] 0.7328516 0.8080500 0.8320779 -#> [496,] 0.5578328 0.5877514 0.5907850 -#> [497,] 0.9729825 0.9912929 1.0054113 -#> [498,] 1.3147718 1.3647384 1.3662988 -#> [499,] 2.7978178 2.8484729 2.9330105 -#> [500,] 0.7264557 0.8516435 0.8679878 -#> [501,] 0.6613612 0.7231428 0.7516601 -#> [502,] 0.8481883 0.8585328 0.8599669 -#> [503,] 0.7367848 0.7974064 0.8334096 -#> [504,] 0.5542075 0.5820559 0.5893635 -#> [505,] 0.6116252 0.6465721 0.7169874 -#> [506,] 0.8556933 0.8736286 0.9100260 -#> [507,] 0.5740050 0.6339344 0.6344504 -#> [508,] 1.0379094 1.0471588 1.0521241 -#> [509,] 0.7351449 0.7733922 0.7906739 -#> [510,] 1.1501040 1.1827899 1.2138401 -#> [511,] 1.0081010 1.0269155 1.0403183 -#> [512,] 1.5049288 1.5205302 1.5471490 -#> [513,] 1.1290831 1.1957300 1.2288811 -#> [514,] 1.0214357 1.0770711 1.1021957 -#> [515,] 0.8460879 0.8489286 0.8553952 -#> [516,] 0.5122100 0.5338072 0.5555859 -#> [517,] 0.8566329 0.8780640 0.9458068 -#> [518,] 0.7053574 0.7396663 0.7529493 -#> [519,] 0.8132218 0.8619528 0.8794552 -#> [520,] 0.7932397 0.8036352 0.8371247 -#> [521,] 0.7600011 0.8030624 0.8731730 -#> [522,] 1.9339702 1.9723446 2.0921342 -#> [523,] 0.8728979 0.8798903 0.9108048 -#> [524,] 0.8602468 0.8898735 0.9355186 -#> [525,] 0.8434020 0.8554912 0.8611303 -#> [526,] 0.6234424 0.6281006 0.6329588 -#> [527,] 0.8075600 0.8575611 0.8953935 -#> [528,] 1.0731621 1.1108133 1.1127074 -#> [529,] 0.8029918 0.8075866 0.8531193 -#> [530,] 0.7844424 0.8029918 0.8055212 -#> [531,] 0.9918341 1.0455495 1.2020781 -#> [532,] 0.6647266 0.6756108 0.6823798 -#> [533,] 0.5475485 0.5560572 0.5752051 -#> [534,] 1.4061900 1.4145051 1.4499503 -#> [535,] 0.5350829 0.5391656 0.5428234 -#> [536,] 0.8666887 0.8838251 0.8856987 -#> [537,] 0.6450181 0.6989626 0.7645513 -#> [538,] 0.6936951 0.6942788 0.7069361 -#> [539,] 0.5448491 0.5483102 0.5591494 -#> [540,] 0.5762100 0.5955601 0.6073795 -#> [541,] 1.3941351 1.4337750 1.4711072 -#> [542,] 1.1020501 1.1086187 1.1164041 -#> [543,] 1.0664033 1.0783751 1.0789541 -#> [544,] 1.1373486 1.1421385 1.1896530 -#> [545,] 0.6098957 0.6264674 0.6398529 -#> [546,] 0.7891117 0.7891957 0.8193688 -#> [547,] 1.0043056 1.0920505 1.1169794 -#> [548,] 0.5197251 0.5388128 0.5394863 -#> [549,] 1.2325702 1.3233724 1.3253074 -#> [550,] 2.3698032 2.4459672 2.4605241 -#> [551,] 0.5694467 0.5983994 0.6216318 -#> [552,] 0.7072118 0.7211100 0.7239720 -#> [553,] 0.5203712 0.5367804 0.5406582 -#> [554,] 1.0371558 1.0512924 1.0639414 -#> [555,] 0.9910236 1.0217891 1.0661520 -#> [556,] 1.0388253 1.0509622 1.0759924 -#> [557,] 0.7511114 0.7523335 0.7737363 -#> [558,] 1.0436830 1.0595302 1.1315020 -#> [559,] 0.7485221 0.7555543 0.7562221 -#> [560,] 0.8110856 0.8442247 0.8590657 -#> [561,] 0.7910970 0.8125575 0.8269329 -#> [562,] 2.0329060 2.0464947 2.1051615 -#> [563,] 1.5614089 1.5792095 1.6496802 -#> [564,] 1.1087357 1.1345552 1.1487033 -#> [565,] 0.6202705 0.6210650 0.6326365 -#> [566,] 1.0882388 1.1474096 1.1573929 -#> [567,] 1.0228863 1.0319975 1.0508046 -#> [568,] 0.5302128 0.5637004 0.5904987 -#> [569,] 1.0220488 1.0539673 1.0574935 -#> [570,] 1.0401324 1.0888612 1.1436175 -#> [571,] 0.4961666 0.5716476 0.5838902 -#> [572,] 0.6315611 0.6399279 0.6997841 -#> [573,] 1.2314421 1.2539958 1.2860329 -#> [574,] 0.8483180 0.8519278 0.8581231 -#> [575,] 0.6048017 0.6223452 0.6331574 -#> [576,] 1.5010928 1.5378015 1.5387295 -#> [577,] 0.7322894 0.7402701 0.7722186 -#> [578,] 0.5335917 0.5458239 0.5537864 -#> [579,] 0.6568820 0.7080392 0.7319985 -#> [580,] 0.6873511 0.7011420 0.7050837 -#> [581,] 0.8928800 0.9238549 0.9298428 -#> [582,] 0.7118597 0.7422491 0.7729236 -#> [583,] 0.9892560 1.0059526 1.0644100 -#> [584,] 1.0792311 1.0849133 1.1084016 -#> [585,] 0.8916000 0.9396140 0.9467054 -#> [586,] 0.8400040 0.8639699 0.9180399 -#> [587,] 1.4627325 1.4824280 1.5210347 -#> [588,] 1.0141649 1.1053538 1.1212797 -#> [589,] 0.6793500 0.6883186 0.7481124 -#> [590,] 0.7648148 0.7906054 0.7945961 -#> [591,] 0.9025918 0.9716701 1.0721897 -#> [592,] 0.6423844 0.6449319 0.6724115 -#> [593,] 0.4725017 0.4901664 0.6232102 -#> [594,] 0.5382786 0.5595756 0.5714908 -#> [595,] 0.8342513 0.8361073 0.9158839 -#> [596,] 0.6828360 0.7077951 0.7093827 -#> [597,] 0.8045615 0.8189127 0.8202734 -#> [598,] 0.6056041 0.6122406 0.6294766 -#> [599,] 0.7319659 0.7665120 0.7830806 -#> [600,] 0.5793024 0.5920239 0.5944527 -#> [601,] 0.5422872 0.5934187 0.6225972 -#> [602,] 0.5038229 0.5088133 0.5333304 -#> [603,] 0.6339928 0.6344587 0.6415068 -#> [604,] 0.5621462 0.5822668 0.5928315 -#> [605,] 0.5043623 0.5152282 0.5412566 -#> [606,] 0.8514101 0.8581030 0.8976943 -#> [607,] 0.8099237 0.8210332 0.8644987 -#> [608,] 1.0553813 1.0852216 1.0892656 -#> [609,] 1.0876542 1.0967448 1.0970276 -#> [610,] 0.6575762 0.6783473 0.6829099 -#> [611,] 0.7092287 0.7793916 0.8176927 -#> [612,] 0.7845856 0.8247635 0.8874659 -#> [613,] 0.8388916 0.8632137 0.9030833 -#> [614,] 1.0231618 1.0235836 1.0425666 -#> [615,] 0.7166701 0.7827488 0.8202734 -#> [616,] 0.6064328 0.6065965 0.6075559 -#> [617,] 0.8378213 0.8425124 0.8486141 -#> [618,] 1.2220645 1.3184867 1.3757140 -#> [619,] 0.5756904 0.5858072 0.5933102 -#> [620,] 1.2608402 1.2822373 1.2869762 -#> [621,] 1.2739743 1.3099188 1.3250280 -#> [622,] 0.6619830 0.6699510 0.7000377 -#> [623,] 0.7164536 0.7257358 0.8023364 -#> [624,] 1.2374534 1.2635508 1.2643068 -#> [625,] 0.6902308 0.7028009 0.7319659 -#> [626,] 0.8958902 0.9030162 0.9093301 -#> [627,] 0.5549466 0.5755035 0.5765267 -#> [628,] 0.7460089 0.7639416 0.8313206 -#> [629,] 0.4905728 0.4942109 0.5031505 -#> [630,] 0.6007005 0.6234864 0.6677731 -#> [631,] 1.5964418 1.6594767 1.6659284 -#> [632,] 0.6744898 0.6937754 0.6943448 -#> [633,] 1.0761527 1.0898412 1.1706041 -#> [634,] 0.6242899 0.6293683 0.6310264 -#> [635,] 0.8992914 1.0140728 1.0649797 -#> [636,] 1.1882590 1.2053993 1.2229430 -#> [637,] 0.5477302 0.6005423 0.6195639 -#> [638,] 0.6460751 0.6486946 0.6554590 -#> [639,] 0.7732483 0.7848387 0.8093511 -#> [640,] 0.7354020 0.7401168 0.7578651 -#> [641,] 0.5115338 0.5376920 0.5490150 -#> [642,] 1.3809669 1.3931236 1.3963649 -#> [643,] 0.6162748 0.6355525 0.6373189 -#> [644,] 0.5609127 0.5977554 0.6404431 -#> [645,] 0.9621100 1.0284746 1.0561041 -#> [646,] 0.8011934 0.8153516 0.8284279 -#> [647,] 1.1757033 1.2322491 1.3049420 -#> [648,] 0.5047042 0.5331247 0.5417924 -#> [649,] 0.7390150 0.7774246 0.7941222 -#> [650,] 1.2468155 1.2588606 1.2648697 -#> [651,] 0.8700253 0.9101938 0.9348567 -#> [652,] 0.7176706 0.7422686 0.7525945 -#> [653,] 1.4647242 1.4649795 1.4841694 -#> [654,] 0.5751650 0.5832221 0.6152891 -#> [655,] 0.8821678 0.9729825 0.9755979 -#> [656,] 0.5068300 0.5140644 0.5700511 -#> [657,] 1.0711251 1.1204842 1.1268332 -#> [658,] 0.6944146 0.7117599 0.7210043 -#> [659,] 0.5651490 0.5871811 0.6401649 -#> [660,] 0.6530109 0.6705030 0.7392660 -#> [661,] 0.9938395 1.0084563 1.0115921 -#> [662,] 0.5564536 0.5589335 0.5876542 -#> [663,] 0.7698231 0.7761523 0.8092654 -#> [664,] 0.6286956 0.6359375 0.6481956 -#> [665,] 0.6136417 0.6709636 0.6793446 -#> [666,] 1.5772505 1.5959408 1.7235659 -#> [667,] 0.5598840 0.5724169 0.5938387 -#> [668,] 0.5214682 0.5598422 0.5650591 -#> [669,] 1.2304458 1.3551871 1.3687091 -#> [670,] 1.1947125 1.2882108 1.2936240 -#> [671,] 0.5446488 0.5522417 0.5537372 -#> [672,] 0.8751083 0.8829570 1.0368283 -#> [673,] 1.0337591 1.0884658 1.0925525 -#> [674,] 1.3091289 1.4285975 1.4426094 -#> [675,] 0.7962729 0.8106037 0.8532589 -#> [676,] 0.5474013 0.5836521 0.6108524 -#> [677,] 0.8050155 0.8197892 0.8250720 -#> [678,] 1.2499746 1.2588606 1.2623633 -#> [679,] 0.7672906 0.8184894 0.8372153 -#> [680,] 0.8309062 0.8499388 0.8522157 -#> [681,] 1.2357405 1.2813162 1.2881833 -#> [682,] 0.7512256 0.7537934 0.8017146 -#> [683,] 0.6924338 0.7604367 0.7621253 -#> [684,] 0.8923240 0.9105257 0.9758638 -#> [685,] 0.5165238 0.5184814 0.5219601 -#> [686,] 0.7218092 0.7418706 0.8362253 -#> [687,] 1.0000655 1.0190872 1.0434870 -#> [688,] 1.4733664 1.4897250 1.5071121 -#> [689,] 0.7361053 0.7372937 0.7560528 -#> [690,] 1.1736718 1.2135157 1.2144153 -#> [691,] 1.6315990 1.6820170 1.6940504 -#> [692,] 0.5508534 0.5760182 0.5859741 -#> [693,] 0.8191035 0.8410250 0.8522157 -#> [694,] 0.9218645 0.9487543 0.9883042 -#> [695,] 0.6830429 0.7555543 0.7700034 -#> [696,] 0.8328559 0.8566730 0.8788406 -#> [697,] 0.7060284 0.7123172 0.7189718 -#> [698,] 0.6966668 0.7095384 0.7583497 -#> [699,] 0.6793757 0.7066244 0.7092152 -#> [700,] 0.8070216 0.8265935 0.8519278 -#> [701,] 0.6302841 0.6342402 0.6365512 -#> [702,] 1.2890853 1.2964382 1.3010610 -#> [703,] 0.9358046 1.1144320 1.1393480 -#> [704,] 0.7081493 0.7151052 0.7405949 -#> [705,] 1.0195369 1.2259891 1.2269391 -#> [706,] 0.5624723 0.5935204 0.6081886 -#> [707,] 0.8580177 0.8863304 0.9252928 -#> [708,] 1.3400658 1.3529517 1.3630672 -#> [709,] 1.0605147 1.0743078 1.0866625 -#> [710,] 0.5703296 0.5793024 0.5853281 -#> [711,] 0.5644427 0.6095891 0.6157921 -#> [712,] 0.6489326 0.6562908 0.6735804 -#> [713,] 0.7164536 0.7185370 0.7198287 -#> [714,] 1.1301325 1.1696451 1.1861796 -#> [715,] 0.6106142 0.6114538 0.6166628 -#> [716,] 0.8472093 0.8533833 0.8807989 -#> [717,] 1.0511908 1.0913276 1.0950373 -#> [718,] 0.7813662 0.7920934 0.8048466 -#> [719,] 1.0322897 1.0772629 1.0996304 -#> [720,] 0.6212383 0.6707692 0.6776307 -#> [721,] 0.8476778 0.8508133 0.9056069 -#> [722,] 0.7818596 0.7991157 0.8047440 -#> [723,] 0.6360874 0.6570015 0.6794117 -#> [724,] 1.5425863 1.5770401 1.5775648 -#> [725,] 1.0469513 1.0730223 1.1365501 -#> [726,] 2.5499001 2.5877972 2.5973348 -#> [727,] 0.9649681 1.0000655 1.0105031 -#> [728,] 0.6682089 0.6689205 0.6925582 -#> [729,] 0.8289745 0.8606617 0.8995628 -#> [730,] 1.1909792 1.2032655 1.2214736 -#> [731,] 0.8155527 0.8330002 0.8910643 -#> [732,] 1.4129700 1.4257205 1.4269882 -#> [733,] 1.2326746 1.2666659 1.2807269 -#> [734,] 0.9759457 1.0941410 1.1131137 -#> [735,] 1.2926398 1.2943778 1.3189782 -#> [736,] 0.8038318 0.9191734 0.9259836 -#> [737,] 0.7771783 0.8173460 0.8183498 -#> [738,] 0.8342751 0.8375110 0.8468821 -#> [739,] 0.7669882 0.7769399 0.7783967 -#> [740,] 0.7676540 0.7703288 0.7959924 -#> [741,] 0.4807659 0.4811913 0.4829322 -#> [742,] 0.7628195 0.7722913 0.8143765 -#> [743,] 0.5038506 0.5872490 0.5902789 -#> [744,] 0.8199415 0.8279275 0.8672934 -#> [745,] 0.7083628 0.7338538 0.7515559 -#> [746,] 1.7624131 1.7831930 1.8529676 -#> [747,] 0.6527544 0.6722586 0.7173171 -#> [748,] 0.6165924 0.6242899 0.6525633 -#> [749,] 0.5591494 0.5773746 0.5871660 -#> [750,] 0.6133688 0.6143176 0.6344504 -#> [751,] 0.5977554 0.6075119 0.6081672 -#> [752,] 0.8201508 0.8217113 0.8258053 -#> [753,] 0.7158419 0.7167084 0.7526110 -#> [754,] 0.5488100 0.5512399 0.5565225 -#> [755,] 0.9689169 0.9702371 0.9763988 -#> [756,] 0.7403582 0.7672499 0.7689766 -#> [757,] 0.6011118 0.6035912 0.6143492 -#> [758,] 0.6690084 0.6855743 0.6954385 -#> [759,] 0.8650603 0.9300395 1.0290951 -#> [760,] 0.9413051 0.9449080 0.9541890 -#> [761,] 0.6814680 0.6873511 0.6933264 -#> [762,] 0.6146394 0.6270975 0.6384403 -#> [763,] 1.2295889 1.2969550 1.3012819 -#> [764,] 0.7912666 0.8175944 0.8652332 -#> [765,] 1.5165658 1.5632683 1.6220706 -#> [766,] 0.7619575 0.7845856 0.7903842 -#> [767,] 0.9105072 0.9167320 0.9350408 -#> [768,] 0.8096945 0.8271271 0.8438052 -#> [769,] 1.0189938 1.0358824 1.0408619 -#> [770,] 0.7793938 0.7906739 0.8376128 -#> [771,] 1.7807055 1.7927447 1.7961446 -#> [772,] 1.2081725 1.2300703 1.2320259 -#> [773,] 0.7095384 0.7634109 0.8012599 -#> [774,] 0.5526499 0.5690779 0.5941848 -#> [775,] 1.0034701 1.0198180 1.0597253 -#> [776,] 0.6628386 0.6747995 0.6812344 -#> [777,] 0.5489606 0.5848354 0.5980778 -#> [778,] 0.6729456 0.6753574 0.6895843 -#> [779,] 0.9989262 1.0340941 1.0549130 -#> [780,] 0.7886171 0.8140232 0.8596395 -#> [781,] 0.9398637 1.0138404 1.0257756 -#> [782,] 0.5196511 0.5300860 0.5441774 -#> [783,] 0.7712708 0.7785486 0.7933427 -#> [784,] 0.7314612 0.7390954 0.7903991 -#> [785,] 0.7497731 0.8423679 0.9455623 -#> [786,] 0.8584332 0.8779746 0.8886868 -#> [787,] 1.1090116 1.1598086 1.1838323 -#> [788,] 1.0667274 1.0709461 1.0753148 -#> [789,] 0.9512250 1.0026992 1.0055707 -#> [790,] 0.6047498 0.6423844 0.6672514 -#> [791,] 0.7644297 0.7823320 0.8289601 -#> [792,] 0.7533340 0.8625143 0.8690115 -#> [793,] 0.8308841 0.8392308 0.8533834 -#> [794,] 0.7378000 0.7576315 0.7722186 -#> [795,] 1.0377217 1.0471588 1.1430132 -#> [796,] 0.6464568 0.6722230 0.7071145 -#> [797,] 0.4768116 0.5038506 0.5119983 -#> [798,] 0.5321714 0.5509819 0.5825465 -#> [799,] 0.5765121 0.6117266 0.6334817 -#> [800,] 1.2780696 1.2879393 1.3291877 -#> [801,] 0.9607605 0.9730312 0.9790967 -#> [802,] 0.9759251 1.0250429 1.0486243 -#> [803,] 0.6575762 0.6844912 0.6890709 -#> [804,] 0.8679566 0.9108230 1.0005149 -#> [805,] 0.6319259 0.6342402 0.7315519 -#> [806,] 0.6782024 0.6794570 0.7198823 -#> [807,] 0.5168748 0.5367804 0.5435968 -#> [808,] 0.5489606 0.6098179 0.6106142 -#> [809,] 0.6365382 0.6373189 0.6435149 -#> [810,] 0.5801760 0.5845530 0.5859639 -#> [811,] 0.7177089 0.7441591 0.7792351 -#> [812,] 0.7422491 0.7606548 0.7615598 -#> [813,] 0.5879726 0.5909601 0.6122462 -#> [814,] 0.5085484 0.5628062 0.5765421 -#> [815,] 0.5026705 0.5189029 0.5284969 -#> [816,] 1.2496203 1.2682469 1.2889422 -#> [817,] 0.9497079 0.9783047 0.9847914 -#> [818,] 0.8094516 0.8416187 0.8517166 -#> [819,] 0.8513099 0.9304029 0.9474305 -#> [820,] 0.8280890 0.8349683 0.8424210 -#> [821,] 0.5074146 0.5448491 0.5822668 -#> [822,] 2.0027772 2.0190620 2.0273821 -#> [823,] 0.6307234 0.6482195 0.6661770 -#> [824,] 2.3750614 2.3903208 2.3941222 -#> [825,] 0.8838251 0.9023417 0.9142908 -#> [826,] 0.9889649 1.0167606 1.0442092 -#> [827,] 1.3254673 1.3315739 1.3326072 -#> [828,] 0.5397715 0.5425306 0.5439430 -#> [829,] 0.6322275 0.6484510 0.7294171 -#> [830,] 0.7303706 0.7766673 0.7895184 -#> [831,] 0.9917530 1.0002187 1.0179955 -#> [832,] 1.0934972 1.0939917 1.1230953 -#> [833,] 1.0198930 1.0874022 1.1050338 -#> [834,] 0.4827478 0.4842416 0.5694467 -#> [835,] 0.9877948 1.1083030 1.1223708 -#> [836,] 0.6776030 0.7596441 0.7681511 -#> [837,] 0.8056446 0.8448444 0.8542834 -#> [838,] 0.8195514 0.8665423 0.8766603 -#> [839,] 0.5893552 0.6070066 0.6161363 -#> [840,] 0.4763757 0.5291550 0.5368799 -#> [841,] 0.7351449 0.8048950 0.8117724 -#> [842,] 0.5595356 0.5705462 0.5735618 -#> [843,] 1.8124457 1.8751240 1.8873251 -#> [844,] 0.7224461 0.7572010 0.7808384 -#> [845,] 0.5486938 0.5735514 0.5755479 -#> [846,] 0.7066244 0.7458233 0.7714272 -#> [847,] 0.6431490 0.6594180 0.6620760 -#> [848,] 0.6721738 0.7166255 0.7308810 -#> [849,] 1.2402478 1.2403056 1.2798592 -#> [850,] 0.8183498 0.8341716 0.8679566 -#> [851,] 0.5682557 0.5983884 0.7436629 -#> [852,] 2.0901283 2.1193404 2.1453606 -#> [853,] 0.8889776 0.9101522 0.9120216 -#> [854,] 0.6801136 0.6814680 0.7064047 -#> [855,] 1.1362938 1.1583177 1.1843568 -#> [856,] 0.5859734 0.6395725 0.6679704 -#> [857,] 0.7928113 0.8106336 0.8582234 -#> [858,] 1.4155497 1.4261048 1.4362454 -#> [859,] 0.6932967 0.7067968 0.7267149 -#> [860,] 0.6422858 0.6720682 0.6826380 -#> [861,] 0.5245846 0.5254051 0.5499494 -#> [862,] 1.9490004 1.9533986 1.9915554 -#> [863,] 0.6024946 0.6290822 0.6487378 -#> [864,] 0.7039406 0.7067968 0.7303706 -#> [865,] 0.8614348 0.9119135 0.9338346 -#> [866,] 0.8723443 0.8807200 0.8847025 -#> [867,] 0.6138500 0.6325047 0.6444264 -#> [868,] 0.8627456 0.9297663 0.9506089 -#> [869,] 0.7957403 0.8158396 0.8162246 -#> [870,] 0.6207546 0.6482118 0.6815168 -#> [871,] 0.6873260 0.6925079 0.7224404 -#> [872,] 0.7004519 0.7115287 0.7141490 -#> [873,] 0.6206424 0.6294411 0.6419992 -#> [874,] 0.7751067 0.7927907 0.8317753 -#> [875,] 0.5477302 0.5905216 0.5968875 -#> [876,] 1.1172828 1.1568073 1.1668856 -#> [877,] 0.8027620 0.8289667 0.8584660 -#> [878,] 1.3446773 1.3871050 1.4087059 -#> [879,] 0.7947447 0.8026697 0.8187020 -#> [880,] 0.7095780 0.7120531 0.7224331 -#> [881,] 0.5165238 0.5197251 0.5563110 -#> [882,] 0.4867866 0.4886340 0.5277014 -#> [883,] 0.6095891 0.6444264 0.6471754 -#> [884,] 1.1103547 1.1321855 1.1509306 -#> [885,] 0.6683102 0.6714550 0.6776343 -#> [886,] 0.6149044 0.6266999 0.6792860 -#> [887,] 1.0134636 1.0375347 1.0748362 -#> [888,] 0.7786041 0.8384083 0.8425124 -#> [889,] 0.6276169 0.6734215 0.6775417 -#> [890,] 0.9176432 0.9291135 0.9400309 -#> [891,] 0.5955329 0.6026508 0.6095924 -#> [892,] 0.4747824 0.5152282 0.5298413 -#> [893,] 0.6624071 0.6869585 0.6930631 -#> [894,] 0.9902618 0.9946636 1.0059933 -#> [895,] 0.6006478 0.6109909 0.7827485 -#> [896,] 1.0739924 1.1353441 1.1362353 -#> [897,] 0.5088133 0.5348194 0.5409899 -#> [898,] 0.8149680 0.8505206 0.8677531 -#> [899,] 0.9816910 0.9847571 0.9914863 -#> [900,] 0.9081888 0.9153452 0.9188084 -#> [901,] 0.6153536 0.6153815 0.6182721 -#> [902,] 0.5495761 0.5507399 0.5603175 -#> [903,] 2.5840580 2.5929933 2.6234895 -#> [904,] 1.1861796 1.1973393 1.2292255 -#> [905,] 0.5724835 0.6038711 0.6115347 -#> [906,] 0.8413863 0.8465449 0.8546885 -#> [907,] 0.8071671 0.8180067 0.8939851 -#> [908,] 0.5879416 0.6358956 0.6381506 -#> [909,] 0.5240560 0.5919193 0.6096382 -#> [910,] 0.7269927 0.8232129 0.8276135 -#> [911,] 0.7996571 0.9101522 0.9129058 -#> [912,] 1.6359869 1.7033136 1.7092960 -#> [913,] 0.7159270 0.7256828 0.7342817 -#> [914,] 0.7425863 0.7431353 0.7449256 -#> [915,] 0.6427402 0.6471053 0.6617013 -#> [916,] 0.7987813 0.8538310 0.8605647 -#> [917,] 0.7536898 0.7766619 0.7871300 -#> [918,] 0.5485144 0.5539596 0.5594923 -#> [919,] 0.6752461 0.7208311 0.7571621 -#> [920,] 0.5653469 0.5664138 0.5700511 -#> [921,] 0.6455285 0.6785113 0.6878116 -#> [922,] 0.5816181 0.6417378 0.6576109 -#> [923,] 0.9541905 0.9633048 1.0140728 -#> [924,] 1.7120622 1.7506400 1.7786597 -#> [925,] 1.1356111 1.1501145 1.1861868 -#> [926,] 0.6844323 0.7211100 0.7277595 -#> [927,] 0.9919870 1.0046539 1.0585988 -#> [928,] 1.0909418 1.0970285 1.1137077 -#> [929,] 0.8215322 0.8246746 0.8262601 -#> [930,] 0.8218535 0.8307444 0.8410435 -#> [931,] 0.9490880 0.9559149 1.0106206 -#> [932,] 1.3404173 1.3431972 1.3540991 -#> [933,] 0.9182950 0.9930687 1.0316881 -#> [934,] 0.5778650 0.6233926 0.6552800 -#> [935,] 0.6513649 0.6688012 0.6800487 -#> [936,] 0.7431773 0.7557377 0.8549699 -#> [937,] 0.5240560 0.5434537 0.5609127 -#> [938,] 0.5520749 0.5642003 0.6029693 -#> [939,] 0.7903432 0.8028419 0.8341896 -#> [940,] 0.8409575 0.8928800 0.9019933 -#> [941,] 0.6343338 0.6726428 0.6845662 -#> [942,] 0.7056023 0.7745546 0.8093433 -#> [943,] 0.9929385 0.9997025 1.0238656 -#> [944,] 0.7246882 0.7409949 0.7639661 -#> [945,] 0.6295790 0.6592961 0.6793446 -#> [946,] 1.1429259 1.2309731 1.2322491 -#> [947,] 0.6787280 0.6887799 0.7126490 -#> [948,] 0.9650437 0.9661742 0.9723720 -#> [949,] 0.8346578 0.8391675 0.8997573 -#> [950,] 0.9614591 0.9675984 0.9725935 -#> [951,] 0.8829771 0.9036908 0.9877948 -#> [952,] 0.8512426 0.8532802 0.8848484 -#> [953,] 0.6166628 0.6170841 0.6371422 -#> [954,] 0.8329126 0.8530069 0.8645209 -#> [955,] 0.5247719 0.5300860 0.6102380 -#> [956,] 1.1619952 1.2036054 1.2282273 -#> [957,] 0.6391615 0.6557125 0.6612431 -#> [958,] 1.0725884 1.0784070 1.1014701 -#> [959,] 1.0767381 1.0916150 1.1107009 -#> [960,] 0.7359664 0.7421168 0.7671997 -#> [961,] 1.1424055 1.1488222 1.1747662 -#> [962,] 0.5546416 0.5719874 0.6326870 -#> [963,] 0.6186984 0.6290006 0.6319184 -#> [964,] 1.0452503 1.0807821 1.0947882 -#> [965,] 0.4862419 0.4933322 0.5164314 -#> [966,] 0.6015028 0.6106738 0.6130888 -#> [967,] 0.9397593 0.9456987 0.9475875 -#> [968,] 1.1989367 1.2039149 1.2430222 -#> [969,] 1.2733077 1.2977592 1.3079020 -#> [970,] 0.6587434 0.7021421 0.7257358 -#> [971,] 0.9298788 0.9332233 0.9786370 -#> [972,] 0.4583481 0.5174262 0.5584686 -#> [973,] 1.2784206 1.3214717 1.3237718 -#> [974,] 0.9892116 0.9895480 1.0097542 -#> [975,] 0.5051712 0.5437751 0.5481820 -#> [976,] 0.7593610 0.7609732 0.7633887 -#> [977,] 0.9492200 0.9543498 0.9551139 -#> [978,] 0.7632394 0.7974951 0.8192408 -#> [979,] 0.5735618 0.5908839 0.6015028 -#> [980,] 0.6197546 0.6206144 0.6359616 -#> [981,] 0.8684410 0.8772932 0.9226718 -#> [982,] 0.7774246 0.7999180 0.8602760 -#> [983,] 0.7194182 0.7592156 0.8063933 -#> [984,] 0.9483649 0.9895036 1.0640046 -#> [985,] 0.9019933 0.9142908 0.9483463 -#> [986,] 0.7750119 0.7840491 0.7910807 -#> [987,] 0.5490150 0.5614692 0.5650591 -#> [988,] 0.6108302 0.6178691 0.6390093 -#> [989,] 0.8721196 0.8953935 0.9019052 -#> [990,] 1.1754947 1.2030136 1.2309854 -#> [991,] 0.7230304 0.7697567 0.7910246 -#> [992,] 0.8350104 0.8966890 0.9137141 -#> [993,] 0.8924814 0.9205566 0.9220425 -#> [994,] 1.6178327 1.6278497 1.6799638 -#> [995,] 0.7267149 0.7846327 0.7985012 -#> [996,] 0.6584006 0.6801919 0.6883392 -#> [997,] 1.1804192 1.2117447 1.2190261 -#> [998,] 0.6526862 0.6584006 0.6732712 -#> [999,] 0.9596488 0.9604054 1.0200492 -#> [1000,] 1.2215756 1.2249653 1.2474735 +#> [1,] 1.5243659 1.5808169 1.6309819 +#> [2,] 0.9611154 0.9978109 1.0056389 +#> [3,] 0.8207332 0.8635637 0.8753390 +#> [4,] 0.9403258 0.9432942 0.9694713 +#> [5,] 0.7043648 0.7952674 0.8274985 +#> [6,] 0.5439281 0.5770422 0.5982568 +#> [7,] 0.7036611 0.7609579 0.8734171 +#> [8,] 0.5276847 0.5397691 0.6196082 +#> [9,] 0.6723708 0.7324915 0.7489528 +#> [10,] 0.6932124 0.7214650 0.7540280 +#> [11,] 1.0114782 1.0153096 1.0194461 +#> [12,] 0.6912918 0.6960641 0.7493430 +#> [13,] 0.7598048 0.8036485 0.8177474 +#> [14,] 0.6430809 0.6726969 0.6822545 +#> [15,] 1.0414107 1.0553590 1.0591613 +#> [16,] 0.8700759 0.8739138 0.8938110 +#> [17,] 0.7917640 0.7923931 0.8010192 +#> [18,] 0.8303925 0.8822084 0.8877890 +#> [19,] 0.7475963 0.7657464 0.7864254 +#> [20,] 0.5630225 0.5770422 0.6040373 +#> [21,] 0.6078729 0.6186548 0.6461841 +#> [22,] 0.8864625 0.9761193 0.9881459 +#> [23,] 0.5940433 0.5999559 0.6055215 +#> [24,] 0.6131153 0.6777096 0.7524123 +#> [25,] 0.6602214 0.6994899 0.7164263 +#> [26,] 0.8288068 0.9339935 0.9397074 +#> [27,] 0.6342003 0.6379932 0.6517197 +#> [28,] 1.1203890 1.1337488 1.2100997 +#> [29,] 0.5249234 0.6053305 0.6375590 +#> [30,] 0.7140120 0.7154912 0.7488831 +#> [31,] 0.4942333 0.5761194 0.5855809 +#> [32,] 0.7520886 0.8040873 0.8396827 +#> [33,] 0.8323721 0.8906242 0.9136292 +#> [34,] 0.6641804 0.6717917 0.6872239 +#> [35,] 1.0939944 1.1142197 1.1249202 +#> [36,] 0.5219579 0.5337046 0.5643711 +#> [37,] 0.9828091 1.0030337 1.0086629 +#> [38,] 0.6305399 0.6523084 0.6545695 +#> [39,] 0.8966548 0.9142811 0.9187279 +#> [40,] 0.6468075 0.7282995 0.7573701 +#> [41,] 1.0466836 1.0745694 1.0879764 +#> [42,] 0.8402753 0.8565728 0.8618696 +#> [43,] 0.6616534 0.6651803 0.6820772 +#> [44,] 0.8484498 0.8826854 0.9821245 +#> [45,] 1.6800755 1.6845896 1.7288681 +#> [46,] 0.8054544 0.8615314 0.8707869 +#> [47,] 0.6353663 0.6555521 0.6722421 +#> [48,] 0.9178182 0.9200243 0.9927841 +#> [49,] 0.5457032 0.5663475 0.5790349 +#> [50,] 0.4860429 0.5075179 0.5379716 +#> [51,] 0.7611719 0.7637640 0.7995270 +#> [52,] 1.2980012 1.3192358 1.3233202 +#> [53,] 0.5446332 0.5478312 0.5535708 +#> [54,] 0.6638055 0.6651148 0.6797352 +#> [55,] 0.5976980 0.6141899 0.6375299 +#> [56,] 0.8755020 0.8962753 0.9500238 +#> [57,] 1.2146188 1.2815139 1.4096943 +#> [58,] 1.0638888 1.1074634 1.1145257 +#> [59,] 0.7242678 0.7270035 0.7293509 +#> [60,] 1.0101676 1.0546030 1.0602406 +#> [61,] 0.6618560 0.6654939 0.6703510 +#> [62,] 0.7501233 0.7567534 0.8105553 +#> [63,] 0.7102408 0.7406132 0.7604855 +#> [64,] 0.6923005 0.7421846 0.7520886 +#> [65,] 1.2056942 1.2894449 1.2991575 +#> [66,] 1.0340444 1.0554123 1.0610237 +#> [67,] 1.3881422 1.3955733 1.4387810 +#> [68,] 0.7332208 0.7571446 0.7770250 +#> [69,] 1.0857663 1.1366361 1.1438864 +#> [70,] 0.7315282 0.7459833 0.7725486 +#> [71,] 0.6440949 0.6446191 0.6983985 +#> [72,] 1.2722014 1.2976706 1.3110711 +#> [73,] 1.3203683 1.3419641 1.3451671 +#> [74,] 1.6801885 1.6853579 1.7120924 +#> [75,] 0.5693952 0.5750238 0.5821031 +#> [76,] 1.0838494 1.1065840 1.1169843 +#> [77,] 0.8086745 0.8116027 0.8389967 +#> [78,] 0.6819764 0.7163205 0.7215572 +#> [79,] 0.7950445 0.8059375 0.8063845 +#> [80,] 0.7012542 0.7164036 0.7310572 +#> [81,] 2.1642842 2.2000876 2.2331132 +#> [82,] 0.8300079 0.8348768 0.8955452 +#> [83,] 0.7831751 0.8878947 0.8923720 +#> [84,] 0.7421833 0.7430611 0.7604475 +#> [85,] 1.1437027 1.2023561 1.3051234 +#> [86,] 0.7069953 0.7164263 0.7207612 +#> [87,] 0.5002889 0.5212093 0.5277063 +#> [88,] 1.0414267 1.0564780 1.1446718 +#> [89,] 0.7087140 0.7210939 0.7300471 +#> [90,] 0.6453419 0.6832413 0.7144920 +#> [91,] 0.6620797 0.6756653 0.7069087 +#> [92,] 1.1947872 1.2003763 1.2043890 +#> [93,] 0.8512671 0.8513194 0.9902324 +#> [94,] 0.8398371 0.8401201 0.8559189 +#> [95,] 0.6780016 0.6897462 0.7036488 +#> [96,] 0.5746850 0.5875314 0.5921158 +#> [97,] 0.8658725 0.9338371 0.9690399 +#> [98,] 1.0853816 1.1052694 1.1294021 +#> [99,] 0.6692727 0.6825616 0.7028998 +#> [100,] 1.1663184 1.1840966 1.1903360 +#> [101,] 0.6834033 0.7182481 0.7783143 +#> [102,] 0.7084309 0.7091938 0.7659119 +#> [103,] 0.6449446 0.6462933 0.6719392 +#> [104,] 0.9233463 0.9835241 0.9947733 +#> [105,] 1.1891001 1.2249966 1.2384766 +#> [106,] 1.1099235 1.1355901 1.1725647 +#> [107,] 0.8123415 0.8325188 0.8671570 +#> [108,] 0.8365915 0.8835575 0.9009966 +#> [109,] 0.6326446 0.6574176 0.6704237 +#> [110,] 1.3458911 1.3519802 1.3581095 +#> [111,] 0.6545029 0.6869348 0.6881646 +#> [112,] 1.0572347 1.0772545 1.0896860 +#> [113,] 0.7767504 0.8143436 0.8462347 +#> [114,] 0.9217313 0.9487040 0.9488802 +#> [115,] 0.7756343 0.8557102 0.8710254 +#> [116,] 0.7891744 0.8032448 0.8230275 +#> [117,] 0.8484029 0.8488996 0.8612993 +#> [118,] 0.8383914 0.8446840 0.8528961 +#> [119,] 0.8192676 0.9261510 0.9664756 +#> [120,] 0.7897595 0.8170575 0.8347737 +#> [121,] 0.9853501 1.0255424 1.0457865 +#> [122,] 0.6665253 0.7115648 0.7229703 +#> [123,] 0.7270004 0.7878174 0.7915487 +#> [124,] 1.8305911 1.9078020 1.9944160 +#> [125,] 0.5928401 0.6368901 0.6445785 +#> [126,] 0.7807276 0.7853142 0.8001476 +#> [127,] 0.7061602 0.7241037 0.7882429 +#> [128,] 0.6003784 0.6088547 0.6383166 +#> [129,] 0.7535734 0.7603356 0.7929358 +#> [130,] 0.9012099 0.9060264 0.9358829 +#> [131,] 0.6657764 0.6753620 0.6839478 +#> [132,] 0.9469076 0.9695313 1.0316372 +#> [133,] 0.9726798 1.0643520 1.0705907 +#> [134,] 0.9120428 0.9666811 1.0409997 +#> [135,] 1.5699467 1.5947757 1.6392738 +#> [136,] 0.8182372 0.8191019 0.8344704 +#> [137,] 0.6936492 0.7024877 0.7115648 +#> [138,] 0.6849311 0.7284462 0.7367644 +#> [139,] 0.8675932 0.8743178 0.8836714 +#> [140,] 0.5796756 0.5953103 0.6121718 +#> [141,] 0.5783303 0.5966800 0.6036024 +#> [142,] 0.6007914 0.6438789 0.6502767 +#> [143,] 0.6620525 0.6806436 0.6867477 +#> [144,] 0.8598790 0.8724186 0.9304678 +#> [145,] 0.7014700 0.7119572 0.7208976 +#> [146,] 0.8137668 0.8385605 0.8450309 +#> [147,] 1.8041595 1.8161606 1.8558945 +#> [148,] 1.1210947 1.1285060 1.1598155 +#> [149,] 0.6452655 0.6583454 0.6593995 +#> [150,] 1.3792715 1.4307066 1.5478979 +#> [151,] 1.0860630 1.1100208 1.1332205 +#> [152,] 0.6403226 0.6421274 0.6443655 +#> [153,] 1.1906040 1.2140674 1.2141235 +#> [154,] 1.0057026 1.0182671 1.0256053 +#> [155,] 1.0403373 1.0696501 1.0827458 +#> [156,] 1.1766876 1.2167468 1.2247420 +#> [157,] 0.6424917 0.6529798 0.6937404 +#> [158,] 0.7134555 0.7617684 0.8326278 +#> [159,] 0.7298838 0.7843733 0.8009879 +#> [160,] 0.5654880 0.5663867 0.5886008 +#> [161,] 0.5649241 0.5693824 0.5812760 +#> [162,] 0.8142111 0.8256681 0.8272541 +#> [163,] 0.8281454 0.8328057 0.8483943 +#> [164,] 0.9857413 1.0088831 1.0380476 +#> [165,] 0.5506159 0.5767945 0.5830846 +#> [166,] 0.8656360 0.8914866 0.9326425 +#> [167,] 0.5233900 0.5292069 0.5416509 +#> [168,] 0.5578011 0.5643164 0.5944404 +#> [169,] 0.7386633 0.7662297 0.9012099 +#> [170,] 1.1387468 1.1658656 1.1679701 +#> [171,] 0.9562380 0.9585808 0.9690550 +#> [172,] 0.7958698 0.8491134 0.8656282 +#> [173,] 0.7329002 0.7548487 0.7614309 +#> [174,] 0.8888326 0.9138311 1.0139692 +#> [175,] 0.8472938 0.8731073 0.9566480 +#> [176,] 0.9184091 0.9184174 0.9258757 +#> [177,] 1.2139058 1.2432262 1.2583021 +#> [178,] 0.9205091 0.9403023 0.9821884 +#> [179,] 1.6050450 1.6194342 1.6253970 +#> [180,] 0.7539741 0.7678168 0.7904194 +#> [181,] 0.7747172 0.8280436 0.8445258 +#> [182,] 0.7610390 0.7624164 0.7719599 +#> [183,] 0.8098735 0.8116027 0.9110454 +#> [184,] 1.1174933 1.1827852 1.2005486 +#> [185,] 0.5391978 0.5443826 0.5470348 +#> [186,] 0.6459826 0.6600433 0.6793389 +#> [187,] 0.5965657 0.6096712 0.6342035 +#> [188,] 0.7144528 0.7317062 0.7411022 +#> [189,] 0.6665549 0.7036611 0.8455571 +#> [190,] 0.6707097 0.6855123 0.7104163 +#> [191,] 0.6880536 0.6951885 0.7146643 +#> [192,] 0.6223099 0.6273539 0.6390304 +#> [193,] 0.6342512 0.6388741 0.6752632 +#> [194,] 0.6327771 0.6469318 0.6602149 +#> [195,] 1.3646138 1.4342775 1.4604686 +#> [196,] 0.5524353 0.5610534 0.5743608 +#> [197,] 0.8344419 0.8614908 0.8924273 +#> [198,] 0.9935013 1.0102038 1.0604318 +#> [199,] 0.9424759 0.9766749 0.9793582 +#> [200,] 0.8082735 0.8481908 0.8727487 +#> [201,] 0.9090066 0.9098650 0.9413194 +#> [202,] 0.5442527 0.5662443 0.5758904 +#> [203,] 0.8915143 0.9333763 0.9400642 +#> [204,] 0.8024589 0.8144466 0.8153928 +#> [205,] 1.4519661 1.6070510 1.6374261 +#> [206,] 1.1985378 1.2227509 1.2647286 +#> [207,] 0.5022303 0.5100027 0.5204578 +#> [208,] 0.5768237 0.5828092 0.6070402 +#> [209,] 0.8405143 0.8704515 0.8730958 +#> [210,] 0.5245977 0.5732797 0.5897997 +#> [211,] 0.6331277 0.6618434 0.6851041 +#> [212,] 0.8040936 0.8173328 0.8276968 +#> [213,] 0.6421244 0.6618434 0.6669356 +#> [214,] 0.8695335 0.8838543 0.9361518 +#> [215,] 0.8780073 0.9322287 0.9854342 +#> [216,] 0.6316852 0.6329600 0.6541418 +#> [217,] 0.7409552 0.7749358 0.7765259 +#> [218,] 0.9633232 0.9700862 0.9714272 +#> [219,] 1.0734137 1.1260716 1.2022306 +#> [220,] 0.7089577 0.7958406 0.8049686 +#> [221,] 0.8087226 0.8820261 0.8979552 +#> [222,] 0.8423325 0.8752929 0.8792610 +#> [223,] 1.1291117 1.1304449 1.1406933 +#> [224,] 0.8237598 0.8477494 0.8525389 +#> [225,] 0.8003785 0.8120694 0.8328057 +#> [226,] 0.7349225 0.7711570 0.7720764 +#> [227,] 0.6475480 0.6542009 0.6584288 +#> [228,] 0.7708681 0.7972449 0.8793620 +#> [229,] 0.6023254 0.6616765 0.7303278 +#> [230,] 0.7363367 0.7637374 0.7667885 +#> [231,] 0.5180424 0.5335359 0.5597946 +#> [232,] 0.6078622 0.6278613 0.6333816 +#> [233,] 0.5992267 0.6017974 0.6051926 +#> [234,] 0.7275223 0.7328934 0.7858247 +#> [235,] 0.8318334 0.8319812 0.8925846 +#> [236,] 0.6797679 0.6834770 0.6946250 +#> [237,] 0.6653248 0.7276097 0.7601208 +#> [238,] 0.7668919 0.7835613 0.7921361 +#> [239,] 0.7590097 0.7645542 0.7747119 +#> [240,] 1.4700593 1.4715203 1.4952204 +#> [241,] 1.2063140 1.2099781 1.2246481 +#> [242,] 1.1757867 1.2450534 1.2567412 +#> [243,] 0.5949664 0.6131437 0.6576266 +#> [244,] 1.8735486 1.8739523 1.9042581 +#> [245,] 0.4875000 0.5057847 0.5298338 +#> [246,] 0.8673738 0.8834692 0.9245452 +#> [247,] 0.9667485 0.9776315 0.9832663 +#> [248,] 0.9853571 1.0121351 1.0502786 +#> [249,] 0.5453723 0.5486410 0.5569566 +#> [250,] 0.6523133 0.7128838 0.7176423 +#> [251,] 1.0271251 1.0428790 1.0444198 +#> [252,] 0.8376048 0.8393177 0.9233463 +#> [253,] 0.7022452 0.7049319 0.7232298 +#> [254,] 0.9378070 0.9465619 0.9539625 +#> [255,] 0.7323271 0.8051645 0.8157303 +#> [256,] 0.8054544 0.8320862 0.8332449 +#> [257,] 0.8221198 0.8385605 0.8713739 +#> [258,] 1.5881133 1.6214879 1.6598806 +#> [259,] 0.4608005 0.4680016 0.4993466 +#> [260,] 0.5204578 0.5257055 0.5325700 +#> [261,] 0.8619552 0.8927870 0.9265075 +#> [262,] 0.9361687 0.9668368 0.9751466 +#> [263,] 0.6995904 0.7329333 0.7573726 +#> [264,] 0.7285425 0.7669078 0.7967487 +#> [265,] 0.6361679 0.6626872 0.6931054 +#> [266,] 0.8464808 0.9042451 0.9361598 +#> [267,] 0.7958406 0.8504945 0.8575893 +#> [268,] 0.9130299 0.9142488 0.9184519 +#> [269,] 0.7023902 0.7036488 0.7190904 +#> [270,] 1.0134838 1.0375951 1.0953411 +#> [271,] 0.9033689 0.9500238 0.9779554 +#> [272,] 0.7569841 0.7713455 0.7743237 +#> [273,] 0.8281020 0.8404081 0.8997864 +#> [274,] 0.6021311 0.6329169 0.6451058 +#> [275,] 0.6116902 0.6584954 0.6982418 +#> [276,] 0.6766993 0.6804542 0.6970547 +#> [277,] 1.1671591 1.1940877 1.2013685 +#> [278,] 0.5385578 0.5524752 0.5540552 +#> [279,] 0.9806397 0.9903009 1.0124594 +#> [280,] 0.7733261 0.7757703 0.7814405 +#> [281,] 1.6651384 1.7565781 1.7573194 +#> [282,] 0.6233547 0.6381087 0.6790567 +#> [283,] 0.8320862 0.8486365 0.8659943 +#> [284,] 0.6353006 0.6440505 0.6491302 +#> [285,] 1.2464875 1.2563855 1.3767573 +#> [286,] 0.9741064 0.9807108 1.0087750 +#> [287,] 0.6533300 0.7024012 0.7046043 +#> [288,] 0.9766857 1.0721776 1.0851837 +#> [289,] 0.6582334 0.6715584 0.7489660 +#> [290,] 0.5736358 0.6016753 0.6031855 +#> [291,] 0.9047764 0.9416227 0.9437803 +#> [292,] 0.6534406 0.6535046 0.6689339 +#> [293,] 0.7839534 0.7906479 0.7915487 +#> [294,] 0.6527325 0.7207140 0.7265732 +#> [295,] 0.8515109 0.8821759 0.8829943 +#> [296,] 1.0891309 1.1238643 1.1280101 +#> [297,] 0.6817921 0.7024531 0.7885705 +#> [298,] 0.7650493 0.7830500 0.7850708 +#> [299,] 0.9253394 0.9618571 0.9675878 +#> [300,] 0.8019273 0.8136202 0.8417530 +#> [301,] 0.8982530 0.9216886 0.9237611 +#> [302,] 0.6387119 0.6701486 0.6924840 +#> [303,] 0.8194054 0.8245683 0.8276968 +#> [304,] 0.8163358 0.8968895 0.9190604 +#> [305,] 0.6911725 0.7333086 0.7364530 +#> [306,] 0.6273922 0.6494594 0.6758489 +#> [307,] 0.6020250 0.6032960 0.6141436 +#> [308,] 1.2462559 1.2627624 1.2734046 +#> [309,] 1.0961978 1.1436299 1.1709643 +#> [310,] 0.6557014 0.6608235 0.6910342 +#> [311,] 0.6901804 0.7042251 0.7121510 +#> [312,] 0.7725283 0.8024589 0.8167136 +#> [313,] 1.3093299 1.3269674 1.3276929 +#> [314,] 0.7285425 0.7598048 0.7688160 +#> [315,] 0.8906903 0.9014016 0.9082654 +#> [316,] 0.6597708 0.6716182 0.7138331 +#> [317,] 1.3444230 1.3969292 1.4297278 +#> [318,] 1.0851945 1.1559070 1.1757867 +#> [319,] 0.6329600 0.6380099 0.6387574 +#> [320,] 0.5443672 0.5488831 0.5624516 +#> [321,] 1.0981134 1.1148162 1.1424652 +#> [322,] 0.6027873 0.6314889 0.6915544 +#> [323,] 0.9562376 1.0873696 1.1439529 +#> [324,] 1.1809562 1.1949260 1.2121301 +#> [325,] 1.2758465 1.2764101 1.3314925 +#> [326,] 0.8488415 0.8858925 0.9049453 +#> [327,] 0.7816092 0.8003785 0.8061216 +#> [328,] 1.5466154 1.6159929 1.6380888 +#> [329,] 1.5863012 1.5911419 1.7342734 +#> [330,] 0.9494797 0.9970640 1.0387814 +#> [331,] 0.5663867 0.5977062 0.6327771 +#> [332,] 0.7786057 0.7926984 0.8472351 +#> [333,] 0.5780262 0.5830670 0.6025930 +#> [334,] 0.8014663 0.8420080 0.8437626 +#> [335,] 0.5649241 0.5889128 0.6072000 +#> [336,] 0.8751113 0.8806370 0.9049909 +#> [337,] 0.9169476 0.9258788 0.9786166 +#> [338,] 1.0001497 1.1000918 1.1152972 +#> [339,] 1.1983213 1.2084839 1.2476221 +#> [340,] 0.8387845 0.8578511 0.8625806 +#> [341,] 1.0503849 1.0581414 1.0981878 +#> [342,] 0.5831863 0.5897049 0.6166476 +#> [343,] 0.8909072 0.8952005 0.9274179 +#> [344,] 0.9235493 0.9288125 0.9731136 +#> [345,] 2.8094835 2.8886078 2.9410917 +#> [346,] 0.5564435 0.5892740 0.6000514 +#> [347,] 0.7551687 0.8334741 0.8581683 +#> [348,] 0.7263683 0.7687818 0.7934345 +#> [349,] 0.6638946 0.7025544 0.7051790 +#> [350,] 1.6618546 1.6642624 1.7089094 +#> [351,] 1.0794372 1.1290306 1.1451370 +#> [352,] 0.6767724 0.7167063 0.7404281 +#> [353,] 0.8472273 0.8487451 0.8598339 +#> [354,] 0.9188042 0.9250145 0.9784457 +#> [355,] 0.6427334 0.6523133 0.6591489 +#> [356,] 0.5867750 0.6096996 0.6609022 +#> [357,] 0.6504095 0.6693203 0.6915823 +#> [358,] 1.0662185 1.0938059 1.1255470 +#> [359,] 0.6849131 0.7253001 0.7567343 +#> [360,] 0.8253925 0.8469022 0.8507445 +#> [361,] 1.2972732 1.3129135 1.3208724 +#> [362,] 0.5900256 0.5966234 0.6255330 +#> [363,] 0.8098279 0.8405189 0.8419364 +#> [364,] 0.8382100 0.8417069 0.8511844 +#> [365,] 0.9920899 0.9990774 1.0466645 +#> [366,] 0.5600479 0.6000714 0.6015735 +#> [367,] 0.8771099 0.8793941 0.8808018 +#> [368,] 1.1420320 1.2435833 1.2576645 +#> [369,] 0.8582952 0.8599602 0.8648614 +#> [370,] 0.6438789 0.6464029 0.6594544 +#> [371,] 0.8132519 0.8348768 0.8718345 +#> [372,] 0.8886290 0.8980959 0.9182436 +#> [373,] 0.6728326 0.6859789 0.7359366 +#> [374,] 0.7909004 0.8103030 0.8672868 +#> [375,] 1.2857568 1.3381113 1.3387273 +#> [376,] 0.7944559 0.8013535 0.8304747 +#> [377,] 1.0194842 1.0607650 1.1032316 +#> [378,] 0.7188675 0.8235059 0.8276617 +#> [379,] 1.0211121 1.1530092 1.1705561 +#> [380,] 0.9923883 1.0221610 1.0783806 +#> [381,] 0.6207979 0.6427334 0.6639098 +#> [382,] 1.2759019 1.2801700 1.3483353 +#> [383,] 0.7151393 0.7507690 0.7514525 +#> [384,] 0.7303102 0.7447334 0.7563054 +#> [385,] 0.7729818 0.7752801 0.8192156 +#> [386,] 0.5042462 0.5132578 0.5347960 +#> [387,] 0.6685003 0.7032316 0.7095077 +#> [388,] 1.2940043 1.3116532 1.3305883 +#> [389,] 0.4817999 0.4878093 0.5410208 +#> [390,] 0.7331032 0.7444346 0.7671677 +#> [391,] 0.7537042 0.7746594 0.7845776 +#> [392,] 0.9354164 0.9916784 1.0038588 +#> [393,] 1.1592034 1.1789048 1.1881525 +#> [394,] 0.6467058 0.6565775 0.6923005 +#> [395,] 0.7310763 0.7552492 0.7594214 +#> [396,] 0.8986675 0.9143464 0.9276981 +#> [397,] 0.8486483 0.8508795 0.8601285 +#> [398,] 0.9976411 1.0011695 1.0739424 +#> [399,] 0.8745644 0.8782130 0.9043587 +#> [400,] 0.9098650 1.0092030 1.0362576 +#> [401,] 0.5597946 0.5927760 0.6031826 +#> [402,] 0.7700191 0.7817334 0.7818776 +#> [403,] 0.9737559 1.0240651 1.0344264 +#> [404,] 0.5936955 0.5965183 0.6031826 +#> [405,] 0.5359840 0.5426235 0.5494953 +#> [406,] 0.4984314 0.5700570 0.5701457 +#> [407,] 0.6150030 0.6689186 0.6761037 +#> [408,] 1.2397482 1.3744765 1.4820356 +#> [409,] 1.1517586 1.1595079 1.1607152 +#> [410,] 0.8970618 0.9174193 0.9612071 +#> [411,] 0.6916650 0.6981241 0.7041224 +#> [412,] 0.6471527 0.6816065 0.6878781 +#> [413,] 0.7327581 0.7406132 0.7452370 +#> [414,] 0.6648266 0.6678141 0.6744941 +#> [415,] 0.6697815 0.6791187 0.6953316 +#> [416,] 0.8168176 0.8194116 0.8211489 +#> [417,] 0.6078729 0.6152754 0.6421274 +#> [418,] 1.7155196 1.7207718 1.7415727 +#> [419,] 0.9481335 0.9612071 0.9820950 +#> [420,] 0.6800200 0.6808957 0.6992552 +#> [421,] 0.6046658 0.6209275 0.6402169 +#> [422,] 0.6835824 0.7585124 0.7881878 +#> [423,] 0.7491987 0.7522365 0.7601292 +#> [424,] 0.5020218 0.5030537 0.5178701 +#> [425,] 2.2932696 2.3723662 2.4223384 +#> [426,] 0.7645542 0.8054434 0.8494835 +#> [427,] 1.4016451 1.4382258 1.4433517 +#> [428,] 0.8341936 0.8344704 0.8387845 +#> [429,] 0.6107487 0.6735196 0.6762099 +#> [430,] 0.7438620 0.7674429 0.7714117 +#> [431,] 0.8219423 0.8234266 0.8463815 +#> [432,] 0.7341805 0.7534588 0.7727361 +#> [433,] 0.6377760 0.6462234 0.6638736 +#> [434,] 0.9333763 0.9422343 0.9488802 +#> [435,] 1.1427459 1.1572862 1.1624329 +#> [436,] 0.7498497 0.7768013 0.7789587 +#> [437,] 0.7549707 0.7669871 0.7987205 +#> [438,] 1.0791655 1.0973817 1.1052959 +#> [439,] 0.4825606 0.5023097 0.5102164 +#> [440,] 0.7093803 0.7365695 0.8450230 +#> [441,] 0.8186225 0.8575886 0.8617306 +#> [442,] 1.1939267 1.2530121 1.2676821 +#> [443,] 1.0690217 1.0938534 1.1098776 +#> [444,] 0.7602211 0.7900737 0.8484472 +#> [445,] 0.7305650 0.7666968 0.7783143 +#> [446,] 0.7870018 0.8203685 0.8417069 +#> [447,] 0.7270035 0.7565922 0.7755959 +#> [448,] 0.8279811 0.8490338 0.8573882 +#> [449,] 0.7283879 0.7563002 0.7800571 +#> [450,] 0.7829672 0.7968122 0.8093141 +#> [451,] 0.9289359 0.9913643 1.0041851 +#> [452,] 0.8580262 0.8603759 0.8625607 +#> [453,] 0.7599411 0.8054832 0.8322838 +#> [454,] 0.5303881 0.5404534 0.5506574 +#> [455,] 0.8750339 0.8754412 0.8896112 +#> [456,] 0.9700862 0.9895414 1.0009251 +#> [457,] 0.7884800 0.7985785 0.8309672 +#> [458,] 0.7219261 0.7259888 0.7685295 +#> [459,] 1.5169999 1.5208390 1.5669053 +#> [460,] 0.9304993 1.0034544 1.0204791 +#> [461,] 0.9252409 0.9413701 1.0006566 +#> [462,] 0.7252742 0.7520877 0.7681750 +#> [463,] 0.5873715 0.5940268 0.6009576 +#> [464,] 1.4102500 1.4254980 1.4289465 +#> [465,] 0.9724693 0.9892153 1.0053203 +#> [466,] 0.6776049 0.6793633 0.7444877 +#> [467,] 0.7234270 0.7373352 0.7395970 +#> [468,] 0.7312486 0.7620866 0.7834036 +#> [469,] 1.9449903 1.9997245 2.0342805 +#> [470,] 1.8673973 1.9165614 1.9322301 +#> [471,] 0.7849593 0.7867677 0.8164522 +#> [472,] 0.8077282 0.8113149 0.8162749 +#> [473,] 0.8092988 0.8227479 0.8581683 +#> [474,] 0.7886554 0.8040224 0.8216809 +#> [475,] 0.9568295 0.9668492 0.9772124 +#> [476,] 0.7496162 0.7635732 0.7935786 +#> [477,] 1.1293331 1.1469452 1.1471375 +#> [478,] 0.6020250 0.6364563 0.6564804 +#> [479,] 0.8828625 0.9178182 0.9357569 +#> [480,] 0.8551747 0.8561162 0.8696071 +#> [481,] 0.7271914 0.7889064 0.8276440 +#> [482,] 0.9167345 0.9432508 0.9734263 +#> [483,] 0.8877356 0.8877721 0.9464073 +#> [484,] 0.6820772 0.6901804 0.7073480 +#> [485,] 0.6032926 0.6355482 0.6519567 +#> [486,] 0.7694448 0.7867677 0.7921361 +#> [487,] 0.8669934 0.8987052 0.9039787 +#> [488,] 0.5386993 0.5510784 0.5615143 +#> [489,] 0.9526243 0.9738814 1.0211781 +#> [490,] 0.5797474 0.5819609 0.5829153 +#> [491,] 1.3827639 1.4192413 1.4736857 +#> [492,] 0.6208781 0.6852571 0.7241467 +#> [493,] 0.5825832 0.6224511 0.6276391 +#> [494,] 1.4299967 1.4388615 1.4452256 +#> [495,] 0.8014326 0.8063517 0.8796086 +#> [496,] 1.0511263 1.0638448 1.0654817 +#> [497,] 0.6331116 0.6969062 0.6987263 +#> [498,] 0.5942840 0.6013444 0.6100400 +#> [499,] 0.8186754 0.8388021 0.8446939 +#> [500,] 1.0481851 1.1458741 1.1547490 +#> [501,] 0.6127121 0.6392152 0.6698288 +#> [502,] 0.7746160 0.8066735 0.8549282 +#> [503,] 0.7550132 0.8247882 0.8276536 +#> [504,] 0.7007891 0.7760447 0.7927290 +#> [505,] 0.6207979 0.6369267 0.7119260 +#> [506,] 0.8450073 0.8506358 0.8947993 +#> [507,] 0.4527564 0.4929968 0.5031262 +#> [508,] 0.8210360 0.8356232 0.8370369 +#> [509,] 0.6196056 0.7501435 0.8137668 +#> [510,] 0.6689186 0.6801213 0.7140120 +#> [511,] 1.6786398 1.6801571 1.6809669 +#> [512,] 1.0270393 1.0378446 1.0830380 +#> [513,] 1.0686446 1.0745639 1.0884353 +#> [514,] 1.2383352 1.3142746 1.3165361 +#> [515,] 0.6923461 0.7222090 0.7349476 +#> [516,] 1.6999924 1.7366141 1.7710182 +#> [517,] 0.7227302 0.7333383 0.7438428 +#> [518,] 0.6412565 0.6487788 0.6527705 +#> [519,] 0.7007891 0.7166611 0.7211109 +#> [520,] 1.0286506 1.1018465 1.1064026 +#> [521,] 0.7539787 0.7646527 0.7749358 +#> [522,] 0.6460914 0.6592550 0.6777096 +#> [523,] 0.6011296 0.6099995 0.6267533 +#> [524,] 0.7527483 0.7889473 0.7951141 +#> [525,] 1.4603744 1.6534632 1.6554812 +#> [526,] 0.6993946 0.7225583 0.7272496 +#> [527,] 1.4180602 1.4306645 1.4584196 +#> [528,] 0.8336929 0.8786696 0.8990421 +#> [529,] 0.8049686 0.9500680 0.9775793 +#> [530,] 1.5558516 1.5605163 1.6140382 +#> [531,] 0.5470348 0.5673608 0.5755022 +#> [532,] 0.7040888 0.7044367 0.7181985 +#> [533,] 0.5600479 0.5693824 0.5705460 +#> [534,] 0.7481863 0.7556583 0.7578103 +#> [535,] 1.6764237 1.6891714 1.6925196 +#> [536,] 0.6892268 0.7104893 0.7386475 +#> [537,] 0.6605804 0.6822545 0.6859765 +#> [538,] 0.6724188 0.6760600 0.6805757 +#> [539,] 0.9236578 0.9779554 0.9825563 +#> [540,] 1.5450570 1.5982529 1.6469659 +#> [541,] 0.7671446 0.7686223 0.7913083 +#> [542,] 0.7749350 0.7760432 0.7970975 +#> [543,] 0.9972164 1.0454226 1.0554706 +#> [544,] 0.9734595 0.9762631 0.9798293 +#> [545,] 1.2253034 1.2347755 1.2356855 +#> [546,] 0.5308653 0.5433494 0.5648950 +#> [547,] 0.6845853 0.6870117 0.7195420 +#> [548,] 0.6836239 0.7093803 0.7112369 +#> [549,] 0.6624672 0.7044367 0.7271320 +#> [550,] 0.5930796 0.6009576 0.6332259 +#> [551,] 1.0512387 1.0828803 1.0834900 +#> [552,] 1.0671486 1.0812531 1.0817206 +#> [553,] 1.0822813 1.0942597 1.1059081 +#> [554,] 0.4990195 0.5265139 0.5447722 +#> [555,] 0.6851350 0.6876713 0.6926234 +#> [556,] 0.6995904 0.7367393 0.7459699 +#> [557,] 1.0643377 1.0757091 1.0772153 +#> [558,] 0.5876273 0.5937526 0.6021311 +#> [559,] 0.6316512 0.6322469 0.6470051 +#> [560,] 0.8883228 0.9206066 0.9323522 +#> [561,] 0.6731440 0.6860345 0.6885872 +#> [562,] 0.6638946 0.7372376 0.8081303 +#> [563,] 0.8289467 0.8349365 0.8653335 +#> [564,] 0.7583382 0.7673915 0.8239604 +#> [565,] 0.7240913 0.7753555 0.7841834 +#> [566,] 0.5998774 0.6128027 0.6591489 +#> [567,] 1.6908154 1.7030627 1.7225823 +#> [568,] 0.7425193 0.7489660 0.7500161 +#> [569,] 0.7170007 0.8089017 0.8122410 +#> [570,] 0.6774092 0.6799657 0.7025544 +#> [571,] 0.4820388 0.5136056 0.5213306 +#> [572,] 0.5566345 0.5922417 0.5935245 +#> [573,] 0.7110430 0.7135245 0.7384764 +#> [574,] 0.8450073 0.8713145 0.8943658 +#> [575,] 0.5573569 0.5840487 0.6198596 +#> [576,] 0.5361367 0.5540552 0.5557784 +#> [577,] 0.5574661 0.5752938 0.5902023 +#> [578,] 0.7813525 0.8041972 0.8239604 +#> [579,] 0.7566974 0.8184313 0.8656965 +#> [580,] 1.1771946 1.1826661 1.1970214 +#> [581,] 1.0231257 1.1010580 1.1155543 +#> [582,] 0.9441800 1.0828442 1.1472337 +#> [583,] 0.5396463 0.5420004 0.5607260 +#> [584,] 1.2029668 1.2605534 1.3054052 +#> [585,] 0.6278613 0.6442174 0.6732055 +#> [586,] 0.8234069 0.8244759 0.8432501 +#> [587,] 0.6341530 0.6353158 0.6772174 +#> [588,] 0.7814405 0.8528844 0.8812575 +#> [589,] 0.6008158 0.6372487 0.6381665 +#> [590,] 0.8535271 0.8584035 0.8604812 +#> [591,] 0.6475480 0.6583511 0.6692462 +#> [592,] 0.6862504 0.7064858 0.7096041 +#> [593,] 0.7141790 0.7540948 0.7743470 +#> [594,] 0.6790748 0.7063940 0.7134847 +#> [595,] 0.8243222 0.8426470 0.8441713 +#> [596,] 0.4841514 0.5657912 0.5755022 +#> [597,] 0.7452370 0.7575772 0.7952843 +#> [598,] 1.4963948 1.5203882 1.6024957 +#> [599,] 1.1383098 1.1702267 1.2238629 +#> [600,] 1.0364890 1.0783860 1.0881871 +#> [601,] 0.9747691 0.9784879 0.9891670 +#> [602,] 0.6325404 0.6790821 0.7024531 +#> [603,] 0.6043368 0.6424482 0.6583511 +#> [604,] 0.5864847 0.6016442 0.6022500 +#> [605,] 0.5472789 0.5627529 0.6346750 +#> [606,] 1.0111774 1.0255424 1.0621965 +#> [607,] 0.8565637 0.8674535 0.8724976 +#> [608,] 1.0922215 1.1643951 1.1807064 +#> [609,] 0.9794582 1.0258198 1.0537422 +#> [610,] 1.8744437 1.8869722 1.9029999 +#> [611,] 0.9482153 1.0703472 1.0738687 +#> [612,] 0.5826571 0.6483689 0.7058580 +#> [613,] 0.7554312 0.7697521 0.7769445 +#> [614,] 0.7722574 0.7785427 0.7852635 +#> [615,] 0.7480086 0.8008770 0.8052328 +#> [616,] 0.4987497 0.5915236 0.6025436 +#> [617,] 1.1530540 1.1624329 1.1696436 +#> [618,] 0.6675875 0.6696642 0.6964581 +#> [619,] 1.9258524 1.9589473 1.9616130 +#> [620,] 0.7694448 0.7945205 0.8112945 +#> [621,] 0.6608919 0.7044144 0.7183474 +#> [622,] 0.8357592 0.8851545 0.9266689 +#> [623,] 1.4453892 1.5597602 1.5799055 +#> [624,] 0.6013444 0.6160422 0.6277272 +#> [625,] 0.6735509 0.7061602 0.7103009 +#> [626,] 0.7594411 0.7819699 0.8162749 +#> [627,] 0.6639936 0.6704440 0.7070445 +#> [628,] 1.4792002 1.4858616 1.5553256 +#> [629,] 0.6801713 0.6803374 0.6807494 +#> [630,] 0.7838042 0.8330158 0.8551235 +#> [631,] 0.5658423 0.5659957 0.5706138 +#> [632,] 0.8275516 0.8398715 0.8894340 +#> [633,] 1.8945030 1.9278525 1.9424763 +#> [634,] 0.7367904 0.7715897 0.7772676 +#> [635,] 0.5245977 0.5419030 0.5470238 +#> [636,] 0.7606916 0.7766755 0.7873526 +#> [637,] 0.8667406 0.9232460 0.9668317 +#> [638,] 0.7093980 0.7179996 0.8043226 +#> [639,] 0.7107465 0.7365392 0.7509218 +#> [640,] 0.6474838 0.6752632 0.7081496 +#> [641,] 0.7407793 0.7601183 0.7669871 +#> [642,] 0.5757339 0.5988755 0.6056370 +#> [643,] 0.8543398 0.9490901 0.9616271 +#> [644,] 0.7037034 0.7089464 0.7225416 +#> [645,] 1.2614159 1.3394622 1.4055323 +#> [646,] 1.0875151 1.1115924 1.1613842 +#> [647,] 1.2023717 1.2228821 1.2469979 +#> [648,] 0.5538165 0.5571109 0.5848674 +#> [649,] 0.6731897 0.6863041 0.7436029 +#> [650,] 1.0723583 1.0829244 1.0888367 +#> [651,] 0.5360911 0.5439281 0.5514637 +#> [652,] 0.9823087 1.0061961 1.0161166 +#> [653,] 0.9615576 0.9772413 1.0220572 +#> [654,] 0.8567612 0.8835575 0.9073879 +#> [655,] 0.8739169 0.9189904 0.9469421 +#> [656,] 0.6440738 0.6553880 0.6621010 +#> [657,] 0.6948833 0.7239879 0.7303132 +#> [658,] 2.0539740 2.0902739 2.1038077 +#> [659,] 0.9031826 0.9270452 0.9469395 +#> [660,] 0.8741287 0.8839707 0.9087493 +#> [661,] 0.8132943 0.8224501 0.8247882 +#> [662,] 0.8186754 0.8330141 0.8343830 +#> [663,] 0.9810485 0.9927890 1.0106749 +#> [664,] 0.7029838 0.7151743 0.7163519 +#> [665,] 0.6817316 0.7015655 0.7115043 +#> [666,] 0.8458111 0.9004967 0.9803732 +#> [667,] 0.7187057 0.7246572 0.7471467 +#> [668,] 0.7927652 0.7950445 0.8107627 +#> [669,] 0.6911190 0.7413602 0.8334453 +#> [670,] 1.1509168 1.1776238 1.1924359 +#> [671,] 0.5475349 0.5505027 0.5639221 +#> [672,] 0.7212985 0.7231728 0.7255583 +#> [673,] 0.6546189 0.6678590 0.6745093 +#> [674,] 0.6236229 0.6316512 0.6448085 +#> [675,] 0.6976790 0.6988070 0.7022311 +#> [676,] 0.8647881 0.8881306 0.9042629 +#> [677,] 0.8584035 0.9732872 0.9841871 +#> [678,] 0.6459826 0.7084309 0.7096870 +#> [679,] 0.6269285 0.6510310 0.7190904 +#> [680,] 0.9532322 0.9611154 0.9822419 +#> [681,] 0.9136462 0.9197724 0.9385553 +#> [682,] 0.6151094 0.6664986 0.6852734 +#> [683,] 0.6181234 0.6336101 0.6550931 +#> [684,] 0.8281020 0.8915035 0.9109314 +#> [685,] 0.7855720 0.8112534 0.8486313 +#> [686,] 0.7968281 0.7993908 0.8096249 +#> [687,] 0.8684122 0.8933293 0.9039735 +#> [688,] 0.6518934 0.6784733 0.7134594 +#> [689,] 0.9237826 0.9400302 0.9471953 +#> [690,] 0.8141279 0.8358715 0.8369607 +#> [691,] 0.5529875 0.5601840 0.5644245 +#> [692,] 0.5517974 0.5693161 0.5873525 +#> [693,] 1.0859972 1.1167549 1.1855703 +#> [694,] 0.6327384 0.6403465 0.6522010 +#> [695,] 0.5369063 0.5844722 0.6025021 +#> [696,] 0.9683329 0.9684100 1.0204332 +#> [697,] 0.8111384 0.8424851 0.8542503 +#> [698,] 1.1814480 1.2263634 1.2384766 +#> [699,] 0.7927652 0.8077602 0.8295842 +#> [700,] 1.2702424 1.2908346 1.3338531 +#> [701,] 0.8291931 0.8403914 0.8738022 +#> [702,] 0.6434446 0.6671026 0.6860087 +#> [703,] 0.7074719 0.7149534 0.7375093 +#> [704,] 0.8169586 0.8272137 0.8283524 +#> [705,] 1.2313260 1.2356855 1.2885794 +#> [706,] 0.8154469 0.8226472 0.8487451 +#> [707,] 0.4983350 0.5262098 0.5327186 +#> [708,] 1.0624992 1.0715707 1.1079968 +#> [709,] 0.6789879 0.7242251 0.7460334 +#> [710,] 0.7968163 0.8044401 0.8081303 +#> [711,] 0.8103030 0.8407096 0.8612372 +#> [712,] 0.6796196 0.7025482 0.7212138 +#> [713,] 0.7080254 0.7608227 0.8142425 +#> [714,] 1.0440521 1.0512421 1.0767011 +#> [715,] 0.8151947 0.8285825 0.8582232 +#> [716,] 0.6284367 0.7223476 0.7408906 +#> [717,] 0.7046757 0.7136841 0.7604333 +#> [718,] 0.8838055 0.9219829 0.9919327 +#> [719,] 0.6121718 0.6380099 0.6388043 +#> [720,] 0.5953103 0.6349862 0.6359090 +#> [721,] 1.8820315 1.9001787 1.9088798 +#> [722,] 0.9795572 0.9823719 1.0485794 +#> [723,] 0.7339828 0.7342629 0.7421833 +#> [724,] 0.7866264 0.8289504 0.8772428 +#> [725,] 0.7079180 0.7419142 0.7869214 +#> [726,] 0.8201243 0.8342268 0.8486467 +#> [727,] 0.6288299 0.6441286 0.6508259 +#> [728,] 0.7837983 0.8149223 0.8295842 +#> [729,] 1.1908658 1.2187422 1.2412926 +#> [730,] 0.6217570 0.6510310 0.6630560 +#> [731,] 0.9160105 0.9928598 1.1118426 +#> [732,] 1.4420573 1.4974156 1.5126267 +#> [733,] 1.7203979 1.7855192 1.8151633 +#> [734,] 0.4929968 0.5297014 0.5530272 +#> [735,] 0.5965755 0.6003747 0.6095929 +#> [736,] 0.6981032 0.7104502 0.7132787 +#> [737,] 1.0985167 1.1041007 1.1451672 +#> [738,] 0.9223074 1.0153096 1.0387814 +#> [739,] 0.7026276 0.7146490 0.7245649 +#> [740,] 0.7207847 0.7329248 0.7348654 +#> [741,] 0.6898966 0.7134847 0.7154010 +#> [742,] 0.6608039 0.7080254 0.7310347 +#> [743,] 0.9439670 1.0013745 1.1012426 +#> [744,] 1.3015360 1.3296636 1.3845750 +#> [745,] 0.6325404 0.6648266 0.6837852 +#> [746,] 0.8505685 0.8759186 0.8890348 +#> [747,] 0.7840299 0.8194621 0.8238080 +#> [748,] 0.7545404 0.7563807 0.8210554 +#> [749,] 1.6518706 1.6901572 1.7073382 +#> [750,] 1.1184452 1.1456799 1.1546014 +#> [751,] 0.9202416 0.9760870 0.9904668 +#> [752,] 0.7601292 0.7789707 0.7888291 +#> [753,] 1.8041411 1.8399981 1.9228155 +#> [754,] 0.5117631 0.5189561 0.5379454 +#> [755,] 0.9188856 0.9264988 0.9320816 +#> [756,] 0.6017029 0.6238293 0.6410903 +#> [757,] 0.7847037 0.7996425 0.8101178 +#> [758,] 0.6032702 0.6093081 0.6393244 +#> [759,] 0.6645667 0.6992010 0.7103009 +#> [760,] 0.6381541 0.7110158 0.7136425 +#> [761,] 0.6806684 0.6858037 0.8278033 +#> [762,] 0.8708349 0.9128123 0.9241553 +#> [763,] 0.6832413 0.6860087 0.7341805 +#> [764,] 0.6157924 0.6343072 0.6558207 +#> [765,] 1.0118846 1.0134499 1.0171610 +#> [766,] 0.5975777 0.6000514 0.6011296 +#> [767,] 0.4531303 0.4674339 0.4860429 +#> [768,] 0.7399173 0.7863414 0.7904919 +#> [769,] 0.9369696 0.9517156 0.9656858 +#> [770,] 1.4471992 1.4562042 1.4820356 +#> [771,] 1.4930419 1.5148422 1.5548330 +#> [772,] 0.7373352 0.7443812 0.7529223 +#> [773,] 1.3959402 1.4042072 1.4333654 +#> [774,] 0.7428682 0.7751488 0.7923230 +#> [775,] 1.6821274 1.7425683 1.8219406 +#> [776,] 0.7952674 0.8616408 0.8749683 +#> [777,] 0.7886363 0.7969842 0.8028407 +#> [778,] 0.7563054 0.7619809 0.7651778 +#> [779,] 0.6701998 0.6882292 0.7097852 +#> [780,] 0.5524353 0.5626330 0.6274188 +#> [781,] 0.6867477 0.6981032 0.7169447 +#> [782,] 0.5279354 0.5476963 0.5488831 +#> [783,] 1.0564007 1.0920713 1.1147534 +#> [784,] 0.8546296 0.9293673 0.9503139 +#> [785,] 1.2862190 1.3487894 1.3838227 +#> [786,] 0.8884697 0.8903167 0.9072002 +#> [787,] 0.5993060 0.6125669 0.6208781 +#> [788,] 1.0283774 1.0756508 1.0760060 +#> [789,] 0.9727410 0.9951119 0.9999906 +#> [790,] 0.6117980 0.6456994 0.6477592 +#> [791,] 0.9312645 0.9560108 0.9569102 +#> [792,] 1.0465306 1.0909766 1.0914729 +#> [793,] 0.9404725 0.9429739 0.9568295 +#> [794,] 0.6590509 0.6658791 0.6763955 +#> [795,] 1.0802310 1.0822813 1.1103753 +#> [796,] 0.7007486 0.7014731 0.7070516 +#> [797,] 1.0239352 1.0372344 1.0721517 +#> [798,] 0.6339919 0.6431744 0.7286949 +#> [799,] 1.0520687 1.0530698 1.1656796 +#> [800,] 0.7208518 0.7217458 0.7246355 +#> [801,] 0.5717532 0.5975777 0.6159771 +#> [802,] 0.9789055 1.0506605 1.1064540 +#> [803,] 0.6408472 0.7736341 0.8250772 +#> [804,] 0.8274985 0.8491893 0.8821759 +#> [805,] 0.5870801 0.5927017 0.6470051 +#> [806,] 1.9180341 1.9459520 2.0220995 +#> [807,] 1.1019302 1.1048826 1.1452958 +#> [808,] 0.7080196 0.7107465 0.7237772 +#> [809,] 0.7306420 0.7346647 0.7392183 +#> [810,] 0.9619491 0.9759745 0.9819215 +#> [811,] 0.7134353 0.7410821 0.7671390 +#> [812,] 0.4397050 0.5120447 0.5158111 +#> [813,] 0.5866931 0.6096429 0.6152754 +#> [814,] 0.7999641 0.8117286 0.8357712 +#> [815,] 0.8746795 0.9115486 0.9335750 +#> [816,] 0.7146225 0.8258685 0.8431029 +#> [817,] 1.0041419 1.0242532 1.0347041 +#> [818,] 0.6384520 0.6638735 0.7109461 +#> [819,] 0.7700035 0.7865491 0.8122098 +#> [820,] 1.0418785 1.0676253 1.1292646 +#> [821,] 0.7532504 0.7747172 0.8473076 +#> [822,] 0.9227195 0.9487566 0.9581983 +#> [823,] 0.5875314 0.5942840 0.5965755 +#> [824,] 1.2553628 1.2694406 1.3343760 +#> [825,] 1.2048136 1.3095050 1.3428537 +#> [826,] 0.9132950 0.9771254 1.0106245 +#> [827,] 0.5661952 0.5750395 0.5822294 +#> [828,] 0.6576110 0.6597499 0.6673354 +#> [829,] 0.9136389 0.9287249 0.9310701 +#> [830,] 0.8903167 0.9201335 0.9323082 +#> [831,] 0.6718409 0.6869043 0.6991622 +#> [832,] 0.6859404 0.7035090 0.7048176 +#> [833,] 0.7420225 0.7588907 0.7750704 +#> [834,] 1.0100578 1.0138136 1.0163078 +#> [835,] 1.8535687 1.9005632 1.9730804 +#> [836,] 0.6506995 0.6590509 0.6599847 +#> [837,] 0.9098862 0.9364781 0.9552696 +#> [838,] 0.6858191 0.6927322 0.7062499 +#> [839,] 1.0220518 1.0238183 1.0621965 +#> [840,] 0.5401356 0.5557784 0.5687479 +#> [841,] 0.6776225 0.6874784 0.6990156 +#> [842,] 1.0768269 1.1514249 1.1546014 +#> [843,] 0.6942737 0.7040666 0.7097852 +#> [844,] 1.0846216 1.1308978 1.1327691 +#> [845,] 1.2357066 1.2465444 1.2791774 +#> [846,] 0.6924840 0.6987541 0.7768013 +#> [847,] 0.5996629 0.6371110 0.6737094 +#> [848,] 0.5578011 0.5624516 0.5842370 +#> [849,] 0.9665835 0.9976055 1.0352665 +#> [850,] 0.7457151 0.7990490 0.8223862 +#> [851,] 0.8717785 0.9060917 0.9801779 +#> [852,] 0.7719812 0.7912119 0.7975241 +#> [853,] 0.9266390 0.9300174 0.9645604 +#> [854,] 0.6398836 0.6412565 0.6775025 +#> [855,] 0.7485899 0.7781986 0.8268827 +#> [856,] 0.9786673 1.0056389 1.0444198 +#> [857,] 0.6893189 0.6929399 0.7584337 +#> [858,] 0.5513524 0.5553564 0.5569566 +#> [859,] 0.8832566 0.9021617 0.9851522 +#> [860,] 0.5454917 0.5477646 0.5658402 +#> [861,] 1.1027024 1.1274677 1.1693164 +#> [862,] 0.7058580 0.8086243 0.8343830 +#> [863,] 0.5877672 0.5956524 0.5991496 +#> [864,] 1.2654594 1.3005064 1.3382089 +#> [865,] 1.1711535 1.1903278 1.2141117 +#> [866,] 0.7920784 0.7935786 0.8320460 +#> [867,] 1.0431132 1.0607671 1.0757144 +#> [868,] 1.4160604 1.4465604 1.4920582 +#> [869,] 0.5573569 0.5675909 0.5761090 +#> [870,] 0.6336309 0.6358192 0.6776049 +#> [871,] 0.8684122 0.8849601 0.8927541 +#> [872,] 1.1902981 1.2012097 1.2430482 +#> [873,] 1.2633157 1.2769944 1.2891117 +#> [874,] 1.1157276 1.1936167 1.1995324 +#> [875,] 0.8294768 0.8344419 0.8362827 +#> [876,] 0.5117953 0.5192327 0.5420004 +#> [877,] 0.8030448 0.8216809 0.8369158 +#> [878,] 0.5102163 0.5178139 0.6454089 +#> [879,] 0.6869348 0.6993587 0.7524557 +#> [880,] 0.5584951 0.5687479 0.5827281 +#> [881,] 0.4919623 0.4922360 0.5161984 +#> [882,] 1.1207087 1.1468858 1.1637005 +#> [883,] 1.4398054 1.4933724 1.5107034 +#> [884,] 0.9321315 0.9481810 0.9830945 +#> [885,] 0.7053919 0.7086441 0.7171960 +#> [886,] 0.8671570 0.8687603 0.8753464 +#> [887,] 0.6857948 0.7112517 0.7573017 +#> [888,] 0.8283524 0.8703998 0.9551885 +#> [889,] 0.7289802 0.8437187 0.8561901 +#> [890,] 0.6797562 0.6992552 0.6998319 +#> [891,] 0.6658791 0.6701998 0.6801707 +#> [892,] 1.3072786 1.3114265 1.3533247 +#> [893,] 0.8628560 0.9132747 0.9165879 +#> [894,] 1.5536419 1.5676981 1.5715458 +#> [895,] 1.1455775 1.1833642 1.2430949 +#> [896,] 1.3007429 1.3456632 1.3779056 +#> [897,] 0.5183205 0.5743258 0.6032926 +#> [898,] 1.3950150 1.4845992 1.5350760 +#> [899,] 0.5721419 0.5732198 0.6285374 +#> [900,] 1.0721517 1.1149484 1.1474075 +#> [901,] 0.5700570 0.5922966 0.6117980 +#> [902,] 0.5699897 0.5910700 0.6163551 +#> [903,] 0.5701457 0.5879070 0.5888147 +#> [904,] 0.6237141 0.6314061 0.6545029 +#> [905,] 0.8616950 0.8821900 0.8904956 +#> [906,] 0.8366904 0.8441713 0.8561901 +#> [907,] 0.4796330 0.5170806 0.5385851 +#> [908,] 0.9966891 1.0304489 1.0453836 +#> [909,] 0.6767015 0.7246652 0.7910346 +#> [910,] 1.0512462 1.0634887 1.1017909 +#> [911,] 0.7690241 0.7835180 0.7842613 +#> [912,] 0.6977815 0.7157679 0.7306703 +#> [913,] 0.6552209 0.6862504 0.7353109 +#> [914,] 0.5776104 0.6021255 0.7304606 +#> [915,] 0.7863488 0.8475656 0.8720974 +#> [916,] 1.0116410 1.0413067 1.0591309 +#> [917,] 0.5229253 0.5461736 0.5666734 +#> [918,] 0.7958120 0.8136202 0.8537410 +#> [919,] 0.7987205 0.8064646 0.8194054 +#> [920,] 0.8142671 0.8244194 0.8358189 +#> [921,] 0.6044993 0.6127121 0.6399026 +#> [922,] 0.8370144 0.8698289 0.9121935 +#> [923,] 0.9604526 0.9776841 1.0087750 +#> [924,] 0.6765533 0.7119572 0.7163205 +#> [925,] 0.7317220 0.7530499 0.7623217 +#> [926,] 0.8837417 0.8855182 0.9463822 +#> [927,] 0.7803086 0.8170575 0.8442191 +#> [928,] 0.7599411 0.7889473 0.7904919 +#> [929,] 1.1081530 1.2327271 1.2911965 +#> [930,] 0.7506026 0.8026476 0.8214476 +#> [931,] 0.7451284 0.7653108 0.7774373 +#> [932,] 0.6848409 0.7093980 0.7568239 +#> [933,] 0.6515893 0.6556246 0.6686640 +#> [934,] 0.6678590 0.6910904 0.7055392 +#> [935,] 0.5962874 0.6394549 0.6976758 +#> [936,] 0.7912512 0.8118788 0.8307374 +#> [937,] 1.1736185 1.2208004 1.2446415 +#> [938,] 0.7667661 0.7940193 0.8452377 +#> [939,] 0.6827288 0.7092455 0.7464431 +#> [940,] 0.8604242 0.8726374 0.8772334 +#> [941,] 0.8093763 0.8289961 0.8344334 +#> [942,] 0.6301110 0.6482104 0.6788163 +#> [943,] 0.5850961 0.6228291 0.6632456 +#> [944,] 1.0421754 1.0446976 1.0731588 +#> [945,] 0.7180052 0.7315810 0.7349476 +#> [946,] 0.8506424 0.8683091 0.9293002 +#> [947,] 1.0121738 1.0359470 1.0360698 +#> [948,] 0.5552995 0.5578163 0.5626172 +#> [949,] 0.8668694 0.8669934 0.8724862 +#> [950,] 0.7558192 0.7727489 0.7882429 +#> [951,] 2.2518509 2.2876735 2.3032924 +#> [952,] 1.3832696 1.4011508 1.4280204 +#> [953,] 0.5844173 0.6092434 0.6248032 +#> [954,] 0.8162019 0.8250067 0.8313569 +#> [955,] 0.5101946 0.5432098 0.5471188 +#> [956,] 0.5132561 0.5241539 0.5257055 +#> [957,] 1.0073150 1.0311136 1.0359089 +#> [958,] 0.5265139 0.5461736 0.5519625 +#> [959,] 0.6008707 0.6280089 0.6345307 +#> [960,] 0.6509555 0.7058413 0.7246035 +#> [961,] 2.1073527 2.1123186 2.1230725 +#> [962,] 0.8408857 0.8688455 0.8930393 +#> [963,] 0.5797989 0.5860902 0.5995586 +#> [964,] 1.0524327 1.0525541 1.0626364 +#> [965,] 0.6757378 0.6832628 0.7136841 +#> [966,] 0.5765295 0.5861157 0.5992267 +#> [967,] 0.6782984 0.6786018 0.6802397 +#> [968,] 0.6391293 0.6621010 0.6837852 +#> [969,] 0.7136425 0.7223476 0.7272496 +#> [970,] 0.5855896 0.5870801 0.5888147 +#> [971,] 0.5590485 0.5654247 0.5661557 +#> [972,] 0.9690550 0.9994436 1.0083670 +#> [973,] 0.7691601 0.7728200 0.7825482 +#> [974,] 0.4869863 0.5158111 0.5624893 +#> [975,] 0.6556246 0.6693918 0.6756131 +#> [976,] 1.0606498 1.0695690 1.0897265 +#> [977,] 0.8508795 0.8643554 0.9045820 +#> [978,] 0.5277032 0.5386993 0.5486410 +#> [979,] 0.5850961 0.5925284 0.5991496 +#> [980,] 0.6278819 0.6315640 0.6414373 +#> [981,] 0.5928978 0.6146377 0.6159974 +#> [982,] 0.9526243 0.9714016 1.0005670 +#> [983,] 0.7373603 0.7435158 0.7604729 +#> [984,] 1.1850158 1.2176165 1.2439219 +#> [985,] 0.7098652 0.7231843 0.7240913 +#> [986,] 0.7024012 0.7333383 0.7539410 +#> [987,] 1.8988976 1.9063222 1.9955230 +#> [988,] 0.8699772 0.9439670 0.9859146 +#> [989,] 0.7045640 0.8301865 0.8381168 +#> [990,] 0.6898966 0.6935705 0.7925446 +#> [991,] 0.7808682 0.8238527 0.8348792 +#> [992,] 0.9184891 0.9223074 0.9293687 +#> [993,] 0.7294261 0.8360314 0.8809900 +#> [994,] 0.6341530 0.6459071 0.6519416 +#> [995,] 0.6254579 0.6277272 0.6626872 +#> [996,] 0.5238755 0.5315577 0.5379967 +#> [997,] 1.3163073 1.3237790 1.3668184 +#> [998,] 0.6553880 0.6724235 0.7223482 +#> [999,] 0.6011789 0.6055215 0.6515013 +#> [1000,] 0.5779394 0.5873525 0.5902023 #> # Find the 10 approximate nearest neighbors @@ -3157,3010 +3157,3010 @@

Examples) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] -#> [1,] 519 950 311 110 409 192 884 753 426 133 -#> [2,] 782 386 902 938 407 955 247 667 756 284 -#> [3,] 789 309 180 707 670 569 900 796 587 554 -#> [4,] 891 845 413 535 37 322 298 269 195 664 -#> [5,] 112 743 962 404 553 957 741 545 155 535 -#> [6,] 10 232 22 765 948 522 820 724 759 368 -#> [7,] 513 486 697 388 650 144 105 374 333 769 -#> [8,] 577 212 951 835 525 116 115 794 210 595 -#> [9,] 193 495 323 430 94 596 784 69 378 791 -#> [10,] 6 232 522 22 765 820 479 27 674 368 -#> [11,] 737 850 448 580 132 804 173 226 65 50 -#> [12,] 602 445 162 348 610 49 375 873 571 83 -#> [13,] 7 513 767 105 244 109 800 544 246 249 -#> [14,] 245 100 848 600 710 883 590 754 711 920 -#> [15,] 686 351 25 265 433 839 863 953 170 263 -#> [16,] 870 793 578 582 872 559 812 762 918 276 -#> [17,] 558 337 520 696 713 510 402 129 841 509 -#> [18,] 668 742 697 95 228 551 68 474 202 892 -#> [19,] 343 856 117 750 796 328 63 422 340 546 -#> [20,] 496 908 127 281 749 575 892 194 318 828 -#> [21,] 937 914 616 184 751 644 909 689 270 875 -#> [22,] 123 332 581 820 759 948 435 227 992 319 -#> [23,] 188 821 157 90 905 882 43 95 274 79 -#> [24,] 615 388 161 344 545 962 745 269 340 333 -#> [25,] 686 15 351 863 265 433 756 170 211 208 -#> [26,] 829 182 646 598 589 249 752 668 819 716 -#> [27,] 556 151 401 555 655 534 465 91 57 820 -#> [28,] 586 358 470 382 32 379 299 645 572 54 -#> [29,] 826 929 530 470 292 110 379 287 54 515 -#> [30,] 161 700 817 739 205 957 5 545 77 388 -#> [31,] 544 675 391 109 327 277 387 251 960 224 -#> [32,] 470 837 367 294 299 54 28 922 887 412 -#> [33,] 216 786 124 334 213 894 72 665 664 180 -#> [34,] 978 694 252 612 488 949 220 720 766 304 -#> [35,] 456 291 272 906 418 500 805 677 64 416 -#> [36,] 541 827 75 178 933 472 152 726 502 380 -#> [37,] 891 664 4 413 322 461 213 298 62 101 -#> [38,] 979 492 965 314 571 302 629 476 533 966 -#> [39,] 149 411 390 104 930 402 337 841 520 185 -#> [40,] 861 458 505 840 594 565 404 155 403 845 -#> [41,] 514 102 857 423 125 936 917 859 911 107 -#> [42,] 774 648 809 851 828 720 821 598 892 643 -#> [43,] 821 918 90 188 604 578 23 762 870 79 -#> [44,] 81 176 695 141 941 818 89 421 768 78 -#> [45,] 319 680 952 756 251 547 948 389 117 608 -#> [46,] 739 957 269 597 307 592 161 436 506 404 -#> [47,] 566 974 391 259 201 224 31 385 109 968 -#> [48,] 127 121 815 261 370 641 676 191 289 194 -#> [49,] 445 425 873 602 12 814 897 662 219 83 -#> [50,] 804 132 138 737 313 324 124 11 945 703 -#> [51,] 847 167 630 979 842 533 314 298 880 38 -#> [52,] 874 911 893 612 949 171 304 736 766 803 -#> [53,] 896 785 684 950 869 99 969 628 352 338 -#> [54,] 470 922 186 292 294 477 808 32 113 758 -#> [55,] 523 537 449 720 813 803 488 477 198 980 -#> [56,] 116 276 951 642 241 928 525 8 490 794 -#> [57,] 507 106 293 342 654 399 750 227 966 532 -#> [58,] 261 191 97 960 48 675 127 575 142 815 -#> [59,] 626 795 755 441 195 866 176 70 836 768 -#> [60,] 830 218 304 102 864 93 190 859 776 606 -#> [61,] 285 397 341 682 372 264 599 126 998 86 -#> [62,] 253 461 474 664 181 685 359 383 988 882 -#> [63,] 136 557 454 224 19 387 856 532 261 142 -#> [64,] 209 594 492 840 629 861 418 272 373 494 -#> [65,] 145 737 850 11 448 173 226 704 324 816 -#> [66,] 161 788 374 858 24 615 30 961 817 246 -#> [67,] 137 373 728 611 898 893 129 278 459 301 -#> [68,] 960 228 392 333 551 18 315 144 202 388 -#> [69,] 871 356 430 184 9 453 937 747 561 914 -#> [70,] 306 836 745 891 37 755 269 615 664 4 -#> [71,] 268 577 320 78 885 941 814 83 872 248 -#> [72,] 508 428 213 334 880 160 780 33 630 939 -#> [73,] 591 166 444 156 721 906 352 596 378 609 -#> [74,] 283 326 150 296 339 920 963 860 656 723 -#> [75,] 964 899 380 718 502 85 196 805 152 416 -#> [76,] 439 366 579 490 234 138 854 313 410 943 -#> [77,] 315 635 144 731 246 242 388 30 700 923 -#> [78,] 268 71 83 941 212 577 934 885 610 794 -#> [79,] 812 23 43 821 188 578 622 237 740 353 -#> [80,] 501 643 776 540 986 606 864 190 120 130 -#> [81,] 695 44 421 176 818 429 89 941 844 872 -#> [82,] 368 232 483 760 522 974 546 999 674 994 -#> [83,] 602 814 794 268 610 78 897 885 445 49 -#> [84,] 419 115 394 910 640 806 317 262 305 8 -#> [85,] 677 416 718 380 209 418 964 805 75 543 -#> [86,] 163 682 371 397 223 998 264 285 466 61 -#> [87,] 692 748 875 751 757 270 165 637 873 572 -#> [88,] 197 984 992 773 698 919 255 916 460 721 -#> [89,] 481 995 359 695 844 177 941 44 81 253 -#> [90,] 188 43 821 23 605 274 95 762 882 604 -#> [91,] 555 497 310 719 986 700 968 288 817 549 -#> [92,] 307 655 46 597 96 506 436 763 917 739 -#> [93,] 373 354 120 209 830 920 797 64 975 190 -#> [94,] 791 495 561 365 193 378 947 9 596 323 -#> [95,] 882 188 90 274 668 905 23 605 18 892 -#> [96,] 498 739 46 857 866 92 506 597 307 269 -#> [97,] 575 552 395 191 712 58 335 641 589 48 -#> [98,] 310 493 242 788 961 731 824 635 91 817 -#> [99,] 558 684 969 717 521 970 53 967 705 877 -#> [100,] 848 883 809 14 711 920 643 963 619 459 -#> [101,] 325 229 216 461 665 328 664 383 284 386 -#> [102,] 41 917 859 423 830 60 857 155 775 40 -#> [103,] 159 295 282 394 300 621 904 114 946 651 -#> [104,] 149 147 583 590 997 39 663 930 312 588 -#> [105,] 716 366 769 528 742 215 7 244 943 513 -#> [106,] 507 342 966 57 654 399 293 386 148 807 -#> [107,] 766 125 911 60 102 41 859 612 830 949 -#> [108,] 460 473 239 733 88 992 647 984 131 981 -#> [109,] 544 31 620 277 68 800 960 224 982 646 -#> [110,] 519 409 292 303 929 753 346 133 29 200 -#> [111,] 881 274 302 516 810 548 685 882 476 965 -#> [112,] 962 5 743 535 344 807 741 845 404 553 -#> [113,] 375 393 808 922 348 292 627 477 659 406 -#> [114,] 300 203 914 324 282 689 450 665 21 704 -#> [115,] 640 317 910 419 262 806 381 210 219 8 -#> [116,] 8 56 276 559 577 835 951 928 212 794 -#> [117,] 328 856 796 19 454 136 319 750 422 399 -#> [118,] 240 260 890 589 646 829 575 649 238 876 -#> [119,] 450 981 180 356 203 216 300 935 324 946 -#> [120,] 975 540 593 797 93 920 290 279 706 245 -#> [121,] 48 370 641 452 437 199 754 127 710 676 -#> [122,] 294 808 922 867 777 361 715 953 360 837 -#> [123,] 332 22 435 759 581 227 871 948 319 747 -#> [124,] 786 350 33 989 313 50 527 945 62 334 -#> [125,] 766 911 995 107 171 853 936 140 844 41 -#> [126,] 192 792 753 158 341 723 61 770 285 230 -#> [127,] 676 48 194 638 20 815 777 261 641 496 -#> [128,] 714 621 831 331 356 946 608 783 184 69 -#> [129,] 137 520 185 728 67 459 611 696 278 713 -#> [130,] 290 986 540 502 553 120 349 80 917 152 -#> [131,] 609 721 832 916 485 88 460 214 239 156 -#> [132,] 138 945 662 50 425 804 11 320 737 798 -#> [133,] 625 139 165 627 530 175 393 637 692 753 -#> [134,] 833 811 699 257 506 773 197 959 916 701 -#> [135,] 744 658 898 416 243 338 283 718 456 500 -#> [136,] 63 454 532 557 261 750 117 856 955 19 -#> [137,] 67 611 129 459 728 373 893 301 304 278 -#> [138,] 132 804 234 313 76 320 50 579 662 703 -#> [139,] 625 993 175 133 299 529 379 637 530 607 -#> [140,] 582 853 872 171 16 766 559 812 125 421 -#> [141,] 443 44 176 768 195 880 842 441 941 524 -#> [142,] 233 202 815 551 454 289 261 392 988 191 -#> [143,] 826 311 884 346 1 29 254 862 929 950 -#> [144,] 315 388 77 486 697 976 545 392 333 68 -#> [145,] 65 850 737 11 324 576 50 804 173 704 -#> [146,] 832 562 396 916 543 134 131 721 487 699 -#> [147,] 104 583 590 149 549 933 639 541 337 574 -#> [148,] 966 979 654 106 791 38 507 322 467 342 -#> [149,] 104 583 185 39 147 520 997 390 312 129 -#> [150,] 230 632 723 339 74 393 420 204 283 753 -#> [151,] 556 27 888 464 899 984 534 655 197 364 -#> [152,] 380 502 472 130 917 290 986 102 540 775 -#> [153,] 990 802 398 412 367 512 837 438 890 973 -#> [154,] 273 855 956 297 681 971 562 959 441 958 -#> [155,] 404 859 423 40 957 157 5 359 995 845 -#> [156,] 444 378 214 591 596 609 193 73 352 495 -#> [157,] 905 190 23 403 353 155 987 882 881 274 -#> [158,] 723 770 792 970 296 509 150 230 921 339 -#> [159,] 295 103 946 300 114 576 282 128 450 324 -#> [160,] 428 780 722 942 919 72 255 778 70 213 -#> [161,] 30 817 24 739 957 46 700 388 615 5 -#> [162,] 980 12 375 610 659 494 348 406 602 330 -#> [163,] 86 223 466 998 407 285 671 568 235 453 -#> [164,] 779 439 636 865 169 491 438 772 528 489 -#> [165,] 572 627 637 692 393 87 211 113 175 217 -#> [166,] 591 200 275 784 73 236 947 434 347 634 -#> [167,] 533 51 972 842 468 571 314 979 377 630 -#> [168,] 802 607 735 990 529 299 379 993 924 887 -#> [169,] 489 258 579 761 604 854 462 897 491 918 -#> [170,] 265 208 839 955 686 676 261 15 247 127 -#> [171,] 799 231 353 889 582 52 995 494 403 766 -#> [172,] 633 389 442 801 680 531 201 391 251 547 -#> [173,] 865 850 226 838 580 11 652 448 725 424 -#> [174,] 679 983 345 369 199 121 327 446 437 712 -#> [175,] 637 299 572 139 863 625 165 993 211 607 -#> [176,] 44 818 440 81 141 695 939 795 941 89 -#> [177,] 253 934 685 320 941 882 359 268 814 89 -#> [178,] 541 521 623 183 933 384 558 99 744 705 -#> [179,] 258 488 978 752 622 694 598 605 578 42 -#> [180,] 119 3 216 33 229 101 450 707 422 124 -#> [181,] 383 461 187 284 62 247 988 318 142 474 -#> [182,] 926 552 663 589 26 819 504 315 829 336 -#> [183,] 414 451 705 178 967 417 877 717 684 536 -#> [184,] 937 783 21 914 430 644 909 902 616 751 -#> [185,] 129 520 459 390 198 402 696 137 611 848 -#> [186,] 296 758 54 841 294 509 922 402 339 808 -#> [187,] 284 383 318 181 194 247 798 548 667 749 -#> [188,] 90 23 821 605 43 762 95 578 668 79 -#> [189,] 446 235 532 293 706 279 996 288 370 593 -#> [190,] 157 774 987 905 834 643 218 80 354 859 -#> [191,] 641 202 551 48 834 97 289 815 58 261 -#> [192,] 126 753 792 950 409 723 628 158 61 230 -#> [193,] 495 378 9 94 940 323 352 156 596 784 -#> [194,] 318 815 127 749 908 667 187 225 289 676 -#> [195,] 443 880 4 626 891 141 298 768 257 842 -#> [196,] 467 805 701 790 888 895 418 699 846 484 -#> [197,] 773 88 919 255 257 722 701 984 811 916 -#> [198,] 459 390 809 930 185 312 523 720 449 851 -#> [199,] 679 983 121 345 710 174 446 437 452 907 -#> [200,] 634 275 886 166 347 784 434 475 519 601 -#> [201,] 172 391 442 369 405 787 174 327 633 801 -#> [202,] 551 233 142 289 191 834 545 504 815 392 -#> [203,] 665 914 689 616 937 935 334 325 847 213 -#> [204,] 628 518 283 869 150 684 785 409 74 338 -#> [205,] 859 864 976 30 237 155 190 739 957 486 -#> [206,] 550 670 587 3 309 554 271 789 569 666 -#> [207,] 524 231 889 799 560 141 768 468 879 594 -#> [208,] 170 265 955 247 839 251 686 454 675 2 -#> [209,] 64 594 93 418 373 861 840 629 492 272 -#> [210,] 525 794 709 781 885 381 115 212 8 317 -#> [211,] 526 863 637 953 448 165 217 175 572 652 -#> [212,] 794 78 977 8 83 610 210 595 577 262 -#> [213,] 216 942 778 325 37 334 665 101 203 891 -#> [214,] 444 698 156 721 378 984 596 940 495 846 -#> [215,] 740 613 769 486 697 976 812 481 622 237 -#> [216,] 213 101 325 229 665 33 37 664 334 942 -#> [217,] 568 627 263 953 777 225 715 671 938 667 -#> [218,] 776 60 830 864 606 501 190 304 643 80 -#> [219,] 757 471 425 640 381 49 445 362 910 873 -#> [220,] 34 252 312 694 198 488 978 185 611 55 -#> [221,] 672 500 321 434 236 660 906 166 657 879 -#> [222,] 904 570 764 475 200 884 875 683 644 1000 -#> [223,] 163 682 998 921 285 230 466 671 715 86 -#> [224,] 63 557 345 391 58 387 907 136 960 566 -#> [225,] 667 671 782 568 777 915 194 217 407 676 -#> [226,] 652 448 580 173 850 526 211 953 265 11 -#> [227,] 435 581 791 654 57 293 747 106 871 123 -#> [228,] 960 68 982 18 424 551 142 908 392 767 -#> [229,] 101 328 216 325 856 664 796 117 461 37 -#> [230,] 150 723 339 627 393 753 420 223 632 263 -#> [231,] 889 799 171 207 594 840 494 403 844 353 -#> [232,] 820 368 10 674 546 944 760 465 82 22 -#> [233,] 142 202 815 289 551 284 962 191 318 261 -#> [234,] 527 313 76 410 742 138 177 320 943 854 -#> [235,] 189 293 446 706 503 996 466 326 288 279 -#> [236,] 500 434 347 456 338 291 303 518 377 601 -#> [237,] 905 976 697 23 668 188 157 95 79 504 -#> [238,] 447 890 638 281 575 412 395 438 265 584 -#> [239,] 460 473 647 108 485 609 131 9 88 954 -#> [240,] 829 589 118 575 649 335 20 496 598 648 -#> [241,] 490 951 931 410 525 729 56 943 517 76 -#> [242,] 731 493 700 310 907 635 77 574 817 315 -#> [243,] 338 135 409 744 250 236 346 969 950 518 -#> [244,] 336 752 716 105 976 513 923 280 26 215 -#> [245,] 600 754 593 14 710 706 975 920 120 279 -#> [246,] 77 635 455 144 315 923 731 388 486 800 -#> [247,] 955 383 284 454 187 2 181 328 208 142 -#> [248,] 440 931 989 517 320 350 71 939 738 818 -#> [249,] 26 646 260 624 620 819 280 829 767 336 -#> [250,] 243 346 932 708 672 969 338 221 898 135 -#> [251,] 389 387 801 675 208 680 454 170 45 955 -#> [252,] 928 34 220 55 803 612 949 736 813 720 -#> [253,] 685 62 359 177 882 474 934 548 413 461 -#> [254,] 311 1 143 884 570 426 519 950 826 222 -#> [255,] 919 778 942 322 654 197 364 722 992 891 -#> [256,] 795 440 939 248 176 818 508 350 363 989 -#> [257,] 811 773 722 919 701 316 197 880 195 443 -#> [258,] 179 605 489 169 598 604 488 918 578 762 -#> [259,] 566 345 907 679 391 199 224 974 310 557 -#> [260,] 646 118 876 829 649 620 589 249 240 819 -#> [261,] 815 454 48 955 58 127 136 839 142 191 -#> [262,] 595 317 538 115 419 212 524 910 806 8 -#> [263,] 217 372 599 627 568 953 863 625 715 230 -#> [264,] 397 86 371 61 267 285 669 372 825 998 -#> [265,] 170 686 15 208 839 638 676 25 955 226 -#> [266,] 511 932 736 611 515 252 977 803 250 893 -#> [267,] 264 585 831 397 372 608 599 783 25 263 -#> [268,] 71 814 78 320 885 83 941 934 662 445 -#> [269,] 597 615 535 306 506 891 4 592 46 845 -#> [270,] 875 637 87 704 683 21 751 757 748 211 -#> [271,] 994 206 550 569 66 554 13 999 858 900 -#> [272,] 565 594 35 805 418 840 64 505 291 699 -#> [273,] 855 956 681 154 959 833 971 441 768 562 -#> [274,] 882 881 111 90 95 23 318 539 548 749 -#> [275,] 634 200 415 660 538 434 347 947 166 167 -#> [276,] 928 16 793 559 116 56 870 794 872 410 -#> [277,] 927 31 544 309 387 109 670 707 760 666 -#> [278,] 623 469 521 384 728 963 67 373 933 137 -#> [279,] 975 370 593 797 706 656 326 754 120 920 -#> [280,] 336 244 923 249 455 26 182 819 588 752 -#> [281,] 649 575 20 496 908 238 838 638 127 829 -#> [282,] 651 683 806 394 689 21 764 114 914 475 -#> [283,] 204 74 456 658 150 518 758 406 901 236 -#> [284,] 187 383 247 810 318 181 194 815 461 233 -#> [285,] 998 913 223 61 825 86 163 466 682 230 -#> [286,] 288 996 536 877 466 967 717 921 719 503 -#> [287,] 709 929 477 427 586 977 515 358 659 29 -#> [288,] 286 235 189 996 465 466 503 401 446 706 -#> [289,] 834 987 815 233 202 191 641 318 194 48 -#> [290,] 540 130 741 797 120 553 986 790 349 502 -#> [291,] 377 972 468 35 347 434 456 314 272 571 -#> [292,] 901 113 515 929 54 758 110 303 659 922 -#> [293,] 235 57 507 106 189 342 227 532 401 407 -#> [294,] 122 922 808 54 837 867 186 715 32 627 -#> [295,] 159 946 300 103 450 618 114 647 282 128 -#> [296,] 758 339 186 963 74 158 901 970 883 723 -#> [297,] 564 855 971 59 818 154 936 661 429 795 -#> [298,] 413 630 685 4 847 842 880 516 934 37 -#> [299,] 607 379 175 529 863 953 599 625 32 139 -#> [300,] 114 450 295 914 946 203 689 119 282 618 -#> [301,] 851 720 304 813 893 449 803 459 980 373 -#> [302,] 476 965 111 810 629 881 492 516 38 539 -#> [303,] 601 330 348 406 659 347 901 434 292 110 -#> [304,] 851 301 60 830 373 893 218 52 611 720 -#> [305,] 542 816 362 703 640 781 355 910 683 115 -#> [306,] 70 836 745 615 269 891 37 755 664 4 -#> [307,] 92 655 46 436 597 592 506 349 957 917 -#> [308,] 632 601 347 393 634 886 692 420 87 571 -#> [309,] 670 927 707 3 789 587 387 952 796 422 -#> [310,] 493 242 700 91 907 731 574 817 555 259 -#> [311,] 254 1 884 143 826 530 29 519 110 993 -#> [312,] 588 997 978 663 198 694 488 34 42 851 -#> [313,] 527 234 742 138 76 50 124 132 181 804 -#> [314,] 842 979 972 167 468 38 533 51 571 965 -#> [315,] 144 77 388 68 731 926 976 635 545 392 -#> [316,] 773 257 722 916 441 811 478 880 443 197 -#> [317,] 115 381 640 262 806 910 219 538 757 471 -#> [318,] 194 908 749 187 289 274 284 815 496 881 -#> [319,] 944 117 435 45 693 820 407 532 546 136 -#> [320,] 268 71 177 941 945 662 934 814 248 234 -#> [321,] 396 221 660 672 906 524 500 166 478 434 -#> [322,] 891 4 37 535 778 845 413 654 255 966 -#> [323,] 784 886 9 634 644 909 430 94 193 947 -#> [324,] 704 737 114 50 616 119 783 145 665 203 -#> [325,] 101 386 935 665 216 747 213 229 203 37 -#> [326,] 593 706 754 921 975 74 279 996 600 915 -#> [327,] 395 58 391 712 97 174 584 675 679 575 -#> [328,] 117 796 856 229 101 454 247 19 422 399 -#> [329,] 771 852 843 657 250 221 672 681 396 321 -#> [330,] 659 901 406 303 348 162 375 601 879 515 -#> [331,] 631 128 946 576 831 608 159 295 356 621 -#> [332,] 123 22 435 581 654 747 227 935 820 992 -#> [333,] 392 388 988 68 545 144 24 474 18 960 -#> [334,] 665 630 213 945 203 298 33 847 216 72 -#> [335,] 711 648 641 452 589 97 552 712 867 643 -#> [336,] 280 923 244 182 455 26 752 663 925 315 -#> [337,] 17 520 558 614 713 521 639 970 841 770 -#> [338,] 243 518 409 236 204 519 950 135 869 283 -#> [339,] 808 296 150 230 393 922 723 74 627 758 -#> [340,] 343 344 615 19 856 999 962 24 750 399 -#> [341,] 991 61 599 868 372 682 126 367 770 792 -#> [342,] 507 106 399 807 57 966 750 743 322 293 -#> [343,] 19 856 750 340 999 117 546 368 63 399 -#> [344,] 962 112 535 399 807 340 988 743 664 233 -#> [345,] 174 679 199 557 983 259 391 224 446 136 -#> [346,] 110 519 409 929 243 515 932 338 250 236 -#> [347,] 601 377 308 291 434 632 236 303 634 365 -#> [348,] 659 601 12 375 406 113 303 162 330 571 -#> [349,] 553 436 743 592 741 790 290 807 5 540 -#> [350,] 989 124 786 248 363 320 939 440 138 256 -#> [351,] 15 433 863 25 953 686 372 599 263 211 -#> [352,] 378 785 869 591 896 193 156 518 444 357 -#> [353,] 403 171 157 23 799 821 79 43 840 494 -#> [354,] 93 797 656 539 476 920 629 302 861 492 -#> [355,] 816 542 305 781 362 703 65 764 683 640 -#> [356,] 69 871 759 608 831 756 184 119 783 935 -#> [357,] 869 985 913 503 365 94 825 628 785 518 -#> [358,] 427 586 28 362 471 572 381 287 477 781 -#> [359,] 253 905 882 89 685 62 155 177 474 23 -#> [360,] 867 712 361 395 122 930 638 452 575 890 -#> [361,] 991 715 638 360 122 867 395 823 712 294 -#> [362,] 471 572 358 219 875 381 640 683 757 270 -#> [363,] 989 350 440 248 786 650 256 795 124 818 -#> [364,] 255 919 597 942 778 506 592 992 197 556 -#> [365,] 947 94 347 518 632 791 357 596 886 291 -#> [366,] 76 943 528 678 234 105 410 716 439 490 -#> [367,] 398 837 32 991 412 294 361 122 341 868 -#> [368,] 483 343 999 820 19 546 340 760 232 422 -#> [369,] 787 983 174 679 442 345 371 682 199 201 -#> [370,] 279 48 121 671 915 815 641 754 777 225 -#> [371,] 397 86 573 682 264 442 61 369 998 163 -#> [372,] 599 263 433 351 61 341 953 863 15 715 -#> [373,] 93 67 209 893 64 137 304 301 830 728 -#> [374,] 858 613 486 513 7 24 215 564 739 66 -#> [375,] 477 113 659 348 162 980 12 901 406 330 -#> [376,] 498 506 597 364 134 92 755 307 197 655 -#> [377,] 291 468 972 571 434 601 347 533 167 314 -#> [378,] 193 495 156 596 352 94 444 214 940 947 -#> [379,] 529 607 299 530 993 175 28 470 139 887 -#> [380,] 502 152 718 130 290 472 85 917 75 384 -#> [381,] 471 317 219 757 49 348 445 692 640 362 -#> [382,] 586 28 645 636 837 54 477 358 470 427 -#> [383,] 181 461 187 284 247 318 988 101 62 142 -#> [384,] 469 623 718 417 933 521 278 860 658 380 -#> [385,] 968 674 719 259 974 91 549 465 555 566 -#> [386,] 902 966 325 2 935 747 782 106 616 284 -#> [387,] 251 796 63 960 454 136 675 707 224 117 -#> [388,] 144 333 545 392 24 486 315 77 697 161 -#> [389,] 801 251 680 172 45 675 531 547 442 686 -#> [390,] 930 402 411 198 841 848 185 696 883 520 -#> [391,] 345 224 327 566 31 201 974 174 259 172 -#> [392,] 333 545 551 68 388 202 142 988 233 960 -#> [393,] 627 113 632 339 165 308 808 692 230 922 -#> [394,] 282 651 806 910 419 764 683 640 103 727 -#> [395,] 712 575 97 638 360 452 361 437 121 552 -#> [396,] 321 832 956 681 478 524 906 487 221 543 -#> [397,] 264 371 86 61 285 267 682 372 573 998 -#> [398,] 367 991 837 361 412 868 153 360 341 433 -#> [399,] 342 750 507 106 807 57 856 344 962 654 -#> [400,] 465 693 401 944 479 319 435 820 163 466 -#> [401,] 465 400 293 57 944 820 27 532 693 435 -#> [402,] 696 841 390 510 520 186 185 411 713 509 -#> [403,] 840 353 516 458 861 157 965 799 40 629 -#> [404,] 155 957 741 5 40 845 112 535 553 743 -#> [405,] 482 201 573 369 442 787 371 868 584 398 -#> [406,] 901 330 348 601 659 303 162 113 375 393 -#> [407,] 782 2 671 225 839 568 938 955 454 915 -#> [408,] 748 757 538 533 806 87 167 634 875 692 -#> [409,] 519 950 110 338 753 204 283 628 243 346 -#> [410,] 943 234 490 559 872 762 579 76 480 578 -#> [411,] 390 402 930 849 198 510 696 841 185 186 -#> [412,] 837 890 367 652 438 122 953 294 398 238 -#> [413,] 298 4 37 685 810 891 322 845 516 253 -#> [414,] 451 705 183 967 717 536 286 877 567 417 -#> [415,] 954 947 485 596 275 51 660 167 784 634 -#> [416,] 677 718 85 658 898 744 35 135 418 456 -#> [417,] 877 384 860 432 469 658 718 623 204 74 -#> [418,] 805 272 209 35 64 196 467 658 456 492 -#> [419,] 115 910 262 640 806 317 595 84 8 394 -#> [420,] 632 913 561 230 150 308 568 393 938 217 -#> [421,] 81 695 853 44 140 559 872 429 582 835 -#> [422,] 952 707 796 19 117 328 856 229 343 760 -#> [423,] 155 859 857 995 102 844 40 404 936 41 -#> [424,] 838 982 228 725 767 649 675 281 173 865 -#> [425,] 49 662 445 873 798 219 814 897 945 132 -#> [426,] 352 570 732 323 784 1 193 785 378 591 -#> [427,] 358 586 287 645 781 28 709 210 382 362 -#> [428,] 780 160 673 894 72 942 70 722 755 457 -#> [429,] 695 872 734 559 480 818 81 481 661 140 -#> [430,] 909 644 937 184 689 914 21 886 9 475 -#> [431,] 725 772 865 876 173 666 424 838 512 447 -#> [432,] 567 417 503 467 196 888 706 617 235 985 -#> [433,] 351 372 15 599 686 25 953 263 863 299 -#> [434,] 377 660 347 236 291 468 601 972 303 275 -#> [435,] 227 123 581 871 319 332 293 57 747 453 -#> [436,] 349 592 790 553 741 290 535 655 404 743 -#> [437,] 452 710 823 121 754 712 983 600 867 395 -#> [438,] 890 412 652 238 226 772 837 281 779 173 -#> [439,] 490 579 76 729 779 169 854 164 865 489 -#> [440,] 818 248 176 256 795 939 44 931 695 989 -#> [441,] 768 316 195 959 141 443 833 560 855 626 -#> [442,] 172 680 371 801 389 633 369 201 174 86 -#> [443,] 195 880 842 141 630 298 768 4 257 314 -#> [444,] 156 214 378 73 591 352 721 596 617 495 -#> [445,] 49 12 425 873 602 814 83 662 219 348 -#> [446,] 189 235 370 279 532 996 199 121 706 293 -#> [447,] 238 265 584 675 638 15 686 801 327 170 -#> [448,] 580 526 226 211 11 652 737 850 270 637 -#> [449,] 537 720 813 301 980 523 803 55 851 918 -#> [450,] 119 300 946 981 114 618 203 295 914 180 -#> [451,] 705 414 717 967 183 536 877 286 684 99 -#> [452,] 437 710 711 712 121 335 14 754 867 823 -#> [453,] 871 938 561 756 902 407 2 782 420 163 -#> [454,] 955 136 261 247 815 117 63 142 328 233 -#> [455,] 923 246 336 244 635 77 925 280 315 144 -#> [456,] 35 658 283 291 236 64 418 500 492 406 -#> [457,] 900 569 745 70 894 780 306 836 554 428 -#> [458,] 861 840 40 629 403 965 505 845 516 594 -#> [459,] 851 809 137 301 198 848 100 185 720 611 -#> [460,] 239 473 108 9 88 485 647 609 954 992 -#> [461,] 664 62 181 383 988 37 101 284 474 253 -#> [462,] 489 854 761 749 798 897 605 90 604 908 -#> [463,] 549 968 259 385 787 566 719 47 98 147 -#> [464,] 899 151 534 888 567 556 432 846 985 75 -#> [465,] 401 400 693 944 288 820 293 532 189 435 -#> [466,] 998 996 163 921 235 223 503 86 285 682 -#> [467,] 196 805 701 888 895 790 418 148 484 846 -#> [468,] 972 377 571 167 533 291 314 660 434 842 -#> [469,] 384 860 278 623 417 933 120 593 521 963 -#> [470,] 54 32 530 28 29 292 379 922 299 929 -#> [471,] 381 572 219 362 757 692 49 87 873 875 -#> [472,] 152 380 933 775 502 278 830 60 384 653 -#> [473,] 647 460 981 239 108 992 69 9 450 88 -#> [474,] 62 988 253 461 527 359 181 664 95 882 -#> [475,] 644 909 886 634 21 430 222 748 875 764 -#> [476,] 302 810 629 656 492 881 111 965 484 354 -#> [477,] 375 113 586 659 980 537 54 761 162 348 -#> [478,] 485 316 727 415 954 508 321 660 524 687 -#> [479,] 400 435 581 465 401 227 123 693 669 820 -#> [480,] 943 769 429 734 410 481 234 818 661 872 -#> [481,] 89 613 359 177 429 215 769 995 695 480 -#> [482,] 405 398 584 868 153 447 367 573 991 433 -#> [483,] 368 343 961 340 999 19 674 555 655 750 -#> [484,] 790 741 797 656 629 807 476 861 492 743 -#> [485,] 954 415 478 727 596 460 51 275 947 239 -#> [486,] 697 976 144 388 237 215 7 613 205 315 -#> [487,] 560 543 699 272 565 677 681 811 85 207 -#> [488,] 978 694 179 720 258 813 449 42 523 34 -#> [489,] 462 169 605 258 761 854 649 604 20 496 -#> [490,] 439 729 579 76 410 241 169 525 854 943 -#> [491,] 169 258 488 179 523 694 489 870 164 16 -#> [492,] 629 656 476 302 965 64 38 861 840 484 -#> [493,] 242 731 310 907 635 700 574 77 817 98 -#> [494,] 610 889 799 162 231 12 879 64 840 353 -#> [495,] 193 94 378 940 9 791 596 214 323 947 -#> [496,] 20 908 892 281 318 575 828 127 191 749 -#> [497,] 555 91 986 130 502 763 827 655 817 349 -#> [498,] 96 755 506 822 376 866 626 597 59 833 -#> [499,] 618 673 894 730 256 690 363 795 350 428 -#> [500,] 906 236 35 672 456 221 434 291 879 338 -#> [501,] 80 776 606 864 643 218 926 190 574 986 -#> [502,] 380 152 130 290 540 986 917 718 120 384 -#> [503,] 235 432 466 357 985 293 996 536 288 706 -#> [504,] 926 834 987 892 551 668 202 648 828 191 -#> [505,] 565 40 458 861 845 840 272 594 404 155 -#> [506,] 597 269 46 722 306 257 919 755 592 255 -#> [507,] 342 106 57 399 807 293 966 654 484 750 -#> [508,] 72 687 939 880 256 141 630 478 795 443 -#> [509,] 713 970 758 296 158 792 186 841 728 770 -#> [510,] 696 402 841 411 186 509 390 54 520 17 -#> [511,] 736 266 893 874 803 611 879 932 330 67 -#> [512,] 924 990 802 153 168 772 438 412 735 607 -#> [513,] 7 769 215 374 486 244 105 613 697 650 -#> [514,] 41 857 936 423 102 866 125 971 844 995 -#> [515,] 929 292 901 330 758 659 54 303 346 110 -#> [516,] 965 111 403 685 934 302 881 548 458 810 -#> [517,] 931 577 248 951 71 958 738 8 440 835 -#> [518,] 869 204 628 338 785 365 283 236 347 357 -#> [519,] 110 409 1 950 338 346 753 200 236 518 -#> [520,] 129 185 713 402 696 728 841 963 390 848 -#> [521,] 623 860 384 278 713 469 970 728 963 933 -#> [522,] 82 368 483 232 10 674 6 27 961 820 -#> [523,] 55 537 449 488 720 198 813 258 477 491 -#> [524,] 207 660 595 141 468 262 321 972 538 231 -#> [525,] 885 210 729 794 781 8 577 490 951 71 -#> [526,] 211 448 863 953 580 217 637 226 667 652 -#> [527,] 234 313 742 474 62 177 253 786 95 989 -#> [528,] 366 624 716 767 105 76 439 742 164 649 -#> [529,] 607 379 993 299 168 139 175 530 735 887 -#> [530,] 379 29 470 133 625 826 299 572 529 139 -#> [531,] 547 172 389 952 760 801 251 45 633 680 -#> [532,] 750 136 189 557 944 446 399 57 293 63 -#> [533,] 167 571 468 972 51 377 408 979 314 842 -#> [534,] 464 27 151 556 655 567 899 555 307 92 -#> [535,] 845 112 4 592 404 741 322 962 891 269 -#> [536,] 967 877 503 286 466 998 684 357 825 985 -#> [537,] 449 55 523 720 803 813 980 477 301 375 -#> [538,] 408 660 275 167 533 262 806 317 468 972 -#> [539,] 603 980 354 619 828 274 302 821 111 749 -#> [540,] 290 120 130 797 741 986 975 553 80 743 -#> [541,] 933 178 36 827 384 472 469 521 705 623 -#> [542,] 816 781 305 362 355 358 427 640 525 729 -#> [543,] 487 906 416 677 699 85 35 75 805 500 -#> [544,] 31 982 109 675 424 960 228 277 68 767 -#> [545,] 202 392 551 388 962 5 233 112 905 504 -#> [546,] 557 760 944 19 820 343 63 136 319 974 -#> [547,] 531 952 45 389 251 760 801 172 948 387 -#> [548,] 685 111 945 662 798 516 934 881 187 274 -#> [549,] 719 286 968 91 288 463 259 385 590 310 -#> [550,] 206 587 670 3 309 271 927 666 569 554 -#> [551,] 202 142 191 233 504 392 545 834 289 68 -#> [552,] 97 589 335 191 182 712 575 395 926 711 -#> [553,] 349 743 741 5 436 112 404 290 807 592 -#> [554,] 900 569 745 457 786 836 650 124 789 3 -#> [555,] 91 497 655 310 719 27 349 986 817 700 -#> [556,] 151 27 888 401 581 364 57 655 227 984 -#> [557,] 546 944 136 63 532 345 224 19 750 693 -#> [558,] 17 713 99 521 337 970 509 520 623 129 -#> [559,] 872 577 16 429 582 410 794 870 695 71 -#> [560,] 487 768 811 699 565 272 441 207 505 833 -#> [561,] 791 453 420 94 913 871 163 227 430 909 -#> [562,] 273 396 956 681 146 154 832 855 771 321 -#> [563,] 787 702 573 369 371 286 717 682 614 983 -#> [564,] 936 613 971 866 995 374 857 514 125 481 -#> [565,] 505 272 40 594 840 458 861 805 811 699 -#> [566,] 259 391 974 224 47 345 907 493 557 310 -#> [567,] 432 502 288 719 497 417 286 503 899 464 -#> [568,] 217 671 225 938 777 782 667 715 263 407 -#> [569,] 900 554 457 745 999 836 789 3 70 988 -#> [570,] 904 222 784 200 475 1000 426 323 166 634 -#> [571,] 533 377 468 965 167 972 38 601 314 12 -#> [572,] 165 637 471 692 175 875 87 362 211 270 -#> [573,] 371 397 405 442 61 264 369 702 682 563 -#> [574,] 907 80 501 242 986 731 310 926 700 493 -#> [575,] 97 395 281 20 712 496 638 240 829 127 -#> [576,] 145 159 946 65 324 587 331 295 114 737 -#> [577,] 71 517 8 559 78 885 268 872 931 794 -#> [578,] 762 43 870 188 812 605 79 918 16 90 -#> [579,] 854 729 897 439 490 169 76 462 662 489 -#> [580,] 448 226 11 652 526 850 425 761 798 211 -#> [581,] 227 435 123 22 332 479 654 940 992 791 -#> [582,] 140 872 171 16 870 853 559 79 812 43 -#> [583,] 590 278 933 14 848 100 104 520 147 149 -#> [584,] 238 327 447 260 395 118 712 646 890 876 -#> [585,] 783 184 453 267 714 644 69 561 139 909 -#> [586,] 358 477 28 382 761 427 287 471 375 54 -#> [587,] 670 309 3 180 666 576 707 145 927 725 -#> [588,] 997 312 663 978 752 218 776 694 606 336 -#> [589,] 240 829 819 552 335 575 97 646 182 648 -#> [590,] 14 710 639 245 583 600 452 848 711 100 -#> [591,] 73 166 156 352 784 378 444 596 947 609 -#> [592,] 436 349 535 553 845 269 597 790 741 807 -#> [593,] 975 706 245 120 279 754 326 600 920 14 -#> [594,] 840 64 209 40 861 272 565 458 889 231 -#> [595,] 262 78 524 212 8 419 317 538 115 141 -#> [596,] 947 698 378 954 415 495 94 214 9 365 -#> [597,] 506 269 592 46 307 364 306 436 655 615 -#> [598,] 668 892 605 188 828 829 622 258 648 179 -#> [599,] 372 263 433 299 351 341 953 625 863 61 -#> [600,] 245 754 14 593 639 963 326 710 823 920 -#> [601,] 303 347 348 377 308 406 571 434 632 533 -#> [602,] 12 83 49 445 604 814 918 610 897 162 -#> [603,] 619 539 980 867 354 749 777 641 828 920 -#> [604,] 918 43 602 897 761 90 870 462 821 605 -#> [605,] 188 762 90 598 489 668 258 578 892 462 -#> [606,] 864 501 776 218 80 925 60 830 643 205 -#> [607,] 529 379 299 993 168 175 802 28 139 530 -#> [608,] 831 356 759 45 948 871 756 267 69 128 -#> [609,] 156 131 721 444 460 596 591 214 378 73 -#> [610,] 494 12 162 799 83 602 889 803 78 659 -#> [611,] 137 67 893 736 304 301 459 612 129 52 -#> [612,] 949 911 52 736 611 874 304 766 34 893 -#> [613,] 481 215 769 89 486 374 740 697 359 564 -#> [614,] 770 639 792 868 841 158 337 970 991 341 -#> [615,] 24 269 306 745 836 340 70 344 161 597 -#> [616,] 751 937 21 902 914 665 203 386 184 704 -#> [617,] 846 895 365 357 985 869 467 698 888 518 -#> [618,] 647 450 300 295 673 894 946 981 119 473 -#> [619,] 603 867 539 641 980 809 828 648 920 883 -#> [620,] 646 260 249 819 109 584 589 552 327 26 -#> [621,] 714 912 128 904 69 585 184 783 356 103 -#> [622,] 752 740 812 179 79 598 578 188 237 762 -#> [623,] 521 860 384 278 469 728 963 713 970 417 -#> [624,] 528 249 716 767 26 649 260 164 876 179 -#> [625,] 133 139 175 627 165 263 637 299 530 599 -#> [626,] 866 59 195 441 269 768 443 70 755 836 -#> [627,] 165 922 393 808 217 230 113 263 953 625 -#> [628,] 204 869 785 684 518 913 150 357 409 753 -#> [629,] 492 861 458 840 965 476 302 64 484 38 -#> [630,] 298 880 51 842 847 334 167 413 443 314 -#> [631,] 331 128 946 608 759 356 948 119 450 831 -#> [632,] 308 150 393 420 347 601 230 365 406 886 -#> [633,] 172 442 693 974 680 531 389 400 944 760 -#> [634,] 886 909 275 200 644 308 347 748 475 323 -#> [635,] 77 731 246 800 242 493 315 144 923 700 -#> [636,] 382 779 164 849 438 491 586 890 772 169 -#> [637,] 175 165 572 211 270 863 87 875 692 526 -#> [638,] 676 127 395 361 575 712 777 265 238 48 -#> [639,] 600 590 245 963 14 614 754 770 710 921 -#> [640,] 910 115 806 219 317 381 419 738 757 362 -#> [641,] 191 834 335 48 711 289 648 121 619 987 -#> [642,] 56 241 490 729 781 439 276 779 491 525 -#> [643,] 776 80 711 648 774 100 501 190 14 809 -#> [644,] 909 430 475 886 21 634 184 937 751 323 -#> [645,] 887 28 382 470 427 379 358 29 32 826 -#> [646,] 260 829 589 26 819 118 240 575 649 620 -#> [647,] 473 618 239 981 460 450 295 300 946 108 -#> [648,] 774 828 42 335 987 892 834 641 643 711 -#> [649,] 281 829 489 838 240 496 20 575 982 462 -#> [650,] 7 363 554 769 527 480 513 989 678 786 -#> [651,] 806 282 687 910 727 394 738 408 538 689 -#> [652,] 226 580 448 953 526 412 211 438 761 281 -#> [653,] 775 472 60 606 830 218 107 152 864 997 -#> [654,] 106 57 778 966 747 322 507 148 227 255 -#> [655,] 307 92 436 592 46 349 597 555 497 27 -#> [656,] 975 797 476 492 915 484 354 629 279 920 -#> [657,] 672 879 677 221 874 500 207 898 736 681 -#> [658,] 456 744 718 416 283 418 135 417 898 35 -#> [659,] 330 375 348 901 162 406 303 113 477 12 -#> [660,] 972 434 468 538 275 377 524 167 291 533 -#> [661,] 734 429 818 958 695 440 559 421 480 81 -#> [662,] 798 814 425 897 548 945 49 268 445 132 -#> [663,] 776 588 182 42 926 997 648 589 312 752 -#> [664,] 461 37 62 988 101 229 344 836 891 253 -#> [665,] 203 334 325 101 616 216 213 847 386 945 -#> [666,] 670 309 587 277 725 544 431 927 31 547 -#> [667,] 225 782 194 568 938 671 2 217 187 284 -#> [668,] 598 892 95 188 605 18 504 237 828 987 -#> [669,] 825 264 732 397 479 285 86 998 61 466 -#> [670,] 309 666 587 3 927 789 707 277 544 725 -#> [671,] 225 568 777 915 370 407 782 217 667 715 -#> [672,] 221 500 434 236 321 657 879 906 660 524 -#> [673,] 894 428 780 72 33 457 160 618 213 942 -#> [674,] 385 968 555 483 91 232 27 719 310 974 -#> [675,] 58 170 251 31 982 960 208 265 387 389 -#> [676,] 127 777 638 839 48 194 225 715 671 261 -#> [677,] 416 85 35 898 209 418 64 456 658 272 -#> [678,] 366 76 313 943 234 527 989 105 650 528 -#> [679,] 983 199 174 345 369 121 437 710 259 452 -#> [680,] 389 45 686 442 839 251 801 319 172 693 -#> [681,] 487 207 273 956 560 657 396 524 768 855 -#> [682,] 223 86 998 61 285 371 921 163 466 341 -#> [683,] 875 764 270 757 282 21 806 748 362 87 -#> [684,] 628 967 785 204 869 877 536 518 357 417 -#> [685,] 548 253 934 516 882 413 111 881 298 177 -#> [686,] 15 25 265 170 351 839 680 433 208 863 -#> [687,] 651 508 738 939 595 141 910 727 806 419 -#> [688,] 793 734 769 740 215 812 480 105 244 622 -#> [689,] 914 203 21 937 430 616 184 909 51 847 -#> [690,] 730 738 248 517 350 931 256 989 687 703 -#> [691,] 394 727 687 651 1000 419 478 282 103 84 -#> [692,] 87 748 165 757 572 875 471 393 308 873 -#> [693,] 400 944 465 319 557 532 401 446 345 680 -#> [694,] 978 488 179 34 312 752 258 622 720 491 -#> [695,] 81 44 429 818 421 89 872 176 559 941 -#> [696,] 402 510 841 520 185 390 129 186 509 713 -#> [697,] 486 976 237 18 144 905 668 95 388 545 -#> [698,] 596 895 846 214 947 701 721 888 773 148 -#> [699,] 811 701 895 805 565 773 272 196 846 560 -#> [700,] 817 30 242 731 310 161 986 493 788 574 -#> [701,] 811 699 895 196 467 773 888 846 805 698 -#> [702,] 61 126 563 573 614 682 998 285 371 792 -#> [703,] 804 138 305 11 737 132 730 50 738 145 -#> [704,] 324 270 616 783 737 21 184 751 937 526 -#> [705,] 451 414 717 967 183 877 286 536 684 417 -#> [706,] 593 975 279 326 235 245 120 189 996 754 -#> [707,] 927 422 309 796 952 387 999 3 789 19 -#> [708,] 243 338 250 969 221 346 73 672 591 166 -#> [709,] 287 210 977 794 427 928 212 525 55 781 -#> [710,] 452 437 245 14 754 711 590 199 600 121 -#> [711,] 335 452 643 641 14 648 100 710 883 848 -#> [712,] 395 97 452 575 360 437 638 335 121 361 -#> [713,] 970 509 728 963 520 860 296 623 521 758 -#> [714,] 621 128 783 585 831 184 912 475 267 904 -#> [715,] 777 361 217 568 676 823 671 808 263 953 -#> [716,] 105 528 622 179 767 605 742 752 598 26 -#> [717,] 705 967 451 877 414 286 536 639 860 684 -#> [718,] 416 384 658 380 418 85 417 469 744 75 -#> [719,] 549 91 555 288 567 968 286 497 827 385 -#> [720,] 813 449 301 851 537 488 42 803 980 304 -#> [721,] 916 214 698 832 131 596 846 156 444 773 -#> [722,] 257 919 160 255 316 197 773 880 506 891 -#> [723,] 158 230 150 339 753 792 921 770 296 74 -#> [724,] 765 479 940 581 759 22 473 123 69 495 -#> [725,] 424 838 173 865 850 982 675 208 431 265 -#> [726,] 36 178 541 75 472 85 543 964 718 416 -#> [727,] 1000 485 651 478 415 954 538 275 687 282 -#> [728,] 713 67 963 137 129 970 278 623 758 373 -#> [729,] 490 579 525 439 885 169 897 854 781 210 -#> [730,] 690 703 350 114 804 738 50 989 124 138 -#> [731,] 242 493 635 77 700 315 907 574 310 144 -#> [732,] 669 825 426 785 352 285 913 628 53 264 -#> [733,] 984 992 108 88 581 197 364 556 22 151 -#> [734,] 429 661 480 559 943 793 872 410 276 140 -#> [735,] 168 529 607 993 802 379 990 924 887 299 -#> [736,] 511 874 611 893 612 52 803 949 879 67 -#> [737,] 11 704 448 270 132 324 50 65 804 850 -#> [738,] 910 640 71 320 248 219 687 939 517 268 -#> [739,] 957 46 161 30 857 155 423 404 859 205 -#> [740,] 812 622 215 79 237 578 793 976 752 762 -#> [741,] 553 743 404 484 807 797 790 112 290 349 -#> [742,] 527 18 95 313 234 605 854 474 668 762 -#> [743,] 807 553 741 112 5 962 349 797 484 535 -#> [744,] 135 658 898 416 283 718 243 623 456 338 -#> [745,] 836 70 306 900 615 569 457 664 24 554 -#> [746,] 1000 904 570 222 884 394 84 727 764 691 -#> [747,] 935 871 386 654 325 902 966 106 227 937 -#> [748,] 757 87 408 692 875 751 909 21 634 308 -#> [749,] 318 194 462 798 908 20 274 539 761 187 -#> [750,] 399 532 856 343 19 136 57 117 342 507 -#> [751,] 616 87 937 21 909 748 902 644 270 692 -#> [752,] 622 179 978 598 740 663 668 694 976 26 -#> [753,] 230 192 723 150 409 110 158 292 126 792 -#> [754,] 245 600 593 710 823 326 14 437 279 121 -#> [755,] 70 306 836 506 745 626 428 59 269 160 -#> [756,] 938 453 902 2 783 782 871 184 25 704 -#> [757,] 748 875 408 87 692 219 471 381 683 270 -#> [758,] 296 901 186 509 339 292 728 406 515 74 -#> [759,] 948 123 356 608 22 871 332 435 69 581 -#> [760,] 546 952 422 387 557 974 547 944 531 19 -#> [761,] 462 604 489 749 897 169 918 854 580 602 -#> [762,] 578 605 188 90 43 870 918 604 812 79 -#> [763,] 497 92 307 827 961 655 555 817 91 788 -#> [764,] 683 875 222 806 475 757 748 270 282 408 -#> [765,] 724 479 581 22 759 940 123 733 473 435 -#> [766,] 107 125 911 171 79 52 949 140 612 304 -#> [767,] 982 424 528 228 716 838 649 742 18 26 -#> [768,] 441 560 844 971 443 141 195 44 833 231 -#> [769,] 215 613 480 481 105 513 740 429 7 650 -#> [770,] 158 792 614 723 970 841 639 713 509 963 -#> [771,] 329 852 681 843 657 956 524 672 221 207 -#> [772,] 876 438 890 164 512 238 636 118 779 153 -#> [773,] 811 916 257 701 197 699 316 698 722 88 -#> [774,] 42 828 648 821 987 190 851 643 892 834 -#> [775,] 653 102 472 60 41 830 152 107 864 606 -#> [776,] 218 643 501 80 606 864 663 190 774 926 -#> [777,] 715 676 671 225 568 217 127 808 370 867 -#> [778,] 942 255 919 213 654 322 992 37 891 325 -#> [779,] 164 439 865 636 729 438 169 173 490 579 -#> [780,] 428 942 160 894 673 213 778 72 457 70 -#> [781,] 542 525 210 427 729 305 358 362 115 640 -#> [782,] 2 407 225 667 938 568 671 386 955 839 -#> [783,] 184 704 756 937 21 585 831 270 902 938 -#> [784,] 323 886 634 947 200 166 596 9 275 378 -#> [785,] 869 628 684 518 352 896 204 357 338 53 -#> [786,] 124 33 350 989 62 527 939 334 253 664 -#> [787,] 369 983 679 174 201 259 563 345 199 371 -#> [788,] 817 961 700 161 30 242 66 77 310 635 -#> [789,] 3 333 988 707 309 228 796 960 68 900 -#> [790,] 484 436 741 467 196 290 349 553 592 807 -#> [791,] 94 561 227 495 148 365 966 9 947 357 -#> [792,] 158 126 770 723 192 509 753 970 614 341 -#> [793,] 16 812 578 740 870 762 622 276 872 79 -#> [794,] 212 83 885 210 525 78 870 559 872 577 -#> [795,] 256 440 176 818 939 59 363 248 508 350 -#> [796,] 328 117 856 19 387 422 707 229 454 247 -#> [797,] 975 540 656 279 120 484 741 354 743 290 -#> [798,] 662 425 749 897 462 814 548 187 854 49 -#> [799,] 889 231 494 171 610 403 840 353 594 516 -#> [800,] 635 246 77 731 493 994 242 315 109 455 -#> [801,] 389 172 251 680 675 442 686 531 170 265 -#> [802,] 990 168 153 607 512 299 529 412 924 379 -#> [803,] 813 893 301 537 449 720 162 610 494 980 -#> [804,] 138 50 703 132 11 737 313 850 76 145 -#> [805,] 418 196 467 272 895 35 565 699 701 209 -#> [806,] 910 640 408 651 317 757 538 115 683 282 -#> [807,] 743 342 507 112 741 962 399 484 553 535 -#> [808,] 922 294 627 339 122 113 393 777 217 715 -#> [809,] 100 883 848 42 851 459 619 711 643 774 -#> [810,] 881 476 111 302 413 284 516 807 548 274 -#> [811,] 699 773 257 701 565 916 560 505 272 316 -#> [812,] 740 79 578 793 622 762 16 582 43 215 -#> [813,] 720 449 301 803 918 851 537 821 43 980 -#> [814,] 897 662 268 83 602 798 49 445 425 885 -#> [815,] 261 48 233 289 194 142 127 454 955 318 -#> [816,] 542 355 305 362 781 65 358 703 11 737 -#> [817,] 700 788 30 161 961 310 242 46 731 349 -#> [818,] 440 176 695 44 81 429 661 248 89 795 -#> [819,] 589 182 646 552 829 663 26 240 260 118 -#> [820,] 944 319 546 401 465 22 123 332 343 435 -#> [821,] 43 188 23 90 828 774 79 918 539 604 -#> [822,] 498 858 96 755 59 374 66 297 866 626 -#> [823,] 754 437 715 867 600 777 452 121 339 361 -#> [824,] 98 827 763 788 310 961 463 493 497 91 -#> [825,] 913 285 998 357 466 669 561 536 163 985 -#> [826,] 29 929 530 470 110 884 292 143 515 346 -#> [827,] 497 719 763 91 541 567 555 502 933 36 -#> [828,] 987 892 774 648 834 821 539 42 188 598 -#> [829,] 240 589 26 649 598 575 646 496 892 668 -#> [830,] 60 218 93 304 102 373 190 864 859 776 -#> [831,] 608 783 356 756 128 267 25 714 585 184 -#> [832,] 916 721 396 773 699 131 811 316 846 906 -#> [833,] 959 441 768 134 560 866 626 971 257 811 -#> [834,] 987 289 504 828 641 892 202 648 191 551 -#> [835,] 958 8 577 116 421 517 595 951 212 661 -#> [836,] 70 306 745 615 664 891 37 269 4 900 -#> [837,] 367 412 32 294 122 398 890 360 991 382 -#> [838,] 424 982 649 173 865 281 725 228 908 767 -#> [839,] 170 676 955 261 265 782 127 407 225 686 -#> [840,] 458 594 861 403 629 965 40 64 799 889 -#> [841,] 402 186 696 390 520 770 713 509 970 296 -#> [842,] 314 167 630 880 51 972 443 298 468 979 -#> [843,] 266 977 771 928 709 511 329 116 212 862 -#> [844,] 995 423 768 89 971 936 231 171 866 155 -#> [845,] 535 4 404 112 458 741 322 891 413 505 -#> [846,] 895 888 698 617 701 467 196 699 899 805 -#> [847,] 51 979 630 298 665 842 167 203 334 314 -#> [848,] 100 883 809 14 711 963 459 920 643 390 -#> [849,] 411 973 636 382 837 890 118 523 438 930 -#> [850,] 173 865 11 226 580 448 65 737 145 804 -#> [851,] 301 304 720 809 42 459 774 813 449 373 -#> [852,] 771 329 84 321 221 262 419 396 862 672 -#> [853,] 140 421 582 949 125 171 874 81 911 695 -#> [854,] 579 462 897 489 798 662 169 605 761 814 -#> [855,] 273 971 441 959 956 768 833 176 44 81 -#> [856,] 19 117 328 750 343 399 796 229 136 340 -#> [857,] 423 514 866 41 936 739 102 917 859 96 -#> [858,] 374 66 513 613 564 7 650 822 24 486 -#> [859,] 155 423 205 102 404 190 957 157 864 995 -#> [860,] 623 877 963 970 521 469 417 74 384 713 -#> [861,] 458 629 40 840 403 594 492 64 484 476 -#> [862,] 143 884 826 346 843 29 932 311 929 84 -#> [863,] 211 953 351 637 526 175 263 217 299 25 -#> [864,] 606 501 218 205 776 80 60 190 859 830 -#> [865,] 173 850 838 226 779 439 580 11 725 424 -#> [866,] 626 857 936 423 844 971 833 995 768 96 -#> [867,] 122 360 619 823 777 603 452 294 361 883 -#> [868,] 991 361 398 341 437 367 360 614 823 770 -#> [869,] 785 518 628 204 357 684 352 365 913 617 -#> [870,] 16 918 578 43 762 604 83 872 582 813 -#> [871,] 453 747 69 935 435 902 356 227 561 123 -#> [872,] 559 582 16 870 429 695 140 78 83 71 -#> [873,] 445 425 49 12 692 87 667 798 757 471 -#> [874,] 52 736 949 893 911 612 879 171 803 511 -#> [875,] 270 87 757 748 683 692 764 637 572 21 -#> [876,] 260 772 118 646 649 238 584 890 438 982 -#> [877,] 860 417 967 921 536 684 717 996 469 204 -#> [878,] 951 517 931 8 835 958 241 577 56 116 -#> [879,] 494 330 893 889 874 456 434 610 64 35 -#> [880,] 443 630 195 842 298 51 847 314 891 257 -#> [881,] 111 274 810 882 302 476 516 685 548 318 -#> [882,] 274 95 881 23 685 905 111 253 90 359 -#> [883,] 848 100 809 14 920 619 963 711 867 296 -#> [884,] 826 29 110 1 311 519 222 530 200 929 -#> [885,] 268 525 71 794 83 814 897 49 729 78 -#> [886,] 634 909 644 323 308 200 784 430 475 275 -#> [887,] 645 32 379 470 529 607 530 299 28 168 -#> [888,] 467 846 196 895 701 899 698 151 432 617 -#> [889,] 799 231 494 171 840 610 594 403 353 207 -#> [890,] 438 238 412 118 837 360 652 281 122 398 -#> [891,] 4 37 322 413 535 845 306 269 298 70 -#> [892,] 828 668 987 598 496 648 834 504 605 188 -#> [893,] 301 803 52 373 611 304 67 736 494 137 -#> [894,] 673 780 428 457 33 72 216 160 213 942 -#> [895,] 846 701 467 698 888 196 805 699 617 418 -#> [896,] 785 352 869 53 444 617 684 518 628 591 -#> [897,] 814 662 604 798 854 462 579 602 49 83 -#> [898,] 67 744 677 416 135 658 456 728 373 879 -#> [899,] 464 888 846 75 196 895 432 151 467 617 -#> [900,] 569 554 745 457 836 664 70 306 229 988 -#> [901,] 406 758 330 659 292 375 113 515 303 162 -#> [902,] 386 938 937 2 616 935 453 756 751 747 -#> [903,] 431 624 876 164 772 528 779 767 249 865 -#> [904,] 222 570 475 764 1000 621 784 714 644 282 -#> [905,] 157 237 23 882 95 359 987 274 190 188 -#> [906,] 500 35 291 660 272 895 236 321 434 543 -#> [907,] 574 242 493 199 310 731 259 552 224 345 -#> [908,] 318 496 194 20 749 127 462 281 187 815 -#> [909,] 644 430 886 634 475 751 21 937 184 748 -#> [910,] 640 806 115 738 419 317 219 651 683 757 -#> [911,] 52 949 612 125 766 874 107 171 853 304 -#> [912,] 621 714 904 128 585 426 69 570 222 475 -#> [913,] 420 285 357 561 825 998 150 628 230 223 -#> [914,] 689 937 21 184 203 616 430 909 644 902 -#> [915,] 671 656 225 370 476 279 326 667 568 407 -#> [916,] 773 832 721 811 699 316 698 701 846 895 -#> [917,] 102 957 130 404 152 859 290 423 380 502 -#> [918,] 604 43 870 602 90 821 578 813 762 897 -#> [919,] 255 778 722 942 197 322 257 891 160 992 -#> [920,] 975 593 120 354 100 14 883 279 754 656 -#> [921,] 996 326 998 466 223 723 877 600 754 158 -#> [922,] 808 294 627 113 122 339 54 393 165 953 -#> [923,] 925 336 455 246 976 77 315 144 182 635 -#> [924,] 512 990 802 168 153 735 607 529 772 433 -#> [925,] 923 606 864 501 205 336 455 218 776 30 -#> [926,] 504 182 648 834 501 551 776 987 552 892 -#> [927,] 707 309 952 422 387 277 670 760 796 999 -#> [928,] 276 252 116 16 794 709 212 870 582 559 -#> [929,] 515 29 292 826 110 287 303 659 470 54 -#> [930,] 390 360 809 198 848 883 335 411 867 100 -#> [931,] 517 248 577 440 951 71 241 958 989 320 -#> [932,] 266 515 346 250 511 929 287 509 696 826 -#> [933,] 384 469 278 472 541 623 521 583 152 502 -#> [934,] 941 685 516 548 177 268 253 298 320 814 -#> [935,] 747 325 386 902 871 203 937 654 966 665 -#> [936,] 564 995 844 971 866 857 423 514 125 41 -#> [937,] 184 616 914 21 902 430 751 909 689 644 -#> [938,] 782 902 568 2 756 453 667 217 225 407 -#> [939,] 941 334 176 248 320 630 934 508 440 141 -#> [940,] 495 193 214 378 94 984 9 791 581 985 -#> [941,] 934 268 177 78 320 71 44 685 939 516 -#> [942,] 778 780 213 919 255 992 160 216 654 322 -#> [943,] 410 480 366 234 76 734 490 559 762 241 -#> [944,] 693 557 546 319 820 532 465 400 136 401 -#> [945,] 548 132 662 685 320 425 334 798 187 665 -#> [946,] 295 450 300 159 119 128 114 618 576 647 -#> [947,] 596 365 415 954 698 275 94 784 634 886 -#> [948,] 759 123 45 22 952 332 319 422 820 608 -#> [949,] 612 911 52 874 766 853 736 140 125 582 -#> [950,] 409 519 1 338 192 628 243 785 753 110 -#> [951,] 878 8 517 241 525 577 931 116 56 835 -#> [952,] 422 547 760 707 45 948 927 796 387 251 -#> [953,] 863 211 217 351 627 263 526 715 652 122 -#> [954,] 415 485 947 596 275 51 698 784 167 9 -#> [955,] 247 454 261 839 170 208 815 2 782 194 -#> [956,] 855 273 681 524 396 441 141 478 207 560 -#> [957,] 404 739 5 46 155 553 112 741 535 859 -#> [958,] 835 517 440 661 931 818 577 248 176 256 -#> [959,] 833 441 768 855 866 971 560 134 273 626 -#> [960,] 68 228 58 392 142 387 551 675 63 982 -#> [961,] 788 817 483 655 555 161 700 763 310 66 -#> 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338 277 +#> [2,] 873 936 304 397 677 479 667 680 230 856 +#> [3,] 480 369 471 186 486 911 569 826 178 10 +#> [4,] 288 481 746 93 802 252 120 849 17 291 +#> [5,] 295 33 212 289 484 556 466 851 776 804 +#> [6,] 999 575 125 386 36 211 695 651 20 1000 +#> [7,] 510 644 30 933 275 407 990 189 483 190 +#> [8,] 953 943 55 278 966 576 730 168 342 870 +#> [9,] 657 493 899 38 71 404 268 401 227 307 +#> [10,] 468 930 569 710 486 238 615 186 981 664 +#> [11,] 456 884 872 375 752 423 218 909 738 430 +#> [12,] 314 64 603 790 421 359 800 478 757 740 +#> [13,] 264 889 46 394 63 131 64 314 972 88 +#> [14,] 720 412 780 352 585 226 320 526 945 537 +#> [15,] 475 215 83 793 521 982 217 411 900 946 +#> [16,] 107 465 86 819 560 101 355 523 592 728 +#> [17,] 252 481 220 675 120 672 446 19 612 302 +#> [18,] 613 32 77 606 121 810 944 12 757 314 +#> [19,] 446 843 836 821 189 931 269 164 988 252 +#> [20,] 651 692 125 386 36 575 1000 901 6 370 +#> [21,] 188 914 627 213 101 629 272 417 777 136 +#> [22,] 283 906 426 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920 537 226 283 14 595 426 893 927 515 +#> [946,] 94 605 521 411 217 215 742 391 292 860 +#> [947,] 285 268 751 326 270 51 657 977 343 106 +#> [948,] 362 631 907 902 858 692 389 558 249 216 +#> [949,] 332 312 508 928 347 204 472 157 487 808 +#> [950,] 816 235 257 759 857 399 377 175 939 127 +#> [951,] 308 516 464 195 817 358 820 749 377 816 +#> [952,] 949 729 317 204 347 312 332 410 487 867 +#> [953,] 8 55 730 943 966 679 278 463 99 836 +#> [954,] 84 268 468 591 723 10 41 561 227 930 +#> [955,] 782 278 707 185 966 734 282 320 572 140 +#> [956,] 53 424 245 583 876 754 827 210 454 260 +#> [957,] 112 238 118 480 569 615 10 65 468 473 +#> [958,] 812 290 137 284 389 31 128 554 917 87 +#> [959,] 208 401 231 566 899 381 232 355 404 728 +#> [960,] 27 592 913 126 346 152 366 161 546 766 +#> [961,] 997 321 88 147 98 330 773 256 908 769 +#> [962,] 457 482 879 809 56 974 902 571 703 274 +#> [963,] 881 767 596 878 490 901 858 631 406 996 +#> [964,] 541 440 683 134 714 380 602 898 580 573 +#> [965,] 715 232 231 250 182 649 585 381 780 717 +#> [966,] 730 278 55 955 8 953 282 559 99 233 +#> [967,] 887 779 594 642 975 618 190 933 796 407 +#> [968,] 305 429 671 691 140 141 978 50 656 745 +#> [969,] 173 649 30 642 644 933 975 760 716 526 +#> [970,] 671 531 980 167 996 782 185 490 805 903 +#> [971,] 828 454 754 903 876 387 192 674 735 191 +#> [972,] 46 301 13 264 687 889 423 171 256 998 +#> [973,] 108 711 113 374 323 37 665 351 718 668 +#> [974,] 571 554 274 902 389 87 917 879 812 111 +#> [975,] 642 594 193 967 533 405 207 933 969 507 +#> [976,] 666 41 626 441 158 268 172 947 751 569 +#> [977,] 667 304 138 241 680 230 528 397 268 270 +#> [978,] 996 707 691 141 490 719 671 589 488 249 +#> [979,] 995 342 583 54 424 75 245 943 876 863 +#> [980,] 805 970 671 995 265 322 278 979 966 583 +#> [981,] 664 570 227 754 96 827 478 876 210 468 +#> [982,] 793 632 111 475 748 593 853 489 15 854 +#> [983,] 736 544 122 78 437 621 709 781 43 924 +#> [984,] 100 92 403 409 628 205 689 299 520 261 +#> [985,] 450 801 216 152 417 333 692 188 665 565 +#> [986,] 555 905 488 91 444 276 327 287 517 890 +#> [987,] 722 409 567 261 403 609 279 689 338 360 +#> [988,] 252 446 19 267 17 85 117 931 743 481 +#> [989,] 340 912 885 517 724 428 682 787 396 890 +#> [990,] 275 407 515 7 510 933 190 741 502 644 +#> [991,] 866 857 939 818 142 209 877 474 476 61 +#> [992,] 499 218 256 731 784 22 906 430 738 330 +#> [993,] 717 652 381 250 200 392 224 915 355 182 +#> [994,] 284 290 128 406 854 958 234 587 294 790 +#> [995,] 979 980 735 863 641 924 75 621 624 265 +#> [996,] 978 249 707 782 185 490 907 970 631 691 +#> [997,] 98 256 892 972 46 992 769 218 330 738 +#> [998,] 387 167 394 423 131 430 921 656 284 413 +#> [999,] 6 695 854 211 36 111 575 748 23 386 +#> [1000,] 125 386 651 523 49 20 801 417 692 577 #> #> $neighbor_distances -#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] -#> [1,] 0.7048266 0.8083694 0.8882599 0.9421757 1.0040249 1.0144722 1.0248596 -#> [2,] 0.2646638 0.4447534 0.4508229 0.4509399 0.4834092 0.5247719 0.5497563 -#> [3,] 0.5625080 0.7485735 0.8228400 0.8580177 0.9447221 1.0220488 1.0268545 -#> [4,] 0.2337535 0.3869610 0.3987572 0.4311430 0.4322313 0.5076585 0.5243604 -#> [5,] 0.3071985 0.4135715 0.4187607 0.4275021 0.4449949 0.4480393 0.4862072 -#> [6,] 0.8162260 1.3583798 1.5773534 1.6751377 1.8243273 1.8572698 1.8800572 -#> [7,] 0.6913829 0.7541327 0.8888739 0.9243094 0.9559779 0.9618115 0.9714105 -#> [8,] 0.5987923 0.6929355 0.7099335 0.7234910 0.7436081 0.7586708 0.7788853 -#> [9,] 0.5972976 0.6019840 0.6145234 0.6544568 0.6621461 0.7077951 0.7314612 -#> [10,] 0.8162260 1.1388998 1.5691586 1.6517703 1.7160733 1.8003366 1.8312074 -#> [11,] 0.4387941 0.4894021 0.5311648 0.5452097 0.6617801 0.6809391 0.7264780 -#> [12,] 0.3486888 0.3676536 0.4109260 0.4335793 0.4539022 0.4627766 0.5623945 -#> [13,] 1.6469234 1.7292788 1.8007623 1.8432932 1.8877758 1.9391614 1.9416047 -#> [14,] 0.3999155 0.4020145 0.4489713 0.4504919 0.4615817 0.4779987 0.5029892 -#> [15,] 0.2436403 0.3935127 0.4089076 0.5083646 0.5488945 0.6392918 0.6604720 -#> [16,] 0.3293306 0.4893257 0.5458239 0.5741937 0.5897778 0.6447046 0.6659995 -#> [17,] 0.6977377 0.8617257 1.0572980 1.1038723 1.1918617 1.2138401 1.2184243 -#> [18,] 0.5040802 0.5627145 0.5672224 0.5718031 0.6170351 0.6306272 0.6928692 -#> [19,] 0.2857866 0.2940478 0.5034045 0.5351798 0.5760647 0.5784438 0.5962825 -#> [20,] 0.2286886 0.4625997 0.4797133 0.4798772 0.5166952 0.5390328 0.5771252 -#> [21,] 0.3915093 0.4433784 0.4566071 0.4575790 0.4861910 0.5110320 0.5232558 -#> [22,] 0.4862339 0.5522699 0.7315827 0.7898797 0.8151923 0.8296489 0.8669828 -#> [23,] 0.3284029 0.3912126 0.4004372 0.4007780 0.4112151 0.4410145 0.4468764 -#> [24,] 0.4052950 0.5732842 0.6229928 0.6652722 0.6817741 0.7026462 0.7338538 -#> [25,] 0.3843435 0.4089076 0.4893418 0.6487378 0.6911578 0.7590991 0.7672499 -#> [26,] 0.5589624 0.6996202 0.7212007 0.7595346 0.7693359 0.7742780 0.8258053 -#> [27,] 0.5874171 0.7553784 0.8066190 0.9306648 0.9755979 1.0063360 1.0732831 -#> [28,] 0.5737651 0.5889834 0.6094010 0.6560304 0.7157484 0.7175131 0.7469774 -#> [29,] 0.3036663 0.5015118 0.5242928 0.6236399 0.7883728 0.8257304 0.9218299 -#> [30,] 0.4241206 0.5605815 0.6033279 0.6619944 0.6658839 0.6841600 0.7411967 -#> [31,] 0.7023488 0.7160217 0.8238013 0.8848219 0.9756494 0.9797639 0.9837719 -#> [32,] 0.5225561 0.5611636 0.5643889 0.6709421 0.6924779 0.7095881 0.7157484 -#> [33,] 0.5907024 0.5912506 0.6060134 0.6538817 0.7120533 0.8081701 0.8266657 -#> [34,] 0.7235467 0.7685739 0.7745123 0.8247635 0.8285483 0.9214560 0.9243558 -#> [35,] 0.4614993 0.4723733 0.4747357 0.5278493 0.5339777 0.5495823 0.6000009 -#> [36,] 1.0280044 1.3326072 1.4438542 1.4950672 1.5100762 1.5186078 1.5462172 -#> [37,] 0.3299522 0.3549475 0.4322313 0.4429551 0.5124888 0.5439571 0.5654315 -#> [38,] 0.2847934 0.4855697 0.4862419 0.4930848 0.4956664 0.5022562 0.5031505 -#> [39,] 1.2646007 1.2969284 1.3367225 1.3488551 1.4737490 1.4945992 1.5523904 -#> [40,] 0.3035479 0.3186834 0.3551038 0.4236751 0.4554516 0.4722940 0.4805071 -#> [41,] 0.4353166 0.5238137 0.6346238 0.7027129 0.8082479 0.8549699 0.8945612 -#> [42,] 0.3158758 0.4122767 0.5134259 0.5156644 0.5397715 0.5698328 0.5908687 -#> [43,] 0.3112028 0.3118351 0.3385731 0.3728544 0.4352734 0.4380835 0.4468764 -#> [44,] 0.3042617 0.4165118 0.4180805 0.5900513 0.6286326 0.6854924 0.7085806 -#> [45,] 0.6570087 0.6574265 0.7661296 0.7933378 0.7996498 0.8144727 0.8169656 -#> [46,] 0.4739766 0.5013117 0.6193471 0.6287011 0.6384725 0.7145823 0.7185547 -#> [47,] 0.8716038 1.1291670 1.2601548 1.2731740 1.4309672 1.5484044 1.6242022 -#> [48,] 0.3418835 0.3449666 0.3816936 0.3847305 0.3990696 0.4634436 0.4700327 -#> [49,] 0.1960542 0.3481627 0.4300678 0.4569880 0.4627766 0.5043400 0.5348194 -#> [50,] 0.5048296 0.6151931 0.7414556 0.7697675 0.7865517 0.7933014 0.8135147 -#> [51,] 0.3107841 0.3815946 0.4300301 0.4810555 0.4820274 0.4976498 0.5715760 -#> [52,] 0.4401777 0.4851220 0.5566957 0.5749747 0.5953573 0.6111659 0.6666715 -#> [53,] 0.8284443 0.9455623 1.0445572 1.1584753 1.2246511 1.2271954 1.2533244 -#> [54,] 0.5165410 0.5483363 0.5577906 0.5610397 0.6063109 0.6596769 0.6916249 -#> [55,] 0.3401918 0.3740531 0.5727056 0.7549157 0.8370006 0.8412854 0.8601071 -#> [56,] 0.7918233 0.8732220 0.9036908 0.9597329 0.9924884 1.1504494 1.1632732 -#> [57,] 0.3175616 0.3724500 0.4292543 0.4828516 0.4994282 0.4997180 0.5676278 -#> [58,] 0.4791532 0.5493708 0.5503179 0.6035151 0.6610069 0.6694261 0.7053166 -#> [59,] 0.6290930 0.9289136 0.9689169 0.9733285 1.0257024 1.0359829 1.0698120 -#> [60,] 0.1793892 0.4433549 0.5704134 0.6528788 0.6883512 0.7304316 0.7677203 -#> [61,] 0.6116325 0.6763739 0.6778514 0.6978583 0.7198049 0.7402864 0.7830806 -#> [62,] 0.3478599 0.3490601 0.3556601 0.4149381 0.5310006 0.5597738 0.6224384 -#> [63,] 0.3444005 0.5210476 0.5378630 0.5725214 0.5962825 0.6565383 0.6822445 -#> [64,] 0.2266562 0.3956464 0.4746320 0.4763757 0.4905728 0.5245846 0.5899013 -#> [65,] 0.5080052 0.7771783 0.8011727 0.8205875 0.9891684 1.0671992 1.0835386 -#> [66,] 0.8743386 1.0018150 1.1016418 1.1232248 1.1248432 1.1419690 1.1658660 -#> [67,] 0.3534918 0.5119499 0.5529916 0.5719831 0.5959716 0.6538127 0.7144411 -#> [68,] 0.4588987 0.5010224 0.5109920 0.5944829 0.6216318 0.6928692 0.6936148 -#> [69,] 0.5602409 0.6469960 0.7002396 0.7106937 0.7325301 0.8068203 0.8211363 -#> [70,] 0.2020652 0.2218200 0.4595513 0.6095924 0.6650239 0.6690871 0.6998387 -#> [71,] 0.3062630 0.4370194 0.4648428 0.4746678 0.5015561 0.6170349 0.6515960 -#> [72,] 0.6308904 0.7111541 0.7114052 0.7174661 0.7458210 0.7520065 0.7886171 -#> [73,] 0.4637712 0.7955241 0.8066526 0.8093809 1.0033759 1.0384308 1.0415756 -#> [74,] 0.4819553 0.5052221 0.5255958 0.5520613 0.5833905 0.6326745 0.6415663 -#> [75,] 0.7557878 0.7656519 0.7914277 0.8048466 0.8943719 0.9215540 0.9592574 -#> [76,] 0.5677734 0.6241592 0.6257073 0.6401146 0.6488231 0.6922880 0.7164694 -#> [77,] 0.4402712 0.4445392 0.4796866 0.5374811 0.5765453 0.7042095 0.7100582 -#> [78,] 0.4369430 0.4746678 0.5246621 0.5705223 0.5949956 0.6440177 0.6726832 -#> [79,] 0.4590312 0.4811924 0.4847473 0.4859729 0.4906804 0.5147732 0.5902184 -#> [80,] 0.2734565 0.4826327 0.5053076 0.5955601 0.5985745 0.6196470 0.6214573 -#> [81,] 0.2202836 0.3042617 0.5492871 0.6267163 0.6952081 0.7114169 0.7539298 -#> [82,] 1.1691834 1.3014405 1.3326076 1.3625383 1.4254770 1.5510179 1.5517087 -#> [83,] 0.3844678 0.4266016 0.4934404 0.5010035 0.5011315 0.5246621 0.5409899 -#> [84,] 0.8915097 1.1654770 1.1700043 1.3160452 1.3369395 1.4012029 1.4224582 -#> [85,] 0.4650003 0.5372201 0.7173710 0.7750321 0.8502619 0.8524977 0.8613864 -#> [86,] 0.4552099 0.5632050 0.6270920 0.6319328 0.6451453 0.6526862 0.6801134 -#> [87,] 0.2697039 0.2989971 0.3722417 0.3864791 0.3979419 0.5044755 0.5115880 -#> [88,] 0.5890583 0.6303962 0.6972553 0.8012599 0.8484478 0.8800725 0.8999855 -#> [89,] 0.4085959 0.6081224 0.6106471 0.6470521 0.6488359 0.6704771 0.7069878 -#> [90,] 0.2682098 0.3385731 0.3977697 0.4007780 0.4088687 0.4110661 0.4334255 -#> [91,] 0.2751204 0.5993097 0.7266816 0.7926226 0.9236902 1.0381235 1.0600730 -#> [92,] 0.3414077 0.6613483 0.7949358 0.9309210 0.9628605 1.0047288 1.0271264 -#> [93,] 0.4188806 0.4482251 0.4985003 0.5105230 0.5590794 0.6109957 0.6160324 -#> [94,] 0.2915407 0.4006096 0.5662374 0.5703396 0.6104983 0.6254522 0.6571560 -#> [95,] 0.3570338 0.4294050 0.4334255 0.4494573 0.4615580 0.4654187 0.4721043 -#> [96,] 0.8064250 0.8357599 0.8473472 0.8582234 0.8847025 0.9628605 0.9966873 -#> [97,] 0.3610341 0.3774352 0.4789762 0.4862033 0.5030681 0.5503179 0.6050880 -#> [98,] 1.0499795 1.0643079 1.2747939 1.2884228 1.3809873 1.4183431 1.4441306 -#> [99,] 0.8425571 1.0012882 1.0613208 1.1160400 1.2090369 1.2196290 1.2271954 -#> [100,] 0.1719823 0.2005349 0.3995663 0.4020145 0.5419647 0.5490831 0.5606193 -#> [101,] 0.2832737 0.4051340 0.4424304 0.5553900 0.5557919 0.5561115 0.6074579 -#> [102,] 0.5238137 0.5270508 0.5384865 0.5802341 0.6482590 0.6528788 0.7393022 -#> [103,] 0.7780846 0.9160502 1.0251646 1.0758883 1.2273985 1.3250280 1.3650345 -#> [104,] 0.8402520 0.9372123 0.9846061 1.1511613 1.2117447 1.3488551 1.3855672 -#> [105,] 0.6289333 0.8665598 0.8765106 0.9286206 0.9619865 0.9700839 0.9714105 -#> [106,] 0.2661266 0.2988244 0.3085853 0.3724500 0.4407825 0.4594065 0.5107381 -#> [107,] 0.4815630 0.7025629 0.7512546 0.8192894 0.8972547 0.9296012 0.9443613 -#> [108,] 0.9098251 0.9994496 1.0043093 1.0074580 1.0819849 1.2459773 1.3049420 -#> [109,] 0.8127954 0.8848219 1.0958495 1.1919957 1.2672147 1.2879393 1.3004464 -#> [110,] 0.3765864 0.5735751 0.6231267 0.6317110 0.6976519 0.7089059 0.7372494 -#> [111,] 0.2312386 0.3439918 0.3618488 0.3632050 0.3725628 0.3938001 0.4741794 -#> [112,] 0.2479778 0.3071985 0.3768641 0.3914624 0.4080576 0.4565760 0.4807659 -#> [113,] 0.3536279 0.4134636 0.4428247 0.4719349 0.4721957 0.5400492 0.5448039 -#> [114,] 0.4448283 0.7417602 0.7560302 0.7727866 0.8194439 0.8326412 0.8550672 -#> [115,] 0.3670521 0.4335027 0.4463284 0.5121596 0.6746002 0.6782024 0.7550491 -#> [116,] 0.7586708 0.7918233 0.8495530 0.8584823 0.8656548 0.8673209 0.8829771 -#> [117,] 0.2824080 0.3407325 0.5028377 0.5034045 0.5364504 0.5724725 0.6111820 -#> [118,] 0.6011015 0.6568896 0.6970957 0.7760345 0.7873279 0.8389859 0.8612169 -#> [119,] 0.4645052 0.7627281 0.7664906 0.8466935 0.8795198 0.9012175 0.9062407 -#> [120,] 0.3015907 0.3781865 0.4005184 0.4426711 0.4985003 0.5100151 0.5129489 -#> [121,] 0.3449666 0.4175508 0.5115338 0.5185349 0.5413713 0.5454344 0.5565225 -#> [122,] 0.2218137 0.4422711 0.4834617 0.4870908 0.6026000 0.6130869 0.6203350 -#> [123,] 0.3457117 0.4862339 0.4869867 0.5838292 0.5907311 0.6927150 0.7224404 -#> [124,] 0.3859147 0.5860999 0.6060134 0.6827474 0.8100991 0.8135147 0.8984934 -#> [125,] 0.5688090 0.5920986 0.6839222 0.7025629 0.7132284 0.7472718 0.7557377 -#> [126,] 0.3214490 0.4776311 0.7167084 0.7890833 0.8072259 0.8133698 0.8167129 -#> [127,] 0.2652173 0.3418835 0.4172473 0.4641082 0.4797133 0.4834878 0.5094085 -#> [128,] 0.8132454 0.8566013 0.8912662 0.9567154 0.9995281 1.0529038 1.0892656 -#> [129,] 0.5173034 0.5385999 0.5909574 0.6495746 0.7144411 0.7530588 0.7793916 -#> [130,] 0.3016603 0.3589144 0.3798359 0.5123829 0.6620639 0.6644755 0.6849573 -#> [131,] 0.7945027 0.8052348 1.0094416 1.0380981 1.1226155 1.1483449 1.1911871 -#> [132,] 0.4773687 0.4896124 0.5876542 0.6151931 0.6152917 0.6525114 0.6617801 -#> [133,] 0.2957885 0.5998256 0.6250829 0.6819637 0.6955587 0.7119675 0.7181709 -#> [134,] 0.8466075 0.9559963 0.9653752 0.9709938 0.9928338 1.0115360 1.0325245 -#> [135,] 0.1418194 0.6279938 0.6904804 0.7189922 0.7214698 0.8149124 0.8672728 -#> [136,] 0.3444005 0.3513501 0.4608625 0.5105701 0.5202147 0.5617529 0.5724725 -#> [137,] 0.3534918 0.3892923 0.5173034 0.5489988 0.5815853 0.6398496 0.6930631 -#> [138,] 0.4773687 0.4978547 0.6620687 0.6726346 0.6922880 0.7011757 0.7414556 -#> [139,] 0.4446937 0.4528535 0.5435197 0.5998256 0.6946994 0.7683952 0.7739046 -#> [140,] 0.3638288 0.4680880 0.6973555 0.7250861 0.7494283 0.7619575 0.7656392 -#> [141,] 0.5383358 0.5900513 0.6787596 0.7081918 0.7108781 0.7493679 0.7631740 -#> [142,] 0.2109421 0.3381355 0.4371760 0.4410435 0.5464723 0.5576133 0.5636682 -#> [143,] 0.9889649 1.0560097 1.1533725 1.1601190 1.2254556 1.2703172 1.3124793 -#> [144,] 0.2954659 0.4437187 0.4796866 0.5504881 0.6164471 0.6256895 0.6496666 -#> [145,] 0.5080052 0.8341716 0.8432496 0.9130185 0.9294930 0.9832351 0.9880707 -#> [146,] 1.4565224 1.7843633 1.7996840 1.8705359 1.8760104 1.8799524 1.9060669 -#> [147,] 0.9372123 1.0059526 1.1388661 1.3188446 1.3606501 1.3640721 1.5037697 -#> [148,] 0.4468204 0.5485547 0.5751650 0.5758143 0.6294308 0.6353628 0.6354754 -#> [149,] 0.8402520 1.0644100 1.1333596 1.2646007 1.3188446 1.3367809 1.4007968 -#> [150,] 0.3891018 0.4304763 0.4883660 0.4925399 0.5255958 0.6052847 0.6073665 -#> [151,] 0.3124576 0.7553784 0.7786041 0.9153356 0.9816910 0.9895036 1.0228704 -#> [152,] 0.4181309 0.4396069 0.5606495 0.7003802 0.7263227 0.8679645 0.8940518 -#> [153,] 0.5745431 0.6589509 0.8750195 0.8975972 0.9072083 0.9513614 0.9557934 -#> [154,] 1.0687995 1.2359980 1.5919794 1.6551029 1.8572032 1.8726503 1.9143099 -#> [155,] 0.3525775 0.4188517 0.4430698 0.5028352 0.5053819 0.5655088 0.5838935 -#> [156,] 0.3304958 0.4964543 0.6085589 0.7224517 0.7508667 0.7620922 0.7791763 -#> [157,] 0.3737895 0.3898532 0.4004372 0.4822703 0.5067166 0.5655088 0.5766819 -#> [158,] 0.2298626 0.4305420 0.4738325 0.5797168 0.6287894 0.6676141 0.6826555 -#> [159,] 0.5807290 0.7780846 0.8875493 1.0127100 1.1450179 1.1916313 1.2690950 -#> [160,] 0.4289125 0.5011922 0.5540639 0.6948436 0.7208311 0.7520065 0.8818430 -#> [161,] 0.4241206 0.6107870 0.6229928 0.6529928 0.7129272 0.7185547 0.7243970 -#> [162,] 0.3859954 0.4109260 0.4555023 0.4626248 0.4633659 0.4918698 0.5110051 -#> [163,] 0.4552099 0.4855611 0.5240031 0.6194519 0.7022538 0.7035018 0.7373904 -#> [164,] 0.5278035 0.7871674 0.9289076 0.9783975 1.0084421 1.0511804 1.0655496 -#> [165,] 0.3799644 0.3806210 0.3934462 0.4135310 0.4970086 0.5115880 0.5358688 -#> [166,] 0.5511508 0.6390218 0.6504903 0.7225953 0.7955241 0.8164118 0.8319241 -#> [167,] 0.2105175 0.3815946 0.3900844 0.4083693 0.4314051 0.4369799 0.4537252 -#> [168,] 0.5598617 0.6075112 0.6221261 0.6616265 0.6686554 0.9067763 0.9191234 -#> [169,] 0.4829863 0.5799477 0.5957137 0.5966500 0.6040105 0.6383378 0.6721866 -#> [170,] 0.2656860 0.3399969 0.4277456 0.4720053 0.5953922 0.6416989 0.6518913 -#> [171,] 0.4612447 0.4740072 0.4972074 0.5100298 0.5698245 0.6111659 0.6466797 -#> [172,] 0.6097647 0.6132011 0.6296337 0.6743795 0.8499388 0.8502969 0.8613284 -#> [173,] 0.2503394 0.2994409 0.5889596 0.6348820 0.7158948 0.7264780 0.8250275 -#> [174,] 0.3672958 0.4160685 0.4725977 0.5723332 0.5830259 0.7428737 0.7919458 -#> [175,] 0.3196900 0.5194182 0.5298376 0.5435197 0.5572317 0.5750432 0.5840255 -#> [176,] 0.4165118 0.5049022 0.5466477 0.6267163 0.6787596 0.6830429 0.7427972 -#> [177,] 0.4497791 0.5191472 0.5219601 0.5395790 0.5416235 0.6160995 0.6362140 -#> [178,] 0.9989923 1.0082055 1.1357055 1.1672237 1.1891288 1.2180572 1.2978906 -#> [179,] 0.4670087 0.5287582 0.5334193 0.5478272 0.5721634 0.5743282 0.6294766 -#> [180,] 0.7664906 0.8228400 0.8585636 0.8969156 0.9115949 1.0017615 1.0335018 -#> [181,] 0.1362916 0.3588431 0.4794574 0.5286231 0.5310006 0.5579764 0.5622382 -#> [182,] 0.4579561 0.6701687 0.6773683 0.6883186 0.6996202 0.7164687 0.7429378 -#> [183,] 0.7755421 0.8804326 0.9078180 1.1672237 1.1684649 1.1729379 1.2657212 -#> [184,] 0.3119060 0.4224714 0.4575790 0.4683933 0.5423035 0.5515163 0.5919193 -#> [185,] 0.5909574 0.6332117 0.6853056 0.7348973 0.7398255 0.7435528 0.7496748 -#> [186,] 0.5069319 0.5264586 0.5577906 0.5580674 0.6443203 0.7141846 0.7309459 -#> [187,] 0.3943873 0.3955992 0.4359817 0.4794574 0.4972336 0.5284220 0.5321714 -#> [188,] 0.2682098 0.3284029 0.3530090 0.3678781 0.3728544 0.4075196 0.4294050 -#> [189,] 0.2991949 0.3692605 0.5451733 0.5594507 0.5624723 0.6126309 0.6801919 -#> [190,] 0.3898532 0.5403542 0.5727275 0.6038711 0.6124425 0.6162748 0.6253421 -#> [191,] 0.4279815 0.4480237 0.4493396 0.4829012 0.4842416 0.4862033 0.4921528 -#> [192,] 0.3214490 0.5980038 0.6570703 0.8534005 0.9127974 0.9277686 0.9352843 -#> [193,] 0.3394844 0.3853540 0.5972976 0.6104983 0.6413421 0.6960078 0.7603939 -#> [194,] 0.2752557 0.4161253 0.4172473 0.4176578 0.4396160 0.4940575 0.4972336 -#> [195,] 0.2578455 0.4328642 0.6321894 0.6537618 0.6839005 0.7108781 0.7116276 -#> [196,] 0.2791779 0.4024938 0.4860003 0.5316131 0.5517135 0.5688071 0.5936018 -#> [197,] 0.5488358 0.5890583 0.5960984 0.6170807 0.6979232 0.7174303 0.7531728 -#> [198,] 0.6311555 0.6573906 0.7012529 0.7290197 0.7398255 0.7983180 0.8183816 -#> [199,] 0.3642713 0.5072499 0.5454344 0.5506138 0.5703296 0.5830259 0.6540970 -#> [200,] 0.4726632 0.5489127 0.5812676 0.6390218 0.6806105 0.6894002 0.7998378 -#> [201,] 0.8613284 0.8640089 0.8871901 1.0109677 1.0149250 1.0495105 1.1598583 -#> [202,] 0.2164501 0.2860089 0.3381355 0.4283662 0.4480237 0.4736562 0.4737440 -#> [203,] 0.3527183 0.4759184 0.5130482 0.5664733 0.5982752 0.6008386 0.6296641 -#> [204,] 0.4037595 0.4106354 0.4734001 0.5075357 0.6279512 0.6381214 0.6766374 -#> [205,] 0.4613472 0.5733389 0.6536408 0.6658839 0.7161500 0.7405805 0.7691944 -#> [206,] 0.7557748 1.9032298 1.9909608 2.0085635 2.1575094 2.1825889 2.2086869 -#> [207,] 0.5352316 0.5703028 0.6775417 0.8012966 0.8110856 0.8860435 0.8907570 -#> [208,] 0.3399969 0.5087585 0.5108792 0.5812280 0.6684315 0.7307516 0.7418706 -#> [209,] 0.2266562 0.4415222 0.5105230 0.5281845 0.5290100 0.5508943 0.5960563 -#> [210,] 0.5144181 0.6123625 0.6639630 0.6989697 0.7102417 0.7557781 0.7588572 -#> [211,] 0.2442281 0.3479301 0.4439951 0.4642895 0.4872823 0.5358688 0.5773974 -#> [212,] 0.4894648 0.5949956 0.6911686 0.6929355 0.6989893 0.7356213 0.7939766 -#> [213,] 0.3559836 0.4992742 0.5002217 0.5591558 0.5654315 0.5836867 0.5955450 -#> [214,] 0.5745717 0.5981669 0.6085589 0.6228606 0.6447953 0.6589045 0.6828360 -#> [215,] 0.4768436 0.5065259 0.5627565 0.7367339 0.7530721 0.7609732 0.7615598 -#> [216,] 0.3559836 0.4424304 0.4907152 0.4921030 0.5727925 0.5907024 0.6593339 -#> [217,] 0.3052188 0.4374722 0.4523524 0.4868032 0.5092464 0.5101514 0.5210982 -#> [218,] 0.3974524 0.4433549 0.4991711 0.5081086 0.5470574 0.6239051 0.6253421 -#> [219,] 0.4592319 0.4923259 0.4934713 0.5198035 0.5287760 0.5481299 0.5616279 -#> [220,] 0.9243558 0.9301088 1.1803583 1.1868249 1.2316803 1.3025945 1.3337114 -#> [221,] 0.1765483 0.6991221 0.7436371 0.8061793 0.8457712 0.8822349 0.9095427 -#> [222,] 0.4114465 0.6579970 0.7081140 0.7223757 1.0008298 1.0602373 1.0841085 -#> [223,] 0.4855611 0.5070293 0.5399607 0.5737582 0.5895435 0.6067046 0.6136829 -#> [224,] 0.5725214 0.6602495 0.7616623 0.7667874 0.8101183 0.8179785 0.8180067 -#> [225,] 0.2694920 0.2950353 0.3692134 0.3719505 0.4592028 0.4992582 0.4999803 -#> [226,] 0.3343100 0.4750387 0.4832977 0.5889596 0.6089304 0.6234424 0.6648360 -#> [227,] 0.4123463 0.4757520 0.5777199 0.5832221 0.6133775 0.6400521 0.6722586 -#> [228,] 0.4657427 0.5010224 0.5418577 0.6170351 0.6605389 0.6997621 0.7465732 -#> [229,] 0.4051340 0.4735834 0.4921030 0.5809545 0.5859734 0.6244618 0.6464568 -#> [230,] 0.3891018 0.4681615 0.4966708 0.5392035 0.5647906 0.5702987 0.5720533 -#> [231,] 0.2547535 0.3056966 0.4740072 0.5703028 0.5714908 0.5925200 0.5940281 -#> [232,] 0.9555092 1.1303360 1.1388998 1.1594726 1.2539825 1.2605820 1.2764892 -#> [233,] 0.2109421 0.2860089 0.3835569 0.4145382 0.4747144 0.5700085 0.5719874 -#> [234,] 0.5355423 0.5872310 0.6488231 0.6511711 0.6514708 0.6620687 0.6808537 -#> [235,] 0.3692605 0.4257540 0.5174856 0.5175952 0.5428824 0.5457228 0.6078203 -#> [236,] 0.5011913 0.5154095 0.5271494 0.6122418 0.6528631 0.6848110 0.7000411 -#> [237,] 0.3951226 0.4605785 0.4844073 0.4856967 0.5214682 0.5475801 0.5896545 -#> [238,] 0.5555567 0.6041729 0.6486946 0.6611702 0.7187924 0.7520435 0.7555620 -#> [239,] 0.4374822 0.9087339 0.9675360 1.0043093 1.0416752 1.1352041 1.2382200 -#> [240,] 0.3848214 0.4431134 0.6011015 0.6048017 0.6541363 0.7072750 0.7132638 -#> [241,] 0.7002668 0.8351929 0.9067863 0.9174519 0.9210116 0.9361946 0.9924884 -#> [242,] 0.2230059 0.3106024 0.5786764 0.6139568 0.6573238 0.6714148 0.7042095 -#> [243,] 0.3631836 0.7214698 0.7647506 0.7948385 0.8413211 0.8450892 0.8465010 -#> [244,] 0.8610764 0.9358077 0.9801249 0.9993196 1.0108796 1.0252545 1.0280008 -#> [245,] 0.3080751 0.3107541 0.3160396 0.3999155 0.4615110 0.5349535 0.5481820 -#> [246,] 0.5765453 0.6558690 0.7005177 0.8138565 0.8658315 0.8670046 1.0029064 -#> [247,] 0.3342053 0.4390372 0.4789193 0.4895538 0.5284220 0.5497563 0.5579764 -#> [248,] 0.5187787 0.5655398 0.5926421 0.6029241 0.6503403 0.6525829 0.7330386 -#> [249,] 0.7742780 0.8390463 0.8879565 0.8886585 0.9933613 1.0113779 1.1279124 -#> [250,] 0.8413211 0.9289766 0.9943680 1.0636714 1.1219632 1.1385667 1.1523223 -#> [251,] 0.4423658 0.4739314 0.6919364 0.7125685 0.7307516 0.7743503 0.7797978 -#> [252,] 0.7704966 0.7745123 0.9301088 0.9765920 0.9806921 1.0033158 1.0061996 -#> [253,] 0.3183395 0.3478599 0.4142914 0.4497791 0.4867866 0.5707005 0.5740675 -#> [254,] 0.6927369 1.1663722 1.3124793 1.3924713 1.6727395 1.6976233 1.7778336 -#> [255,] 0.1963591 0.4167413 0.5624760 0.5832461 0.6152891 0.6170807 0.6645390 -#> [256,] 0.3642874 0.6713886 0.8404900 0.8498604 0.8597038 0.9465393 0.9807787 -#> [257,] 0.4127776 0.4727399 0.4924958 0.6556979 0.6588958 0.6865332 0.6979232 -#> [258,] 0.4670087 0.4929666 0.5014880 0.5799477 0.6056041 0.6182922 0.6302956 -#> [259,] 0.4788930 0.7232206 0.7920760 0.8184894 0.8846523 0.8963857 0.9163078 -#> [260,] 0.3298885 0.6568896 0.6602341 0.8319435 0.8572144 0.8684830 0.8820444 -#> [261,] 0.3461297 0.3779047 0.3847305 0.4344955 0.4791532 0.5155695 0.5202147 -#> [262,] 0.4019718 0.5540441 0.6682981 0.6746002 0.7254377 0.8308921 0.8470057 -#> [263,] 0.4523524 0.4550712 0.4623505 0.5549466 0.5637004 0.5863843 0.5943596 -#> [264,] 0.2353301 0.6801134 0.7381024 0.7402864 0.7923956 0.8268060 0.8741396 -#> [265,] 0.2656860 0.4940036 0.5083646 0.5087585 0.5427427 0.6460751 0.6637709 -#> [266,] 0.6714131 0.8008904 0.9622620 1.2575237 1.2897530 1.2925865 1.3800389 -#> [267,] 0.7923956 0.8258942 0.8966985 0.9029819 1.0015818 1.0553813 1.1269123 -#> [268,] 0.3062630 0.3782222 0.4369430 0.4465721 0.4803380 0.5010035 0.5324977 -#> [269,] 0.5009687 0.5250248 0.5428234 0.5473297 0.5942332 0.5955329 0.6020509 -#> [270,] 0.3694694 0.4990745 0.5044755 0.5073828 0.5221797 0.5618657 0.6075119 -#> [271,] 2.1686806 2.2086869 2.2810268 2.3181527 2.3606029 2.4186803 2.4244825 -#> [272,] 0.3530585 0.4734049 0.4747357 0.4937261 0.5221003 0.5710723 0.6007936 -#> [273,] 0.7566811 0.9849265 1.0073145 1.0687995 1.0916150 1.2922580 1.3093768 -#> [274,] 0.2474450 0.2828653 0.3439918 0.4110661 0.4494573 0.4789884 0.5098917 -#> [275,] 0.4648660 0.5489127 0.5699855 0.5739238 0.5982141 0.6347572 0.6426404 -#> [276,] 0.6293818 0.6993759 0.8308841 0.8464172 0.8495530 0.8732220 0.9309170 -#> [277,] 0.9493566 0.9797639 1.1373486 1.1400396 1.1867739 1.1919957 1.1947125 -#> [278,] 0.5676543 0.5906277 0.6551925 0.6584415 0.6663706 0.7028327 0.7198973 -#> [279,] 0.3376254 0.3556077 0.4071768 0.4304381 0.4513943 0.5140644 0.5481116 -#> [280,] 0.4443316 1.0295641 1.0404369 1.1279124 1.1370093 1.1961986 1.2268191 -#> [281,] 0.4701398 0.4753663 0.4798772 0.5016437 0.5879416 0.6611702 0.6612903 -#> [282,] 0.5963667 0.6405775 0.7198823 0.7262374 0.7851440 0.7967534 0.8175944 -#> [283,] 0.4734001 0.4819553 0.5610950 0.6125552 0.6348908 0.6632266 0.7207668 -#> [284,] 0.3943873 0.4328513 0.4789193 0.4850523 0.5263223 0.5286231 0.5301923 -#> [285,] 0.3747992 0.5221909 0.5895435 0.6116325 0.6512875 0.6895740 0.7035018 -#> [286,] 0.6183229 0.7769803 0.7859109 0.8773568 0.9343448 0.9549166 0.9764120 -#> [287,] 0.6583572 0.7055782 0.7864954 0.8128896 0.8280572 0.8424534 0.8690105 -#> [288,] 0.6183229 0.6824711 0.6825961 0.6918185 0.7775237 0.7884203 0.7974064 -#> [289,] 0.3075964 0.3862536 0.4024244 0.4145382 0.4283662 0.4921528 0.5025113 -#> [290,] 0.1998153 0.3016603 0.4811913 0.5119983 0.5129489 0.5203712 0.5852687 -#> [291,] 0.3000201 0.4148677 0.4633867 0.4723733 0.4938870 0.5325850 0.5666957 -#> [292,] 0.5232328 0.5400492 0.5425748 0.5460978 0.5610397 0.6117036 0.6231267 -#> [293,] 0.4257540 0.4292543 0.4966009 0.5107381 0.5594507 0.6267861 0.6400521 -#> [294,] 0.2218137 0.3576403 0.4037885 0.6063109 0.6104833 0.6138500 0.6443203 -#> [295,] 0.5807290 0.6296239 0.7645480 0.9160502 0.9723325 1.0319325 1.1000059 -#> [296,] 0.3246618 0.4604986 0.5069319 0.5312717 0.5520613 0.6287894 0.6300649 -#> [297,] 1.3482078 1.4732361 1.4893027 1.5836625 1.6339643 1.6551029 1.6707667 -#> [298,] 0.2296156 0.3813579 0.5184814 0.5243604 0.5431865 0.5595356 0.5634057 -#> [299,] 0.3972822 0.4965400 0.5194182 0.6076082 0.6290822 0.6760092 0.6789478 -#> [300,] 0.4448283 0.5734645 0.7645480 0.7941773 0.8390236 0.8422752 0.8519435 -#> [301,] 0.3365770 0.4171835 0.4481301 0.4496889 0.4506818 0.4891075 0.5171006 -#> [302,] 0.2569293 0.3341905 0.3618488 0.4208733 0.4286491 0.4562478 0.4609700 -#> [303,] 0.3582576 0.4393601 0.4750677 0.4935474 0.5200496 0.6106381 0.6153815 -#> [304,] 0.3985065 0.4481301 0.5704134 0.5758475 0.6468491 0.6484340 0.6493102 -#> [305,] 0.5517353 0.6800489 0.8270838 0.8313950 0.8533623 0.8767628 0.8883322 -#> [306,] 0.2020652 0.2753051 0.4636687 0.5449661 0.5473297 0.5852984 0.6668215 -#> [307,] 0.3414077 0.3471195 0.6384725 0.7448607 0.7551961 0.8762055 0.9248971 -#> [308,] 0.3537939 0.4440057 0.4910911 0.5114391 0.5476241 0.5530247 0.5760182 -#> [309,] 0.4916895 0.5995342 0.6447800 0.7485735 0.8892563 0.9886686 0.9982018 -#> [310,] 0.5312083 0.6139568 0.6994112 0.7266816 0.7379023 0.8330002 0.8425017 -#> [311,] 0.6927369 0.8882599 1.0306228 1.0560097 1.2125442 1.3396713 1.3635505 -#> [312,] 0.4636346 0.5320051 0.7262585 0.7761523 0.7983180 0.8087218 0.9527683 -#> [313,] 0.5474971 0.5872310 0.6326847 0.6726346 0.7840795 0.7865517 0.8100991 -#> [314,] 0.3160586 0.3882206 0.4023933 0.4537252 0.4826127 0.4930848 0.5560572 -#> [315,] 0.2954659 0.4402712 0.6798214 0.6936148 0.7349776 0.7459878 0.7593610 -#> [316,] 0.6696794 0.6865332 0.6996609 0.7618664 0.7626236 0.7792351 0.8628118 -#> [317,] 0.4335027 0.4585807 0.5434027 0.5540441 0.6385622 0.6481007 0.6811298 -#> [318,] 0.2752557 0.2994951 0.3710504 0.4359817 0.5036908 0.5098917 0.5263223 -#> [319,] 0.5254022 0.6111820 0.6481640 0.6570087 0.6724047 0.6794556 0.7683324 -#> [320,] 0.4465721 0.4648428 0.5395790 0.5984565 0.6111190 0.6136581 0.6233926 -#> [321,] 0.7127578 0.7436371 0.7673176 0.8291398 0.8413863 0.8558235 1.0156060 -#> [322,] 0.4470890 0.5076585 0.5124888 0.5231660 0.5373169 0.5393927 0.5440362 -#> [323,] 0.4066972 0.4586935 0.6145234 0.6310264 0.6404431 0.6568517 0.6626158 -#> [324,] 0.4874757 0.7017439 0.7727866 0.7933014 0.8831344 0.9190963 0.9253171 -#> [325,] 0.2832737 0.4406743 0.4510282 0.4699401 0.4907152 0.5575169 0.5591558 -#> [326,] 0.4557063 0.4871143 0.4931461 0.5019206 0.5051712 0.5052221 0.5481116 -#> [327,] 0.7143952 0.7606851 0.7677387 0.7730754 0.7746293 0.7919458 0.8392739 -#> [328,] 0.2824080 0.3459307 0.3787681 0.4735834 0.5561115 0.5666532 0.5784076 -#> [329,] 1.0676613 1.5983391 1.7461672 1.8718903 1.9509921 1.9752659 1.9934865 -#> [330,] 0.2656600 0.4197421 0.4314120 0.4393601 0.5153055 0.5641456 0.6239847 -#> [331,] 0.8832363 0.9567154 1.2394640 1.4999657 1.5093249 1.5724634 1.6109561 -#> [332,] 0.3457117 0.5522699 0.6965300 0.7437559 0.7467575 0.7628491 0.7762495 -#> [333,] 0.3895432 0.4687791 0.5209139 0.5944829 0.6893626 0.7648775 0.7656143 -#> [334,] 0.4374755 0.5166567 0.5836867 0.6258101 0.6296641 0.6343839 0.6538817 -#> [335,] 0.2724458 0.4483847 0.4595417 0.5615556 0.5907221 0.6050880 0.6080600 -#> [336,] 0.4443316 0.6424847 0.8610764 0.8815996 0.9384542 0.9568739 1.0199679 -#> [337,] 0.8617257 0.8710661 0.9524282 1.0021075 1.0310961 1.0416989 1.0630547 -#> [338,] 0.3631836 0.5953177 0.6319675 0.6528631 0.7359611 0.7627102 0.8105955 -#> [339,] 0.4309298 0.4604986 0.4925399 0.4966708 0.4968930 0.5229099 0.5622740 -#> [340,] 0.5696837 0.5945664 0.6140291 0.6607465 0.6679704 0.6915614 0.7433810 -#> [341,] 0.5259341 0.6778514 0.7219848 0.7570375 0.7698148 0.8017146 0.8072259 -#> [342,] 0.2217617 0.2988244 0.3202706 0.3326814 0.4828516 0.4954094 0.6143176 -#> [343,] 0.2857866 0.4578845 0.5097455 0.5696837 0.7037830 0.7221809 0.7342913 -#> [344,] 0.2499468 0.4080576 0.5500536 0.5505802 0.5763671 0.5945664 0.6108302 -#> [345,] 0.4725977 0.5025481 0.5506138 0.6319652 0.7194182 0.7232206 0.7233274 -#> [346,] 0.7372494 0.7678418 0.7828068 0.8265162 0.8465010 0.8489286 0.8980172 -#> [347,] 0.4087473 0.4442985 0.4910911 0.4938870 0.4999916 0.5135788 0.5271494 -#> [348,] 0.3932012 0.4194612 0.4335793 0.4401427 0.4459262 0.4721957 0.4750677 -#> [349,] 0.2273583 0.3806511 0.4652016 0.4704623 0.4829322 0.5894456 0.5898104 -#> [350,] 0.1983044 0.5860999 0.5923463 0.6525829 0.6622607 0.8927730 0.9317428 -#> [351,] 0.3935127 0.4116965 0.4475573 0.4893418 0.5434911 0.5978571 0.6817049 -#> [352,] 0.6198571 0.6364684 0.6631117 0.7236107 0.7350833 0.7603939 0.8215576 -#> [353,] 0.3747890 0.4972074 0.5067166 0.5456494 0.5765121 0.5823562 0.6178335 -#> [354,] 0.4482251 0.4768116 0.4777298 0.4975465 0.5205927 0.5351490 0.5767044 -#> [355,] 0.5921002 0.8509328 0.8883322 1.3493029 1.4194159 1.5123797 1.5465838 -#> [356,] 0.6469960 0.6535593 0.6651903 0.6765484 0.7467857 0.8138460 0.8155243 -#> [357,] 0.5297576 0.5460581 0.5752158 0.6501874 0.6887962 0.6977100 0.7395676 -#> [358,] 0.4777599 0.5337423 0.5889834 0.6460212 0.6903412 0.7210871 0.8240193 -#> [359,] 0.4142914 0.5176881 0.5277014 0.6106471 0.6123415 0.6224384 0.6269097 -#> [360,] 0.4919594 0.5453030 0.5484186 0.5550756 0.6550249 0.6896709 0.7104783 -#> [361,] 0.4577316 0.5203527 0.5270669 0.5484186 0.6130869 0.6325047 0.6629479 -#> [362,] 0.5048736 0.6315611 0.6460212 0.6517485 0.7300337 0.7449540 0.7578651 -#> [363,] 0.6151594 0.6622607 0.9363069 0.9611949 0.9692649 0.9902827 1.0039163 -#> [364,] 0.6645390 0.7595542 0.7690297 0.8238071 0.8250804 0.9323085 0.9492159 -#> [365,] 0.4801832 0.5703396 0.6265655 0.6522410 0.6744898 0.6847840 0.6887962 -#> [366,] 0.6241592 0.6385163 0.6849981 0.7652407 0.8636321 0.8665598 0.8747543 -#> [367,] 0.4036686 0.4074138 0.5643889 0.5962516 0.6315293 0.7345245 0.7536984 -#> [368,] 0.5929305 0.7351369 0.8083417 0.9341815 0.9427737 1.0242711 1.0624381 -#> [369,] 0.4695051 0.4996085 0.5723332 0.6187645 0.8859597 0.8961061 0.9085786 -#> [370,] 0.3556077 0.3990696 0.4175508 0.4701749 0.5415097 0.5704060 0.5748674 -#> [371,] 0.5189913 0.6270920 0.7058054 0.7151584 0.7381024 0.7822497 0.8412011 -#> [372,] 0.1821476 0.4550712 0.5155402 0.6817049 0.7198049 0.7698148 0.7935792 -#> [373,] 0.4188806 0.5119499 0.5290100 0.5673679 0.6157251 0.6398496 0.6468491 -#> [374,] 0.6158580 0.7952401 0.8465456 0.9759081 0.9771859 1.0338753 1.0490758 -#> [375,] 0.3524337 0.3536279 0.3891551 0.4401427 0.4555023 0.4699200 0.5623945 -#> [376,] 1.2005248 1.2138753 1.3149943 1.3170209 1.3290479 1.3774349 1.4527436 -#> [377,] 0.3000201 0.3066120 0.3272235 0.3809927 0.4125014 0.4249279 0.4442985 -#> [378,] 0.3853540 0.4068972 0.4964543 0.5422557 0.6198571 0.6254522 0.6384770 -#> [379,] 0.3454563 0.3713510 0.4965400 0.5224370 0.6194128 0.6682066 0.7175131 -#> [380,] 0.3392109 0.4181309 0.6375285 0.7202229 0.7429733 0.7588076 0.7750321 -#> [381,] 0.3905628 0.4585807 0.5287760 0.6011118 0.6414148 0.6638869 0.6706265 -#> [382,] 0.6376604 0.6560304 0.7972184 0.8490657 0.8542834 0.9027296 0.9437868 -#> [383,] 0.1362916 0.3912933 0.3955992 0.4328513 0.4390372 0.5727553 0.6178691 -#> [384,] 0.3091575 0.5322739 0.5451491 0.5872667 0.6306143 0.6480375 0.6584415 -#> [385,] 0.1628608 0.7103740 1.0996304 1.1373904 1.1416821 1.1607203 1.2325702 -#> [386,] 0.3784988 0.4051373 0.4406743 0.4447534 0.4783833 0.4970822 0.5196511 -#> [387,] 0.4739314 0.5851950 0.6565383 0.6904380 0.7537516 0.8005908 0.8106037 -#> [388,] 0.4437187 0.4687791 0.5177161 0.5509537 0.5732842 0.6291464 0.6798214 -#> [389,] 0.3113292 0.4423658 0.6108399 0.6132011 0.8419828 0.8532589 0.8539395 -#> [390,] 0.5042632 0.5047298 0.6332989 0.6573906 0.6640788 0.7308810 0.7348973 -#> [391,] 0.7233274 0.7667874 0.7677387 0.8072730 0.8238013 0.8640089 0.8686242 -#> [392,] 0.3895432 0.4814243 0.5017388 0.5109920 0.5509537 0.5559009 0.5684539 -#> [393,] 0.3934241 0.4134636 0.4875948 0.4968930 0.4970086 0.5114391 0.5413453 -#> [394,] 0.7262374 0.7883433 0.8249225 0.8961636 0.9529310 1.0486522 1.0517503 -#> [395,] 0.1257555 0.4669204 0.4789762 0.5074462 0.5550756 0.6272511 0.6629479 -#> [396,] 0.7127578 0.9761487 1.0934914 1.1792269 1.2529735 1.2647336 1.2691116 -#> [397,] 0.2353301 0.5189913 0.6319328 0.6763739 0.8770099 0.9029819 0.9047004 -#> [398,] 0.4036686 0.6553439 0.7098716 0.7248484 0.7313914 0.7385971 0.8750195 -#> [399,] 0.3202706 0.3913705 0.4149331 0.4594065 0.4824973 0.4997180 0.5302900 -#> [400,] 0.3830567 0.4335794 0.6856147 0.7246882 0.7279501 0.8605461 0.8679605 -#> [401,] 0.3458029 0.6856147 0.6893942 0.7525894 0.7639661 0.7671241 0.8066190 -#> [402,] 0.3184559 0.4518358 0.5047298 0.6410868 0.6615776 0.7332994 0.7435528 -#> [403,] 0.3710223 0.3747890 0.3890036 0.4005514 0.4566556 0.4822703 0.4933322 -#> [404,] 0.3525775 0.3633023 0.3969505 0.4275021 0.4805071 0.4826465 0.5098671 -#> [405,] 0.7915908 1.0149250 1.1095494 1.1976677 1.2020657 1.2915119 1.4095128 -#> [406,] 0.3285568 0.4314120 0.4459262 0.4767470 0.4802200 0.4935474 0.5290869 -#> [407,] 0.3213510 0.4834092 0.5131636 0.5381790 0.5893552 0.5904987 0.6029693 -#> [408,] 0.3178739 0.3953197 0.5304649 0.5385771 0.5808212 0.6046218 0.6353393 -#> [409,] 0.4980218 0.5544410 0.5735751 0.6319675 0.6597138 0.7081038 0.7502911 -#> [410,] 0.4111658 0.6511711 0.6553934 0.7264462 0.7492739 0.7716554 0.8001210 -#> [411,] 0.6332989 0.7615951 0.8218535 0.8311973 0.9326075 0.9754550 0.9913331 -#> [412,] 0.5250252 0.6174626 0.6315293 0.6413620 0.6457151 0.6603378 0.6631487 -#> [413,] 0.2296156 0.3987572 0.4429551 0.4651612 0.4720911 0.5008382 0.5440362 -#> [414,] 0.4051202 0.4769180 0.7755421 0.9211892 0.9332052 0.9669258 1.0150922 -#> [415,] 0.1457126 0.5002871 0.5610436 0.5681428 0.5699855 0.7477874 0.7981500 -#> [416,] 0.4056079 0.5185333 0.5372201 0.5717254 0.6669696 0.6682361 0.6959216 -#> [417,] 0.5261482 0.5872667 0.5905954 0.6268633 0.6326371 0.6944146 0.7490721 -#> [418,] 0.2546616 0.5221003 0.5281845 0.5339777 0.5899013 0.5936018 0.6222072 -#> [419,] 0.5121596 0.6132402 0.7254377 0.7297429 0.7689811 0.7979053 0.8296740 -#> [420,] 0.5019129 0.5067543 0.5348742 0.5720533 0.6073665 0.6227840 0.6371878 -#> [421,] 0.5492871 0.6242689 0.6266138 0.7856933 0.8007903 0.8282300 0.8867929 -#> [422,] 0.4955922 0.5857594 0.5938233 0.6401902 0.6480952 0.6741597 0.6915875 -#> [423,] 0.4430698 0.4549399 0.4942273 0.5141040 0.5802341 0.5968755 0.6428334 -#> [424,] 0.3802711 0.3884973 0.6605389 0.7177136 0.7344810 0.8601750 0.8970716 -#> [425,] 0.3481627 0.3920226 0.3926587 0.4059614 0.4381897 0.4934713 0.5628062 -#> [426,] 0.9883830 1.0004716 1.0509800 1.0558282 1.1139603 1.1253654 1.1421931 -#> [427,] 0.4777599 0.7755872 0.8128896 0.8477208 0.8490090 0.8849198 0.9162768 -#> [428,] 0.3918320 0.4289125 0.6084791 0.6263253 0.7111541 0.8128610 0.8505465 -#> [429,] 0.5138186 0.6642021 0.6690111 0.6806061 0.6947922 0.7058257 0.7114169 -#> [430,] 0.3688427 0.3948392 0.4609358 0.5423035 0.5549392 0.5716828 0.6080963 -#> [431,] 1.0730223 1.4578942 1.4752809 1.5188541 1.5335735 1.5639044 1.5855170 -#> [432,] 0.6152174 0.6268633 0.6339550 0.7537199 0.8068748 0.8384083 0.8432481 -#> [433,] 0.4116965 0.5155402 0.5488945 0.5653569 0.7218092 0.7590991 0.7820999 -#> [434,] 0.4125014 0.4983567 0.4999916 0.5154095 0.5325850 0.5391964 0.5422872 -#> [435,] 0.4123463 0.4869867 0.5505204 0.5944104 0.6481640 0.6965300 0.7600829 -#> [436,] 0.3806511 0.3838156 0.4368219 0.4897840 0.5797199 0.6907963 0.6946080 -#> [437,] 0.2618901 0.4174240 0.5035822 0.5413713 0.5488100 0.5831975 0.5879309 -#> [438,] 0.4959007 0.6457151 0.7176706 0.7920279 0.8368992 0.8614404 0.9128021 -#> [439,] 0.4485787 0.5459737 0.5677734 0.6356550 0.6956782 0.7170209 0.7448506 -#> [440,] 0.3514631 0.5187787 0.5466477 0.6713886 0.6968107 0.8028419 0.8264479 -#> [441,] 0.5425146 0.7626236 0.7633689 0.7675038 0.7677695 0.7694295 0.7838109 -#> [442,] 0.6296337 0.7434690 0.7822497 0.8595808 0.8613229 0.8841422 0.8859597 -#> [443,] 0.2578455 0.3417088 0.5196555 0.5383358 0.6234864 0.6611541 0.6897736 -#> [444,] 0.3304958 0.5745717 0.6384770 0.8066526 0.8386210 0.8411821 0.8508133 -#> [445,] 0.1960542 0.3676536 0.3926587 0.3980096 0.4613971 0.5085484 0.5577556 -#> [446,] 0.2991949 0.5174856 0.5902651 0.6158555 0.6207376 0.6400436 0.6540970 -#> [447,] 0.5555567 0.8119474 0.9274681 0.9510667 0.9553364 0.9636199 0.9896622 -#> [448,] 0.3136491 0.4379508 0.4750387 0.4872823 0.5311648 0.6108173 0.6707253 -#> [449,] 0.2922924 0.2928507 0.3874939 0.4891075 0.4980564 0.5681757 0.5693549 -#> [450,] 0.4645052 0.5734645 0.6892862 0.7327970 0.8550672 0.9059494 0.9226508 -#> [451,] 0.3776611 0.4051202 0.7571150 0.7891617 0.8804326 0.9623114 1.1344319 -#> [452,] 0.2618901 0.3390154 0.4804254 0.5135363 0.5185349 0.5615556 0.5796006 -#> [453,] 0.4366422 0.5072311 0.5238620 0.5409284 0.5454542 0.7176306 0.7218152 -#> [454,] 0.3352511 0.3513501 0.3779047 0.4895538 0.5026705 0.5364504 0.5378630 -#> [455,] 0.6907413 0.7005177 0.9384542 1.0699758 1.0746198 1.0962976 1.1049130 -#> [456,] 0.4614993 0.5081797 0.5610950 0.5666957 0.6122418 0.6170191 0.6381189 -#> [457,] 0.5955406 0.6458231 0.7036961 0.7863549 0.8026338 0.8140232 0.8283215 -#> [458,] 0.2289517 0.3067851 0.3186834 0.3819500 0.4005514 0.4741237 0.4938784 -#> [459,] 0.5252485 0.5339298 0.5489988 0.5691434 0.6311555 0.6720264 0.6812624 -#> [460,] 0.4374822 0.7195262 0.9098251 0.9343713 0.9505368 0.9764738 1.0424884 -#> [461,] 0.2996762 0.3490601 0.3588431 0.3912933 0.5119789 0.5439571 0.5553900 -#> [462,] 0.3660383 0.3848525 0.4423240 0.4423984 0.4764335 0.5007229 0.5412566 -#> [463,] 1.1788565 1.3423411 1.4133923 1.4198957 1.4895292 1.5667001 1.6822037 -#> [464,] 0.6225775 0.9153356 1.0056298 1.0156434 1.0508046 1.1443681 1.1523387 -#> [465,] 0.3458029 0.3830567 0.5383580 0.6264371 0.7775237 0.7781291 0.8263970 -#> [466,] 0.3757904 0.4253779 0.5240031 0.5697414 0.6078203 0.6136829 0.6402618 -#> [467,] 0.2791779 0.4828984 0.4913224 0.5192799 0.5194278 0.5242562 0.6222072 -#> [468,] 0.1412353 0.3066120 0.3913374 0.4314051 0.4435253 0.4633867 0.4826127 -#> [469,] 0.3091575 0.5814689 0.5906277 0.5992562 0.6326371 0.6469669 0.6984444 -#> [470,] 0.5165410 0.5225561 0.5504840 0.6094010 0.6236399 0.7190391 0.7255185 -#> [471,] 0.3905628 0.4570374 0.4923259 0.5048736 0.5072869 0.5268461 0.5910757 -#> [472,] 0.5606495 0.7588076 0.7928859 0.8473664 0.9024861 0.9034418 0.9504198 -#> [473,] 0.6265314 0.7195262 0.8195750 0.9087339 0.9994496 1.0561105 1.1280151 -#> [474,] 0.3556601 0.5642782 0.5707005 0.5888508 0.6033136 0.6389466 0.6528188 -#> [475,] 0.4024483 0.5136618 0.6266999 0.6293683 0.6331598 0.6568448 0.7223757 -#> [476,] 0.2569293 0.3657331 0.4026997 0.4276793 0.4572070 0.4812868 0.5029748 -#> [477,] 0.3524337 0.5567038 0.5635622 0.5871811 0.6359616 0.6450181 0.6596769 -#> [478,] 0.6504698 0.8628118 0.8934793 0.9492043 0.9711269 1.0379094 1.0430789 -#> [479,] 0.7279501 0.8232968 0.8815718 0.8875119 0.9731088 1.0534073 1.0644012 -#> [480,] 0.5943301 0.6619937 0.6947922 0.7085971 0.8332180 0.8358105 0.9264773 -#> [481,] 0.4085959 0.4556442 0.6605592 0.6979482 0.7189606 0.7891139 0.7960983 -#> [482,] 0.7915908 1.0648047 1.2543197 1.3099819 1.4000270 1.4309635 1.4379298 -#> [483,] 0.5929305 0.8490390 0.9457200 1.0348564 1.0396577 1.1022982 1.1396698 -#> [484,] 0.3525252 0.4063249 0.4582774 0.4732585 0.4942109 0.5168748 0.5171654 -#> [485,] 0.5038661 0.5610436 0.6504698 0.7018391 0.9611765 0.9764738 1.0006022 -#> [486,] 0.3358029 0.4505269 0.5504881 0.6291464 0.7264237 0.7367339 0.7541327 -#> [487,] 0.5383990 0.6707741 0.7384176 0.8436659 0.9150972 0.9363727 0.9406192 -#> [488,] 0.4432166 0.4470028 0.5287582 0.5528294 0.6302956 0.6820235 0.7083550 -#> [489,] 0.3660383 0.4829863 0.4848390 0.5014880 0.5230184 0.5234596 0.5705091 -#> [490,] 0.4485787 0.4863853 0.5623340 0.6401146 0.6553934 0.7002668 0.8068167 -#> [491,] 0.6932394 0.7742510 0.8789513 0.8817063 0.9108048 0.9883042 1.0338475 -#> [492,] 0.2094617 0.4414033 0.4572070 0.4609700 0.4661846 0.4746320 0.4855697 -#> [493,] 0.3106024 0.4460617 0.5312083 0.6673814 0.7340519 0.8070216 0.8581231 -#> [494,] 0.3301119 0.3437702 0.3669123 0.4918698 0.5940281 0.6070454 0.6111134 -#> [495,] 0.3394844 0.4006096 0.4068972 0.4172339 0.6019840 0.6138800 0.6667747 -#> [496,] 0.2286886 0.3686147 0.4272458 0.5016437 0.5448599 0.5484320 0.5511875 -#> [497,] 0.5624785 0.5993097 0.7017688 0.7706361 0.8810112 0.9182541 0.9193467 -#> [498,] 0.8064250 1.0526432 1.1023809 1.1811835 1.2005248 1.2230364 1.2752681 -#> [499,] 2.4787351 2.5249703 2.6359338 2.6802160 2.6815841 2.6952508 2.7314304 -#> [500,] 0.4542098 0.5011913 0.5495823 0.6193803 0.6722716 0.6991221 0.7031971 -#> [501,] 0.2734565 0.4199832 0.4287305 0.4473554 0.5979192 0.6239051 0.6380812 -#> [502,] 0.3392109 0.4396069 0.5123829 0.6164692 0.7387613 0.7750119 0.7871300 -#> [503,] 0.5428824 0.6339550 0.6402618 0.6501874 0.6507848 0.7304828 0.7361710 -#> [504,] 0.3386233 0.3930878 0.4120583 0.4747824 0.4762068 0.5085280 0.5215113 -#> [505,] 0.2612405 0.3551038 0.4938784 0.5573365 0.5755479 0.5987590 0.6044852 -#> [506,] 0.3083919 0.5942332 0.7722180 0.7991157 0.8329750 0.8516392 0.8547082 -#> [507,] 0.2217617 0.2661266 0.3175616 0.4149331 0.4537911 0.4966009 0.5105782 -#> [508,] 0.6308904 0.7466674 0.7903432 0.9064238 0.9807787 1.0042939 1.0348121 -#> [509,] 0.4242651 0.4347131 0.5480419 0.6643007 0.6676141 0.7039359 0.7141846 -#> [510,] 0.5904378 0.6410868 0.8222237 0.9754550 1.0094423 1.0567393 1.0831111 -#> [511,] 0.4332651 0.6714131 0.7998238 0.8317753 0.8457471 0.8535646 0.8628611 -#> [512,] 0.5671601 0.7282968 0.8362417 0.9513614 1.1567506 1.1991442 1.3318198 -#> [513,] 0.6913829 0.8888571 0.9334695 0.9759081 0.9892210 1.0252545 1.0327843 -#> [514,] 0.4353166 0.5455117 0.7431773 0.8553774 0.8819996 0.9236206 0.9959342 -#> [515,] 0.4870615 0.5425748 0.6153536 0.6784835 0.6855743 0.7353062 0.7886938 -#> [516,] 0.3604918 0.3632050 0.3890036 0.4383867 0.4599237 0.4645308 0.4826099 -#> [517,] 0.3329671 0.5828192 0.6029241 0.7347236 0.7795816 0.8302082 0.8375110 -#> [518,] 0.3944994 0.4106354 0.5612706 0.5953177 0.6034767 0.6522410 0.6632266 -#> [519,] 0.3765864 0.4980218 0.7048266 0.7224488 0.7627102 0.7678418 0.8104953 -#> [520,] 0.5385999 0.6332117 0.6527146 0.6615776 0.6865330 0.7093842 0.7124826 -#> [521,] 0.1923351 0.5812571 0.6480375 0.6551925 0.7185370 0.7214546 0.7555407 -#> [522,] 1.4254770 1.4553427 1.5319170 1.5682789 1.5691586 1.6937886 1.8572698 -#> [523,] 0.3401918 0.4893371 0.5681757 0.7157430 0.7289997 0.8183816 0.8595664 -#> [524,] 0.5352316 0.6435275 0.7486387 0.7818217 0.8367021 0.8470057 0.8558235 -#> [525,] 0.4876600 0.5144181 0.5706278 0.6483658 0.6916804 0.7436081 0.8408944 -#> [526,] 0.2442281 0.4379508 0.5443972 0.5917659 0.5943638 0.6069509 0.6195639 -#> [527,] 0.5355423 0.5474971 0.5550847 0.6033136 0.6927700 0.6934277 0.7839221 -#> [528,] 0.6849981 0.7236067 0.7239378 0.7559130 0.9286206 0.9476253 1.0313606 -#> [529,] 0.3109425 0.3454563 0.4489389 0.6076082 0.6686554 0.7683952 0.7746529 -#> [530,] 0.5224370 0.5242928 0.5504840 0.6955587 0.7028009 0.7331899 0.7756818 -#> [531,] 0.2686635 0.8502969 0.8539395 0.8875681 0.9449080 0.9607605 0.9725843 -#> [532,] 0.3968053 0.4608625 0.5451733 0.5693529 0.5913540 0.6207376 0.6556169 -#> [533,] 0.2105175 0.2849864 0.4435253 0.4451457 0.4976498 0.5039508 0.5385771 -#> [534,] 1.0056298 1.0063360 1.0228704 1.1758045 1.3061648 1.3131092 1.3539577 -#> [535,] 0.1795017 0.3914624 0.4311430 0.4808685 0.5177915 0.5188129 0.5231660 -#> [536,] 0.3886122 0.6940858 0.7367848 0.7859109 0.7948439 0.8092575 0.8410188 -#> [537,] 0.2922924 0.3740531 0.4893371 0.5506097 0.5691912 0.5700471 0.5963322 -#> [538,] 0.5304649 0.5727852 0.5982141 0.5984345 0.6064560 0.6682981 0.6768179 -#> [539,] 0.3723138 0.4811191 0.4975465 0.5152524 0.5242217 0.5337690 0.5374942 -#> [540,] 0.1998153 0.3781865 0.3798359 0.4114479 0.5380592 0.5418090 0.5437751 -#> [541,] 0.8346431 0.9989923 1.0280044 1.2451451 1.2641855 1.3222055 1.3797486 -#> [542,] 0.5150732 0.5405132 0.5517353 0.8327506 0.8509328 1.0145762 1.0188366 -#> [543,] 0.6707741 0.8546885 0.8712047 0.9233843 0.9337107 0.9400282 1.0088752 -#> [544,] 0.7023488 0.7678536 0.8127954 0.8866871 0.9579694 1.0152249 1.0761013 -#> [545,] 0.4737440 0.4814243 0.5159818 0.5177161 0.5302001 0.5374756 0.6089900 -#> [546,] 0.4570762 0.4936120 0.5217608 0.6644822 0.7165986 0.7342913 0.7596182 -#> [547,] 0.2686635 0.6729536 0.8144727 0.8584639 0.8972973 0.9212723 0.9984089 -#> [548,] 0.2965186 0.3938001 0.4227960 0.4627563 0.5003201 0.5122100 0.5155835 -#> [549,] 0.7471292 1.0120172 1.0738716 1.0850179 1.1114957 1.1788565 1.2134379 -#> [550,] 0.7557748 1.9640711 2.0158992 2.1958536 2.2025514 2.2810268 2.3287320 -#> [551,] 0.2164501 0.4410435 0.4493396 0.4747144 0.4762068 0.5017388 0.5159818 -#> [552,] 0.3774352 0.5874570 0.6080600 0.6632698 0.6701687 0.6766880 0.6805240 -#> [553,] 0.2273583 0.3103047 0.3158317 0.4449949 0.4897840 0.5127951 0.5189196 -#> [554,] 0.4215890 0.4280685 0.7515559 0.8982119 0.9799687 0.9824180 1.0017711 -#> [555,] 0.2751204 0.5624785 0.8821678 0.8904358 0.9163542 0.9306648 0.9887995 -#> [556,] 0.3124576 0.5874171 0.9109125 0.9132635 0.9570289 0.9756815 0.9871541 -#> [557,] 0.4570762 0.4896084 0.5105701 0.5210476 0.5693529 0.6319652 0.6602495 -#> [558,] 0.6977377 0.8379934 0.8425571 0.9253071 0.9524282 0.9583905 1.0047693 -#> [559,] 0.2618401 0.6252074 0.6447046 0.6806061 0.6927381 0.7264462 0.7378000 -#> [560,] 0.5383990 0.5511466 0.6680109 0.7092152 0.7109173 0.7745257 0.8069808 -#> [561,] 0.4650941 0.5238620 0.5348742 0.5662374 0.6147478 0.6925079 0.7901603 -#> [562,] 1.4131752 1.4267515 1.5172372 1.6144240 1.7843633 1.9143099 2.0157166 -#> [563,] 1.0995921 1.1604199 1.2860329 1.3602894 1.4155908 1.4740372 1.5191169 -#> [564,] 0.5700973 0.9030833 1.0028225 1.0354115 1.0786533 1.0800418 1.0995374 -#> [565,] 0.2612405 0.3530585 0.4722940 0.5048796 0.5476488 0.5617994 0.5865857 -#> [566,] 0.4788930 0.8072730 0.8456541 0.8498218 0.8716038 0.9956315 1.0053389 -#> [567,] 0.6152174 0.9260792 0.9534972 0.9594619 1.0066028 1.0099486 1.0219929 -#> [568,] 0.3052188 0.3113680 0.3719505 0.4422055 0.4989853 0.5074590 0.5246449 -#> [569,] 0.2343413 0.4280685 0.6458231 0.6796532 0.9604054 0.9616049 1.0077803 -#> [570,] 0.6254268 0.6579970 0.8277229 0.9013307 0.9356341 0.9553533 1.0004716 -#> [571,] 0.2849864 0.3809927 0.3913374 0.4213421 0.4369799 0.4488936 0.4956664 -#> [572,] 0.3799644 0.3981264 0.4570374 0.5037029 0.5298376 0.5905216 0.5940546 -#> [573,] 0.7058054 0.9816568 1.1095494 1.1333906 1.1793584 1.2026618 1.2271717 -#> [574,] 0.4938878 0.7164475 0.7231428 0.7274085 0.7840491 0.8155527 0.8425017 -#> [575,] 0.3610341 0.4669204 0.4753663 0.5390328 0.5429686 0.5484320 0.5824740 -#> [576,] 0.9832351 1.1916313 1.2309731 1.2885887 1.3348366 1.4022129 1.4999657 -#> [577,] 0.4370194 0.5828192 0.5987923 0.6252074 0.6440177 0.7103820 0.7120553 -#> [578,] 0.1907358 0.4380835 0.4398420 0.4453320 0.4968742 0.5043623 0.5147732 -#> [579,] 0.3394250 0.5017704 0.5073744 0.5459737 0.5623340 0.5957137 0.6257073 -#> [580,] 0.3136491 0.4832977 0.5452097 0.5469488 0.5943638 0.6684409 0.6788909 -#> [581,] 0.4757520 0.5505204 0.5907311 0.7315827 0.7437559 0.8815718 0.8903385 -#> [582,] 0.3638288 0.5512129 0.5698245 0.5741937 0.6482118 0.6834861 0.6927381 -#> [583,] 0.7201322 0.8014768 0.9182950 0.9211684 0.9495949 0.9767439 0.9846061 -#> [584,] 0.8286330 0.8392739 0.9274681 0.9415304 0.9662324 0.9756150 1.0326113 -#> [585,] 0.7150264 0.8001651 0.8241293 0.8258942 0.8378235 0.8571228 0.8893262 -#> [586,] 0.5337423 0.5635622 0.5737651 0.6376604 0.7493295 0.7755872 0.8280572 -#> [587,] 0.9260981 0.9886686 1.0634161 1.2119385 1.3173416 1.4022129 1.4536936 -#> [588,] 0.2770382 0.4636346 0.6602455 0.8192408 0.9824257 0.9941006 1.0064931 -#> [589,] 0.4431134 0.4585248 0.4858187 0.5874570 0.5907221 0.6573963 0.6631667 -#> [590,] 0.5029892 0.5650864 0.6707272 0.6804693 0.7201322 0.7361470 0.7548057 -#> [591,] 0.4637712 0.5511508 0.7224517 0.7236107 0.7981595 0.8298472 0.8386210 -#> [592,] 0.3838156 0.4704623 0.4808685 0.5406582 0.6070318 0.6076115 0.6214845 -#> [593,] 0.2780944 0.2938909 0.3160396 0.4005184 0.4071768 0.4305712 0.4557063 -#> [594,] 0.3080777 0.3956464 0.4415222 0.4554516 0.4652289 0.4734049 0.5048796 -#> [595,] 0.4019718 0.7367865 0.7486387 0.7984211 0.8065179 0.8296740 0.8335389 -#> [596,] 0.3458876 0.5025590 0.5422557 0.5644321 0.5681428 0.6667747 0.6730841 -#> [597,] 0.3083919 0.5009687 0.6214845 0.6287011 0.7551961 0.7690297 0.7985806 -#> [598,] 0.3176937 0.3592673 0.4412460 0.5300126 0.5439430 0.5769023 0.6025091 -#> [599,] 0.1821476 0.4623505 0.5653569 0.6789478 0.6922921 0.7219848 0.7308414 -#> [600,] 0.3080751 0.3136977 0.4504919 0.4725017 0.4993532 0.5012870 0.5669852 -#> [601,] 0.3582576 0.4087473 0.4194612 0.4249279 0.4440057 0.4767470 0.4961666 -#> [602,] 0.3486888 0.3844678 0.4569880 0.4613971 0.4621962 0.4693899 0.4812505 -#> [603,] 0.2676504 0.3723138 0.5245181 0.5998886 0.6102405 0.6116010 0.6204448 -#> [604,] 0.1880397 0.4352734 0.4621962 0.4660285 0.4711256 0.4978754 0.5305005 -#> [605,] 0.3678781 0.4005174 0.4088687 0.4412460 0.4848390 0.4860993 0.4929666 -#> [606,] 0.3445994 0.4287305 0.5211516 0.5470574 0.6196470 0.7423847 0.8015919 -#> [607,] 0.3109425 0.3713510 0.3972822 0.5843780 0.6075112 0.6639107 0.7612510 -#> [608,] 0.6211108 0.6765484 0.7801991 0.9374454 0.9723720 1.0410421 1.0413188 -#> [609,] 0.7620922 0.7945027 0.9671000 1.0241726 1.0526528 1.0645023 1.0721897 -#> [610,] 0.3301119 0.4539022 0.4626248 0.4929649 0.5011315 0.5038229 0.5371010 -#> [611,] 0.3892923 0.5719831 0.6193540 0.6307219 0.6754799 0.6939600 0.7055080 -#> [612,] 0.3590217 0.5622607 0.5749747 0.7050365 0.7092287 0.7328549 0.7642209 -#> [613,] 0.4556442 0.5065259 0.6415368 0.7681048 0.7847803 0.7952401 0.7995223 -#> [614,] 0.4841278 0.7330356 0.8625143 0.8627456 0.8871369 0.9040311 1.0021075 -#> [615,] 0.4052950 0.5250248 0.5449661 0.5487680 0.5836566 0.6140291 0.7035311 -#> [616,] 0.3342382 0.3528047 0.4566071 0.4588823 0.5595899 0.5650627 0.5664733 -#> [617,] 0.5910692 0.6109909 0.7725971 0.7880406 0.8080487 0.8162246 0.8352604 -#> [618,] 0.8995747 0.9059494 1.0124701 1.0319325 1.0337591 1.1374333 1.1429259 -#> [619,] 0.2676504 0.4989698 0.5152524 0.5376920 0.5667576 0.5704259 0.5736706 -#> [620,] 0.8284279 0.8684830 0.9933613 0.9954375 1.0958495 1.1693827 1.2511751 -#> [621,] 0.7137572 0.7962522 0.8566013 1.1097381 1.1318377 1.1977082 1.2191116 -#> [622,] 0.3788236 0.4559011 0.5616415 0.5721634 0.5902184 0.6025091 0.6110487 -#> [623,] 0.1923351 0.4792305 0.5322739 0.5676543 0.5992562 0.6682089 0.7108051 -#> [624,] 0.7236067 0.8886585 0.9266796 0.9953299 1.1276024 1.2146687 1.2330272 -#> [625,] 0.2957885 0.4446937 0.5750432 0.5765267 0.5925585 0.6037244 0.6568023 -#> [626,] 0.5509826 0.6290930 0.6537618 0.8620734 0.8626699 0.8700212 0.8840395 -#> [627,] 0.3806210 0.3815088 0.3934241 0.4213650 0.4374722 0.5392035 0.5448039 -#> [628,] 0.4037595 0.4367022 0.4729371 0.5086459 0.5612706 0.7159270 0.7336171 -#> [629,] 0.2094617 0.3032933 0.3819500 0.3945596 0.3978667 0.4026997 0.4286491 -#> [630,] 0.3813579 0.4197812 0.4300301 0.4399549 0.4519320 0.5166567 0.5815696 -#> [631,] 0.8832363 1.3477283 1.3545155 1.4381920 1.4890247 1.5271940 1.5304553 -#> [632,] 0.3537939 0.4304763 0.4875948 0.5019129 0.5135788 0.5934187 0.6370304 -#> [633,] 0.6097647 0.8841422 0.9312233 0.9659309 0.9901034 1.0455495 1.0524537 -#> [634,] 0.2411622 0.4635766 0.4648660 0.4726632 0.5298530 0.5476241 0.6141360 -#> [635,] 0.4445392 0.5229273 0.6558690 0.6608258 0.6714148 0.7340519 0.7681936 -#> [636,] 0.8490657 0.9090991 0.9289076 0.9717083 1.0127865 1.0738554 1.1702953 -#> [637,] 0.3196900 0.3934462 0.3981264 0.4439951 0.4990745 0.5239454 0.5315435 -#> [638,] 0.3856247 0.4641082 0.5074462 0.5270669 0.5824740 0.5874906 0.6256009 -#> [639,] 0.4993532 0.6707272 0.6866497 0.7076178 0.7314473 0.7330356 0.7394303 -#> [640,] 0.2615611 0.3670521 0.5169540 0.5198035 0.5434027 0.7077364 0.7297429 -#> [641,] 0.4279815 0.4513086 0.4595417 0.4634436 0.4923078 0.5025113 0.5047042 -#> [642,] 0.9597329 1.1546740 1.1975968 1.3252038 1.3591368 1.3595380 1.3768743 -#> [643,] 0.4094364 0.4826327 0.4858038 0.5331247 0.5526499 0.5606193 0.5979192 -#> [644,] 0.1502014 0.3948392 0.4024483 0.4363727 0.5110320 0.5298530 0.5515163 -#> [645,] 0.7483794 0.7660779 0.7972184 0.8423217 0.8477208 0.9264165 0.9535310 -#> [646,] 0.3298885 0.6257554 0.6793500 0.7212007 0.7591843 0.7873279 0.7969979 -#> [647,] 0.6265314 0.8995747 0.9675360 1.0166948 1.0424884 1.0450260 1.1418658 -#> [648,] 0.3706053 0.3744725 0.4122767 0.4483847 0.4539152 0.4645888 0.4827478 -#> [649,] 0.4701398 0.5616011 0.5705091 0.6293527 0.6541363 0.6560097 0.7152830 -#> [650,] 0.9559779 0.9902827 1.0017711 1.0408619 1.0470346 1.2111807 1.2288811 -#> [651,] 0.5931816 0.5963667 0.6109270 0.7269927 0.7596997 0.7883433 0.8504473 -#> [652,] 0.3343100 0.5469488 0.6108173 0.6170841 0.6329588 0.6413620 0.6597046 -#> [653,] 0.8118327 1.0229776 1.2321380 1.3246079 1.3556170 1.3676437 1.4039970 -#> [654,] 0.4407825 0.4994282 0.5063334 0.5119253 0.5206700 0.5721931 0.5740050 -#> [655,] 0.3471195 0.6613483 0.7012632 0.7894252 0.7945076 0.8148472 0.8189127 -#> [656,] 0.4028254 0.4151472 0.4276793 0.4414033 0.4663869 0.4732585 0.4777298 -#> [657,] 0.8341872 0.8865120 0.9124553 0.9776484 0.9869563 0.9919096 1.0184556 -#> [658,] 0.5081797 0.5225944 0.5464530 0.5717254 0.6125552 0.6259600 0.6279938 -#> [659,] 0.2656600 0.3891551 0.3932012 0.4618651 0.4633659 0.4802200 0.5200496 -#> [660,] 0.4583481 0.4983567 0.5065117 0.5727852 0.5739238 0.6249346 0.6435275 -#> [661,] 0.7064498 0.7354697 0.7449229 0.8980629 0.9261128 0.9273094 0.9797472 -#> [662,] 0.2599636 0.3310614 0.3920226 0.4318124 0.4627563 0.5120180 0.5462467 -#> [663,] 0.6329682 0.6602455 0.6773683 0.7154039 0.7537751 0.7591667 0.7627073 -#> [664,] 0.2996762 0.3549475 0.4149381 0.6040388 0.6074579 0.6244618 0.6274167 -#> [665,] 0.3527183 0.4374755 0.4699401 0.5557919 0.5650627 0.5727925 0.5955450 -#> [666,] 0.8355623 1.1958227 1.3173416 1.3620397 1.3930836 1.4061571 1.5639044 -#> [667,] 0.2694920 0.4093251 0.4940575 0.5246449 0.5301315 0.5522417 0.5525923 -#> [668,] 0.3176937 0.3298639 0.4615580 0.4781089 0.4860993 0.5040802 0.5085280 -#> [669,] 0.8076283 0.8741396 0.9529027 1.0189253 1.1386040 1.1676503 1.2302778 -#> [670,] 0.4916895 0.8355623 0.9260981 0.9447221 0.9853316 1.0694053 1.1113730 -#> [671,] 0.2950353 0.3113680 0.4127848 0.4388591 0.4701749 0.5131636 0.5181448 -#> [672,] 0.1765483 0.6193803 0.7609701 0.7924176 0.8291398 0.8341872 0.8631459 -#> [673,] 0.1882941 0.6084791 0.6591222 0.8985321 0.9114107 0.9670695 0.9727846 -#> [674,] 0.7103740 0.7996086 1.1310247 1.1396698 1.1415995 1.1594726 1.2106312 -#> [675,] 0.6694261 0.7091689 0.7125685 0.7160217 0.7290825 0.7359664 0.7543159 -#> [676,] 0.2652173 0.3591586 0.3856247 0.4302680 0.4700327 0.5284957 0.5455016 -#> [677,] 0.4056079 0.4650003 0.6410389 0.6644238 0.7346686 0.7799487 0.7987587 -#> [678,] 0.7652407 1.0127818 1.1077200 1.1214226 1.1279028 1.2002891 1.2068199 -#> [679,] 0.3086478 0.3642713 0.3672958 0.5025481 0.6187645 0.7493980 0.7529638 -#> [680,] 0.6108399 0.6574265 0.6846185 0.7434690 0.7667026 0.7743503 0.8051438 -#> [681,] 0.9406192 0.9900879 1.0073145 1.0223147 1.0868001 1.1268332 1.1792269 -#> [682,] 0.5070293 0.5632050 0.6182612 0.6978583 0.7107505 0.7151584 0.7312178 -#> [683,] 0.4334864 0.4357644 0.5221797 0.6035912 0.6405775 0.6628211 0.6794570 -#> [684,] 0.5086459 0.5793263 0.5985444 0.6381214 0.6499291 0.7309148 0.8410188 -#> [685,] 0.2965186 0.3183395 0.3937665 0.4383867 0.4429527 0.4651612 0.4741794 -#> [686,] 0.2436403 0.3843435 0.4940036 0.5953922 0.5978571 0.6161363 0.6846185 -#> [687,] 0.6109270 0.7466674 0.8328029 0.8611830 0.9437925 0.9840271 0.9916199 -#> [688,] 1.1609350 1.1780794 1.2971786 1.2993309 1.3046307 1.3752090 1.4155252 -#> [689,] 0.3227764 0.5130482 0.5373405 0.5434537 0.5549392 0.6665069 0.7139751 -#> [690,] 0.6298396 0.9606412 0.9806925 1.0191859 1.0786642 1.0977456 1.1067650 -#> [691,] 1.1912198 1.2343525 1.3290869 1.3660693 1.3753267 1.5181986 1.6123363 -#> [692,] 0.2697039 0.3892588 0.4135310 0.4528358 0.5037029 0.5203917 0.5268461 -#> [693,] 0.4335794 0.4556055 0.5383580 0.6724047 0.7737363 0.7917263 0.8150843 -#> [694,] 0.3496784 0.4470028 0.5743282 0.7685739 0.8087218 0.8201508 0.9122154 -#> [695,] 0.2202836 0.4180805 0.5138186 0.6213372 0.6242689 0.6470521 0.6813480 -#> [696,] 0.3184559 0.5904378 0.6486461 0.6865330 0.7496748 0.7809224 0.8325244 -#> [697,] 0.3358029 0.3893338 0.4844073 0.5672224 0.6164471 0.6320225 0.6561801 -#> [698,] 0.5025590 0.5278623 0.5865902 0.5981669 0.6182001 0.6365512 0.6947839 -#> [699,] 0.3784456 0.4625917 0.6006478 0.6319259 0.6326365 0.6393936 0.6485100 -#> [700,] 0.4097627 0.5605815 0.5786764 0.6970361 0.6994112 0.7243970 0.7910807 -#> [701,] 0.4532875 0.4625917 0.4769697 0.4860003 0.4913224 0.5144246 0.5899088 -#> [702,] 1.0514347 1.1351614 1.1604199 1.2314421 1.2675142 1.2719898 1.2757552 -#> [703,] 0.6039762 0.8073952 0.8313950 0.8410883 0.8428926 0.8934076 0.8941275 -#> [704,] 0.4874757 0.5073828 0.6075559 0.6104783 0.6204127 0.6582852 0.7073790 -#> [705,] 0.3776611 0.4769180 0.5101209 0.8239215 0.9078180 0.9702416 1.0187160 -#> [706,] 0.2938909 0.4308291 0.4513943 0.4871143 0.5175952 0.5349535 0.5568170 -#> [707,] 0.4360400 0.5857594 0.6447800 0.6451698 0.7482271 0.8122301 0.8444847 -#> [708,] 0.9514395 1.0606779 1.0636714 1.1426683 1.2643485 1.2781764 1.2947481 -#> [709,] 0.6583572 0.6639630 0.7268477 0.9004944 0.9162768 0.9609299 1.0435224 -#> [710,] 0.3390154 0.4174240 0.4615110 0.4615817 0.4617750 0.5644427 0.5650864 -#> [711,] 0.2724458 0.4804254 0.4858038 0.4923078 0.5294087 0.5417924 0.5419647 -#> [712,] 0.1257555 0.5030681 0.5135363 0.5429686 0.5453030 0.5831975 0.5874906 -#> [713,] 0.2316066 0.4242651 0.5503053 0.5668125 0.6527146 0.6826380 0.7104982 -#> [714,] 0.7137572 0.8132454 0.8288236 0.8378235 0.9917530 1.0033330 1.1027254 -#> [715,] 0.3445357 0.5203527 0.5210982 0.5302128 0.5474013 0.5504232 0.5537372 -#> [716,] 0.6289333 0.7239378 0.7605889 0.8364011 0.8404026 0.8441677 0.8458550 -#> [717,] 0.5101209 0.7502501 0.7571150 0.7914278 0.9332052 0.9764120 1.0077118 -#> [718,] 0.5185333 0.5451491 0.5464530 0.6375285 0.7150237 0.7173710 0.7490721 -#> [719,] 0.7471292 0.7926226 0.9163542 0.9244381 0.9594619 0.9813975 0.9902617 -#> [720,] 0.2798545 0.2928507 0.4171835 0.4241446 0.5506097 0.5528294 0.5698328 -#> [721,] 0.5810662 0.6228606 0.6947839 0.6994219 0.8052348 0.8134730 0.8163599 -#> [722,] 0.4924958 0.4981826 0.5540639 0.6878055 0.6996609 0.7174303 0.7634109 -#> [723,] 0.2298626 0.4681615 0.4883660 0.5622740 0.6198420 0.6236002 0.6291796 -#> [724,] 0.2985586 1.2498559 1.3073808 1.3259615 1.4278049 1.4933361 1.5392211 -#> [725,] 0.7177136 0.7769373 0.8442932 0.9119135 0.9680577 0.9692823 1.0137121 -#> [726,] 1.6054625 2.0848020 2.2737335 2.3440355 2.4777165 2.4800381 2.5488145 -#> [727,] 0.6776881 0.7018391 0.7596997 0.8934793 0.8953289 0.9017456 0.9556454 -#> [728,] 0.5503053 0.5529916 0.5644517 0.5815853 0.6495746 0.6587434 0.6663706 -#> [729,] 0.4863853 0.5017704 0.5706278 0.6356550 0.6714550 0.7973914 0.8065675 -#> [730,] 0.6298396 0.8941275 1.0586703 1.1020911 1.1238132 1.1308614 1.1605411 -#> [731,] 0.2230059 0.4460617 0.5229273 0.5374811 0.6970361 0.7349776 0.7393258 -#> [732,] 0.9529027 1.0492829 1.0509800 1.2951662 1.3222785 1.3498134 1.4098043 -#> [733,] 0.9483649 0.9880734 1.0074580 1.0216102 1.1741660 1.2103866 1.2133149 -#> [734,] 0.6690111 0.7064498 0.7085971 0.8331035 0.8896609 0.9334063 0.9730671 -#> [735,] 0.6221261 0.8075866 0.9425628 1.0028363 1.0798067 1.1136847 1.1192310 -#> [736,] 0.4332651 0.5409303 0.6307219 0.6624071 0.7050365 0.7191833 0.7874377 -#> [737,] 0.4387941 0.6204127 0.6707253 0.6965939 0.6972482 0.7017439 0.7697675 -#> [738,] 0.5950760 0.7354020 0.7369217 0.7641021 0.8088560 0.8308253 0.8328029 -#> [739,] 0.4441841 0.4739766 0.6529928 0.6619944 0.7024932 0.7527359 0.7571572 -#> [740,] 0.3433086 0.4559011 0.4768436 0.6148437 0.6968936 0.7092597 0.7328150 -#> [741,] 0.3158317 0.3165580 0.3969505 0.4063249 0.4579166 0.4631483 0.4714148 -#> [742,] 0.5550847 0.5627145 0.6103244 0.6326847 0.6514708 0.7510046 0.7562212 -#> [743,] 0.3056034 0.3103047 0.3165580 0.3768641 0.4135715 0.4179349 0.4652016 -#> [744,] 0.1418194 0.5225944 0.6063061 0.6682361 0.7811779 0.7920934 0.7948385 -#> [745,] 0.3113118 0.4595513 0.4636687 0.5151912 0.5487680 0.6796532 0.7036961 -#> [746,] 1.2094194 1.2693452 1.2838527 1.2862101 1.5435401 1.6377179 1.6807627 -#> [747,] 0.1921174 0.4963909 0.4970822 0.5206700 0.5575169 0.5603175 0.5684175 -#> [748,] 0.2709125 0.2989971 0.3178739 0.3892588 0.3984968 0.5257014 0.6096382 -#> [749,] 0.3710504 0.4176578 0.4423984 0.4646938 0.4921990 0.5166952 0.5442631 -#> [750,] 0.3913705 0.3968053 0.4414737 0.5097455 0.5351798 0.5617529 0.5676278 -#> [751,] 0.3342382 0.3864791 0.4802595 0.4861910 0.5138721 0.5257014 0.5507399 -#> [752,] 0.3788236 0.5478272 0.5989136 0.6381861 0.7703288 0.8092654 0.8158994 -#> [753,] 0.5702987 0.5980038 0.6198420 0.6559528 0.6597138 0.7089059 0.7139787 -#> [754,] 0.3107541 0.3136977 0.4305712 0.4617750 0.4785309 0.4931461 0.5282038 -#> [755,] 0.6690871 0.6860968 0.7725749 0.8556933 0.8632565 0.9030162 0.9208716 -#> [756,] 0.4915624 0.5409284 0.5495761 0.5752191 0.6137225 0.7168790 0.7279401 -#> [757,] 0.2709125 0.3940243 0.3953197 0.3979419 0.4528358 0.4592319 0.5072869 -#> [758,] 0.3246618 0.4099349 0.5264586 0.5480419 0.5910737 0.6117036 0.6689205 -#> [759,] 0.5578534 0.5838292 0.6651903 0.7801991 0.8151923 0.8256253 0.8408133 -#> [760,] 0.4936120 0.7140313 0.8417973 0.8946692 0.9161463 0.9171797 0.9212723 -#> [761,] 0.4423240 0.4711256 0.5230184 0.5773746 0.5822046 0.5966500 0.6572810 -#> [762,] 0.1907358 0.4005174 0.4075196 0.4639866 0.4674898 0.5261269 0.5539596 -#> [763,] 0.9182541 1.0350864 1.1025945 1.1219863 1.1424055 1.1723399 1.1851861 -#> [764,] 0.4357644 0.5396707 0.7081140 0.7458473 0.7559599 0.7662393 0.7817104 -#> [765,] 0.2985586 1.2673941 1.3162858 1.3490841 1.3812083 1.4572480 1.4857447 -#> [766,] 0.4815630 0.5688090 0.6190446 0.7048870 0.7183197 0.7194659 0.7259945 -#> [767,] 0.7216168 0.7344810 0.7559130 0.8101955 0.8404026 0.8766603 0.8847948 -#> [768,] 0.5425146 0.5511466 0.6194563 0.6747291 0.6897736 0.7081918 0.7287542 -#> [769,] 0.5627565 0.6415368 0.6619937 0.7960983 0.8765106 0.8888571 0.8970740 -#> [770,] 0.4305420 0.4811124 0.4841278 0.6360874 0.6567670 0.7184861 0.7732483 -#> [771,] 1.0676613 1.4296416 1.4939067 1.5614253 1.5694705 1.7011895 1.7708172 -#> [772,] 0.7779108 0.8614404 1.0133924 1.0792046 1.1991442 1.2035680 1.2053993 -#> [773,] 0.3852675 0.4621747 0.4727399 0.5144246 0.5488358 0.6393936 0.6696794 -#> [774,] 0.3158758 0.3585868 0.3706053 0.4252485 0.4941148 0.5403542 0.5470744 -#> [775,] 0.8118327 0.8399439 0.8473664 0.8689506 0.9741627 0.9763009 0.9807302 -#> [776,] 0.3974524 0.4094364 0.4199832 0.5053076 0.5211516 0.6099352 0.6329682 -#> [777,] 0.3445357 0.3591586 0.4127848 0.4592028 0.4989853 0.5092464 0.5094085 -#> [778,] 0.3027772 0.4167413 0.4696109 0.5002217 0.5063334 0.5373169 0.6704545 -#> [779,] 0.5278035 0.6956782 0.8427137 0.9090991 0.9683068 0.9759108 0.9917875 -#> [780,] 0.3918320 0.4896400 0.5011922 0.6041616 0.6591222 0.7215146 0.7815183 -#> [781,] 0.5405132 0.6916804 0.6989697 0.8490090 0.8606617 0.8767628 0.9370817 -#> [782,] 0.2646638 0.3213510 0.3692134 0.4093251 0.4185317 0.5074590 0.5181448 -#> [783,] 0.4224714 0.6104783 0.6137225 0.6729815 0.7095745 0.7150264 0.7208141 -#> [784,] 0.4066972 0.6110123 0.6401066 0.6787280 0.6894002 0.7225953 0.7293708 -#> [785,] 0.2995764 0.4729371 0.5985444 0.6034767 0.6364684 0.6724590 0.6766374 -#> [786,] 0.3859147 0.5912506 0.5923463 0.6539348 0.7856543 0.8075600 0.8499710 -#> [787,] 0.4695051 0.8063933 0.8487997 0.9466795 1.0495105 1.0972245 1.0995921 -#> [788,] 0.5394996 0.7051774 0.8265935 0.8760955 0.8869731 0.9950556 1.0018150 -#> [789,] 0.5625080 0.8329584 0.8397169 0.8863304 0.8892563 0.8977614 0.9131567 -#> [790,] 0.3525252 0.4368219 0.4714148 0.5242562 0.5316131 0.5881955 0.5894456 -#> [791,] 0.2915407 0.4650941 0.5777199 0.6138800 0.6294308 0.6847840 0.7266085 -#> [792,] 0.4738325 0.4776311 0.4811124 0.6236002 0.6570703 0.7039359 0.7526110 -#> [793,] 0.4893257 0.5173880 0.6514329 0.7328150 0.7466487 0.7937440 0.7984116 -#> [794,] 0.4894648 0.4934404 0.5353879 0.6123625 0.6483658 0.6927912 0.7029612 -#> [795,] 0.3642874 0.6968107 0.7434605 0.8517166 0.9269132 0.9289136 1.0205662 -#> [796,] 0.3459307 0.5028377 0.5434625 0.5760647 0.5851950 0.5938233 0.6451698 -#> [797,] 0.3974026 0.4114479 0.4151472 0.4304381 0.4426711 0.4582774 0.4631483 -#> [798,] 0.2599636 0.4381897 0.4646938 0.4704474 0.4764335 0.4929968 0.5003201 -#> [799,] 0.1603362 0.3056966 0.3669123 0.4612447 0.4929649 0.5254921 0.5291550 -#> [800,] 0.6608258 1.0429812 1.0439467 1.1099186 1.1286047 1.2167245 1.2207269 -#> [801,] 0.3113292 0.6743795 0.6919364 0.8051438 0.8565539 0.8595808 0.8784011 -#> [802,] 0.3474395 0.5598617 0.6589509 0.7612510 0.8362417 0.9492731 0.9708233 -#> [803,] 0.4604155 0.4981237 0.5171006 0.5691912 0.5693549 0.6212383 0.6379603 -#> [804,] 0.4978547 0.5048296 0.6039762 0.6525114 0.6809391 0.8173460 0.8338392 -#> [805,] 0.2546616 0.4024938 0.4828984 0.4937261 0.5813304 0.6000009 0.6202705 -#> [806,] 0.4080902 0.5169540 0.5808212 0.5931816 0.6385622 0.6709518 0.6768179 -#> [807,] 0.3056034 0.3326814 0.4537911 0.4565760 0.4579166 0.4781016 0.4824973 -#> [808,] 0.2136091 0.4037885 0.4213650 0.4309298 0.4422711 0.4428247 0.5413453 -#> [809,] 0.3995663 0.4340247 0.4436905 0.5134259 0.5156347 0.5339298 0.5704259 -#> [810,] 0.3092599 0.3657331 0.3725628 0.4208733 0.4720911 0.4850523 0.5555859 -#> [811,] 0.3784456 0.3852675 0.4127776 0.4532875 0.6210650 0.6525644 0.6680109 -#> [812,] 0.3433086 0.4590312 0.4968742 0.5173880 0.5616415 0.6270975 0.6659995 -#> [813,] 0.2798545 0.3874939 0.4496889 0.4604155 0.5485144 0.5682557 0.5700471 -#> [814,] 0.3082097 0.3310614 0.3782222 0.4266016 0.4693899 0.4929968 0.5043400 -#> [815,] 0.3461297 0.3816936 0.3835569 0.4024244 0.4161253 0.4371760 0.4834878 -#> [816,] 0.5150732 0.5921002 0.6800489 0.9387221 1.0449819 1.1255987 1.2031884 -#> [817,] 0.4097627 0.5394996 0.6033279 0.6107870 0.7756786 0.8485907 0.8577339 -#> [818,] 0.3514631 0.5049022 0.6213372 0.6854924 0.6952081 0.7058257 0.7449229 -#> [819,] 0.4858187 0.7164687 0.7591843 0.8094750 0.8311792 0.8322227 0.8456569 -#> [820,] 0.5556390 0.6794556 0.7165986 0.7671241 0.7781291 0.7898797 0.8203556 -#> [821,] 0.3112028 0.3530090 0.3912126 0.3977697 0.4197831 0.4252485 0.4859729 -#> [822,] 1.1811835 1.4155497 1.6946680 1.7408778 1.7963002 1.9419126 2.0003276 -#> [823,] 0.4785309 0.5035822 0.5504232 0.5581019 0.5920239 0.6022480 0.6114413 -#> [824,] 1.4441306 1.9944252 2.0704969 2.2442786 2.2650094 2.3348776 2.3617711 -#> [825,] 0.6218947 0.6512875 0.6732712 0.7395676 0.7976003 0.8076283 0.8764037 -#> [826,] 0.3036663 0.6271310 0.7331899 0.8259215 0.9237299 0.9609905 0.9817945 -#> [827,] 0.9193467 1.0772629 1.1219863 1.2334469 1.2451451 1.2538902 1.3005034 -#> [828,] 0.2696111 0.2853528 0.3585868 0.3744725 0.4054881 0.4197831 0.5242217 -#> [829,] 0.3848214 0.4585248 0.5589624 0.5616011 0.5769023 0.6223452 0.6257554 -#> [830,] 0.1793892 0.4991711 0.5590794 0.5758475 0.6482590 0.6809989 0.6897370 -#> [831,] 0.6211108 0.7208141 0.7467857 0.7814715 0.8912662 0.8966985 0.9665416 -#> [832,] 0.5439116 0.6994219 0.9761487 0.9915919 1.0092772 1.0094416 1.0673060 -#> [833,] 0.3097309 0.7838109 0.8271271 0.8466075 0.8590657 0.8687430 1.0163926 -#> [834,] 0.1657857 0.3075964 0.3930878 0.4054881 0.4513086 0.4665484 0.4736562 -#> [835,] 0.6569257 0.7234910 0.8336439 0.8673209 0.9341469 0.9458068 0.9533946 -#> [836,] 0.2218200 0.2753051 0.3113118 0.5836566 0.6286956 0.6558379 0.6649380 -#> [837,] 0.4074138 0.5250252 0.5611636 0.6104833 0.6594435 0.7098716 0.7220400 -#> [838,] 0.3802711 0.5821115 0.6293527 0.6348820 0.6427768 0.6612903 0.7769373 -#> [839,] 0.4277456 0.4302680 0.4351358 0.5361542 0.5427427 0.5441774 0.5844532 -#> [840,] 0.3067851 0.3080777 0.3261829 0.3710223 0.3945596 0.4150834 0.4236751 -#> [841,] 0.4518358 0.5580674 0.6486461 0.6640788 0.7124826 0.7184861 0.7256895 -#> [842,] 0.3160586 0.4083693 0.4399549 0.4689018 0.4820274 0.5174262 0.5196555 -#> [843,] 1.5311992 1.5536753 1.5614253 1.6258930 1.7115210 1.7434298 1.7461672 -#> [844,] 0.2972695 0.5968755 0.6194563 0.6488359 0.6492446 0.6546223 0.7091232 -#> [845,] 0.1795017 0.3869610 0.4826465 0.4837839 0.4985066 0.5214377 0.5393927 -#> [846,] 0.2536980 0.5370576 0.5865902 0.5910692 0.6302841 0.6917589 0.7002640 -#> [847,] 0.3107841 0.4453761 0.4519320 0.5431865 0.6136417 0.6244927 0.6254025 -#> [848,] 0.1719823 0.1765733 0.4436905 0.4489713 0.6157921 0.6186984 0.6720264 -#> [849,] 0.8311973 0.8747926 0.9717083 1.0013779 1.0122960 1.0163732 1.2066618 -#> [850,] 0.2994409 0.4768582 0.4894021 0.6089304 0.6684409 0.7042698 0.8011727 -#> [851,] 0.3365770 0.3985065 0.4241446 0.5156347 0.5156644 0.5252485 0.5470744 -#> [852,] 1.4296416 1.5983391 1.9137894 2.0242065 2.0456072 2.0512273 2.0535124 -#> [853,] 0.4680880 0.6266138 0.6834861 0.7391465 0.7472718 0.8071015 0.8707201 -#> [854,] 0.3394250 0.3848525 0.4798373 0.5234596 0.5509819 0.6162140 0.6383378 -#> [855,] 0.7566811 0.8401697 0.8518542 0.9411251 0.9457062 0.9563972 1.1295818 -#> [856,] 0.2940478 0.3407325 0.3787681 0.4414737 0.4578845 0.5302900 0.5434625 -#> [857,] 0.4942273 0.5455117 0.6028087 0.6346238 0.6826033 0.7024932 0.7393022 -#> [858,] 0.6158580 1.1232248 1.2848476 1.3117549 1.3278298 1.3405774 1.3982930 -#> [859,] 0.4188517 0.4549399 0.4613472 0.5384865 0.6145878 0.6387264 0.6612431 -#> [860,] 0.4792305 0.5141196 0.5410854 0.5430704 0.5812571 0.5814689 0.5905954 -#> [861,] 0.2289517 0.3032933 0.3035479 0.3261829 0.4566556 0.4652289 0.4926226 -#> [862,] 1.3375979 1.5159084 1.7651675 1.7951556 1.8873251 1.9282501 1.9311304 -#> [863,] 0.3479301 0.4158467 0.4475573 0.5239454 0.5443972 0.5572317 0.5943596 -#> [864,] 0.3445994 0.4473554 0.5081086 0.5733389 0.6099352 0.6214573 0.6883512 -#> [865,] 0.2503394 0.4768582 0.6427768 0.7949227 0.8427137 0.8543760 0.8600206 -#> [866,] 0.5509826 0.6028087 0.6743603 0.7374324 0.7572010 0.8214049 0.8687430 -#> [867,] 0.4870908 0.4919594 0.4989698 0.5581019 0.5980778 0.5998886 0.6074706 -#> [868,] 0.4299956 0.6966550 0.7385971 0.7570375 0.8153590 0.8393366 0.8576094 -#> [869,] 0.2995764 0.3944994 0.4367022 0.5075357 0.5297576 0.6499291 0.6631117 -#> [870,] 0.3293306 0.3850889 0.4398420 0.4766956 0.5261269 0.5305005 0.6059146 -#> [871,] 0.4366422 0.4963909 0.5602409 0.5644773 0.5944104 0.6285445 0.6535593 -#> [872,] 0.2618401 0.5512129 0.5897778 0.6207546 0.6642021 0.6813480 0.6973555 -#> [873,] 0.3980096 0.4059614 0.4300678 0.5654316 0.5859741 0.5926003 0.6126095 -#> [874,] 0.4401777 0.5409303 0.6310690 0.6985039 0.7151776 0.7328549 0.7661509 -#> [875,] 0.3694694 0.3722417 0.3940243 0.3984968 0.4334864 0.5203917 0.5396707 -#> [876,] 0.6602341 0.7779108 0.8882337 0.9693348 1.0649957 1.0676211 1.1084016 -#> [877,] 0.5141196 0.5261482 0.5798880 0.6404703 0.6940858 0.7309148 0.7914278 -#> [878,] 0.6389136 0.9553313 1.0718025 1.1883763 1.2044469 1.2161923 1.2191719 -#> [879,] 0.6111134 0.6330021 0.7264062 0.7466766 0.7661509 0.7662488 0.7706794 -#> [880,] 0.3417088 0.4197812 0.4328642 0.4689018 0.5634057 0.6811357 0.6952935 -#> [881,] 0.2312386 0.2828653 0.3092599 0.3927840 0.4562478 0.4812868 0.4826099 -#> [882,] 0.2474450 0.3570338 0.3927840 0.4410145 0.4429527 0.4597591 0.4845243 -#> [883,] 0.1765733 0.2005349 0.4340247 0.4779987 0.5640750 0.5933102 0.5941373 -#> [884,] 0.9609905 0.9755831 1.0158348 1.0248596 1.0306228 1.0421030 1.0602373 -#> [885,] 0.4803380 0.4876600 0.5015561 0.5353879 0.5473466 0.5765421 0.6334912 -#> [886,] 0.2411622 0.3859884 0.4363727 0.4586935 0.5530247 0.5812676 0.6110123 -#> [887,] 0.7483794 0.7858414 0.7975968 0.8328092 0.8531193 0.9108383 0.9761211 -#> [888,] 0.5192799 0.5370576 0.5517135 0.5649657 0.5899088 0.6580907 0.6966668 -#> [889,] 0.1603362 0.2547535 0.3437702 0.5100298 0.5368799 0.5371010 0.5595756 -#> [890,] 0.4959007 0.6041729 0.6174626 0.6970957 0.7220400 0.7628530 0.9100871 -#> [891,] 0.2337535 0.3299522 0.4470890 0.5008382 0.5391656 0.5486938 0.5852984 -#> [892,] 0.2853528 0.3298639 0.3561649 0.3592673 0.4272458 0.4645888 0.4665484 -#> [893,] 0.4506818 0.4981237 0.5566957 0.5673679 0.6193540 0.6484340 0.6538127 -#> [894,] 0.1882941 0.6041616 0.6263253 0.8026338 0.8081701 0.9258872 0.9768949 -#> [895,] 0.2536980 0.4769697 0.5194278 0.5278623 0.5649657 0.5688071 0.5813304 -#> [896,] 0.6724590 0.7350833 0.8214040 0.8284443 0.9983396 1.0588523 1.0601857 -#> [897,] 0.3082097 0.4318124 0.4660285 0.4704474 0.4798373 0.5007229 0.5073744 -#> [898,] 0.5959716 0.6063061 0.6644238 0.6669696 0.6904804 0.7117599 0.7358067 -#> [899,] 0.6225775 0.6580907 0.7458233 0.7656519 0.8636642 0.9055562 0.9134872 -#> [900,] 0.2343413 0.4215890 0.5151912 0.5955406 0.7681511 0.8515844 0.8669462 -#> [901,] 0.3285568 0.4099349 0.4197421 0.4618651 0.5232328 0.5740169 0.6015343 -#> [902,] 0.3784988 0.4220740 0.4358854 0.4508229 0.4588823 0.5052489 0.5454542 -#> [903,] 2.0556048 2.1108382 2.1577122 2.1925334 2.2083299 2.2801831 2.5363151 -#> [904,] 0.4114465 0.6254268 0.8252143 1.0101165 1.0390967 1.1097381 1.1741516 -#> [905,] 0.3737895 0.3951226 0.4112151 0.4597591 0.4654187 0.5176881 0.5691951 -#> [906,] 0.4542098 0.5278493 0.6860703 0.7970234 0.8091093 0.8225765 0.8265693 -#> [907,] 0.4938878 0.6573238 0.6673814 0.7260920 0.7379023 0.7393258 0.7920760 -#> [908,] 0.2994951 0.3686147 0.4396160 0.4625997 0.4921990 0.5790150 0.5847977 -#> [909,] 0.1502014 0.3688427 0.3859884 0.4635766 0.5136618 0.5138721 0.5232558 -#> [910,] 0.2615611 0.4080902 0.4463284 0.5950760 0.6132402 0.6481007 0.6598114 -#> [911,] 0.4851220 0.5057987 0.5622607 0.5920986 0.6190446 0.7151776 0.7512546 -#> [912,] 0.7962522 1.1027254 1.3672287 1.3807148 1.4204162 1.4331734 1.6037038 -#> [913,] 0.5067543 0.5221909 0.5752158 0.6147478 0.6218947 0.6348818 0.6790847 -#> [914,] 0.3227764 0.3627995 0.4433784 0.4683933 0.4759184 0.5595899 0.5716828 -#> [915,] 0.4388591 0.4663869 0.4992582 0.5415097 0.5598902 0.5853602 0.5982363 -#> [916,] 0.4621747 0.5439116 0.5810662 0.6525644 0.7261151 0.7618664 0.7742697 -#> [917,] 0.5270508 0.6896589 0.6949101 0.6962610 0.7263227 0.7282629 0.7321677 -#> [918,] 0.1880397 0.3118351 0.3850889 0.4812505 0.4999104 0.5074146 0.5335917 -#> [919,] 0.1963591 0.4696109 0.4981826 0.5449325 0.5960984 0.6329302 0.6556979 -#> [920,] 0.3811083 0.4901664 0.5100151 0.5351490 0.5490831 0.5580313 0.5640750 -#> [921,] 0.3875109 0.5019206 0.5601844 0.5697414 0.5737582 0.6291796 0.6404703 -#> [922,] 0.2136091 0.3576403 0.3815088 0.4719349 0.4834617 0.5229099 0.5483363 -#> [923,] 0.6154598 0.6424847 0.6907413 0.8670046 0.8818672 0.8885824 0.8975248 -#> [924,] 0.5671601 0.8629524 1.0250429 1.0556881 1.2399047 1.2926398 1.5970988 -#> [925,] 0.6154598 0.7423847 0.8346140 1.0411140 1.0660808 1.0823524 1.1049130 -#> [926,] 0.3386233 0.4579561 0.6172867 0.6309674 0.6380812 0.6801436 0.6812344 -#> [927,] 0.4360400 0.5995342 0.8487750 0.8763325 0.9250326 0.9493566 0.9853316 -#> [928,] 0.6293818 0.7704966 0.8845181 0.9375603 0.9499213 0.9609299 1.0233418 -#> [929,] 0.4870615 0.5015118 0.5460978 0.6271310 0.6976519 0.7055782 0.8089936 -#> [930,] 0.5042632 0.6896709 0.7113964 0.7290197 0.7380223 0.7713337 0.7809407 -#> [931,] 0.3329671 0.5655398 0.7402701 0.8392612 0.8733825 0.8951593 0.9067863 -#> [932,] 0.8008904 0.8786278 0.8980172 0.9943680 1.0081010 1.0912510 1.3251528 -#> [933,] 0.6306143 0.6469669 0.7280660 0.7928859 0.8346431 0.8725721 0.8731730 -#> [934,] 0.2743603 0.3937665 0.4599237 0.5155835 0.5191472 0.5414595 0.5740675 -#> [935,] 0.1921174 0.4510282 0.4783833 0.5052489 0.5644773 0.6008386 0.6455745 -#> [936,] 0.5700973 0.6075683 0.6546223 0.6574239 0.6743603 0.6826033 0.6853944 -#> [937,] 0.3119060 0.3528047 0.3627995 0.3915093 0.4358854 0.4609358 0.4802595 -#> [938,] 0.4185317 0.4220740 0.4422055 0.4509399 0.4915624 0.5072311 0.5301315 -#> [939,] 0.6726428 0.7354445 0.7427972 0.7515865 0.7576142 0.7620362 0.7784925 -#> [940,] 0.4172339 0.6413421 0.6954323 0.7064193 0.7385654 0.8145048 0.8370775 -#> [941,] 0.2743603 0.5324977 0.5416235 0.5705223 0.5984565 0.6170349 0.6286326 -#> [942,] 0.3027772 0.4896400 0.4992742 0.5449325 0.5624760 0.6325971 0.6948436 -#> [943,] 0.4111658 0.5943301 0.6385163 0.6979967 0.8446133 0.8896609 0.9001202 -#> [944,] 0.4556055 0.4896084 0.5217608 0.5254022 0.5556390 0.5913540 0.6264371 -#> [945,] 0.4227960 0.4896124 0.5120180 0.5913042 0.6111190 0.6146042 0.6258101 -#> [946,] 0.6296239 0.6892862 0.8390236 0.8875493 0.9461592 1.0529038 1.1175567 -#> [947,] 0.3458876 0.4801832 0.5002871 0.5415791 0.6182001 0.6493688 0.6571560 -#> [948,] 0.5578534 0.7378963 0.8169656 0.8296489 0.8388325 0.8670486 0.8909451 -#> [949,] 0.3590217 0.5057987 0.5953573 0.6310690 0.7259945 0.7391465 0.8038318 -#> [950,] 0.5544410 0.7224488 0.8083694 0.8105955 0.8534005 0.8792767 0.8823584 -#> [951,] 0.6389136 0.7099335 0.7347236 0.8351929 0.8554912 0.8708675 0.8733825 -#> [952,] 0.4955922 0.6729536 0.7140313 0.7482271 0.7661296 0.8388325 0.8487750 -#> [953,] 0.4158467 0.4642895 0.4868032 0.5434911 0.5755035 0.5863843 0.5917659 -#> [954,] 0.1457126 0.5038661 0.5415791 0.5644321 0.6778771 0.7278628 0.8170445 -#> [955,] 0.3342053 0.3352511 0.4344955 0.4351358 0.4720053 0.5108792 0.5189029 -#> [956,] 0.9457062 0.9849265 1.0223147 1.0483017 1.0934914 1.0951222 1.1603080 -#> [957,] 0.3633023 0.4441841 0.4480393 0.5013117 0.5053819 0.5627093 0.6093778 -#> [958,] 0.6569257 0.8302082 0.8922407 0.8980629 0.9490880 0.9769237 1.0046860 -#> [959,] 0.3097309 0.7675038 0.9328969 0.9411251 1.0235728 1.0326252 1.0389430 -#> [960,] 0.4588987 0.4657427 0.6035151 0.6461901 0.6718601 0.6904380 0.7021603 -#> [961,] 0.7051774 0.7756786 0.9457200 1.0963949 1.1187557 1.1253954 1.1275522 -#> [962,] 0.2479778 0.2499468 0.4179349 0.4187607 0.4781016 0.5302001 0.5350829 -#> [963,] 0.5012870 0.5258843 0.5312717 0.5410854 0.5644517 0.5668125 0.5941373 -#> [964,] 0.7557878 0.8613864 0.9285815 0.9622023 1.0018294 1.0047035 1.0277976 -#> [965,] 0.3341905 0.3604918 0.3978667 0.4150834 0.4213421 0.4661846 0.4741237 -#> [966,] 0.3085853 0.4051373 0.4468204 0.4954094 0.5105782 0.5119253 0.5684175 -#> [967,] 0.3886122 0.5793263 0.5798880 0.7502501 0.7891617 0.8239215 0.9211892 -#> [968,] 0.1628608 0.7996086 0.9813975 1.0457239 1.0600730 1.0738716 1.1739681 -#> [969,] 0.8815454 0.9994666 1.0613208 1.1361912 1.1385667 1.1426683 1.2533244 -#> [970,] 0.2316066 0.4347131 0.5258843 0.5430704 0.5797168 0.6455426 0.6567670 -#> [971,] 0.6492446 0.6574239 0.6747291 0.8214049 0.8362544 0.8401697 0.8707394 -#> [972,] 0.1412353 0.3272235 0.3900844 0.4023933 0.4148677 0.4451457 0.4488936 -#> [973,] 0.8747926 1.0726503 1.1068952 1.1648686 1.2204814 1.2386833 1.2633442 -#> [974,] 0.8193688 0.8456541 0.8527007 0.8686242 0.9171797 0.9520110 0.9659309 -#> [975,] 0.2780944 0.3015907 0.3376254 0.3811083 0.3974026 0.4028254 0.4308291 -#> [976,] 0.3893338 0.4505269 0.4605785 0.6256895 0.6536408 0.7331622 0.7586668 -#> [977,] 0.6911686 0.7268477 0.8231290 0.8424534 0.9000961 0.9033437 0.9233767 -#> [978,] 0.3496784 0.4432166 0.5334193 0.5989136 0.7096008 0.7235467 0.7262585 -#> [979,] 0.2847934 0.3882206 0.4453761 0.4810555 0.5301634 0.5475485 0.5485547 -#> [980,] 0.3859954 0.4699200 0.4811191 0.4980564 0.5245181 0.5667576 0.5963322 -#> [981,] 0.7278976 0.7327970 0.7627281 0.7788747 0.7951149 0.8195750 0.8378108 -#> [982,] 0.3884973 0.5418577 0.5821115 0.7216168 0.7290825 0.7671997 0.7678536 -#> [983,] 0.3086478 0.4160685 0.4996085 0.5072499 0.5879309 0.7115002 0.7117846 -#> [984,] 0.6303962 0.6589045 0.8001376 0.8145048 0.8338672 0.9112486 0.9150192 -#> [985,] 0.5460581 0.6507848 0.8080487 0.8499740 0.8614721 0.8856987 0.8991177 -#> [986,] 0.3589144 0.5418090 0.5852687 0.5985745 0.7017688 0.7516601 0.7699749 -#> [987,] 0.1657857 0.2696111 0.3561649 0.3862536 0.4120583 0.4539152 0.4941148 -#> [988,] 0.5119789 0.5209139 0.5622382 0.5642782 0.5787497 0.5996913 0.6040388 -#> [989,] 0.1983044 0.5926421 0.6151594 0.6539348 0.6827474 0.8271119 0.8582468 -#> [990,] 0.3474395 0.5745431 0.6616265 0.7282968 0.8629524 1.0278892 1.1192310 -#> [991,] 0.4299956 0.4577316 0.5259341 0.5962516 0.6553439 0.7172299 0.7221793 -#> [992,] 0.6325971 0.6704545 0.6972553 0.7056987 0.7278976 0.7571621 0.7676339 -#> [993,] 0.4489389 0.4528535 0.5843780 0.6116490 0.6194128 0.7036732 0.8328092 -#> [994,] 1.2167245 1.4022221 1.4315399 1.4493745 1.4979496 1.5439557 1.6053293 -#> [995,] 0.2972695 0.5141040 0.6075683 0.6081224 0.6466797 0.6477152 0.6839222 -#> [996,] 0.3875109 0.4253779 0.5457228 0.5545367 0.5935204 0.6400436 0.6516956 -#> [997,] 0.2770382 0.5320051 0.7591667 1.0625637 1.1118740 1.1502454 1.1741015 -#> [998,] 0.3747992 0.3757904 0.5399607 0.5601844 0.6182612 0.6194519 0.6348818 -#> [999,] 0.6915614 0.7037830 0.7320299 0.8083417 0.8444847 0.8632591 0.9026419 -#> [1000,] 0.6776881 0.9553533 1.0390967 1.1113306 1.1381286 1.1394759 1.2094194 +#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] +#> [1,] 1.21046951 1.2814328 1.3088671 1.3903896 1.4093499 1.5123118 1.5177298 +#> [2,] 0.55047599 0.7540306 0.7634534 0.7722017 0.8163211 0.8700257 0.9226647 +#> [3,] 0.35784179 0.4869218 0.5242352 0.5781613 0.6461551 0.6900840 0.7158303 +#> [4,] 0.53401809 0.5887559 0.7787472 0.7808913 0.7860686 0.8376048 0.9398310 +#> [5,] 0.34914673 0.4117812 0.4147237 0.5550794 0.5681887 0.5832709 0.6670932 +#> [6,] 0.34558386 0.3718587 0.3866957 0.4058981 0.4168423 0.4366775 0.4563907 +#> [7,] 0.46565599 0.4989607 0.5344266 0.5990700 0.6116902 0.6150030 0.6260920 +#> [8,] 0.24524756 0.3766672 0.4190034 0.4619930 0.4910142 0.4985297 0.5080894 +#> [9,] 0.26592251 0.5272305 0.5303869 0.5855830 0.5917343 0.5936955 0.6659433 +#> [10,] 0.20624216 0.4890596 0.6274324 0.6552598 0.6766045 0.6837926 0.6886020 +#> [11,] 0.57189888 0.5912276 0.6606174 0.6676028 0.7789707 0.9019471 0.9936128 +#> [12,] 0.59154586 0.5943194 0.6043368 0.6477592 0.6792485 0.6849131 0.6892158 +#> [13,] 0.19424836 0.2125101 0.6348915 0.6565775 0.7102408 0.7548653 0.7556356 +#> [14,] 0.50366019 0.5213843 0.5306987 0.5441858 0.5766456 0.6146114 0.6332020 +#> [15,] 0.66621597 0.7634713 0.9313510 0.9380089 0.9422618 0.9714016 1.0029979 +#> [16,] 0.42796979 0.5702875 0.6811840 0.7296882 0.7650716 0.8027896 0.8264574 +#> [17,] 0.43980417 0.5712633 0.6833969 0.7022311 0.7234893 0.7255583 0.7870018 +#> [18,] 0.32998788 0.3715182 0.4313687 0.7057181 0.7156661 0.7459431 0.7606691 +#> [19,] 0.53175341 0.5804025 0.6025482 0.6220356 0.6549309 0.6895867 0.7023902 +#> [20,] 0.25537990 0.4888065 0.5012188 0.5132578 0.5201907 0.5244126 0.5297184 +#> [21,] 0.44869504 0.5176139 0.5201538 0.5333403 0.5528347 0.5637833 0.6056317 +#> [22,] 0.24270294 0.4181342 0.4994289 0.7301984 0.7687906 0.8701522 0.8722797 +#> [23,] 0.27916247 0.4182686 0.4627760 0.5187923 0.5625505 0.5745288 0.5912516 +#> [24,] 0.27801534 0.2808169 0.4150215 0.4842020 0.5161527 0.5207735 0.5940268 +#> [25,] 0.07900148 0.2370228 0.3602234 0.3902051 0.5426235 0.6006307 0.6474137 +#> [26,] 0.61071508 0.6573590 0.7340550 0.7405824 0.7735499 0.7830390 0.8050931 +#> [27,] 0.34159301 0.3748690 0.3932452 0.5942108 0.6043088 0.6143347 0.6237749 +#> [28,] 0.62085681 0.6916244 0.7386633 0.9016987 1.0270393 1.0317743 1.1095360 +#> [29,] 0.22512765 0.3803994 0.3970135 0.4499811 0.4840932 0.4850417 0.4858446 +#> [30,] 0.39126729 0.4931540 0.5295156 0.5344266 0.5987257 0.6567391 0.6665549 +#> [31,] 0.30912486 0.3716844 0.4069067 0.4383080 0.4538033 0.4771445 0.4805272 +#> [32,] 0.37151820 0.4253336 0.4780872 0.6146927 0.6674207 0.7188448 0.7326479 +#> [33,] 0.09113993 0.4117812 0.6715584 0.7239671 0.7447867 0.7682577 0.8076352 +#> [34,] 0.37734773 0.4000469 0.4212806 0.5560209 0.5659865 0.5809123 0.6173314 +#> [35,] 0.69441613 0.7249473 0.7624164 0.8119654 0.9839406 1.0092612 1.0173604 +#> [36,] 0.24808700 0.2723521 0.4092183 0.4168423 0.4383710 0.5023097 0.5201907 +#> [37,] 0.52469938 0.6230936 0.7173692 0.7371090 0.7860783 0.9153443 0.9223275 +#> [38,] 0.45160491 0.5060944 0.5127088 0.5314992 0.5812371 0.5855830 0.6128921 +#> [39,] 0.33992786 0.6105890 0.7697521 0.8120088 0.8502975 0.8543398 0.8718331 +#> [40,] 0.45826699 0.4952567 0.5293771 0.5759559 0.5790945 0.5833047 0.5974994 +#> [41,] 0.78237982 0.8547462 0.9179658 0.9656688 1.0256551 1.0375951 1.0403373 +#> [42,] 0.34658311 0.5914036 0.6843618 0.7177654 0.8091027 0.8208785 0.8380186 +#> [43,] 0.36424210 0.4869968 0.5056829 0.5323747 0.5782066 0.5927128 0.6488748 +#> [44,] 0.45940455 0.6715617 0.6978360 0.7154144 0.7471025 0.7953747 0.8342867 +#> [45,] 0.66295655 1.0285802 1.1348424 1.1780126 1.5407360 1.5456936 1.5925654 +#> [46,] 0.59517291 0.6055320 0.6175850 0.6348915 0.6934208 0.7472936 0.7604855 +#> [47,] 0.26748597 0.4514422 0.5305996 0.5355726 0.5653137 0.5940433 0.6347029 +#> [48,] 0.44182012 0.4790345 0.7311678 0.7590097 0.7912512 0.8369607 0.8826947 +#> [49,] 0.39492438 0.4265521 0.4639095 0.4944305 0.4999843 0.5031262 0.5226127 +#> [50,] 0.36671734 0.3998036 0.4010056 0.4295132 0.4577139 0.4684129 0.4841514 +#> [51,] 0.48536186 0.5327137 0.6370667 0.6729774 0.6874784 0.6910342 0.6948833 +#> [52,] 0.58197751 0.8388852 0.9412836 1.0082361 1.0723705 1.1975280 1.2467210 +#> [53,] 0.28197372 0.4617600 0.4898238 0.5057847 0.5060944 0.5117953 0.5443672 +#> [54,] 0.31425204 0.4628165 0.4717548 0.4836014 0.4837092 0.5745749 0.6417850 +#> [55,] 0.33077154 0.3722042 0.3859907 0.4190034 0.4615879 0.5161005 0.5482435 +#> [56,] 0.35792720 0.5561728 0.6250605 0.6409314 0.7018025 0.7086441 0.7429670 +#> [57,] 0.47956545 0.7789857 0.9671439 1.1418139 1.1795548 1.2010407 1.2126780 +#> [58,] 0.63049552 0.9036267 0.9673234 0.9727745 1.0094522 1.0096174 1.0156655 +#> [59,] 0.46314350 0.5494480 0.5521143 0.5776104 0.6092869 0.6229224 0.7165581 +#> [60,] 0.66813811 0.7942737 0.8143436 0.9562376 0.9588842 0.9783526 1.0074788 +#> [61,] 0.30665876 0.3083442 0.5477442 0.5914676 0.6283700 0.6488153 0.6576743 +#> [62,] 0.48137672 0.5174087 0.5922966 0.6447556 0.6930791 0.6942257 0.7407218 +#> [63,] 0.53136017 0.5715258 0.6273539 0.6438647 0.6653796 0.6855298 0.6951885 +#> [64,] 0.29071253 0.3427768 0.5943194 0.6082906 0.6168765 0.6543068 0.6855298 +#> [65,] 0.90797943 1.0011695 1.0073150 1.0568285 1.1074157 1.1380730 1.1655519 +#> [66,] 0.51356621 0.6649191 0.7882149 0.8889239 0.9799349 1.0152143 1.0274019 +#> [67,] 0.91009161 1.1452958 1.1939267 1.2397482 1.2778259 1.3142367 1.3691782 +#> [68,] 0.43896058 0.6626579 0.7063940 0.7192270 0.7217734 0.7289212 0.7324372 +#> [69,] 0.89069032 0.9313217 0.9673234 0.9976411 0.9996854 1.0331601 1.0587833 +#> [70,] 0.33989198 0.4327614 0.5350378 0.6258618 0.6469255 0.7164036 0.7242920 +#> [71,] 0.38397973 0.3862299 0.4516049 0.4913964 0.4920131 0.5414338 0.5917343 +#> [72,] 1.00848047 1.0768269 1.0943501 1.1057459 1.1238643 1.1548307 1.2246481 +#> [73,] 0.49674503 1.1517222 1.1530540 1.1780126 1.2000227 1.2599610 1.2891031 +#> [74,] 0.95888418 1.4328568 1.4894015 1.6543722 1.6595918 1.6635901 1.6692800 +#> [75,] 0.20286252 0.2775229 0.3058515 0.4774863 0.4874333 0.5379454 0.5445072 +#> [76,] 0.56327896 0.6449463 0.8223549 0.8228872 0.9294344 0.9357540 1.0041295 +#> [77,] 0.42533361 0.4313687 0.5218097 0.6681459 0.6745087 0.7003972 0.7146743 +#> [78,] 0.31406018 0.4260126 0.4445334 0.5684864 0.5926834 0.6159768 0.6696777 +#> [79,] 0.58282320 0.5919036 0.6241585 0.6445785 0.6560100 0.7319469 0.7938257 +#> [80,] 0.19979753 0.5161356 0.5565107 0.5836934 0.5945889 0.6472249 0.6842989 +#> [81,] 1.81951854 1.9922656 2.0205986 2.0790480 2.0946907 2.1277494 2.1302952 +#> [82,] 0.55400715 0.5834456 0.7181371 0.7294277 0.7365553 0.7525134 0.7698621 +#> [83,] 0.38538472 0.4618722 0.6916650 0.7051790 0.7148987 0.7293677 0.7540948 +#> [84,] 0.47027993 0.5917161 0.6165220 0.6246316 0.6297133 0.7374528 0.7390052 +#> [85,] 0.72936937 0.8543112 0.9746944 1.0084146 1.1090694 1.1174333 1.1207087 +#> [86,] 0.59078288 0.6462234 0.6689212 0.6699648 0.6811840 0.6858087 0.6860345 +#> [87,] 0.27254531 0.3651141 0.3716844 0.3921957 0.4445022 0.4630358 0.4686448 +#> [88,] 0.66562908 0.8177474 0.8235249 0.9084179 0.9557719 1.0141622 1.0408833 +#> [89,] 0.31747977 0.4114420 0.4268198 0.6100400 0.6136000 0.6282401 0.6387592 +#> [90,] 0.43156060 0.4952567 0.5908792 0.6033091 0.6173670 0.6258699 0.6387592 +#> [91,] 0.31421513 0.5338823 0.5412133 0.5900256 0.6272943 0.6422183 0.6592550 +#> [92,] 0.81196536 0.9115486 0.9346957 0.9929918 1.0456172 1.0754124 1.0787575 +#> [93,] 0.64736887 0.7271914 0.7541340 0.7808913 0.7979371 0.8244759 0.8497188 +#> [94,] 0.49533234 0.6598227 0.7076012 0.7186929 0.7863789 0.8153246 0.8174131 +#> [95,] 0.34692452 0.5627264 0.6081151 0.6135268 0.6495568 0.6515160 0.6583607 +#> [96,] 0.39060104 0.4790116 0.4927501 0.4989591 0.5117631 0.5192327 0.5327137 +#> [97,] 0.55207656 0.5697016 0.6301777 0.6495728 0.6696607 0.7135412 0.8605164 +#> [98,] 0.63783218 0.7148571 0.7776584 0.8507334 0.9494797 1.0558888 1.0583920 +#> [99,] 0.39020552 0.4962837 0.5675716 0.5861157 0.5976980 0.6081151 0.6092434 +#> [100,] 0.89978391 0.9308147 0.9408754 0.9696101 0.9858472 0.9923786 1.0627488 +#> [101,] 0.55283471 0.5907829 0.5922631 0.6141458 0.6261074 0.6392327 0.6421244 +#> [102,] 0.35502072 0.4501754 0.6099109 0.6260703 0.6280210 0.6670661 0.6866346 +#> [103,] 0.37801086 0.4721603 0.4913964 0.5124604 0.5450710 0.6028122 0.6238292 +#> [104,] 0.34134019 0.5929541 0.6372357 0.7820931 0.7897595 0.8339584 0.8447999 +#> [105,] 0.54679682 0.9584708 0.9619867 0.9668903 1.1132133 1.1147389 1.1797309 +#> [106,] 0.42115831 0.5175675 0.5697016 0.7871844 1.0125592 1.0360698 1.0951413 +#> [107,] 0.37049110 0.4279698 0.7430611 0.7789587 0.7795453 0.7839516 0.7865491 +#> [108,] 0.37950826 0.5067155 0.6800699 0.6945929 0.7168134 0.7420902 0.7524010 +#> [109,] 0.31009228 0.4000469 0.4590026 0.5164836 0.5549018 0.6026765 0.6227651 +#> [110,] 1.01791160 1.0815005 1.0840109 1.2249167 1.2279099 1.2384120 1.2896179 +#> [111,] 0.51360556 0.5314622 0.5624893 0.5767651 0.5827102 0.6198561 0.6221631 +#> [112,] 0.70836509 0.7513724 0.7882149 0.8676796 0.8703300 0.9034353 0.9156993 +#> [113,] 0.49869553 0.5067155 0.5460506 0.5801585 0.6696494 0.7173692 0.7608483 +#> [114,] 0.40062803 0.7368107 0.8528844 0.8633145 0.8855950 0.9016987 0.9022142 +#> [115,] 0.39939033 0.5879220 0.6793512 0.6805757 0.7058957 0.7344912 0.7391128 +#> [116,] 0.31704666 0.3983268 0.5037551 0.5216523 0.5296416 0.6908122 0.7452978 +#> [117,] 0.37326479 0.5340094 0.5787218 0.5794222 0.7135245 0.7212985 0.7981541 +#> [118,] 0.58711013 0.6636078 0.6993385 0.7835613 0.7958064 0.8146638 0.8318024 +#> [119,] 0.38781056 0.6292556 0.6395271 0.6717917 0.6905921 0.7365392 0.7809893 +#> [120,] 0.51972264 0.5565621 0.5588898 0.6499334 0.6994073 0.7234893 0.7733899 +#> [121,] 0.52007377 0.7156661 0.7769445 0.8729608 0.9187279 0.9307765 0.9801876 +#> [122,] 0.37462522 0.5608964 0.5684864 0.5867999 0.6280210 0.6488748 0.6532210 +#> [123,] 0.28637481 0.5232306 0.5870548 0.5945889 0.6033274 0.6372778 0.6648775 +#> [124,] 1.46107996 1.6845896 1.6912515 1.6925196 1.7416590 1.7471112 1.8158605 +#> [125,] 0.34397845 0.3866957 0.3924534 0.4412515 0.5012188 0.5058887 0.5131453 +#> [126,] 0.37486905 0.4751257 0.5087351 0.5887325 0.7051331 0.7298008 0.7491495 +#> [127,] 0.29550828 0.3681418 0.5015297 0.6060122 0.6288299 0.6308819 0.6992010 +#> [128,] 0.21279658 0.3983268 0.4456823 0.4821855 0.5067848 0.5608770 0.5763736 +#> [129,] 0.33082271 0.3812432 0.4656621 0.5812384 0.6536513 0.7041940 0.7333086 +#> [130,] 0.59124589 0.7267236 0.7863414 0.8405639 0.8473076 0.8762039 0.8769079 +#> [131,] 0.29341112 0.3539795 0.5754289 0.5817286 0.6082906 0.6362094 0.6390304 +#> [132,] 0.38097849 0.4882768 0.5360774 0.7430517 0.7626059 0.8503862 0.8728612 +#> [133,] 0.34767639 0.5731660 0.6695476 0.8528346 0.8894494 0.8955452 0.9042629 +#> [134,] 0.67153583 0.7344912 0.7583382 0.7931788 0.8128573 0.8623701 0.9081630 +#> [135,] 1.14665624 1.2219094 1.2304212 1.3831278 1.4038045 1.4243506 1.5006148 +#> [136,] 0.44825377 0.6461841 0.7169382 0.7503173 0.7732450 0.7867631 0.8033412 +#> [137,] 0.26411020 0.4260126 0.4456823 0.4673949 0.4828882 0.5533188 0.6529094 +#> [138,] 0.52191310 0.5914450 0.5995905 0.6128027 0.6217932 0.6322603 0.6677923 +#> [139,] 0.58668618 0.6357454 0.6994073 0.7920784 0.8237250 0.8393177 0.8492497 +#> [140,] 0.29261567 0.4439449 0.5403259 0.5471188 0.5475349 0.5604020 0.5725399 +#> [141,] 0.25265749 0.4204692 0.4459950 0.4995554 0.5339018 0.5646986 0.5659258 +#> [142,] 0.44731276 0.4693085 0.4891228 0.4957363 0.5366771 0.5708846 0.5962150 +#> [143,] 0.43630431 0.5608964 0.5913644 0.6018143 0.6031855 0.6383166 0.6529094 +#> [144,] 0.55029717 0.6149651 0.6536513 0.6960493 0.7244467 0.7711151 0.8378862 +#> [145,] 0.46758775 0.5323747 0.6548214 0.6687391 0.6954821 0.6959315 0.6987302 +#> [146,] 0.56358593 0.6714041 0.7109351 0.7311678 0.7458959 0.7665936 0.7959432 +#> [147,] 1.27924469 1.3343597 1.4185485 1.4773048 1.7597464 1.7606057 1.7904207 +#> [148,] 0.69682063 0.8924923 0.9030081 0.9054818 1.0016268 1.0311475 1.0401675 +#> [149,] 0.47105967 0.5015533 0.5732198 0.5992964 0.6070402 0.6333816 0.6404007 +#> [150,] 1.08881693 1.1088618 1.1437963 1.2449947 1.2620592 1.2672230 1.3379752 +#> [151,] 0.60289777 0.6267536 0.6512268 0.8387839 0.8905984 0.9767534 1.0555368 +#> [152,] 0.38434510 0.5474227 0.5580321 0.6047128 0.6159771 0.6368901 0.6379932 +#> [153,] 0.76091799 1.0115355 1.0132570 1.0881376 1.0951688 1.1263791 1.1440797 +#> [154,] 0.42601353 0.7362994 0.7374528 0.9115949 0.9399216 0.9703215 0.9797734 +#> [155,] 0.55380071 0.5868136 0.8971739 0.9859653 0.9871506 1.0210517 1.0258789 +#> [156,] 0.38074638 0.5154705 0.8954715 0.9021617 0.9227504 1.0985167 1.1493428 +#> [157,] 0.33667180 0.3653168 0.3902051 0.4978214 0.5171986 0.6065168 0.6329947 +#> [158,] 0.35433111 0.4546433 0.5162549 0.5985664 0.6256440 0.6345276 0.7123310 +#> [159,] 0.46309918 0.4648272 0.4978386 0.5025315 0.6583607 0.6659381 0.7088686 +#> [160,] 0.43700057 0.4744331 0.5245809 0.5412125 0.5517345 0.5612849 0.5615328 +#> [161,] 0.26497463 0.3009462 0.3889990 0.4135342 0.4214572 0.4266499 0.4639095 +#> [162,] 0.42839864 0.5531608 0.5698067 0.6107278 0.6536423 0.7160076 0.7929313 +#> [163,] 0.35057975 0.4309376 0.5965018 0.7168258 0.7637036 0.7787982 0.7869576 +#> [164,] 0.37272716 0.6543357 0.6840445 0.7475963 0.8614908 0.9399279 0.9457978 +#> [165,] 0.28816770 0.3682785 0.3965755 0.4383710 0.4658402 0.4771445 0.5262443 +#> [166,] 0.49137345 0.6205306 0.6278612 0.7186285 0.7435339 0.7440504 0.7887943 +#> [167,] 0.32875221 0.4161305 0.4295132 0.4324372 0.4674339 0.4922360 0.5073235 +#> [168,] 0.34534255 0.4533731 0.4648813 0.4786173 0.5276847 0.5344793 0.5385578 +#> [169,] 0.23154039 0.4996064 0.5751151 0.6726457 0.6773536 0.7041913 0.7237772 +#> [170,] 0.52243421 0.7852364 0.8055764 0.8948322 0.9694110 1.0355170 1.0355223 +#> [171,] 0.57831635 0.6068026 0.7323271 0.8061708 0.9309761 0.9451110 0.9462643 +#> [172,] 0.35433111 0.4766277 0.5867573 0.5916953 0.6389778 0.6756414 0.7301892 +#> [173,] 0.39210704 0.5408687 0.5812758 0.5987257 0.6104140 0.6381541 0.7037034 +#> [174,] 0.60447661 0.6267536 0.7537042 0.7770416 0.7910509 0.7976067 0.8122335 +#> [175,] 0.37131688 0.4732668 0.6357584 0.7240616 0.7558192 0.8210686 0.8438541 +#> [176,] 0.38097849 0.6842423 0.6960493 0.7390536 0.7940312 0.8275767 0.8500671 +#> [177,] 0.61987271 0.9503676 0.9604526 1.1101986 1.1372493 1.1962238 1.1983213 +#> [178,] 0.86356366 0.8696071 0.8979664 0.9135622 0.9136056 0.9146832 0.9156993 +#> [179,] 0.90251624 1.2391223 1.2551132 1.3247592 1.3892696 1.4173955 1.5960577 +#> [180,] 0.65482141 0.6582334 0.6583989 0.6872535 0.7058957 0.7233368 0.7298838 +#> [181,] 0.47581826 0.5598178 0.6172547 0.6436547 0.7041913 0.7288602 0.7499769 +#> [182,] 0.46401044 0.5627313 0.5956964 0.6124989 0.6997715 0.7332486 0.7392277 +#> [183,] 0.31704666 0.3587281 0.6026883 0.6088547 0.6555160 0.7286017 0.7831765 +#> [184,] 0.78523641 0.9052024 0.9599744 1.0731588 1.0797704 1.1046482 1.1092652 +#> [185,] 0.26858368 0.3132308 0.3914943 0.4122333 0.4825606 0.4826539 0.4865361 +#> [186,] 0.42992809 0.4314484 0.4412957 0.5306193 0.5781613 0.6099109 0.6317479 +#> [187,] 0.32558630 0.4538033 0.4867114 0.5004484 0.5212093 0.5499718 0.5767945 +#> [188,] 0.27986585 0.4486950 0.6261074 0.6478857 0.6791720 0.6830734 0.7098652 +#> [189,] 0.35281360 0.4997944 0.5622268 0.6197868 0.6285987 0.6537978 0.6549309 +#> [190,] 0.27383529 0.3569462 0.3955108 0.4536409 0.4845361 0.5086319 0.6688656 +#> [191,] 0.43967830 0.4634355 0.5661557 0.6033091 0.6095929 0.6202265 0.6597499 +#> [192,] 0.44065919 0.4634355 0.5367478 0.5504818 0.5631576 0.5679693 0.6168765 +#> [193,] 0.35144341 0.4494418 0.4993466 0.5087696 0.5155247 0.5654223 0.5736881 +#> [194,] 0.22112194 0.3996389 0.5086507 0.5654880 0.5935245 0.5983421 0.6027282 +#> [195,] 0.80557643 0.9636475 0.9959560 1.0036229 1.2343316 1.2907756 1.2967553 +#> [196,] 0.39149426 0.4101772 0.4281875 0.4773816 0.4779779 0.4999843 0.5505670 +#> [197,] 0.55400715 0.7314181 0.7574815 0.7799084 0.7873526 0.8264940 0.8332343 +#> [198,] 0.50253147 0.6811196 0.6893140 0.8128573 0.8158326 0.8572884 0.9836412 +#> [199,] 0.72804450 0.8080975 0.8230186 0.8388021 0.9040104 0.9090553 0.9122911 +#> [200,] 0.36816787 0.5370509 0.5996214 0.6130515 0.7065292 0.7703608 0.7938257 +#> [201,] 0.63311158 0.7397344 0.7983179 0.8409059 0.8487797 0.8629053 0.8852884 +#> [202,] 0.35553709 0.3656315 0.3725824 0.4895812 0.4997822 0.5088901 0.5389288 +#> [203,] 0.65886490 0.7329002 0.7347903 0.7411261 0.7580537 0.8624063 0.8720974 +#> [204,] 0.55651070 0.5587885 0.5830566 0.5955075 0.6618140 0.7156208 0.7315282 +#> [205,] 1.12746773 1.1447031 1.1611430 1.2061711 1.2973666 1.2998117 1.4372318 +#> [206,] 1.04008113 1.0741286 1.0838163 1.0959222 1.1050789 1.1226859 1.1568236 +#> [207,] 0.21900050 0.2742296 0.3514434 0.4414084 0.4509631 0.4979605 0.4985219 +#> [208,] 0.21563769 0.3590978 0.3669434 0.3987107 0.4993616 0.5127696 0.5669487 +#> [209,] 0.55611113 0.5794918 0.7240616 0.7361423 0.8079934 0.8221198 0.8290688 +#> [210,] 0.20603309 0.2903457 0.3807584 0.3999190 0.4606817 0.4924360 0.5132561 +#> [211,] 0.35146422 0.4366775 0.4686757 0.4812793 0.5317383 0.5949025 0.6197411 +#> [212,] 0.41472370 0.5742801 0.6313599 0.6807774 0.6951039 0.7447867 0.7760244 +#> [213,] 0.24306608 0.2691089 0.4780553 0.5333403 0.5817011 0.6043088 0.6178096 +#> [214,] 0.34134019 0.5565621 0.5584969 0.6664560 0.6867161 0.8019273 0.8237250 +#> [215,] 0.46304774 0.7293677 0.7351532 0.7634713 0.8081023 0.8122335 0.8499778 +#> [216,] 0.40235927 0.4250036 0.5136656 0.5626172 0.5915929 0.5997113 0.6013005 +#> [217,] 0.36827211 0.4581167 0.5627529 0.6870920 0.6881646 0.7117143 0.7378429 +#> [218,] 0.33982358 0.5976765 0.7207886 0.7449855 0.8507334 0.9094096 0.9321405 +#> [219,] 0.73585058 0.9232594 0.9565458 1.0190818 1.0440702 1.0562080 1.0697942 +#> [220,] 0.55849685 0.5866862 0.6150630 0.6372357 0.6499334 0.6833969 0.6999140 +#> [221,] 0.63004997 0.6805731 0.7160076 0.7317062 0.7545404 0.7969842 0.7992506 +#> [222,] 0.44727767 0.5974994 0.7193678 0.7340550 0.7608659 0.7996507 0.7997487 +#> [223,] 0.90362672 0.9146832 0.9180192 0.9874210 0.9927248 1.0419432 1.1020623 +#> [224,] 0.46826716 0.6439832 0.7046757 0.7119260 0.7290976 0.7391057 0.7963469 +#> [225,] 0.49106116 0.5244854 0.6788071 0.7176650 0.7177654 0.7749013 0.7870105 +#> [226,] 0.32322310 0.3357362 0.4338062 0.6146114 0.6173491 0.6359929 0.6747795 +#> [227,] 0.28556909 0.4419782 0.4688051 0.4989591 0.5661041 0.5827281 0.6393244 +#> [228,] 0.43303596 0.5174087 0.5792943 0.5882164 0.6120067 0.6697537 0.6772856 +#> [229,] 0.24611185 0.3842782 0.3983348 0.4293245 0.4612341 0.4862838 0.5753730 +#> [230,] 0.39388994 0.4755652 0.5219131 0.5983490 0.6630029 0.6829525 0.6841384 +#> [231,] 0.30036760 0.3198414 0.3590978 0.4168866 0.4785825 0.4997144 0.5040527 +#> [232,] 0.30036760 0.3669434 0.3815305 0.5653624 0.5656248 0.5965183 0.5996629 +#> [233,] 0.50544474 0.5182948 0.5418878 0.5623380 0.5669282 0.5773719 0.5893375 +#> [234,] 0.31343717 0.4221985 0.5792943 0.6308612 0.6487788 0.6565327 0.6942257 +#> [235,] 0.32559725 0.4732668 0.6090162 0.6755251 0.7244467 0.7776300 0.8144492 +#> [236,] 0.42319471 0.4568666 0.5344793 0.5792409 0.5955864 0.6276450 0.6599847 +#> [237,] 0.46543336 0.4792014 0.5336176 0.5733939 0.5851822 0.6046285 0.6290967 +#> [238,] 0.42144588 0.5031239 0.6132328 0.6837926 0.7049319 0.7312486 0.7506026 +#> [239,] 0.46484263 0.5704845 0.7109351 0.7118330 0.7417987 0.7526987 0.7569339 +#> [240,] 1.09317617 1.1040206 1.1193103 1.2529562 1.2863927 1.2939069 1.3269674 +#> [241,] 0.69303540 0.8205277 0.9671439 0.9847831 1.0134838 1.0473081 1.1663187 +#> [242,] 0.78317649 0.8697816 0.8849601 0.9668492 1.0168150 1.0595519 1.1677669 +#> [243,] 0.35963054 0.3806475 0.3983348 0.4068962 0.4617965 0.4657141 0.5526758 +#> [244,] 1.26864772 1.6713056 1.7214903 1.7557255 1.7616370 1.7898021 1.8336938 +#> [245,] 0.23444596 0.2765470 0.2852333 0.2970063 0.4048737 0.4606817 0.4664689 +#> [246,] 0.47238131 0.4935583 0.5652610 0.6414176 0.6576266 0.6836239 0.8132373 +#> [247,] 0.78307298 0.8013535 0.8650611 0.8930465 0.9029668 0.9399767 0.9486970 +#> [248,] 0.51792634 0.6692384 0.7227889 0.7777086 0.7952843 0.8018866 0.9766410 +#> [249,] 0.31072384 0.3755687 0.4014202 0.4074870 0.4573402 0.5314506 0.5385851 +#> [250,] 0.36816787 0.4060399 0.5202421 0.5714498 0.5858707 0.5880933 0.5919036 +#> [251,] 0.60180716 0.6018285 0.9662172 0.9774597 0.9816729 1.0145107 1.0180882 +#> [252,] 0.43980417 0.4874431 0.5197226 0.6150630 0.6634790 0.6664560 0.7864254 +#> [253,] 0.44190042 0.4631435 0.5505767 0.5886008 0.6377367 0.6873164 0.7012542 +#> [254,] 0.39100186 0.7409369 0.7806299 0.8388192 0.9217313 0.9222943 0.9345085 +#> [255,] 0.34632790 0.3812432 0.5636713 0.6431622 0.6769138 0.6824575 0.6911725 +#> [256,] 0.54436133 0.5948165 0.7289802 0.7301984 0.7438620 0.7875841 0.7967487 +#> [257,] 0.57458790 0.5926552 0.6283424 0.6438393 0.6785323 0.6825467 0.7500568 +#> [258,] 1.27783404 1.2900982 1.2950464 1.4985037 1.5286311 1.5460156 1.5713194 +#> [259,] 0.21900050 0.3075903 0.3096341 0.3999190 0.4035350 0.4148944 0.4527564 +#> [260,] 0.40353499 0.4664689 0.4852165 0.4871731 0.4873610 0.4924360 0.5023658 +#> [261,] 0.41794591 0.5664209 0.5781964 0.5864097 0.6685038 0.6909388 0.8368906 +#> [262,] 0.48463135 0.6728008 0.8050962 0.8370144 0.8376331 0.8393648 0.8598953 +#> [263,] 0.32319175 0.4870720 0.4978386 0.5671789 0.6692245 0.6793512 0.6843618 +#> [264,] 0.19424836 0.2040277 0.4992867 0.5313602 0.6055320 0.6362094 0.6543068 +#> [265,] 0.36833863 0.4654334 0.5017085 0.5656454 0.6003747 0.6170498 0.6251389 +#> [266,] 0.58386724 0.6659381 0.6715358 0.6811196 0.6829526 0.7913083 0.8226197 +#> [267,] 0.53151133 0.5787218 0.6020524 0.6985332 0.7286656 0.7835030 0.7867163 +#> [268,] 0.64847362 0.6573676 0.6659433 0.7604574 0.7968490 0.8643554 0.9069869 +#> [269,] 0.49223996 0.5229711 0.5686139 0.6100679 0.6674642 0.6830877 0.6999881 +#> [270,] 0.47282196 0.5868136 0.8988946 0.9045820 0.9142488 0.9619491 0.9636785 +#> [271,] 0.56905828 0.7277196 0.7705092 0.8684019 0.8710113 0.8973821 0.8976672 +#> [272,] 0.32987731 0.5085852 0.6028978 0.6044766 0.6056317 0.7368727 0.7378429 +#> [273,] 0.57827386 0.6399215 0.6848409 0.7001828 0.7300695 0.7999589 0.8096686 +#> [274,] 0.38353125 0.4820388 0.5002889 0.5103997 0.5439551 0.5473826 0.5618381 +#> [275,] 0.31634825 0.3453730 0.4488479 0.4783741 0.5086319 0.5134158 0.6014621 +#> [276,] 0.42046922 0.4730321 0.6023254 0.6381665 0.6473181 0.6619429 0.6708163 +#> [277,] 0.75559906 0.8434938 0.9864432 1.0654337 1.0883128 1.1004546 1.1442323 +#> [278,] 0.23177341 0.3876206 0.4300517 0.4489872 0.4615879 0.4619930 0.5083644 +#> [279,] 0.17043615 0.6115139 0.6144349 0.7220758 0.7441820 0.9432558 0.9551188 +#> [280,] 0.31136211 0.4758183 0.5105971 0.5345327 0.6773536 0.7440504 0.7614294 +#> [281,] 1.28735206 1.4495158 1.5862658 1.5876154 1.6128527 1.6299672 1.6443932 +#> [282,] 0.29261567 0.4436452 0.5262098 0.5505027 0.5601840 0.5625149 0.5846818 +#> [283,] 0.24270294 0.3663440 0.3688916 0.6605804 0.6611236 0.8047881 0.8244194 +#> [284,] 0.34324427 0.3852723 0.4807027 0.5292069 0.5543009 0.5608770 0.5657912 +#> [285,] 0.62918180 1.0125592 1.1663187 1.2016624 1.2094716 1.2117365 1.2418795 +#> [286,] 0.29686347 0.5360774 0.7940312 0.8762371 0.9201335 0.9503676 0.9619168 +#> [287,] 0.40142020 0.4634532 0.4824160 0.5110722 0.5282545 0.6008158 0.6378858 +#> [288,] 0.53401809 0.6609695 0.7889064 0.7917791 0.8890348 0.8939098 0.9408754 +#> [289,] 0.38448622 0.4870720 0.5550794 0.5776140 0.5852819 0.5914036 0.6358499 +#> [290,] 0.21279658 0.3871573 0.4590166 0.5166758 0.5296416 0.5533188 0.5543009 +#> [291,] 0.35604642 0.5907578 0.6481719 0.6609695 0.7840776 0.8362565 0.8475800 +#> [292,] 0.32558630 0.4674089 0.5277063 0.5832308 0.6203573 0.6430868 0.6531550 +#> [293,] 0.58301167 0.6375903 0.6964726 0.7071698 0.7245649 0.7315685 0.7625511 +#> [294,] 0.27397800 0.4990195 0.5213306 0.5736358 0.5985232 0.6072090 0.6459071 +#> [295,] 0.09113993 0.3491467 0.6358499 0.6807774 0.6979795 0.7459699 0.7863417 +#> [296,] 0.64120027 0.8388852 0.8525389 0.9818075 1.0069490 1.0293006 1.0614266 +#> [297,] 0.46566207 0.5201454 0.5636713 0.5652610 0.6131437 0.6701657 0.6707414 +#> [298,] 0.57944873 0.5921952 0.6205306 0.6440024 0.6743616 0.6780611 0.7077063 +#> [299,] 0.35056102 0.4323315 0.5741677 0.6964146 0.7629855 0.7754890 0.8597179 +#> [300,] 0.59295405 0.6744941 0.6985332 0.6999140 0.7287384 0.7367904 0.7500358 +#> [301,] 0.58706661 0.5951729 0.6874442 0.7326062 0.7522365 0.8230275 0.8272819 +#> [302,] 0.36175024 0.4209899 0.4813064 0.5223277 0.5475791 0.6160059 0.6233547 +#> [303,] 0.63812120 0.6595335 0.6641357 0.7310347 0.7366293 0.7377899 0.7552492 +#> [304,] 0.28967890 0.4755652 0.5373530 0.6050027 0.7367644 0.7569945 0.7634534 +#> [305,] 0.39860107 0.5109188 0.5967728 0.6581444 0.6616765 0.6637005 0.6784733 +#> [306,] 0.35967592 0.3691845 0.3996389 0.4213417 0.5454917 0.5615328 0.5832308 +#> [307,] 0.39541408 0.5084299 0.5127088 0.5249234 0.5292019 0.5643657 0.5927760 +#> [308,] 0.72699233 1.0108327 1.0447182 1.0627107 1.1576053 1.1857721 1.2353420 +#> [309,] 0.72936937 0.7679370 0.8357712 0.9106449 0.9236965 1.0219507 1.0300689 +#> [310,] 0.38622987 0.4114420 0.4536031 0.5582578 0.6083764 0.6128921 0.6449446 +#> [311,] 0.22968159 0.3896564 0.4692602 0.5056829 0.6336309 0.6535046 0.6674393 +#> [312,] 0.49782138 0.6603331 0.6654162 0.6993385 0.7263683 0.7397532 0.7719971 +#> [313,] 0.94691536 1.0304489 1.2154122 1.2366640 1.2861856 1.3044798 1.3060265 +#> [314,] 0.34277683 0.5761198 0.5915459 0.5993767 0.6146927 0.6753620 0.7003972 +#> [315,] 0.50998616 0.5479445 0.6304955 0.7377899 0.7726233 0.8401201 0.8418575 +#> [316,] 0.35402695 0.4413732 0.4589638 0.4667636 0.5178635 0.5726849 0.6361679 +#> [317,] 0.54679682 1.1183533 1.1625312 1.2716053 1.2726941 1.2864367 1.3436808 +#> [318,] 0.78307298 0.8124752 0.8480663 0.8515825 0.8729608 0.9198239 1.0690410 +#> [319,] 0.30066148 0.4654537 0.4985146 0.5389288 0.5498350 0.5507061 0.6302704 +#> [320,] 0.31021064 0.3132308 0.4101772 0.4694301 0.4810949 0.5101946 0.5164189 +#> [321,] 0.75567952 0.7687906 0.8486365 0.8781762 0.8926911 0.9084179 0.9230808 +#> [322,] 0.34928645 0.3637145 0.3923042 0.4792014 0.5017085 0.5937359 0.6005207 +#> [323,] 0.58015850 0.6230936 0.6470833 0.7195649 0.7420902 0.7616721 0.7977826 +#> [324,] 0.79251236 0.9054818 0.9668903 1.0041571 1.0512183 1.1058004 1.1671591 +#> [325,] 0.60182850 0.9306545 0.9786673 1.0296871 1.0457754 1.1541700 1.2631363 +#> [326,] 0.64957283 0.7590596 0.7749350 0.7850495 0.7871844 0.7927290 0.8156598 +#> [327,] 0.42672090 0.5879220 0.6760600 0.6836464 0.6876713 0.7225715 0.7573887 +#> [328,] 0.91433588 0.9158306 0.9784031 1.2583021 1.2764300 1.3413194 1.5356199 +#> [329,] 1.01843729 1.2450534 1.4353399 1.4878778 1.4932276 1.5408669 1.5720413 +#> [330,] 0.53075473 0.5583400 0.6635461 0.8393648 0.8629734 0.9292874 0.9293687 +#> [331,] 0.26707323 0.2953271 0.3320452 0.4213417 0.4737726 0.4925101 0.5484479 +#> [332,] 0.28263796 0.4708422 0.5357388 0.5955864 0.6937404 0.7394286 0.7719971 +#> [333,] 0.30066148 0.4250036 0.4539085 0.4947684 0.4997310 0.5221296 0.5411184 +#> [334,] 0.60462854 0.6625574 0.6892968 0.7561637 0.7664300 0.7800571 0.7937415 +#> [335,] 0.35753455 0.3898205 0.4122781 0.4397189 0.4454174 0.4694301 0.5457032 +#> [336,] 0.71541443 0.7496531 0.7674448 0.7797565 0.8114114 0.8249625 0.8688823 +#> [337,] 0.44798338 0.4910612 0.5965018 0.6305607 0.8125750 0.8208785 0.8604156 +#> [338,] 0.59733251 0.6119211 0.7441820 0.7640671 0.8495275 0.9484934 0.9795572 +#> [339,] 0.34767639 0.4231106 0.7018844 0.7598041 1.1616720 1.1800716 1.1930159 +#> [340,] 0.25283902 0.5982736 0.6251264 0.6512282 0.6701734 0.6992505 0.8306882 +#> [341,] 0.62170404 0.7770250 0.8326278 0.8458111 0.9135953 1.0200667 1.0227937 +#> [342,] 0.33306395 0.4476540 0.4836014 0.5161005 0.5396148 0.5397691 0.5607260 +#> [343,] 0.51756753 0.5550128 0.6301777 0.7850495 0.7903039 0.8444186 0.8738890 +#> [344,] 0.46604194 0.6543357 0.6882759 0.7544034 0.8082659 0.8203685 0.8405639 +#> [345,] 2.08627568 2.2164901 2.3605552 2.4335841 2.4893736 2.7627295 2.8010679 +#> [346,] 0.13034995 0.4238135 0.4250519 0.4266499 0.5022303 0.5382494 0.5479820 +#> [347,] 0.34099743 0.4887354 0.5830566 0.6372778 0.6603331 0.6619228 0.6842989 +#> [348,] 0.51517039 0.5962294 0.6121996 0.6451439 0.6529798 0.6540016 0.6602214 +#> [349,] 0.26748597 0.4683940 0.5793739 0.6053595 0.6260703 0.6309714 0.6394856 +#> [350,] 0.76858466 1.1761635 1.2582593 1.2940643 1.5105286 1.6101623 1.6421377 +#> [351,] 0.76916011 0.7860783 0.7977826 0.8974579 0.9256936 0.9290629 0.9301239 +#> [352,] 0.52825894 0.5441858 0.5634698 0.5812371 0.6239770 0.6274126 0.6737094 +#> [353,] 0.58578655 0.7566644 0.7721850 0.7867631 0.7960317 0.8069427 0.8407096 +#> [354,] 0.58458911 0.6064475 0.7186929 0.7366293 0.8120694 0.8515900 0.9056615 +#> [355,] 0.52335784 0.5589289 0.5656257 0.5669487 0.5721419 0.5722827 0.6008707 +#> [356,] 0.34534255 0.4616561 0.5112414 0.5470238 0.5596469 0.5750012 0.5792409 +#> [357,] 0.43156060 0.4582670 0.5476464 0.5485833 0.5877672 0.6220212 0.6434446 +#> [358,] 0.73220521 0.8099764 0.8445454 0.9186037 0.9566207 0.9656704 1.0108327 +#> [359,] 0.31343717 0.5510211 0.6036509 0.6120067 0.6209275 0.6326844 0.6540326 +#> [360,] 0.36655786 0.3690922 0.5734929 0.5834994 0.5864097 0.6540622 0.7491659 +#> [361,] 0.76858466 1.0874815 1.1149484 1.1430548 1.2046681 1.2715879 1.2920916 +#> [362,] 0.19062462 0.3839844 0.4583266 0.4943451 0.5136656 0.5699897 0.5736296 +#> [363,] 0.28951401 0.4910873 0.6248821 0.6611893 0.6696607 0.7183489 0.7233776 +#> [364,] 0.42181553 0.4877192 0.5912459 0.6743616 0.7056267 0.7461754 0.8017895 +#> [365,] 0.76086589 0.7760302 0.8728537 0.9342568 0.9361687 0.9381583 0.9653260 +#> [366,] 0.13034995 0.3167348 0.3422170 0.3889990 0.4509631 0.4680016 0.4929429 +#> [367,] 0.57134342 0.7193678 0.8413470 0.8535926 0.8536220 0.8554087 0.8668236 +#> [368,] 0.63441274 0.6715617 0.8162489 1.0910088 1.1201771 1.1260417 1.1408357 +#> [369,] 0.48692180 0.5494740 0.5578253 0.7312511 0.8092917 0.8365102 0.8545020 +#> [370,] 0.30834417 0.3717870 0.5505670 0.5663475 0.5913376 0.6025436 0.6040373 +#> [371,] 0.37423009 0.4064087 0.5369217 0.5752935 0.6668281 0.7664228 0.7809228 +#> [372,] 0.54015491 0.5936938 0.6481829 0.6765533 0.7104502 0.8064179 0.8720588 +#> [373,] 0.43896058 0.4437918 0.4920131 0.5450710 0.5919320 0.6239316 0.6245348 +#> [374,] 0.44397251 0.5654979 0.6834941 0.6972669 0.7510266 0.7524010 0.7836004 +#> [375,] 0.66760280 0.7469794 0.9887768 1.0101594 1.1013004 1.1914199 1.2538680 +#> [376,] 0.59437982 0.6026883 0.6497441 0.6697146 0.6745087 0.7748304 0.7891744 +#> [377,] 0.66407082 0.7500568 0.7535016 0.8290688 0.8438541 0.9890276 1.0188957 +#> [378,] 0.41627299 0.5488501 0.6116729 0.6436547 0.6610865 0.6855123 0.7180888 +#> [379,] 0.69345760 0.7210034 0.8104446 0.8915143 0.9844797 1.0110316 1.0172064 +#> [380,] 0.76087780 0.8230186 0.9407532 0.9450609 0.9547859 0.9661827 0.9709767 +#> [381,] 0.40603994 0.5180424 0.5222549 0.5370509 0.5608112 0.5768237 0.5923872 +#> [382,] 0.73832735 1.0173604 1.1611430 1.1846141 1.1984804 1.2295837 1.2735942 +#> [383,] 0.53100533 0.5449393 0.5474227 0.5828232 0.5858707 0.6840361 0.6868059 +#> [384,] 0.57134342 0.5851683 0.6375299 0.6651148 0.6688456 0.6987302 0.6993011 +#> [385,] 0.27373125 0.2955083 0.5659957 0.6637059 0.6880188 0.6971506 0.7213557 +#> [386,] 0.24808700 0.3510083 0.3794034 0.3877919 0.4058981 0.4412515 0.4755460 +#> [387,] 0.43967830 0.4726402 0.4932683 0.5055582 0.5679693 0.5897617 0.6561252 +#> [388,] 0.84819084 1.1595079 1.1723107 1.1948092 1.2608081 1.2782794 1.2918580 +#> [389,] 0.32217673 0.3829084 0.4074870 0.4187954 0.4443748 0.4634532 0.4686448 +#> [390,] 0.52825894 0.5631576 0.6271125 0.6557014 0.6611893 0.6657764 0.6932175 +#> [391,] 0.45811669 0.5054670 0.5085852 0.6096429 0.6646026 0.7051882 0.7080762 +#> [392,] 0.46826716 0.6150778 0.6268764 0.6573377 0.7076093 0.8563626 0.9239508 +#> [393,] 0.95746914 1.0086629 1.0406724 1.0532450 1.1052959 1.1057283 1.1518480 +#> [394,] 0.29341112 0.4992867 0.5268116 0.5280320 0.5652156 0.5715258 0.6271125 +#> [395,] 0.22968159 0.3101792 0.3454661 0.5909986 0.5927128 0.6689339 0.6976758 +#> [396,] 0.36120649 0.4103122 0.6250605 0.6992505 0.7602595 0.8301865 0.8850193 +#> [397,] 0.28967890 0.3938899 0.7029221 0.7187057 0.7722017 0.8253453 0.8307571 +#> [398,] 0.75147404 0.7849116 0.9170330 0.9208913 0.9440306 0.9802764 0.9832832 +#> [399,] 0.24282195 0.5745879 0.6237231 0.7167928 0.7501435 0.7539793 0.7585124 +#> [400,] 0.44085544 0.6105890 0.6449463 0.6787343 0.7155752 0.7958527 0.8565443 +#> [401,] 0.33143558 0.3987107 0.4398471 0.4584524 0.4812428 0.5219024 0.5589289 +#> [402,] 0.33082271 0.5502972 0.6431622 0.6637005 0.6701657 0.6755251 0.7168742 +#> [403,] 0.56642090 0.6540622 0.8223332 0.8576855 0.8731733 0.8882479 0.9480153 +#> [404,] 0.26714363 0.3198414 0.4024143 0.4422043 0.5714739 0.5722827 0.5828092 +#> [405,] 0.30823539 0.4929429 0.5151064 0.5155247 0.5171986 0.5267013 0.5308653 +#> [406,] 0.38527232 0.4324372 0.4494279 0.4531303 0.4787794 0.4858446 0.4919623 +#> [407,] 0.27383529 0.2797752 0.3163482 0.4943825 0.5064923 0.5986820 0.6116729 +#> [408,] 0.38240555 0.8434938 0.8584295 0.8924923 0.9832103 1.1613203 1.2121301 +#> [409,] 0.57819639 0.6505936 0.8731733 0.9346957 0.9573444 1.0097077 1.0714018 +#> [410,] 0.52591639 0.6012587 0.7080196 0.7624489 0.7958698 0.8552217 0.8827765 +#> [411,] 0.36827211 0.4711137 0.4719894 0.4929756 0.6203573 0.6707708 0.6902118 +#> [412,] 0.23216678 0.3232231 0.3311819 0.5213843 0.5366771 0.5760775 0.5889128 +#> [413,] 0.58976169 0.6202265 0.6450097 0.6692384 0.6843983 0.7223482 0.7326062 +#> [414,] 0.24263568 0.4612341 0.4657141 0.4723813 0.5201454 0.6181234 0.6518934 +#> [415,] 0.36476374 0.5452436 0.5489041 0.6107487 0.6231284 0.6486865 0.6606610 +#> [416,] 0.60728448 0.6157924 0.6258618 0.7511654 0.7687882 0.7726233 0.7889009 +#> [417,] 0.26910893 0.3252515 0.4800824 0.5574661 0.5587419 0.5779394 0.5942108 +#> [418,] 1.12512471 1.3314374 1.4135245 1.4562553 1.5906817 1.6334235 1.6924701 +#> [419,] 0.62786122 0.6749549 0.6909721 0.7614294 0.7672396 0.8301462 0.8979768 +#> [420,] 0.22112194 0.3691845 0.5145285 0.5252724 0.5844169 0.6151094 0.6728585 +#> [421,] 0.27916247 0.4592070 0.4632790 0.4938952 0.5738974 0.5998774 0.6008497 +#> [422,] 0.50152971 0.5310501 0.5585375 0.6059301 0.6115119 0.6137666 0.6637059 +#> [423,] 0.53080944 0.5963091 0.6491302 0.6666116 0.6843983 0.6985799 0.7032316 +#> [424,] 0.27654704 0.2951427 0.3070175 0.3842054 0.4358474 0.4817677 0.4873610 +#> [425,] 0.97840314 1.7536070 1.8131297 2.0017496 2.1434535 2.1819352 2.2393017 +#> [426,] 0.36634397 0.4353989 0.4994289 0.5350266 0.7159151 0.7472615 0.7556795 +#> [427,] 0.70836509 1.0437403 1.1130162 1.2179101 1.2229333 1.3177732 1.3912272 +#> [428,] 0.55813831 0.5829462 0.5906029 0.6822739 0.7171960 0.8027568 0.8069427 +#> [429,] 0.40100564 0.4380599 0.4580945 0.4843531 0.5109188 0.5388405 0.5403259 +#> [430,] 0.57831635 0.5799145 0.6269897 0.6892983 0.6985799 0.7360508 0.7376631 +#> [431,] 0.44682494 0.4837806 0.5786943 0.6870117 0.7904464 0.8035448 0.8086243 +#> [432,] 0.38781056 0.4518521 0.5390001 0.5638302 0.6139850 0.6920587 0.7199420 +#> [433,] 0.47805534 0.5401356 0.5505767 0.5521143 0.5617291 0.6032702 0.6186321 +#> [434,] 0.64271917 0.7210034 0.7781986 0.8294768 0.8347201 0.9185371 0.9272091 +#> [435,] 0.78213116 0.8018866 0.8228872 0.8803566 1.0575084 1.0734745 1.0943855 +#> [436,] 0.42797926 0.5164301 0.5924125 0.6018320 0.6691727 0.6717369 0.6841384 +#> [437,] 0.46247887 0.5067058 0.6335023 0.6481829 0.6948617 0.7102101 0.7358506 +#> [438,] 0.21124475 0.8079009 0.8139015 0.8850193 0.9155204 0.9589698 1.0491241 +#> [439,] 0.25940823 0.3564569 0.3725663 0.3965755 0.4547488 0.4755460 0.4823437 +#> [440,] 0.40146034 0.5058961 0.5291942 0.5949664 0.6414176 0.6517776 0.6678141 +#> [441,] 0.43381139 0.5162549 0.5867573 0.6451439 0.6698491 0.6805422 0.7958064 +#> [442,] 0.27226466 0.7965124 0.8139015 0.9680754 1.0481851 1.0729468 1.1314613 +#> [443,] 0.67495487 0.6964098 0.7077063 0.7435339 0.8941679 0.9820487 0.9932132 +#> [444,] 0.42672090 0.4742585 0.6577665 0.6621273 0.7176650 0.7227302 0.7306703 +#> [445,] 0.27374113 0.3398920 0.4336246 0.4878851 0.5836934 0.7156208 0.7161151 +#> [446,] 0.49775036 0.5057079 0.5317534 0.6670638 0.6812482 0.7011247 0.7810805 +#> [447,] 0.19979753 0.4336246 0.5156453 0.5955075 0.6469255 0.6540243 0.6858037 +#> [448,] 0.57611984 0.6540326 0.6697537 0.7188448 0.7964828 0.8270086 0.8270891 +#> [449,] 0.35402695 0.5317214 0.5661899 0.6237384 0.6483689 0.6932376 0.7014995 +#> [450,] 0.37853720 0.5857865 0.6541418 0.6932700 0.7015655 0.7664324 0.7808781 +#> [451,] 0.49137345 0.6440024 0.6964098 0.7731717 0.8979768 0.9132668 0.9222713 +#> [452,] 0.34099743 0.3787914 0.6033274 0.6649191 0.6806684 0.7186290 0.8443421 +#> [453,] 0.32777468 0.4084296 0.5964598 0.6985484 0.7299506 0.7334949 0.7418415 +#> [454,] 0.24444572 0.2539434 0.3194772 0.3515583 0.4927501 0.5041885 0.5241539 +#> [455,] 0.55947715 0.6226706 0.7406254 0.7418054 0.8031751 0.8122835 0.8346231 +#> [456,] 0.57189888 0.7630114 0.7777086 0.8392729 0.8726374 0.9481810 0.9678224 +#> [457,] 0.41927666 0.4611625 0.6163551 0.6427449 0.6779266 0.6880188 0.7513486 +#> [458,] 0.39230424 0.5280235 0.5472313 0.5493735 0.6251389 0.6625574 0.6653248 +#> [459,] 0.77560656 0.9673221 1.3283529 1.4040438 1.4118502 1.4425507 1.4774432 +#> [460,] 0.61725469 0.7662297 0.7672396 0.7733261 0.7887943 0.8812575 0.9060264 +#> [461,] 0.43303596 0.5888637 0.7407218 0.8279811 0.8655723 0.8826947 0.9146768 +#> [462,] 0.42912632 0.4845361 0.6626579 0.6728326 0.6761037 0.6790748 0.7104630 +#> [463,] 0.41043967 0.4491331 0.4698574 0.5527091 0.5605946 0.5686139 0.5844173 +#> [464,] 0.72699233 0.8099764 0.9777157 1.2406322 1.2518960 1.3226513 1.3457507 +#> [465,] 0.57028752 0.5922631 0.7839516 0.7948862 0.7992506 0.8687603 0.9287249 +#> [466,] 0.52735350 0.5742801 0.5845891 0.6067881 0.6068605 0.6641357 0.6670932 +#> [467,] 0.28297282 0.4491331 0.4689097 0.6411230 0.6674642 0.6797679 0.6916542 +#> [468,] 0.20624216 0.5091761 0.5980774 0.6159974 0.6453148 0.6593648 0.7085769 +#> [469,] 1.19141986 1.5441863 1.5456936 1.7348901 1.8111149 1.8559001 1.9191438 +#> [470,] 1.41774477 1.4487873 1.4903795 1.6992258 1.7577959 1.7602376 1.8522810 +#> [471,] 0.29797572 0.4299281 0.5242352 0.5578253 0.6776225 0.7056283 0.7425035 +#> [472,] 0.38302962 0.5127490 0.5259164 0.5390705 0.6286051 0.6704237 0.7531162 +#> [473,] 0.50312387 0.5099862 0.6608039 0.7022452 0.7096227 0.7186290 0.7293509 +#> [474,] 0.32559725 0.3713169 0.7130241 0.7659304 0.7700191 0.7789670 0.7808682 +#> [475,] 0.66621597 0.7830272 0.7831751 0.8182106 0.8966966 0.9351781 0.9509069 +#> [476,] 0.31706208 0.4752431 0.5834994 0.6357454 0.6537978 0.6696843 0.7443812 +#> [477,] 0.63307436 0.8746795 0.9048494 1.0334246 1.0734693 1.0859972 1.1214003 +#> [478,] 0.24835875 0.2855691 0.4485579 0.5506574 0.5675909 0.5897147 0.5921158 +#> [479,] 0.67299483 0.6867017 0.6929659 0.7637374 0.8307571 0.8561672 0.8700257 +#> [480,] 0.35784179 0.5513518 0.5670087 0.7056283 0.7706672 0.8031751 0.8092917 +#> [481,] 0.48744310 0.5588898 0.5712633 0.5887559 0.6020757 0.6867161 0.6954605 +#> [482,] 0.64034442 0.7109808 0.7513486 0.7552150 0.7938263 0.8022374 0.9097428 +#> [483,] 0.65673910 0.6934576 0.7347903 0.7368107 0.7609579 0.8347201 0.8816674 +#> [484,] 0.46758775 0.5681887 0.5852819 0.6067881 0.6260452 0.6605836 0.6614731 +#> [485,] 0.47111371 0.4812793 0.4850204 0.5201222 0.5477646 0.5577346 0.5793739 +#> [486,] 0.43144845 0.4391736 0.5432727 0.5923682 0.6461551 0.6766045 0.7690241 +#> [487,] 0.37879138 0.4887354 0.5135662 0.7270004 0.7552587 0.7578103 0.8245740 +#> [488,] 0.31421513 0.4441723 0.4573402 0.4961898 0.5060788 0.5282545 0.5339018 +#> [489,] 0.65656076 0.7463260 0.7929313 0.8194621 0.9020570 0.9177525 0.9422374 +#> [490,] 0.30104083 0.3744047 0.4592138 0.5041361 0.5161984 0.5169359 0.5456778 +#> [491,] 0.96364754 1.1528099 1.1904007 1.2277327 1.2608628 1.3413665 1.3659466 +#> [492,] 0.27801534 0.4372083 0.4698574 0.5177640 0.5229711 0.5237595 0.6135268 +#> [493,] 0.49796048 0.5036863 0.5219024 0.5233578 0.5272305 0.5284553 0.5654223 +#> [494,] 0.98742099 1.0437403 1.2940643 1.3715098 1.3899578 1.3997916 1.4121160 +#> [495,] 0.44397251 0.7141122 0.7220758 0.7276547 0.7367931 0.7933537 0.7945902 +#> [496,] 0.78865536 0.8210686 0.8651220 0.8884697 0.9370270 1.0128090 1.0499836 +#> [497,] 0.44065919 0.4790116 0.4853619 0.5303881 0.5448443 0.6220982 0.6282401 +#> [498,] 0.34765312 0.4310536 0.5417388 0.5560209 0.5638302 0.5759559 0.5821031 +#> [499,] 0.62476464 0.6853526 0.7231728 0.7364530 0.7754434 0.7937415 0.8122482 +#> [500,] 0.86382960 0.9100916 0.9574691 0.9589698 0.9872226 1.0080292 1.0085337 +#> [501,] 0.44876414 0.5102163 0.5347131 0.5379716 0.5645399 0.5704845 0.6053305 +#> [502,] 0.41627299 0.6481719 0.6935705 0.6963693 0.6982418 0.7340551 0.7619814 +#> [503,] 0.32777468 0.4800625 0.6171153 0.6411230 0.6927322 0.7000504 0.7321665 +#> [504,] 0.35071989 0.3780109 0.4437918 0.5414338 0.6048693 0.6083764 0.6248821 +#> [505,] 0.33721916 0.4024143 0.4997144 0.5299611 0.5741004 0.6124989 0.6187411 +#> [506,] 0.29866995 0.5794222 0.6637734 0.6932376 0.7236006 0.7750488 0.7979371 +#> [507,] 0.21629621 0.2649746 0.3422170 0.3558626 0.4238135 0.4414084 0.4454174 +#> [508,] 0.28263796 0.4568666 0.4619486 0.7048069 0.7134353 0.7177440 0.7321665 +#> [509,] 0.48137672 0.4824022 0.5419171 0.5632433 0.5882164 0.5888637 0.6137666 +#> [510,] 0.46565599 0.4783741 0.5334498 0.5335707 0.6197868 0.6302965 0.6626189 +#> [511,] 1.08972495 1.4503325 1.4872049 1.6333087 1.6377813 1.6615663 1.6656201 +#> [512,] 0.40062803 0.7811736 0.9422343 0.9619867 0.9771794 0.9813070 1.0157656 +#> [513,] 0.24363560 0.7233368 0.9136292 0.9197246 0.9389733 0.9935159 1.0471151 +#> [514,] 0.57508362 0.8877721 0.9844797 1.0681754 1.0911114 1.1350714 1.1527929 +#> [515,] 0.33573616 0.5134158 0.5348730 0.5760775 0.6140883 0.6615503 0.6626189 +#> [516,] 1.34136645 1.3838227 1.4342775 1.4943989 1.5133858 1.5837403 1.6842036 +#> [517,] 0.31680663 0.5132193 0.5207735 0.5239781 0.5288084 0.6530337 0.6701734 +#> [518,] 0.33083441 0.3806689 0.4182686 0.4632790 0.5653137 0.6036509 0.6394856 +#> [519,] 0.31747977 0.4444787 0.4536031 0.6238292 0.6440949 0.6693203 0.6872239 +#> [520,] 0.63307436 0.6750776 0.6964146 0.7806849 0.7888541 0.9482153 0.9678648 +#> [521,] 0.43546948 0.4618722 0.4630477 0.4719894 0.6224500 0.6884615 0.7096870 +#> [522,] 0.27135405 0.4652345 0.4995554 0.5058957 0.5669282 0.6064614 0.6081116 +#> [523,] 0.24306608 0.3252515 0.4779854 0.5131453 0.5564435 0.5580321 0.5617291 +#> [524,] 0.40842964 0.4800625 0.5511625 0.5870548 0.6589856 0.6864307 0.7071698 +#> [525,] 0.86978163 1.0184373 1.2608628 1.2638072 1.3526222 1.4484502 1.4599987 +#> [526,] 0.23216678 0.3463831 0.4338062 0.4473128 0.6140883 0.6430809 0.6863041 +#> [527,] 0.73832735 1.0898115 1.1625550 1.2302076 1.2953288 1.3461254 1.3687894 +#> [528,] 0.64120027 0.7090600 0.7290976 0.8012898 0.8137912 0.8145357 0.8163358 +#> [529,] 0.31341527 0.3253701 0.5315113 0.5945311 0.6297303 0.6668281 0.7500358 +#> [530,] 1.14549023 1.2158055 1.3127295 1.4186153 1.4811360 1.5310853 1.5420888 +#> [531,] 0.39980361 0.4032872 0.4148328 0.4161305 0.4579549 0.5164189 0.5330906 +#> [532,] 0.37766750 0.4218155 0.5093474 0.6618992 0.6812482 0.6834770 0.6870574 +#> [533,] 0.27422963 0.3075903 0.3558626 0.3898205 0.4871731 0.5087696 0.5242883 +#> [534,] 0.38302962 0.5250651 0.5477875 0.6636078 0.7090112 0.7146490 0.7397532 +#> [535,] 1.12806137 1.1348424 1.1938812 1.4175963 1.5498325 1.5511832 1.6104801 +#> [536,] 0.41300116 0.5232306 0.5273535 0.5511625 0.5844169 0.6403226 0.6632456 +#> [537,] 0.48435314 0.5178139 0.5347131 0.5489041 0.5685398 0.5879447 0.6548786 +#> [538,] 0.49628373 0.5831077 0.6269285 0.6550708 0.6605836 0.6656679 0.6692245 +#> [539,] 0.35792720 0.6560213 0.7569771 0.7602595 0.7759812 0.7785397 0.8970102 +#> [540,] 0.76904320 1.4661106 1.4702632 1.4773048 1.5262415 1.5297211 1.5432717 +#> [541,] 0.44133972 0.5291942 0.6068363 0.6208701 0.7134594 0.7263323 0.7303278 +#> [542,] 0.62170404 0.6256440 0.6446191 0.7070516 0.7112517 0.7289212 0.7560268 +#> [543,] 0.24363560 0.6583989 0.8760443 0.8771099 0.8894776 0.8955944 0.9222511 +#> [544,] 0.65932573 0.8223836 0.8684019 0.8693571 0.8740993 0.8921200 0.9172862 +#> [545,] 0.71813707 0.8264940 0.9067110 1.0099780 1.1022822 1.2036360 1.2095566 +#> [546,] 0.21629621 0.3009462 0.3167348 0.4250519 0.4616561 0.5050785 0.5242883 +#> [547,] 0.45699431 0.4590581 0.5355108 0.5476464 0.5826571 0.6701486 0.6719392 +#> [548,] 0.35963054 0.5976765 0.6206061 0.6336101 0.6707414 0.6790821 0.6824575 +#> [549,] 0.58301167 0.5851683 0.6196863 0.6228291 0.6402223 0.6417850 0.6535752 +#> [550,] 0.27135405 0.4842020 0.4882325 0.5237595 0.5327186 0.5418878 0.5643148 +#> [551,] 0.80281248 0.8087226 0.9071681 0.9365881 0.9549767 0.9721787 1.0265847 +#> [552,] 0.71862846 0.7597058 0.8234266 0.8728537 0.9222713 0.9789055 1.0440381 +#> [553,] 0.45094915 0.8421501 0.8582952 0.8886290 0.9141369 0.9763113 0.9821884 +#> [554,] 0.13934368 0.2428452 0.3342411 0.3651141 0.3679166 0.3829084 0.4805272 +#> [555,] 0.27237342 0.4441723 0.5132193 0.5338823 0.6621273 0.6778590 0.6797562 +#> [556,] 0.34658311 0.3844862 0.5832709 0.6088823 0.6749278 0.6793633 0.6951039 +#> [557,] 0.39100186 0.8816674 0.9568694 0.9716891 1.0118846 1.0173781 1.0519565 +#> [558,] 0.31072384 0.4064494 0.4824160 0.4943451 0.4961898 0.5423066 0.5552995 +#> [559,] 0.36175024 0.4205963 0.4667636 0.5322349 0.5562671 0.5765295 0.5846818 +#> [560,] 0.37049110 0.7604475 0.7650716 0.8009941 0.8012898 0.8748209 0.8785264 +#> [561,] 0.21399890 0.5146832 0.5962294 0.6301110 0.6329947 0.6412104 0.6431744 +#> [562,] 0.33083441 0.4327513 0.4589491 0.5305996 0.5625505 0.6008497 0.6177383 +#> [563,] 0.39939033 0.5776140 0.6556661 0.6749278 0.6872535 0.7165905 0.7329333 +#> [564,] 0.54723126 0.5513452 0.6829526 0.7028998 0.7263323 0.7384764 0.7391128 +#> [565,] 0.50873514 0.5717532 0.6554691 0.6807494 0.6955825 0.7046757 0.7062653 +#> [566,] 0.33143558 0.4451621 0.4826764 0.4883416 0.5127696 0.5912516 0.5924125 +#> [567,] 1.41926620 1.4453761 1.5806618 1.6063033 1.6172418 1.6276178 1.6809096 +#> [568,] 0.32319175 0.4648272 0.5217926 0.5503828 0.5627264 0.6550708 0.7109863 +#> [569,] 0.56700865 0.5871101 0.6226706 0.6274324 0.6453148 0.7142274 0.7158303 +#> [570,] 0.43535072 0.4501754 0.5306193 0.5830972 0.5962874 0.6341743 0.6489536 +#> [571,] 0.13267935 0.1393437 0.4187954 0.4336526 0.4364860 0.4371461 0.4445022 +#> [572,] 0.36563150 0.4882325 0.5004472 0.5279354 0.5432098 0.5498350 0.5527091 +#> [573,] 0.50642499 0.5753730 0.6208701 0.6528180 0.6671471 0.6804542 0.6851323 +#> [574,] 0.48378059 0.6244787 0.6473689 0.6603144 0.7580379 0.7597058 0.7760302 +#> [575,] 0.37185869 0.3924534 0.4451621 0.5187923 0.5219579 0.5244126 0.5347960 +#> [576,] 0.23445424 0.2852333 0.4086348 0.4174691 0.4786173 0.4985297 0.5030537 +#> [577,] 0.37256625 0.3725824 0.4192504 0.4744331 0.5042462 0.5308093 0.5538165 +#> [578,] 0.37326479 0.5838672 0.6528180 0.6782440 0.7643309 0.7686223 0.7750488 +#> [579,] 0.47903449 0.5110472 0.5121367 0.5338226 0.6714041 0.6867017 0.7523310 +#> [580,] 0.42311058 0.6695476 0.7311306 0.9776841 1.0525541 1.0632890 1.0634033 +#> [581,] 0.57508362 0.6840445 0.8620530 0.9108403 0.9978395 1.0064777 1.0194699 +#> [582,] 0.76091799 0.7711151 0.8032878 0.8050413 0.8144492 0.8472938 0.9161961 +#> [583,] 0.23444596 0.3015421 0.3070175 0.4174691 0.4489872 0.4617600 0.4706032 +#> [584,] 0.27226466 0.8079009 0.8499023 1.0784853 1.0893290 1.1458741 1.1687427 +#> [585,] 0.44220434 0.4669321 0.5040527 0.5766456 0.6187411 0.6245348 0.6274126 +#> [586,] 0.29866995 0.5317214 0.6603144 0.7035090 0.7042937 0.7435667 0.8003562 +#> [587,] 0.27397800 0.3872981 0.5268575 0.5447722 0.5489092 0.6181197 0.6198561 +#> [588,] 0.23154039 0.5680358 0.6208568 0.6608245 0.7288602 0.7413602 0.7439166 +#> [589,] 0.37440467 0.5032150 0.5277032 0.5510784 0.5646986 0.5658666 0.5828211 +#> [590,] 0.64620569 0.6604637 0.7526997 0.7527220 0.8270891 0.8307374 0.8406601 +#> [591,] 0.40406787 0.4514422 0.5745288 0.5820708 0.6177383 0.6246316 0.6402169 +#> [592,] 0.38434510 0.3900757 0.3932452 0.5887325 0.6267533 0.6392327 0.6516217 +#> [593,] 0.38066893 0.4327513 0.4683940 0.4974019 0.5355726 0.6497441 0.7012273 +#> [594,] 0.38085583 0.4536409 0.4904569 0.5187908 0.5235230 0.5986820 0.6696586 +#> [595,] 0.53502656 0.6239770 0.6597689 0.6757738 0.7233776 0.7877170 0.8047881 +#> [596,] 0.27831665 0.3010408 0.3120374 0.3287522 0.3432095 0.4494279 0.4723983 +#> [597,] 0.37616048 0.6107151 0.6290967 0.6931054 0.6972586 0.7213988 0.7268775 +#> [598,] 0.76376970 0.8028125 1.4043754 1.4072762 1.4190972 1.4209938 1.4947212 +#> [599,] 0.44182012 0.7566974 0.8450309 0.8655723 0.9357569 1.0665545 1.0948709 +#> [600,] 0.55397237 0.7960216 0.8821900 0.9143359 0.9184174 0.9304678 0.9681963 +#> [601,] 0.45940455 0.6344127 0.7640671 0.8751113 0.9069941 0.9339558 0.9425497 +#> [602,] 0.24263568 0.4617965 0.4862838 0.4935583 0.5058961 0.5164612 0.6160175 +#> [603,] 0.24835875 0.3899307 0.4419782 0.4938952 0.5140316 0.5292019 0.6025951 +#> [604,] 0.33306395 0.4201660 0.5164836 0.5178701 0.5361367 0.5477875 0.5745749 +#> [605,] 0.40116324 0.4737726 0.4747191 0.4929756 0.5054670 0.5098325 0.5183205 +#> [606,] 0.26497430 0.6516321 0.7057181 0.7326479 0.8690916 0.9142811 0.9759745 +#> [607,] 0.43233153 0.6705407 0.7719599 0.7760562 0.7888541 0.8285201 0.8285825 +#> [608,] 0.21124475 0.7965124 0.8499023 0.8638296 0.9862154 1.0242589 1.0566198 +#> [609,] 0.17043615 0.5791031 0.6119211 0.7568780 0.7933537 0.9401932 0.9784879 +#> [610,] 1.27251818 1.4007409 1.5872242 1.6502714 1.6759689 1.7931794 1.7962503 +#> [611,] 0.35056102 0.5386376 0.6705407 0.8652738 0.8902867 0.9165879 0.9442007 +#> [612,] 0.36081713 0.4053927 0.4144134 0.4205963 0.4209899 0.4413732 0.5342375 +#> [613,] 0.32998788 0.4780872 0.5829748 0.6451118 0.6516321 0.6681459 0.7481556 +#> [614,] 0.35146422 0.3814274 0.5201222 0.7057621 0.7107186 0.7137567 0.7610594 +#> [615,] 0.36291605 0.4214459 0.4391736 0.6600433 0.6886020 0.7072703 0.7096227 +#> [616,] 0.34646260 0.4265521 0.4487823 0.4702363 0.4947684 0.4957363 0.4985146 +#> [617,] 0.62805720 0.6972586 0.7227889 1.0564521 1.0855136 1.0942662 1.1260716 +#> [618,] 0.31136211 0.5369502 0.5598178 0.5751151 0.6118213 0.6281894 0.6608245 +#> [619,] 1.11726240 1.3848146 1.6063874 1.6113179 1.6482571 1.8290001 1.8811300 +#> [620,] 0.38538472 0.4354695 0.4614661 0.6278003 0.7072703 0.7150355 0.7591022 +#> [621,] 0.36424210 0.4117843 0.4445334 0.5619969 0.5867999 0.5987444 0.6254579 +#> [622,] 0.42107740 0.5226775 0.6905425 0.8072431 0.8114114 0.8218081 0.8353734 +#> [623,] 0.90251624 0.9179658 1.0056713 1.0084805 1.1554840 1.3880295 1.4135907 +#> [624,] 0.20286252 0.3610021 0.4372544 0.4740533 0.4997761 0.5182454 0.5524235 +#> [625,] 0.25229778 0.4824022 0.5822296 0.6059301 0.6334506 0.6447556 0.6600611 +#> [626,] 0.55947715 0.5916953 0.6345276 0.6698491 0.7170007 0.7471335 0.7557454 +#> [627,] 0.46573585 0.4878851 0.5195055 0.5201538 0.5315147 0.5350378 0.6540243 +#> [628,] 0.93623884 1.0977995 1.1947872 1.2876165 1.2998117 1.4436202 1.4755800 +#> [629,] 0.44825377 0.5195055 0.5581383 0.5637833 0.5975474 0.5977062 0.6021255 +#> [630,] 0.37536730 0.5164301 0.5531608 0.6666422 0.7220469 0.7574397 0.7691004 +#> [631,] 0.32903489 0.3839844 0.4796330 0.5315577 0.5368963 0.5412133 0.5453723 +#> [632,] 0.49740194 0.5447744 0.6280775 0.6527705 0.6697146 0.7372376 0.7830272 +#> [633,] 1.11726240 1.2423258 1.5862658 1.6544705 1.6930723 1.7550223 1.8218150 +#> [634,] 0.27820372 0.3253701 0.3660151 0.5369217 0.6020524 0.7003384 0.7089577 +#> [635,] 0.23445424 0.4048737 0.4322466 0.4608005 0.4648813 0.4977342 0.5050785 +#> [636,] 0.40640874 0.5834456 0.6100679 0.6851942 0.7146722 0.7241467 0.7301356 +#> [637,] 0.46252459 0.6297303 0.6331314 0.7003384 0.8112534 0.8124345 0.8651220 +#> [638,] 0.60088987 0.6339919 0.6388741 0.6474137 0.6474838 0.6696586 0.6988707 +#> [639,] 0.37734773 0.4590026 0.5390705 0.5921952 0.6671026 0.6974902 0.7090112 +#> [640,] 0.21399890 0.4031256 0.5151704 0.5494136 0.5664058 0.6006307 0.6424917 +#> [641,] 0.29207978 0.5401549 0.5474909 0.5524235 0.5796832 0.6059761 0.6585259 +#> [642,] 0.29137886 0.5112414 0.5187908 0.5307719 0.5494953 0.5495123 0.5756728 +#> [643,] 0.44085544 0.5514176 0.5632790 0.7421846 0.7821312 0.7895657 0.7912623 +#> [644,] 0.35281360 0.3912673 0.4989607 0.5335707 0.5414298 0.6156885 0.6527913 +#> [645,] 0.78117363 0.8633145 1.0734705 1.0875151 1.0911114 1.1794586 1.2188109 +#> [646,] 0.69162439 0.7439166 0.9022142 0.9147525 0.9972726 1.0179116 1.0717781 +#> [647,] 0.58197751 0.7391057 0.8008340 0.9239508 0.9941564 1.0069490 1.0591957 +#> [648,] 0.25940823 0.2881677 0.4069067 0.4167995 0.4490281 0.5004484 0.5369063 +#> [649,] 0.43361530 0.4576543 0.5310053 0.5408687 0.5907444 0.6274188 0.6276467 +#> [650,] 0.55397237 0.7664153 0.9252118 0.9803168 1.0258493 1.0333822 1.0508506 +#> [651,] 0.25537990 0.3510083 0.3896725 0.4092183 0.4114427 0.5009519 0.5058887 +#> [652,] 0.50856221 0.6573377 0.6668035 0.7638306 0.8564216 0.8851545 0.9487532 +#> [653,] 0.72771956 0.7701128 0.8085942 0.8565908 0.8612755 0.8616950 0.9033424 +#> [654,] 0.63004997 0.6790883 0.6930265 0.7471025 0.7796087 0.8001579 0.8162489 +#> [655,] 0.62415853 0.6790883 0.8149223 0.8164451 0.8245908 0.8249625 0.8542619 +#> [656,] 0.36371447 0.5336176 0.5416509 0.5799145 0.5927017 0.5963091 0.5968608 +#> [657,] 0.26592251 0.3839797 0.5314992 0.5582578 0.6573676 0.6859789 0.6869186 +#> [658,] 0.96860515 1.5596684 1.7214903 1.8390047 1.8732701 2.0095196 2.0340435 +#> [659,] 0.33992786 0.6787343 0.7953610 0.7995270 0.8406601 0.8451771 0.8946463 +#> [660,] 0.57850160 0.6149651 0.6331314 0.7958120 0.8032878 0.8500671 0.8728612 +#> [661,] 0.59645976 0.6882759 0.7294277 0.7314181 0.7398650 0.7451284 0.7905992 +#> [662,] 0.51562884 0.6948035 0.6954605 0.7612766 0.7820931 0.7821108 0.8110738 +#> [663,] 0.63575836 0.6640708 0.7659304 0.7776300 0.8079934 0.8763540 0.9216423 +#> [664,] 0.32642149 0.5204109 0.5661041 0.5980774 0.6062228 0.6408244 0.6936385 +#> [665,] 0.34583666 0.5094248 0.5822296 0.6019879 0.6508259 0.6516076 0.6744814 +#> [666,] 0.43381139 0.4546433 0.5788702 0.6756414 0.7471335 0.8011819 0.8455020 +#> [667,] 0.53467357 0.5373530 0.5983490 0.5995905 0.6462057 0.7008084 0.7061636 +#> [668,] 0.37654869 0.4283986 0.5094248 0.6696494 0.7168134 0.7303654 0.7825482 +#> [669,] 0.45033714 0.5345327 0.5369502 0.5948893 0.5985664 0.6389778 0.6726457 +#> [670,] 0.83931843 0.8628560 0.9748641 0.9761721 1.0343402 1.0928670 1.0943788 +#> [671,] 0.36481889 0.3875701 0.4533006 0.4579549 0.5029217 0.5244214 0.5263868 +#> [672,] 0.53423748 0.6205033 0.6237384 0.6597708 0.6637734 0.6859404 0.7042937 +#> [673,] 0.22512765 0.3954141 0.4057245 0.4066714 0.5754289 0.5981497 0.6459042 +#> [674,] 0.41796157 0.5450184 0.5485833 0.5590485 0.5908792 0.5971899 0.6076136 +#> [675,] 0.41441336 0.4468249 0.4590581 0.4647662 0.5223277 0.5907237 0.6244787 +#> [676,] 0.35057975 0.6053708 0.6305607 0.7109863 0.7912119 0.8047230 0.8227136 +#> [677,] 0.47357398 0.6408472 0.7471467 0.7822333 0.8163211 0.8184313 0.8571742 +#> [678,] 0.36291605 0.4614661 0.5432727 0.5830972 0.5858808 0.6132328 0.6351667 +#> [679,] 0.34692452 0.4372083 0.5217926 0.5248431 0.5675716 0.5873715 0.6131153 +#> [680,] 0.47956545 0.7061636 0.7550567 0.7569945 0.8113778 0.8205277 0.8486483 +#> [681,] 0.69219525 0.7959432 0.7989554 0.8713739 0.8858275 0.8900467 0.9009966 +#> [682,] 0.23817426 0.2808169 0.3592144 0.5177640 0.5288084 0.5930796 0.5983421 +#> [683,] 0.38064751 0.4014603 0.4293245 0.4413397 0.5164612 0.5171589 0.5566918 +#> [684,] 0.27374113 0.4327614 0.5156453 0.5587885 0.6472249 0.7160519 0.7567857 +#> [685,] 0.36601515 0.3742301 0.4121055 0.5945311 0.7146722 0.7312741 0.7746565 +#> [686,] 0.52802346 0.5513452 0.6314889 0.6345006 0.7183474 0.7496084 0.7939503 +#> [687,] 0.50375515 0.5870666 0.6666116 0.7169169 0.7286017 0.8465621 0.8663543 +#> [688,] 0.24611185 0.3370508 0.5064250 0.5171589 0.5526758 0.6160175 0.6205033 +#> [689,] 0.41794591 0.6505936 0.6588649 0.7485899 0.7491659 0.7548487 0.8576855 +#> [690,] 0.48897230 0.5338226 0.5631145 0.6876064 0.7249473 0.7610390 0.7665936 +#> [691,] 0.25265749 0.3648189 0.4231851 0.4983350 0.5054447 0.5058957 0.5379967 +#> [692,] 0.37194528 0.3896725 0.4518190 0.4888065 0.5286499 0.5368963 0.5411184 +#> [693,] 0.42115831 0.5520766 0.5550128 0.9107576 0.9213139 0.9311899 1.0733480 +#> [694,] 0.30665876 0.3717870 0.3719453 0.4997310 0.5480230 0.6036892 0.6316852 +#> [695,] 0.27235215 0.3682785 0.3877919 0.4563907 0.4686757 0.4790775 0.5102164 +#> [696,] 0.44798338 0.5244854 0.8689665 0.9423840 0.9442252 0.9472707 0.9680754 +#> [697,] 0.27562530 0.3560464 0.6615503 0.7733899 0.7821108 0.7931469 0.8066735 +#> [698,] 0.64271917 0.8104446 0.8518589 0.9819177 1.0527555 1.0704130 1.1284419 +#> [699,] 0.37536730 0.4279793 0.5698067 0.6805403 0.6930791 0.7037863 0.7775509 +#> [700,] 0.77560656 0.8556852 1.1021752 1.1159335 1.1821154 1.2422865 1.2640127 +#> [701,] 0.54944796 0.6858087 0.7160519 0.7161151 0.7514740 0.8011942 0.8040033 +#> [702,] 0.43725443 0.4518521 0.5293771 0.5417388 0.5617081 0.5693952 0.5809123 +#> [703,] 0.27373125 0.3681418 0.5585375 0.5629239 0.6276305 0.6427449 0.6776797 +#> [704,] 0.44727767 0.6573590 0.7268775 0.7276097 0.7664300 0.7956606 0.8151043 +#> [705,] 0.48215564 0.7168258 0.7797592 0.8584295 0.8881306 0.9258788 1.1747701 +#> [706,] 0.32987731 0.5539253 0.6512268 0.6805731 0.7051882 0.7770416 0.8028310 +#> [707,] 0.34290313 0.3916555 0.4384343 0.4439449 0.4451151 0.4652345 0.4779779 +#> [708,] 0.72429204 0.8485679 0.9030081 1.0299906 1.0379055 1.0509786 1.0511232 +#> [709,] 0.26411020 0.5763736 0.5913644 0.6009726 0.6159768 0.6332756 0.6387827 +#> [710,] 0.59236817 0.6317479 0.6552598 0.6905037 0.7620866 0.7751092 0.7835180 +#> [711,] 0.49777424 0.4986955 0.5246994 0.6800699 0.6932700 0.7195649 0.7310794 +#> [712,] 0.30585146 0.3569595 0.3610021 0.4310536 0.5617081 0.6139850 0.6173314 +#> [713,] 0.22730905 0.3101792 0.3896564 0.6595335 0.6614731 0.6616534 0.7026276 +#> [714,] 0.73113062 0.7404447 0.7598041 0.8894494 0.9218340 0.9772646 1.0149214 +#> [715,] 0.53546582 0.6488153 0.6827288 0.7065292 0.7176423 0.7196598 0.7858885 +#> [716,] 0.31706208 0.3824672 0.4997944 0.5211668 0.5414298 0.5734929 0.6104140 +#> [717,] 0.29573626 0.5202421 0.5274417 0.5923872 0.6130515 0.6268764 0.6668035 +#> [718,] 0.51787090 0.6681381 0.6921952 0.6945929 0.7616721 0.7728200 0.7767504 +#> [719,] 0.44511515 0.4773816 0.5208903 0.5221296 0.5708846 0.6036892 0.6071663 +#> [720,] 0.31021064 0.3667173 0.4148328 0.5036602 0.5388405 0.5443826 0.5743608 +#> [721,] 0.76376970 1.4945173 1.5618344 1.6583313 1.7226438 1.7337409 1.8270387 +#> [722,] 0.57910311 0.6115139 0.6685038 0.8223332 0.8940572 0.9323082 0.9573444 +#> [723,] 0.40406787 0.4589491 0.5738974 0.6282063 0.6353663 0.6645386 0.6905037 +#> [724,] 0.26361352 0.3612065 0.5829462 0.6409314 0.6512282 0.6560213 0.6666938 +#> [725,] 0.37766750 0.4877192 0.4977504 0.6141899 0.6374305 0.6463524 0.7035879 +#> [726,] 0.40235927 0.7095820 0.7211537 0.7390536 0.7565762 0.8014326 0.8093141 +#> [727,] 0.25229778 0.5443461 0.5480230 0.5517974 0.5632433 0.6230362 0.6285004 +#> [728,] 0.60180716 0.6345307 0.7319469 0.7498497 0.7510102 0.7522254 0.7795453 +#> [729,] 0.79251236 0.9169129 0.9624720 1.0016268 1.1132133 1.1183533 1.1703586 +#> [730,] 0.21651187 0.3307715 0.3902055 0.4048998 0.4300517 0.5080894 0.5970754 +#> [731,] 0.66354613 0.8080975 0.8122482 0.8448919 0.8598953 0.8703998 0.8736572 +#> [732,] 0.76793703 0.9746944 1.0603844 1.1809858 1.3134783 1.3312986 1.4184369 +#> [733,] 1.41806022 1.4979673 1.5334406 1.6314486 1.6429539 1.7030105 1.7172150 +#> [734,] 0.35753455 0.3949244 0.4214572 0.4281875 0.4408832 0.4810949 0.4826539 +#> [735,] 0.16898732 0.4774863 0.4997761 0.5450184 0.5577090 0.5654247 0.5790945 +#> [736,] 0.27779408 0.4117843 0.4624789 0.4869968 0.5152503 0.5926834 0.6532210 +#> [737,] 0.45094915 0.7870609 0.8365102 0.9197372 0.9669580 0.9771254 1.0148486 +#> [738,] 0.33982358 0.7319139 0.7776584 0.7905012 0.8014663 0.8392729 0.9160105 +#> [739,] 0.39752836 0.4628165 0.5139286 0.5557389 0.6299496 0.6648775 0.6892268 +#> [740,] 0.35502072 0.5015861 0.5160277 0.6083196 0.6489536 0.6874109 0.6959152 +#> [741,] 0.35694620 0.4291263 0.5064923 0.6014621 0.6239316 0.6610865 0.6857948 +#> [742,] 0.54794447 0.5909986 0.6224500 0.6278003 0.6351667 0.6394549 0.6607352 +#> [743,] 0.53400936 0.5574238 0.7236006 0.7286656 0.7608778 0.7813525 0.8909473 +#> [744,] 0.81247524 1.0111774 1.0421754 1.0457865 1.1062559 1.1693766 1.2517755 +#> [745,] 0.33705076 0.3842782 0.4068962 0.5566918 0.5967728 0.6206061 0.6247646 +#> [746,] 0.57869430 0.6643098 0.6963693 0.7499769 0.7532504 0.7787472 0.7840776 +#> [747,] 0.34583666 0.3765487 0.6485353 0.6536423 0.6600611 0.6950010 0.7608483 +#> [748,] 0.55392527 0.6011789 0.6107278 0.6565608 0.6666422 0.7067511 0.7295667 +#> [749,] 1.28096940 1.3035623 1.4027108 1.4503325 1.5398309 1.6228690 1.6251855 +#> [750,] 0.75682393 0.9759578 1.0086455 1.0171610 1.0781083 1.0961189 1.1095360 +#> [751,] 0.47282196 0.5538007 0.6370667 0.7604574 0.8299728 0.8469233 0.8905465 +#> [752,] 0.50321505 0.6012756 0.6440738 0.6763688 0.6811249 0.6878932 0.7024877 +#> [753,] 1.12512471 1.5002459 1.6186691 1.6746142 1.6971242 1.7244525 1.7529364 +#> [754,] 0.19126137 0.2539434 0.3530391 0.4211737 0.4610017 0.4735912 0.4812976 +#> [755,] 0.74738137 0.8490869 0.8598790 0.8819419 0.8834692 0.8956083 0.9020044 +#> [756,] 0.40863482 0.4201660 0.4875000 0.4977342 0.5139286 0.5250651 0.5548998 +#> [757,] 0.59144499 0.6451118 0.6556036 0.6960641 0.7246572 0.7526997 0.7562335 +#> [758,] 0.29034567 0.3153831 0.4148944 0.4420203 0.5036863 0.5100027 0.5967067 +#> [759,] 0.24282195 0.5158832 0.5926552 0.6115119 0.6196056 0.6231284 0.6441286 +#> [760,] 0.34646260 0.4576543 0.5116226 0.5403305 0.5449393 0.5790349 0.5962150 +#> [761,] 0.28637481 0.5161356 0.6072845 0.6403226 0.6589856 0.6618140 0.6619228 +#> [762,] 0.22663217 0.7094030 0.7527483 0.8047230 0.8173328 0.8380186 0.8391967 +#> [763,] 0.34765312 0.4212806 0.4268198 0.4444787 0.6220212 0.6292556 0.6796196 +#> [764,] 0.25529604 0.4244684 0.4925101 0.5098325 0.5315147 0.5866931 0.6092869 +#> [765,] 0.63149423 0.6344471 0.8072431 0.8556852 0.9310300 0.9539625 0.9673221 +#> [766,] 0.41353422 0.4800824 0.4997822 0.5004472 0.5308093 0.5517345 0.5817011 +#> [767,] 0.17192229 0.3120374 0.3274886 0.3970135 0.4186993 0.4204261 0.4487641 +#> [768,] 0.49960642 0.5680358 0.5989883 0.6187675 0.6281894 0.6901499 0.7043782 +#> [769,] 0.53075473 0.5948165 0.7371089 0.7845360 0.8615314 0.8746930 0.9299736 +#> [770,] 1.08831280 1.1290978 1.2708480 1.2778259 1.2814328 1.3589799 1.4092152 +#> [771,] 0.84050232 1.2081018 1.3537660 1.3676314 1.3998444 1.4217575 1.4250399 +#> [772,] 0.49874972 0.5116226 0.5348619 0.5606675 0.6284367 0.6302704 0.6997016 +#> [773,] 1.01823009 1.1625109 1.1724161 1.2807257 1.3346802 1.3372022 1.3953495 +#> [774,] 0.42107740 0.5228137 0.5782739 0.6344471 0.6408634 0.6552209 0.7278126 +#> [775,] 1.44425777 1.5024005 1.5148908 1.5275841 1.5392507 1.5788080 1.6184525 +#> [776,] 0.51100732 0.6797352 0.6979795 0.7239671 0.7271320 0.7607239 0.7625511 +#> [777,] 0.27986585 0.6141458 0.6186548 0.6930265 0.6978360 0.7064858 0.7565171 +#> [778,] 0.61968631 0.6495568 0.6692727 0.6764916 0.6782440 0.7079180 0.7088686 +#> [779,] 0.45979711 0.4943986 0.5124604 0.5573640 0.5791735 0.6387119 0.6584954 +#> [780,] 0.33118191 0.3463831 0.4397189 0.4891228 0.4944305 0.5297014 0.5306987 +#> [781,] 0.31406018 0.3746252 0.4828882 0.5987444 0.6003784 0.6580683 0.6789879 +#> [782,] 0.26858368 0.3672683 0.4606300 0.4823437 0.4895812 0.5083644 0.5182948 +#> [783,] 0.80509624 0.9642723 0.9748641 0.9932183 1.0175262 1.0440972 1.0506605 +#> [784,] 0.34632790 0.5812384 0.6068026 0.6817921 0.7782817 0.7818776 0.8135992 +#> [785,] 0.95031389 1.1390276 1.1488276 1.1717946 1.1885282 1.2093484 1.2547582 +#> [786,] 0.57850160 0.7428819 0.7626059 0.7664228 0.7746565 0.8124345 0.8786955 +#> [787,] 0.23817426 0.3740172 0.4150215 0.5086507 0.5239781 0.5252724 0.5643148 +#> [788,] 0.80083402 0.8688661 0.8952005 0.9025712 1.0016799 1.0082361 1.0213946 +#> [789,] 0.47662769 0.5948893 0.6012587 0.7134555 0.7582208 0.8186225 0.8845991 +#> [790,] 0.38039938 0.4066714 0.4787794 0.5510211 0.6025951 0.6032960 0.6046658 +#> [791,] 0.52281365 0.6314942 0.6905425 0.7001828 0.7370280 0.7496531 0.8518589 +#> [792,] 0.87344003 0.8786955 0.9130445 0.9356086 1.0061377 1.0099780 1.0406190 +#> [793,] 0.39295645 0.6883166 0.7067511 0.7409552 0.7463260 0.9268664 0.9380089 +#> [794,] 0.45337305 0.5127075 0.5475791 0.5705612 0.5791735 0.5867750 0.6463524 +#> [795,] 0.38074638 0.5428344 0.7870609 0.8122835 0.8285331 0.9000570 1.0503715 +#> [796,] 0.65741762 0.6595386 0.6786018 0.6805422 0.6911190 0.6945954 0.6964581 +#> [797,] 0.42601353 0.6297133 0.8849188 0.9089493 0.9556250 0.9724911 1.0142959 +#> [798,] 0.07900148 0.2134423 0.3366718 0.3990870 0.5151064 0.5494136 0.6121996 +#> [799,] 0.75505674 0.7705614 0.7789857 0.8690916 0.9599744 0.9880098 1.0048817 +#> [800,] 0.29071253 0.5367478 0.5514176 0.5993767 0.6653796 0.6892158 0.7155752 +#> [801,] 0.45390851 0.4654537 0.4702363 0.4998753 0.5088901 0.5587419 0.5677993 +#> [802,] 0.66430980 0.7860686 0.7917791 0.8035448 0.8512671 0.8943658 0.9688910 +#> [803,] 0.43339983 0.4889723 0.5015533 0.5121367 0.5496062 0.5623404 0.6369267 +#> [804,] 0.22663217 0.6737981 0.6864307 0.7741433 0.7760244 0.7914695 0.8054832 +#> [805,] 0.34928645 0.3683386 0.3883146 0.5029217 0.5178635 0.5493735 0.5851822 +#> [806,] 0.96860515 1.5474225 1.6916781 1.7508751 1.8336938 1.8432269 1.8601614 +#> [807,] 0.81823719 0.8905984 0.9877874 1.0080292 1.0574307 1.0694985 1.0751693 +#> [808,] 0.51274896 0.5794487 0.5989883 0.6016442 0.6227651 0.6675875 0.7007486 +#> [809,] 0.41927666 0.4758018 0.5229936 0.6353158 0.6485353 0.7109808 0.7184761 +#> [810,] 0.52007377 0.5829748 0.7459431 0.7847037 0.7953610 0.8120088 0.8905465 +#> [811,] 0.52670130 0.5495123 0.5750012 0.5960382 0.6278278 0.6401071 0.6482104 +#> [812,] 0.30912486 0.3221767 0.3445838 0.3679166 0.3921957 0.4167995 0.4364860 +#> [813,] 0.26707323 0.3675232 0.3676174 0.4747191 0.5430170 0.5577346 0.5612849 +#> [814,] 0.66246718 0.6764916 0.6870574 0.7056267 0.7653108 0.7741433 0.7975241 +#> [815,] 0.46401044 0.6718409 0.6750776 0.6944161 0.8141279 0.8415446 0.8674535 +#> [816,] 0.40585333 0.5158832 0.5310501 0.6237231 0.6283424 0.6308819 0.6735509 +#> [817,] 0.51787090 0.7322052 0.7942737 0.8858275 0.8992017 0.9529758 0.9777157 +#> [818,] 0.44878229 0.4693085 0.5348619 0.5403305 0.5507061 0.5780262 0.5890797 +#> [819,] 0.40312556 0.4724889 0.5146832 0.6540016 0.6699648 0.7296882 0.7548694 +#> [820,] 0.52243421 0.9528990 0.9566207 0.9690615 0.9959560 1.0041419 1.0048973 +#> [821,] 0.61876751 0.6220356 0.6670638 0.7302815 0.7320587 0.7336890 0.7419142 +#> [822,] 0.45033714 0.5105971 0.6909721 0.7200852 0.7301892 0.7582208 0.7617684 +#> [823,] 0.27752292 0.3530391 0.3569595 0.4105468 0.4740533 0.5041885 0.5822294 +#> [824,] 0.92369646 0.9935013 1.0084146 1.0603844 1.1688281 1.2238993 1.2536617 +#> [825,] 0.93623884 0.9670659 0.9716891 1.0213059 1.0791829 1.0908452 1.2003763 +#> [826,] 0.51547045 0.5428344 0.5513518 0.7418054 0.7596351 0.8207332 0.8464236 +#> [827,] 0.36357445 0.3906010 0.4358474 0.4812976 0.4851370 0.5204109 0.5300283 +#> [828,] 0.21918209 0.3515583 0.4460121 0.4735912 0.5015861 0.5055582 0.5928341 +#> [829,] 0.47248888 0.7390052 0.7862303 0.7934345 0.8240780 0.8774079 0.8816378 +#> [830,] 0.61443490 0.7276547 0.7430517 0.7568780 0.8004466 0.8797075 0.8800598 +#> [831,] 0.33721916 0.4333998 0.4494589 0.5627313 0.5631145 0.6392648 0.6425977 +#> [832,] 0.35584388 0.3608171 0.4589638 0.4647662 0.5355108 0.5409202 0.5661899 +#> [833,] 0.31425204 0.3975284 0.5110073 0.6375903 0.6674449 0.7254397 0.7415100 +#> [834,] 0.43093765 0.4821556 0.6053708 0.7107931 0.8125750 0.9832103 1.0043076 +#> [835,] 0.84050232 1.4143479 1.5009154 1.6691936 1.6755054 1.6854697 1.8534532 +#> [836,] 0.46890975 0.4922400 0.5605946 0.5643164 0.5666706 0.6025482 0.6248032 +#> [837,] 0.29686347 0.4882768 0.6842423 0.7095820 0.7367931 0.8004466 0.8982905 +#> [838,] 0.28297282 0.4104397 0.5606675 0.5922417 0.5930066 0.6435596 0.6506995 +#> [839,] 0.66046375 0.7587216 0.7705614 0.7822333 0.8118788 0.9794113 0.9822419 +#> [840,] 0.20603309 0.3096341 0.3153831 0.4322466 0.4852165 0.4985219 0.5298338 +#> [841,] 0.51426500 0.5160277 0.5448443 0.6115975 0.6209217 0.6602422 0.6673354 +#> [842,] 0.87682996 0.8871343 0.9657971 0.9972174 1.0056713 1.0089594 1.0226326 +#> [843,] 0.53344981 0.5666706 0.5705612 0.5804025 0.6160059 0.6285987 0.6888110 +#> [844,] 0.63329928 0.7035094 0.8909473 0.9090553 0.9450609 1.0034860 1.0322017 +#> [845,] 0.59733251 0.9069941 1.0315972 1.0651302 1.1004546 1.1819033 1.2104695 +#> [846,] 0.35584388 0.4053927 0.4569943 0.4801926 0.5726849 0.5907237 0.6562850 +#> [847,] 0.44945895 0.4669321 0.4710597 0.5335359 0.5496062 0.5714739 0.5741004 +#> [848,] 0.41227811 0.4813064 0.5127075 0.5322349 0.5396463 0.5524752 0.5573640 +#> [849,] 0.60207570 0.6332993 0.7541340 0.7612766 0.8339584 0.8695335 0.9403258 +#> [850,] 0.56667337 0.5690583 0.6806436 0.6841255 0.7057482 0.7196784 0.7266933 +#> [851,] 0.60644755 0.6068605 0.6088823 0.6313599 0.6381212 0.7043648 0.8091027 +#> [852,] 0.46309918 0.5503828 0.5671789 0.6737981 0.6893140 0.6897462 0.7094030 +#> [853,] 0.54477441 0.7328934 0.7748304 0.7982394 0.8948322 0.9052024 0.9099494 +#> [854,] 0.42219845 0.5288742 0.5313286 0.5314622 0.5489092 0.6208746 0.6326844 +#> [855,] 0.36655786 0.4248826 0.5211668 0.5812758 0.6696843 0.6909388 0.7411261 +#> [856,] 0.66906906 0.6929659 0.7029221 0.7363367 0.8576981 0.9298216 0.9509733 +#> [857,] 0.10961117 0.5561111 0.5914676 0.6253939 0.6720140 0.6791187 0.6825467 +#> [858,] 0.34458382 0.3564569 0.4068168 0.4490281 0.4817999 0.5008421 0.5009519 +#> [859,] 0.53900013 0.7596351 0.7809893 0.7977962 0.8110831 0.8285331 0.8717266 +#> [860,] 0.26056421 0.3320452 0.3675232 0.4011632 0.4244684 0.5245809 0.5277923 +#> [861,] 0.53863757 0.5741677 0.7806849 0.9257462 0.9832158 1.0343402 1.0652415 +#> [862,] 0.48019263 0.5409202 0.6188566 0.6728008 0.6845853 0.6976790 0.6983050 +#> [863,] 0.16898732 0.4179616 0.4874333 0.5182454 0.5581737 0.5727718 0.5833047 +#> [864,] 0.49674503 0.8623701 0.9361598 1.0285802 1.0919945 1.1454716 1.1467348 +#> [865,] 0.74356670 0.8506358 0.8988180 0.9653260 0.9658914 1.0456813 1.1495489 +#> [866,] 0.19274415 0.5890797 0.6656190 0.6929399 0.7092455 0.7130241 0.7300549 +#> [867,] 0.82780332 0.8682733 0.8819994 0.9382707 0.9624720 1.0012975 1.0401675 +#> [868,] 0.80504130 1.0132570 1.1390276 1.2647887 1.2893205 1.3519468 1.4066961 +#> [869,] 0.38993068 0.4592070 0.4627760 0.4812428 0.4883416 0.5284553 0.5554485 +#> [870,] 0.41300116 0.4726289 0.5145285 0.5831077 0.6027282 0.6196082 0.6260452 +#> [871,] 0.35872808 0.5216523 0.5218097 0.5943798 0.8396827 0.8405865 0.8480663 +#> [872,] 0.66061744 0.9751220 0.9887768 1.0250606 1.1462059 1.1488878 1.1533324 +#> [873,] 0.55047599 0.9306545 0.9634239 1.0486355 1.0540035 1.1314857 1.1924546 +#> [874,] 0.74044467 0.8490869 0.8839707 1.0353342 1.0634033 1.0914634 1.1081549 +#> [875,] 0.36909222 0.3824672 0.4248826 0.4752431 0.5622268 0.6527913 0.7932064 +#> [876,] 0.19126137 0.3194772 0.3331275 0.3635745 0.3842054 0.4105468 0.4783768 +#> [877,] 0.52330958 0.5452436 0.6420042 0.6720140 0.7052971 0.7300549 0.7423220 +#> [878,] 0.35754567 0.3647637 0.3856706 0.4204261 0.4580945 0.4684129 0.4723983 +#> [879,] 0.48698634 0.5103997 0.5561728 0.5943264 0.6441364 0.6522988 0.6841255 +#> [880,] 0.38075836 0.4420203 0.4485579 0.4658402 0.5140316 0.5446332 0.5554485 +#> [881,] 0.17192229 0.2007145 0.2783166 0.3575457 0.4577139 0.4804332 0.4840932 +#> [882,] 0.55742381 0.7035094 0.8815920 0.9407532 0.9665835 1.0070158 1.0256505 +#> [883,] 1.06671323 1.1098776 1.1939526 1.2708891 1.3019662 1.3272026 1.3346704 +#> [884,] 0.59122759 0.5924289 0.6878932 0.7469794 0.7701128 0.7939503 0.9124692 +#> [885,] 0.26361352 0.4103122 0.5966234 0.6251264 0.6412155 0.6638039 0.6959499 +#> [886,] 0.38142744 0.4702799 0.6183745 0.6692462 0.7132491 0.7992174 0.8603296 +#> [887,] 0.41103442 0.4721603 0.4943986 0.5488501 0.5919320 0.6048693 0.6696642 +#> [888,] 0.48463135 0.5583400 0.6188566 0.7207993 0.7371089 0.7768013 0.7927239 +#> [889,] 0.20402766 0.2125101 0.5652156 0.6438647 0.6656291 0.6934208 0.7234980 +#> [890,] 0.40644938 0.4742585 0.5110722 0.5993060 0.6140650 0.6602692 0.6758489 +#> [891,] 0.31009228 0.5020218 0.5300283 0.5325700 0.5659865 0.6462933 0.6560148 +#> [892,] 0.63783218 0.7207886 0.7319139 1.1488815 1.2363910 1.2394352 1.2466568 +#> [893,] 0.51562884 0.6047001 0.6736678 0.7180052 0.8424851 0.8446939 0.8627030 +#> [894,] 1.16058388 1.2652724 1.3307926 1.3720424 1.4087473 1.4230225 1.4442578 +#> [895,] 0.76904320 0.8050931 1.0306609 1.0424817 1.0576121 1.1172876 1.1341513 +#> [896,] 1.03405675 1.0807985 1.1629530 1.1726818 1.2371444 1.2431209 1.2989847 +#> [897,] 0.26056421 0.2953271 0.3596759 0.3676174 0.4370006 0.4674089 0.4867114 +#> [898,] 0.66295655 1.0524327 1.1454716 1.1938812 1.2348503 1.2659794 1.3541057 +#> [899,] 0.26714363 0.4168866 0.4398471 0.4827427 0.4993616 0.5299611 0.5303869 +#> [900,] 0.99383792 1.0207571 1.0337819 1.0365731 1.0417322 1.0553590 1.0669284 +#> [901,] 0.41869934 0.4499811 0.4804332 0.4892405 0.5419171 0.5443461 0.5630225 +#> [902,] 0.41001145 0.4336526 0.4356013 0.4651105 0.5229936 0.5439551 0.5553379 +#> [903,] 0.44601213 0.4739712 0.4898238 0.5262443 0.5330906 0.5404534 0.5571109 +#> [904,] 0.38729811 0.4356013 0.4758018 0.5605504 0.5629239 0.6072090 0.6208746 +#> [905,] 0.43376457 0.6284165 0.6473181 0.6926234 0.7473814 0.7664153 0.8196974 +#> [906,] 0.36889156 0.4181342 0.4353989 0.5443613 0.6925852 0.7820596 0.8175952 +#> [907,] 0.35553709 0.3639759 0.4068168 0.4192504 0.4518190 0.4547488 0.4583266 +#> [908,] 0.62373689 0.7183489 0.8926911 0.9374606 0.9533518 0.9709443 0.9932183 +#> [909,] 0.47303215 0.5924289 0.6284165 0.6345006 0.6550931 0.6738575 0.6763688 +#> [910,] 0.37272716 0.7574815 0.8620530 0.9235493 1.0293627 1.0346818 1.0506003 +#> [911,] 0.29797572 0.4412957 0.5142650 0.5494740 0.6083196 0.6670661 0.6900840 +#> [912,] 0.31680663 0.3592144 0.3740172 0.4725692 0.5161527 0.5982736 0.6788071 +#> [913,] 0.40154240 0.5960382 0.6015735 0.6039505 0.6143347 0.6173411 0.6399215 +#> [914,] 0.25529604 0.4657359 0.5176139 0.5277923 0.5430170 0.5472789 0.5484479 +#> [915,] 0.52267751 0.5274417 0.7076093 0.7294261 0.7614309 0.7638306 0.7763127 +#> [916,] 0.60183197 0.6690691 0.7574397 0.7667885 0.7775509 0.8601285 0.9643448 +#> [917,] 0.27254531 0.3342411 0.4363043 0.4371461 0.4383080 0.4857113 0.5166758 +#> [918,] 0.27820372 0.3134153 0.4121055 0.4625246 0.5752935 0.7428819 0.7835030 +#> [919,] 0.22730905 0.3454661 0.4692602 0.5782066 0.7374615 0.7477163 0.7979180 +#> [920,] 0.38219942 0.5233096 0.6548786 0.6736678 0.6747795 0.7224611 0.7872064 +#> [921,] 0.35397953 0.4057245 0.4850417 0.5075179 0.5084299 0.5268116 0.5634698 +#> [922,] 0.49108733 0.6237369 0.6983050 0.7207993 0.7317220 0.7329495 0.8043957 +#> [923,] 0.57316599 0.7018844 0.7698621 0.9067110 0.9072002 0.9167711 0.9358035 +#> [924,] 0.27779408 0.5067058 0.5474909 0.5619969 0.5990116 0.6007823 0.6651803 +#> [925,] 0.28951401 0.3507199 0.6028122 0.6597689 0.6932175 0.7135412 0.7213954 +#> [926,] 0.65566614 0.7996484 0.8013142 0.8076352 0.8094535 0.8484571 0.8515109 +#> [927,] 0.27562530 0.5907578 0.6047001 0.6923461 0.6948035 0.7315810 0.7754890 +#> [928,] 0.42319471 0.4619486 0.5357388 0.6964726 0.7374605 0.7408393 0.7504980 +#> [929,] 0.38240555 0.6968206 0.7107931 0.7555991 0.7797592 1.0041571 1.0511232 +#> [930,] 0.48905962 0.5091761 0.6165220 0.6183745 0.6802482 0.7362994 0.7480086 +#> [931,] 0.46604194 0.5057079 0.5093474 0.6374305 0.6515160 0.6895867 0.7152979 +#> [932,] 0.36022339 0.3990870 0.4527244 0.6065168 0.6401071 0.6408634 0.6579255 +#> [933,] 0.27977522 0.3955108 0.4488479 0.4904569 0.4931540 0.5990700 0.6173134 +#> [934,] 0.38153048 0.4648426 0.5635859 0.6371110 0.6375488 0.6404007 0.6428293 +#> [935,] 0.44190042 0.4850204 0.5412125 0.5499718 0.5658402 0.5743258 0.5858808 +#> [936,] 0.47357398 0.5110472 0.5623404 0.6583454 0.6729948 0.7008084 0.7540306 +#> [937,] 0.61987271 0.7960216 0.9158306 1.0644285 1.1518480 1.1549978 1.1687427 +#> [938,] 0.29207978 0.5936938 0.5990116 0.6335023 0.6814437 0.6869994 0.7562474 +#> [939,] 0.10961117 0.5477442 0.5794918 0.6271646 0.6420042 0.6697815 0.6785323 +#> [940,] 0.37616048 0.5179263 0.5733939 0.6280572 0.6450097 0.7735499 0.7956606 +#> [941,] 0.59060294 0.5975474 0.6558207 0.6976132 0.7076012 0.7717883 0.8033412 +#> [942,] 0.21344226 0.2370228 0.3082354 0.3653168 0.4527244 0.5664058 0.6008899 +#> [943,] 0.37666715 0.4403305 0.4726289 0.4837092 0.5482435 0.5557389 0.5831863 +#> [944,] 0.26497430 0.6674207 0.7481556 0.7606691 0.9812453 1.0048817 1.0092092 +#> [945,] 0.38219942 0.5879447 0.6173491 0.6611236 0.6726969 0.6757738 0.7159151 +#> [946,] 0.49533234 0.6346750 0.6884615 0.7041224 0.7872879 0.8081023 0.8233290 +#> [947,] 0.62918180 0.7968490 0.8299728 0.8488415 0.9636785 0.9671748 0.9727649 +#> [948,] 0.19062462 0.3290349 0.3639759 0.4651105 0.5008421 0.5286499 0.5410208 +#> [949,] 0.47084221 0.6654162 0.7048069 0.7374605 0.7551687 0.8144466 0.8210441 +#> [950,] 0.40585333 0.6090162 0.6438393 0.6645667 0.6893189 0.7167928 0.7535016 +#> [951,] 1.71535420 1.7710182 1.8012323 1.9644858 2.0306185 2.1379828 2.1948562 +#> [952,] 0.94719990 1.1703586 1.2716053 1.2792180 1.2884615 1.3356232 1.3512644 +#> [953,] 0.24524756 0.3722042 0.4048998 0.4403305 0.4920482 0.5248431 0.5715971 +#> [954,] 0.59171612 0.6484736 0.6593648 0.7002319 0.7342629 0.7738593 0.7823798 +#> [955,] 0.36726831 0.3876206 0.3916555 0.4122333 0.4146105 0.4408832 0.4436452 +#> [956,] 0.28197372 0.2951427 0.2970063 0.3015421 0.3331275 0.4610017 0.4851370 +#> [957,] 0.75137243 0.7668919 0.8383914 0.8551747 0.8874871 0.8942799 0.9154840 +#> [958,] 0.43970503 0.4590166 0.4673949 0.4807027 0.4878093 0.4942333 0.5067848 +#> [959,] 0.21563769 0.4584524 0.4785825 0.4826764 0.4827427 0.5608112 0.5656248 +#> [960,] 0.34159301 0.3900757 0.4015424 0.4751257 0.5479820 0.6047128 0.6139697 +#> [961,] 1.64730746 1.7606569 1.8298994 1.8558945 1.8647541 1.9069203 2.0772323 +#> [962,] 0.46116246 0.6403444 0.6522988 0.7392183 0.7429670 0.7526534 0.8085424 +#> [963,] 0.20071453 0.3274886 0.3432095 0.3856706 0.4592138 0.4892405 0.5513524 +#> [964,] 0.60683629 0.6517776 0.8600679 0.9081630 0.9218340 0.9547859 0.9884917 +#> [965,] 0.53546582 0.5653624 0.5850334 0.5880933 0.5956964 0.6276467 0.6442174 +#> [966,] 0.21651187 0.2317734 0.3859907 0.4146105 0.4910142 0.4920482 0.5625149 +#> [967,] 0.41103442 0.4597971 0.5235230 0.5307719 0.5510616 0.6118213 0.6688656 +#> [968,] 0.39860107 0.4380599 0.4533006 0.5529875 0.5604020 0.5783303 0.5932555 +#> [969,] 0.39210704 0.4336153 0.5295156 0.6056370 0.6156885 0.6515893 0.6693918 +#> [970,] 0.38757011 0.4032872 0.4681296 0.5233900 0.5238755 0.5789343 0.5793320 +#> [971,] 0.21918209 0.2444457 0.4211737 0.4739712 0.4783768 0.4932683 0.5504818 +#> [972,] 0.61758497 0.6874442 0.8036485 0.8796937 0.8933293 0.9167823 0.9520005 +#> [973,] 0.37950826 0.4977742 0.5460506 0.5654979 0.6470833 0.7371090 0.7557208 +#> [974,] 0.13267935 0.2428452 0.3835312 0.4100114 0.4443748 0.4630358 0.4857113 +#> [975,] 0.29137886 0.3808558 0.4494418 0.5510616 0.5705460 0.5788722 0.5844677 +#> [976,] 0.57887022 0.8547462 0.8845617 0.8948754 0.9142141 0.9572253 1.0449902 +#> [977,] 0.53467357 0.6050027 0.6217932 0.6930354 0.8113778 0.8167004 0.8336929 +#> [978,] 0.24855464 0.3429031 0.4231851 0.4459950 0.5169359 0.5208903 0.5263868 +#> [979,] 0.38428962 0.4476540 0.4706032 0.4717548 0.4817677 0.5750238 0.5753789 +#> [980,] 0.38831457 0.4681296 0.5244214 0.5359329 0.5656454 0.6005207 0.6054502 +#> [981,] 0.32642149 0.4353507 0.4688051 0.5189561 0.5746850 0.5750395 0.5897147 +#> [982,] 0.39295645 0.6280775 0.7546048 0.8182106 0.8942453 0.9120783 0.9266390 +#> [983,] 0.51525033 0.6593257 0.6665253 0.6819764 0.6948617 0.7044144 0.7242251 +#> [984,] 0.93081475 0.9929918 1.0686850 1.0714018 1.0977995 1.1447031 1.1805190 +#> [985,] 0.37853720 0.4998753 0.6013005 0.6443655 0.6587918 0.6663823 0.6978056 +#> [986,] 0.27237342 0.4337646 0.5060788 0.6272943 0.6577665 0.6708163 0.6836464 +#> [987,] 1.38856912 1.6361155 1.7030627 1.7118549 1.7578300 1.7924477 1.8974918 +#> [988,] 0.66347900 0.7011247 0.7657464 0.7867163 0.8439553 0.8543112 0.8612993 +#> [989,] 0.25283902 0.4725692 0.6412155 0.6530337 0.6666938 0.6822739 0.6852734 +#> [990,] 0.34537301 0.4943825 0.5348730 0.6260920 0.6302965 0.6686640 0.6707097 +#> [991,] 0.19274415 0.6253939 0.6271646 0.6384520 0.7050826 0.7361423 0.7423220 +#> [992,] 0.68535263 0.7449855 0.8332449 0.8448919 0.8546296 0.8701522 0.9151551 +#> [993,] 0.29573626 0.5085622 0.5222549 0.5714498 0.5996214 0.6150778 0.6439832 +#> [994,] 0.34324427 0.3871573 0.4821855 0.4984314 0.5288742 0.5620869 0.6308612 +#> [995,] 0.38428962 0.5359329 0.5577090 0.5581737 0.5796832 0.6007823 0.6205757 +#> [996,] 0.24855464 0.3755687 0.4384343 0.4606300 0.4865361 0.5041361 0.5170806 +#> [997,] 0.71485709 1.0349787 1.1488815 1.1793684 1.1988625 1.2671123 1.2780407 +#> [998,] 0.47264023 0.5073235 0.5280320 0.5308094 0.5817286 0.6269897 0.6459467 +#> [999,] 0.34558386 0.4790775 0.5313286 0.5317383 0.5337046 0.5827102 0.5840487 +#> [1000,] 0.34397845 0.3794034 0.4114427 0.4779854 0.5226127 0.5297184 0.5677993 #> [,8] [,9] [,10] -#> [1,] 1.0948143 1.1253654 1.1294703 -#> [2,] 0.5525923 0.5752191 0.5948832 -#> [3,] 1.0433555 1.0634161 1.0639414 -#> [4,] 0.6020509 0.6321894 0.6900302 -#> [5,] 0.5374756 0.5838935 0.6076137 -#> [6,] 1.9607194 1.9707421 1.9894195 -#> [7,] 0.9771859 1.0105006 1.0358824 -#> [8,] 0.7905344 0.7948463 0.8065179 -#> [9,] 0.7325301 0.7529760 0.7644297 -#> [10,] 1.8648328 1.8954785 1.9155196 -#> [11,] 0.7809546 0.8205875 0.8244164 -#> [12,] 0.5654316 0.5838902 0.5863865 -#> [13,] 1.9906122 2.0133325 2.0347324 -#> [14,] 0.5282038 0.5294087 0.5580313 -#> [15,] 0.6695476 0.6720961 0.7860751 -#> [16,] 0.6848587 0.6942770 0.6993759 -#> [17,] 1.2454930 1.2909212 1.3323128 -#> [18,] 0.7167810 0.7366055 0.7377562 -#> [19,] 0.6401902 0.6607465 0.6644822 -#> [20,] 0.5855031 0.5910505 0.6080686 -#> [21,] 0.5373405 0.5618657 0.5968875 -#> [22,] 1.0337314 1.1039228 1.1077311 -#> [23,] 0.4721043 0.4789884 0.4811924 -#> [24,] 0.7415321 0.7484844 0.7656143 -#> [25,] 0.8201046 0.8592889 0.8610294 -#> [26,] 0.8449646 0.8456569 0.8807989 -#> [27,] 1.1437594 1.1803415 1.2018161 -#> [28,] 0.7660779 0.7776805 0.8061198 -#> [29,] 0.9298050 0.9307238 0.9379767 -#> [30,] 0.7981114 0.8339729 0.8374160 -#> [31,] 0.9943953 1.0235233 1.0283907 -#> [32,] 0.7459984 0.7858414 0.7963569 -#> [33,] 0.8277493 0.8512057 0.8969156 -#> [34,] 0.9271726 0.9673539 0.9850058 -#> [35,] 0.6410389 0.6600794 0.6959216 -#> [36,] 1.6054625 1.6865838 1.6911640 -#> [37,] 0.5849180 0.6452064 0.6531977 -#> [38,] 0.5227291 0.5905755 0.6106738 -#> [39,] 1.5553362 1.6244928 1.6247010 -#> [40,] 0.5028352 0.5363079 0.5814556 -#> [41,] 0.9051968 0.9132779 0.9296012 -#> [42,] 0.6478459 0.6729798 0.6803337 -#> [43,] 0.4674898 0.4766956 0.4847473 -#> [44,] 0.7856933 0.8096945 0.8112277 -#> [45,] 0.8419828 0.8642496 0.9374454 -#> [46,] 0.7466119 0.7722180 0.7869417 -#> [47,] 1.6690312 1.6896383 1.6958277 -#> [48,] 0.4829012 0.5538597 0.5852644 -#> [49,] 0.5462467 0.5481299 0.5585343 -#> [50,] 0.8244164 0.8805266 0.9358046 -#> [51,] 0.6613715 0.6811357 0.7124117 -#> [52,] 0.7191833 0.7194659 0.7649574 -#> [53,] 1.2564305 1.3031879 1.3709338 -#> [54,] 0.7095881 0.7101002 0.7457393 -#> [55,] 0.8632450 0.9452734 0.9582362 -#> [56,] 1.2330285 1.2860053 1.2878207 -#> [57,] 0.6133775 0.6570875 0.6647266 -#> [58,] 0.7066226 0.7161633 0.7200286 -#> [59,] 1.1455786 1.1821034 1.2087668 -#> [60,] 0.7898192 0.7939243 0.8015919 -#> [61,] 0.8167129 0.8387293 0.8398155 -#> [62,] 0.6275538 0.6390093 0.6403610 -#> [63,] 0.6823798 0.6855014 0.6864298 -#> [64,] 0.6007936 0.6157251 0.6158978 -#> [65,] 1.0931949 1.1069983 1.1255987 -#> [66,] 1.1747662 1.1762794 1.1793043 -#> [67,] 0.7198973 0.8448278 0.8528679 -#> [68,] 0.7744335 0.8008949 0.8113809 -#> [69,] 0.8337477 0.8546016 0.8666153 -#> [70,] 0.7035311 0.7185677 0.7657909 -#> [71,] 0.6959320 0.7141490 0.7330386 -#> [72,] 0.8266657 0.8555373 0.8824980 -#> [73,] 1.0689941 1.0765964 1.0970276 -#> [74,] 0.6422858 0.6753351 0.6794117 -#> [75,] 0.9881594 1.0049256 1.0092698 -#> [76,] 0.7840795 0.8029412 0.8446133 -#> [77,] 0.8339729 0.8852561 0.8885824 -#> [78,] 0.6776343 0.6783473 0.6927912 -#> [79,] 0.5990214 0.6148437 0.6178335 -#> [80,] 0.6261561 0.6705603 0.6917049 -#> [81,] 0.8183042 0.8248413 0.8452508 -#> [82,] 1.5711966 1.5921858 1.6799638 -#> [83,] 0.5473466 0.5577556 0.5585343 -#> [84,] 1.4575733 1.4748398 1.5450134 -#> [85,] 0.9101200 0.9215540 0.9400282 -#> [86,] 0.6895740 0.6964980 0.8398155 -#> [87,] 0.5315435 0.5926003 0.5940546 -#> [88,] 0.9466164 0.9505368 0.9819191 -#> [89,] 0.7085806 0.7539298 0.7576612 -#> [90,] 0.4639866 0.4886340 0.4978754 -#> [91,] 1.0661671 1.0703207 1.0850179 -#> [92,] 1.0350864 1.1246410 1.1410353 -#> [93,] 0.6273846 0.6396846 0.6729936 -#> [94,] 0.6621461 0.6730841 0.6755372 -#> [95,] 0.5589076 0.5718031 0.5726976 -#> [96,] 1.0967401 1.1063678 1.1619855 -#> [97,] 0.6227062 0.6631667 0.7028310 -#> [98,] 1.4545619 1.4804709 1.4940935 -#> [99,] 1.2369132 1.2409701 1.2629280 -#> [100,] 0.6414267 0.6420026 0.6812624 -#> [101,] 0.6256541 0.6270762 0.6340315 -#> [102,] 0.8030102 0.8399439 0.8470966 -#> [103,] 1.3906688 1.4321209 1.4531103 -#> [104,] 1.4258069 1.4359298 1.4368440 -#> [105,] 0.9993196 1.0286461 1.0327843 -#> [106,] 0.5507339 0.5758143 0.6218453 -#> [107,] 0.9608068 0.9634866 0.9682306 -#> [108,] 1.3341929 1.3603603 1.4866068 -#> [109,] 1.3107763 1.3393935 1.3667934 -#> [110,] 0.8175726 0.8257304 0.8447845 -#> [111,] 0.4845243 0.5029748 0.5270942 -#> [112,] 0.4837839 0.5098671 0.5127951 -#> [113,] 0.5567038 0.5651490 0.5693620 -#> [114,] 0.8781318 0.9038817 0.9218376 -#> [115,] 0.7588572 0.7636821 0.7788853 -#> [116,] 0.8845181 0.9186031 0.9341500 -#> [117,] 0.6133688 0.6480952 0.6680145 -#> [118,] 0.8748226 0.8857262 0.8882337 -#> [119,] 0.9168692 0.9190963 0.9461592 -#> [120,] 0.5529531 0.5568170 0.5956290 -#> [121,] 0.5807087 0.5853281 0.6225125 -#> [122,] 0.6371422 0.6550249 0.6594435 -#> [123,] 0.7378963 0.8014425 0.8158279 -#> [124,] 0.9562657 0.9765916 0.9771331 -#> [125,] 0.7866932 0.7870167 0.8082479 -#> [126,] 0.8753202 0.9776262 0.9876411 -#> [127,] 0.5155695 0.5517497 0.5578328 -#> [128,] 1.1372116 1.2638671 1.2847125 -#> [129,] 0.8325244 0.8735306 0.8902842 -#> [130,] 0.6917049 0.6949101 0.7003802 -#> [131,] 1.2206230 1.2382200 1.2576681 -#> [132,] 0.6918563 0.6972482 0.6981607 -#> [133,] 0.7293444 0.7451747 0.7648917 -#> [134,] 1.0767381 1.1098807 1.1120624 -#> [135,] 0.8793499 0.9069460 0.9134570 -#> [136,] 0.6395725 0.6667419 0.6688439 -#> [137,] 0.7184914 0.7327227 0.7652532 -#> [138,] 0.7553037 0.8062697 0.8073952 -#> [139,] 0.7791502 0.8055212 0.8210332 -#> [140,] 0.7756952 0.7866932 0.8007903 -#> [141,] 0.7677695 0.7754018 0.7818217 -#> [142,] 0.5684539 0.5996913 0.6007714 -#> [143,] 1.3375979 1.4037835 1.4188600 -#> [144,] 0.7343907 0.7648775 0.7744335 -#> [145,] 1.0005149 1.0839008 1.1359603 -#> [146,] 1.9247666 2.0528754 2.1450529 -#> [147,] 1.5396802 1.5606996 1.5799396 -#> [148,] 0.6387329 0.6418468 0.7399409 -#> [149,] 1.4024336 1.4556944 1.4722021 -#> [150,] 0.6279512 0.6348908 0.6559528 -#> [151,] 1.0326327 1.0389256 1.0914895 -#> [152,] 0.9378915 0.9794508 0.9807302 -#> [153,] 1.0243505 1.0493163 1.0726503 -#> [154,] 1.9435902 2.0558332 2.0685179 -#> [155,] 0.6269097 0.6477152 0.6494768 -#> [156,] 0.8093809 0.8215576 0.8361150 -#> [157,] 0.5778372 0.5838077 0.5843167 -#> [158,] 0.6856160 0.6878116 0.6889679 -#> [159,] 1.3000231 1.3299072 1.4954100 -#> [160,] 0.9060245 0.9248323 0.9391637 -#> [161,] 0.7344118 0.7827488 0.8158820 -#> [162,] 0.5290869 0.5333304 0.5641456 -#> [163,] 0.7405819 0.7453283 0.7493836 -#> [164,] 1.0792046 1.1108133 1.1561070 -#> [165,] 0.5784775 0.5840255 0.5874667 -#> [166,] 0.8497432 0.8526097 0.8793478 -#> [167,] 0.5301634 0.5773118 0.5815696 -#> [168,] 0.9205566 1.0556881 1.0748362 -#> [169,] 0.6847138 0.6932394 0.7051132 -#> [170,] 0.6720961 0.6875287 0.7077823 -#> [171,] 0.6625082 0.6636391 0.7048870 -#> [172,] 0.9430009 0.9596114 1.0043056 -#> [173,] 0.8368947 0.8442932 0.9303547 -#> [174,] 0.8347588 0.8516636 0.8723330 -#> [175,] 0.6116490 0.6363949 0.6639107 -#> [176,] 0.7434605 0.7671118 0.8219937 -#> [177,] 0.6475037 0.6693719 0.6704771 -#> [178,] 1.3141811 1.3641529 1.3654928 -#> [179,] 0.7288562 0.7513864 0.8175451 -#> [180,] 1.0607921 1.0778866 1.0892902 -#> [181,] 0.5993173 0.6505006 0.6528188 -#> [182,] 0.7856517 0.8236180 0.8815996 -#> [183,] 1.2932255 1.2943257 1.3099257 -#> [184,] 0.5943086 0.6065965 0.6508316 -#> [185,] 0.8558937 1.0087461 1.0173097 -#> [186,] 0.7332994 0.7524957 0.7620211 -#> [187,] 0.5388128 0.5724169 0.5871660 -#> [188,] 0.4453320 0.4781089 0.4906804 -#> [189,] 0.6825961 0.7315208 0.7662661 -#> [190,] 0.6261561 0.6322562 0.6387264 -#> [191,] 0.5431800 0.5493708 0.5842524 -#> [192,] 0.9366650 1.0115112 1.0139018 -#> [193,] 0.7791763 0.7877686 0.8267271 -#> [194,] 0.4999803 0.5224957 0.5284957 -#> [195,] 0.7287542 0.7338708 0.7382701 -#> [196,] 0.6793757 0.7002640 0.7527591 -#> [197,] 0.8001376 0.8348291 0.8683633 -#> [198,] 0.8281828 0.8604421 0.8701024 -#> [199,] 0.7081871 0.7107376 0.7260920 -#> [200,] 0.8064651 0.8132218 0.8245745 -#> [201,] 1.1696744 1.1757446 1.1955097 -#> [202,] 0.5215113 0.5506487 0.5559009 -#> [203,] 0.6368902 0.6431490 0.6779259 -#> [204,] 0.7081038 0.7217274 0.7359611 -#> [205,] 0.7783967 0.7897575 0.8099471 -#> [206,] 2.2261040 2.2775297 2.3224374 -#> [207,] 0.9126229 0.9135974 0.9346452 -#> [208,] 0.7500319 0.7543159 0.8307144 -#> [209,] 0.5990616 0.6094973 0.6518840 -#> [210,] 0.7939766 0.7948463 0.8174748 -#> [211,] 0.6363949 0.6399279 0.6597046 -#> [212,] 0.7984211 0.8140872 0.8308921 -#> [213,] 0.6526012 0.6779259 0.7076093 -#> [214,] 0.6954323 0.7328516 0.8023101 -#> [215,] 0.7891139 0.8550059 0.8793124 -#> [216,] 0.6767596 0.6819420 0.7056023 -#> [217,] 0.5446488 0.5520749 0.5598840 -#> [218,] 0.6493102 0.6569956 0.7001344 -#> [219,] 0.6517485 0.6598114 0.6637741 -#> [220,] 1.3616232 1.3623973 1.3651631 -#> [221,] 0.9490913 0.9776484 1.0237415 -#> [222,] 1.0844905 1.0989884 1.1113306 -#> [223,] 0.6221887 0.6253999 0.6451453 -#> [224,] 0.8194787 0.8278785 0.8498218 -#> [225,] 0.5101514 0.5381790 0.5455016 -#> [226,] 0.7723712 0.7773210 0.7809546 -#> [227,] 0.6798027 0.6873260 0.6927150 -#> [228,] 0.7488809 0.7905154 0.8101955 -#> [229,] 0.6734346 0.6924004 0.7396594 -#> [230,] 0.6067046 0.6370304 0.6404394 -#> [231,] 0.6918033 0.7091232 0.7606984 -#> [232,] 1.2896599 1.3014405 1.3156883 -#> [233,] 0.5900934 0.6004548 0.6081852 -#> [234,] 0.6822182 0.6979967 0.7666046 -#> [235,] 0.6520138 0.6824711 0.6960982 -#> [236,] 0.7053574 0.7241683 0.7403172 -#> [237,] 0.5986689 0.5990214 0.6377405 -#> [238,] 0.7920279 0.8213955 0.8286330 -#> [239,] 1.2819247 1.2855510 1.3117287 -#> [240,] 0.7161184 0.7414333 0.7443708 -#> [241,] 1.0238656 1.0239953 1.0652112 -#> [242,] 0.7274085 0.8577339 0.9286956 -#> [243,] 0.8815454 0.8823584 0.9279361 -#> [244,] 1.0295641 1.0425783 1.0451755 -#> [245,] 0.5792627 0.5956290 0.5963923 -#> [246,] 1.0161725 1.0426072 1.0429812 -#> [247,] 0.5784076 0.5812280 0.6161878 -#> [248,] 0.7515865 0.8088560 0.8094516 -#> [249,] 1.1293264 1.1461501 1.1804159 -#> [250,] 1.1818696 1.1882331 1.1978149 -#> [251,] 0.7825243 0.7996498 0.8301877 -#> [252,] 1.0085870 1.0653633 1.0755396 -#> [253,] 0.5808555 0.5864900 0.6044754 -#> [254,] 1.7971424 1.8032660 1.8215755 -#> [255,] 0.6878055 0.7056987 0.7316238 -#> [256,] 0.9891129 1.0039163 1.0513837 -#> [257,] 0.7224331 0.7338708 0.7404499 -#> [258,] 0.6437494 0.6840457 0.6865839 -#> [259,] 0.9520110 1.0119328 1.0333963 -#> [260,] 0.8879565 0.8959714 0.9304029 -#> [261,] 0.5361542 0.5636682 0.5842524 -#> [262,] 0.8899253 0.8908305 0.9057293 -#> [263,] 0.6037244 0.6114538 0.6404394 -#> [264,] 1.0037048 1.0046564 1.0069419 -#> [265,] 0.6911578 0.7127932 0.7773210 -#> [266,] 1.4057024 1.4076882 1.4225578 -#> [267,] 1.1279720 1.1550765 1.1581750 -#> [268,] 0.5414595 0.5564536 0.6357178 -#> [269,] 0.6076115 0.6193471 0.6276841 -#> [270,] 0.6143492 0.6556173 0.6836048 -#> [271,] 2.4496041 2.5344793 2.5391836 -#> [272,] 0.6044852 0.6194560 0.6485100 -#> [273,] 1.3404538 1.4055332 1.4131752 -#> [274,] 0.5337690 0.5394863 0.5442631 -#> [275,] 0.6493688 0.6504903 0.6757886 -#> [276,] 0.9664330 1.0029752 1.0502675 -#> [277,] 1.2916295 1.3270275 1.3620397 -#> [278,] 0.7264431 0.7280660 0.7652532 -#> [279,] 0.5512399 0.5529531 0.5653469 -#> [280,] 1.2674576 1.2947856 1.3007113 -#> [281,] 0.6846347 0.6856328 0.7447192 -#> [282,] 0.8194439 0.8902137 0.9103053 -#> [283,] 0.7289972 0.7363352 0.7464540 -#> [284,] 0.5464866 0.5698526 0.5700085 -#> [285,] 0.7070283 0.7107505 0.7586351 -#> [286,] 0.9786823 0.9902617 1.0043523 -#> [287,] 0.8835670 0.9166792 0.9298050 -#> [288,] 0.8211376 0.8313677 0.8479914 -#> [289,] 0.5036908 0.5224957 0.5538597 -#> [290,] 0.5881955 0.5898104 0.6164692 -#> [291,] 0.5928864 0.6194560 0.6233224 -#> [292,] 0.6303293 0.6526242 0.6651987 -#> [293,] 0.6756108 0.6893942 0.7012269 -#> [294,] 0.6567613 0.6709421 0.6779583 -#> [295,] 1.1418658 1.3219935 1.3361117 -#> [296,] 0.6455426 0.6471754 0.6570015 -#> [297,] 1.6822661 1.7729263 1.7731140 -#> [298,] 0.5762717 0.5778650 0.5849180 -#> [299,] 0.6902308 0.6924779 0.6946994 -#> [300,] 0.9062407 0.9221555 1.0124701 -#> [301,] 0.5691434 0.6206144 0.6520764 -#> [302,] 0.4645308 0.5022562 0.5374942 -#> [303,] 0.6195858 0.6303293 0.6317110 -#> [304,] 0.6666715 0.6754799 0.6776307 -#> [305,] 0.9657700 1.0268108 1.0743191 -#> [306,] 0.6860968 0.7290304 0.7363749 -#> [307,] 0.9330724 1.0323207 1.0533723 -#> [308,] 0.6227840 0.6391521 0.6412936 -#> [309,] 1.0081923 1.0499037 1.1157758 -#> [310,] 0.8485907 0.8904358 1.0119328 -#> [311,] 1.4585826 1.5491043 1.5699872 -#> [312,] 1.0084942 1.0480537 1.1049122 -#> [313,] 0.8173120 0.8294177 0.8338392 -#> [314,] 0.5715760 0.5716476 0.5847812 -#> [315,] 0.7681936 0.7818949 0.7836927 -#> [316,] 0.8939884 0.9396375 0.9799018 -#> [317,] 0.6936951 0.7488810 0.7905965 -#> [318,] 0.5284969 0.5448599 0.5563110 -#> [319,] 0.7708202 0.7891957 0.7910154 -#> [320,] 0.6448727 0.6503403 0.6822182 -#> [321,] 1.0162726 1.0430789 1.0781098 -#> [322,] 0.5721931 0.5832461 0.6130888 -#> [323,] 0.6755372 0.6960078 0.7344727 -#> [324,] 0.9294930 0.9347328 0.9469777 -#> [325,] 0.5809545 0.6368902 0.6649969 -#> [326,] 0.5545367 0.5669852 0.5982363 -#> [327,] 0.8570095 0.8741171 0.9018141 -#> [328,] 0.5784438 0.6741597 0.6947247 -#> [329,] 2.1946260 2.1992391 2.2446966 -#> [330,] 0.6299160 0.6330021 0.6784835 -#> [331,] 1.6457078 1.6651054 1.7061774 -#> [332,] 0.8167660 0.8280890 0.8350104 -#> [333,] 0.7697932 0.7825019 0.8039611 -#> [334,] 0.6594180 0.6819420 0.7174661 -#> [335,] 0.6489326 0.6538819 0.6575023 -#> [336,] 1.0346965 1.0823524 1.1103062 -#> [337,] 1.1231594 1.1492845 1.1676271 -#> [338,] 0.8149124 0.8261038 0.8263901 -#> [339,] 0.5833905 0.5875335 0.5910737 -#> [340,] 0.7484844 0.7489488 0.8072147 -#> [341,] 0.8174948 0.8685083 0.8690115 -#> [342,] 0.6255115 0.6255987 0.6267861 -#> [343,] 0.7351369 0.7598897 0.7924194 -#> [344,] 0.6258388 0.6274167 0.6378654 -#> [345,] 0.7616623 0.7817998 0.7882697 -#> [346,] 0.9191435 0.9289766 0.9628716 -#> [347,] 0.6106381 0.6141360 0.6265655 -#> [348,] 0.5110051 0.5153055 0.5965174 -#> [349,] 0.6150592 0.6544731 0.6558452 -#> [350,] 0.9379206 0.9586946 0.9891129 -#> [351,] 0.6922921 0.7232509 0.7310245 -#> [352,] 0.8284646 0.8411821 0.8562097 -#> [353,] 0.6232940 0.6251972 0.6336534 -#> [354,] 0.5802153 0.5900908 0.5905871 -#> [355,] 1.6016927 1.6183683 1.6376652 -#> [356,] 0.8466935 0.8662803 0.8833108 -#> [357,] 0.7460089 0.7497731 0.7529493 -#> [358,] 0.8835670 0.9017609 0.9370817 -#> [359,] 0.6362140 0.6389466 0.6555353 -#> [360,] 0.7257879 0.7571024 0.7628530 -#> [361,] 0.6661770 0.6735804 0.6826631 -#> [362,] 0.7604367 0.7631046 0.7719649 -#> [363,] 1.0205662 1.0823203 1.1001316 -#> [364,] 0.9601521 0.9658617 0.9756815 -#> [365,] 0.7093827 0.7349957 0.7540449 -#> [366,] 0.9733015 0.9780837 0.9795397 -#> [367,] 0.8142208 0.8174948 0.8393366 -#> [368,] 1.1288409 1.1303360 1.1309967 -#> [369,] 0.9086849 0.9438448 1.0109677 -#> [370,] 0.5769253 0.5848354 0.5854538 -#> [371,] 0.9085786 1.0613554 1.0737194 -#> [372,] 0.8003550 0.8014943 0.8155339 -#> [373,] 0.6520764 0.6809989 0.6925582 -#> [374,] 1.0800418 1.0875153 1.1016418 -#> [375,] 0.5740169 0.5790913 0.6239847 -#> [376,] 1.4694834 1.4839979 1.5690349 -#> [377,] 0.5039508 0.5773118 0.6093329 -#> [378,] 0.6447953 0.7064193 0.7456880 -#> [379,] 0.7255185 0.7739046 0.7975968 -#> [380,] 0.7766619 0.7914277 0.7957228 -#> [381,] 0.6988580 0.7077364 0.7449540 -#> [382,] 0.9485530 0.9536875 0.9786161 -#> [383,] 0.6256541 0.6275538 0.6362574 -#> [384,] 0.6720682 0.7787870 0.7957228 -#> [385,] 1.2687172 1.2802805 1.3047140 -#> [386,] 0.5507339 0.6064328 0.6108049 -#> [387,] 0.8122301 0.8179785 0.8191682 -#> [388,] 0.7100582 0.7123172 0.7344118 -#> [389,] 0.8584639 0.8613229 0.8715004 -#> [390,] 0.7809224 0.7943283 0.8036352 -#> [391,] 0.8789989 0.8846523 0.9430009 -#> [392,] 0.5787497 0.6434306 0.6461901 -#> [393,] 0.5508534 0.5647906 0.5816181 -#> [394,] 1.0615136 1.0758883 1.1293451 -#> [395,] 0.6867323 0.7006231 0.7072118 -#> [396,] 1.3040997 1.3157568 1.3392170 -#> [397,] 0.9589776 0.9816568 1.0123912 -#> [398,] 0.8932279 0.9187887 0.9317281 -#> [399,] 0.5505802 0.6370801 0.6504363 -#> [400,] 0.9190036 0.9236847 0.9552591 -#> [401,] 0.8119278 0.8150843 0.8183023 -#> [402,] 0.7615951 0.9273662 0.9407729 -#> [403,] 0.5254921 0.5363079 0.5819930 -#> [404,] 0.5177915 0.5189196 0.5980605 -#> [405,] 1.4713607 1.4878362 1.5052571 -#> [406,] 0.5693620 0.5790913 0.6271961 -#> [407,] 0.6229257 0.6454792 0.6617013 -#> [408,] 0.6365288 0.6380717 0.6629856 -#> [409,] 0.7639416 0.7647506 0.7828068 -#> [410,] 0.8029412 0.8332180 0.8366698 -#> [411,] 1.0092043 1.1255428 1.1476501 -#> [412,] 0.7288516 0.7313914 0.7520435 -#> [413,] 0.5735514 0.5819185 0.5864900 -#> [414,] 1.1381939 1.2571642 1.2603551 -#> [415,] 0.8042587 0.8070106 0.8208756 -#> [416,] 0.7189922 0.7228040 0.7890711 -#> [417,] 0.8023364 0.8728391 0.8812638 -#> [418,] 0.6259600 0.6381189 0.6573023 -#> [419,] 0.8915097 0.9321882 0.9529310 -#> [420,] 0.6882820 0.7178746 0.7298191 -#> [421,] 0.8980328 0.9007722 0.9341469 -#> [422,] 0.7858526 0.8211451 0.8417973 -#> [423,] 0.6611121 0.6853944 0.7027129 -#> [424,] 0.9034129 0.9303547 0.9338346 -#> [425,] 0.6103830 0.6146042 0.6152917 -#> [426,] 1.2741930 1.3048889 1.3166447 -#> [427,] 0.9660454 0.9786161 1.0160631 -#> [428,] 0.9027740 0.9208716 0.9272575 -#> [429,] 0.7189606 0.7354697 0.8051510 -#> [430,] 0.6149044 0.6544568 0.6568448 -#> [431,] 1.6014659 1.6124795 1.6464965 -#> [432,] 0.8645507 0.8864328 0.8991177 -#> [433,] 0.7941475 0.7968045 0.8960429 -#> [434,] 0.5584686 0.6195858 0.6347572 -#> [435,] 0.7867618 0.7902651 0.8077762 -#> [436,] 0.7012632 0.7241992 0.7251975 -#> [437,] 0.6840500 0.6843970 0.6867323 -#> [438,] 0.9685332 0.9759108 0.9967674 -#> [439,] 0.7871674 0.8543760 0.9412158 -#> [440,] 0.8392612 0.8716437 0.8721196 -#> [441,] 0.8069808 0.8518542 0.8620734 -#> [442,] 0.8871901 0.9332700 1.0400032 -#> [443,] 0.7402080 0.7404499 0.7449671 -#> [444,] 0.9053295 0.9441010 0.9513810 -#> [445,] 0.5589335 0.5616279 0.6127602 -#> [446,] 0.6576721 0.6804033 0.7033662 -#> [447,] 0.9927727 0.9967219 1.0073378 -#> [448,] 0.7042698 0.7459097 0.7554355 -#> [449,] 0.5727056 0.5983884 0.7005419 -#> [450,] 0.9723325 0.9768080 1.0335018 -#> [451,] 1.1559923 1.2082916 1.3320827 -#> [452,] 0.5845112 0.6074706 0.6114413 -#> [453,] 0.7398085 0.7490561 0.7493836 -#> [454,] 0.5464723 0.5666532 0.6183374 -#> [455,] 1.1370093 1.2190428 1.2428198 -#> [456,] 0.6722716 0.6847392 0.6861309 -#> [457,] 0.8434437 0.8982119 0.9272575 -#> [458,] 0.4985066 0.5338072 0.5382786 -#> [459,] 0.6853056 0.6895883 0.7055080 -#> [460,] 1.0526528 1.0920031 1.1344009 -#> [461,] 0.5698526 0.5888508 0.6044754 -#> [462,] 0.5562461 0.5621462 0.5847977 -#> [463,] 1.7375101 1.8041639 1.8503639 -#> [464,] 1.2224570 1.2429012 1.2841455 -#> [465,] 0.8482913 0.8697166 0.8788324 -#> [466,] 0.6964980 0.7070283 0.7537934 -#> [467,] 0.6418468 0.6671150 0.6917589 -#> [468,] 0.5065117 0.5391964 0.5705462 -#> [469,] 0.7010090 0.7214546 0.7667716 -#> [470,] 0.7275787 0.8099892 0.8246746 -#> [471,] 0.6374177 0.6419992 0.6515068 -#> [472,] 0.9687647 0.9824862 1.0229776 -#> [473,] 1.1807701 1.2286957 1.2921542 -#> [474,] 0.6557559 0.6844510 0.7066562 -#> [475,] 0.7262108 0.7448291 0.7559599 -#> [476,] 0.5164314 0.5171654 0.5205927 -#> [477,] 0.6980515 0.7317562 0.7372731 -#> [478,] 1.0509450 1.0538652 1.0635850 -#> [479,] 1.1200334 1.1386040 1.1417755 -#> [480,] 0.9911805 1.0084563 1.0218342 -#> [481,] 0.7985012 0.8332130 0.8358105 -#> [482,] 1.4960626 1.5213869 1.5779222 -#> [483,] 1.1499770 1.1650330 1.1797567 -#> [484,] 0.5254051 0.5810606 0.5872490 -#> [485,] 1.0248897 1.0299032 1.0416752 -#> [486,] 0.7847803 0.8099471 0.8112365 -#> [487,] 0.9444033 0.9586442 0.9673000 -#> [488,] 0.7103499 0.7157430 0.8285483 -#> [489,] 0.6194508 0.6614908 0.6766100 -#> [490,] 0.8434020 0.8588308 0.9001202 -#> [491,] 1.0468715 1.0511804 1.0698476 -#> [492,] 0.4926226 0.5391086 0.5810606 -#> [493,] 0.9021870 1.0212893 1.0643079 -#> [494,] 0.6158978 0.6229634 0.6336534 -#> [495,] 0.7328516 0.8080500 0.8320779 -#> [496,] 0.5578328 0.5877514 0.5907850 -#> [497,] 0.9729825 0.9912929 1.0054113 -#> [498,] 1.3147718 1.3647384 1.3662988 -#> [499,] 2.7978178 2.8484729 2.9330105 -#> [500,] 0.7264557 0.8516435 0.8679878 -#> [501,] 0.6613612 0.7231428 0.7516601 -#> [502,] 0.8481883 0.8585328 0.8599669 -#> [503,] 0.7367848 0.7974064 0.8334096 -#> [504,] 0.5542075 0.5820559 0.5893635 -#> [505,] 0.6116252 0.6465721 0.7169874 -#> [506,] 0.8556933 0.8736286 0.9100260 -#> [507,] 0.5740050 0.6339344 0.6344504 -#> [508,] 1.0379094 1.0471588 1.0521241 -#> [509,] 0.7351449 0.7733922 0.7906739 -#> [510,] 1.1501040 1.1827899 1.2138401 -#> [511,] 1.0081010 1.0269155 1.0403183 -#> [512,] 1.5049288 1.5205302 1.5471490 -#> [513,] 1.1290831 1.1957300 1.2288811 -#> [514,] 1.0214357 1.0770711 1.1021957 -#> [515,] 0.8460879 0.8489286 0.8553952 -#> [516,] 0.5122100 0.5338072 0.5555859 -#> [517,] 0.8566329 0.8780640 0.9458068 -#> [518,] 0.7053574 0.7396663 0.7529493 -#> [519,] 0.8132218 0.8619528 0.8794552 -#> [520,] 0.7932397 0.8036352 0.8371247 -#> [521,] 0.7600011 0.8030624 0.8731730 -#> [522,] 1.9339702 1.9723446 2.0921342 -#> [523,] 0.8728979 0.8798903 0.9108048 -#> [524,] 0.8602468 0.8898735 0.9355186 -#> [525,] 0.8434020 0.8554912 0.8611303 -#> [526,] 0.6234424 0.6281006 0.6329588 -#> [527,] 0.8075600 0.8575611 0.8953935 -#> [528,] 1.0731621 1.1108133 1.1127074 -#> [529,] 0.8029918 0.8075866 0.8531193 -#> [530,] 0.7844424 0.8029918 0.8055212 -#> [531,] 0.9918341 1.0455495 1.2020781 -#> [532,] 0.6647266 0.6756108 0.6823798 -#> [533,] 0.5475485 0.5560572 0.5752051 -#> [534,] 1.4061900 1.4145051 1.4499503 -#> [535,] 0.5350829 0.5391656 0.5428234 -#> [536,] 0.8666887 0.8838251 0.8856987 -#> [537,] 0.6450181 0.6989626 0.7645513 -#> [538,] 0.6936951 0.6942788 0.7069361 -#> [539,] 0.5448491 0.5483102 0.5591494 -#> [540,] 0.5762100 0.5955601 0.6073795 -#> [541,] 1.3941351 1.4337750 1.4711072 -#> [542,] 1.1020501 1.1086187 1.1164041 -#> [543,] 1.0664033 1.0783751 1.0789541 -#> [544,] 1.1373486 1.1421385 1.1896530 -#> [545,] 0.6098957 0.6264674 0.6398529 -#> [546,] 0.7891117 0.7891957 0.8193688 -#> [547,] 1.0043056 1.0920505 1.1169794 -#> [548,] 0.5197251 0.5388128 0.5394863 -#> [549,] 1.2325702 1.3233724 1.3253074 -#> [550,] 2.3698032 2.4459672 2.4605241 -#> [551,] 0.5694467 0.5983994 0.6216318 -#> [552,] 0.7072118 0.7211100 0.7239720 -#> [553,] 0.5203712 0.5367804 0.5406582 -#> [554,] 1.0371558 1.0512924 1.0639414 -#> [555,] 0.9910236 1.0217891 1.0661520 -#> [556,] 1.0388253 1.0509622 1.0759924 -#> [557,] 0.7511114 0.7523335 0.7737363 -#> [558,] 1.0436830 1.0595302 1.1315020 -#> [559,] 0.7485221 0.7555543 0.7562221 -#> [560,] 0.8110856 0.8442247 0.8590657 -#> [561,] 0.7910970 0.8125575 0.8269329 -#> [562,] 2.0329060 2.0464947 2.1051615 -#> [563,] 1.5614089 1.5792095 1.6496802 -#> [564,] 1.1087357 1.1345552 1.1487033 -#> [565,] 0.6202705 0.6210650 0.6326365 -#> [566,] 1.0882388 1.1474096 1.1573929 -#> [567,] 1.0228863 1.0319975 1.0508046 -#> [568,] 0.5302128 0.5637004 0.5904987 -#> [569,] 1.0220488 1.0539673 1.0574935 -#> [570,] 1.0401324 1.0888612 1.1436175 -#> [571,] 0.4961666 0.5716476 0.5838902 -#> [572,] 0.6315611 0.6399279 0.6997841 -#> [573,] 1.2314421 1.2539958 1.2860329 -#> [574,] 0.8483180 0.8519278 0.8581231 -#> [575,] 0.6048017 0.6223452 0.6331574 -#> [576,] 1.5010928 1.5378015 1.5387295 -#> [577,] 0.7322894 0.7402701 0.7722186 -#> [578,] 0.5335917 0.5458239 0.5537864 -#> [579,] 0.6568820 0.7080392 0.7319985 -#> [580,] 0.6873511 0.7011420 0.7050837 -#> [581,] 0.8928800 0.9238549 0.9298428 -#> [582,] 0.7118597 0.7422491 0.7729236 -#> [583,] 0.9892560 1.0059526 1.0644100 -#> [584,] 1.0792311 1.0849133 1.1084016 -#> [585,] 0.8916000 0.9396140 0.9467054 -#> [586,] 0.8400040 0.8639699 0.9180399 -#> [587,] 1.4627325 1.4824280 1.5210347 -#> [588,] 1.0141649 1.1053538 1.1212797 -#> [589,] 0.6793500 0.6883186 0.7481124 -#> [590,] 0.7648148 0.7906054 0.7945961 -#> [591,] 0.9025918 0.9716701 1.0721897 -#> [592,] 0.6423844 0.6449319 0.6724115 -#> [593,] 0.4725017 0.4901664 0.6232102 -#> [594,] 0.5382786 0.5595756 0.5714908 -#> [595,] 0.8342513 0.8361073 0.9158839 -#> [596,] 0.6828360 0.7077951 0.7093827 -#> [597,] 0.8045615 0.8189127 0.8202734 -#> [598,] 0.6056041 0.6122406 0.6294766 -#> [599,] 0.7319659 0.7665120 0.7830806 -#> [600,] 0.5793024 0.5920239 0.5944527 -#> [601,] 0.5422872 0.5934187 0.6225972 -#> [602,] 0.5038229 0.5088133 0.5333304 -#> [603,] 0.6339928 0.6344587 0.6415068 -#> [604,] 0.5621462 0.5822668 0.5928315 -#> [605,] 0.5043623 0.5152282 0.5412566 -#> [606,] 0.8514101 0.8581030 0.8976943 -#> [607,] 0.8099237 0.8210332 0.8644987 -#> [608,] 1.0553813 1.0852216 1.0892656 -#> [609,] 1.0876542 1.0967448 1.0970276 -#> [610,] 0.6575762 0.6783473 0.6829099 -#> [611,] 0.7092287 0.7793916 0.8176927 -#> [612,] 0.7845856 0.8247635 0.8874659 -#> [613,] 0.8388916 0.8632137 0.9030833 -#> [614,] 1.0231618 1.0235836 1.0425666 -#> [615,] 0.7166701 0.7827488 0.8202734 -#> [616,] 0.6064328 0.6065965 0.6075559 -#> [617,] 0.8378213 0.8425124 0.8486141 -#> [618,] 1.2220645 1.3184867 1.3757140 -#> [619,] 0.5756904 0.5858072 0.5933102 -#> [620,] 1.2608402 1.2822373 1.2869762 -#> [621,] 1.2739743 1.3099188 1.3250280 -#> [622,] 0.6619830 0.6699510 0.7000377 -#> [623,] 0.7164536 0.7257358 0.8023364 -#> [624,] 1.2374534 1.2635508 1.2643068 -#> [625,] 0.6902308 0.7028009 0.7319659 -#> [626,] 0.8958902 0.9030162 0.9093301 -#> [627,] 0.5549466 0.5755035 0.5765267 -#> [628,] 0.7460089 0.7639416 0.8313206 -#> [629,] 0.4905728 0.4942109 0.5031505 -#> [630,] 0.6007005 0.6234864 0.6677731 -#> [631,] 1.5964418 1.6594767 1.6659284 -#> [632,] 0.6744898 0.6937754 0.6943448 -#> [633,] 1.0761527 1.0898412 1.1706041 -#> [634,] 0.6242899 0.6293683 0.6310264 -#> [635,] 0.8992914 1.0140728 1.0649797 -#> [636,] 1.1882590 1.2053993 1.2229430 -#> [637,] 0.5477302 0.6005423 0.6195639 -#> [638,] 0.6460751 0.6486946 0.6554590 -#> [639,] 0.7732483 0.7848387 0.8093511 -#> [640,] 0.7354020 0.7401168 0.7578651 -#> [641,] 0.5115338 0.5376920 0.5490150 -#> [642,] 1.3809669 1.3931236 1.3963649 -#> [643,] 0.6162748 0.6355525 0.6373189 -#> [644,] 0.5609127 0.5977554 0.6404431 -#> [645,] 0.9621100 1.0284746 1.0561041 -#> [646,] 0.8011934 0.8153516 0.8284279 -#> [647,] 1.1757033 1.2322491 1.3049420 -#> [648,] 0.5047042 0.5331247 0.5417924 -#> [649,] 0.7390150 0.7774246 0.7941222 -#> [650,] 1.2468155 1.2588606 1.2648697 -#> [651,] 0.8700253 0.9101938 0.9348567 -#> [652,] 0.7176706 0.7422686 0.7525945 -#> [653,] 1.4647242 1.4649795 1.4841694 -#> [654,] 0.5751650 0.5832221 0.6152891 -#> [655,] 0.8821678 0.9729825 0.9755979 -#> [656,] 0.5068300 0.5140644 0.5700511 -#> [657,] 1.0711251 1.1204842 1.1268332 -#> [658,] 0.6944146 0.7117599 0.7210043 -#> [659,] 0.5651490 0.5871811 0.6401649 -#> [660,] 0.6530109 0.6705030 0.7392660 -#> [661,] 0.9938395 1.0084563 1.0115921 -#> [662,] 0.5564536 0.5589335 0.5876542 -#> [663,] 0.7698231 0.7761523 0.8092654 -#> [664,] 0.6286956 0.6359375 0.6481956 -#> [665,] 0.6136417 0.6709636 0.6793446 -#> [666,] 1.5772505 1.5959408 1.7235659 -#> [667,] 0.5598840 0.5724169 0.5938387 -#> [668,] 0.5214682 0.5598422 0.5650591 -#> [669,] 1.2304458 1.3551871 1.3687091 -#> [670,] 1.1947125 1.2882108 1.2936240 -#> [671,] 0.5446488 0.5522417 0.5537372 -#> [672,] 0.8751083 0.8829570 1.0368283 -#> [673,] 1.0337591 1.0884658 1.0925525 -#> [674,] 1.3091289 1.4285975 1.4426094 -#> [675,] 0.7962729 0.8106037 0.8532589 -#> [676,] 0.5474013 0.5836521 0.6108524 -#> [677,] 0.8050155 0.8197892 0.8250720 -#> [678,] 1.2499746 1.2588606 1.2623633 -#> [679,] 0.7672906 0.8184894 0.8372153 -#> [680,] 0.8309062 0.8499388 0.8522157 -#> [681,] 1.2357405 1.2813162 1.2881833 -#> [682,] 0.7512256 0.7537934 0.8017146 -#> [683,] 0.6924338 0.7604367 0.7621253 -#> [684,] 0.8923240 0.9105257 0.9758638 -#> [685,] 0.5165238 0.5184814 0.5219601 -#> [686,] 0.7218092 0.7418706 0.8362253 -#> [687,] 1.0000655 1.0190872 1.0434870 -#> [688,] 1.4733664 1.4897250 1.5071121 -#> [689,] 0.7361053 0.7372937 0.7560528 -#> [690,] 1.1736718 1.2135157 1.2144153 -#> [691,] 1.6315990 1.6820170 1.6940504 -#> [692,] 0.5508534 0.5760182 0.5859741 -#> [693,] 0.8191035 0.8410250 0.8522157 -#> [694,] 0.9218645 0.9487543 0.9883042 -#> [695,] 0.6830429 0.7555543 0.7700034 -#> [696,] 0.8328559 0.8566730 0.8788406 -#> [697,] 0.7060284 0.7123172 0.7189718 -#> [698,] 0.6966668 0.7095384 0.7583497 -#> [699,] 0.6793757 0.7066244 0.7092152 -#> [700,] 0.8070216 0.8265935 0.8519278 -#> [701,] 0.6302841 0.6342402 0.6365512 -#> [702,] 1.2890853 1.2964382 1.3010610 -#> [703,] 0.9358046 1.1144320 1.1393480 -#> [704,] 0.7081493 0.7151052 0.7405949 -#> [705,] 1.0195369 1.2259891 1.2269391 -#> [706,] 0.5624723 0.5935204 0.6081886 -#> [707,] 0.8580177 0.8863304 0.9252928 -#> [708,] 1.3400658 1.3529517 1.3630672 -#> [709,] 1.0605147 1.0743078 1.0866625 -#> [710,] 0.5703296 0.5793024 0.5853281 -#> [711,] 0.5644427 0.6095891 0.6157921 -#> [712,] 0.6489326 0.6562908 0.6735804 -#> [713,] 0.7164536 0.7185370 0.7198287 -#> [714,] 1.1301325 1.1696451 1.1861796 -#> [715,] 0.6106142 0.6114538 0.6166628 -#> [716,] 0.8472093 0.8533833 0.8807989 -#> [717,] 1.0511908 1.0913276 1.0950373 -#> [718,] 0.7813662 0.7920934 0.8048466 -#> [719,] 1.0322897 1.0772629 1.0996304 -#> [720,] 0.6212383 0.6707692 0.6776307 -#> [721,] 0.8476778 0.8508133 0.9056069 -#> [722,] 0.7818596 0.7991157 0.8047440 -#> [723,] 0.6360874 0.6570015 0.6794117 -#> [724,] 1.5425863 1.5770401 1.5775648 -#> [725,] 1.0469513 1.0730223 1.1365501 -#> [726,] 2.5499001 2.5877972 2.5973348 -#> [727,] 0.9649681 1.0000655 1.0105031 -#> [728,] 0.6682089 0.6689205 0.6925582 -#> [729,] 0.8289745 0.8606617 0.8995628 -#> [730,] 1.1909792 1.2032655 1.2214736 -#> [731,] 0.8155527 0.8330002 0.8910643 -#> [732,] 1.4129700 1.4257205 1.4269882 -#> [733,] 1.2326746 1.2666659 1.2807269 -#> [734,] 0.9759457 1.0941410 1.1131137 -#> [735,] 1.2926398 1.2943778 1.3189782 -#> [736,] 0.8038318 0.9191734 0.9259836 -#> [737,] 0.7771783 0.8173460 0.8183498 -#> [738,] 0.8342751 0.8375110 0.8468821 -#> [739,] 0.7669882 0.7769399 0.7783967 -#> [740,] 0.7676540 0.7703288 0.7959924 -#> [741,] 0.4807659 0.4811913 0.4829322 -#> [742,] 0.7628195 0.7722913 0.8143765 -#> [743,] 0.5038506 0.5872490 0.5902789 -#> [744,] 0.8199415 0.8279275 0.8672934 -#> [745,] 0.7083628 0.7338538 0.7515559 -#> [746,] 1.7624131 1.7831930 1.8529676 -#> [747,] 0.6527544 0.6722586 0.7173171 -#> [748,] 0.6165924 0.6242899 0.6525633 -#> [749,] 0.5591494 0.5773746 0.5871660 -#> [750,] 0.6133688 0.6143176 0.6344504 -#> [751,] 0.5977554 0.6075119 0.6081672 -#> [752,] 0.8201508 0.8217113 0.8258053 -#> [753,] 0.7158419 0.7167084 0.7526110 -#> [754,] 0.5488100 0.5512399 0.5565225 -#> [755,] 0.9689169 0.9702371 0.9763988 -#> [756,] 0.7403582 0.7672499 0.7689766 -#> [757,] 0.6011118 0.6035912 0.6143492 -#> [758,] 0.6690084 0.6855743 0.6954385 -#> [759,] 0.8650603 0.9300395 1.0290951 -#> [760,] 0.9413051 0.9449080 0.9541890 -#> [761,] 0.6814680 0.6873511 0.6933264 -#> [762,] 0.6146394 0.6270975 0.6384403 -#> [763,] 1.2295889 1.2969550 1.3012819 -#> [764,] 0.7912666 0.8175944 0.8652332 -#> [765,] 1.5165658 1.5632683 1.6220706 -#> [766,] 0.7619575 0.7845856 0.7903842 -#> [767,] 0.9105072 0.9167320 0.9350408 -#> [768,] 0.8096945 0.8271271 0.8438052 -#> [769,] 1.0189938 1.0358824 1.0408619 -#> [770,] 0.7793938 0.7906739 0.8376128 -#> [771,] 1.7807055 1.7927447 1.7961446 -#> [772,] 1.2081725 1.2300703 1.2320259 -#> [773,] 0.7095384 0.7634109 0.8012599 -#> [774,] 0.5526499 0.5690779 0.5941848 -#> [775,] 1.0034701 1.0198180 1.0597253 -#> [776,] 0.6628386 0.6747995 0.6812344 -#> [777,] 0.5489606 0.5848354 0.5980778 -#> [778,] 0.6729456 0.6753574 0.6895843 -#> [779,] 0.9989262 1.0340941 1.0549130 -#> [780,] 0.7886171 0.8140232 0.8596395 -#> [781,] 0.9398637 1.0138404 1.0257756 -#> [782,] 0.5196511 0.5300860 0.5441774 -#> [783,] 0.7712708 0.7785486 0.7933427 -#> [784,] 0.7314612 0.7390954 0.7903991 -#> [785,] 0.7497731 0.8423679 0.9455623 -#> [786,] 0.8584332 0.8779746 0.8886868 -#> [787,] 1.1090116 1.1598086 1.1838323 -#> [788,] 1.0667274 1.0709461 1.0753148 -#> [789,] 0.9512250 1.0026992 1.0055707 -#> [790,] 0.6047498 0.6423844 0.6672514 -#> [791,] 0.7644297 0.7823320 0.8289601 -#> [792,] 0.7533340 0.8625143 0.8690115 -#> [793,] 0.8308841 0.8392308 0.8533834 -#> [794,] 0.7378000 0.7576315 0.7722186 -#> [795,] 1.0377217 1.0471588 1.1430132 -#> [796,] 0.6464568 0.6722230 0.7071145 -#> [797,] 0.4768116 0.5038506 0.5119983 -#> [798,] 0.5321714 0.5509819 0.5825465 -#> [799,] 0.5765121 0.6117266 0.6334817 -#> [800,] 1.2780696 1.2879393 1.3291877 -#> [801,] 0.9607605 0.9730312 0.9790967 -#> [802,] 0.9759251 1.0250429 1.0486243 -#> [803,] 0.6575762 0.6844912 0.6890709 -#> [804,] 0.8679566 0.9108230 1.0005149 -#> [805,] 0.6319259 0.6342402 0.7315519 -#> [806,] 0.6782024 0.6794570 0.7198823 -#> [807,] 0.5168748 0.5367804 0.5435968 -#> [808,] 0.5489606 0.6098179 0.6106142 -#> [809,] 0.6365382 0.6373189 0.6435149 -#> [810,] 0.5801760 0.5845530 0.5859639 -#> [811,] 0.7177089 0.7441591 0.7792351 -#> [812,] 0.7422491 0.7606548 0.7615598 -#> [813,] 0.5879726 0.5909601 0.6122462 -#> [814,] 0.5085484 0.5628062 0.5765421 -#> [815,] 0.5026705 0.5189029 0.5284969 -#> [816,] 1.2496203 1.2682469 1.2889422 -#> [817,] 0.9497079 0.9783047 0.9847914 -#> [818,] 0.8094516 0.8416187 0.8517166 -#> [819,] 0.8513099 0.9304029 0.9474305 -#> [820,] 0.8280890 0.8349683 0.8424210 -#> [821,] 0.5074146 0.5448491 0.5822668 -#> [822,] 2.0027772 2.0190620 2.0273821 -#> [823,] 0.6307234 0.6482195 0.6661770 -#> [824,] 2.3750614 2.3903208 2.3941222 -#> [825,] 0.8838251 0.9023417 0.9142908 -#> [826,] 0.9889649 1.0167606 1.0442092 -#> [827,] 1.3254673 1.3315739 1.3326072 -#> [828,] 0.5397715 0.5425306 0.5439430 -#> [829,] 0.6322275 0.6484510 0.7294171 -#> [830,] 0.7303706 0.7766673 0.7895184 -#> [831,] 0.9917530 1.0002187 1.0179955 -#> [832,] 1.0934972 1.0939917 1.1230953 -#> [833,] 1.0198930 1.0874022 1.1050338 -#> [834,] 0.4827478 0.4842416 0.5694467 -#> [835,] 0.9877948 1.1083030 1.1223708 -#> [836,] 0.6776030 0.7596441 0.7681511 -#> [837,] 0.8056446 0.8448444 0.8542834 -#> [838,] 0.8195514 0.8665423 0.8766603 -#> [839,] 0.5893552 0.6070066 0.6161363 -#> [840,] 0.4763757 0.5291550 0.5368799 -#> [841,] 0.7351449 0.8048950 0.8117724 -#> [842,] 0.5595356 0.5705462 0.5735618 -#> [843,] 1.8124457 1.8751240 1.8873251 -#> [844,] 0.7224461 0.7572010 0.7808384 -#> [845,] 0.5486938 0.5735514 0.5755479 -#> [846,] 0.7066244 0.7458233 0.7714272 -#> [847,] 0.6431490 0.6594180 0.6620760 -#> [848,] 0.6721738 0.7166255 0.7308810 -#> [849,] 1.2402478 1.2403056 1.2798592 -#> [850,] 0.8183498 0.8341716 0.8679566 -#> [851,] 0.5682557 0.5983884 0.7436629 -#> [852,] 2.0901283 2.1193404 2.1453606 -#> [853,] 0.8889776 0.9101522 0.9120216 -#> [854,] 0.6801136 0.6814680 0.7064047 -#> [855,] 1.1362938 1.1583177 1.1843568 -#> [856,] 0.5859734 0.6395725 0.6679704 -#> [857,] 0.7928113 0.8106336 0.8582234 -#> [858,] 1.4155497 1.4261048 1.4362454 -#> [859,] 0.6932967 0.7067968 0.7267149 -#> [860,] 0.6422858 0.6720682 0.6826380 -#> [861,] 0.5245846 0.5254051 0.5499494 -#> [862,] 1.9490004 1.9533986 1.9915554 -#> [863,] 0.6024946 0.6290822 0.6487378 -#> [864,] 0.7039406 0.7067968 0.7303706 -#> [865,] 0.8614348 0.9119135 0.9338346 -#> [866,] 0.8723443 0.8807200 0.8847025 -#> [867,] 0.6138500 0.6325047 0.6444264 -#> [868,] 0.8627456 0.9297663 0.9506089 -#> [869,] 0.7957403 0.8158396 0.8162246 -#> [870,] 0.6207546 0.6482118 0.6815168 -#> [871,] 0.6873260 0.6925079 0.7224404 -#> [872,] 0.7004519 0.7115287 0.7141490 -#> [873,] 0.6206424 0.6294411 0.6419992 -#> [874,] 0.7751067 0.7927907 0.8317753 -#> [875,] 0.5477302 0.5905216 0.5968875 -#> [876,] 1.1172828 1.1568073 1.1668856 -#> [877,] 0.8027620 0.8289667 0.8584660 -#> [878,] 1.3446773 1.3871050 1.4087059 -#> [879,] 0.7947447 0.8026697 0.8187020 -#> [880,] 0.7095780 0.7120531 0.7224331 -#> [881,] 0.5165238 0.5197251 0.5563110 -#> [882,] 0.4867866 0.4886340 0.5277014 -#> [883,] 0.6095891 0.6444264 0.6471754 -#> [884,] 1.1103547 1.1321855 1.1509306 -#> [885,] 0.6683102 0.6714550 0.6776343 -#> [886,] 0.6149044 0.6266999 0.6792860 -#> [887,] 1.0134636 1.0375347 1.0748362 -#> [888,] 0.7786041 0.8384083 0.8425124 -#> [889,] 0.6276169 0.6734215 0.6775417 -#> [890,] 0.9176432 0.9291135 0.9400309 -#> [891,] 0.5955329 0.6026508 0.6095924 -#> [892,] 0.4747824 0.5152282 0.5298413 -#> [893,] 0.6624071 0.6869585 0.6930631 -#> [894,] 0.9902618 0.9946636 1.0059933 -#> [895,] 0.6006478 0.6109909 0.7827485 -#> [896,] 1.0739924 1.1353441 1.1362353 -#> [897,] 0.5088133 0.5348194 0.5409899 -#> [898,] 0.8149680 0.8505206 0.8677531 -#> [899,] 0.9816910 0.9847571 0.9914863 -#> [900,] 0.9081888 0.9153452 0.9188084 -#> [901,] 0.6153536 0.6153815 0.6182721 -#> [902,] 0.5495761 0.5507399 0.5603175 -#> [903,] 2.5840580 2.5929933 2.6234895 -#> [904,] 1.1861796 1.1973393 1.2292255 -#> [905,] 0.5724835 0.6038711 0.6115347 -#> [906,] 0.8413863 0.8465449 0.8546885 -#> [907,] 0.8071671 0.8180067 0.8939851 -#> [908,] 0.5879416 0.6358956 0.6381506 -#> [909,] 0.5240560 0.5919193 0.6096382 -#> [910,] 0.7269927 0.8232129 0.8276135 -#> [911,] 0.7996571 0.9101522 0.9129058 -#> [912,] 1.6359869 1.7033136 1.7092960 -#> [913,] 0.7159270 0.7256828 0.7342817 -#> [914,] 0.7425863 0.7431353 0.7449256 -#> [915,] 0.6427402 0.6471053 0.6617013 -#> [916,] 0.7987813 0.8538310 0.8605647 -#> [917,] 0.7536898 0.7766619 0.7871300 -#> [918,] 0.5485144 0.5539596 0.5594923 -#> [919,] 0.6752461 0.7208311 0.7571621 -#> [920,] 0.5653469 0.5664138 0.5700511 -#> [921,] 0.6455285 0.6785113 0.6878116 -#> [922,] 0.5816181 0.6417378 0.6576109 -#> [923,] 0.9541905 0.9633048 1.0140728 -#> [924,] 1.7120622 1.7506400 1.7786597 -#> [925,] 1.1356111 1.1501145 1.1861868 -#> [926,] 0.6844323 0.7211100 0.7277595 -#> [927,] 0.9919870 1.0046539 1.0585988 -#> [928,] 1.0909418 1.0970285 1.1137077 -#> [929,] 0.8215322 0.8246746 0.8262601 -#> [930,] 0.8218535 0.8307444 0.8410435 -#> [931,] 0.9490880 0.9559149 1.0106206 -#> [932,] 1.3404173 1.3431972 1.3540991 -#> [933,] 0.9182950 0.9930687 1.0316881 -#> [934,] 0.5778650 0.6233926 0.6552800 -#> [935,] 0.6513649 0.6688012 0.6800487 -#> [936,] 0.7431773 0.7557377 0.8549699 -#> [937,] 0.5240560 0.5434537 0.5609127 -#> [938,] 0.5520749 0.5642003 0.6029693 -#> [939,] 0.7903432 0.8028419 0.8341896 -#> [940,] 0.8409575 0.8928800 0.9019933 -#> [941,] 0.6343338 0.6726428 0.6845662 -#> [942,] 0.7056023 0.7745546 0.8093433 -#> [943,] 0.9929385 0.9997025 1.0238656 -#> [944,] 0.7246882 0.7409949 0.7639661 -#> [945,] 0.6295790 0.6592961 0.6793446 -#> [946,] 1.1429259 1.2309731 1.2322491 -#> [947,] 0.6787280 0.6887799 0.7126490 -#> [948,] 0.9650437 0.9661742 0.9723720 -#> [949,] 0.8346578 0.8391675 0.8997573 -#> [950,] 0.9614591 0.9675984 0.9725935 -#> [951,] 0.8829771 0.9036908 0.9877948 -#> [952,] 0.8512426 0.8532802 0.8848484 -#> [953,] 0.6166628 0.6170841 0.6371422 -#> [954,] 0.8329126 0.8530069 0.8645209 -#> [955,] 0.5247719 0.5300860 0.6102380 -#> [956,] 1.1619952 1.2036054 1.2282273 -#> [957,] 0.6391615 0.6557125 0.6612431 -#> [958,] 1.0725884 1.0784070 1.1014701 -#> [959,] 1.0767381 1.0916150 1.1107009 -#> [960,] 0.7359664 0.7421168 0.7671997 -#> [961,] 1.1424055 1.1488222 1.1747662 -#> [962,] 0.5546416 0.5719874 0.6326870 -#> [963,] 0.6186984 0.6290006 0.6319184 -#> [964,] 1.0452503 1.0807821 1.0947882 -#> [965,] 0.4862419 0.4933322 0.5164314 -#> [966,] 0.6015028 0.6106738 0.6130888 -#> [967,] 0.9397593 0.9456987 0.9475875 -#> [968,] 1.1989367 1.2039149 1.2430222 -#> [969,] 1.2733077 1.2977592 1.3079020 -#> [970,] 0.6587434 0.7021421 0.7257358 -#> [971,] 0.9298788 0.9332233 0.9786370 -#> [972,] 0.4583481 0.5174262 0.5584686 -#> [973,] 1.2784206 1.3214717 1.3237718 -#> [974,] 0.9892116 0.9895480 1.0097542 -#> [975,] 0.5051712 0.5437751 0.5481820 -#> [976,] 0.7593610 0.7609732 0.7633887 -#> [977,] 0.9492200 0.9543498 0.9551139 -#> [978,] 0.7632394 0.7974951 0.8192408 -#> [979,] 0.5735618 0.5908839 0.6015028 -#> [980,] 0.6197546 0.6206144 0.6359616 -#> [981,] 0.8684410 0.8772932 0.9226718 -#> [982,] 0.7774246 0.7999180 0.8602760 -#> [983,] 0.7194182 0.7592156 0.8063933 -#> [984,] 0.9483649 0.9895036 1.0640046 -#> [985,] 0.9019933 0.9142908 0.9483463 -#> [986,] 0.7750119 0.7840491 0.7910807 -#> [987,] 0.5490150 0.5614692 0.5650591 -#> [988,] 0.6108302 0.6178691 0.6390093 -#> [989,] 0.8721196 0.8953935 0.9019052 -#> [990,] 1.1754947 1.2030136 1.2309854 -#> [991,] 0.7230304 0.7697567 0.7910246 -#> [992,] 0.8350104 0.8966890 0.9137141 -#> [993,] 0.8924814 0.9205566 0.9220425 -#> [994,] 1.6178327 1.6278497 1.6799638 -#> [995,] 0.7267149 0.7846327 0.7985012 -#> [996,] 0.6584006 0.6801919 0.6883392 -#> [997,] 1.1804192 1.2117447 1.2190261 -#> [998,] 0.6526862 0.6584006 0.6732712 -#> [999,] 0.9596488 0.9604054 1.0200492 -#> [1000,] 1.2215756 1.2249653 1.2474735 +#> [1,] 1.5243659 1.5808169 1.6309819 +#> [2,] 0.9611154 0.9978109 1.0056389 +#> [3,] 0.8207332 0.8635637 0.8753390 +#> [4,] 0.9403258 0.9432942 0.9694713 +#> [5,] 0.7043648 0.7952674 0.8274985 +#> [6,] 0.5439281 0.5770422 0.5982568 +#> [7,] 0.7036611 0.7609579 0.8734171 +#> [8,] 0.5276847 0.5397691 0.6196082 +#> [9,] 0.6723708 0.7324915 0.7489528 +#> [10,] 0.6932124 0.7214650 0.7540280 +#> [11,] 1.0114782 1.0153096 1.0194461 +#> [12,] 0.6912918 0.6960641 0.7493430 +#> [13,] 0.7598048 0.8036485 0.8177474 +#> [14,] 0.6430809 0.6726969 0.6822545 +#> [15,] 1.0414107 1.0553590 1.0591613 +#> [16,] 0.8700759 0.8739138 0.8938110 +#> [17,] 0.7917640 0.7923931 0.8010192 +#> [18,] 0.8303925 0.8822084 0.8877890 +#> [19,] 0.7475963 0.7657464 0.7864254 +#> [20,] 0.5630225 0.5770422 0.6040373 +#> [21,] 0.6078729 0.6186548 0.6461841 +#> [22,] 0.8864625 0.9761193 0.9881459 +#> [23,] 0.5940433 0.5999559 0.6055215 +#> [24,] 0.6131153 0.6777096 0.7524123 +#> [25,] 0.6602214 0.6994899 0.7164263 +#> [26,] 0.8288068 0.9339935 0.9397074 +#> [27,] 0.6342003 0.6379932 0.6517197 +#> [28,] 1.1203890 1.1337488 1.2100997 +#> [29,] 0.5249234 0.6053305 0.6375590 +#> [30,] 0.7140120 0.7154912 0.7488831 +#> [31,] 0.4942333 0.5761194 0.5855809 +#> [32,] 0.7520886 0.8040873 0.8396827 +#> [33,] 0.8323721 0.8906242 0.9136292 +#> [34,] 0.6641804 0.6717917 0.6872239 +#> [35,] 1.0939944 1.1142197 1.1249202 +#> [36,] 0.5219579 0.5337046 0.5643711 +#> [37,] 0.9828091 1.0030337 1.0086629 +#> [38,] 0.6305399 0.6523084 0.6545695 +#> [39,] 0.8966548 0.9142811 0.9187279 +#> [40,] 0.6468075 0.7282995 0.7573701 +#> [41,] 1.0466836 1.0745694 1.0879764 +#> [42,] 0.8402753 0.8565728 0.8618696 +#> [43,] 0.6616534 0.6651803 0.6820772 +#> [44,] 0.8484498 0.8826854 0.9821245 +#> [45,] 1.6800755 1.6845896 1.7288681 +#> [46,] 0.8054544 0.8615314 0.8707869 +#> [47,] 0.6353663 0.6555521 0.6722421 +#> [48,] 0.9178182 0.9200243 0.9927841 +#> [49,] 0.5457032 0.5663475 0.5790349 +#> [50,] 0.4860429 0.5075179 0.5379716 +#> [51,] 0.7611719 0.7637640 0.7995270 +#> [52,] 1.2980012 1.3192358 1.3233202 +#> [53,] 0.5446332 0.5478312 0.5535708 +#> [54,] 0.6638055 0.6651148 0.6797352 +#> [55,] 0.5976980 0.6141899 0.6375299 +#> [56,] 0.8755020 0.8962753 0.9500238 +#> [57,] 1.2146188 1.2815139 1.4096943 +#> [58,] 1.0638888 1.1074634 1.1145257 +#> [59,] 0.7242678 0.7270035 0.7293509 +#> [60,] 1.0101676 1.0546030 1.0602406 +#> [61,] 0.6618560 0.6654939 0.6703510 +#> [62,] 0.7501233 0.7567534 0.8105553 +#> [63,] 0.7102408 0.7406132 0.7604855 +#> [64,] 0.6923005 0.7421846 0.7520886 +#> [65,] 1.2056942 1.2894449 1.2991575 +#> [66,] 1.0340444 1.0554123 1.0610237 +#> [67,] 1.3881422 1.3955733 1.4387810 +#> [68,] 0.7332208 0.7571446 0.7770250 +#> [69,] 1.0857663 1.1366361 1.1438864 +#> [70,] 0.7315282 0.7459833 0.7725486 +#> [71,] 0.6440949 0.6446191 0.6983985 +#> [72,] 1.2722014 1.2976706 1.3110711 +#> [73,] 1.3203683 1.3419641 1.3451671 +#> [74,] 1.6801885 1.6853579 1.7120924 +#> [75,] 0.5693952 0.5750238 0.5821031 +#> [76,] 1.0838494 1.1065840 1.1169843 +#> [77,] 0.8086745 0.8116027 0.8389967 +#> [78,] 0.6819764 0.7163205 0.7215572 +#> [79,] 0.7950445 0.8059375 0.8063845 +#> [80,] 0.7012542 0.7164036 0.7310572 +#> [81,] 2.1642842 2.2000876 2.2331132 +#> [82,] 0.8300079 0.8348768 0.8955452 +#> [83,] 0.7831751 0.8878947 0.8923720 +#> [84,] 0.7421833 0.7430611 0.7604475 +#> [85,] 1.1437027 1.2023561 1.3051234 +#> [86,] 0.7069953 0.7164263 0.7207612 +#> [87,] 0.5002889 0.5212093 0.5277063 +#> [88,] 1.0414267 1.0564780 1.1446718 +#> [89,] 0.7087140 0.7210939 0.7300471 +#> [90,] 0.6453419 0.6832413 0.7144920 +#> [91,] 0.6620797 0.6756653 0.7069087 +#> [92,] 1.1947872 1.2003763 1.2043890 +#> [93,] 0.8512671 0.8513194 0.9902324 +#> [94,] 0.8398371 0.8401201 0.8559189 +#> [95,] 0.6780016 0.6897462 0.7036488 +#> [96,] 0.5746850 0.5875314 0.5921158 +#> [97,] 0.8658725 0.9338371 0.9690399 +#> [98,] 1.0853816 1.1052694 1.1294021 +#> [99,] 0.6692727 0.6825616 0.7028998 +#> [100,] 1.1663184 1.1840966 1.1903360 +#> [101,] 0.6834033 0.7182481 0.7783143 +#> [102,] 0.7084309 0.7091938 0.7659119 +#> [103,] 0.6449446 0.6462933 0.6719392 +#> [104,] 0.9233463 0.9835241 0.9947733 +#> [105,] 1.1891001 1.2249966 1.2384766 +#> [106,] 1.1099235 1.1355901 1.1725647 +#> [107,] 0.8123415 0.8325188 0.8671570 +#> [108,] 0.8365915 0.8835575 0.9009966 +#> [109,] 0.6326446 0.6574176 0.6704237 +#> [110,] 1.3458911 1.3519802 1.3581095 +#> [111,] 0.6545029 0.6869348 0.6881646 +#> [112,] 1.0572347 1.0772545 1.0896860 +#> [113,] 0.7767504 0.8143436 0.8462347 +#> [114,] 0.9217313 0.9487040 0.9488802 +#> [115,] 0.7756343 0.8557102 0.8710254 +#> [116,] 0.7891744 0.8032448 0.8230275 +#> [117,] 0.8484029 0.8488996 0.8612993 +#> [118,] 0.8383914 0.8446840 0.8528961 +#> [119,] 0.8192676 0.9261510 0.9664756 +#> [120,] 0.7897595 0.8170575 0.8347737 +#> [121,] 0.9853501 1.0255424 1.0457865 +#> [122,] 0.6665253 0.7115648 0.7229703 +#> [123,] 0.7270004 0.7878174 0.7915487 +#> [124,] 1.8305911 1.9078020 1.9944160 +#> [125,] 0.5928401 0.6368901 0.6445785 +#> [126,] 0.7807276 0.7853142 0.8001476 +#> [127,] 0.7061602 0.7241037 0.7882429 +#> [128,] 0.6003784 0.6088547 0.6383166 +#> [129,] 0.7535734 0.7603356 0.7929358 +#> [130,] 0.9012099 0.9060264 0.9358829 +#> [131,] 0.6657764 0.6753620 0.6839478 +#> [132,] 0.9469076 0.9695313 1.0316372 +#> [133,] 0.9726798 1.0643520 1.0705907 +#> [134,] 0.9120428 0.9666811 1.0409997 +#> [135,] 1.5699467 1.5947757 1.6392738 +#> [136,] 0.8182372 0.8191019 0.8344704 +#> [137,] 0.6936492 0.7024877 0.7115648 +#> [138,] 0.6849311 0.7284462 0.7367644 +#> [139,] 0.8675932 0.8743178 0.8836714 +#> [140,] 0.5796756 0.5953103 0.6121718 +#> [141,] 0.5783303 0.5966800 0.6036024 +#> [142,] 0.6007914 0.6438789 0.6502767 +#> [143,] 0.6620525 0.6806436 0.6867477 +#> [144,] 0.8598790 0.8724186 0.9304678 +#> [145,] 0.7014700 0.7119572 0.7208976 +#> [146,] 0.8137668 0.8385605 0.8450309 +#> [147,] 1.8041595 1.8161606 1.8558945 +#> [148,] 1.1210947 1.1285060 1.1598155 +#> [149,] 0.6452655 0.6583454 0.6593995 +#> [150,] 1.3792715 1.4307066 1.5478979 +#> [151,] 1.0860630 1.1100208 1.1332205 +#> [152,] 0.6403226 0.6421274 0.6443655 +#> [153,] 1.1906040 1.2140674 1.2141235 +#> [154,] 1.0057026 1.0182671 1.0256053 +#> [155,] 1.0403373 1.0696501 1.0827458 +#> [156,] 1.1766876 1.2167468 1.2247420 +#> [157,] 0.6424917 0.6529798 0.6937404 +#> [158,] 0.7134555 0.7617684 0.8326278 +#> [159,] 0.7298838 0.7843733 0.8009879 +#> [160,] 0.5654880 0.5663867 0.5886008 +#> [161,] 0.5649241 0.5693824 0.5812760 +#> [162,] 0.8142111 0.8256681 0.8272541 +#> [163,] 0.8281454 0.8328057 0.8483943 +#> [164,] 0.9857413 1.0088831 1.0380476 +#> [165,] 0.5506159 0.5767945 0.5830846 +#> [166,] 0.8656360 0.8914866 0.9326425 +#> [167,] 0.5233900 0.5292069 0.5416509 +#> [168,] 0.5578011 0.5643164 0.5944404 +#> [169,] 0.7386633 0.7662297 0.9012099 +#> [170,] 1.1387468 1.1658656 1.1679701 +#> [171,] 0.9562380 0.9585808 0.9690550 +#> [172,] 0.7958698 0.8491134 0.8656282 +#> [173,] 0.7329002 0.7548487 0.7614309 +#> [174,] 0.8888326 0.9138311 1.0139692 +#> [175,] 0.8472938 0.8731073 0.9566480 +#> [176,] 0.9184091 0.9184174 0.9258757 +#> [177,] 1.2139058 1.2432262 1.2583021 +#> [178,] 0.9205091 0.9403023 0.9821884 +#> [179,] 1.6050450 1.6194342 1.6253970 +#> [180,] 0.7539741 0.7678168 0.7904194 +#> [181,] 0.7747172 0.8280436 0.8445258 +#> [182,] 0.7610390 0.7624164 0.7719599 +#> [183,] 0.8098735 0.8116027 0.9110454 +#> [184,] 1.1174933 1.1827852 1.2005486 +#> [185,] 0.5391978 0.5443826 0.5470348 +#> [186,] 0.6459826 0.6600433 0.6793389 +#> [187,] 0.5965657 0.6096712 0.6342035 +#> [188,] 0.7144528 0.7317062 0.7411022 +#> [189,] 0.6665549 0.7036611 0.8455571 +#> [190,] 0.6707097 0.6855123 0.7104163 +#> [191,] 0.6880536 0.6951885 0.7146643 +#> [192,] 0.6223099 0.6273539 0.6390304 +#> [193,] 0.6342512 0.6388741 0.6752632 +#> [194,] 0.6327771 0.6469318 0.6602149 +#> [195,] 1.3646138 1.4342775 1.4604686 +#> [196,] 0.5524353 0.5610534 0.5743608 +#> [197,] 0.8344419 0.8614908 0.8924273 +#> [198,] 0.9935013 1.0102038 1.0604318 +#> [199,] 0.9424759 0.9766749 0.9793582 +#> [200,] 0.8082735 0.8481908 0.8727487 +#> [201,] 0.9090066 0.9098650 0.9413194 +#> [202,] 0.5442527 0.5662443 0.5758904 +#> [203,] 0.8915143 0.9333763 0.9400642 +#> [204,] 0.8024589 0.8144466 0.8153928 +#> [205,] 1.4519661 1.6070510 1.6374261 +#> [206,] 1.1985378 1.2227509 1.2647286 +#> [207,] 0.5022303 0.5100027 0.5204578 +#> [208,] 0.5768237 0.5828092 0.6070402 +#> [209,] 0.8405143 0.8704515 0.8730958 +#> [210,] 0.5245977 0.5732797 0.5897997 +#> [211,] 0.6331277 0.6618434 0.6851041 +#> [212,] 0.8040936 0.8173328 0.8276968 +#> [213,] 0.6421244 0.6618434 0.6669356 +#> [214,] 0.8695335 0.8838543 0.9361518 +#> [215,] 0.8780073 0.9322287 0.9854342 +#> [216,] 0.6316852 0.6329600 0.6541418 +#> [217,] 0.7409552 0.7749358 0.7765259 +#> [218,] 0.9633232 0.9700862 0.9714272 +#> [219,] 1.0734137 1.1260716 1.2022306 +#> [220,] 0.7089577 0.7958406 0.8049686 +#> [221,] 0.8087226 0.8820261 0.8979552 +#> [222,] 0.8423325 0.8752929 0.8792610 +#> [223,] 1.1291117 1.1304449 1.1406933 +#> [224,] 0.8237598 0.8477494 0.8525389 +#> [225,] 0.8003785 0.8120694 0.8328057 +#> [226,] 0.7349225 0.7711570 0.7720764 +#> [227,] 0.6475480 0.6542009 0.6584288 +#> [228,] 0.7708681 0.7972449 0.8793620 +#> [229,] 0.6023254 0.6616765 0.7303278 +#> [230,] 0.7363367 0.7637374 0.7667885 +#> [231,] 0.5180424 0.5335359 0.5597946 +#> [232,] 0.6078622 0.6278613 0.6333816 +#> [233,] 0.5992267 0.6017974 0.6051926 +#> [234,] 0.7275223 0.7328934 0.7858247 +#> [235,] 0.8318334 0.8319812 0.8925846 +#> [236,] 0.6797679 0.6834770 0.6946250 +#> [237,] 0.6653248 0.7276097 0.7601208 +#> [238,] 0.7668919 0.7835613 0.7921361 +#> [239,] 0.7590097 0.7645542 0.7747119 +#> [240,] 1.4700593 1.4715203 1.4952204 +#> [241,] 1.2063140 1.2099781 1.2246481 +#> [242,] 1.1757867 1.2450534 1.2567412 +#> [243,] 0.5949664 0.6131437 0.6576266 +#> [244,] 1.8735486 1.8739523 1.9042581 +#> [245,] 0.4875000 0.5057847 0.5298338 +#> [246,] 0.8673738 0.8834692 0.9245452 +#> [247,] 0.9667485 0.9776315 0.9832663 +#> [248,] 0.9853571 1.0121351 1.0502786 +#> [249,] 0.5453723 0.5486410 0.5569566 +#> [250,] 0.6523133 0.7128838 0.7176423 +#> [251,] 1.0271251 1.0428790 1.0444198 +#> [252,] 0.8376048 0.8393177 0.9233463 +#> [253,] 0.7022452 0.7049319 0.7232298 +#> [254,] 0.9378070 0.9465619 0.9539625 +#> [255,] 0.7323271 0.8051645 0.8157303 +#> [256,] 0.8054544 0.8320862 0.8332449 +#> [257,] 0.8221198 0.8385605 0.8713739 +#> [258,] 1.5881133 1.6214879 1.6598806 +#> [259,] 0.4608005 0.4680016 0.4993466 +#> [260,] 0.5204578 0.5257055 0.5325700 +#> [261,] 0.8619552 0.8927870 0.9265075 +#> [262,] 0.9361687 0.9668368 0.9751466 +#> [263,] 0.6995904 0.7329333 0.7573726 +#> [264,] 0.7285425 0.7669078 0.7967487 +#> [265,] 0.6361679 0.6626872 0.6931054 +#> [266,] 0.8464808 0.9042451 0.9361598 +#> [267,] 0.7958406 0.8504945 0.8575893 +#> [268,] 0.9130299 0.9142488 0.9184519 +#> [269,] 0.7023902 0.7036488 0.7190904 +#> [270,] 1.0134838 1.0375951 1.0953411 +#> [271,] 0.9033689 0.9500238 0.9779554 +#> [272,] 0.7569841 0.7713455 0.7743237 +#> [273,] 0.8281020 0.8404081 0.8997864 +#> [274,] 0.6021311 0.6329169 0.6451058 +#> [275,] 0.6116902 0.6584954 0.6982418 +#> [276,] 0.6766993 0.6804542 0.6970547 +#> [277,] 1.1671591 1.1940877 1.2013685 +#> [278,] 0.5385578 0.5524752 0.5540552 +#> [279,] 0.9806397 0.9903009 1.0124594 +#> [280,] 0.7733261 0.7757703 0.7814405 +#> [281,] 1.6651384 1.7565781 1.7573194 +#> [282,] 0.6233547 0.6381087 0.6790567 +#> [283,] 0.8320862 0.8486365 0.8659943 +#> [284,] 0.6353006 0.6440505 0.6491302 +#> [285,] 1.2464875 1.2563855 1.3767573 +#> [286,] 0.9741064 0.9807108 1.0087750 +#> [287,] 0.6533300 0.7024012 0.7046043 +#> [288,] 0.9766857 1.0721776 1.0851837 +#> [289,] 0.6582334 0.6715584 0.7489660 +#> [290,] 0.5736358 0.6016753 0.6031855 +#> [291,] 0.9047764 0.9416227 0.9437803 +#> [292,] 0.6534406 0.6535046 0.6689339 +#> [293,] 0.7839534 0.7906479 0.7915487 +#> [294,] 0.6527325 0.7207140 0.7265732 +#> [295,] 0.8515109 0.8821759 0.8829943 +#> [296,] 1.0891309 1.1238643 1.1280101 +#> [297,] 0.6817921 0.7024531 0.7885705 +#> [298,] 0.7650493 0.7830500 0.7850708 +#> [299,] 0.9253394 0.9618571 0.9675878 +#> [300,] 0.8019273 0.8136202 0.8417530 +#> [301,] 0.8982530 0.9216886 0.9237611 +#> [302,] 0.6387119 0.6701486 0.6924840 +#> [303,] 0.8194054 0.8245683 0.8276968 +#> [304,] 0.8163358 0.8968895 0.9190604 +#> [305,] 0.6911725 0.7333086 0.7364530 +#> [306,] 0.6273922 0.6494594 0.6758489 +#> [307,] 0.6020250 0.6032960 0.6141436 +#> [308,] 1.2462559 1.2627624 1.2734046 +#> [309,] 1.0961978 1.1436299 1.1709643 +#> [310,] 0.6557014 0.6608235 0.6910342 +#> [311,] 0.6901804 0.7042251 0.7121510 +#> [312,] 0.7725283 0.8024589 0.8167136 +#> [313,] 1.3093299 1.3269674 1.3276929 +#> [314,] 0.7285425 0.7598048 0.7688160 +#> [315,] 0.8906903 0.9014016 0.9082654 +#> [316,] 0.6597708 0.6716182 0.7138331 +#> [317,] 1.3444230 1.3969292 1.4297278 +#> [318,] 1.0851945 1.1559070 1.1757867 +#> [319,] 0.6329600 0.6380099 0.6387574 +#> [320,] 0.5443672 0.5488831 0.5624516 +#> [321,] 1.0981134 1.1148162 1.1424652 +#> [322,] 0.6027873 0.6314889 0.6915544 +#> [323,] 0.9562376 1.0873696 1.1439529 +#> [324,] 1.1809562 1.1949260 1.2121301 +#> [325,] 1.2758465 1.2764101 1.3314925 +#> [326,] 0.8488415 0.8858925 0.9049453 +#> [327,] 0.7816092 0.8003785 0.8061216 +#> [328,] 1.5466154 1.6159929 1.6380888 +#> [329,] 1.5863012 1.5911419 1.7342734 +#> [330,] 0.9494797 0.9970640 1.0387814 +#> [331,] 0.5663867 0.5977062 0.6327771 +#> [332,] 0.7786057 0.7926984 0.8472351 +#> [333,] 0.5780262 0.5830670 0.6025930 +#> [334,] 0.8014663 0.8420080 0.8437626 +#> [335,] 0.5649241 0.5889128 0.6072000 +#> [336,] 0.8751113 0.8806370 0.9049909 +#> [337,] 0.9169476 0.9258788 0.9786166 +#> [338,] 1.0001497 1.1000918 1.1152972 +#> [339,] 1.1983213 1.2084839 1.2476221 +#> [340,] 0.8387845 0.8578511 0.8625806 +#> [341,] 1.0503849 1.0581414 1.0981878 +#> [342,] 0.5831863 0.5897049 0.6166476 +#> [343,] 0.8909072 0.8952005 0.9274179 +#> [344,] 0.9235493 0.9288125 0.9731136 +#> [345,] 2.8094835 2.8886078 2.9410917 +#> [346,] 0.5564435 0.5892740 0.6000514 +#> [347,] 0.7551687 0.8334741 0.8581683 +#> [348,] 0.7263683 0.7687818 0.7934345 +#> [349,] 0.6638946 0.7025544 0.7051790 +#> [350,] 1.6618546 1.6642624 1.7089094 +#> [351,] 1.0794372 1.1290306 1.1451370 +#> [352,] 0.6767724 0.7167063 0.7404281 +#> [353,] 0.8472273 0.8487451 0.8598339 +#> [354,] 0.9188042 0.9250145 0.9784457 +#> [355,] 0.6427334 0.6523133 0.6591489 +#> [356,] 0.5867750 0.6096996 0.6609022 +#> [357,] 0.6504095 0.6693203 0.6915823 +#> [358,] 1.0662185 1.0938059 1.1255470 +#> [359,] 0.6849131 0.7253001 0.7567343 +#> [360,] 0.8253925 0.8469022 0.8507445 +#> [361,] 1.2972732 1.3129135 1.3208724 +#> [362,] 0.5900256 0.5966234 0.6255330 +#> [363,] 0.8098279 0.8405189 0.8419364 +#> [364,] 0.8382100 0.8417069 0.8511844 +#> [365,] 0.9920899 0.9990774 1.0466645 +#> [366,] 0.5600479 0.6000714 0.6015735 +#> [367,] 0.8771099 0.8793941 0.8808018 +#> [368,] 1.1420320 1.2435833 1.2576645 +#> [369,] 0.8582952 0.8599602 0.8648614 +#> [370,] 0.6438789 0.6464029 0.6594544 +#> [371,] 0.8132519 0.8348768 0.8718345 +#> [372,] 0.8886290 0.8980959 0.9182436 +#> [373,] 0.6728326 0.6859789 0.7359366 +#> [374,] 0.7909004 0.8103030 0.8672868 +#> [375,] 1.2857568 1.3381113 1.3387273 +#> [376,] 0.7944559 0.8013535 0.8304747 +#> [377,] 1.0194842 1.0607650 1.1032316 +#> [378,] 0.7188675 0.8235059 0.8276617 +#> [379,] 1.0211121 1.1530092 1.1705561 +#> [380,] 0.9923883 1.0221610 1.0783806 +#> [381,] 0.6207979 0.6427334 0.6639098 +#> [382,] 1.2759019 1.2801700 1.3483353 +#> [383,] 0.7151393 0.7507690 0.7514525 +#> [384,] 0.7303102 0.7447334 0.7563054 +#> [385,] 0.7729818 0.7752801 0.8192156 +#> [386,] 0.5042462 0.5132578 0.5347960 +#> [387,] 0.6685003 0.7032316 0.7095077 +#> [388,] 1.2940043 1.3116532 1.3305883 +#> [389,] 0.4817999 0.4878093 0.5410208 +#> [390,] 0.7331032 0.7444346 0.7671677 +#> [391,] 0.7537042 0.7746594 0.7845776 +#> [392,] 0.9354164 0.9916784 1.0038588 +#> [393,] 1.1592034 1.1789048 1.1881525 +#> [394,] 0.6467058 0.6565775 0.6923005 +#> [395,] 0.7310763 0.7552492 0.7594214 +#> [396,] 0.8986675 0.9143464 0.9276981 +#> [397,] 0.8486483 0.8508795 0.8601285 +#> [398,] 0.9976411 1.0011695 1.0739424 +#> [399,] 0.8745644 0.8782130 0.9043587 +#> [400,] 0.9098650 1.0092030 1.0362576 +#> [401,] 0.5597946 0.5927760 0.6031826 +#> [402,] 0.7700191 0.7817334 0.7818776 +#> [403,] 0.9737559 1.0240651 1.0344264 +#> [404,] 0.5936955 0.5965183 0.6031826 +#> [405,] 0.5359840 0.5426235 0.5494953 +#> [406,] 0.4984314 0.5700570 0.5701457 +#> [407,] 0.6150030 0.6689186 0.6761037 +#> [408,] 1.2397482 1.3744765 1.4820356 +#> [409,] 1.1517586 1.1595079 1.1607152 +#> [410,] 0.8970618 0.9174193 0.9612071 +#> [411,] 0.6916650 0.6981241 0.7041224 +#> [412,] 0.6471527 0.6816065 0.6878781 +#> [413,] 0.7327581 0.7406132 0.7452370 +#> [414,] 0.6648266 0.6678141 0.6744941 +#> [415,] 0.6697815 0.6791187 0.6953316 +#> [416,] 0.8168176 0.8194116 0.8211489 +#> [417,] 0.6078729 0.6152754 0.6421274 +#> [418,] 1.7155196 1.7207718 1.7415727 +#> [419,] 0.9481335 0.9612071 0.9820950 +#> [420,] 0.6800200 0.6808957 0.6992552 +#> [421,] 0.6046658 0.6209275 0.6402169 +#> [422,] 0.6835824 0.7585124 0.7881878 +#> [423,] 0.7491987 0.7522365 0.7601292 +#> [424,] 0.5020218 0.5030537 0.5178701 +#> [425,] 2.2932696 2.3723662 2.4223384 +#> [426,] 0.7645542 0.8054434 0.8494835 +#> [427,] 1.4016451 1.4382258 1.4433517 +#> [428,] 0.8341936 0.8344704 0.8387845 +#> [429,] 0.6107487 0.6735196 0.6762099 +#> [430,] 0.7438620 0.7674429 0.7714117 +#> [431,] 0.8219423 0.8234266 0.8463815 +#> [432,] 0.7341805 0.7534588 0.7727361 +#> [433,] 0.6377760 0.6462234 0.6638736 +#> [434,] 0.9333763 0.9422343 0.9488802 +#> [435,] 1.1427459 1.1572862 1.1624329 +#> [436,] 0.7498497 0.7768013 0.7789587 +#> [437,] 0.7549707 0.7669871 0.7987205 +#> [438,] 1.0791655 1.0973817 1.1052959 +#> [439,] 0.4825606 0.5023097 0.5102164 +#> [440,] 0.7093803 0.7365695 0.8450230 +#> [441,] 0.8186225 0.8575886 0.8617306 +#> [442,] 1.1939267 1.2530121 1.2676821 +#> [443,] 1.0690217 1.0938534 1.1098776 +#> [444,] 0.7602211 0.7900737 0.8484472 +#> [445,] 0.7305650 0.7666968 0.7783143 +#> [446,] 0.7870018 0.8203685 0.8417069 +#> [447,] 0.7270035 0.7565922 0.7755959 +#> [448,] 0.8279811 0.8490338 0.8573882 +#> [449,] 0.7283879 0.7563002 0.7800571 +#> [450,] 0.7829672 0.7968122 0.8093141 +#> [451,] 0.9289359 0.9913643 1.0041851 +#> [452,] 0.8580262 0.8603759 0.8625607 +#> [453,] 0.7599411 0.8054832 0.8322838 +#> [454,] 0.5303881 0.5404534 0.5506574 +#> [455,] 0.8750339 0.8754412 0.8896112 +#> [456,] 0.9700862 0.9895414 1.0009251 +#> [457,] 0.7884800 0.7985785 0.8309672 +#> [458,] 0.7219261 0.7259888 0.7685295 +#> [459,] 1.5169999 1.5208390 1.5669053 +#> [460,] 0.9304993 1.0034544 1.0204791 +#> [461,] 0.9252409 0.9413701 1.0006566 +#> [462,] 0.7252742 0.7520877 0.7681750 +#> [463,] 0.5873715 0.5940268 0.6009576 +#> [464,] 1.4102500 1.4254980 1.4289465 +#> [465,] 0.9724693 0.9892153 1.0053203 +#> [466,] 0.6776049 0.6793633 0.7444877 +#> [467,] 0.7234270 0.7373352 0.7395970 +#> [468,] 0.7312486 0.7620866 0.7834036 +#> [469,] 1.9449903 1.9997245 2.0342805 +#> [470,] 1.8673973 1.9165614 1.9322301 +#> [471,] 0.7849593 0.7867677 0.8164522 +#> [472,] 0.8077282 0.8113149 0.8162749 +#> [473,] 0.8092988 0.8227479 0.8581683 +#> [474,] 0.7886554 0.8040224 0.8216809 +#> [475,] 0.9568295 0.9668492 0.9772124 +#> [476,] 0.7496162 0.7635732 0.7935786 +#> [477,] 1.1293331 1.1469452 1.1471375 +#> [478,] 0.6020250 0.6364563 0.6564804 +#> [479,] 0.8828625 0.9178182 0.9357569 +#> [480,] 0.8551747 0.8561162 0.8696071 +#> [481,] 0.7271914 0.7889064 0.8276440 +#> [482,] 0.9167345 0.9432508 0.9734263 +#> [483,] 0.8877356 0.8877721 0.9464073 +#> [484,] 0.6820772 0.6901804 0.7073480 +#> [485,] 0.6032926 0.6355482 0.6519567 +#> [486,] 0.7694448 0.7867677 0.7921361 +#> [487,] 0.8669934 0.8987052 0.9039787 +#> [488,] 0.5386993 0.5510784 0.5615143 +#> [489,] 0.9526243 0.9738814 1.0211781 +#> [490,] 0.5797474 0.5819609 0.5829153 +#> [491,] 1.3827639 1.4192413 1.4736857 +#> [492,] 0.6208781 0.6852571 0.7241467 +#> [493,] 0.5825832 0.6224511 0.6276391 +#> [494,] 1.4299967 1.4388615 1.4452256 +#> [495,] 0.8014326 0.8063517 0.8796086 +#> [496,] 1.0511263 1.0638448 1.0654817 +#> [497,] 0.6331116 0.6969062 0.6987263 +#> [498,] 0.5942840 0.6013444 0.6100400 +#> [499,] 0.8186754 0.8388021 0.8446939 +#> [500,] 1.0481851 1.1458741 1.1547490 +#> [501,] 0.6127121 0.6392152 0.6698288 +#> [502,] 0.7746160 0.8066735 0.8549282 +#> [503,] 0.7550132 0.8247882 0.8276536 +#> [504,] 0.7007891 0.7760447 0.7927290 +#> [505,] 0.6207979 0.6369267 0.7119260 +#> [506,] 0.8450073 0.8506358 0.8947993 +#> [507,] 0.4527564 0.4929968 0.5031262 +#> [508,] 0.8210360 0.8356232 0.8370369 +#> [509,] 0.6196056 0.7501435 0.8137668 +#> [510,] 0.6689186 0.6801213 0.7140120 +#> [511,] 1.6786398 1.6801571 1.6809669 +#> [512,] 1.0270393 1.0378446 1.0830380 +#> [513,] 1.0686446 1.0745639 1.0884353 +#> [514,] 1.2383352 1.3142746 1.3165361 +#> [515,] 0.6923461 0.7222090 0.7349476 +#> [516,] 1.6999924 1.7366141 1.7710182 +#> [517,] 0.7227302 0.7333383 0.7438428 +#> [518,] 0.6412565 0.6487788 0.6527705 +#> [519,] 0.7007891 0.7166611 0.7211109 +#> [520,] 1.0286506 1.1018465 1.1064026 +#> [521,] 0.7539787 0.7646527 0.7749358 +#> [522,] 0.6460914 0.6592550 0.6777096 +#> [523,] 0.6011296 0.6099995 0.6267533 +#> [524,] 0.7527483 0.7889473 0.7951141 +#> [525,] 1.4603744 1.6534632 1.6554812 +#> [526,] 0.6993946 0.7225583 0.7272496 +#> [527,] 1.4180602 1.4306645 1.4584196 +#> [528,] 0.8336929 0.8786696 0.8990421 +#> [529,] 0.8049686 0.9500680 0.9775793 +#> [530,] 1.5558516 1.5605163 1.6140382 +#> [531,] 0.5470348 0.5673608 0.5755022 +#> [532,] 0.7040888 0.7044367 0.7181985 +#> [533,] 0.5600479 0.5693824 0.5705460 +#> [534,] 0.7481863 0.7556583 0.7578103 +#> [535,] 1.6764237 1.6891714 1.6925196 +#> [536,] 0.6892268 0.7104893 0.7386475 +#> [537,] 0.6605804 0.6822545 0.6859765 +#> [538,] 0.6724188 0.6760600 0.6805757 +#> [539,] 0.9236578 0.9779554 0.9825563 +#> [540,] 1.5450570 1.5982529 1.6469659 +#> [541,] 0.7671446 0.7686223 0.7913083 +#> [542,] 0.7749350 0.7760432 0.7970975 +#> [543,] 0.9972164 1.0454226 1.0554706 +#> [544,] 0.9734595 0.9762631 0.9798293 +#> [545,] 1.2253034 1.2347755 1.2356855 +#> [546,] 0.5308653 0.5433494 0.5648950 +#> [547,] 0.6845853 0.6870117 0.7195420 +#> [548,] 0.6836239 0.7093803 0.7112369 +#> [549,] 0.6624672 0.7044367 0.7271320 +#> [550,] 0.5930796 0.6009576 0.6332259 +#> [551,] 1.0512387 1.0828803 1.0834900 +#> [552,] 1.0671486 1.0812531 1.0817206 +#> [553,] 1.0822813 1.0942597 1.1059081 +#> [554,] 0.4990195 0.5265139 0.5447722 +#> [555,] 0.6851350 0.6876713 0.6926234 +#> [556,] 0.6995904 0.7367393 0.7459699 +#> [557,] 1.0643377 1.0757091 1.0772153 +#> [558,] 0.5876273 0.5937526 0.6021311 +#> [559,] 0.6316512 0.6322469 0.6470051 +#> [560,] 0.8883228 0.9206066 0.9323522 +#> [561,] 0.6731440 0.6860345 0.6885872 +#> [562,] 0.6638946 0.7372376 0.8081303 +#> [563,] 0.8289467 0.8349365 0.8653335 +#> [564,] 0.7583382 0.7673915 0.8239604 +#> [565,] 0.7240913 0.7753555 0.7841834 +#> [566,] 0.5998774 0.6128027 0.6591489 +#> [567,] 1.6908154 1.7030627 1.7225823 +#> [568,] 0.7425193 0.7489660 0.7500161 +#> [569,] 0.7170007 0.8089017 0.8122410 +#> [570,] 0.6774092 0.6799657 0.7025544 +#> [571,] 0.4820388 0.5136056 0.5213306 +#> [572,] 0.5566345 0.5922417 0.5935245 +#> [573,] 0.7110430 0.7135245 0.7384764 +#> [574,] 0.8450073 0.8713145 0.8943658 +#> [575,] 0.5573569 0.5840487 0.6198596 +#> [576,] 0.5361367 0.5540552 0.5557784 +#> [577,] 0.5574661 0.5752938 0.5902023 +#> [578,] 0.7813525 0.8041972 0.8239604 +#> [579,] 0.7566974 0.8184313 0.8656965 +#> [580,] 1.1771946 1.1826661 1.1970214 +#> [581,] 1.0231257 1.1010580 1.1155543 +#> [582,] 0.9441800 1.0828442 1.1472337 +#> [583,] 0.5396463 0.5420004 0.5607260 +#> [584,] 1.2029668 1.2605534 1.3054052 +#> [585,] 0.6278613 0.6442174 0.6732055 +#> [586,] 0.8234069 0.8244759 0.8432501 +#> [587,] 0.6341530 0.6353158 0.6772174 +#> [588,] 0.7814405 0.8528844 0.8812575 +#> [589,] 0.6008158 0.6372487 0.6381665 +#> [590,] 0.8535271 0.8584035 0.8604812 +#> [591,] 0.6475480 0.6583511 0.6692462 +#> [592,] 0.6862504 0.7064858 0.7096041 +#> [593,] 0.7141790 0.7540948 0.7743470 +#> [594,] 0.6790748 0.7063940 0.7134847 +#> [595,] 0.8243222 0.8426470 0.8441713 +#> [596,] 0.4841514 0.5657912 0.5755022 +#> [597,] 0.7452370 0.7575772 0.7952843 +#> [598,] 1.4963948 1.5203882 1.6024957 +#> [599,] 1.1383098 1.1702267 1.2238629 +#> [600,] 1.0364890 1.0783860 1.0881871 +#> [601,] 0.9747691 0.9784879 0.9891670 +#> [602,] 0.6325404 0.6790821 0.7024531 +#> [603,] 0.6043368 0.6424482 0.6583511 +#> [604,] 0.5864847 0.6016442 0.6022500 +#> [605,] 0.5472789 0.5627529 0.6346750 +#> [606,] 1.0111774 1.0255424 1.0621965 +#> [607,] 0.8565637 0.8674535 0.8724976 +#> [608,] 1.0922215 1.1643951 1.1807064 +#> [609,] 0.9794582 1.0258198 1.0537422 +#> [610,] 1.8744437 1.8869722 1.9029999 +#> [611,] 0.9482153 1.0703472 1.0738687 +#> [612,] 0.5826571 0.6483689 0.7058580 +#> [613,] 0.7554312 0.7697521 0.7769445 +#> [614,] 0.7722574 0.7785427 0.7852635 +#> [615,] 0.7480086 0.8008770 0.8052328 +#> [616,] 0.4987497 0.5915236 0.6025436 +#> [617,] 1.1530540 1.1624329 1.1696436 +#> [618,] 0.6675875 0.6696642 0.6964581 +#> [619,] 1.9258524 1.9589473 1.9616130 +#> [620,] 0.7694448 0.7945205 0.8112945 +#> [621,] 0.6608919 0.7044144 0.7183474 +#> [622,] 0.8357592 0.8851545 0.9266689 +#> [623,] 1.4453892 1.5597602 1.5799055 +#> [624,] 0.6013444 0.6160422 0.6277272 +#> [625,] 0.6735509 0.7061602 0.7103009 +#> [626,] 0.7594411 0.7819699 0.8162749 +#> [627,] 0.6639936 0.6704440 0.7070445 +#> [628,] 1.4792002 1.4858616 1.5553256 +#> [629,] 0.6801713 0.6803374 0.6807494 +#> [630,] 0.7838042 0.8330158 0.8551235 +#> [631,] 0.5658423 0.5659957 0.5706138 +#> [632,] 0.8275516 0.8398715 0.8894340 +#> [633,] 1.8945030 1.9278525 1.9424763 +#> [634,] 0.7367904 0.7715897 0.7772676 +#> [635,] 0.5245977 0.5419030 0.5470238 +#> [636,] 0.7606916 0.7766755 0.7873526 +#> [637,] 0.8667406 0.9232460 0.9668317 +#> [638,] 0.7093980 0.7179996 0.8043226 +#> [639,] 0.7107465 0.7365392 0.7509218 +#> [640,] 0.6474838 0.6752632 0.7081496 +#> [641,] 0.7407793 0.7601183 0.7669871 +#> [642,] 0.5757339 0.5988755 0.6056370 +#> [643,] 0.8543398 0.9490901 0.9616271 +#> [644,] 0.7037034 0.7089464 0.7225416 +#> [645,] 1.2614159 1.3394622 1.4055323 +#> [646,] 1.0875151 1.1115924 1.1613842 +#> [647,] 1.2023717 1.2228821 1.2469979 +#> [648,] 0.5538165 0.5571109 0.5848674 +#> [649,] 0.6731897 0.6863041 0.7436029 +#> [650,] 1.0723583 1.0829244 1.0888367 +#> [651,] 0.5360911 0.5439281 0.5514637 +#> [652,] 0.9823087 1.0061961 1.0161166 +#> [653,] 0.9615576 0.9772413 1.0220572 +#> [654,] 0.8567612 0.8835575 0.9073879 +#> [655,] 0.8739169 0.9189904 0.9469421 +#> [656,] 0.6440738 0.6553880 0.6621010 +#> [657,] 0.6948833 0.7239879 0.7303132 +#> [658,] 2.0539740 2.0902739 2.1038077 +#> [659,] 0.9031826 0.9270452 0.9469395 +#> [660,] 0.8741287 0.8839707 0.9087493 +#> [661,] 0.8132943 0.8224501 0.8247882 +#> [662,] 0.8186754 0.8330141 0.8343830 +#> [663,] 0.9810485 0.9927890 1.0106749 +#> [664,] 0.7029838 0.7151743 0.7163519 +#> [665,] 0.6817316 0.7015655 0.7115043 +#> [666,] 0.8458111 0.9004967 0.9803732 +#> [667,] 0.7187057 0.7246572 0.7471467 +#> [668,] 0.7927652 0.7950445 0.8107627 +#> [669,] 0.6911190 0.7413602 0.8334453 +#> [670,] 1.1509168 1.1776238 1.1924359 +#> [671,] 0.5475349 0.5505027 0.5639221 +#> [672,] 0.7212985 0.7231728 0.7255583 +#> [673,] 0.6546189 0.6678590 0.6745093 +#> [674,] 0.6236229 0.6316512 0.6448085 +#> [675,] 0.6976790 0.6988070 0.7022311 +#> [676,] 0.8647881 0.8881306 0.9042629 +#> [677,] 0.8584035 0.9732872 0.9841871 +#> [678,] 0.6459826 0.7084309 0.7096870 +#> [679,] 0.6269285 0.6510310 0.7190904 +#> [680,] 0.9532322 0.9611154 0.9822419 +#> [681,] 0.9136462 0.9197724 0.9385553 +#> [682,] 0.6151094 0.6664986 0.6852734 +#> [683,] 0.6181234 0.6336101 0.6550931 +#> [684,] 0.8281020 0.8915035 0.9109314 +#> [685,] 0.7855720 0.8112534 0.8486313 +#> [686,] 0.7968281 0.7993908 0.8096249 +#> [687,] 0.8684122 0.8933293 0.9039735 +#> [688,] 0.6518934 0.6784733 0.7134594 +#> [689,] 0.9237826 0.9400302 0.9471953 +#> [690,] 0.8141279 0.8358715 0.8369607 +#> [691,] 0.5529875 0.5601840 0.5644245 +#> [692,] 0.5517974 0.5693161 0.5873525 +#> [693,] 1.0859972 1.1167549 1.1855703 +#> [694,] 0.6327384 0.6403465 0.6522010 +#> [695,] 0.5369063 0.5844722 0.6025021 +#> [696,] 0.9683329 0.9684100 1.0204332 +#> [697,] 0.8111384 0.8424851 0.8542503 +#> [698,] 1.1814480 1.2263634 1.2384766 +#> [699,] 0.7927652 0.8077602 0.8295842 +#> [700,] 1.2702424 1.2908346 1.3338531 +#> [701,] 0.8291931 0.8403914 0.8738022 +#> [702,] 0.6434446 0.6671026 0.6860087 +#> [703,] 0.7074719 0.7149534 0.7375093 +#> [704,] 0.8169586 0.8272137 0.8283524 +#> [705,] 1.2313260 1.2356855 1.2885794 +#> [706,] 0.8154469 0.8226472 0.8487451 +#> [707,] 0.4983350 0.5262098 0.5327186 +#> [708,] 1.0624992 1.0715707 1.1079968 +#> [709,] 0.6789879 0.7242251 0.7460334 +#> [710,] 0.7968163 0.8044401 0.8081303 +#> [711,] 0.8103030 0.8407096 0.8612372 +#> [712,] 0.6796196 0.7025482 0.7212138 +#> [713,] 0.7080254 0.7608227 0.8142425 +#> [714,] 1.0440521 1.0512421 1.0767011 +#> [715,] 0.8151947 0.8285825 0.8582232 +#> [716,] 0.6284367 0.7223476 0.7408906 +#> [717,] 0.7046757 0.7136841 0.7604333 +#> [718,] 0.8838055 0.9219829 0.9919327 +#> [719,] 0.6121718 0.6380099 0.6388043 +#> [720,] 0.5953103 0.6349862 0.6359090 +#> [721,] 1.8820315 1.9001787 1.9088798 +#> [722,] 0.9795572 0.9823719 1.0485794 +#> [723,] 0.7339828 0.7342629 0.7421833 +#> [724,] 0.7866264 0.8289504 0.8772428 +#> [725,] 0.7079180 0.7419142 0.7869214 +#> [726,] 0.8201243 0.8342268 0.8486467 +#> [727,] 0.6288299 0.6441286 0.6508259 +#> [728,] 0.7837983 0.8149223 0.8295842 +#> [729,] 1.1908658 1.2187422 1.2412926 +#> [730,] 0.6217570 0.6510310 0.6630560 +#> [731,] 0.9160105 0.9928598 1.1118426 +#> [732,] 1.4420573 1.4974156 1.5126267 +#> [733,] 1.7203979 1.7855192 1.8151633 +#> [734,] 0.4929968 0.5297014 0.5530272 +#> [735,] 0.5965755 0.6003747 0.6095929 +#> [736,] 0.6981032 0.7104502 0.7132787 +#> [737,] 1.0985167 1.1041007 1.1451672 +#> [738,] 0.9223074 1.0153096 1.0387814 +#> [739,] 0.7026276 0.7146490 0.7245649 +#> [740,] 0.7207847 0.7329248 0.7348654 +#> [741,] 0.6898966 0.7134847 0.7154010 +#> [742,] 0.6608039 0.7080254 0.7310347 +#> [743,] 0.9439670 1.0013745 1.1012426 +#> [744,] 1.3015360 1.3296636 1.3845750 +#> [745,] 0.6325404 0.6648266 0.6837852 +#> [746,] 0.8505685 0.8759186 0.8890348 +#> [747,] 0.7840299 0.8194621 0.8238080 +#> [748,] 0.7545404 0.7563807 0.8210554 +#> [749,] 1.6518706 1.6901572 1.7073382 +#> [750,] 1.1184452 1.1456799 1.1546014 +#> [751,] 0.9202416 0.9760870 0.9904668 +#> [752,] 0.7601292 0.7789707 0.7888291 +#> [753,] 1.8041411 1.8399981 1.9228155 +#> [754,] 0.5117631 0.5189561 0.5379454 +#> [755,] 0.9188856 0.9264988 0.9320816 +#> [756,] 0.6017029 0.6238293 0.6410903 +#> [757,] 0.7847037 0.7996425 0.8101178 +#> [758,] 0.6032702 0.6093081 0.6393244 +#> [759,] 0.6645667 0.6992010 0.7103009 +#> [760,] 0.6381541 0.7110158 0.7136425 +#> [761,] 0.6806684 0.6858037 0.8278033 +#> [762,] 0.8708349 0.9128123 0.9241553 +#> [763,] 0.6832413 0.6860087 0.7341805 +#> [764,] 0.6157924 0.6343072 0.6558207 +#> [765,] 1.0118846 1.0134499 1.0171610 +#> [766,] 0.5975777 0.6000514 0.6011296 +#> [767,] 0.4531303 0.4674339 0.4860429 +#> [768,] 0.7399173 0.7863414 0.7904919 +#> [769,] 0.9369696 0.9517156 0.9656858 +#> [770,] 1.4471992 1.4562042 1.4820356 +#> [771,] 1.4930419 1.5148422 1.5548330 +#> [772,] 0.7373352 0.7443812 0.7529223 +#> [773,] 1.3959402 1.4042072 1.4333654 +#> [774,] 0.7428682 0.7751488 0.7923230 +#> [775,] 1.6821274 1.7425683 1.8219406 +#> [776,] 0.7952674 0.8616408 0.8749683 +#> [777,] 0.7886363 0.7969842 0.8028407 +#> [778,] 0.7563054 0.7619809 0.7651778 +#> [779,] 0.6701998 0.6882292 0.7097852 +#> [780,] 0.5524353 0.5626330 0.6274188 +#> [781,] 0.6867477 0.6981032 0.7169447 +#> [782,] 0.5279354 0.5476963 0.5488831 +#> [783,] 1.0564007 1.0920713 1.1147534 +#> [784,] 0.8546296 0.9293673 0.9503139 +#> [785,] 1.2862190 1.3487894 1.3838227 +#> [786,] 0.8884697 0.8903167 0.9072002 +#> [787,] 0.5993060 0.6125669 0.6208781 +#> [788,] 1.0283774 1.0756508 1.0760060 +#> [789,] 0.9727410 0.9951119 0.9999906 +#> [790,] 0.6117980 0.6456994 0.6477592 +#> [791,] 0.9312645 0.9560108 0.9569102 +#> [792,] 1.0465306 1.0909766 1.0914729 +#> [793,] 0.9404725 0.9429739 0.9568295 +#> [794,] 0.6590509 0.6658791 0.6763955 +#> [795,] 1.0802310 1.0822813 1.1103753 +#> [796,] 0.7007486 0.7014731 0.7070516 +#> [797,] 1.0239352 1.0372344 1.0721517 +#> [798,] 0.6339919 0.6431744 0.7286949 +#> [799,] 1.0520687 1.0530698 1.1656796 +#> [800,] 0.7208518 0.7217458 0.7246355 +#> [801,] 0.5717532 0.5975777 0.6159771 +#> [802,] 0.9789055 1.0506605 1.1064540 +#> [803,] 0.6408472 0.7736341 0.8250772 +#> [804,] 0.8274985 0.8491893 0.8821759 +#> [805,] 0.5870801 0.5927017 0.6470051 +#> [806,] 1.9180341 1.9459520 2.0220995 +#> [807,] 1.1019302 1.1048826 1.1452958 +#> [808,] 0.7080196 0.7107465 0.7237772 +#> [809,] 0.7306420 0.7346647 0.7392183 +#> [810,] 0.9619491 0.9759745 0.9819215 +#> [811,] 0.7134353 0.7410821 0.7671390 +#> [812,] 0.4397050 0.5120447 0.5158111 +#> [813,] 0.5866931 0.6096429 0.6152754 +#> [814,] 0.7999641 0.8117286 0.8357712 +#> [815,] 0.8746795 0.9115486 0.9335750 +#> [816,] 0.7146225 0.8258685 0.8431029 +#> [817,] 1.0041419 1.0242532 1.0347041 +#> [818,] 0.6384520 0.6638735 0.7109461 +#> [819,] 0.7700035 0.7865491 0.8122098 +#> [820,] 1.0418785 1.0676253 1.1292646 +#> [821,] 0.7532504 0.7747172 0.8473076 +#> [822,] 0.9227195 0.9487566 0.9581983 +#> [823,] 0.5875314 0.5942840 0.5965755 +#> [824,] 1.2553628 1.2694406 1.3343760 +#> [825,] 1.2048136 1.3095050 1.3428537 +#> [826,] 0.9132950 0.9771254 1.0106245 +#> [827,] 0.5661952 0.5750395 0.5822294 +#> [828,] 0.6576110 0.6597499 0.6673354 +#> [829,] 0.9136389 0.9287249 0.9310701 +#> [830,] 0.8903167 0.9201335 0.9323082 +#> [831,] 0.6718409 0.6869043 0.6991622 +#> [832,] 0.6859404 0.7035090 0.7048176 +#> [833,] 0.7420225 0.7588907 0.7750704 +#> [834,] 1.0100578 1.0138136 1.0163078 +#> [835,] 1.8535687 1.9005632 1.9730804 +#> [836,] 0.6506995 0.6590509 0.6599847 +#> [837,] 0.9098862 0.9364781 0.9552696 +#> [838,] 0.6858191 0.6927322 0.7062499 +#> [839,] 1.0220518 1.0238183 1.0621965 +#> [840,] 0.5401356 0.5557784 0.5687479 +#> [841,] 0.6776225 0.6874784 0.6990156 +#> [842,] 1.0768269 1.1514249 1.1546014 +#> [843,] 0.6942737 0.7040666 0.7097852 +#> [844,] 1.0846216 1.1308978 1.1327691 +#> [845,] 1.2357066 1.2465444 1.2791774 +#> [846,] 0.6924840 0.6987541 0.7768013 +#> [847,] 0.5996629 0.6371110 0.6737094 +#> [848,] 0.5578011 0.5624516 0.5842370 +#> [849,] 0.9665835 0.9976055 1.0352665 +#> [850,] 0.7457151 0.7990490 0.8223862 +#> [851,] 0.8717785 0.9060917 0.9801779 +#> [852,] 0.7719812 0.7912119 0.7975241 +#> [853,] 0.9266390 0.9300174 0.9645604 +#> [854,] 0.6398836 0.6412565 0.6775025 +#> [855,] 0.7485899 0.7781986 0.8268827 +#> [856,] 0.9786673 1.0056389 1.0444198 +#> [857,] 0.6893189 0.6929399 0.7584337 +#> [858,] 0.5513524 0.5553564 0.5569566 +#> [859,] 0.8832566 0.9021617 0.9851522 +#> [860,] 0.5454917 0.5477646 0.5658402 +#> [861,] 1.1027024 1.1274677 1.1693164 +#> [862,] 0.7058580 0.8086243 0.8343830 +#> [863,] 0.5877672 0.5956524 0.5991496 +#> [864,] 1.2654594 1.3005064 1.3382089 +#> [865,] 1.1711535 1.1903278 1.2141117 +#> [866,] 0.7920784 0.7935786 0.8320460 +#> [867,] 1.0431132 1.0607671 1.0757144 +#> [868,] 1.4160604 1.4465604 1.4920582 +#> [869,] 0.5573569 0.5675909 0.5761090 +#> [870,] 0.6336309 0.6358192 0.6776049 +#> [871,] 0.8684122 0.8849601 0.8927541 +#> [872,] 1.1902981 1.2012097 1.2430482 +#> [873,] 1.2633157 1.2769944 1.2891117 +#> [874,] 1.1157276 1.1936167 1.1995324 +#> [875,] 0.8294768 0.8344419 0.8362827 +#> [876,] 0.5117953 0.5192327 0.5420004 +#> [877,] 0.8030448 0.8216809 0.8369158 +#> [878,] 0.5102163 0.5178139 0.6454089 +#> [879,] 0.6869348 0.6993587 0.7524557 +#> [880,] 0.5584951 0.5687479 0.5827281 +#> [881,] 0.4919623 0.4922360 0.5161984 +#> [882,] 1.1207087 1.1468858 1.1637005 +#> [883,] 1.4398054 1.4933724 1.5107034 +#> [884,] 0.9321315 0.9481810 0.9830945 +#> [885,] 0.7053919 0.7086441 0.7171960 +#> [886,] 0.8671570 0.8687603 0.8753464 +#> [887,] 0.6857948 0.7112517 0.7573017 +#> [888,] 0.8283524 0.8703998 0.9551885 +#> [889,] 0.7289802 0.8437187 0.8561901 +#> [890,] 0.6797562 0.6992552 0.6998319 +#> [891,] 0.6658791 0.6701998 0.6801707 +#> [892,] 1.3072786 1.3114265 1.3533247 +#> [893,] 0.8628560 0.9132747 0.9165879 +#> [894,] 1.5536419 1.5676981 1.5715458 +#> [895,] 1.1455775 1.1833642 1.2430949 +#> [896,] 1.3007429 1.3456632 1.3779056 +#> [897,] 0.5183205 0.5743258 0.6032926 +#> [898,] 1.3950150 1.4845992 1.5350760 +#> [899,] 0.5721419 0.5732198 0.6285374 +#> [900,] 1.0721517 1.1149484 1.1474075 +#> [901,] 0.5700570 0.5922966 0.6117980 +#> [902,] 0.5699897 0.5910700 0.6163551 +#> [903,] 0.5701457 0.5879070 0.5888147 +#> [904,] 0.6237141 0.6314061 0.6545029 +#> [905,] 0.8616950 0.8821900 0.8904956 +#> [906,] 0.8366904 0.8441713 0.8561901 +#> [907,] 0.4796330 0.5170806 0.5385851 +#> [908,] 0.9966891 1.0304489 1.0453836 +#> [909,] 0.6767015 0.7246652 0.7910346 +#> [910,] 1.0512462 1.0634887 1.1017909 +#> [911,] 0.7690241 0.7835180 0.7842613 +#> [912,] 0.6977815 0.7157679 0.7306703 +#> [913,] 0.6552209 0.6862504 0.7353109 +#> [914,] 0.5776104 0.6021255 0.7304606 +#> [915,] 0.7863488 0.8475656 0.8720974 +#> [916,] 1.0116410 1.0413067 1.0591309 +#> [917,] 0.5229253 0.5461736 0.5666734 +#> [918,] 0.7958120 0.8136202 0.8537410 +#> [919,] 0.7987205 0.8064646 0.8194054 +#> [920,] 0.8142671 0.8244194 0.8358189 +#> [921,] 0.6044993 0.6127121 0.6399026 +#> [922,] 0.8370144 0.8698289 0.9121935 +#> [923,] 0.9604526 0.9776841 1.0087750 +#> [924,] 0.6765533 0.7119572 0.7163205 +#> [925,] 0.7317220 0.7530499 0.7623217 +#> [926,] 0.8837417 0.8855182 0.9463822 +#> [927,] 0.7803086 0.8170575 0.8442191 +#> [928,] 0.7599411 0.7889473 0.7904919 +#> [929,] 1.1081530 1.2327271 1.2911965 +#> [930,] 0.7506026 0.8026476 0.8214476 +#> [931,] 0.7451284 0.7653108 0.7774373 +#> [932,] 0.6848409 0.7093980 0.7568239 +#> [933,] 0.6515893 0.6556246 0.6686640 +#> [934,] 0.6678590 0.6910904 0.7055392 +#> [935,] 0.5962874 0.6394549 0.6976758 +#> [936,] 0.7912512 0.8118788 0.8307374 +#> [937,] 1.1736185 1.2208004 1.2446415 +#> [938,] 0.7667661 0.7940193 0.8452377 +#> [939,] 0.6827288 0.7092455 0.7464431 +#> [940,] 0.8604242 0.8726374 0.8772334 +#> [941,] 0.8093763 0.8289961 0.8344334 +#> [942,] 0.6301110 0.6482104 0.6788163 +#> [943,] 0.5850961 0.6228291 0.6632456 +#> [944,] 1.0421754 1.0446976 1.0731588 +#> [945,] 0.7180052 0.7315810 0.7349476 +#> [946,] 0.8506424 0.8683091 0.9293002 +#> [947,] 1.0121738 1.0359470 1.0360698 +#> [948,] 0.5552995 0.5578163 0.5626172 +#> [949,] 0.8668694 0.8669934 0.8724862 +#> [950,] 0.7558192 0.7727489 0.7882429 +#> [951,] 2.2518509 2.2876735 2.3032924 +#> [952,] 1.3832696 1.4011508 1.4280204 +#> [953,] 0.5844173 0.6092434 0.6248032 +#> [954,] 0.8162019 0.8250067 0.8313569 +#> [955,] 0.5101946 0.5432098 0.5471188 +#> [956,] 0.5132561 0.5241539 0.5257055 +#> [957,] 1.0073150 1.0311136 1.0359089 +#> [958,] 0.5265139 0.5461736 0.5519625 +#> [959,] 0.6008707 0.6280089 0.6345307 +#> [960,] 0.6509555 0.7058413 0.7246035 +#> [961,] 2.1073527 2.1123186 2.1230725 +#> [962,] 0.8408857 0.8688455 0.8930393 +#> [963,] 0.5797989 0.5860902 0.5995586 +#> [964,] 1.0524327 1.0525541 1.0626364 +#> [965,] 0.6757378 0.6832628 0.7136841 +#> [966,] 0.5765295 0.5861157 0.5992267 +#> [967,] 0.6782984 0.6786018 0.6802397 +#> [968,] 0.6391293 0.6621010 0.6837852 +#> [969,] 0.7136425 0.7223476 0.7272496 +#> [970,] 0.5855896 0.5870801 0.5888147 +#> [971,] 0.5590485 0.5654247 0.5661557 +#> [972,] 0.9690550 0.9994436 1.0083670 +#> [973,] 0.7691601 0.7728200 0.7825482 +#> [974,] 0.4869863 0.5158111 0.5624893 +#> [975,] 0.6556246 0.6693918 0.6756131 +#> [976,] 1.0606498 1.0695690 1.0897265 +#> [977,] 0.8508795 0.8643554 0.9045820 +#> [978,] 0.5277032 0.5386993 0.5486410 +#> [979,] 0.5850961 0.5925284 0.5991496 +#> [980,] 0.6278819 0.6315640 0.6414373 +#> [981,] 0.5928978 0.6146377 0.6159974 +#> [982,] 0.9526243 0.9714016 1.0005670 +#> [983,] 0.7373603 0.7435158 0.7604729 +#> [984,] 1.1850158 1.2176165 1.2439219 +#> [985,] 0.7098652 0.7231843 0.7240913 +#> [986,] 0.7024012 0.7333383 0.7539410 +#> [987,] 1.8988976 1.9063222 1.9955230 +#> [988,] 0.8699772 0.9439670 0.9859146 +#> [989,] 0.7045640 0.8301865 0.8381168 +#> [990,] 0.6898966 0.6935705 0.7925446 +#> [991,] 0.7808682 0.8238527 0.8348792 +#> [992,] 0.9184891 0.9223074 0.9293687 +#> [993,] 0.7294261 0.8360314 0.8809900 +#> [994,] 0.6341530 0.6459071 0.6519416 +#> [995,] 0.6254579 0.6277272 0.6626872 +#> [996,] 0.5238755 0.5315577 0.5379967 +#> [997,] 1.3163073 1.3237790 1.3668184 +#> [998,] 0.6553880 0.6724235 0.7223482 +#> [999,] 0.6011789 0.6055215 0.6515013 +#> [1000,] 0.5779394 0.5873525 0.5902023 #> diff --git a/reference/tof_get_model_penalty.html b/reference/tof_get_model_penalty.html index 3c2fa27..256bf45 100644 --- a/reference/tof_get_model_penalty.html +++ b/reference/tof_get_model_penalty.html @@ -125,7 +125,7 @@

Examples ) tof_get_model_penalty(regression_model) -#> [1] 1e-10 +#> [1] 0.003162278 diff --git a/reference/tof_get_model_training_data.html b/reference/tof_get_model_training_data.html index 2e31ca9..f631371 100644 --- a/reference/tof_get_model_training_data.html +++ b/reference/tof_get_model_training_data.html @@ -126,18 +126,18 @@

Examples tof_get_model_training_data(regression_model) #> # A tibble: 100 × 9 -#> sample cd45 pstat5 cd34 outcome class multiclass event time_to_event -#> <chr> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <dbl> -#> 1 65 0.822 0.619 0.815 5.72 class1 class3 1 7.41 -#> 2 84 0.597 0.767 0.260 6.34 class1 class3 1 12.5 -#> 3 17 0.902 0.723 0.749 2.97 class2 class1 0 12.4 -#> 4 24 0.667 0.0417 0.801 3.15 class2 class1 0 10.2 -#> 5 3 0.293 0.230 0.443 2.70 class2 class1 0 11.1 -#> 6 49 0.374 0.951 0.286 4.82 class1 class2 1 11.0 -#> 7 9 0.280 0.0778 0.509 1.96 class2 class1 0 10.3 -#> 8 87 0.295 0.651 0.226 4.22 class1 class3 1 8.02 -#> 9 53 0.231 0.793 0.335 5.00 class1 class2 1 8.03 -#> 10 70 0.917 0.183 0.255 2.38 class2 class3 1 12.7 +#> sample cd45 pstat5 cd34 outcome class multiclass event time_to_event +#> <chr> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <dbl> +#> 1 28 0.691 0.785 0.439 5.97 class1 class1 0 13.3 +#> 2 55 0.857 0.819 0.811 6.65 class1 class2 1 11.9 +#> 3 16 0.695 0.106 0.627 1.49 class2 class1 0 7.10 +#> 4 11 0.0496 0.115 0.372 0.193 class2 class1 0 10.8 +#> 5 67 0.112 0.227 0.784 2.74 class2 class3 1 9.07 +#> 6 23 0.355 0.796 0.815 4.99 class1 class1 0 9.64 +#> 7 89 0.407 0.191 0.282 2.69 class2 class3 1 12.0 +#> 8 21 0.0873 0.348 0.944 2.32 class2 class1 0 16.2 +#> 9 26 0.672 0.420 0.183 2.89 class2 class1 0 10.8 +#> 10 20 0.708 0.504 0.430 3.69 class1 class1 0 10.6 #> # ℹ 90 more rows diff --git a/reference/tof_get_model_x.html b/reference/tof_get_model_x.html index a4b60ac..1e78b6b 100644 --- a/reference/tof_get_model_x.html +++ b/reference/tof_get_model_x.html @@ -125,107 +125,107 @@

Examples ) tof_get_model_x(regression_model) -#> cd45 pstat5 cd34 -#> [1,] 0.097430219 0.82910106 -0.94376493 -#> [2,] -1.311838342 -0.83388574 2.00234953 -#> [3,] 0.608982549 0.25588910 -0.45198780 -#> [4,] -0.302489013 0.63513275 -0.78947643 -#> [5,] 0.814591515 1.35424932 -0.06893559 -#> [6,] -1.490715046 -0.60767688 0.20873417 -#> [7,] -1.068171324 -0.98809671 -0.92545271 -#> [8,] 1.720644052 1.03349182 -0.61031206 -#> [9,] 0.344938617 1.45622241 -0.58876885 -#> [10,] -0.344691402 -1.64191753 1.46032139 -#> [11,] -0.316963867 -0.29841413 -0.19475752 -#> [12,] -1.364896702 -0.21680095 1.06028628 -#> [13,] -0.788463388 1.56784234 1.34496699 -#> [14,] 1.465392231 -0.02392575 -0.38715496 -#> [15,] -0.983790203 1.22733473 -0.57337552 -#> [16,] -1.351760250 -1.48437184 -1.45299400 -#> [17,] -1.021288982 -0.99886311 -0.84663647 -#> [18,] -1.389544154 0.53088546 1.88944653 -#> [19,] -1.218894884 -0.25109334 1.20528647 -#> [20,] 0.238289453 -0.60142456 -0.79626050 -#> [21,] 0.989845642 -0.56050792 1.00459207 -#> [22,] -1.449966131 -1.21051222 -0.26514832 -#> [23,] -1.482798231 0.09343527 -0.39684951 -#> [24,] 1.463977818 1.26356640 1.72149337 -#> [25,] 1.405791893 0.17131523 -1.38922709 -#> [26,] -0.620979465 1.69803167 0.91600303 -#> [27,] -0.629277673 -0.27514976 1.12814102 -#> [28,] 0.500584250 1.06722334 0.30449975 -#> [29,] -1.043556082 1.27808977 0.77334193 -#> [30,] -1.024425108 -0.97478798 0.84206340 -#> [31,] 1.554943783 1.06852314 -0.35906208 -#> [32,] 1.169379658 0.08050339 -0.16781256 -#> [33,] -1.177645648 -0.13092913 -0.02108945 -#> [34,] -0.172509694 -1.46016260 0.06155719 -#> [35,] -0.748479429 -1.13536404 -0.67258770 -#> [36,] -1.402700354 1.34348410 -0.05211684 -#> [37,] -1.062300509 -0.75774393 1.81266694 -#> [38,] -1.282857700 0.41580356 1.22121612 -#> [39,] 0.875433929 1.68290377 -1.39538817 -#> [40,] -1.164910939 0.56025763 -1.40528210 -#> [41,] 0.668840929 1.63436095 -0.41448278 -#> [42,] -0.002996884 -1.64653309 -0.59163196 -#> [43,] 0.172427732 -0.26530729 -0.91681641 -#> [44,] 1.468934435 -0.49443278 1.26750344 -#> [45,] -0.924013752 -0.96065024 -1.41537556 -#> [46,] 0.181290611 0.45321197 -0.55039255 -#> [47,] 0.274956688 -0.03118150 0.48177377 -#> [48,] -0.282324875 1.57603526 0.23943288 -#> [49,] 1.147478504 -1.24884927 0.19638588 -#> [50,] 1.010348840 0.61371718 -0.76901762 -#> [51,] 1.110491475 1.29781210 -1.04943965 -#> [52,] 1.078293933 0.56541751 -0.10336833 -#> [53,] -0.176056382 -0.71155325 -1.09782905 -#> [54,] 0.621151974 -1.64261801 -0.69870671 -#> [55,] -1.656946890 -0.83395761 -0.06055877 -#> [56,] -0.305423555 0.57731463 -1.08112087 -#> [57,] 0.702750034 0.06664733 1.73234041 -#> [58,] -0.513923518 -1.59376747 1.88020934 -#> [59,] 0.401774984 0.90797477 1.00688706 -#> [60,] -1.251357033 0.27391588 -1.47560501 -#> [61,] 0.249423289 0.54384053 -0.57632363 -#> [62,] -0.563953322 0.18670903 -0.87890035 -#> [63,] -0.134333347 -1.47950062 0.52142003 -#> [64,] -0.447376307 -1.63419639 -1.17733125 -#> [65,] 1.056123410 -0.77591397 1.55592024 -#> [66,] -0.649333161 -1.60929699 0.36112138 -#> [67,] -0.314440154 -0.15318593 -1.57390336 -#> [68,] -1.574189375 1.36296254 0.43201458 -#> [69,] -1.236690266 0.39641178 1.61239377 -#> [70,] 1.059948573 -0.91541842 -1.36370039 -#> [71,] 1.660572266 -0.70944415 1.05667214 -#> [72,] -1.493476736 -0.49845220 -1.63788023 -#> [73,] 1.655897949 -0.39093657 0.29411358 -#> [74,] 0.451123291 0.98262889 1.98757738 -#> [75,] -0.414424253 -1.07530968 0.31514068 -#> [76,] 0.453726423 0.85686567 1.01130980 -#> [77,] 0.999958797 -1.57167820 -1.63538826 -#> [78,] 1.423268538 1.18211735 0.61977295 -#> [79,] 0.599291206 -0.11074027 -1.43890299 -#> [80,] -0.283921239 -0.69847066 1.61055618 -#> [81,] 1.233219158 0.37126964 1.08175076 -#> [82,] 0.803109507 1.66628276 -0.70233496 -#> [83,] 0.694205481 1.36237235 0.33442099 -#> [84,] -0.227147094 -0.07495812 -1.03451647 -#> [85,] 0.316764480 -1.28795998 -0.58610671 -#> [86,] 1.566441362 0.32637161 -0.12839293 -#> [87,] 0.442802106 -0.54085031 -0.07227478 -#> [88,] 1.197063319 0.26925504 -1.00240896 -#> [89,] -0.135714814 -1.60973478 -0.74200950 -#> [90,] -1.478638693 0.60936144 0.52842898 -#> [91,] 0.264647737 0.60063557 -1.04296196 -#> [92,] 1.531517096 -1.16694306 -0.11886303 -#> [93,] 1.526609706 0.46163629 0.26221660 -#> [94,] -1.248391947 0.26145114 -1.14140504 -#> [95,] -0.116980597 0.41800529 -0.30383907 -#> [96,] -1.150374900 -0.89023524 0.88019565 -#> [97,] 1.376741715 1.42083375 0.32368321 -#> [98,] -0.065986285 -0.07541222 0.28225801 -#> [99,] 0.551185862 -1.35040975 0.34747512 -#> [100,] -0.338277123 1.64082156 0.95628936 +#> cd45 pstat5 cd34 +#> [1,] 0.59528468 1.44049135 -0.83714724 +#> [2,] 1.54466688 -0.22824576 1.06582350 +#> [3,] -0.12522254 -1.08701937 1.13310841 +#> [4,] -0.76518793 -0.15699067 0.77008930 +#> [5,] 1.12500430 -0.59512774 1.49386093 +#> [6,] 0.62293193 0.42242236 -1.15306393 +#> [7,] 0.95787530 0.69707908 -1.53772848 +#> [8,] -1.51437535 -1.26969989 0.71432329 +#> [9,] 0.71836259 -0.16856184 1.23580735 +#> [10,] -0.17814153 -0.99433548 1.60091909 +#> [11,] -0.17615269 -0.31469660 -0.71168946 +#> [12,] 1.49710327 -0.49062257 -1.09733437 +#> [13,] 0.60861274 -0.90280890 -0.02380285 +#> [14,] -0.97021357 -1.66032177 0.97382990 +#> [15,] -1.12931291 1.04706206 0.98000587 +#> [16,] 1.36839304 1.30151098 -0.77030751 +#> [17,] -1.00794017 -1.24661367 -0.07513261 +#> [18,] 1.57525546 0.89661144 -0.24023615 +#> [19,] -1.37678241 0.26408240 0.82416993 +#> [20,] 0.46284376 -1.74386667 0.59860382 +#> [21,] 1.53204332 0.75290827 -0.39625612 +#> [22,] -0.54577299 1.39097230 0.16327280 +#> [23,] 0.86148076 -0.52197611 -0.37413485 +#> [24,] 0.90588132 0.42532148 1.26113010 +#> [25,] -0.11179582 -0.80614650 -1.19038292 +#> [26,] -0.17196734 0.74510860 -0.81294208 +#> [27,] -1.01322479 -0.07601260 -1.09422140 +#> [28,] -0.21067085 -1.67342491 1.44540044 +#> [29,] 0.06241313 -1.75624527 -1.13753404 +#> [30,] 1.35966090 0.91896495 0.38898307 +#> [31,] -0.42218411 0.43046533 1.33648001 +#> [32,] 0.29022671 -0.16514078 0.19730942 +#> [33,] -0.85397979 1.44423593 0.25040494 +#> [34,] 0.57594925 -0.93603822 1.39197131 +#> [35,] 0.66512784 -0.66210884 0.21891150 +#> [36,] 1.29937014 0.26633988 -1.06070517 +#> [37,] 0.47025773 0.60766544 1.20720669 +#> [38,] 1.74216883 1.29773790 -0.92743740 +#> [39,] -0.05671371 -0.21378531 -0.44117363 +#> [40,] 0.90578150 1.36117276 -0.69817984 +#> [41,] -0.30202868 1.40239907 -0.72668353 +#> [42,] -1.52848056 1.51200754 -1.15051494 +#> [43,] 1.37175592 -0.85830784 0.33996543 +#> [44,] 0.10292442 1.47296414 1.54534688 +#> [45,] 0.87231483 -0.66998719 -0.75471131 +#> [46,] 0.59973216 1.52335287 0.88083634 +#> [47,] 0.03172790 0.88023205 0.25640078 +#> [48,] -0.93905443 -1.76333291 -1.50980937 +#> [49,] 0.68597932 1.01875328 0.54721470 +#> [50,] -1.34154855 -1.47231339 -1.23182257 +#> [51,] -1.19759029 1.11769827 1.37274402 +#> [52,] -0.77676834 -0.24984412 1.28737788 +#> [53,] 1.57761598 -0.80905492 -1.46397424 +#> [54,] -1.46234273 0.53946793 0.83992867 +#> [55,] -1.44160006 -0.92726804 -0.72762774 +#> [56,] -1.12277586 -0.44741260 -1.23962862 +#> [57,] -1.53422704 -1.75313915 -1.40023083 +#> [58,] -1.52850730 -0.09200885 -1.26759899 +#> [59,] 0.25170208 -1.66114265 -1.43033089 +#> [60,] 1.41142859 0.06305778 0.03403432 +#> [61,] 1.61192397 1.53805764 0.20902184 +#> [62,] 0.81832934 0.05420090 1.13949815 +#> [63,] -1.48387133 -0.94285481 0.50179554 +#> [64,] -0.29774219 1.41597976 -0.13233003 +#> [65,] -0.11098242 0.33902408 0.53157601 +#> [66,] 1.60395232 -0.14373650 -1.30828061 +#> [67,] 1.37038390 -0.95215370 -1.07137337 +#> [68,] -1.08615289 -0.33296319 0.99702174 +#> [69,] 0.96390776 0.70442885 -0.52280306 +#> [70,] -0.92393107 1.26050968 -1.25652288 +#> [71,] 0.82670256 1.01989361 0.96623132 +#> [72,] -0.12792110 0.61884228 -0.94373227 +#> [73,] -0.03773112 -0.36445157 1.30015191 +#> [74,] 0.53737663 1.15735483 -1.28048161 +#> [75,] -0.43960698 -0.57800199 -1.07667595 +#> [76,] -1.16969056 -1.49595434 -1.37999938 +#> [77,] -0.74519037 -1.35082752 -1.47184025 +#> [78,] -0.49350483 0.12421461 0.95061511 +#> [79,] 0.06086406 -0.34898140 -0.32547597 +#> [80,] 1.10918997 0.06044862 1.54905017 +#> [81,] -0.86538695 -0.61392745 1.27208174 +#> [82,] 1.14162107 1.17549231 0.56257925 +#> [83,] 1.23388738 0.16430646 1.59824413 +#> [84,] -1.50351537 -0.04168292 1.44026256 +#> [85,] -0.68300922 0.71941312 0.24602419 +#> [86,] -1.08604708 -1.61860127 -0.62076171 +#> [87,] -1.52548098 -0.06910424 -0.43377568 +#> [88,] 1.22128478 0.09523296 -0.03669461 +#> [89,] -0.85895991 1.11803238 -0.36124931 +#> [90,] -0.46602823 -0.60026305 0.43627494 +#> [91,] -0.06529655 -1.23539097 -0.79265925 +#> [92,] -0.65848390 -1.13040670 1.20447999 +#> [93,] -1.29812461 0.25648772 1.26211649 +#> [94,] -0.64842523 -1.01460888 -1.51738573 +#> [95,] -1.14939283 0.99348930 0.17886208 +#> [96,] 0.16810639 0.16164857 0.18501520 +#> [97,] -1.12685763 1.54704470 -0.82246960 +#> [98,] 1.64024070 1.31257818 1.21617554 +#> [99,] -0.41100871 -1.27935540 -1.29617247 +#> [100,] 0.10725702 1.41010101 0.03048824 diff --git a/reference/tof_get_model_y.html b/reference/tof_get_model_y.html index 1e5c88e..d76ca18 100644 --- a/reference/tof_get_model_y.html +++ b/reference/tof_get_model_y.html @@ -125,23 +125,21 @@

Examples ) tof_get_model_y(regression_model) -#> [1] 5.7621216 2.2316713 0.6277567 3.8226163 3.4438041 3.2022874 -#> [7] 4.6158093 2.9033474 3.6741484 3.1352677 2.4676448 5.1249311 -#> [13] 4.7087302 6.2543310 1.1120282 4.7324417 5.9082607 2.0386707 -#> [19] 5.4174583 3.5122708 4.8392255 0.8281352 5.2922102 5.3983678 -#> [25] 3.5386303 4.1805624 3.7113123 0.8101075 2.2566154 7.1559778 -#> [31] 5.1942710 4.4812019 3.2366547 1.4674999 0.8180363 2.3338306 -#> [37] 0.8810946 3.6455296 3.3840711 5.5595769 4.7384607 4.4243745 -#> [43] 4.7496679 2.0974114 2.7392128 4.5135190 2.7730292 3.1200798 -#> [49] 2.7923983 2.4138013 4.5699212 4.5719286 0.7985220 1.0197147 -#> [55] 3.5951921 2.8275037 4.5476347 3.3280893 3.6705805 2.8785254 -#> [61] 6.7006236 1.5374820 1.2341384 2.6680193 2.7916082 5.3360892 -#> [67] -0.2466384 5.0882917 1.9183955 2.9068708 5.0615714 5.6398780 -#> [73] 1.1183529 1.6471065 5.3397798 3.8092044 3.4540926 1.2882773 -#> [79] 1.9441636 2.3991209 5.9633846 5.1459914 3.5421044 6.5684994 -#> [85] 1.6638710 2.6644894 3.1831984 2.6593735 2.0910804 4.8734370 -#> [91] 1.8225112 4.0398150 2.8298920 3.1629248 2.0121100 3.8578668 -#> [97] 0.8822519 3.6211117 4.9356237 6.4689079 +#> [1] 2.5193384 4.5149299 4.0560885 5.9611669 4.5580430 3.8717242 2.0115023 +#> [8] 2.3287751 1.2558387 2.7106398 8.0353582 3.1109667 0.0956430 3.8845026 +#> [15] 2.2893241 2.2838848 6.6738549 4.3610705 2.6677311 1.2832516 5.0359106 +#> [22] 5.3490989 3.5828511 4.2151462 3.9590472 3.5700904 2.8944736 5.6814454 +#> [29] 1.5711894 4.9576279 5.2051262 2.9132805 0.7297537 5.1965211 1.1214608 +#> [36] 1.7391494 2.4467109 3.9139769 5.0194642 2.6534669 2.7843119 6.1812425 +#> [43] 4.3700452 1.5681085 4.5762836 4.9653675 6.3941393 1.4707955 6.8116263 +#> [50] 2.5860246 3.1838962 3.8527498 3.6197843 0.3490233 6.4851868 1.8348396 +#> [57] 1.9694412 3.2396971 3.0333514 2.4014812 5.1867053 0.6855628 5.7730632 +#> [64] 3.4127052 5.0764523 2.9876150 3.6740342 3.8951042 3.8605096 2.4412410 +#> [71] 6.9218832 1.6564309 2.7859638 1.6670116 4.8367688 4.9466905 5.3100786 +#> [78] 5.4125562 1.9711748 4.9535322 5.8852148 2.2287108 4.1071774 4.6672257 +#> [85] 5.6143727 5.9681251 1.7989624 3.9874215 6.6877886 3.3037816 2.2971176 +#> [92] 3.9744913 3.5935729 0.8340110 8.2184186 2.9458438 4.9067439 0.5873825 +#> [99] 3.1074093 5.5015267 diff --git a/reference/tof_metacluster.html b/reference/tof_metacluster.html index 3989a0e..01b787d 100644 --- a/reference/tof_metacluster.html +++ b/reference/tof_metacluster.html @@ -175,18 +175,18 @@

Examples method = "flowsom" ) #> # A tibble: 1,000 × 6 -#> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster -#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> -#> 1 -0.443 1.22 -0.322 0.477 z 1 -#> 2 0.342 0.282 0.616 1.37 z 1 -#> 3 -1.67 -1.54 0.615 -0.475 l 2 -#> 4 -1.53 -0.707 0.280 -1.11 a 1 -#> 5 -0.539 -0.923 -0.239 0.145 w 1 -#> 6 1.19 -0.295 -0.661 0.493 f 3 -#> 7 -0.418 -0.110 -0.557 -1.10 z 1 -#> 8 -0.315 0.803 -2.72 0.0648 m 4 -#> 9 -0.846 0.127 0.117 -0.162 f 3 -#> 10 -0.669 2.21 -0.608 -0.494 o 1 +#> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster +#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> +#> 1 -1.58 -0.768 -0.195 0.900 e 4 +#> 2 -2.39 0.525 -2.68 1.29 v 2 +#> 3 0.664 -1.03 -0.206 -0.00204 t 6 +#> 4 1.04 -0.160 -0.922 0.622 y 2 +#> 5 -0.350 0.344 0.463 -0.365 l 1 +#> 6 -2.19 -0.237 0.670 0.159 r 3 +#> 7 -0.365 -0.370 0.445 -0.568 d 1 +#> 8 0.512 -0.625 1.01 -1.68 f 1 +#> 9 -1.08 -0.935 0.582 -0.970 x 4 +#> 10 0.352 -0.135 0.276 -0.193 e 4 #> # ℹ 990 more rows tof_metacluster( @@ -195,18 +195,18 @@

Examples method = "phenograph" ) #> # A tibble: 1,000 × 6 -#> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster -#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> -#> 1 -0.443 1.22 -0.322 0.477 z 1 -#> 2 0.342 0.282 0.616 1.37 z 1 -#> 3 -1.67 -1.54 0.615 -0.475 l 3 -#> 4 -1.53 -0.707 0.280 -1.11 a 1 -#> 5 -0.539 -0.923 -0.239 0.145 w 2 -#> 6 1.19 -0.295 -0.661 0.493 f 4 -#> 7 -0.418 -0.110 -0.557 -1.10 z 1 -#> 8 -0.315 0.803 -2.72 0.0648 m 3 -#> 9 -0.846 0.127 0.117 -0.162 f 4 -#> 10 -0.669 2.21 -0.608 -0.494 o 1 +#> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster +#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> +#> 1 -1.58 -0.768 -0.195 0.900 e 1 +#> 2 -2.39 0.525 -2.68 1.29 v 2 +#> 3 0.664 -1.03 -0.206 -0.00204 t 1 +#> 4 1.04 -0.160 -0.922 0.622 y 2 +#> 5 -0.350 0.344 0.463 -0.365 l 3 +#> 6 -2.19 -0.237 0.670 0.159 r 2 +#> 7 -0.365 -0.370 0.445 -0.568 d 3 +#> 8 0.512 -0.625 1.01 -1.68 f 3 +#> 9 -1.08 -0.935 0.582 -0.970 x 1 +#> 10 0.352 -0.135 0.276 -0.193 e 1 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_consensus.html b/reference/tof_metacluster_consensus.html index 7061f9c..b8fd3ee 100644 --- a/reference/tof_metacluster_consensus.html +++ b/reference/tof_metacluster_consensus.html @@ -210,16 +210,16 @@

Examples#> # A tibble: 1,000 × 1 #> .consensus_metacluster #> <chr> -#> 1 1 -#> 2 1 -#> 3 5 -#> 4 2 +#> 1 4 +#> 2 10 +#> 3 4 +#> 4 3 #> 5 1 -#> 6 1 -#> 7 1 -#> 8 5 -#> 9 2 -#> 10 7 +#> 6 4 +#> 7 4 +#> 8 7 +#> 9 3 +#> 10 1 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_flowsom.html b/reference/tof_metacluster_flowsom.html index ba50c19..09809b5 100644 --- a/reference/tof_metacluster_flowsom.html +++ b/reference/tof_metacluster_flowsom.html @@ -197,16 +197,16 @@

Examples#> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> <chr> -#> 1 3 -#> 2 4 -#> 3 3 -#> 4 4 -#> 5 4 -#> 6 3 -#> 7 4 -#> 8 2 -#> 9 4 -#> 10 1 +#> 1 1 +#> 2 1 +#> 3 1 +#> 4 1 +#> 5 1 +#> 6 1 +#> 7 1 +#> 8 1 +#> 9 1 +#> 10 2 #> # ℹ 990 more rows tof_metacluster_flowsom( @@ -217,16 +217,16 @@

Examples#> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> <chr> -#> 1 2 -#> 2 1 -#> 3 1 -#> 4 1 +#> 1 4 +#> 2 4 +#> 3 6 +#> 4 2 #> 5 1 -#> 6 2 -#> 7 1 +#> 6 1 +#> 7 2 #> 8 4 -#> 9 1 -#> 10 2 +#> 9 4 +#> 10 5 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_hierarchical.html b/reference/tof_metacluster_hierarchical.html index 0f534c1..26cde7d 100644 --- a/reference/tof_metacluster_hierarchical.html +++ b/reference/tof_metacluster_hierarchical.html @@ -180,16 +180,16 @@

Examples#> # A tibble: 1,000 × 1 #> .hierarchical_metacluster #> <chr> -#> 1 10 -#> 2 5 -#> 3 3 -#> 4 6 -#> 5 8 -#> 6 7 -#> 7 7 +#> 1 4 +#> 2 3 +#> 3 1 +#> 4 9 +#> 5 10 +#> 6 1 +#> 7 6 #> 8 7 -#> 9 2 -#> 10 3 +#> 9 3 +#> 10 5 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_kmeans.html b/reference/tof_metacluster_kmeans.html index 29adb10..77c5f00 100644 --- a/reference/tof_metacluster_kmeans.html +++ b/reference/tof_metacluster_kmeans.html @@ -164,16 +164,16 @@

Examples#> # A tibble: 1,000 × 1 #> .kmeans_metacluster #> <chr> -#> 1 3 -#> 2 8 -#> 3 9 +#> 1 1 +#> 2 10 +#> 3 10 #> 4 2 -#> 5 5 -#> 6 3 -#> 7 8 -#> 8 2 -#> 9 4 -#> 10 7 +#> 5 7 +#> 6 8 +#> 7 1 +#> 8 9 +#> 9 1 +#> 10 1 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_phenograph.html b/reference/tof_metacluster_phenograph.html index efae38c..98d2e5e 100644 --- a/reference/tof_metacluster_phenograph.html +++ b/reference/tof_metacluster_phenograph.html @@ -169,16 +169,16 @@

Examples#> # A tibble: 1,000 × 1 #> .phenograph_metacluster #> <chr> -#> 1 2 -#> 2 1 -#> 3 3 +#> 1 3 +#> 2 4 +#> 3 2 #> 4 1 #> 5 1 -#> 6 2 -#> 7 1 -#> 8 1 -#> 9 3 -#> 10 2 +#> 6 1 +#> 7 2 +#> 8 3 +#> 9 4 +#> 10 4 #> # ℹ 990 more rows diff --git a/reference/tof_predict.html b/reference/tof_predict.html index 048e295..26f8922 100644 --- a/reference/tof_predict.html +++ b/reference/tof_predict.html @@ -179,26 +179,26 @@

Examples#> # A tibble: 20 × 1 #> .pred #> <dbl> -#> 1 3.57 -#> 2 2.35 -#> 3 4.91 -#> 4 4.72 -#> 5 2.93 -#> 6 1.97 -#> 7 3.57 -#> 8 3.52 -#> 9 5.53 -#> 10 2.48 -#> 11 2.21 -#> 12 3.35 -#> 13 2.38 -#> 14 2.91 -#> 15 3.89 -#> 16 3.45 -#> 17 5.20 -#> 18 5.53 -#> 19 3.45 -#> 20 3.69 +#> 1 3.90 +#> 2 4.77 +#> 3 3.33 +#> 4 0.388 +#> 5 2.43 +#> 6 3.07 +#> 7 3.38 +#> 8 2.59 +#> 9 5.27 +#> 10 4.30 +#> 11 0.824 +#> 12 0.987 +#> 13 2.44 +#> 14 5.04 +#> 15 3.86 +#> 16 5.88 +#> 17 4.41 +#> 18 4.80 +#> 19 4.44 +#> 20 3.14 diff --git a/reference/tof_reduce_dimensions.html b/reference/tof_reduce_dimensions.html index da3942b..ac5f4b2 100644 --- a/reference/tof_reduce_dimensions.html +++ b/reference/tof_reduce_dimensions.html @@ -144,35 +144,35 @@

Examples# calculate pca tof_reduce_dimensions(tof_tibble = sim_data, method = "pca") #> # A tibble: 100 × 8 -#> cd45 cd38 cd34 cd19 .pc1 .pc2 .pc3 .pc4 -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.435 -1.61 0.636 0.405 1.04 0.0683 -1.27 0.666 -#> 2 -1.43 -1.12 0.532 0.673 0.138 -0.144 -1.95 0.503 -#> 3 -0.506 -0.435 -0.170 -0.587 -0.203 0.702 -0.453 0.0480 -#> 4 1.38 -0.210 0.971 -0.444 1.41 -0.0798 0.697 -0.790 -#> 5 -0.755 0.0102 -0.913 -1.88 -1.05 1.85 -0.0182 -0.250 -#> 6 1.45 -0.217 0.0959 1.06 0.835 -1.07 0.949 0.387 -#> 7 -0.164 0.0759 0.638 0.248 0.172 -0.426 -0.459 -0.510 -#> 8 -0.0347 -1.10 -1.07 1.47 -0.198 -0.565 -0.135 1.90 -#> 9 -0.528 0.557 -3.12 0.487 -2.77 0.297 1.04 1.80 -#> 10 0.520 -0.244 0.676 -2.51 0.837 1.83 0.352 -1.38 +#> cd45 cd38 cd34 cd19 .pc1 .pc2 .pc3 .pc4 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.561 -0.259 2.01 0.845 1.12 0.283 1.46 1.16 +#> 2 0.332 -1.31 1.39 0.389 1.53 0.121 0.00464 0.920 +#> 3 -2.33 1.63 0.112 -0.119 -2.14 0.519 1.72 0.914 +#> 4 -1.18 1.42 0.597 -0.744 -1.48 -0.628 1.14 0.854 +#> 5 0.666 0.469 -0.181 -0.449 -0.306 -0.736 -0.256 -0.428 +#> 6 1.70 -1.30 1.42 0.239 2.22 -0.718 -0.496 0.184 +#> 7 -0.474 0.138 -1.46 0.995 -0.717 1.50 0.0360 -0.949 +#> 8 -0.0121 -1.38 -0.248 0.624 0.656 1.11 -0.614 0.183 +#> 9 0.728 -1.33 -0.111 0.147 0.870 0.315 -1.00 0.0103 +#> 10 1.03 0.181 -0.171 -0.463 0.0189 -0.830 -0.546 -0.520 #> # ℹ 90 more rows # calculate tsne tof_reduce_dimensions(tof_tibble = sim_data, method = "tsne") #> # A tibble: 100 × 6 -#> cd45 cd38 cd34 cd19 .tsne1 .tsne2 -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.435 -1.61 0.636 0.405 -2.95 3.44 -#> 2 -1.43 -1.12 0.532 0.673 -2.36 4.03 -#> 3 -0.506 -0.435 -0.170 -0.587 -0.234 2.25 -#> 4 1.38 -0.210 0.971 -0.444 -3.05 -0.949 -#> 5 -0.755 0.0102 -0.913 -1.88 0.443 3.37 -#> 6 1.45 -0.217 0.0959 1.06 2.10 -3.11 -#> 7 -0.164 0.0759 0.638 0.248 -0.676 -0.522 -#> 8 -0.0347 -1.10 -1.07 1.47 0.142 -3.74 -#> 9 -0.528 0.557 -3.12 0.487 4.06 1.46 -#> 10 0.520 -0.244 0.676 -2.51 -3.85 1.09 +#> cd45 cd38 cd34 cd19 .tsne1 .tsne2 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.561 -0.259 2.01 0.845 -1.12 -3.02 +#> 2 0.332 -1.31 1.39 0.389 -3.26 -1.49 +#> 3 -2.33 1.63 0.112 -0.119 5.46 -0.0530 +#> 4 -1.18 1.42 0.597 -0.744 4.78 0.0140 +#> 5 0.666 0.469 -0.181 -0.449 2.38 -1.80 +#> 6 1.70 -1.30 1.42 0.239 -3.61 -2.86 +#> 7 -0.474 0.138 -1.46 0.995 1.38 2.17 +#> 8 -0.0121 -1.38 -0.248 0.624 -3.08 0.895 +#> 9 0.728 -1.33 -0.111 0.147 -3.65 0.923 +#> 10 1.03 0.181 -0.171 -0.463 2.36 -2.19 #> # ℹ 90 more rows # calculate umap @@ -182,18 +182,18 @@

Examples#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 100 × 6 -#> cd45 cd38 cd34 cd19 .umap1 .umap2 -#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.435 -1.61 0.636 0.405 2.01 4.48 -#> 2 -1.43 -1.12 0.532 0.673 2.85 4.72 -#> 3 -0.506 -0.435 -0.170 -0.587 1.76 -1.98 -#> 4 1.38 -0.210 0.971 -0.444 2.21 2.25 -#> 5 -0.755 0.0102 -0.913 -1.88 2.20 -2.82 -#> 6 1.45 -0.217 0.0959 1.06 -3.10 -1.22 -#> 7 -0.164 0.0759 0.638 0.248 0.942 -0.870 -#> 8 -0.0347 -1.10 -1.07 1.47 -4.01 -0.354 -#> 9 -0.528 0.557 -3.12 0.487 -0.849 -0.123 -#> 10 0.520 -0.244 0.676 -2.51 2.54 0.216 +#> cd45 cd38 cd34 cd19 .umap1 .umap2 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.561 -0.259 2.01 0.845 -0.133 -0.963 +#> 2 0.332 -1.31 1.39 0.389 -4.09 0.285 +#> 3 -2.33 1.63 0.112 -0.119 4.83 -1.72 +#> 4 -1.18 1.42 0.597 -0.744 4.61 -1.28 +#> 5 0.666 0.469 -0.181 -0.449 2.49 1.01 +#> 6 1.70 -1.30 1.42 0.239 -3.36 0.151 +#> 7 -0.474 0.138 -1.46 0.995 -0.808 -2.59 +#> 8 -0.0121 -1.38 -0.248 0.624 -5.92 -0.166 +#> 9 0.728 -1.33 -0.111 0.147 -4.87 0.746 +#> 10 1.03 0.181 -0.171 -0.463 2.25 0.798 #> # ℹ 90 more rows diff --git a/reference/tof_reduce_pca.html b/reference/tof_reduce_pca.html index 912b595..46585e6 100644 --- a/reference/tof_reduce_pca.html +++ b/reference/tof_reduce_pca.html @@ -166,18 +166,18 @@

Examples# calculate pca tof_reduce_pca(tof_tibble = sim_data, num_comp = 2) #> # A tibble: 200 × 2 -#> .pc1 .pc2 -#> <dbl> <dbl> -#> 1 -0.489 0.432 -#> 2 -0.486 -0.514 -#> 3 0.422 -0.325 -#> 4 -0.565 1.28 -#> 5 1.55 -2.16 -#> 6 0.664 0.406 -#> 7 -1.20 -0.136 -#> 8 0.518 0.481 -#> 9 -0.0566 0.370 -#> 10 0.739 -1.34 +#> .pc1 .pc2 +#> <dbl> <dbl> +#> 1 -0.244 1.51 +#> 2 -0.553 0.502 +#> 3 -0.503 -1.25 +#> 4 -0.538 1.31 +#> 5 -0.528 -1.23 +#> 6 -2.61 0.0402 +#> 7 -1.39 1.82 +#> 8 0.298 0.459 +#> 9 -1.15 0.779 +#> 10 1.21 0.0252 #> # ℹ 190 more rows # return recipe instead of embeddings @@ -186,18 +186,18 @@

Examples# apply recipe to new data recipes::bake(pca_recipe, new_data = new_data) #> # A tibble: 50 × 4 -#> PC1 PC2 PC3 PC4 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.737 0.656 -0.752 -0.199 -#> 2 -0.938 0.936 -0.101 -0.158 -#> 3 0.290 -0.395 -0.00400 -0.517 -#> 4 -0.157 -0.421 -0.593 1.12 -#> 5 -1.31 1.01 0.753 -0.205 -#> 6 -0.617 -2.82 0.273 -0.925 -#> 7 -0.404 -0.134 0.271 0.228 -#> 8 0.929 -0.0137 0.839 0.000129 -#> 9 0.312 2.42 1.67 -0.0232 -#> 10 -1.89 2.14 -1.09 0.0440 +#> PC1 PC2 PC3 PC4 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.370 -0.725 -0.491 0.695 +#> 2 -1.58 -1.37 -1.03 2.04 +#> 3 1.00 0.601 0.390 -1.64 +#> 4 -0.617 1.70 0.101 -1.45 +#> 5 0.549 0.832 0.827 -0.329 +#> 6 0.808 0.344 1.62 0.295 +#> 7 -0.761 -0.428 0.360 -1.21 +#> 8 -0.758 -0.632 -1.11 0.153 +#> 9 -0.851 0.897 -0.937 0.828 +#> 10 0.154 -0.190 -1.40 -0.915 #> # ℹ 40 more rows diff --git a/reference/tof_reduce_tsne.html b/reference/tof_reduce_tsne.html index 7f46d6f..fc9a6f2 100644 --- a/reference/tof_reduce_tsne.html +++ b/reference/tof_reduce_tsne.html @@ -162,18 +162,18 @@

Examples# calculate tsne tof_reduce_tsne(tof_tibble = sim_data) #> # A tibble: 200 × 2 -#> .tsne1 .tsne2 -#> <dbl> <dbl> -#> 1 0.491 4.86 -#> 2 5.76 1.73 -#> 3 3.00 -4.96 -#> 4 -2.56 -4.14 -#> 5 -4.99 -4.90 -#> 6 2.02 -3.28 -#> 7 -1.76 -7.35 -#> 8 1.36 -6.55 -#> 9 -5.08 4.51 -#> 10 4.82 7.52 +#> .tsne1 .tsne2 +#> <dbl> <dbl> +#> 1 7.65 4.09 +#> 2 -5.58 -6.48 +#> 3 0.309 -5.97 +#> 4 -6.72 -6.01 +#> 5 2.42 -0.467 +#> 6 0.0232 1.01 +#> 7 -4.22 -3.00 +#> 8 7.07 3.76 +#> 9 6.67 1.84 +#> 10 -5.70 2.25 #> # ℹ 190 more rows # calculate tsne with only 2 columns @@ -181,16 +181,16 @@

Examples#> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 3.22 -5.14 -#> 2 5.77 -8.41 -#> 3 0.381 2.88 -#> 4 -6.70 3.60 -#> 5 -5.71 7.82 -#> 6 -3.06 0.412 -#> 7 -7.00 6.05 -#> 8 1.10 3.51 -#> 9 4.94 2.74 -#> 10 -5.72 -5.85 +#> 1 -4.88 -2.18 +#> 2 2.85 3.32 +#> 3 8.34 -0.916 +#> 4 -0.846 6.80 +#> 5 -5.65 1.18 +#> 6 7.16 -1.99 +#> 7 -2.02 -2.87 +#> 8 -3.53 -5.04 +#> 9 -2.12 -4.92 +#> 10 -4.89 8.78 #> # ℹ 190 more rows diff --git a/reference/tof_reduce_umap.html b/reference/tof_reduce_umap.html index cfea7d4..cced253 100644 --- a/reference/tof_reduce_umap.html +++ b/reference/tof_reduce_umap.html @@ -192,16 +192,16 @@

Examples#> # A tibble: 200 × 2 #> .umap1 .umap2 #> <dbl> <dbl> -#> 1 -2.83 -2.74 -#> 2 0.581 -1.52 -#> 3 3.81 0.0325 -#> 4 -3.04 0.595 -#> 5 0.267 -0.993 -#> 6 -3.85 -0.836 -#> 7 -1.89 1.52 -#> 8 5.78 -0.180 -#> 9 3.51 -1.97 -#> 10 -2.14 0.703 +#> 1 -0.671 2.77 +#> 2 -0.398 1.34 +#> 3 -2.67 -1.34 +#> 4 -0.473 -0.356 +#> 5 -0.327 3.20 +#> 6 -2.18 2.84 +#> 7 4.18 -2.06 +#> 8 -0.190 -0.0974 +#> 9 4.08 -2.51 +#> 10 -2.04 0.786 #> # ℹ 190 more rows # calculate umap with only 2 columns @@ -209,16 +209,16 @@

Examples#> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 -7.59 -1.06 -#> 2 -1.97 5.26 -#> 3 -1.46 -4.92 -#> 4 -4.09 0.256 -#> 5 -2.76 4.66 -#> 6 -5.98 -1.23 -#> 7 3.30 -0.344 -#> 8 7.05 2.68 -#> 9 1.88 7.55 -#> 10 -3.34 1.99 +#> 1 8.31 -4.23 +#> 2 3.03 -3.13 +#> 3 -2.08 6.18 +#> 4 -4.30 1.17 +#> 5 7.53 -2.50 +#> 6 5.96 0.366 +#> 7 -8.31 -2.83 +#> 8 1.13 -4.12 +#> 9 -6.24 -2.37 +#> 10 4.00 -0.209 #> # ℹ 190 more rows # return recipe @@ -231,18 +231,18 @@

Examples# apply recipe to new data recipes::bake(umap_recipe, new_data = new_data) #> # A tibble: 50 × 2 -#> UMAP1 UMAP2 -#> <dbl> <dbl> -#> 1 -0.114 3.45 -#> 2 2.39 -2.68 -#> 3 2.72 -0.965 -#> 4 1.07 3.15 -#> 5 2.33 1.42 -#> 6 -4.80 -1.13 -#> 7 -5.03 -0.379 -#> 8 -0.114 2.93 -#> 9 -0.00782 -3.22 -#> 10 2.49 2.88 +#> UMAP1 UMAP2 +#> <dbl> <dbl> +#> 1 1.47 -1.53 +#> 2 2.64 0.782 +#> 3 1.63 -4.23 +#> 4 2.06 1.96 +#> 5 -1.47 -1.47 +#> 6 0.206 -1.21 +#> 7 -1.75 3.22 +#> 8 2.88 1.60 +#> 9 -2.78 -0.962 +#> 10 -0.322 -1.91 #> # ℹ 40 more rows diff --git a/reference/tof_spade_density.html b/reference/tof_spade_density.html index 9a585c9..8a83f16 100644 --- a/reference/tof_spade_density.html +++ b/reference/tof_spade_density.html @@ -184,16 +184,16 @@

Examples#> # A tibble: 1,000 × 1 #> .spade_density #> <dbl> -#> 1 1 -#> 2 1 -#> 3 1 -#> 4 1 -#> 5 1 -#> 6 1 -#> 7 1 -#> 8 1 -#> 9 0.6 -#> 10 1 +#> 1 1 +#> 2 1 +#> 3 1 +#> 4 1 +#> 5 1 +#> 6 1 +#> 7 1 +#> 8 1 +#> 9 1 +#> 10 1 #> # ℹ 990 more rows # perform the density estimation using cosine distance @@ -205,16 +205,16 @@

Examples#> # A tibble: 1,000 × 1 #> .spade_density #> <dbl> -#> 1 0.375 -#> 2 0.125 -#> 3 0.125 -#> 4 0.375 -#> 5 0.25 -#> 6 0.75 -#> 7 0.25 -#> 8 0.25 -#> 9 0.25 -#> 10 0.375 +#> 1 0 +#> 2 0.143 +#> 3 0.286 +#> 4 0.143 +#> 5 0.286 +#> 6 0.286 +#> 7 0.143 +#> 8 0.143 +#> 9 0.429 +#> 10 0 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around @@ -226,15 +226,15 @@

Examples#> # A tibble: 1,000 × 1 #> .spade_density #> <dbl> -#> 1 0.9 -#> 2 0.3 -#> 3 0 +#> 1 0.7 +#> 2 1 +#> 3 1 #> 4 1 -#> 5 0.1 +#> 5 0.4 #> 6 1 -#> 7 0.4 -#> 8 0.1 -#> 9 0 +#> 7 1 +#> 8 1 +#> 9 1 #> 10 1 #> # ℹ 990 more rows diff --git a/reference/tof_split_data.html b/reference/tof_split_data.html index 83877c4..b9836a9 100644 --- a/reference/tof_split_data.html +++ b/reference/tof_split_data.html @@ -212,15 +212,15 @@

Examples#> # A tibble: 10 × 2 #> splits id #> <list> <chr> -#> 1 <split [100/39]> Bootstrap01 -#> 2 <split [100/35]> Bootstrap02 +#> 1 <split [100/36]> Bootstrap01 +#> 2 <split [100/36]> Bootstrap02 #> 3 <split [100/35]> Bootstrap03 -#> 4 <split [100/39]> Bootstrap04 +#> 4 <split [100/40]> Bootstrap04 #> 5 <split [100/34]> Bootstrap05 -#> 6 <split [100/33]> Bootstrap06 -#> 7 <split [100/38]> Bootstrap07 -#> 8 <split [100/33]> Bootstrap08 -#> 9 <split [100/39]> Bootstrap09 +#> 6 <split [100/37]> Bootstrap06 +#> 7 <split [100/36]> Bootstrap07 +#> 8 <split [100/34]> Bootstrap08 +#> 9 <split [100/37]> Bootstrap09 #> 10 <split [100/41]> Bootstrap10 # split the dataset into a single training/test set diff --git a/reference/tof_train_model.html b/reference/tof_train_model.html index ab1210f..d0f6efd 100644 --- a/reference/tof_train_model.html +++ b/reference/tof_train_model.html @@ -189,18 +189,18 @@

Arguments

  • For "two-class" models, choices are "roc_auc" (the area under the Receiver-Operating Curve for the classification; the default), "misclassification error" (the proportion of -misclassified observations), "binomial_deviance" (see deviance.glmnet), +misclassified observations), "binomial_deviance" (see deviance.glmnet), "mse" (the mean squared error of the logit function), and "mae" (the mean absolute error of the logit function).

  • For "multiclass" models, choices are "roc_auc" (the area under the Receiver-Operating Curve for the classification using the Hand-Till generalization of the ROC AUC for multiclass models in roc_auc; the default), "misclassification error" (the proportion of -misclassified observations), "multinomial_deviance" (see deviance.glmnet), +misclassified observations), "multinomial_deviance" (see deviance.glmnet), and "mse" and "mae" as above.

  • For "survival" models, choices are "concordance_index" (Harrel's C index; -see deviance.glmnet) and "partial_likelihood_deviance" -(see deviance.glmnet).

    • +see deviance.glmnet) and "partial_likelihood_deviance" +(see deviance.glmnet).

    @@ -309,10 +309,10 @@

    Examples#> # A tibble: 4 × 2 #> feature coefficient #> <chr> <dbl> -#> 1 (Intercept) 3.61 -#> 2 pstat5 1.16 -#> 3 cd45 0.749 -#> 4 cd34 0.0891 +#> 1 (Intercept) 3.51 +#> 2 pstat5 1.28 +#> 3 cd45 0.836 +#> 4 cd34 0.123 # train a logistic regression classifier tof_train_model( @@ -321,12 +321,14 @@

    Examples response_col = class, model_type = "two-class" ) -#> A two-class `tof_model` with a mixture parameter (alpha) of 0.25 and a penalty parameter (lambda) of 1e+00 -#> # A tibble: 2 × 2 +#> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 +#> # A tibble: 4 × 2 #> feature coefficient #> <chr> <dbl> -#> 1 pstat5 -0.0512 -#> 2 (Intercept) 0.00000244 +#> 1 pstat5 -2.31 +#> 2 cd45 -1.19 +#> 3 cd34 -0.275 +#> 4 (Intercept) -0.0137 # train a cox regression survival model tof_train_model( @@ -336,13 +338,13 @@

    Examples event_col = event, model_type = "survival" ) -#> A survival `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 +#> A survival `tof_model` with a mixture parameter (alpha) of 0 and a penalty parameter (lambda) of 1e+00 #> # A tibble: 3 × 2 #> feature coefficient #> <chr> <dbl> -#> 1 cd45 -0.314 -#> 2 cd34 0.238 -#> 3 pstat5 -0.145 +#> 1 pstat5 0.0633 +#> 2 cd45 -0.0608 +#> 3 cd34 0.0493 diff --git a/reference/tof_upsample.html b/reference/tof_upsample.html index 67cc87e..ff227ce 100644 --- a/reference/tof_upsample.html +++ b/reference/tof_upsample.html @@ -184,18 +184,18 @@

    Examples method = "distance" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .upsample_cluster -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -0.814 0.0914 0.0564 -0.667 a -#> 2 -0.450 -1.16 0.0757 1.25 a -#> 3 -0.459 0.707 -0.119 1.27 a -#> 4 -0.973 -0.875 -0.715 0.809 a -#> 5 -0.994 1.14 -0.691 -1.24 b -#> 6 0.471 -1.62 0.389 0.621 b -#> 7 0.490 -0.331 1.02 -0.268 a -#> 8 0.395 0.234 -0.0729 1.65 b -#> 9 -0.789 0.679 0.464 -1.01 b -#> 10 0.199 -1.54 -0.116 0.434 b +#> cd45 cd38 cd34 cd19 .upsample_cluster +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 1.88 -0.666 -0.879 -1.05 b +#> 2 0.717 -1.30 0.351 -0.598 b +#> 3 -0.175 0.328 1.06 -0.521 a +#> 4 -0.600 0.0668 -0.331 -0.147 a +#> 5 0.452 0.359 -3.45 -0.699 a +#> 6 0.585 -0.176 2.05 -0.112 a +#> 7 0.701 1.20 1.74 0.252 a +#> 8 -1.77 -2.10 -0.554 -0.625 a +#> 9 1.99 -0.590 1.45 -0.588 b +#> 10 0.807 0.0548 1.46 0.435 a #> # ℹ 990 more rows # upsample using distance to nearest neighbor @@ -206,18 +206,18 @@

    Examples method = "neighbor" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .upsample_cluster -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -0.814 0.0914 0.0564 -0.667 a -#> 2 -0.450 -1.16 0.0757 1.25 b -#> 3 -0.459 0.707 -0.119 1.27 b -#> 4 -0.973 -0.875 -0.715 0.809 a -#> 5 -0.994 1.14 -0.691 -1.24 a -#> 6 0.471 -1.62 0.389 0.621 b -#> 7 0.490 -0.331 1.02 -0.268 b -#> 8 0.395 0.234 -0.0729 1.65 b -#> 9 -0.789 0.679 0.464 -1.01 b -#> 10 0.199 -1.54 -0.116 0.434 a +#> cd45 cd38 cd34 cd19 .upsample_cluster +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 1.88 -0.666 -0.879 -1.05 b +#> 2 0.717 -1.30 0.351 -0.598 a +#> 3 -0.175 0.328 1.06 -0.521 b +#> 4 -0.600 0.0668 -0.331 -0.147 b +#> 5 0.452 0.359 -3.45 -0.699 a +#> 6 0.585 -0.176 2.05 -0.112 b +#> 7 0.701 1.20 1.74 0.252 b +#> 8 -1.77 -2.10 -0.554 -0.625 b +#> 9 1.99 -0.590 1.45 -0.588 a +#> 10 0.807 0.0548 1.46 0.435 b #> # ℹ 990 more rows diff --git a/reference/tof_upsample_distance.html b/reference/tof_upsample_distance.html index e46e7e9..d14a906 100644 --- a/reference/tof_upsample_distance.html +++ b/reference/tof_upsample_distance.html @@ -212,7 +212,7 @@

    Examples#> 4 b #> 5 b #> 6 b -#> 7 a +#> 7 b #> 8 b #> 9 b #> 10 b @@ -230,14 +230,14 @@

    Examples#> <chr> #> 1 a #> 2 a -#> 3 b -#> 4 b +#> 3 a +#> 4 a #> 5 a -#> 6 b +#> 6 a #> 7 a #> 8 b -#> 9 b -#> 10 b +#> 9 a +#> 10 a #> # ℹ 990 more rows diff --git a/reference/tof_upsample_neighbor.html b/reference/tof_upsample_neighbor.html index 7e7dfce..25fbd6b 100644 --- a/reference/tof_upsample_neighbor.html +++ b/reference/tof_upsample_neighbor.html @@ -182,14 +182,14 @@

    Examples#> .upsample_cluster #> <chr> #> 1 a -#> 2 a -#> 3 a +#> 2 b +#> 3 b #> 4 a #> 5 a -#> 6 b -#> 7 a -#> 8 b -#> 9 a +#> 6 a +#> 7 b +#> 8 a +#> 9 b #> 10 b #> # ℹ 990 more rows @@ -203,16 +203,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .upsample_cluster #> <chr> -#> 1 a -#> 2 a -#> 3 a -#> 4 a -#> 5 b -#> 6 b -#> 7 b +#> 1 b +#> 2 b +#> 3 b +#> 4 b +#> 5 a +#> 6 a +#> 7 a #> 8 b -#> 9 b -#> 10 a +#> 9 a +#> 10 b #> # ℹ 990 more rows diff --git a/search.json b/search.json index 9aeb45a..4b9e253 100644 --- a/search.json +++ b/search.json @@ -1 +1 @@ -[{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tidytof","title":"Contributing to tidytof","text":"outlines propose change tidytof. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to tidytof","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to tidytof","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to tidytof","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"keyes-timothy/tidytof\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to tidytof","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to tidytof","text":"Please note tidytof project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Timothy Keyes Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/clustering.html","id":"clustering-with-tof_cluster","dir":"Articles","previous_headings":"","what":"Clustering with tof_cluster()","title":"Clustering and metaclustering","text":"demonstrate, can apply PhenoGraph clustering algorithm tidytof’s built-phenograph_data. Note phenograph_data contains 3000 total cells (1000 3 clusters identified original PhenoGraph publication). demonstration purposes, also metacluster PhenoGraph clusters using k-means clustering. outputs tof_cluster() tof_metacluster() tof_tbl identical input tibble, now addition additional column (case, “.phenograph_cluster” “.kmeans_metacluster”) encodes cluster id cell input tof_tbl. Note output columns added tibble tof_tbl tidytof begin full-stop (“.”) reduce likelihood collisions existing column names. output tof_cluster tof_tbl, can use dplyr’s count method assess accuracy clustering procedure compared original clustering PhenoGraph paper. , can see clustering procedure groups cells PhenoGraph cluster one another (small number mistakes). change clustering algorithm tof_cluster uses, alter method flag. change columns used compute clusters, change cluster_cols flag. finally, want return one-column tibble includes cluster labels (opposed cluster labels added new column input tof_tbl), set augment FALSE.","code":"data(phenograph_data) set.seed(203L) phenograph_clusters <- phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = starts_with(\"cd\"), num_neighbors = 50L, distance_function = \"cosine\", method = \"phenograph\" ) |> tof_metacluster( cluster_col = .phenograph_cluster, metacluster_cols = starts_with(\"cd\"), num_metaclusters = 3L, method = \"kmeans\" ) phenograph_clusters |> dplyr::select(sample_name, .phenograph_cluster, .kmeans_metacluster) |> head() #> # A tibble: 6 × 3 #> sample_name .phenograph_cluster .kmeans_metacluster #> #> 1 H1_PhenoGraph_cluster1 6 2 #> 2 H1_PhenoGraph_cluster1 1 2 #> 3 H1_PhenoGraph_cluster1 6 2 #> 4 H1_PhenoGraph_cluster1 6 2 #> 5 H1_PhenoGraph_cluster1 6 2 #> 6 H1_PhenoGraph_cluster1 6 2 phenograph_clusters |> dplyr::count(phenograph_cluster, .kmeans_metacluster, sort = TRUE) #> # A tibble: 4 × 3 #> phenograph_cluster .kmeans_metacluster n #> #> 1 cluster2 3 1000 #> 2 cluster3 1 1000 #> 3 cluster1 2 995 #> 4 cluster1 1 5 # use the kmeans algorithm phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"kmeans\" ) # use the flowsom algorithm phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"flowsom\" ) # will result in a tibble with only 1 column (the cluster labels) phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"kmeans\", augment = FALSE ) |> head() #> # A tibble: 6 × 1 #> .kmeans_cluster #> #> 1 2 #> 2 1 #> 3 19 #> 4 9 #> 5 2 #> 6 9"},{"path":"https://keyes-timothy.github.io/tidytof/articles/clustering.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Clustering and metaclustering","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.1 viridisLite_0.4.2 timeDate_4032.109 #> [4] farver_2.1.1 viridis_0.6.5 ggraph_2.2.1 #> [7] fastmap_1.1.1 tweenr_2.0.3 rpart_4.1.23 #> [10] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.1 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.9 #> [19] tools_4.3.3 igraph_2.0.3 utf8_1.2.4 #> [22] yaml_2.3.8 data.table_1.15.2 knitr_1.45 #> [25] graphlayouts_1.1.1 withr_3.0.0 purrr_1.0.2 #> [28] RProtoBufLib_2.14.1 BiocGenerics_0.48.1 desc_1.4.3 #> [31] nnet_7.3-19 grid_4.3.3 polyclip_1.10-6 #> [34] stats4_4.3.3 fansi_1.0.6 RcppHNSW_0.6.0 #> [37] future_1.33.1 colorspace_2.1-0 ggplot2_3.5.0 #> [40] globals_0.16.3 scales_1.3.0 iterators_1.0.14 #> [43] MASS_7.3-60.0.1 cli_3.6.2 rmarkdown_2.26 #> [46] ragg_1.3.0 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.2 #> [52] ggforce_0.4.2 stringr_1.5.1 splines_4.3.3 #> [55] parallel_4.3.3 matrixStats_1.2.0 vctrs_0.6.5 #> [58] hardhat_1.3.1 glmnet_4.1-8 Matrix_1.6-5 #> [61] jsonlite_1.8.8 cytolib_2.14.1 hms_1.1.3 #> [64] S4Vectors_0.40.2 ggrepel_0.9.5 listenv_0.9.1 #> [67] systemfonts_1.0.6 foreach_1.5.2 gower_1.0.1 #> [70] tidyr_1.3.1 jquerylib_0.1.4 recipes_1.0.10 #> [73] parallelly_1.37.1 glue_1.7.0 pkgdown_2.0.7 #> [76] codetools_0.2-19 stringi_1.8.3 lubridate_1.9.3 #> [79] gtable_0.3.4 shape_1.4.6.1 munsell_0.5.0 #> [82] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.7 #> [85] ipred_0.9-14 lava_1.8.0 R6_2.5.1 #> [88] textshaping_0.3.7 doParallel_1.0.17 tidygraph_1.3.1 #> [91] evaluate_0.23 Biobase_2.62.0 lattice_0.22-5 #> [94] readr_2.1.5 memoise_2.0.1 bslib_0.6.2 #> [97] class_7.3-22 Rcpp_1.0.12 prodlim_2023.08.28 #> [100] gridExtra_2.3 xfun_0.42 fs_1.6.3 #> [103] pkgconfig_2.0.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"general-guidelines","dir":"Articles","previous_headings":"","what":"General Guidelines","title":"How to contribute code","text":"extend tidytof include new algorithm - example, one ’ve just developed - can take 1 2 general strategies (cases, may take !). first write tidytof-style verb algorithm can included standalone package. case, benefit writing tidytof-style verb algorithm taking advantage tidytof’s design schema make algorithm easy users access without learning much () new syntax still allowing maintain code base independently team. second approach write tidytof-style function ’d like team add tidytof next release. case, code review process take bit time, also allow teams collaborate provide greater degree critical feedback one another well share burden code maintenance future. either case, ’re welcome contact tidytof team review code via pull request /issue tidytof GitHub page. tutorial may helpful don’t lot experience collaborating programmers via GitHub. open request, can submit code team reviewed. Whether want method incorporated tidytof ’re simply looking external code review/feedback team, please mention request.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"code-style","dir":"Articles","previous_headings":"","what":"Code style","title":"How to contribute code","text":"tidytof uses tidyverse style guide. Adhering tidyverse style something team expect code incorporated tidytof, ’s also something encourage functions write analysis packages. experience, best code written just executed, also read humans! also many tools can use lint automatically style R code, {lintr} {styler} packages.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"testing","dir":"Articles","previous_headings":"","what":"Testing","title":"How to contribute code","text":"addition written well-styled code, encourage write unit tests every function write. common practice software engineering world, common probably (!) bioinformatics community. tidytof team uses {testthat} package unit tests, ’s great tutorial .","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"general-principles","dir":"Articles","previous_headings":"How to contribute","what":"General principles","title":"How to contribute code","text":"important part writing function extends tidytof adhere tidytof verb syntax. exceptions, tidytof functions follow specific, shared syntax involves 3 types arguments always occur order. argument types follows: almost tidytof functions, first argument data frame (tibble). enables use pipe (|>) multi-step calculations, means first argument functions implicit (passed previous function using pipe). second group arguments called column specifications, end suffix _col _cols. Column specifications unquoted column names tell tidytof verb columns compute particular operation. example, cluster_cols argument tof_cluster allows user specify column input data frames used perform clustering. Regardless verb requires , column specifications support tidyselect helpers follow rules tidyselection tidyverse verbs like dplyr::select() tidyr::pivot_longer(). Finally, third group arguments tidytof verb called method specifications, ’re comprised every argument isn’t input data frame column specification. Whereas column specifications represent columns used perform operation, method specifications represent details operation performed. example, tof_cluster_phenograph() function requires method specification num_neighbors, specifies many nearest neighbors used construct PhenoGraph algorithm’s k-nearest-neighbor graph. exceptions, tidytof extension include 3 argument types (order). addition, functions extend tidytof name starts prefix tof_. make easier users find tidytof functions using text completion functionality included development environments.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"contributing-a-new-method-to-an-existing-tidytof-verb","dir":"Articles","previous_headings":"How to contribute","what":"Contributing a new method to an existing {tidytof} verb","title":"How to contribute code","text":"tidytof currently includes multiple verbs perform fundamental single-cell data manipulation tasks. Currently, tidytof’s extensible verbs following: tof_analyze_abundance: Perform differential cluster abundance analysis tof_analyze_expression: Perform differential marker expression analysis tof_annotate_clusters: Annotate clusters manual IDs tof_batch_correct: Perform batch correction tof_cluster: Cluster cells subpopulations tof_downsample: Subsample dataset smaller number cells tof_extract: Calculate sample-level summary statistics tof_metacluster: Metacluster clusters smaller number subpopulations tof_plot_cells: Plot cell-level data tof_plot_clusters: Plot cluster-level data tof_plot_model: Plot results sample-level model tof_read_data: Read data memory disk tof_reduce_dimensions: Perform dimensionality reduction tof_transform: Transform marker expression values vectorized fashion tof_upsample: Assign new cells existing clusters (defined downsample dataset) tof_write_data: Write data memory disk tidytof verb wraps family related functions perform basic task. example, tof_cluster verb wrapper following functions: tof_cluster_ddpr, tof_cluster_flowsom, tof_cluster_kmeans, tof_cluster_phenograph. functions implement different clustering algorithm, share underlying logic standardized tof_cluster abstraction. practice, means users can apply DDPR, FlowSOM, K-means, PhenoGraph clustering algorithms datasets either calling one tof_cluster_* functions directly, calling tof_cluster method argument set appropriate value (“ddpr”, “flowsom”, “kmeans”, “phenograph”, respectively). extend existing tidytof verb, write function whose name fits pattern tof_{verb name}_*, “*” represents name algorithm used perform computation. function definition, try share many arguments possible tidytof verb ’re extending, return output object described “Value” heading help file verb extended. example, suppose wanted write tidytof-style interface new clustering algorithm “supercluster”, performs k-means clustering dataset twice outputs final cluster assignment equal two k-means cluster assignments spliced together. add supercluster algorithm tidytof, might write function like : example , note tof_cluster_supercluster named using tof_{verb name}_* style, function definition uses tof_tibble cluster_cols arguments tof_cluster, returned output object tof_tbl single column encoding cluster ids cells tof_tibble.","code":"#' Perform superclustering on high-dimensional cytometry data. #' #' This function applies the silly, hypothetical clustering algorithm #' \"supercluster\" to high-dimensional cytometry data using user-specified #' input variables/cytometry measurements. #' #' @param tof_tibble A `tof_tbl` or `tibble`. #' #' @param cluster_cols Unquoted column names indicating which columns in #' `tof_tibble` to use in computing the supercluster clusters. #' Supports tidyselect helpers. #' #' @param num_kmeans_clusters An integer indicating how many clusters should be #' used for the two k-means clustering steps. #' #' @param sep A string to use when splicing the 2 k-means clustering assignments #' to one another. #' #' @param ... Optional additional parameters to pass to #' \\code{\\link[tidytof]{tof_cluster_kmeans}} #' #' @return A tibble with one column named `.supercluster_cluster` containing #' a character vector of length `nrow(tof_tibble)` indicating the id of the #' supercluster cluster to which each cell (i.e. each row) in `tof_tibble` was #' assigned. #' #' @importFrom dplyr tibble #' tof_cluster_supercluster <- function(tof_tibble, cluster_cols, num_kmeans_clusters = 10L, sep = \"_\", ...) { kmeans_1 <- tof_tibble |> tof_cluster_kmeans( cluster_cols = {{ cluster_cols }}, num_clusters = num_kmeans_clusters, ... ) kmeans_2 <- tof_tibble |> tof_cluster_kmeans( cluster_cols = {{ cluster_cols }}, num_clusters = num_kmeans_clusters, ... ) final_result <- dplyr::tibble( .supercluster_cluster = paste(kmeans_1$.kmeans_cluster, kmeans_2$.kmeans_cluster, sep = sep) ) return(final_result) }"},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"creating-a-new-tidytof-verb","dir":"Articles","previous_headings":"How to contribute","what":"Creating a new {tidytof} verb","title":"How to contribute code","text":"want contribute function tidytof represents new operation encompassed existing verbs , include suggestion create new verb pull request tidytof team. case, ’ll considerably flexibility define interface tidytof use implement new verb, tidytof team happy work figure makes sense (least brainstorm together).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"a-note-about-modeling-functions","dir":"Articles","previous_headings":"How to contribute","what":"A note about modeling functions","title":"How to contribute code","text":"point development, don’t recommend extending tidytof’s modeling functionality, likely abstracted standalone package (emphasis interoperability tidymodels ecosystem) point future.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"contact-us","dir":"Articles","previous_headings":"","what":"Contact us","title":"How to contribute code","text":"general questions/comments/concerns tidytof, feel free reach team GitHub .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"How to contribute code","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> loaded via a namespace (and not attached): #> [1] digest_0.6.35 desc_1.4.3 R6_2.5.1 fastmap_1.1.1 #> [5] xfun_0.42 magrittr_2.0.3 cachem_1.0.8 knitr_1.45 #> [9] memoise_2.0.1 htmltools_0.5.7 rmarkdown_2.26 lifecycle_1.0.4 #> [13] cli_3.6.2 vctrs_0.6.5 sass_0.4.9 pkgdown_2.0.7 #> [17] textshaping_0.3.7 jquerylib_0.1.4 systemfonts_1.0.6 compiler_4.3.3 #> [21] purrr_1.0.2 tools_4.3.3 ragg_1.3.0 bslib_0.6.2 #> [25] evaluate_0.23 yaml_2.3.8 jsonlite_1.8.8 rlang_1.1.3 #> [29] fs_1.6.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Differential discovery analysis","text":"demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = 1:length(file_name), patient = stringr::str_extract(file_name, \"patient[:digit:]\"), stimulation = stringr::str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"differential-abundance-analysis-using-tof_analyze_abundance","dir":"Articles","previous_headings":"","what":"Differential abundance analysis using tof_analyze_abundance()","title":"Differential discovery analysis","text":"might wonder certain clusters expand deplete within patients two stimulation conditions described - question requires differential abundance analysis (DAA). tidytof’s tof_analyze_abundance() verb supports use 3 statistical approaches performing DAA: diffcyt, generalized-linear mixed modeling (GLMMs), simple t-tests. setup described uses paired design 2 experimental conditions interest (Basal vs. BCR-XL), can use paired t-test method: Based output, can see 6 8 clusters statistically different abundance two stimulation conditions. Using tidytof easy integration tidyverse packages, can use result visualize fold-changes cluster (within patient) BCR-XL condition compared Basal condition using ggplot2: Importantly, output tof_analyze_abundance depends slightly underlying statistical method used, details can found documentation tof_analyze_abundance_* function family member: tof_analyze_abundance_diffcyt tof_analyze_abundance_glmm tof_analyze_abundance_ttest","code":"daa_result <- citrus_data |> tof_analyze_abundance( cluster_col = population_id, effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) daa_result #> # A tibble: 8 × 8 #> population_id p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 0.000924 0.00535 \"*\" -5.48 7 -0.00743 0.644 #> 2 2 0.00623 0.0166 \"*\" -3.86 7 -0.0156 0.674 #> 3 3 0.0235 0.0314 \"*\" -2.88 7 -0.0638 0.849 #> 4 4 0.0235 0.0314 \"*\" 2.88 7 0.0832 1.38 #> 5 5 0.0116 0.0232 \"*\" 3.39 7 0.00246 1.08 #> 6 6 0.371 0.371 \"\" -0.955 7 -0.0168 0.919 #> 7 7 0.00134 0.00535 \"*\" 5.14 7 0.0202 1.14 #> 8 8 0.236 0.270 \"\" -1.30 7 -0.00228 0.901 plot_data <- citrus_data |> mutate(population_id = as.character(population_id)) |> left_join( select(daa_result, population_id, significant, mean_fc), by = \"population_id\" ) |> dplyr::count(patient, stimulation, population_id, significant, mean_fc, name = \"n\") |> group_by(patient, stimulation) |> mutate(prop = n / sum(n)) |> ungroup() |> pivot_wider( names_from = stimulation, values_from = c(prop, n), ) |> mutate( diff = `prop_BCR-XL` - prop_Basal, fc = `prop_BCR-XL` / prop_Basal, population_id = fct_reorder(population_id, diff), direction = case_when( mean_fc > 1 & significant == \"*\" ~ \"increase\", mean_fc < 1 & significant == \"*\" ~ \"decrease\", TRUE ~ NA_character_ ) ) significance_data <- plot_data |> group_by(population_id, significant, direction) |> summarize(diff = max(diff), fc = max(fc)) |> ungroup() plot_data |> ggplot(aes(x = population_id, y = fc, fill = direction)) + geom_violin(trim = FALSE) + geom_hline(yintercept = 1, color = \"red\", linetype = \"dotted\", size = 0.5) + geom_point() + geom_text( aes(x = population_id, y = fc, label = significant), data = significance_data, size = 8, nudge_x = 0.2, nudge_y = 0.06 ) + scale_x_discrete(labels = function(x) str_c(\"cluster \", x)) + scale_fill_manual( values = c(\"decrease\" = \"#cd5241\", \"increase\" = \"#207394\"), na.translate = FALSE ) + labs( x = NULL, y = \"Abundance fold-change (stimulated / basal)\", fill = \"Effect\", caption = \"Asterisks indicate significance at an adjusted p-value of 0.05\" ) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"differential-expression-analysis-with-tof_analyze_expression","dir":"Articles","previous_headings":"","what":"Differential expression analysis with tof_analyze_expression()","title":"Differential discovery analysis","text":"Similarly, suppose ’re interested intracellular signaling proteins change expression levels two stimulation conditions clusters. Differential Expression Analysis (DEA) can performed using tidytof’s tof_analyze_expression verb. , can use paired t-tests multiple-hypothesis correction test significant differences cluster’s expression signaling markers stimulation conditions. output tof_analyze_expression() also depends underlying test used, can see result looks relatively similar output tof_analyze_abundance(). , output tibble row represents differential expression results single cluster-marker pair - example, first row represents difference expression pS6 cluster 1 BCR-XL Basal conditions. row includes raw p-value multiple-hypothesis-corrected p-value cluster-marker pair. result can used make volcano plot visualize results cluster-marker pairs: , details can found documentation tof_analyze_expression_* function family member: tof_analyze_expression_diffcyt tof_analyze_expression_lmm tof_analyze_expression_ttest","code":"signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) dea_result <- citrus_data |> tof_preprocess(channel_cols = any_of(signaling_markers)) |> tof_analyze_expression( method = \"ttest\", cluster_col = population_id, marker_cols = any_of(signaling_markers), effect_col = stimulation, group_cols = patient, test_type = \"paired\" ) dea_result |> head() #> # A tibble: 6 × 9 #> population_id marker p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 pS6_Y… 7.58e-8 2.12e-6 * 22.9 7 2.56 4.31 #> 2 2 pS6_Y… 1.16e-7 2.12e-6 * 21.6 7 2.13 2.49 #> 3 3 pBtk_… 1.32e-7 2.12e-6 * -21.2 7 -0.475 0.289 #> 4 7 pBtk_… 1.18e-7 2.12e-6 * -21.5 7 -0.518 0.286 #> 5 8 pBtk_… 1.30e-7 2.12e-6 * -21.2 7 -0.516 0.324 #> 6 4 pBtk_… 7.85e-7 1.05e-5 * -16.3 7 -0.462 0.296 volcano_data <- dea_result |> mutate( log2_fc = log(mean_fc, base = 2), log_p = -log(p_adj), significance = case_when( p_adj < 0.05 & mean_fc > 1 ~ \"increased\", p_adj < 0.05 & mean_fc < 1 ~ \"decreased\", TRUE ~ NA_character_ ), marker = str_extract(marker, \".+_\") |> str_remove(\"_\"), pair = str_c(marker, str_c(\"cluster \", population_id), sep = \"@\") ) volcano_data |> ggplot(aes(x = log2_fc, y = log_p, fill = significance)) + geom_vline(xintercept = 0, linetype = \"dashed\", color = \"gray50\") + geom_hline(yintercept = -log(0.05), linetype = \"dashed\", color = \"red\") + geom_point(shape = 21, size = 2) + ggrepel::geom_text_repel( aes(label = pair), data = slice_head(volcano_data, n = 10L), size = 2 ) + scale_fill_manual( values = c(\"decreased\" = \"#cd5241\", \"increased\" = \"#207394\"), na.value = \"#cdcdcd\" ) + labs( x = \"log2(Fold-change)\", y = \"-log10(p-value)\", fill = NULL, caption = \"Labels indicate the 10 most significant marker-cluster pairs\" )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Differential discovery analysis","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] tidyr_1.3.1 ggplot2_3.5.0 #> [19] stringr_1.5.1 dplyr_1.1.4 #> [21] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 labeling_0.4.3 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] highr_0.10 ggforce_0.4.2 #> [55] MASS_7.3-60.0.1 lava_1.8.0 #> [57] rappdirs_0.3.3 DelayedArray_0.28.0 #> [59] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [61] httpuv_1.6.14 future.apply_1.11.1 #> [63] nnet_7.3-19 glue_1.7.0 #> [65] promises_1.2.1 grid_4.3.3 #> [67] generics_0.1.3 recipes_1.0.10 #> [69] gtable_0.3.4 tzdb_0.4.0 #> [71] class_7.3-22 data.table_1.15.2 #> [73] hms_1.1.3 tidygraph_1.3.1 #> [75] utf8_1.2.4 XVector_0.42.0 #> [77] ggrepel_0.9.5 BiocVersion_3.18.1 #> [79] foreach_1.5.2 pillar_1.9.0 #> [81] RcppHNSW_0.6.0 later_1.3.2 #> [83] splines_4.3.3 tweenr_2.0.3 #> [85] lattice_0.22-5 survival_3.5-8 #> [87] bit_4.0.5 RProtoBufLib_2.14.1 #> [89] tidyselect_1.2.1 Biostrings_2.70.3 #> [91] knitr_1.45 gridExtra_2.3 #> [93] xfun_0.42 graphlayouts_1.1.1 #> [95] hardhat_1.3.1 timeDate_4032.109 #> [97] stringi_1.8.3 yaml_2.3.8 #> [99] evaluate_0.23 codetools_0.2-19 #> [101] ggraph_2.2.1 tibble_3.2.1 #> [103] BiocManager_1.30.22 cli_3.6.2 #> [105] rpart_4.1.23 xtable_1.8-4 #> [107] systemfonts_1.0.6 munsell_0.5.0 #> [109] jquerylib_0.1.4 Rcpp_1.0.12 #> [111] globals_0.16.3 png_0.1-8 #> [113] parallel_4.3.3 ellipsis_0.3.2 #> [115] pkgdown_2.0.7 gower_1.0.1 #> [117] readr_2.1.5 blob_1.2.4 #> [119] bitops_1.0-7 listenv_0.9.1 #> [121] glmnet_4.1-8 viridisLite_0.4.2 #> [123] ipred_0.9-14 scales_1.3.0 #> [125] prodlim_2023.08.28 purrr_1.0.2 #> [127] crayon_1.5.2 rlang_1.1.3 #> [129] KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"dimensionality-reduction-with-tof_reduce_dimensions-","dir":"Articles","previous_headings":"","what":"Dimensionality reduction with tof_reduce_dimensions().","title":"Dimensionality reduction","text":"example call tof_reduce_dimensions() use tSNE visualize data tidytof’s built-phenograph_data dataset. default, tof_reduce_dimensions add reduced-dimension feature embeddings input tof_tbl return augmented tof_tbl (, tof_tbl new columns embedding dimension) result. return features embeddings , set augment FALSE (tof_cluster). Changing method argument results different low-dimensional embeddings:","code":"data(phenograph_data) # perform the dimensionality reduction phenograph_tsne <- phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\") #> Loading required namespace: Rtsne # select only the tsne embedding columns phenograph_tsne |> select(contains(\"tsne\")) |> head() #> # A tibble: 6 × 2 #> .tsne1 .tsne2 #> #> 1 -13.8 -8.35 #> 2 -2.20 -13.5 #> 3 -19.7 -29.0 #> 4 -8.65 -20.0 #> 5 -14.5 -12.1 #> 6 -3.06 -26.1 phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .tsne1 .tsne2 #> #> 1 9.32 -14.4 #> 2 3.31 -14.6 #> 3 -8.80 -34.1 #> 4 -1.49 -21.9 #> 5 8.69 -17.5 #> 6 -9.96 -19.1 #> 7 -0.924 -13.3 #> 8 -11.2 -20.4 #> 9 -6.23 -18.6 #> 10 5.46 -8.96 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"umap\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .umap1 .umap2 #> #> 1 -10.4 4.32 #> 2 -9.51 3.42 #> 3 -8.27 0.695 #> 4 -6.87 -0.268 #> 5 -10.6 4.13 #> 6 0.129 -4.19 #> 7 -10.7 3.77 #> 8 -8.40 -0.172 #> 9 -6.22 0.175 #> 10 -9.22 5.39 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"pca\", augment = FALSE) #> # A tibble: 3,000 × 5 #> .pc1 .pc2 .pc3 .pc4 .pc5 #> #> 1 -2.77 1.23 -0.868 0.978 3.49 #> 2 -0.969 -1.02 -0.787 1.22 0.329 #> 3 -2.36 2.54 -1.95 -0.882 -1.30 #> 4 -3.68 -0.00565 0.962 0.410 0.788 #> 5 -4.03 2.07 -0.829 1.59 5.39 #> 6 -2.59 -0.108 1.32 -1.41 -1.24 #> 7 -1.55 -0.651 -0.233 1.08 0.129 #> 8 -1.18 -0.446 0.134 -0.771 -0.932 #> 9 -2.00 -0.485 0.593 -0.0416 -0.658 #> 10 -0.0356 -0.924 -0.692 1.45 0.270 #> # ℹ 2,990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"method-specifications-for-tof_reduce_-functions","dir":"Articles","previous_headings":"","what":"Method specifications for tof_reduce_*() functions","title":"Dimensionality reduction","text":"tof_reduce_dimensions() provides high-level API three lower-level functions: tof_reduce_pca(), tof_reduce_umap(), tof_reduce_tsne(). help files functions provide details algorithm-specific method specifications associated dimensionality reduction approaches. example, tof_reduce_pca takes num_comp argument determine many principal components returned: see ?tof_reduce_pca, ?tof_reduce_umap, ?tof_reduce_tsne additional details.","code":"# 2 principal components phenograph_data |> tof_reduce_pca(num_comp = 2) #> # A tibble: 3,000 × 2 #> .pc1 .pc2 #> #> 1 -2.77 1.23 #> 2 -0.969 -1.02 #> 3 -2.36 2.54 #> 4 -3.68 -0.00565 #> 5 -4.03 2.07 #> 6 -2.59 -0.108 #> 7 -1.55 -0.651 #> 8 -1.18 -0.446 #> 9 -2.00 -0.485 #> 10 -0.0356 -0.924 #> # ℹ 2,990 more rows # 3 principal components phenograph_data |> tof_reduce_pca(num_comp = 3) #> # A tibble: 3,000 × 3 #> .pc1 .pc2 .pc3 #> #> 1 -2.77 1.23 -0.868 #> 2 -0.969 -1.02 -0.787 #> 3 -2.36 2.54 -1.95 #> 4 -3.68 -0.00565 0.962 #> 5 -4.03 2.07 -0.829 #> 6 -2.59 -0.108 1.32 #> 7 -1.55 -0.651 -0.233 #> 8 -1.18 -0.446 0.134 #> 9 -2.00 -0.485 0.593 #> 10 -0.0356 -0.924 -0.692 #> # ℹ 2,990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"visualization-using-tof_plot_cells_embedding","dir":"Articles","previous_headings":"","what":"Visualization using tof_plot_cells_embedding()","title":"Dimensionality reduction","text":"Regardless method used, reduced-dimension feature embeddings can visualized using ggplot2 (graphics package). tidytof also provides helper functions easily generating dimensionality reduction plots tof_tbl tibble columns representing embedding dimensions: visualizations can helpful qualitatively describing phenotypic differences clusters dataset. example, example , can see one clusters high CD11b expression, whereas others lower CD11b expression.","code":"# plot the tsne embeddings using color to distinguish between clusters phenograph_tsne |> tof_plot_cells_embedding( embedding_cols = contains(\".tsne\"), color_col = phenograph_cluster ) # plot the tsne embeddings using color to represent CD11b expression phenograph_tsne |> tof_plot_cells_embedding( embedding_cols = contains(\".tsne\"), color_col = cd11b ) + ggplot2::scale_fill_viridis_c()"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Dimensionality reduction","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] ggplot2_3.5.0 dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] RcppAnnoy_0.0.22 matrixStats_1.2.0 compiler_4.3.3 #> [7] systemfonts_1.0.6 vctrs_0.6.5 stringr_1.5.1 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ellipsis_0.3.2 labeling_0.4.3 ggraph_2.2.1 #> [16] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [19] tzdb_0.4.0 ragg_1.3.0 purrr_1.0.2 #> [22] xfun_0.42 glmnet_4.1-8 cachem_1.0.8 #> [25] jsonlite_1.8.8 recipes_1.0.10 highr_0.10 #> [28] tweenr_2.0.3 irlba_2.3.5.1 parallel_4.3.3 #> [31] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [34] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [37] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [40] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [43] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [46] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [49] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [52] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [55] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [58] Biobase_2.62.0 withr_3.0.0 evaluate_0.23 #> [61] Rtsne_0.17 future_1.33.1 desc_1.4.3 #> [64] survival_3.5-8 polyclip_1.10-6 embed_1.1.4 #> [67] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [70] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [73] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [76] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [79] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [82] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [85] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [88] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [91] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [94] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [97] lava_1.8.0 uwot_0.1.16 gtable_0.3.4 #> [100] sass_0.4.9 digest_0.6.35 BiocGenerics_0.48.1 #> [103] ggrepel_0.9.5 farver_2.1.1 memoise_2.0.1 #> [106] htmltools_0.5.7 pkgdown_2.0.7 lifecycle_1.0.4 #> [109] hardhat_1.3.1 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"downsampling-with-tof_downsample","dir":"Articles","previous_headings":"","what":"Downsampling with tof_downsample()","title":"Downsampling","text":"Using tidytof’s built-dataset phenograph_data, can see original size dataset 1000 cells per cluster, 3000 cells total: randomly sample 200 cells per cluster, can use tof_downsample() using “constant” method: Alternatively, wanted sample 50% cells cluster, use “prop” method: finally, might also interested taking slightly different approach downsampling reduces number cells fixed constant proportion, fixed density phenotypic space. example, following scatterplot demonstrates certain areas phenotypic density phenograph_data contain cells others along cd34/cd38 axes: reduce number cells dataset local density around cell dataset relatively constant, can use “density” method tof_downsample: Thus, can see density downsampling uniform (though exactly uniform) across range cd34/cd38 values phenograph_data.","code":"data(phenograph_data) phenograph_data |> dplyr::count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 1000 #> 2 cluster2 1000 #> 3 cluster3 1000 phenograph_data |> # downsample tof_downsample( group_cols = phenograph_cluster, method = \"constant\", num_cells = 200 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 200 #> 2 cluster2 200 #> 3 cluster3 200 phenograph_data |> # downsample tof_downsample( group_cols = phenograph_cluster, method = \"prop\", prop_cells = 0.5 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 500 #> 2 cluster2 500 #> 3 cluster3 500 rescale_max <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE)) { x / from[2] * to[2] } phenograph_data |> # preprocess all numeric columns in the dataset tof_preprocess(undo_noise = FALSE) |> # plot ggplot(aes(x = cd34, y = cd38)) + geom_hex() + coord_fixed(ratio = 0.4) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + scale_fill_viridis_c( labels = function(x) round(rescale_max(x), 2) ) + labs( fill = \"relative density\" ) phenograph_data |> tof_preprocess(undo_noise = FALSE) |> tof_downsample(method = \"density\", density_cols = c(cd34, cd38)) |> # plot ggplot(aes(x = cd34, y = cd38)) + geom_hex() + coord_fixed(ratio = 0.4) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + scale_fill_viridis_c( labels = function(x) round(rescale_max(x), 2) ) + labs( fill = \"relative density\" )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"additional-documentation","dir":"Articles","previous_headings":"","what":"Additional documentation","title":"Downsampling","text":"details, check documentation 3 underlying members tof_downsample_* function family (wrapped tof_downsample): tof_downsample_constant tof_downsample_prop tof_downsample_density","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Downsampling","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] ggplot2_3.5.0 dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [10] shape_1.4.6.1 fastmap_1.1.1 ggraph_2.2.1 #> [13] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [16] tzdb_0.4.0 ragg_1.3.0 purrr_1.0.2 #> [19] xfun_0.42 glmnet_4.1-8 cachem_1.0.8 #> [22] jsonlite_1.8.8 recipes_1.0.10 highr_0.10 #> [25] tweenr_2.0.3 parallel_4.3.3 R6_2.5.1 #> [28] bslib_0.6.2 stringi_1.8.3 parallelly_1.37.1 #> [31] rpart_4.1.23 lubridate_1.9.3 jquerylib_0.1.4 #> [34] Rcpp_1.0.12 iterators_1.0.14 knitr_1.45 #> [37] future.apply_1.11.1 readr_2.1.5 flowCore_2.14.2 #> [40] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [43] igraph_2.0.3 timechange_0.3.0 tidyselect_1.2.1 #> [46] yaml_2.3.8 viridis_0.6.5 timeDate_4032.109 #> [49] doParallel_1.0.17 codetools_0.2-19 listenv_0.9.1 #> [52] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [55] withr_3.0.0 evaluate_0.23 future_1.33.1 #> [58] desc_1.4.3 survival_3.5-8 polyclip_1.10-6 #> [61] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [64] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [67] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [70] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [73] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [76] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [79] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [82] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [85] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [88] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [91] lava_1.8.0 gtable_0.3.4 sass_0.4.9 #> [94] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [97] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [100] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [103] MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Feature extraction","text":"demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = 1:length(file_name), patient = stringr::str_extract(file_name, \"patient[:digit:]\"), stimulation = stringr::str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-cluster-proportions-using-tof_extract_proportion","dir":"Articles","previous_headings":"","what":"Calculating cluster proportions using tof_extract_proportion()","title":"Feature extraction","text":"First, tof_extract_proportion(), extracts proportion cells cluster within sample (samples defined using group_cols argument): Like members tof_extract_* function family, tof_extract_proportion() returns one row sample (defined unique combination values columns specified group_cols) one column extracted feature (, one column proportion 8 clusters citrus_data). values can also returned “long” format changing format argument:","code":"# preprocess the numeric columns in the citrus dataset citrus_data <- citrus_data |> mutate(cluster = str_c(\"cluster\", population_id)) |> tof_preprocess() citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation) ) |> head() #> # A tibble: 6 × 10 #> patient stimulation `prop@cluster1` `prop@cluster2` `prop@cluster3` #> #> 1 patient1 Basal 0.0190 0.0482 0.447 #> 2 patient1 BCR-XL 0.0109 0.0395 0.268 #> 3 patient2 Basal 0.0130 0.0280 0.491 #> 4 patient2 BCR-XL 0.0101 0.0143 0.358 #> 5 patient3 Basal 0.0326 0.0830 0.397 #> 6 patient3 BCR-XL 0.0200 0.0412 0.323 #> # ℹ 5 more variables: `prop@cluster4` , `prop@cluster5` , #> # `prop@cluster6` , `prop@cluster7` , `prop@cluster8` citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation), format = \"long\" ) |> head() #> # A tibble: 6 × 4 #> patient stimulation cluster prop #> #> 1 patient1 Basal cluster1 0.0190 #> 2 patient1 Basal cluster2 0.0482 #> 3 patient1 Basal cluster3 0.447 #> 4 patient1 Basal cluster4 0.237 #> 5 patient1 Basal cluster5 0.00219 #> 6 patient1 Basal cluster6 0.0759"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-cluster-marker-expression-measures-using-tof_extract_central_tendency","dir":"Articles","previous_headings":"","what":"Calculating cluster marker expression measures using tof_extract_central_tendency()","title":"Feature extraction","text":"Another member tof_extract_*() function family, tof_extract_central_tendency(), computes central tendency (e.g. mean median) user-specified markers cluster. argument central_tendency_function can used compute summary statistic. example, following choice central_tendency_function compute 75th percentile marker-cluster pair citrus_data:","code":"citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = mean ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 4.80 0.0967 #> 2 patient1 Basal 4.68 0.765 #> 3 patient2 BCR-XL 5.00 -0.0579 #> 4 patient2 Basal 4.88 0.808 #> 5 patient3 BCR-XL 5.04 -0.0432 #> 6 patient3 Basal 4.98 0.745 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , … citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = function(x) quantile(x = x, probs = 0.75) ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 5.30 -0.0186 #> 2 patient1 Basal 5.18 1.32 #> 3 patient2 BCR-XL 5.41 -0.0201 #> 4 patient2 Basal 5.28 1.39 #> 5 patient3 BCR-XL 5.42 -0.0362 #> 6 patient3 Basal 5.41 1.27 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-the-proportion-of-cells-with-marker-expression-above-a-threshold-using-tof_extract_proportion","dir":"Articles","previous_headings":"","what":"Calculating the proportion of cells with marker expression above a threshold using tof_extract_proportion()","title":"Feature extraction","text":"tof_extract_threshold() similar tof_extract_central_tendency(), calculates proportion cells user-specified expression value marker instead measure central tendency:","code":"citrus_data |> tof_extract_threshold( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), threshold = 5 ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_threshold` CD4_Nd145@cluster1_thre…¹ #> #> 1 patient1 BCR-XL 0.516 0 #> 2 patient1 Basal 0.365 0 #> 3 patient2 BCR-XL 0.554 0 #> 4 patient2 Basal 0.452 0 #> 5 patient3 BCR-XL 0.547 0 #> 6 patient3 Basal 0.549 0 #> # ℹ abbreviated name: ¹​`CD4_Nd145@cluster1_threshold` #> # ℹ 22 more variables: `CD20_Sm147@cluster1_threshold` , #> # `CD45_In115@cluster2_threshold` , #> # `CD4_Nd145@cluster2_threshold` , #> # `CD20_Sm147@cluster2_threshold` , #> # `CD45_In115@cluster3_threshold` , #> # `CD4_Nd145@cluster3_threshold` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-differences-in-marker-distributions-using-tof_extract_emd-and-tof_extract_jsd","dir":"Articles","previous_headings":"","what":"Calculating differences in marker distributions using tof_extract_emd() and tof_extract_jsd()","title":"Feature extraction","text":"two final members tof_extract_* function family – tof_extract_emd tof_extract_jsd – designed specifically comparing distributions marker expression stimulation conditions. , must given stimulation column (using emd_col jsd_col argument) identifies stimulation condition cell , reference_level specifies reference (.e. unstimulated) condition within emd_col jsd_col. additional arguments, tof_extract_emd computes Earth-mover’s distance marker’s distribution stimulation conditions (within cluster) basal condition; similarly, tof_extract_jsd computes Jensen-Shannon divergence index distributions. values ways compare different 2 distributions one another computationally expensive (also higher-resolution) simply comparing measures central tendency.","code":"# Earth-mover's distance citrus_data |> tof_extract_emd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), emd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.864 2.47 13.0 #> 2 patient2 1.11 7.05 10.8 #> 3 patient3 0.670 6.23 10.5 #> 4 patient4 2.64 5.86 9.90 #> 5 patient5 0.594 7.56 8.13 #> 6 patient6 0.661 4.77 7.97 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_emd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_emd`, ³​`BCR-XL_CD20_Sm147@cluster3_emd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_emd` , #> # `BCR-XL_CD4_Nd145@cluster7_emd` , #> # `BCR-XL_CD20_Sm147@cluster7_emd` , #> # `BCR-XL_CD45_In115@cluster4_emd` , #> # `BCR-XL_CD4_Nd145@cluster4_emd` , … # Jensen-Shannon Divergence citrus_data |> tof_extract_jsd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), jsd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.0367 0.0513 0.347 #> 2 patient2 0.00831 0.168 0.401 #> 3 patient3 0.0104 0.115 0.357 #> 4 patient4 0.0301 0.135 0.205 #> 5 patient5 0.00911 0.0789 0.274 #> 6 patient6 0.00972 0.0346 0.214 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_jsd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_jsd`, ³​`BCR-XL_CD20_Sm147@cluster3_jsd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_jsd` , #> # `BCR-XL_CD4_Nd145@cluster7_jsd` , #> # `BCR-XL_CD20_Sm147@cluster7_jsd` , #> # `BCR-XL_CD45_In115@cluster4_jsd` , #> # `BCR-XL_CD4_Nd145@cluster4_jsd` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"putting-it-all-together-with-tof_extract_features","dir":"Articles","previous_headings":"","what":"Putting it all together with tof_extract_features()","title":"Feature extraction","text":"Finally, tof_extract_features() verb provides wrapper members function family, allowing users extract multiple features types . example, following code extracts proportion cluster, median several markers cluster, EMD basal condition stimulated condition cluster patients citrus_data.","code":"signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) citrus_data |> tof_extract_features( cluster_col = cluster, group_cols = patient, stimulation_col = stimulation, lineage_cols = any_of(c(\"CD45_In115\", \"CD20_Sm147\", \"CD33_Nd148\")), signaling_cols = any_of(signaling_markers), signaling_method = \"emd\", basal_level = \"Basal\" ) |> head() #> # A tibble: 6 × 193 #> patient `prop@cluster1` `prop@cluster2` `prop@cluster3` `prop@cluster4` #> #> 1 patient1 0.0149 0.0438 0.356 0.351 #> 2 patient2 0.0115 0.0212 0.425 0.323 #> 3 patient3 0.0255 0.0594 0.355 0.217 #> 4 patient4 0.0127 0.0418 0.320 0.223 #> 5 patient5 0.0207 0.0423 0.377 0.269 #> 6 patient6 0.0183 0.0493 0.459 0.250 #> # ℹ 188 more variables: `prop@cluster5` , `prop@cluster6` , #> # `prop@cluster7` , `prop@cluster8` , #> # `CD45_In115@cluster1_ct` , `CD20_Sm147@cluster1_ct` , #> # `CD33_Nd148@cluster1_ct` , `CD45_In115@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD33_Nd148@cluster2_ct` , #> # `CD45_In115@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD33_Nd148@cluster3_ct` , `CD45_In115@cluster4_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Feature extraction","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 stringr_1.5.1 #> [17] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 philentropy_0.8.0 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] ggforce_0.4.2 MASS_7.3-60.0.1 #> [55] lava_1.8.0 rappdirs_0.3.3 #> [57] DelayedArray_0.28.0 tools_4.3.3 #> [59] interactiveDisplayBase_1.40.0 httpuv_1.6.14 #> [61] future.apply_1.11.1 nnet_7.3-19 #> [63] glue_1.7.0 promises_1.2.1 #> [65] grid_4.3.3 generics_0.1.3 #> [67] recipes_1.0.10 gtable_0.3.4 #> [69] tzdb_0.4.0 class_7.3-22 #> [71] tidyr_1.3.1 data.table_1.15.2 #> [73] hms_1.1.3 tidygraph_1.3.1 #> [75] utf8_1.2.4 XVector_0.42.0 #> [77] ggrepel_0.9.5 BiocVersion_3.18.1 #> [79] foreach_1.5.2 pillar_1.9.0 #> [81] RcppHNSW_0.6.0 later_1.3.2 #> [83] splines_4.3.3 tweenr_2.0.3 #> [85] lattice_0.22-5 survival_3.5-8 #> [87] bit_4.0.5 emdist_0.3-3 #> [89] RProtoBufLib_2.14.1 tidyselect_1.2.1 #> [91] Biostrings_2.70.3 knitr_1.45 #> [93] gridExtra_2.3 xfun_0.42 #> [95] graphlayouts_1.1.1 hardhat_1.3.1 #> [97] timeDate_4032.109 stringi_1.8.3 #> [99] yaml_2.3.8 evaluate_0.23 #> [101] codetools_0.2-19 ggraph_2.2.1 #> [103] tibble_3.2.1 BiocManager_1.30.22 #> [105] cli_3.6.2 rpart_4.1.23 #> [107] xtable_1.8-4 systemfonts_1.0.6 #> [109] munsell_0.5.0 jquerylib_0.1.4 #> [111] Rcpp_1.0.12 globals_0.16.3 #> [113] png_0.1-8 parallel_4.3.3 #> [115] ellipsis_0.3.2 pkgdown_2.0.7 #> [117] gower_1.0.1 ggplot2_3.5.0 #> [119] readr_2.1.5 blob_1.2.4 #> [121] bitops_1.0-7 listenv_0.9.1 #> [123] glmnet_4.1-8 viridisLite_0.4.2 #> [125] ipred_0.9-14 scales_1.3.0 #> [127] prodlim_2023.08.28 purrr_1.0.2 #> [129] crayon_1.5.2 rlang_1.1.3 #> [131] KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Building predictive models","text":"illustrate work, first download patient-level data paper combine sample-level clinical annotations one tidytof’s built-datasets (ddpr_metadata). data processing steps result tibble called ddpr_patients. numeric columns ddpr_patients represent aggregated cell population features sample (see Supplementary Table 5 paper details). non-numeric columns represent clinical metadata sample (run ?ddpr_metadata information). metadata columns, important ones indicate patient develop refractory disease (“relapse”), /happen. information stored relapse_status time_to_relapse columns, respectively. also preprocessing steps might want perform now save us headaches ’re fitting models later. next part vignette, ’ll use patient-level data build predictive models using resampling procedures like k-fold cross-validation bootstrapping.","code":"data(ddpr_metadata) # link for downloading the sample-level data from the Nature Medicine website data_link <- \"https://static-content.springer.com/esm/art%3A10.1038%2Fnm.4505/MediaObjects/41591_2018_BFnm4505_MOESM3_ESM.csv\" # download the data and combine it with clinical annotations ddpr_patients <- readr::read_csv(data_link, skip = 2L, n_max = 78L, show_col_types = FALSE) |> dplyr::rename(patient_id = Patient_ID) |> dplyr::left_join(ddpr_metadata, by = \"patient_id\") |> dplyr::filter(!str_detect(patient_id, \"Healthy\")) # preview only the metadata (i.e. non-numeric) columns ddpr_patients |> dplyr::select(where(~ !is.numeric(.x))) |> head() #> # A tibble: 6 × 8 #> patient_id gender mrd_risk nci_rome_risk relapse_status type_of_relapse cohort #> #> 1 UPN1 Male Interme… Standard Yes Early Train… #> 2 UPN1-Rx Male Interme… Standard Yes Early Train… #> 3 UPN2 Male Interme… Standard No NA Train… #> 4 UPN3 Female Standard Standard No NA Train… #> 5 UPN4 Male Standard Standard No NA Valid… #> 6 UPN5 Female Standard High No NA Valid… #> # ℹ 1 more variable: ddpr_risk ddpr_patients <- ddpr_patients |> # convert the relapse_status variable to a factor # and create the time_to_event and event columns for survival modeling dplyr::mutate( relapse_status = as.factor(relapse_status), time_to_event = dplyr::if_else(relapse_status == \"Yes\", time_to_relapse, ccr), event = dplyr::if_else(relapse_status == \"Yes\", 1, 0) )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"building-a-classifier-using-elastic-net-regularized-logistic-regression","dir":"Articles","previous_headings":"","what":"Building a classifier using elastic net-regularized logistic regression","title":"Building predictive models","text":"First, can build elastic net classifier predict patients relapse patients won’t (ignoring time--event data now). , can use relapse_status column ddpr_patients outcome variable: can see samples annotated, can throw away samples don’t clinical outcome associated . original DDPR paper, 10-fold cross-validation used tune glmnet model estimate error model new datasets. , can use tof_split_data() function split cohort training test set either 10 times using k-fold cross-validation bootstrapping. Reading documentation tof_split_data() demonstrates use resampling methods (like bootstrapping). output tof_split_data() varies depending split_method used. cross-validation, result rset object rsample package. rset objects type tibble two columns: splits - column entry rsplit object (contains single resample full dataset) id - character column entry represents name fold entry splits belongs . can inspect one resamples splits column see contain: Note can use rsample::training rsample::testing return training test observations resampling: , can feed training_split tof_train_model function tune logistic regression model predicts relapse_status leukemia patient. sure check tof_create_grid documentation learn make hyperparameter search grid model tuning (case, limit mixture parameter value 1, fits sparse lasso model). Also note , case, illustrative purposes ’re incorporating features one populations interest (population 2) model, whereas original model incorporated features 12 populations (likely required quite bit computational power result). output tof_train_model tof_model, object containing information trained model (can passed tof_predict tof_assess_model verbs). tof_model printed, information optimal hyperparamters printed, table nonzero model coefficients model. training model, might interested seeing performs. One way assess classification model see well works applied directly back data trained (model’s “training data”). , can use tof_assess_model() function arguments: tof_assess_model() returns list several model assessment metrics differ depending kind tof_model trained. two-class classifier models, among useful confusion_matrix, shows classifier classified observation relative true class assignment. case, can see model performed perfectly training data (expected, model optimized using data !). can also visualize model’s performance using tof_plot_model() verb, case two-class model give us Receiver-Operating Characteristic (ROC) curve: shown , tof_plot_model() return receiver-operating curve two-class model. ’s unusual get AUC 1 machine learning world, can note case, classification problem wasn’t particularly difficult (lot input features work ). training model, generally isn’t sufficient evaluate model performs training data alone, provide overly-optimistic representation model perform data ’s never seen (problem often called “overfitting” model training data). get fairer estimate model’s performance new datasets, can also evaluate cross-validation error calling tof_assess_model() tof_plot_model() new_data argument set “tuning”. case, plot ROC Curve using predictions observation excluded model training cross-validation, approach gives accurate estimate model’s performance new data simple evaluation training dataset.","code":"# find how many of each outcome we have in our cohort ddpr_patients |> dplyr::count(relapse_status) #> # A tibble: 3 × 2 #> relapse_status n #> #> 1 No 37 #> 2 Yes 24 #> 3 NA 12 ddpr_patients_unannotated <- ddpr_patients |> dplyr::filter(is.na(relapse_status)) ddpr_patients <- ddpr_patients |> dplyr::filter(!is.na(relapse_status)) set.seed(3000L) training_split <- ddpr_patients |> tof_split_data( split_method = \"k-fold\", num_cv_folds = 10, strata = relapse_status ) training_split #> # 10-fold cross-validation using stratification #> # A tibble: 10 × 2 #> splits id #> #> 1 Fold01 #> 2 Fold02 #> 3 Fold03 #> 4 Fold04 #> 5 Fold05 #> 6 Fold06 #> 7 Fold07 #> 8 Fold08 #> 9 Fold09 #> 10 Fold10 my_resample <- training_split$splits[[1]] print(my_resample) #> #> <54/7/61> my_resample |> rsample::training() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN1 3.06 0.583 0.00449 0.164 1.94 0.416 #> 2 UPN1-Rx 0.0395 0.618 0.0634 0.572 2.93 0.944 #> 3 UPN2 0.139 0.0662 0.0221 0.0825 2.25 0.454 #> 4 UPN3 0.633 0.0234 0.0165 0.0327 2.25 0.226 #> 5 UPN4 0.0443 0.129 0.0447 0.232 2.47 0.336 #> 6 UPN5 0.0647 0.0577 0.0163 0.162 2.89 0.406 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … my_resample |> rsample::testing() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN6 5.62 0.550 0.00374 0.622 2.86 0.342 #> 2 UPN10-Rx 0.00240 0.167 0.203 0.802 2.57 0.822 #> 3 UPN13 0.0634 0.0300 0.0219 0.109 2.34 0.314 #> 4 UPN22-Rx 0.0643 1.68 0.0804 1.56 3.06 0.529 #> 5 UPN58 0.00546 0.00918 0.0168 0.480 2.70 0.112 #> 6 UPN95 0.300 0.389 0.00454 0.697 2.45 0.247 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … hyperparams <- tof_create_grid(mixture_values = 1) class_mod <- training_split |> tof_train_model( predictor_cols = c(contains(\"Pop2\")), response_col = relapse_status, model_type = \"two-class\", hyperparameter_grid = hyperparams, impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(class_mod) #> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e-10 #> # A tibble: 28 × 2 #> feature coefficient #> #> 1 p4EBP1_dP_IL7_Pop2 -3.10 #> 2 pCreb_dP_PVO4_Pop2 -2.66 #> 3 TSLPr_Pop2 2.07 #> 4 CD43_Pop2 2.00 #> 5 pSTAT5_FC_PVO4_Pop2 -1.80 #> 6 pS6_dP_IL7_Pop2 1.56 #> 7 pPLCg1_2_dP_PVO4_Pop2 1.44 #> 8 (Intercept) -1.43 #> 9 pSTAT5_FC_BCR_Pop2 1.24 #> 10 pErk_dP_IL7_Pop2 -1.23 #> # ℹ 18 more rows training_classifier_metrics <- class_mod |> tof_assess_model() training_classifier_metrics$confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 37 #> 2 No Yes 0 #> 3 Yes No 0 #> 4 Yes Yes 24 class_mod |> tof_plot_model() cv_classifier_metrics <- class_mod |> tof_assess_model(new_data = \"tuning\") class_mod |> tof_plot_model(new_data = \"tuning\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"building-a-survival-model-using-elastic-net-regularized-cox-regression","dir":"Articles","previous_headings":"","what":"Building a survival model using elastic net-regularized cox regression","title":"Building predictive models","text":"Building ideas , sophisticated way model data simply predict relapse won’t, build time--event model estimates patients’ probabilities relapse function time since diagnosis. approach called “survival modeling” (specifically, case use Cox-proportional hazards modeling) takes account patients adverse events different times course disease (.e. everyone relapses time). build survival model using tidytof, use tof_train_model() verb setting model_type flag “survival”. addition, need provide two outcome columns. first columns (event_col) indicates patient relapsed (.e. experienced event--interest) censored certain amount follow-time. second (time_col) indicates much time took patient relapse censored analysis. survival model trained, can used predict patient’s probability event--interest different times post-diagnosis. However, common way survival models applied practice use patient’s predicted relative risk event--interest divide patients low- high-risk subgroups. tidytof can automatically according optimal split obtained using log-rank test possible split points dataset tof_assess_model(). addition, return predicted survival curve patient time: survival models, tof_plot_model() plots average survival curves low- high-risk groups:","code":"hyperparams <- tof_create_grid(mixture_values = 1) survival_mod <- training_split |> tof_train_model( predictor_cols = c(contains(\"Pop2\")), time_col = time_to_event, event_col = event, model_type = \"survival\", hyperparameter_grid = hyperparams, impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(survival_mod) #> A survival `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 40 × 2 #> feature coefficient #> #> 1 pErk_dP_TSLP_Pop2 -7.03 #> 2 pCreb_dP_PVO4_Pop2 -5.47 #> 3 CD19_Pop2 -3.73 #> 4 CD34_Pop2 3.63 #> 5 pSTAT5_FC_BCR_Pop2 3.40 #> 6 HLADR_Pop2 -3.38 #> 7 pPLCg1_2_dP_IL7_Pop2 3.33 #> 8 pPLCg1_2_dP_PVO4_Pop2 3.14 #> 9 pSyk_dP_TSLP_Pop2 2.88 #> 10 CD123_Pop2 2.77 #> # ℹ 30 more rows survival_metrics <- survival_mod |> tof_assess_model() survival_metrics #> $model_metrics #> # A tibble: 3 × 2 #> metric value #> #> 1 neg_log_partial_likelihood 1.76e+ 1 #> 2 concordance_index 1 e+ 0 #> 3 log_rank_p_value 1.47e-22 #> #> $survival_curves #> # A tibble: 61 × 6 #> row_index survival_curve relative_risk time_to_event event risk_group #> #> 1 1 2.83e+3 1043 1 low #> 2 2 2.61e+3 1043 1 low #> 3 3 1.58e-8 5406 0 low #> 4 4 2.09e-4 4917 0 low #> 5 5 9.98e-3 4538 0 low #> 6 6 6.62e-1 4490 0 low #> 7 7 4.09e+9 136 1 high #> 8 8 2.57e+8 364 1 high #> 9 9 1.27e+9 237 1 high #> 10 10 2.31e+4 886 1 low #> # ℹ 51 more rows survival_mod |> tof_plot_model() cv_survival_metrics <- survival_mod |> tof_assess_model(new_data = \"tuning\") survival_mod |> tof_plot_model(new_data = \"tuning\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Building predictive models","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] stringr_1.5.1 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] furrr_0.3.1 matrixStats_1.2.0 compiler_4.3.3 #> [7] systemfonts_1.0.6 vctrs_0.6.5 crayon_1.5.2 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ellipsis_0.3.2 labeling_0.4.3 ggraph_2.2.1 #> [16] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [19] tzdb_0.4.0 ragg_1.3.0 bit_4.0.5 #> [22] purrr_1.0.2 xfun_0.42 glmnet_4.1-8 #> [25] cachem_1.0.8 jsonlite_1.8.8 recipes_1.0.10 #> [28] highr_0.10 tweenr_2.0.3 parallel_4.3.3 #> [31] R6_2.5.1 rsample_1.2.1 bslib_0.6.2 #> [34] stringi_1.8.3 parallelly_1.37.1 rpart_4.1.23 #> [37] lubridate_1.9.3 jquerylib_0.1.4 Rcpp_1.0.12 #> [40] iterators_1.0.14 knitr_1.45 future.apply_1.11.1 #> [43] readr_2.1.5 flowCore_2.14.2 Matrix_1.6-5 #> [46] splines_4.3.3 nnet_7.3-19 igraph_2.0.3 #> [49] timechange_0.3.0 tidyselect_1.2.1 yaml_2.3.8 #> [52] viridis_0.6.5 timeDate_4032.109 doParallel_1.0.17 #> [55] codetools_0.2-19 curl_5.2.1 listenv_0.9.1 #> [58] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [61] withr_3.0.0 evaluate_0.23 future_1.33.1 #> [64] desc_1.4.3 survival_3.5-8 polyclip_1.10-6 #> [67] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [70] generics_0.1.3 vroom_1.6.5 RcppHNSW_0.6.0 #> [73] S4Vectors_0.40.2 hms_1.1.3 ggplot2_3.5.0 #> [76] munsell_0.5.0 scales_1.3.0 globals_0.16.3 #> [79] class_7.3-22 glue_1.7.0 tools_4.3.3 #> [82] data.table_1.15.2 gower_1.0.1 fs_1.6.3 #> [85] graphlayouts_1.1.1 tidygraph_1.3.1 grid_4.3.3 #> [88] yardstick_1.3.1 tidyr_1.3.1 RProtoBufLib_2.14.1 #> [91] ipred_0.9-14 colorspace_2.1-0 ggforce_0.4.2 #> [94] cli_3.6.2 textshaping_0.3.7 fansi_1.0.6 #> [97] cytolib_2.14.1 viridisLite_0.4.2 lava_1.8.0 #> [100] dplyr_1.1.4 gtable_0.3.4 sass_0.4.9 #> [103] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [106] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [109] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [112] bit64_4.0.5 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"preprocessing-with-tof_preprocess","dir":"Articles","previous_headings":"","what":"Preprocessing with tof_preprocess","title":"Preprocessing","text":"Generally speaking, raw ion counts measured analyte mass cytometer (content raw FCS files obtained directly mass cytometer) need transformed CyTOF data analysis. Common preprocessing steps may include variance-stabilizing transformations - hyperbolic arcsine (arcsinh) transformation log transformation - scaling/centering, /denoising. perform standard preprocessing tasks tidytof, use tof_preprocess. tof_preprocess’s default behavior apply arcsinh transformation (cofactor 5) numeric column input tof_tibble well remove gaussian noise Fluidigm software adds ion count (noise added visualization purposes, analyses, removing recommended). example, can preprocess tidytof’s built-phenograph_data tof_tibble see first measurements change . alter tof_preprocess’s default behavior, change channel_cols argument specify columns tof_tibble transformed. Alter transform_fun argument specify vector-valued function used transform channel_cols. example, suppose want center scale numeric columns instead arcsinh-transforming : keep gaussian noise added Fluidigm software (working dataset noise), set undo_noise argument FALSE.","code":"data(phenograph_data) # before preprocessing phenograph_data %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 131. 3.23 1.51 #> 2 230. -0.582 11.4 #> 3 293. 5.20 1.84 #> 4 431. 0.363 13.3 #> # ℹ 2 more rows phenograph_data %>% # perform preprocessing tof_preprocess() %>% # inspect new values select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 3.96 0.608 0.298 #> 2 4.52 -0.116 1.56 #> 3 4.76 0.909 0.360 #> 4 5.15 0.0725 1.70 #> # ℹ 2 more rows phenograph_data %>% # preprocess tof_preprocess(transform_fun = scale) %>% # inspect new values select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45[,1] cd34[,1] cd38[,1] #> #> 1 -1.40 1.01 -0.437 #> 2 -1.15 -0.911 0.0316 #> 3 -0.999 2.00 -0.422 #> 4 -0.661 -0.436 0.120 #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"postprocessing-with-tof_postprocess","dir":"Articles","previous_headings":"","what":"Postprocessing with tof_postprocess","title":"Preprocessing","text":"final note, note built-function tof_postprocess works nearly identically tof_preprocess, provides different default behavior (namely, applying reverse arcsinh transformation cofactor 5 numeric columns. See ?tof_postprocess details).","code":"print(phenograph_data) %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 3,000 × 25 #> sample_name phenograph_cluster cd19 cd11b cd34 cd45 cd123 cd33 cd47 #> #> 1 H1_PhenoGra… cluster1 -0.168 29.0 3.23 131. -0.609 1.21 13.0 #> 2 H1_PhenoGra… cluster1 1.65 4.83 -0.582 230. 2.53 -0.507 12.9 #> 3 H1_PhenoGra… cluster1 2.79 36.1 5.20 293. -0.265 3.67 27.1 #> 4 H1_PhenoGra… cluster1 0.0816 48.8 0.363 431. 2.04 9.40 41.0 #> # ℹ 2,996 more rows #> # ℹ 16 more variables: cd7 , cd44 , cd38 , cd3 , #> # cd117 , cd64 , cd41 , pstat3 , pstat5 , #> # pampk , p4ebp1 , ps6 , pcreb , `pzap70-syk` , #> # prb , `perk1-2` #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 131. 3.23 1.51 #> 2 230. -0.582 11.4 #> 3 293. 5.20 1.84 #> 4 431. 0.363 13.3 #> # ℹ 2 more rows # after preprocessing and post-processing, the data are the same # except that the re-added noise component is different for each value phenograph_data %>% tof_preprocess() %>% tof_postprocess(redo_noise = TRUE) %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 131. 2.36 0.619 #> 2 229. -1.13 11.2 #> 3 292. 4.32 1.17 #> 4 431. -0.635 12.7 #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Preprocessing","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.1 viridisLite_0.4.2 timeDate_4032.109 #> [4] farver_2.1.1 viridis_0.6.5 ggraph_2.2.1 #> [7] fastmap_1.1.1 tweenr_2.0.3 rpart_4.1.23 #> [10] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.1 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.9 #> [19] tools_4.3.3 igraph_2.0.3 utf8_1.2.4 #> [22] yaml_2.3.8 data.table_1.15.2 knitr_1.45 #> [25] graphlayouts_1.1.1 withr_3.0.0 purrr_1.0.2 #> [28] RProtoBufLib_2.14.1 BiocGenerics_0.48.1 desc_1.4.3 #> [31] nnet_7.3-19 grid_4.3.3 polyclip_1.10-6 #> [34] stats4_4.3.3 fansi_1.0.6 RcppHNSW_0.6.0 #> [37] future_1.33.1 colorspace_2.1-0 ggplot2_3.5.0 #> [40] globals_0.16.3 scales_1.3.0 iterators_1.0.14 #> [43] MASS_7.3-60.0.1 cli_3.6.2 rmarkdown_2.26 #> [46] ragg_1.3.0 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.2 #> [52] ggforce_0.4.2 stringr_1.5.1 splines_4.3.3 #> [55] parallel_4.3.3 matrixStats_1.2.0 vctrs_0.6.5 #> [58] hardhat_1.3.1 glmnet_4.1-8 Matrix_1.6-5 #> [61] jsonlite_1.8.8 cytolib_2.14.1 hms_1.1.3 #> [64] S4Vectors_0.40.2 ggrepel_0.9.5 listenv_0.9.1 #> [67] systemfonts_1.0.6 foreach_1.5.2 gower_1.0.1 #> [70] tidyr_1.3.1 jquerylib_0.1.4 recipes_1.0.10 #> [73] parallelly_1.37.1 glue_1.7.0 pkgdown_2.0.7 #> [76] codetools_0.2-19 stringi_1.8.3 lubridate_1.9.3 #> [79] gtable_0.3.4 shape_1.4.6.1 munsell_0.5.0 #> [82] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.7 #> [85] ipred_0.9-14 lava_1.8.0 R6_2.5.1 #> [88] textshaping_0.3.7 doParallel_1.0.17 tidygraph_1.3.1 #> [91] evaluate_0.23 Biobase_2.62.0 lattice_0.22-5 #> [94] readr_2.1.5 memoise_2.0.1 bslib_0.6.2 #> [97] class_7.3-22 Rcpp_1.0.12 prodlim_2023.08.28 #> [100] gridExtra_2.3 xfun_0.42 fs_1.6.3 #> [103] pkgconfig_2.0.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Quality control","text":"demonstrate use {tidytof}’s quality control verbs, use combination simulated real data vignette. Simulated data generated --fly sections , walk download real dataset section. want download dataset originally collected development PhenoGraph algorithm. data built HDCytoData package, available Bioconductor can downloaded following command: load PhenoGraph data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s. also add lines dplyr code clean column names perform standard arcsinh transformation. Thus, can see levine tof_tbl 265627 cells (one row) 40 pieces information cell (one column).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") levine <- HDCytoData::Levine_32dim_flowSet() |> as_tof_tbl() |> # a bit of data cleaning dplyr::mutate(population_id = as.character(population_id)) |> dplyr::rename_with( .fn = \\(x) stringr::str_to_lower(stringr::str_remove(x, \"\\\\|.+\")) ) |> dplyr::mutate(dplyr::across(c(file_number, population_id), as.character)) |> # arcsinh transformation tof_preprocess( channel_cols = c(-time, -cell_length, -event_number, -file_number, -population_id) )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"detect-low-expression-i-e--potentially-failed-channels-with-tof_assess_channels","dir":"Articles","previous_headings":"","what":"Detect low-expression (i.e. potentially failed) channels with tof_assess_channels()","title":"Quality control","text":"data collection, might wonder channels include downstream analyses. particular, might want exclude channels positive values, situation indicates antibody may failed, signal particular channel may weak detected. , can use tof_assess_channels(), verb calculates many cells negative (.e. use-specified threshold, negative_threshold) channel flags channels user-specified proportion negative cells (negative_proportion_flag). levine dataset, look markers 97.5% cells threshold 5 ion counts: can see two channels 97.5% cells dataset 5 counts. given experiment, might expected (.e. marker expressed rare cell population) unexpected (marker expressed many cells). case, can visualize marker negative cells manually inspect (recommend flagged channels). case, looks like small population cells slightly positive cd14, simply measurement noise/nonspecific binding antibody. user whether include cd14 downstream analyses.","code":"# convert 5 counts to asinh value with a cofactor of 5 threshold <- asinh(5 / 5) levine |> tof_assess_channels( negative_threshold = threshold, negative_proportion_flag = 0.975 ) #> # A tibble: 38 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd14 0.988 TRUE #> 2 cd133 0.975 TRUE #> 3 cd117 0.969 FALSE #> 4 cd16 0.967 FALSE #> 5 flt3 0.960 FALSE #> 6 cd15 0.940 FALSE #> 7 cd41 0.923 FALSE #> 8 cd34 0.909 FALSE #> 9 cd61 0.890 FALSE #> 10 cd33 0.885 FALSE #> # ℹ 28 more rows levine |> tof_plot_cells_density(marker_col = cd14)"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"identify-time-periods-of-abnormally-low-or-high-flow-rate-during-data-acquisition-using-tof_assess_flow_rate","dir":"Articles","previous_headings":"","what":"Identify time periods of abnormally low or high flow rate during data acquisition using tof_assess_flow_rate()","title":"Quality control","text":"Large changes flow rate cytometer can impact quality signal acquired data collection: example, abnormally low flow rates can caused partial occlusions cytometer’s flow cell, leading debris air infiltration cytometer’s microfluidics system. Thus, can useful perform quality control step explicitly interrogates flow rate course cytometry experiment order flag cells collected unusually high low rates acquisition. , {tidytof} provides tof_assess_flow_rate(), function implements simplified version FlowAI’s flow rate analysis. short, relative flow rates timestep cytometry experiment calculated, outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged. size timesteps user-defined parameter calculation, significance level (0 1) within t-distribution determines anomalous time step’s flow rate must cells flagged. can apply tof_assess_flow_rate() levine dataset , using result , can see last several timesteps flagged potentially low flow rates. decision include exclude cells analyses left user. group_cols argument can also used analyze flow rates samples, patients, mass cytometry barcode plates, etc. separately.","code":"levine |> tof_assess_flow_rate( time_col = time, num_timesteps = 200, # flag timepoints in which flow rates are high or low at a signicance level # of p = 0.01 alpha_threshold = 0.01, # plot the number of cells in each timestep, and whether or not the # rates were flagged as too high or too low visualize = TRUE ) levine |> tof_assess_flow_rate( time_col = time, # analyze two files in the levine dataset separately group_cols = file_number, alpha_threshold = 0.01, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"flag-cells-that-are-unusually-far-from-the-centroid-of-a-cluster-to-which-they-have-been-assigned-with-tof_assess_clusters_distance","dir":"Articles","previous_headings":"","what":"Flag cells that are unusually far from the centroid of a cluster to which they have been assigned with tof_assess_clusters_distance()","title":"Quality control","text":"using favorite clustering algorithm define cell subpopulations (example, using tof_cluster()), may wonder well clustering procedure worked. example, may clusters contain outliers - , cells less similar cells cluster typical. detect cells, tidytof provides tof_assess_clusters_distance() verb, computes Mahalanobis distance cell centroid cluster assigned. , computes z-score mahalanobis distances cells cluster flags cells z-score user-specified threshold. Altogether, procedure flags cells unusually far cluster centroid - .e. candidate outliers. demonstrate use tof_assess_clusters_distance() simulated data. simulate data 3 clusters, large population cells “truly” belong cluster well small population outliers cells erroneously assigned cluster. following plots visualize simulated data described : Using dataset, see tof_assess_clusters_distance() can successfully flag majority outlier cells cluster.","code":"set.seed(2020L) # simulate large population of cells that truly belong in their assigned cluster sim_data_base <- dplyr::tibble( cd45 = c(rnorm(n = 600), rnorm(n = 500, mean = -4)), cd38 = c( rnorm(n = 100, sd = 0.5), rnorm(n = 500, mean = -3), rnorm(n = 500, mean = 8) ), cd34 = c( rnorm(n = 100, sd = 0.2, mean = -10), rnorm(n = 500, mean = 4), rnorm(n = 500, mean = 60) ), cd19 = c(rnorm(n = 100, sd = 0.3, mean = 10), rnorm(n = 1000)), cluster_id = c(rep(\"a\", 100), rep(\"b\", 500), rep(\"c\", 500)), dataset = \"non-outlier\" ) # simulate outlier cells that do not belong in their assigned cluster sim_data_outlier <- dplyr::tibble( cd45 = c(rnorm(n = 10), rnorm(50, mean = 3), rnorm(n = 50, mean = -12)), cd38 = c( rnorm(n = 10, sd = 0.5), rnorm(n = 50, mean = -10), rnorm(n = 50, mean = 10) ), cd34 = c( rnorm(n = 10, sd = 0.2, mean = -15), rnorm(n = 50, mean = 15), rnorm(n = 50, mean = 70) ), cd19 = c(rnorm(n = 10, sd = 0.3, mean = 19), rnorm(n = 100)), cluster_id = c(rep(\"a\", 10), rep(\"b\", 50), rep(\"c\", 50)), dataset = \"outlier\" ) # bind simulated data together sim_data <- bind_rows(sim_data_base, sim_data_outlier) sim_data |> tof_plot_cells_embedding(color_col = cluster_id) sim_data |> tof_plot_cells_embedding(color_col = dataset) sim_data |> tof_assess_clusters_distance( cluster_col = cluster_id, # flag cells with a mahalanobis distance z-score over 3 z_threshold = 3, augment = TRUE ) |> # visualize result as above dplyr::select(-dplyr::starts_with(\".mahala\"), -z_score) |> dplyr::mutate(flagged_cell = as.character(flagged_cell)) |> tof_plot_cells_embedding(color_col = flagged_cell) + scale_fill_manual(values = tof_generate_palette(num_colors = 2))"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"flag-cells-whose-cluster-assignment-is-ambiguous-with-tof_assess_clusters_entropy","dir":"Articles","previous_headings":"","what":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","title":"Quality control","text":"may also wish evaluate clustering result based cell’s absolute distance centroid cluster assigned, based relative distances cell cluster centroids. , order confident cell’s cluster assignment, ideally want cell close centroid cluster assigned, relatively distant clusters. contrasts scenario cell might similarly close centroids 2-3 clusters, case might think cell “ambiguous” phenotype, phenotype intermediate clusters clustering algorithm identified. flag “ambiguous” clusters, {tidytof} provides tof_assess_clusters_entropy() verb. tof_assess_clusters_entropy() computes entropy L1-scaled mahalanobis distance vector (.e. mahalanobis distance cell centroids clusters dataset) - entropy low (close 0) confident cell’s cluster assignment, high (near 1) equally close multiple cluster centroids. demonstrate use function simulated data levine dataset .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"simulated-data","dir":"Articles","previous_headings":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","what":"Simulated data","title":"Quality control","text":"First, simulate fake dataset 3000 cells 4 channels. simulated dataset, two well-defined clusters (“b” “c”) dispersed cluster intermediate others (“”). data visualized : dataset, can imagine first analysis approach might involve clustering cells 2 distinct clusters. data simulated, already know number clusters small - can calculating entropy cells resulting clusters help us realize without prior knowledge? check, can use tof_assess_clusters_entropy(): plots , can see cells middle 2 k-means clusters (correspond well ground-truth clusters “b” “c” ) high entropy values, whereas cells closer one centroids low entropy values. can also see tof_assess_clusters_entropy() flags cells potentially anomalous (.e. intermediate phenotype two clusters identified) entropy values 75th percentile (user-specified parameter) entropy values dataset. user wish recluster dataset, filter anomalous cells, processing steps. can see , expected, intermediate cluster (“”) higher entropies either distinct clusters (“b” “c”).","code":"sim_data <- dplyr::tibble( cd45 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd38 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd34 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd19 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) sim_data |> tof_reduce_dimensions(method = \"pca\") |> tof_plot_cells_embedding( embedding_cols = c(.pc1, .pc2), color_col = cluster_id ) set.seed(17L) entropy_result <- sim_data |> # cluster into 2 clusters tof_cluster( num_clusters = 2, method = \"kmeans\" ) |> # calculate the entropy of all cells tof_assess_clusters_entropy( cluster_col = .kmeans_cluster, marker_cols = starts_with(\"cd\"), entropy_quantile = 0.8, augment = TRUE ) # plot the clusters in PCA space entropy_result |> select(-starts_with(\".mahala\"), -flagged_cell) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = .kmeans_cluster) # show the entropy values for each cell entropy_result |> select(-starts_with(\".mahala\"), -flagged_cell) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = entropy) + scale_fill_viridis_c() entropy_result |> select(-starts_with(\".mahala\")) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = flagged_cell) + scale_fill_viridis_d() entropy_result |> ggplot(aes(x = entropy, fill = cluster_id)) + geom_density(alpha = 0.4) + theme_bw()"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"real-data","dir":"Articles","previous_headings":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","what":"Real data","title":"Quality control","text":"can also apply tof_assess_clusters_entropy() dataset derived levine data. Suppose take 5 largest clusters levine save result small_levine tof_tbl. , can perturb dataset replacing labels cells smallest cluster (cluster 9) random labels. effectively creates population cells dataset whose “true” cluster label absent. scenario, expect cells perturbed cluster relatively distant remaining clusters, whereas unperturbed cells relatively close cluster centroid (correct centroid). Thus, can test tof_assess_clusters_entropy() successfully flags cells perturbed cluster relative others: plot , can see cells cluster 9 larger entropy values cells clusters, expected. Similarly, can see majority cells 9 successfully flagged tof_assess_clusters_entropy() using entropy quantile threshold 0.9. Conversely, cells cluster (cluster 15) flagged.","code":"clusters_to_keep <- levine |> dplyr::count(population_id) |> dplyr::slice_max(order_by = n, n = 5L) |> dplyr::arrange(n) |> pull(population_id) smallest_cluster <- clusters_to_keep[1] largest_cluster <- clusters_to_keep[[length(clusters_to_keep)]] small_levine <- levine |> dplyr::filter(population_id %in% clusters_to_keep) # perform the perturbation small_levine <- small_levine |> dplyr::mutate( new_population_id = dplyr::if_else( population_id %in% smallest_cluster, sample( clusters_to_keep[-which(clusters_to_keep %in% smallest_cluster)], size = nrow(small_levine), replace = TRUE ), population_id ) ) # perform the entropy assessment entropy_levine <- small_levine |> tof_assess_clusters_entropy( cluster_col = new_population_id, marker_cols = starts_with(\"cd\"), augment = TRUE ) entropy_levine |> mutate(population_id = fct_reorder(population_id, entropy)) |> tof_plot_cells_density( marker_col = entropy, group_col = population_id, use_ggridges = TRUE, scale = 0.1 ) + ggplot2::theme(legend.position = \"none\") + ggplot2::labs(x = \"Entropy\", y = \"Cluster ID\") entropy_levine |> mutate(flagged_cell = entropy > quantile(entropy, prob = 0.9)) |> dplyr::count(population_id, flagged_cell) |> group_by(population_id) |> mutate(prop = n / sum(n)) |> ungroup() |> dplyr::filter(flagged_cell) #> # A tibble: 2 × 4 #> population_id flagged_cell n prop #> #> 1 15 TRUE 12888 0.0798 #> 2 9 TRUE 11666 0.706"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Quality control","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] ggplot2_3.5.0 dplyr_1.1.4 #> [19] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 labeling_0.4.3 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] highr_0.10 ggforce_0.4.2 #> [55] MASS_7.3-60.0.1 lava_1.8.0 #> [57] rappdirs_0.3.3 DelayedArray_0.28.0 #> [59] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [61] httpuv_1.6.14 future.apply_1.11.1 #> [63] nnet_7.3-19 glue_1.7.0 #> [65] promises_1.2.1 grid_4.3.3 #> [67] generics_0.1.3 recipes_1.0.10 #> [69] gtable_0.3.4 tzdb_0.4.0 #> [71] class_7.3-22 tidyr_1.3.1 #> [73] data.table_1.15.2 hms_1.1.3 #> [75] tidygraph_1.3.1 utf8_1.2.4 #> [77] XVector_0.42.0 ggrepel_0.9.5 #> [79] BiocVersion_3.18.1 foreach_1.5.2 #> [81] pillar_1.9.0 stringr_1.5.1 #> [83] RcppHNSW_0.6.0 later_1.3.2 #> [85] splines_4.3.3 tweenr_2.0.3 #> [87] lattice_0.22-5 survival_3.5-8 #> [89] bit_4.0.5 RProtoBufLib_2.14.1 #> [91] tidyselect_1.2.1 Biostrings_2.70.3 #> [93] knitr_1.45 gridExtra_2.3 #> [95] xfun_0.42 graphlayouts_1.1.1 #> [97] hardhat_1.3.1 timeDate_4032.109 #> [99] stringi_1.8.3 yaml_2.3.8 #> [101] evaluate_0.23 codetools_0.2-19 #> [103] ggraph_2.2.1 tibble_3.2.1 #> [105] BiocManager_1.30.22 cli_3.6.2 #> [107] rpart_4.1.23 xtable_1.8-4 #> [109] systemfonts_1.0.6 munsell_0.5.0 #> [111] jquerylib_0.1.4 Rcpp_1.0.12 #> [113] globals_0.16.3 png_0.1-8 #> [115] parallel_4.3.3 ellipsis_0.3.2 #> [117] pkgdown_2.0.7 gower_1.0.1 #> [119] readr_2.1.5 blob_1.2.4 #> [121] bitops_1.0-7 listenv_0.9.1 #> [123] glmnet_4.1-8 viridisLite_0.4.2 #> [125] ipred_0.9-14 ggridges_0.5.6 #> [127] scales_1.3.0 prodlim_2023.08.28 #> [129] purrr_1.0.2 crayon_1.5.2 #> [131] rlang_1.1.3 KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Reading and writing data","text":"tidytof comes bundled several example mass cytometry datasets. access raw FCS CSV files containing data, use tidytof_example_data function. called arguments, tidytof_example_data return character vector naming datasets contained tidytof: details datasets contained directories isn’t particularly important, basic information follows: aml - one FCS file containing myeloid cells healthy bone marrow one FCS file containing myeloid cells AML patient bone marrow ddpr - two FCS files containing B-cell lineage cells paper mix - two FCS files different CyTOF antigen panels (one FCS file “aml” directory one “phenograph” directory) mix2 - three files different CyTOF antigen panels different file extensions (one FCS file “aml” directory two CSV files “phenograph_csv directory) phenograph - three FCS files containing AML cells paper phenograph_csv - cells “phenograph” directory, stored CSV files scaffold - three FCS files paper statistical_scaffold - three FCS files paper surgery - three FCS files paper obtain file path directory containing dataset, call tidytof_example_data one dataset names argument. example, obtain directory phenograph data, use following command:","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"surgery\" tidytof_example_data(\"phenograph\") #> [1] \"/home/runner/work/_temp/Library/tidytof/extdata/phenograph\""},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"reading-data-with-tof_read_data","dir":"Articles","previous_headings":"","what":"Reading Data with tof_read_data","title":"Reading and writing data","text":"Using one directories (directory containing CyTOF data local machine), can use tof_read_data read CyTOF data raw files. Acceptable formats include FCS files CSV files. Importantly, tof_read_data smart enough read single FCS/CSV files multiple FCS/CSV files depending whether first argument (path) leads single file directory files. , can use tof_read_data read FCS files “phenograph” example dataset bundled tidytof store phenograph variable. Regardless input data file type, tidytof reads data extended tibble class called tof_tbl (pronounced “tof tibble”). tof tibbles S3 class identical tbl_df, one additional attribute (“panel”). tidytof stores additional attribute tof_tbls , addition analyzing CyTOF data individual experiments, CyTOF users often want compare panels experiments find common markers compare metals associated particular markers across panels. retrieve panel information tof_tbl, use tof_get_panel: additional notes tof_tbls: tof_tbls contains one cell per row one CyTOF channel per column (provide data “tidy” format). tof_read_data adds additional column output tof_tbl encoding name file cell read (“file_name” column). tof_tbls inherit tbl_df class, methods available tibbles also available tof_tbls.","code":"phenograph <- tidytof_example_data(\"phenograph\") %>% tof_read_data() phenograph %>% class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph %>% tof_get_panel() #> # A tibble: 44 × 2 #> metals antigens #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 Ir191 DNA1 #> 4 Ir193 DNA2 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"using-tibble-methods-with-tidytof-tibbles","dir":"Articles","previous_headings":"","what":"Using tibble methods with {tidytof} tibbles","title":"Reading and writing data","text":"extension tbl_df class, tof_tbls get access dplyr tidyr free. can useful performing variety common operations. example, phenograph object two columns - PhenoGraph Condition - encode categorical variables numeric codes. might interested converting types columns strings make sure don’t accidentally perform quantitative operations later. Thus, dplyr’s useful mutate method can applied phenograph convert two columns character vectors. note tof_tbl class preserved even transformations. Importantly, tof_read_data uses opinionated heuristic mine different keyword slots input FCS file(s) guess metals antigens used data acquisition. Thus, CSV files read using tof_read_data, recommended use panel_info argument provide panel manually (CSV files, unlike FCS files, provide built-metadata columns contain).","code":"phenograph <- phenograph %>% # mutate the input tof_tbl mutate( PhenoGraph = as.character(PhenoGraph), Condition = as.character(Condition) ) phenograph %>% # use dplyr's select method to show # that the columns have been changed select(where(is.character)) #> # A tibble: 300 × 3 #> file_name PhenoGraph Condition #> #> 1 H1_PhenoGraph_cluster1.fcs 7 7 #> 2 H1_PhenoGraph_cluster1.fcs 6 6 #> 3 H1_PhenoGraph_cluster1.fcs 9 9 #> 4 H1_PhenoGraph_cluster1.fcs 2 2 #> # ℹ 296 more rows phenograph %>% class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" # when csv files are read, the tof_tibble's \"panel\" # attribute will be empty by default tidytof_example_data(\"phenograph_csv\") %>% tof_read_data() %>% tof_get_panel() #> # A tibble: 0 × 0 # to add a panel manually, provide it as a tibble # to tof_read_data phenograph_panel <- phenograph %>% tof_get_panel() tidytof_example_data(\"phenograph_csv\") %>% tof_read_data(panel_info = phenograph_panel) %>% tof_get_panel() #> # A tibble: 44 × 2 #> antigens metals #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 DNA1 Ir191 #> 4 DNA2 Ir193 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"writing-data-from-a-tof_tbl-to-disk","dir":"Articles","previous_headings":"","what":"Writing data from a tof_tbl to disk","title":"Reading and writing data","text":"Users may wish store CyTOF data FCS CSV files transformation, concatenation, filtering, data processing. write single-cell data tof_tbl FCS CSV files, use tof_write_data. illustrate use verb, use tidytof’s built-phenograph_data dataset. tof_write_data’s trickiest argument group_cols, argument used specify columns tof_tibble used group cells (rows tof_tibble) separate FCS CSV files. Simply put, argument allows tof_write_data create single FCS CSV file unique combination values group_cols columns specified user. example , cells grouped 3 output FCS files - one 3 clusters encoded phenograph_cluster column phenograph_data. files following names (derived values phenograph_cluster column): cluster1.fcs cluster2.fcs cluster3.fcs Note file names match distinct values group_cols column (phenograph_cluster): However, suppose wanted write multiple files cluster breaking cells two groups: express high levels pstat5 express low levels pstat5. can use dplyr::mutate create new column phenograph_data breaks cells high- low-pstat5 expression groups, add column group_cols specification: write 6 files following names (derived values phenograph_cluster expression_group). cluster1_low.fcs cluster1_high.fcs cluster2_low.fcs cluster2_high.fcs cluster3_low.fcs cluster3_high.fcs , note file names match distinct values group_cols columns (phenograph_cluster expression_group): useful feature tof_write_data automatically concatenate cells single FCS CSV files based specified group_cols regardless many unique files cells came . allows easy concatenation FCS CSV files containing data single sample acquired multiple CyTOF runs, example.","code":"data(phenograph_data) print(phenograph_data) #> # A tibble: 3,000 × 25 #> sample_name phenograph_cluster cd19 cd11b cd34 cd45 cd123 cd33 cd47 #> #> 1 H1_PhenoGra… cluster1 -0.168 29.0 3.23 131. -0.609 1.21 13.0 #> 2 H1_PhenoGra… cluster1 1.65 4.83 -0.582 230. 2.53 -0.507 12.9 #> 3 H1_PhenoGra… cluster1 2.79 36.1 5.20 293. -0.265 3.67 27.1 #> 4 H1_PhenoGra… cluster1 0.0816 48.8 0.363 431. 2.04 9.40 41.0 #> # ℹ 2,996 more rows #> # ℹ 16 more variables: cd7 , cd44 , cd38 , cd3 , #> # cd117 , cd64 , cd41 , pstat3 , pstat5 , #> # pampk , p4ebp1 , ps6 , pcreb , `pzap70-syk` , #> # prb , `perk1-2` # when copying and pasting this code, feel free to change this path # to wherever you'd like to save your output files my_path <- file.path(\"~\", \"Desktop\", \"tidytof_vignette_files\") phenograph_data %>% tof_write_data( group_cols = phenograph_cluster, out_path = my_path, format = \"fcs\" ) phenograph_data %>% distinct(phenograph_cluster) #> # A tibble: 3 × 1 #> phenograph_cluster #> #> 1 cluster1 #> 2 cluster2 #> 3 cluster3 phenograph_data %>% # create a variable representing if a cell is above or below # the median expression level of pstat5 mutate( expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\") ) %>% tof_write_data( group_cols = c(phenograph_cluster, expression_group), out_path = my_path, format = \"fcs\" ) phenograph_data %>% mutate( expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\") ) %>% distinct(phenograph_cluster, expression_group) #> # A tibble: 6 × 2 #> phenograph_cluster expression_group #> #> 1 cluster1 low #> 2 cluster1 high #> 3 cluster2 low #> 4 cluster2 high #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Reading and writing data","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 crayon_1.5.2 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [16] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.3.0 #> [19] bit_4.0.5 purrr_1.0.2 xfun_0.42 #> [22] glmnet_4.1-8 cachem_1.0.8 jsonlite_1.8.8 #> [25] recipes_1.0.10 tweenr_2.0.3 parallel_4.3.3 #> [28] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [31] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [34] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [37] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [40] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [46] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [49] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [52] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [55] Biobase_2.62.0 withr_3.0.0 evaluate_0.23 #> [58] future_1.33.1 desc_1.4.3 survival_3.5-8 #> [61] polyclip_1.10-6 pillar_1.9.0 foreach_1.5.2 #> [64] stats4_4.3.3 generics_0.1.3 vroom_1.6.5 #> [67] RcppHNSW_0.6.0 S4Vectors_0.40.2 hms_1.1.3 #> [70] ggplot2_3.5.0 munsell_0.5.0 scales_1.3.0 #> [73] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [76] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [79] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [82] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [88] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [91] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [94] lava_1.8.0 gtable_0.3.4 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [100] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [103] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [106] bit64_4.0.5 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"Getting started with tidytof","text":"tidytof makes heavy use two concepts may unfamiliar R beginners. first pipe (|>), can read . second “grouping” data data.frame tibble using dplyr::group_by, can read . tidytof users also benefit relatively -depth understanding dplyr package, wonderful introductory vignette : Everything else self-explanatory beginner advanced R users, though zero background running R code, read chapter R Data Science Hadley Wickham.","code":"vignette(\"dplyr\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"workflow-basics","dir":"Articles","previous_headings":"","what":"Workflow basics","title":"Getting started with tidytof","text":"Broadly speaking, tidytof’s functionality organized support 3 levels analysis inherent single-cell data described : Reading, writing, preprocessing, visualizing data level individual cells Identifying describing cell subpopulations clusters Building models (inference prediction) level patients samples tidytof provides functions (“verbs”) operate levels analysis: Cell-level data: tof_read_data() reads single-cell data FCS CSV files disk tidy data frame called tof_tbl. tof_tbls represent cell row protein measurement (piece information associated given cell) column. tof_preprocess() transforms protein expression values using user-provided function (.e. log-transformation, centering, scaling) tof_downsample() reduces number cells tof_tibble via subsampling. tof_reduce_dimensions() performs dimensionality reduction (across columns) tof_write_data writes single-cell data tof_tibble back disk form FCS CSV file. Cluster-level data: tof_cluster() clusters cells using one several algorithms commonly applied high-dimensional cytometry data tof_metacluster() agglomerates clusters smaller number metaclusters tof_analyze_abundance() performs differential abundance analysis (DAA) clusters metaclusters across experimental groups tof_analyze_expression() performs differential expression analysis (DEA) clusters’ metaclusters’ marker expression levels across experimental groups tof_extract_features() computes summary statistics (mean marker expression) cluster. Also (optionally) pivots summary statistics sample-level tidy data frame row represents sample column represents cluster-level summary statistic. Sample-level data: tof_split_data() splits sample-level data training test set predictive modeling tof_create_grid() creates elastic net hyperparameter search grid model tuning tof_train_model() trains sample-level elastic net model saves tof_model object tof_predict() Applies trained tof_model new data predict sample-level outcomes tof_assess_model() calculates performance metrics trained tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"tidytof-verb-syntax","dir":"Articles","previous_headings":"","what":"{tidytof} verb syntax","title":"Getting started with tidytof","text":"exceptions, tidytof functions follow specific, shared syntax involves 3 types arguments always occur order. argument types follows: almost tidytof functions, first argument data frame (tibble). enables use pipe (|>) multi-step calculations, means first argument functions implicit (passed previous function using pipe). also means tidytof functions -called “single-table verbs,” exception tof_cluster_ddpr, “two-table verb” (details use tof_cluster_ddpr, see “clustering--metaclustering” vignette). second group arguments called column specifications, end suffix _col _cols. Column specifications unquoted column names tell tidytof verb columns compute particular operation. example, cluster_cols argument tof_cluster allows user specify column input data frames used perform clustering. Regardless verb requires , column specifications support tidyselect helpers follow rules tidyselection tidyverse verbs like dplyr::select() tidyr::pivot_longer(). Finally, third group arguments tidytof verb called method specifications, ’re comprised every argument isn’t input data frame column specification. Whereas column specifications represent columns used perform operation, method specifications represent details operation performed. example, tof_cluster_phenograph() function requires method specification num_neighbors, specifies many nearest neighbors used construct PhenoGraph algorithm’s k-nearest-neighbor graph. cases, tidytof sets reasonable defaults verb’s particular method specifications, workflows can also customized experimenting non-default values. following code demonstrates tidytof verb syntax looks practice, column method specifications explicitly pointed :","code":"data(ddpr_data) set.seed(777L) ddpr_data |> tof_preprocess() |> tof_cluster( cluster_cols = starts_with(\"cd\"), # column specification method = \"phenograph\", # method specification, ) |> tof_metacluster( cluster_col = .phenograph_cluster, # column specification num_metaclusters = 4, # method specification method = \"kmeans\" # method specification ) |> tof_downsample( group_cols = .kmeans_metacluster, # column specification num_cells = 200, # method specification method = \"constant\" # method specification ) |> tof_plot_cells_layout( knn_cols = starts_with(\"cd\"), # column specification color_col = .kmeans_metacluster, # column specification num_neighbors = 7L, # method specification node_size = 2L # method specification )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"pipelines","dir":"Articles","previous_headings":"","what":"Pipelines","title":"Getting started with tidytof","text":"tidytof verbs can used combination one another using pipe (|>) operator. example, multistep “pipeline” takes built-tidytof dataset performs following analytical steps: Arcsinh-transform column protein measurements (default behavior tof_preprocess verb Cluster cells based surface markers panel Downsample dataset 100 random cells picked cluster Perform dimensionality reduction downsampled dataset using tSNE Visualize clusters using low-dimensional tSNE embedding","code":"ddpr_data |> # step 1 tof_preprocess() |> # step 2 tof_cluster( cluster_cols = starts_with(\"cd\"), method = \"phenograph\", # num_metaclusters = 4L, seed = 2020L ) |> # step 3 tof_downsample( group_cols = .phenograph_cluster, method = \"constant\", num_cells = 400 ) |> # step 4 tof_reduce_dimensions(method = \"tsne\") |> # step 5 tof_plot_cells_embedding( embedding_cols = contains(\"tsne\"), color_col = .phenograph_cluster ) + ggplot2::theme(legend.position = \"none\") #> Loading required namespace: Rtsne"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"other-tips","dir":"Articles","previous_headings":"","what":"Other tips","title":"Getting started with tidytof","text":"tidytof designed multidisciplinary team wet-lab biologists, bioinformaticians, physician-scientists analyze high-dimensional cytometry kinds single-cell data solve variety problems. result, tidytof’s high-level API designed great care mirror tidyverse - , human-centered, consistent, composable, inclusive wide userbase. Practically speaking, means things using tidytof. First, means tidytof designed quality--life features mind. example, may notice tidytof functions begin prefix tof_. intentional, allow use development environment’s code-completing software search tidytof functions easily (even can’t remember specific function name). reason, recommend using tidytof within RStudio development environment; however, many code editors predictive text functionality serves similar function. general, tidytof verbs organized way IDE’s code-completion tools also allow search (compare) related functions relative ease. (instance, tof_cluster_ prefix used clustering functions, tof_downsample_ prefix used downsampling functions). Second, means tidytof functions relatively intuitive use due shared logic - words, understand use one tidytof function, understand use others. example shared logic across tidytof functions argument group_cols, shows multiple verbs (tof_downsample, tof_cluster, tof_daa, tof_dea, tof_extract_features, tof_write_data). case, group_cols works way: accepts unquoted vector column names (specified manually using tidyselection) used group cells operation performed. idea generalizes throughout tidytof: see argument one place, behave identically (least similarly) wherever else encounter . Finally, means tidytof optimized first ease--use, performance. humans computers interact data differently, always trade-choosing data representation intuitive human user vs. choosing data representation optimized computational speed memory efficiency. design choices conflict one another, team tends err side choosing representation easy--understand users even expense small performance costs. Ultimately, means tidytof may optimal tool every high-dimensional cytometry analysis, though hopefully general framework provide users useful functionality.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Getting started with tidytof","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [10] shape_1.4.6.1 fastmap_1.1.1 labeling_0.4.3 #> [13] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [16] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.3.0 #> [19] purrr_1.0.2 xfun_0.42 glmnet_4.1-8 #> [22] cachem_1.0.8 jsonlite_1.8.8 recipes_1.0.10 #> [25] highr_0.10 tweenr_2.0.3 parallel_4.3.3 #> [28] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [31] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [34] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [37] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [40] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [46] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [49] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [52] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [55] Biobase_2.62.0 withr_3.0.0 Rtsne_0.17 #> [58] evaluate_0.23 future_1.33.1 desc_1.4.3 #> [61] survival_3.5-8 polyclip_1.10-6 pillar_1.9.0 #> [64] foreach_1.5.2 stats4_4.3.3 generics_0.1.3 #> [67] RcppHNSW_0.6.0 S4Vectors_0.40.2 hms_1.1.3 #> [70] ggplot2_3.5.0 munsell_0.5.0 scales_1.3.0 #> [73] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [76] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [79] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [82] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [88] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [91] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [94] lava_1.8.0 dplyr_1.1.4 gtable_0.3.4 #> [97] sass_0.4.9 digest_0.6.35 BiocGenerics_0.48.1 #> [100] ggrepel_0.9.5 farver_2.1.1 memoise_2.0.1 #> [103] htmltools_0.5.7 pkgdown_2.0.7 lifecycle_1.0.4 #> [106] hardhat_1.3.1 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Timothy Keyes. Maintainer. Kara Davis. Research team head, owner. Garry Nolan. Research team head, owner.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Keyes T, Davis K, Nolan G (2024). tidytof: user-friendly framework scalable reproducible high-dimensional cytometry data analysis. doi:10.18129/B9.bioc.tidytof, https://github.com/keyes-timothy/tidytof/tidytof - R package version 0.99.5, http://www.bioconductor.org/packages/tidytof. Keyes, J. T, Koladiya, Abhishek, Lo, Yu-Chen, Nolan, P. G, Davis, L. K (2023). “tidytof: user-friendly framework scalable reproducible high-dimensional cytometry data analysis.” Bioinformatics Advances, 3(1). doi:10.1093/bioadv/vbad071, https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad071/7192984.","code":"@Manual{, title = {tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis}, author = {Timothy Keyes and Kara Davis and Garry Nolan}, year = {2024}, url = {http://www.bioconductor.org/packages/tidytof}, note = {https://github.com/keyes-timothy/tidytof/tidytof - R package version 0.99.5}, doi = {10.18129/B9.bioc.tidytof}, } @Article{, title = {tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis}, author = {{Keyes} and Timothy J. and {Koladiya} and {Abhishek} and {Lo} and {Yu-Chen} and {Nolan} and Garry P. and {Davis} and Kara L.}, year = {2023}, journal = {Bioinformatics Advances}, volume = {3}, number = {1}, doi = {10.1093/bioadv/vbad071}, url = {https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad071/7192984}, }"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"tidytof-a-user-friendly-framework-for-interactive-and-reproducible-cytometry-data-analysis-","dir":"","previous_headings":"","what":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof R package implements open-source, integrated “grammar” single-cell data analysis high-dimensional cytometry data (.e. mass cytometry, full-spectrum flow cytometry, sequence-based cytometry). Specifically, tidytof provides easy--use pipeline handling high-dimensional cytometry data multiple levels observation - single-cell level, cell subpopulation (cluster) level, whole-sample level - automating many common data-processing tasks common “tidy data” interface. extension tidyverse ecosystem data manipulation tools R, tidytof’s functions developed internally consistent, human-centered set design principles. means using tidytof equally intuitive among scientists wide range coding experience (including beginners).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"prerequisites","dir":"","previous_headings":"Getting started","what":"Prerequisites","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof makes heavy use two concepts R beginners may unfamiliar . first pipe (|>), can read . second “grouping” data data.frame tibble using dplyr::group_by, can read . Everything else self-explanatory beginner advanced R users, though zero background running R code, read chapter R Data Science Hadley Wickham.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"package-structure","dir":"","previous_headings":"Getting started","what":"Package structure","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Broadly speaking, tidytof’s functionality organized support 3 levels analysis inherent single-cell data: Reading, writing, preprocessing, visualizing data level single cells Identifying describing cell subpopulations clusters Building models (inference prediction) level patients samples use tidytof levels cytometry data analysis detailed “Usage” section .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"can install development version tidytof GitHub following command: tidytof installed, can attach current R session using following code: addition, can install load packages need vignette:","code":"if (!require(devtools)) install.packages(\"devtools\") devtools::install_github(\"keyes-timothy/tidytof\") library(tidytof) if (!require(FlowSOM)) BiocManager::install(\"FlowSOM\") library(FlowSOM) if (!require(tidyverse)) install.packages(\"tidyverse\") library(tidyverse)"},{"path":[]},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"reading-data-with-tof_read_data","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Reading data with tof_read_data","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof comes bundled several example mass cytometry datasets. access raw .fcs .csv files containing data, use tidytof_example_data function. called arguments, tidytof_example_data return character vector naming datasets contained tidytof: obtain file path directory containing dataset, call tidytof_example_data one dataset names argument. example, obtain directory phenograph data, use following command: Using one directories (directory containing cytometry data local machine), can use tof_read_data read cytometry data raw files. Acceptable formats include .fcs files .csv files. Importantly, tof_read_data smart enough read single .fcs/.csv files multiple .fcs/.csv files depending whether first argument (path) leads single file directory files. , can use tof_read_data read .fcs files “phenograph” example dataset bundled tidytof store phenograph variable. Regardless input format, tidytof reads data extended tibble called tof_tbl (pronounced “tof tibble”), S3 class identical tbl_df, one additional attribute (“panel”). tidytof stores additional attribute tof_tbls , addition analyzing cytometry data individual experiments, cytometry users often want compare panels experiments find common markers compare metals associated particular markers across panels. notes tof_tbls: tof_tbls contains one cell per row one cytometry channel per column (provide data “tidy” format). tof_read_data adds additional column output tof_tbl encoding name file cell read (“file_name” column). tof_tbls inherit tbl_df class, methods available tibbles also available tof_tbls. example, dplyr’s useful mutate method can applied tof_tbl named phenograph convert columns encoding phenograph cluster ID stimulation condition cell belongs character vectors (instead original numeric codes uncleaned dataset). tof_tbl class preserved even transformations. Finally, retrieve panel information tof_tbl, use tof_get_panel: Importantly, tof_read_data uses opinionated heuristic mine different keyword slots input .fcs file(s) guess metals antigens used data collection. Thus, .csv files read using tof_read_data, recommended use panel_info argument provide panel manually (.csv files, unlike .fcs files, provide built-metadata columns contain).","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"scaffold\" \"statistical_scaffold\" #> [10] \"surgery\" tidytof_example_data(\"phenograph\") #> [1] \"/Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library/tidytof/extdata/phenograph\" phenograph <- tidytof_example_data(\"phenograph\") |> tof_read_data() phenograph |> class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph <- phenograph |> # mutate the input tof_tbl mutate( PhenoGraph = as.character(PhenoGraph), Condition = as.character(Condition) ) phenograph |> # use dplyr's select method to show that the columns have been changed select(where(is.character)) |> head() #> # A tibble: 6 × 3 #> file_name PhenoGraph Condition #> #> 1 H1_PhenoGraph_cluster1.fcs 7 7 #> 2 H1_PhenoGraph_cluster1.fcs 6 6 #> 3 H1_PhenoGraph_cluster1.fcs 9 9 #> 4 H1_PhenoGraph_cluster1.fcs 2 2 #> 5 H1_PhenoGraph_cluster1.fcs 15 15 #> 6 H1_PhenoGraph_cluster1.fcs 12 12 phenograph |> class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph |> tof_get_panel() |> head() #> # A tibble: 6 × 2 #> metals antigens #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 Ir191 DNA1 #> 4 Ir193 DNA2 #> 5 Pd104 BC1 #> 6 Pd106 BC2"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"pre-processing-with-tof_preprocess","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Pre-processing with tof_preprocess","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Generally, raw ion counts analyte measured mass cytometer need transformed cytometry data analysis. Common preprocessing steps may include variance-stabilizing transformations - hyperbolic arcsine (arcsinh) transformation log transformation - scaling/centering, /denoising. perform standard preprocessing tasks tidytof, use tof_preprocess. tof_preprocess’s default behavior apply arcsinh transformation (cofactor 5) numeric column input tof_tibble well remove gaussian noise Fluidigm software adds ion count (noise added visualization purposes, analyses, removing recommended). example, can preprocess phenograph tof_tibble see first measurements change . alter tof_preprocess’s default behavior, change channel_cols argument (specify columns tof_tibble transformed) transform_fun argument (specify vector-valued function used transform channel_cols). keep gaussian noise added Fluidigm software (working dataset noise), set undo_noise argument FALSE. Finally, note built-function tof_postprocess works nearly identically tof_preprocess, provides different default behavior (namely, applying reverse arcsinh transformation cofactor 5 numeric columns. See ?tof_postprocess details).","code":"# before preprocessing phenograph |> select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> head() #> # A tibble: 6 × 3 #> `CD45|Sm154` `CD34|Nd148` `CD38|Er167` #> #> 1 440. 0.256 18.7 #> 2 705. 1.96 41.2 #> 3 383. -0.302 6.51 #> 4 44.4 2.74 27.2 #> 5 892. 4.08 24.5 #> 6 448. 2.69 11.1 # perform preprocessing phenograph <- phenograph |> tof_preprocess() # inspect new values phenograph |> select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> head() #> # A tibble: 6 × 3 #> `CD45|Sm154` `CD34|Nd148` `CD38|Er167` #> #> 1 5.17 0.0512 2.03 #> 2 5.64 0.382 2.81 #> 3 5.03 -0.0603 1.08 #> 4 2.88 0.524 2.40 #> 5 5.88 0.746 2.29 #> 6 5.19 0.515 1.54"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"downsampling-with-tof_downsample","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Downsampling with tof_downsample","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Often, cytometry experiments collect tens hundreds millions cells total, can useful downsample smaller, computationally tractable number cells - either final analysis developing code. , tidytof implements tof_downsample verb, allows downsampling using 3 methods. Using tidytof’s built-dataset phenograph_data (smaller version dataset read ), can see original size dataset 1000 cells per cluster, 3000 cells total: randomly sample 200 cells per cluster, can use tof_downsample using “constant” method: Alternatively, wanted sample 50% cells cluster, use “prop” method: finally, might also interested taking slightly different approach downsampling downsamples number cells fixed constant proportion, fixed density phenotypic space. example, following scatterplot demonstrates certain areas phenotypic density phenograph_data contain cells others along cd34/cd38 axes: reduce number cells dataset local density around cell dataset relatively constant, can use “density” method tof_downsample: details, check documentation 3 underlying members tof_downsample_* function family (wrapped tof_downsample): tof_downsample_constant tof_downsample_prop tof_downsample_density","code":"data(phenograph_data) phenograph_data |> count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 1000 #> 2 cluster2 1000 #> 3 cluster3 1000 phenograph_data |> # downsample tof_downsample( method = \"constant\", group_cols = phenograph_cluster, num_cells = 200 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 200 #> 2 cluster2 200 #> 3 cluster3 200 phenograph_data |> # downsample tof_downsample( method = \"prop\", group_cols = phenograph_cluster, prop_cells = 0.5 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 500 #> 2 cluster2 500 #> 3 cluster3 500 phenograph_data |> # preprocess all numeric columns in the dataset tof_preprocess(undo_noise = FALSE) |> # make a scatterplot ggplot(aes(x = cd34, y = cd38)) + geom_point(alpha = 0.5) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + theme_bw() phenograph_data |> tof_preprocess(undo_noise = FALSE) |> tof_downsample( density_cols = c(cd34, cd38), target_prop_cells = 0.25, method = \"density\", ) |> ggplot(aes(x = cd34, y = cd38)) + geom_point(alpha = 0.5) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + theme_bw()"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"writing-data-with-tof_write_data","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Writing data with tof_write_data","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Finally, users may wish store single-cell data .fcs .csv files transformation, concatenation, filtering, data processing steps dimensionality reduction /clustering (see ). write single-cell data tof_tbl .fcs .csv files, use tof_write_data. tof_write_data’s trickiest argument group_cols, argument used specify columns tof_tibble used group cells (.e. rows tof_tibble) separate .fcs .csv files. Simply put, argument allows tof_write_data create single .fcs .csv file unique combination values columns specified user. example , cells grouped 3 output .fcs files - one 3 clusters encoded phenograph_cluster column phenograph_data. files following names (derived values phenograph_cluster column): cluster1.fcs cluster2.fcs cluster3.fcs However, suppose wanted write multiple files cluster breaking cells two groups: express high levels pstat5 express low levels pstat5. can use dplyr::mutate create new column phenograph_data breaks cells high- low-pstat5 expression groups, add column group_cols specification: write 6 files following names (derived values phenograph_cluster expression_group). cluster1_low.fcs cluster1_high.fcs cluster2_low.fcs cluster2_high.fcs cluster3_low.fcs cluster3_high.fcs useful feature tof_write_data automatically concatenate cells single .fcs .csv files based specified group_cols regardless many unique files cells came , allowing easy concatenation .fcs .csv files containing data single sample acquired multiple cytometry runs.","code":"# when copying and pasting this code, feel free to change this path # to wherever you'd like to save your output files my_path <- file.path(\"~\", \"Desktop\", \"tidytof_vignette_files\") phenograph_data |> tof_write_data( group_cols = phenograph_cluster, out_path = my_path, format = \"fcs\" ) phenograph_data |> # create a variable representing if a cell is above or below the median # expression level of pstat5 mutate(expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\")) |> tof_write_data( group_cols = c(phenograph_cluster, expression_group), out_path = my_path, format = \"fcs\" )"},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"identifying-clusters-with-tof_cluster","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Identifying clusters with tof_cluster","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"input files read tabular format preprocessed/downsampled, might interested clustering data define communities cells shared characteristics. , can use tof_cluster verb. Several clustering methods implemented tidytof, including FlowSOM, PhenoGraph, k-means, others. demonstrate, can apply FlowSOM clustering algorithm phenograph_data . Note phenograph_data contains 6000 total cells (2000 3 clusters identified original PhenoGraph publication). output tof_cluster tof_tbl identical input tibble, now addition additional column (“.flowsom_metacluster”) encodes cluster id cell input tof_tbl. Note output columns added tibble tof_tbl tidytof begin full-stop (“.”) reduce likelihood collisions existing column names. output tof_cluster tof_tbl, can use dplyr’s count method assess accuracy FlowSOM clustering compared original clustering PhenoGraph paper. , can see FlowSOM algorithm groups cells PhenoGraph cluster one another (small number mistakes per PhenoGraph cluster). change clustering algorithm tof_cluster uses, alter method flag; change columns used compute clusters, change cluster_cols flag. finally, want return tibble includes cluster labels (cluster labels added new column input tof_tbl), set augment FALSE.","code":"phenograph_clusters <- phenograph_data |> tof_preprocess() |> tof_cluster(method = \"flowsom\", cluster_cols = contains(\"cd\")) phenograph_clusters |> select(sample_name, .flowsom_metacluster, everything()) |> head() #> # A tibble: 6 × 26 #> sample_name .flowsom_metacluster phenograph_cluster cd19 cd11b cd34 #> #> 1 H1_PhenoGraph_c… 13 cluster1 -0.0336 2.46 0.608 #> 2 H1_PhenoGraph_c… 18 cluster1 0.324 0.856 -0.116 #> 3 H1_PhenoGraph_c… 10 cluster1 0.532 2.67 0.909 #> 4 H1_PhenoGraph_c… 8 cluster1 0.0163 2.97 0.0725 #> 5 H1_PhenoGraph_c… 13 cluster1 0.144 2.98 0.128 #> 6 H1_PhenoGraph_c… 8 cluster1 0.742 3.41 0.336 #> # ℹ 20 more variables: cd45 , cd123 , cd33 , cd47 , #> # cd7 , cd44 , cd38 , cd3 , cd117 , cd64 , #> # cd41 , pstat3 , pstat5 , pampk , p4ebp1 , #> # ps6 , pcreb , `pzap70-syk` , prb , `perk1-2` phenograph_clusters |> count(phenograph_cluster, .flowsom_metacluster, sort = TRUE) #> # A tibble: 23 × 3 #> phenograph_cluster .flowsom_metacluster n #> #> 1 cluster3 12 323 #> 2 cluster3 15 318 #> 3 cluster2 3 309 #> 4 cluster1 17 234 #> 5 cluster2 2 218 #> 6 cluster2 4 206 #> 7 cluster1 8 182 #> 8 cluster1 18 167 #> 9 cluster1 9 162 #> 10 cluster3 20 162 #> # ℹ 13 more rows # will result in a tibble with only 1 column (the cluster labels) phenograph_data |> tof_preprocess() |> tof_cluster(method = \"flowsom\", cluster_cols = contains(\"cd\"), augment = FALSE) |> head() #> # A tibble: 6 × 1 #> .flowsom_metacluster #> #> 1 13 #> 2 3 #> 3 10 #> 4 11 #> 5 10 #> 6 11"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"dimensionality-reduction-with-tof_reduce_dimensions","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Dimensionality reduction with tof_reduce_dimensions()","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"clusters identified, useful tool visualizing dimensionality reduction, form unsupervised machine learning used represent high-dimensional datasets smaller, easier--visualize number dimensions. tidytof includes several algorithms commonly used biologists dimensionality reduction: Principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), uniform manifold approximation projection (UMAP). apply dataset, use tof_reduce_dimensions: default, tof_reduce_dimensions add reduced-dimension feature embeddings input tof_tbl return augmented tof_tbl (, tof_tbl new columns embedding dimension) result. return features embeddings , set augment FALSE (tof_cluster). Regardless method used, reduced-dimension feature embeddings can visualized using ggplot2 (graphics package): visualizations can helpful qualitatively describing phenotypic differences clusters dataset. example, example , can see one clusters high CD11b expression, whereas others lower CD11b expression.","code":"# perform the dimensionality reduction phenograph_tsne <- phenograph_clusters |> tof_reduce_dimensions(method = \"tsne\") # select only the tsne embedding columns using a tidyselect helper (contains) phenograph_tsne |> select(contains(\"tsne\")) |> head() #> # A tibble: 6 × 2 #> .tsne1 .tsne2 #> #> 1 -8.41 17.2 #> 2 1.91 13.6 #> 3 23.9 20.1 #> 4 4.79 22.3 #> 5 -4.99 22.4 #> 6 11.0 20.2 # plot the tsne embeddings using color to distinguish between clusters phenograph_tsne |> ggplot(aes(x = .tsne1, y = .tsne2, fill = phenograph_cluster)) + geom_point(shape = 21) + theme_bw() + labs(fill = NULL) # plot the tsne embeddings using color to represent CD11b expression phenograph_tsne |> ggplot(aes(x = .tsne1, y = .tsne2, fill = cd11b)) + geom_point(shape = 21) + scale_fill_viridis_c() + theme_bw() + labs(fill = \"CD11b expression\")"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"differential-discovery-analysis-with-tof_analyze_abundance-and-tof_analyze_expression","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Differential discovery analysis with tof_analyze_abundance and tof_analyze_expression","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"dimensionality reduction can used visualize clustering result, many cytometry users also want use statistical tools rigorously quantify clusters(s) dataset associate particular experimental clinical variable. analyses often grouped umbrella term differential discovery analysis include comparing relative size clusters experimental conditions (differential abundance analysis; DAA) well comparing marker expression patterns clusters experimental conditions (differential expression analysis; DEA). tidytof provides tof_analyze_abundance tof_analyze_expression verbs differential abundance differential expression analyses, respectively. demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console). might wonder certain clusters expand deplete within patients two stimulation conditions described - question requires differential abundance analysis (DAA). tidytof’s tof_analyze_abundance verb supports use 3 statistical approaches performing DAA: diffcyt, generalized-linear mixed modeling (GLMMs), simple t-tests. setup described uses paired design 2 experimental conditions interest (Basal vs. BCR-XL), can use paired t-test method: Based output, can see 6 8 clusters statistically different abundance two stimulation conditions. Using tidytof easy integration tidyverse packages, can use result visualize fold-changes cluster (within patient) BCR-XL condition compared Basal condition using ggplot2: Importantly, output tof_analyze_abundance depends slightly underlying statistical method used, details can found documentation tof_analyze_abundance_* function family member: tof_analyze_abundance_diffcyt tof_analyze_abundance_glmm tof_analyze_abundance_ttest Similarly, suppose ’re interested intracellular signaling proteins change expression levels two stimulation conditions clusters. Differential Expression Analysis (DEA) can performed using tidytof’s tof_analyze_expression verb. , can use paired t-tests multiple-hypothesis correction test significant differences cluster’s expression signaling markers stimulation conditions. output tof_analyze_expression() also depends underlying test used, can see result looks relatively similar output tof_analyze_abundance(). , output tibble row represents differential expression results single cluster-marker pair - example, first row represents difference expression pS6 cluster 1 BCR-XL Basal conditions. row includes raw p-value multiple-hypothesis-corrected p-value cluster-marker pair. result can used make volcano plot visualize results cluster-marker pairs:","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = seq_along(file_name), patient = str_extract(file_name, \"patient[:digit:]\"), stimulation = str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\") daa_result <- citrus_data |> tof_analyze_abundance( cluster_col = population_id, effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) daa_result #> # A tibble: 8 × 8 #> population_id p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 0.000924 0.00535 \"*\" -5.48 7 -0.00743 0.644 #> 2 2 0.00623 0.0166 \"*\" -3.86 7 -0.0156 0.674 #> 3 3 0.0235 0.0314 \"*\" -2.88 7 -0.0638 0.849 #> 4 4 0.0235 0.0314 \"*\" 2.88 7 0.0832 1.38 #> 5 5 0.0116 0.0232 \"*\" 3.39 7 0.00246 1.08 #> 6 6 0.371 0.371 \"\" -0.955 7 -0.0168 0.919 #> 7 7 0.00134 0.00535 \"*\" 5.14 7 0.0202 1.14 #> 8 8 0.236 0.270 \"\" -1.30 7 -0.00228 0.901 plot_data <- citrus_data |> mutate(population_id = as.character(population_id)) |> left_join( select(daa_result, population_id, significant, mean_fc), by = \"population_id\" ) |> dplyr::count(patient, stimulation, population_id, significant, mean_fc, name = \"n\") |> group_by(patient, stimulation) |> mutate(prop = n / sum(n)) |> ungroup() |> pivot_wider( names_from = stimulation, values_from = c(prop, n), ) |> mutate( diff = `prop_BCR-XL` - prop_Basal, fc = `prop_BCR-XL` / prop_Basal, population_id = fct_reorder(population_id, diff), direction = case_when( mean_fc > 1 & significant == \"*\" ~ \"increase\", mean_fc < 1 & significant == \"*\" ~ \"decrease\", TRUE ~ NA_character_ ) ) significance_data <- plot_data |> group_by(population_id, significant, direction) |> summarize(diff = max(diff), fc = max(fc)) |> ungroup() plot_data |> ggplot(aes(x = population_id, y = fc, fill = direction)) + geom_violin(trim = FALSE) + geom_hline(yintercept = 1, color = \"red\", linetype = \"dotted\", size = 0.5) + geom_point() + geom_text( aes(x = population_id, y = fc, label = significant), data = significance_data, size = 8, nudge_x = 0.2, nudge_y = 0.06 ) + scale_x_discrete(labels = function(x) str_c(\"cluster \", x)) + scale_fill_manual( values = c(\"decrease\" = \"#cd5241\", \"increase\" = \"#207394\"), na.translate = FALSE ) + labs( x = NULL, y = \"Abundance fold-change (stimulated / basal)\", fill = \"Effect\", caption = \"Asterisks indicate significance at an adjusted p-value of 0.05\" ) signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) dea_result <- citrus_data |> tof_preprocess(channel_cols = any_of(signaling_markers)) |> tof_analyze_expression( cluster_col = population_id, marker_cols = any_of(signaling_markers), effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) dea_result |> head() #> # A tibble: 6 × 9 #> population_id marker p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 pS6_Y… 7.58e-8 2.12e-6 * 22.9 7 2.56 4.31 #> 2 2 pS6_Y… 1.16e-7 2.12e-6 * 21.6 7 2.13 2.49 #> 3 3 pBtk_… 1.32e-7 2.12e-6 * -21.2 7 -0.475 0.289 #> 4 7 pBtk_… 1.18e-7 2.12e-6 * -21.5 7 -0.518 0.286 #> 5 8 pBtk_… 1.30e-7 2.12e-6 * -21.2 7 -0.516 0.324 #> 6 4 pBtk_… 7.85e-7 1.05e-5 * -16.3 7 -0.462 0.296 volcano_plot <- dea_result |> tof_plot_clusters_volcano( use_ggrepel = TRUE ) volcano_plot"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"analyzing-data-at-the-patient--and-sample-level","dir":"","previous_headings":"Usage","what":"Analyzing data at the patient- and sample-level","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"addition verbs operate single-cell data directly, tidytof implements functions aggregating single-cell measurements cluster- sample-level summary statistics can analyzed using variety statistical models.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"feature-extraction-with-tof_extract_features","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level","what":"Feature extraction with tof_extract_features","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"addition functions analyzing visualizing cytometry data single-cell cluster levels, tidytof’s tof_extract_features verb allows users aggregate single-cell cluster-level information order summarize whole-samples (whole-patients) cells collected. features can useful visualizing differences patients samples different experimental conditions building machine learning models. understand tof_extract_features verb works, ’s easiest look subroutines (members tof_extract_* function family) independently. First, tof_extract_proportion, extracts proportion cells cluster within sample (samples defined using group_cols argument): Like members tof_extract_* function family, tof_extract_proportion() returns one row sample (defined unique combination values group_cols) one column extracted feature (, one column proportion 8 clusters citrus_data). values can also returned “long” format changing format argument: Another member function family, tof_extract_central_tendency, computes central tendency (e.g. mean median) user-specified markers cluster. tof_extract_threshold similar tof_extract_central_tendency, calculates proportion cells user-specified expression value marker instead measure central tendency: two final members tof_extract_* function family – tof_extract_emd tof_extract_jsd designed specifically comparing distributions marker expression stimulation conditions. , must given stimulation_col identifies stimulation condition cell , basal_level specifies reference (.e. unstimulated) condition within stimulation_col. additional arguments, tof_extract_emd computes Earth-mover’s distance marker’s distribution stimulation conditions (within cluster) basal condition; similarly, tof_extract_jsd computes Jensen-Shannon divergence index distributions. values ways compare different 2 distributions one another computationally expensive (also higher-resolution) simply comparing measures central tendency. Finally, tof_extract_features verb provides wrapper members function family, allowing users extract multiple features types . example, following code extracts proportion cluster, median several markers cluster, EMD basal condition stimulated condition cluster patients citrus_data.","code":"# preprocess the numeric columns in the citrus dataset citrus_data <- citrus_data |> mutate(cluster = str_c(\"cluster\", population_id)) |> tof_preprocess() citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation) ) |> head() #> # A tibble: 6 × 10 #> patient stimulation `prop@cluster1` `prop@cluster2` `prop@cluster3` #> #> 1 patient1 Basal 0.0190 0.0482 0.447 #> 2 patient1 BCR-XL 0.0109 0.0395 0.268 #> 3 patient2 Basal 0.0130 0.0280 0.491 #> 4 patient2 BCR-XL 0.0101 0.0143 0.358 #> 5 patient3 Basal 0.0326 0.0830 0.397 #> 6 patient3 BCR-XL 0.0200 0.0412 0.323 #> # ℹ 5 more variables: `prop@cluster4` , `prop@cluster5` , #> # `prop@cluster6` , `prop@cluster7` , `prop@cluster8` citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation), format = \"long\" ) |> head() #> # A tibble: 6 × 4 #> patient stimulation cluster prop #> #> 1 patient1 Basal cluster1 0.0190 #> 2 patient1 Basal cluster2 0.0482 #> 3 patient1 Basal cluster3 0.447 #> 4 patient1 Basal cluster4 0.237 #> 5 patient1 Basal cluster5 0.00219 #> 6 patient1 Basal cluster6 0.0759 citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = mean ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 4.80 0.0967 #> 2 patient1 Basal 4.68 0.765 #> 3 patient2 BCR-XL 5.00 -0.0579 #> 4 patient2 Basal 4.88 0.808 #> 5 patient3 BCR-XL 5.04 -0.0432 #> 6 patient3 Basal 4.98 0.745 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , … citrus_data |> tof_extract_threshold( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), threshold = 5 ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_threshold` CD4_Nd145@cluster1_thre…¹ #> #> 1 patient1 BCR-XL 0.516 0 #> 2 patient1 Basal 0.365 0 #> 3 patient2 BCR-XL 0.554 0 #> 4 patient2 Basal 0.452 0 #> 5 patient3 BCR-XL 0.547 0 #> 6 patient3 Basal 0.549 0 #> # ℹ abbreviated name: ¹​`CD4_Nd145@cluster1_threshold` #> # ℹ 22 more variables: `CD20_Sm147@cluster1_threshold` , #> # `CD45_In115@cluster2_threshold` , #> # `CD4_Nd145@cluster2_threshold` , #> # `CD20_Sm147@cluster2_threshold` , #> # `CD45_In115@cluster3_threshold` , #> # `CD4_Nd145@cluster3_threshold` , … # Earth-mover's distance citrus_data |> tof_extract_emd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), emd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.864 2.47 13.0 #> 2 patient2 1.11 7.05 10.8 #> 3 patient3 0.670 6.23 10.5 #> 4 patient4 2.64 5.86 9.90 #> 5 patient5 0.594 7.56 8.13 #> 6 patient6 0.661 4.77 7.97 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_emd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_emd`, ³​`BCR-XL_CD20_Sm147@cluster3_emd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_emd` , #> # `BCR-XL_CD4_Nd145@cluster7_emd` , #> # `BCR-XL_CD20_Sm147@cluster7_emd` , #> # `BCR-XL_CD45_In115@cluster4_emd` , #> # `BCR-XL_CD4_Nd145@cluster4_emd` , … # Jensen-Shannon Divergence citrus_data |> tof_extract_jsd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), jsd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.0367 0.0513 0.347 #> 2 patient2 0.00831 0.168 0.401 #> 3 patient3 0.0104 0.115 0.357 #> 4 patient4 0.0301 0.135 0.205 #> 5 patient5 0.00911 0.0789 0.274 #> 6 patient6 0.00972 0.0346 0.214 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_jsd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_jsd`, ³​`BCR-XL_CD20_Sm147@cluster3_jsd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_jsd` , #> # `BCR-XL_CD4_Nd145@cluster7_jsd` , #> # `BCR-XL_CD20_Sm147@cluster7_jsd` , #> # `BCR-XL_CD45_In115@cluster4_jsd` , #> # `BCR-XL_CD4_Nd145@cluster4_jsd` , … citrus_data |> tof_extract_features( cluster_col = cluster, group_cols = patient, stimulation_col = stimulation, lineage_cols = any_of(c(\"CD45_In115\", \"CD20_Sm147\", \"CD33_Nd148\")), signaling_cols = any_of(signaling_markers), signaling_method = \"emd\", basal_level = \"Basal\" ) |> head()"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"outcomes-modeling-with-tof_model","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level","what":"Outcomes modeling with tof_model","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"[brief intro building predictive models might motivated .] tidytof implements several functions building predictive models using sample- patient-level data. illustrate work, first download patient-level data paper combining sample-level clinical annotations one tidytof’s built-data objects (ddpr_metadata). data processing steps result ddpr_patients tibble. numeric columns ddpr_patients represent aggregated cell population features sample (see Supplementary Table 5 paper details). non-numeric columns represent clinical metadata sample (run ?ddpr_metadata information). also preprocessing steps might want perform now save us headaches ’re fitting models later.","code":"data(ddpr_metadata) # link for downloading the sample-level data from the Nature Medicine website data_link <- \"https://static-content.springer.com/esm/art%3A10.1038%2Fnm.4505/MediaObjects/41591_2018_BFnm4505_MOESM3_ESM.csv\" # downloading the data and combining it with clinical annotations ddpr_patients <- readr::read_csv(data_link, skip = 2L, n_max = 78L, show_col_types = FALSE) |> dplyr::rename(patient_id = Patient_ID) |> left_join(ddpr_metadata, by = \"patient_id\") |> dplyr::filter(!str_detect(patient_id, \"Healthy\")) ddpr_patients |> select(where(~ !is.numeric(.x))) |> head() #> # A tibble: 6 × 8 #> patient_id gender mrd_risk nci_rome_risk relapse_status type_of_relapse cohort #> #> 1 UPN1 Male Interme… Standard Yes Early Train… #> 2 UPN1-Rx Male Interme… Standard Yes Early Train… #> 3 UPN2 Male Interme… Standard No Train… #> 4 UPN3 Female Standard Standard No Train… #> 5 UPN4 Male Standard Standard No Valid… #> 6 UPN5 Female Standard High No Valid… #> # ℹ 1 more variable: ddpr_risk ddpr_patients <- ddpr_patients |> # convert the relapse_status variable to a factor first, # which is something we'll want for fitting the model later # and create the time_to_event and event columns for survival modeling mutate( relapse_status = as.factor(relapse_status), time_to_event = if_else(relapse_status == \"Yes\", time_to_relapse, ccr), event = if_else(relapse_status == \"Yes\", 1, 0) )"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"separating-the-training-and-validation-cohorts","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level > Outcomes modeling with tof_model","what":"Separating the training and validation cohorts","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"original DDPR paper, patients used fit model rest used assess model tuned. can separate training validation cohorts using cohort variable ddpr_patients","code":"ddpr_training <- ddpr_patients |> dplyr::filter(cohort == \"Training\") ddpr_validation <- ddpr_patients |> dplyr::filter(cohort == \"Validation\") nrow(ddpr_training) #> [1] 49 nrow(ddpr_validation) #> [1] 12"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"building-a-classifier-using-logistic-regression","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level > Outcomes modeling with tof_model","what":"Building a classifier using logistic regression","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"First, can build elastic net classifier predict patients relapse patients won’t (ignoring time--event data now). , can use relapse_status column ddpr_training outcome variable: Specifically, can use tof_split_data function split cohort training test set either (“simple” split) multiple times (using either k-fold cross-validation bootstrapping). case, use 5-fold cross-validation, reading documentation tof_split_data demonstrates use methods. output tof_split_data varies depending split_method used. cross-validation, result rset object rsample package. rset objects type tibble two columns: splits - column entry rsplit object (contains single resample full dataset) id - character column entry represents name fold entry splits belongs . can inspect one resamples splits column see contain: Note can use rsample::training rsample::testing return training test obeservations resampling: , can feed training_split tof_train_model function tune logistic regression model predicts relapse_status leukemia patient. sure check tof_create_grid documentation learn make hyperparameter search grid model tuning (case, limit mixture parameter value 1, fits sparse lasso model). Also note demonstration purposes, include features come one cell population (“Population 2”) original dataset, means probably shouldn’t expect model perform well one original paper (select many features). output tof_train_model tof_model, object containing information trained model (can passed tof_predict tof_assess_model verbs). tof_model printed, information optimal hyperparamters printed, table nonzero model coefficients model. can use trained model make predictions validation data set aside earlier: can see model gets (!) predictions correct validation set set aside. can also assess model directly using tof_assess_model can make ROC curve using metrics: can assess model validation data…","code":"# find how many of each outcome we have in our cohort ddpr_training |> dplyr::count(relapse_status) #> # A tibble: 2 × 2 #> relapse_status n #> #> 1 No 31 #> 2 Yes 18 training_split <- ddpr_training |> tof_split_data( split_method = \"k-fold\", num_cv_folds = 5, strata = relapse_status ) training_split #> # 5-fold cross-validation using stratification #> # A tibble: 5 × 2 #> splits id #> #> 1 Fold1 #> 2 Fold2 #> 3 Fold3 #> 4 Fold4 #> 5 Fold5 my_resample <- training_split$splits[[1]] print(my_resample) #> #> <38/11/49> class(my_resample) #> [1] \"vfold_split\" \"rsplit\" my_resample |> rsample::training() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN1 3.06 0.583 0.00449 0.164 1.94 0.416 #> 2 UPN1-Rx 0.0395 0.618 0.0634 0.572 2.93 0.944 #> 3 UPN3 0.633 0.0234 0.0165 0.0327 2.25 0.226 #> 4 UPN8 0.951 0.958 0.161 0.556 3.18 0.556 #> 5 UPN10 0.00374 0.761 0.000696 0.829 3.19 0.886 #> 6 UPN10-Rx 0.00240 0.167 0.203 0.802 2.57 0.822 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … my_resample |> rsample::testing() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN2 0.139 0.0662 0.0221 0.0825 2.25 0.454 #> 2 UPN6 5.62 0.550 0.00374 0.622 2.86 0.342 #> 3 UPN7 0.474 0.966 0.124 1.24 2.59 0.243 #> 4 UPN9 15.6 0.446 0.0445 0.163 2.86 0.434 #> 5 UPN12 0.0565 0.185 0.0115 0.142 2.49 0.254 #> 6 UPN17 1.40 1.52 0.0128 0.284 3.46 0.656 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … class_mod <- training_split |> tof_train_model( predictor_cols = contains(\"Pop2\"), response_col = relapse_status, model_type = \"two-class\", hyperparameter_grid = tof_create_grid(mixture_values = 1), impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(class_mod) #> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e-05 #> # A tibble: 25 × 2 #> feature coefficient #> #> 1 p4EBP1_dP_IL7_Pop2 -2.59 #> 2 CD58_Pop2 2.23 #> 3 (Intercept) -1.83 #> 4 pSTAT5_dP_TSLP_Pop2 1.69 #> 5 p4EBP1_FC_IL7_Pop2 1.46 #> 6 CD43_Pop2 1.37 #> 7 HLADR_Pop2 -1.32 #> 8 pSyk_dP_TSLP_Pop2 1.08 #> 9 pErk_dP_IL7_Pop2 -1.05 #> 10 Ki67_Pop2 -1.05 #> # ℹ 15 more rows class_predictions <- class_mod |> tof_predict(new_data = ddpr_validation, prediction_type = \"class\") class_predictions |> dplyr::mutate( truth = ddpr_validation$relapse_status ) #> # A tibble: 12 × 2 #> .pred truth #> #> 1 Yes No #> 2 No No #> 3 No Yes #> 4 No No #> 5 No No #> 6 Yes Yes #> 7 Yes Yes #> 8 No No #> 9 No No #> 10 No Yes #> 11 No Yes #> 12 No Yes # calling the function with no new_data evaluates the # the nodel using its training data training_assessment <- class_mod |> tof_assess_model() training_assessment #> $model_metrics #> # A tibble: 6 × 2 #> metric value #> #> 1 binomial_deviance 0.0291 #> 2 misclassification_error 0 #> 3 roc_auc 1 #> 4 mse 0.00119 #> 5 mae 0.0285 #> 6 accuracy 1 #> #> $roc_curve #> # A tibble: 51 × 5 #> .threshold specificity sensitivity tpr fpr #> #> 1 -Inf 0 1 1 1 #> 2 0.00000114 0 1 1 1 #> 3 0.0000955 0.0323 1 1 0.968 #> 4 0.000160 0.0645 1 1 0.935 #> 5 0.000190 0.0968 1 1 0.903 #> 6 0.000612 0.129 1 1 0.871 #> 7 0.000896 0.161 1 1 0.839 #> 8 0.00135 0.194 1 1 0.806 #> 9 0.00142 0.226 1 1 0.774 #> 10 0.00194 0.258 1 1 0.742 #> # ℹ 41 more rows #> #> $confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 31 #> 2 No Yes 0 #> 3 Yes No 0 #> 4 Yes Yes 18 class_mod |> tof_plot_model() + labs(subtitle = \"ROC Curve (Training data)\") validation_assessment <- class_mod |> tof_assess_model(new_data = ddpr_validation) validation_assessment #> $model_metrics #> # A tibble: 6 × 2 #> metric value #> #> 1 binomial_deviance 4.75 #> 2 misclassification_error 0.417 #> 3 roc_auc 0.639 #> 4 mse 0.759 #> 5 mae 0.879 #> 6 accuracy 0.583 #> #> $roc_curve #> # A tibble: 14 × 5 #> .threshold specificity sensitivity tpr fpr #> #> 1 -Inf 0 1 1 1 #> 2 0.000240 0 1 1 1 #> 3 0.00105 0.167 1 1 0.833 #> 4 0.00195 0.167 0.833 0.833 0.833 #> 5 0.00230 0.333 0.833 0.833 0.667 #> 6 0.00472 0.5 0.833 0.833 0.5 #> 7 0.00618 0.667 0.833 0.833 0.333 #> 8 0.0464 0.667 0.667 0.667 0.333 #> 9 0.273 0.667 0.5 0.5 0.333 #> 10 0.286 0.667 0.333 0.333 0.333 #> 11 0.844 0.833 0.333 0.333 0.167 #> 12 0.852 0.833 0.167 0.167 0.167 #> 13 1.00 0.833 0 0 0.167 #> 14 Inf 1 0 0 0 #> #> $confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 5 #> 2 No Yes 1 #> 3 Yes No 4 #> 4 Yes Yes 2 class_mod |> tof_plot_model(new_data = ddpr_validation) + labs(subtitle = \"ROC Curve (Validation data)\")"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"tidytofs-design-principles-and-some-tips","dir":"","previous_headings":"","what":"{tidytof}’s Design Principles (and some tips)","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"{tidytof} designed multidisciplinary team wet-lab biologists, bioinformaticians, physician-scientists analyze cytometry kinds single-cell data solve variety problems. result, tidytof’s high-level API designed great care mirror tidyverse - , human-centered, consistent, composable, inclusive wide userbase. section, describe miscellaneous design decisions tips using tidytof may help enthusiastic user.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_1-use-the-tof_-prefix-to-your-advantage","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"1. Use the tof_ prefix to your advantage.","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"may notice tidytof functions begin prefix tof_. intentional, allow use development environment’s code-completing software search functions easily (even don’t remember function name). reason, recommend using tidytof within RStudio development environment; however, many code editors predictive text functionality serves similar function. general, tidytof verbs organized way IDE’s code-completion tools also allow search (compare) related functions relative ease. (instance, tof_cluster_ prefix used clustering functions, tof_downsample_ prefix used downsampling functions).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_2-tidytof-functions-use-2-kinds-of-arguments","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"2. {tidytof} functions use 2 kinds of arguments","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof functions optimized working “tidy” data form tibbles data.frames. means tidytof functions share basic design principles terms arguments work. details design principles, check Getting Started tidytof vignette","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_3-use-tidytof-to-write-human-readable-pipelines","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"3. Use {tidytof} to write human-readable pipelines","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"real “magic” tidytof derives ability simplify multistep data-processing tasks simple readable chunk code. example, suppose just acquired .fcs files mass cytometer want perform following analysis: Read .fcs files R session Arcsinh-transform column protein measurements Cluster cells based surface markers panel Downsample dataset 100 random cells picked cluster Perform dimensionality reduction downsampled dataset using tSNE Visualize clusters using low-dimensional tSNE embedding using appropriate tidytof verbs step analysis, can easily write code function call corresponds exactly one step pipeline: shown , stringing together tidytof verbs creates pipeline can read easily left--right top--bottom – means relatively easy return code later (modify , write methods section next high-impact manuscript!) , perhaps importantly, one colleagues return later want recreate analysis.","code":"input_path <- tidytof_example_data(\"phenograph\") set.seed(0012) input_path |> # step 1 tof_read_data() |> # step 2 tof_preprocess() |> # step 3 tof_cluster(method = \"phenograph\") |> # step 4 tof_downsample( group_cols = .phenograph_cluster, num_cells = 100, method = \"constant\" ) |> # step 5 tof_reduce_dimensions(perplexity = 50, method = \"tsne\") |> # step 6 tof_plot_cells_embedding( embedding_cols = starts_with(\".tsne\"), color_col = .phenograph_cluster )"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_4-additional-resources","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"4. Additional resources","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof built top tidyverse family R packages. result, users tidytof benefit substantially spending hours dplyr, ggplot2, tidyr package vignettes learn many useful functions packages provide. access recommended list package vignettes, run following lines R code console:","code":"# dplyr vignette(topic = \"dplyr\", package = \"dplyr\") vignette(topic = \"grouping\", package = \"dplyr\") vignette(topic = \"colwise\", package = \"dplyr\") # ggplot2 vignette(topic = \"ggplot2-specs\", package = \"ggplot2\") # tidyr vignette(topic = \"tidy-data\", package = \"tidyr\") vignette(topic = \"nest\", package = \"tidyr\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"Coerce object SingleCellExperiment Coerce tof_tbl SingleCellExperiment","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"","code":"as_SingleCellExperiment(x, ...) # S3 method for tof_tbl as_SingleCellExperiment( x, channel_cols = where(tof_is_numeric), reduced_dimensions_cols, metadata_cols = where(function(.x) !tof_is_numeric(.x)), split_reduced_dimensions = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"x tof_tbl ... Unused. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default numeric columns. reduced_dimensions_cols Unquoted column names representing columns contain dimensionality reduction embeddings, tSNE UMAP embeddings. Supports tidyselect helpers. metadata_cols Unquoted column names representing columns contain metadata samples cell collected. nothing specified, default non-numeric columns. split_reduced_dimensions boolean value indicating whether dimensionality results x split separate slots resulting SingleCellExperiment. FALSE (default), split performed reducedDims slot result single entry (\"tidytof_reduced_dimensions\"). TRUE, split performed reducedDims slot result 1-4 entries depending dimensionality reduction results present x (\"tidytof_pca\", \"tidytof_tsne\", \"tidytof_umap\", \"tidytof_reduced_dimensions\"). Note \"tidytof_reduced_dimensions\" include dimensionality reduction results named according tidytof's pca, umap, tsne conventions.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"SingleCellExperiment SingleCellExperiment.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a flowFrame — as_flowFrame","title":"Coerce an object into a flowFrame — as_flowFrame","text":"Coerce object flowFrame Coerce tof_tbl flowFrame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a flowFrame — as_flowFrame","text":"","code":"as_flowFrame(x, ...) # S3 method for tof_tbl as_flowFrame(x, ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a flowFrame — as_flowFrame","text":"x tof_tbl. ... Unused.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a flowFrame — as_flowFrame","text":"flowFrame flowFrame. Note non-numeric columns `x` removed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a flowFrame — as_flowFrame","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a flowSet — as_flowSet","title":"Coerce an object into a flowSet — as_flowSet","text":"Coerce object flowSet Coerce tof_tbl flowSet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a flowSet — as_flowSet","text":"","code":"as_flowSet(x, ...) # S3 method for tof_tbl as_flowSet(x, group_cols, ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a flowSet — as_flowSet","text":"x tof_tbl. ... Unused. group_cols Unquoted names columns `x` used group cells separate flowFrames. Supports tidyselect helpers. Defaults NULL (cells written single flowFrame).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a flowSet — as_flowSet","text":"flowSet flowSet. Note non-numeric columns `x` removed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a flowSet — as_flowSet","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a SeuratObject — as_seurat","title":"Coerce an object into a SeuratObject — as_seurat","text":"Coerce object SeuratObject Coerce tof_tbl SeuratObject","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a SeuratObject — as_seurat","text":"","code":"as_seurat(x, ...) # S3 method for tof_tbl as_seurat( x, channel_cols = where(tof_is_numeric), reduced_dimensions_cols, metadata_cols = where(function(.x) !tof_is_numeric(.x)), split_reduced_dimensions = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a SeuratObject — as_seurat","text":"x tof_tbl ... Unused. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default numeric columns. reduced_dimensions_cols Unquoted column names representing columns contain dimensionality reduction embeddings, tSNE UMAP embeddings. Supports tidyselect helpers. metadata_cols Unquoted column names representing columns contain metadata samples cell collected. nothing specified, default non-numeric columns. split_reduced_dimensions boolean value indicating whether dimensionality results x split separate slots resulting SingleCellExperiment. FALSE (default), split performed reducedDims slot result single entry (\"tidytof_reduced_dimensions\"). TRUE, split performed reducedDims slot result 1-4 entries depending dimensionality reduction results present x (\"tidytof_pca\", \"tidytof_tsne\", \"tidytof_umap\", \"tidytof_reduced_dimensions\"). Note \"tidytof_reduced_dimensions\" include dimensionality reduction results named according tidytof's pca, umap, tsne conventions.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a SeuratObject — as_seurat","text":"SeuratObject SeuratObject.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a SeuratObject — as_seurat","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"Convert object tof_tbl","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"","code":"# S3 method for flowSet as_tof_tbl(flow_data, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"flow_data FlowSet sep string use separate antigen name associated metal column names output tibble. Defaults \"|\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"`tof_tbl`","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"Coerce flowFrames flowSets tof_tbl's.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"","code":"as_tof_tbl(flow_data, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"flow_data flowFrame flowSet sep string indicating symbol used separate antigen names metal names columns output tof_tbl.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"tof_tbl.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] input_flowframe <- flowCore::read.FCS(input_file) tof_tibble <- as_tof_tbl(input_flowframe)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the cosine similarity between two vectors — cosine_similarity","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"Find cosine similarity two vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"","code":"cosine_similarity(x, y)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"x numeric vector y numeric vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"scalar value representing cosine similarity x y","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":null,"dir":"Reference","previous_headings":"","what":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"dataset containing CyTOF measurements immune cells originally studied following paper: Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"","code":"data(ddpr_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"data frame 10000 rows 24 variables: sample_name name sample data read cd45 CyTOF measurement raw ion counts cd19 CyTOF measurement raw ion counts cd22 CyTOF measurement raw ion counts cd79b CyTOF measurement raw ion counts cd20 CyTOF measurement raw ion counts cd34 CyTOF measurement raw ion counts cd123 CyTOF measurement raw ion counts cd10 CyTOF measurement raw ion counts cd24 CyTOF measurement raw ion counts cd127 CyTOF measurement raw ion counts cd43 CyTOF measurement raw ion counts cd38 CyTOF measurement raw ion counts cd58 CyTOF measurement raw ion counts psyk CyTOF measurement raw ion counts p4ebp1 CyTOF measurement raw ion counts pstat5 CyTOF measurement raw ion counts pakt CyTOF measurement raw ion counts ps6 CyTOF measurement raw ion counts perk CyTOF measurement raw ion counts pcreb CyTOF measurement raw ion counts","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"https://github.com/kara-davis-lab/DDPR","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"dataset containing patient-level clinical metadata samples originally studied following paper: Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"","code":"data(ddpr_metadata)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"data frame 10000 rows 12 variables: patient_id Name sample data read gender Gender patient sample collected age_at_diagnosis Age (years) patient sample collected wbc_count diagnostic White Blood Cell (WBC) count patient sample collected mrd_risk Risk stratification category patient using minimal residual disease (MRD) criteria nci_rome_risk Risk stratification category patient using National Cancer Institute (NCI) criteria relapse_status string representing whether patient relapsed time_to_relapse time (days) took patient relapse. Patients relapse value NA type_of_relapse string representing timing relapse patient. \"early\" relapses occurred less 18 months diagnosis; \"Early\" relapses occurred 18 months 32 months diagnosis; \"Late\" relapses occurred later 32 months diagnosis. ccr number documented days continuous complete remission (CCR) patients relapse. patients relapsed value NA. cohort string representing sample used \"Training\" \"Validation\" cohort original study ddpr_risk risk category (\"Low\" \"High\") assigned sample using original paper's risk-stratification algorithm","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207. Supplementary Table 1.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the dot product between two vectors. — dot","title":"Find the dot product between two vectors. — dot","text":"Find dot product two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the dot product between two vectors. — dot","text":"","code":"dot(x, y)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the dot product between two vectors. — dot","text":"x numeric vector. y numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the dot product between two vectors. — dot","text":"dot product x y.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the extension for a file — get_extension","title":"Find the extension for a file — get_extension","text":"Find extension file","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the extension for a file — get_extension","text":"","code":"get_extension(filename)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the extension for a file — get_extension","text":"filename string representing name file local 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l2_normalize","text":"","code":"l2_normalize(x)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"x numeric vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"vector length length(x) magnitude 1","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the magnitude of a vector. — magnitude","title":"Find the magnitude of a vector. — magnitude","text":"Find magnitude vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the magnitude of a vector. — magnitude","text":"","code":"magnitude(x)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the magnitude of a vector. — magnitude","text":"x numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the magnitude of a vector. — magnitude","text":"scalar value (magnitude x).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"Make AnnotatedDataFrame needed flowFrame class","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"","code":"make_flowcore_annotated_data_frame(maxes_and_mins)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"maxes_and_mins data.frame containing information max min values channel saved flowFrame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"AnnotatedDataFrame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":null,"dir":"Reference","previous_headings":"","what":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"character vector used `tof_read_fcs` `tof_read_data` detect parse CyTOF metals correspond channel input .fcs file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"","code":"data(metal_masterlist)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"character vector entry pattern tidytof searches every CyTOF channel input .fcs files. patterns amalgamate example .fcs files sampled studies linked .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"https://github.com/kara-davis-lab/DDPR https://cytobank.org/nolanlab/reports/Levine2015.html https://cytobank.org/nolanlab/reports/Spitzer2015.html https://cytobank.org/nolanlab/reports/Spitzer2017.html https://community.cytobank.org/cytobank/projects/609","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"named character vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for a tof_model. — new_tof_model","title":"Constructor for a tof_model. — new_tof_model","text":"Constructor tof_model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for a tof_model. — new_tof_model","text":"","code":"new_tof_model( model, recipe, penalty, mixture, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), outcome_colnames, training_data )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for a tof_model. — new_tof_model","text":"model glmnet model. recipe prepped recipe object. penalty double indicating lambda value used within glmnet path. mixture double indicating alpha value used fit glmnet model. model_type string indicating type glmnet model fit. outcome_colnames training_data ","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for a tof_model. — new_tof_model","text":"`tof_model`, S3 class includes trained glmnet model recipe used perform associated preprocessing.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for a tof_tibble. — new_tof_tibble","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"Constructor tof_tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"","code":"new_tof_tibble(x = dplyr::tibble(), panel = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"x data.frame tibble containing single-cell mass cytometry data rows cells columns CyTOF measurements. panel data.frame tibble containing information panel mass cytometry data x.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"`tof_tbl`, tibble extension tracks attributes useful CyTOF data analysis.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":null,"dir":"Reference","previous_headings":"","what":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"dataset containing CyTOF measurements healthy control cells originally studied following paper: Levine JH, Simonds EF, et al. Data-Driven Phenotypic Dissection AML Reveals Progenitor-like Cells Correlate Prognosis. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. Epub 2015 Jun 18. PMID: 26095251; PMCID: PMC4508757.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"","code":"data(phenograph_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"data frame 6000 rows 26 variables: sample_name Name sample data read phenograph_cluster Numeric ID cluster assignment row cd19 CyTOF measurement raw ion counts cd11b CyTOF measurement raw ion counts cd34 CyTOF measurement raw ion counts cd45 CyTOF measurement raw ion counts cd123 CyTOF measurement raw ion counts cd33 CyTOF measurement raw ion counts cd47 CyTOF measurement raw ion counts cd7 CyTOF measurement raw ion counts cd44 CyTOF measurement raw ion counts cd38 CyTOF measurement raw ion counts cd3 CyTOF measurement raw ion counts cd117 CyTOF measurement raw ion counts cd64 CyTOF measurement raw ion counts cd41 CyTOF measurement raw ion counts pstat3 CyTOF measurement raw ion counts pstat5 CyTOF measurement raw ion counts pampk CyTOF measurement raw ion counts p4ebp1 CyTOF measurement raw ion counts ps6 CyTOF measurement raw ion counts pcreb CyTOF measurement raw ion counts pzap70-syk CyTOF measurement raw ion counts prb CyTOF measurement raw ion counts perk1-2 CyTOF measurement raw ion counts","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"https://cytobank.org/nolanlab/reports/Levine2015.html","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"2000 cells 3 clusters identified original paper sampled.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% rlang :=, .data tidyselect all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, starts_with","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Objects exported from other packages — reexports","text":"See documentation object's original package.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Objects exported from other packages — reexports","text":"","code":"# See examples in each object's original package NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"Reverses arcsinh transformation cofactor `scale_factor` shift `shift_factor`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"","code":"rev_asinh(x, shift_factor, scale_factor)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"x numeric vector. shift_factor scalar value `` following equation used transform high-dimensional cytometry raw data ion counts using hyperbolic arcsinh function: `new_x <- asinh(+ b * x)`. scale_factor scalar value `b` following equation used transform high-dimensional cytometry raw data ion counts using hyperbolic arcsinh function: `new_x <- asinh(+ b * x)`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"numeric vector undergoing reverse arcsinh transformation","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"","code":"shift_factor <- 0 scale_factor <- 1 / 5 input_value <- 20 asinh_value <- asinh(shift_factor + input_value * scale_factor) restored_value <- rev_asinh(asinh_value, shift_factor, scale_factor)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get paths to tidytof example data — tidytof_example_data","title":"Get paths to tidytof example data — tidytof_example_data","text":"tidytof comes bundled number sample .fcs files inst/extdata directory. function makes easy access.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get paths to tidytof example data — tidytof_example_data","text":"","code":"tidytof_example_data(dataset_name = NULL)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get paths to tidytof example data — tidytof_example_data","text":"dataset_name Name dataset want access. NULL, names datasets (different study) listed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get paths to tidytof example data — tidytof_example_data","text":"character vector file paths requested .fcs files located. `dataset_name` NULL, character vector dataset names (can used values `dataset_name`) returned instead.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get paths to tidytof example data — tidytof_example_data","text":"","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"surgery\" tidytof_example_data(dataset_name = \"phenograph\") #> [1] \"/home/runner/work/_temp/Library/tidytof/extdata/phenograph\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using one three methods (\"diffcyt\", \"glmm\", \"ttest\"). wraps members `tof_analyze_abundance_*` function family: tof_analyze_abundance_diffcyt, tof_analyze_abundance_glmm, tof_analyze_abundance_ttest.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"","code":"tof_analyze_abundance(tof_tibble, method = c(\"diffcyt\", \"glmm\", \"ttest\"), ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"tof_tibble `tof_tbl` `tibble`. method string indicating statistical method used. Valid values include \"diffcyt\", \"glmm\", \"ttest\". ... Additional arguments pass onto `tof_analyze_abundance_*` function family member corresponding chosen method.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"tibble nested tibble containing differential abundance results chosen method. See tof_analyze_abundance_diffcyt, tof_analyze_abundance_glmm, tof_analyze_abundance_ttest details.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using one three methods implemented diffcyt package differential discovery analysis high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"","code":"tof_analyze_abundance_diffcyt( tof_tibble, sample_col, cluster_col, fixed_effect_cols, random_effect_cols, diffcyt_method = c(\"glmm\", \"edgeR\", \"voom\"), include_observation_level_random_effects = FALSE, min_cells = 3, min_samples = 5, alpha = 0.05, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential abundance analysis. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Optional. Unquoted column names representing columns `tof_tibble` used model random effects differential abundance analysis. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without multiple samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. diffcyt_method string indicating diffcyt method used differential abundance analysis. Valid methods include \"glmm\" (default), \"edgeR\", \"voom\". include_observation_level_random_effects boolean value indicating \"observation-level random effects\" (OLREs) included random effect terms \"glmm\" differential abundance model. details OLREs , see diffcyt paper. \"glmm\" method can model observation-level random effects, values ignore argument (throw warning set TRUE). Defaults FALSE. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. ... Optional additional arguments pass --hood diffcyt function used perform differential abundance analysis. See testDA_GLMM, testDA_edgeR, testDA_voom details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"nested tibble two columns: `tested_effect` `daa_results`. first column, `tested_effect` character vector indicating term differential abundance model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named `fixed_effect` levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential abundance model represent difference cluster abundances samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `daa_results` list tibbles entry gives differential abundance results tested_effect. Within entry `daa_results`, find several columns including following: * `p_val`, p-value associated tested effect input cluster * `p_adj`, multiple-comparison adjusted p-value (using p.adjust function) * values associated underlying method used perform differential abundance analysis (log-fold change cluster abundance levels compared). details, see glmFit, voom, topTable, testDA_GLMM.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"three methods based generalized linear mixed models (\"glmm\"), edgeR (\"edgeR\"), voom (\"voom\"). \"glmm\" \"voom\" methods can model fixed effects random effects, \"edgeR\" method can model fixed effects.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using generalized linear mixed-models. Users specify columns represent sample, cluster, fixed effect, random effect information, (mixed) binomial regression model fit using either glmer glm.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"","code":"tof_analyze_abundance_glmm( tof_tibble, sample_col, cluster_col, fixed_effect_cols, random_effect_cols, min_cells = 3, min_samples = 5, alpha = 0.05 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential abundance analysis. Supports tidyselect helpers. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Unquoted column names representing columns `tof_tibble` used model random effects differential abundance analysis. Supports tidyselection. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without many samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"nested tibble two columns: `tested_effect` `daa_results`. first column, `tested_effect`, character vector indicating term differential abundance model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential abundance model represent difference cluster abundances samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `daa_results`, list tibbles entry gives differential abundance results tested_effect. Within entry `daa_results`, find `p_value`, p-value associated tested effect input cluster; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential abundance analysis (log-fold change cluster abundance levels compared).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using simple t-tests. Users specify columns represent sample, cluster, effect information, either paired unpaired t-test (one per cluster) used detect significant differences sample types.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"","code":"tof_analyze_abundance_ttest( tof_tibble, cluster_col, effect_col, group_cols, test_type = c(\"unpaired\", \"paired\"), min_cells = 3, min_samples = 5, alpha = 0.05, quiet = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. effect_col Unquoted column name representing column `tof_tibble` used break samples groups t-test. 2 unique values. group_cols Unquoted names columns `effect_col` used group cells independent observations. Fills similar role `sample_col` `tof_analyze_abundance_*` functions. example, experiment involves analyzing samples taken multiple patients two timepoints (`effect_col = timepoint`), group_cols name column representing patient IDs. test_type string indicating whether t-test \"unpaired\" (default) \"paired\". min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. quiet boolean value indicating whether warnings printed. Defaults `TRUE`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"tibble 7 columns: {cluster_col} name/ID cluster tested. entry column match unique value input {cluster_col}. t t-statistic computed cluster. df degrees freedom used t-test cluster. p_val (unadjusted) p-value t-test cluster. p_adj p.adjust-adjusted p-value t-test cluster. significant character vector \"*\" clusters p_adj < alpha \"\" otherwise. mean_diff unpaired t-test, difference average proportions cluster two levels `effect_col`. paired t-test, average difference proportions cluster two levels `effect_col` within given patient. mean_fc unpaired t-test, ratio average proportions cluster two levels `effect_col`. paired t-test, average ratio proportions cluster two levels `effect_col` within given patient. 0.001 added denominator ratio avoid divide--zero errors. \"levels\" attribute result indicates order different levels `effect_col` considered. `mean_diff` value row output computed subtracting second level first level, `mean_fc` value row computed dividing first level second level.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using one three methods (\"diffcyt\", \"glmm\", \"ttest\"). wraps members `tof_analyze_expression_*` function family: tof_analyze_expression_diffcyt, tof_analyze_expression_lmm, tof_analyze_expression_ttest.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"","code":"tof_analyze_expression(tof_tibble, method = c(\"diffcyt\", \"glmm\", \"ttest\"), ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"tof_tibble `tof_tbl` `tibble`. method string indicating statistical method used. Valid values include \"diffcyt\", \"lmm\", \"ttest\". ... Additional arguments pass onto `tof_analyze_expression_*` function family member corresponding chosen method.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"tibble nested tibble containing differential abundance results chosen method. See tof_analyze_expression_diffcyt, tof_analyze_expression_lmm, tof_analyze_expression_ttest details.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using one two methods implemented diffcyt package differential discovery analysis high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"","code":"tof_analyze_expression_diffcyt( tof_tibble, sample_col, cluster_col, marker_cols = where(tof_is_numeric), fixed_effect_cols, random_effect_cols, diffcyt_method = c(\"lmm\", \"limma\"), include_observation_level_random_effects = FALSE, min_cells = 3, min_samples = 5, alpha = 0.05, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) tested differential expression levels `fixed_effect_cols`. Defaults numeric (integer double) columns. Supports tidyselect helpers. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential expression analysis. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Unquoted column names representing columns `tof_tibble` used model random effects differential expression analysis. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without many samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. diffcyt_method string indicating diffcyt method used differential expression analysis. Valid methods include \"lmm\" (default) \"limma\". include_observation_level_random_effects boolean value indicating \"observation-level random effects\" (OLREs) included random effect terms \"lmm\" differential expression model. details OLREs , see diffcyt paper. Defaults FALSE. min_cells integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. ... Optional additional arguments pass --hood diffcyt function used perform differential expression analysis. See testDS_LMM testDS_limma details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"nested tibble two columns: `tested_effect` `dea_results`. first column, `tested_effect` character vector indicating term differential expression model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential expression model represent difference cluster median expression values marker samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `dea_results` list tibbles entry gives differential expression results tested_effect. Within entry `dea_results`, find `p_val`, p-value associated tested effect input cluster/marker pair; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential expression analysis (log-fold change clusters' median marker expression values conditions compared). tibble `dea_results` also two columns representing cluster marker corresponding p-value row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"two methods based linear mixed models (\"lmm\") limma (\"limma\"). \"lmm\" \"limma\" methods can model fixed effects random effects.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using linear mixed-models. Users specify columns represent sample, cluster, marker, fixed effect, random effect information, (mixed) linear regression model fit using either lmer glm.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"","code":"tof_analyze_expression_lmm( tof_tibble, sample_col, cluster_col, marker_cols = where(tof_is_numeric), fixed_effect_cols, random_effect_cols, central_tendency_function = median, min_cells = 3, min_samples = 5, alpha = 0.05 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) included differential discovery analysis. Defaults numeric (integer double) columns. Supports tidyselection. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential expression analysis. Supports tidyselection. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Optional. Unquoted column names representing columns `tof_tibble` used model random effects differential expression analysis. Supports tidyselection. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. analyses include random effects. central_tendency_function function used calculate measurement central tendency cluster/marker pair (used dependent variable linear model). Defaults median. min_cells integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"nested tibble two columns: `tested_effect` `dea_results`. first column, `tested_effect` character vector indicating term differential expression model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential expression model represent difference cluster median expression values marker samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `dea_results` list tibbles entry gives differential expression results tested_effect. Within entry `daa_results`, find `p_val`, p-value associated tested effect input cluster/marker pair; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential expression analysis (log-fold change clusters' median marker expression values levels compared).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"Specifically, one linear model fit cluster/marker pair. cluster/marker pair, user-supplied measurement central tendency (`central_tendency_function`), mean median, calculated across cells cluster sample--sample basis. , central tendency value used dependent variable linear model `fixed_effect_cols` fixed effects predictors `random_effect_cols` random effects predictors. models (one per cluster/marker pair) fit, p-values coefficient model multiple-comparisons adjusted using p.adjust function.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using simple t-tests. Specifically, either unpaired paired t-test compare samples' marker expression distributions (two conditions) within cluster using user-specified summary function (.e. mean median). One t-test conducted per cluster/marker pair significant differences sample types detected multiple-hypothesis correction.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"","code":"tof_analyze_expression_ttest( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), effect_col, group_cols, test_type = c(\"unpaired\", \"paired\"), summary_function = mean, min_cells = 3, min_samples = 5, alpha = 0.05, quiet = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) tested differential expression levels `effect_col`. Defaults numeric (integer double) columns. Supports tidyselect helpers. effect_col Unquoted column name representing column `tof_tibble` used break samples groups t-test. 2 unique values. group_cols Unquoted names columns `effect_col` used group cells independent observations. Fills similar role `sample_col` `tof_analyze_abundance_*` functions. example, experiment involves analyzing samples taken multiple patients two timepoints (`effect_col = timepoint`), group_cols name column representing patient IDs. test_type string indicating whether t-test \"unpaired\" (default) \"paired\". summary_function vector-valued function used summarize distribution marker cluster (within sample, grouped `group_cols`). Defaults `mean`. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. quiet boolean value indicating whether warnings printed. Defaults `TRUE`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"tibble 7 columns: {cluster_col} name/ID cluster cluster/marker pair tested. entry column match unique value input {cluster_col}. marker name marker cluster/marker pair tested. t t-statistic computed cluster. df degrees freedom used t-test cluster. p_val (unadjusted) p-value t-test cluster. p_adj p.adjust-adjusted p-value t-test cluster. significant character vector \"*\" clusters p_adj < alpha \"\" otherwise. mean_diff unpaired t-test, difference average proportions cluster two levels `effect_col`. paired t-test, average difference proportions cluster two levels `effect_col` within given patient. mean_fc unpaired t-test, ratio average proportions cluster two levels `effect_col`. paired t-test, average ratio proportions cluster two levels `effect_col` within given patient. 0.001 added denominator ratio avoid divide--zero errors. \"levels\" attribute result indicates order different levels `effect_col` considered. `mean_diff` value row output computed subtracting second level first level, `mean_fc` value row computed dividing first level second level.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"function adds additional column `tibble` `tof_tbl` allow users incorporate manual cell type labels clusters identified using unsupervised algorithms.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"","code":"tof_annotate_clusters(tof_tibble, cluster_col, annotations)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` contains ids unsupervised cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. annotations data structure indicating annotate cluster id `cluster_col`. `annotations` can provided data.frame two columns (first name `cluster_col` contain unique cluster id; second can name contain character vector indicating manual annotation matched cluster id first column). `annotations` can also provided named character vector; case, entry `annotations` unique cluster id, names entry corresponding manual cluster annotation. See examples.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"`tof_tbl` number rows `tof_tibble` one additional column containing manual cluster annotations cell (character vector). `annotations` provided data.frame, new column name column containing cluster annotations `annotations`. `annotations` provided named character vector, new column named `{cluster_col}_annotation`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # using named character vector sim_data |> tof_annotate_clusters( cluster_col = cluster_id, annotations = c(\"macrophage\" = \"a\", \"dendritic cell\" = \"b\") ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id cluster_id_annotation #> #> 1 -1.40 -0.337 -0.166 1.12 a macrophage #> 2 0.255 -0.216 0.120 0.400 a macrophage #> 3 -2.44 0.621 -0.662 -0.985 a macrophage #> 4 -0.00557 -1.28 -0.531 -0.503 a macrophage #> 5 0.622 -1.30 -0.301 0.987 a macrophage #> 6 1.15 -0.377 -0.602 2.19 a macrophage #> 7 -1.82 0.104 -0.318 -0.165 a macrophage #> 8 -0.247 -0.704 0.308 -0.686 a macrophage #> 9 -0.244 1.50 0.799 0.941 a macrophage #> 10 -0.283 -0.303 1.75 -0.164 a macrophage #> # ℹ 990 more rows # using two-column data.frame annotation_data_frame <- data.frame( cluster_id = c(\"a\", \"b\"), cluster_annotation = c(\"macrophage\", \"dendritic cell\") ) sim_data |> tof_annotate_clusters( cluster_col = cluster_id, annotations = annotation_data_frame ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id cluster_annotation #> #> 1 -1.40 -0.337 -0.166 1.12 a macrophage #> 2 0.255 -0.216 0.120 0.400 a macrophage #> 3 -2.44 0.621 -0.662 -0.985 a macrophage #> 4 -0.00557 -1.28 -0.531 -0.503 a macrophage #> 5 0.622 -1.30 -0.301 0.987 a macrophage #> 6 1.15 -0.377 -0.602 2.19 a macrophage #> 7 -1.82 0.104 -0.318 -0.165 a macrophage #> 8 -0.247 -0.704 0.308 -0.686 a macrophage #> 9 -0.244 1.50 0.799 0.941 a macrophage #> 10 -0.283 -0.303 1.75 -0.164 a macrophage #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"Perform developmental clustering CyTOF data using pre-fit classifier","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"","code":"tof_apply_classifier( cancer_tibble = NULL, classifier_fit = NULL, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1, parallel_vars )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"cancer_tibble `tibble` `tof_tibble` containing cells classified nearest healthy subpopulation (generally cancer cells). classifier_fit nested `tibble` produced `tof_build_classifier` row represents healthy cell subpopulation cells `cancer_tibble` classified using minimum distance. distance_function string indicating distance function used perform classification. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). parallel_vars Unquoted column names indicating columns `cancer_tibble` use breaking data order parallelize classification. Defaults NULL. Supports tidyselect helpers.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"tibble `nrow(cancer_tibble)` rows `nrow(classifier_fit) + 1` columns. row represents cell `cancer_tibble`, `nrow(classifier_fit)` columns represent distance cell healthy subpopulations' cluster centroids. final column represents cluster id healthy subpopulation minimum distance cell represented row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"Detect low-expression (.e. potentially failed) channels high-dimensional cytometry data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"","code":"tof_assess_channels( tof_tibble, channel_cols = where(tof_is_numeric), negative_threshold = asinh(10/5), negative_proportion_flag = 0.95 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"tof_tibble `tof_tbl` `tibble`. channel_cols vector unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default analyze numeric columns. negative_threshold scalar indicating threshold measurement considered negative. Defaults hyperbolic arcsine transformation 10 counts. negative_proportion_flag scalar 0 1 indicating proportion cells tof_tibble need `negative_threshold` given marker order marker flagged. Defaults 0.95.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"tibble 3 columns number rows equal number columns `tof_tibble` chosen `channel_cols`. three columns \"channel\", character vector channel names, \"negative_proportion\", numeric vector values 0 1 indicating many cells `tof_tibble` `negative_threshold` channel, `flagged_channel`, boolean vector indicating whether channel flagged potentially failed (TRUE means channel large number cells `negative_threshold`).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"","code":"# simulate some data sim_data <- data.frame( cd4 = rnorm(n = 100, mean = 5, sd = 0.5), cd8 = rnorm(n = 100, mean = 0, sd = 0.1), cd33 = rnorm(n = 100, mean = 10, sd = 0.1) ) tof_assess_channels(tof_tibble = sim_data) #> # A tibble: 3 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE #> 3 cd33 0 FALSE tof_assess_channels(tof_tibble = sim_data, channel_cols = c(cd4, cd8)) #> # A tibble: 2 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE tof_assess_channels(tof_tibble = sim_data, negative_threshold = 2) #> # A tibble: 3 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE #> 3 cd33 0 FALSE"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"function evaluates result clustering procedure comparing mahalanobis distance cell centroid cluster assigned among cells given cluster. cells mahalanobis-distance z-score user-specified threshold flagged potentially anomalous. Note z-score calculated using modified formula minimize effect outliers (Z = x - median(x) / mad(x)).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"","code":"tof_assess_clusters_distance( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), z_threshold = 3, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. z_threshold scalar indicating distance z-score threshold cell considered anomalous. Defaults 3. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"augment = FALSE (default), tibble 3 columns: \".mahalanobis_distance\" (mahalanobis distance cell centroid tits assigned cluster), \"z_score\" (modified z-score cell's mahalanobis distance relative cells dataset), \"flagged_cell\" (boolean indicating whether cell flagged z-score z_threshold). augment = TRUE, 3 columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"","code":"# simulate data sim_data_inner <- dplyr::tibble( cd45 = c(rnorm(n = 600), rnorm(n = 500, mean = -4)), cd38 = c( rnorm(n = 100, sd = 0.5), rnorm(n = 500, mean = -3), rnorm(n = 500, mean = 8) ), cd34 = c( rnorm(n = 100, sd = 0.2, mean = -10), rnorm(n = 500, mean = 4), rnorm(n = 500, mean = 60) ), cd19 = c(rnorm(n = 100, sd = 0.3, mean = 10), rnorm(n = 1000)), cluster_id = c(rep(\"a\", 100), rep(\"b\", 500), rep(\"c\", 500)), dataset = \"inner\" ) sim_data_outer <- dplyr::tibble( cd45 = c(rnorm(n = 10), rnorm(50, mean = 3), rnorm(n = 50, mean = -12)), cd38 = c( rnorm(n = 10, sd = 0.5), rnorm(n = 50, mean = -10), rnorm(n = 50, mean = 10) ), cd34 = c( rnorm(n = 10, sd = 0.2, mean = -15), rnorm(n = 50, mean = 15), rnorm(n = 50, mean = 70) ), cd19 = c(rnorm(n = 10, sd = 0.3, mean = 19), rnorm(n = 100)), cluster_id = c(rep(\"a\", 10), rep(\"b\", 50), rep(\"c\", 50)), dataset = \"outer\" ) sim_data <- rbind(sim_data_inner, sim_data_outer) # detect anomalous cells (in this case, the \"outer\" dataset contains small # clusters that get lumped into the larger clusters in the \"inner\" dataset) z_result <- sim_data |> tof_assess_clusters_distance(cluster_col = cluster_id, z_threshold = 2.5)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"function evaluates result clustering procedure calculating mahalanobis distance cell centroids clusters dataset finding shannon entropy resulting vector distances. cells entropy threshold user-specified threshold flagged potentially anomalous. Entropy minimized (0) cell close one (small number) clusters, far rest . cell close multiple cluster centroids (.e. ambiguous phenotype), entropy large.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"","code":"tof_assess_clusters_entropy( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), entropy_threshold, entropy_quantile = 0.9, num_closest_clusters, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. entropy_threshold scalar indicating entropy threshold cell considered anomalous. unspecified, threshold computed using `entropy_quantile` (see ). (Note: Entropy often 0 1, can larger many classes/clusters). entropy_quantile scalar 0 1 indicating entropy quantile cell considered anomalous. Defaults 0.9, means cells entropy 90th percentile flagged. Ignored entropy_threshold specified directly. num_closest_clusters integer indicating many cell's closest cluster centroids mahalanobis distance included entropy calculation. Playing argument allow ignore distances clusters far away cell (thus may distort result, many distant centroids large distances can artificially inflate cells' entropy value; said, rarely issue empirically). Defaults clusters tof_tibble. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"augment = FALSE (default), tibble 2 + NUM_CLUSTERS columns. NUM_CLUSTERS number unique clusters cluster_col. Two columns \"entropy\" (entropy value cell) \"flagged_cell\" (boolean value indicating cell entropy value entropy_threshold). NUM_CLUSTERS columns contain mahalanobis distances cell clusters cluster_col (named \".mahalanobis_{cluster_name}\"). augment = TRUE, 2 + NUM_CLUSTERS columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"","code":"# simulate data sim_data <- dplyr::tibble( cd45 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd38 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd34 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd19 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) # imagine a \"reference\" dataset in which \"cluster a\" isn't present sim_data_reference <- sim_data |> dplyr::filter(cluster_id %in% c(\"b\", \"c\")) # if we cluster into the reference dataset, we will force all cells in # cluster a into a population where they don't fit very well sim_data <- sim_data |> tof_cluster( healthy_tibble = sim_data_reference, healthy_label_col = cluster_id, method = \"ddpr\" ) # we can evaluate the clustering quality by calculating by the entropy of the # mahalanobis distance vector for each cell to all cluster centroids entropy_result <- sim_data |> tof_assess_clusters_entropy( cluster_col = .mahalanobis_cluster, marker_cols = starts_with(\"cd\"), entropy_quantile = 0.8, augment = TRUE ) # most cells in \"cluster a\" are flagged, and few cells in the other clusters are flagged_cluster_proportions <- entropy_result |> dplyr::group_by(cluster_id) |> dplyr::summarize( prop_flagged = mean(flagged_cell) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"function evaluates result clustering procedure finding cell's K nearest neighbors, determining cluster majority assigned , checking matches cell's cluster assignment. cluster assignment majority cell's nearest neighbors match cell's cluster assignment, cell flagged potentially anomalous.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"","code":"tof_assess_clusters_knn( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), num_neighbors = min(10, nrow(tof_tibble)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. num_neighbors integer indicating many neighbors found nearest neighbor calculation. distance_function string indicating distance function used perform k nearest neighbor calculation. Options \"euclidean\" (default) \"cosine\". augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"augment = FALSE (default), tibble 2 columns: \".knn_cluster\" (character vector indicating cluster received majority vote cell's k nearest neighbors) \"flagged_cell\" (boolean value indicating cell's cluster assignment matched majority vote (TRUE) (FALSE)). augment = TRUE, 2 columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"","code":"sim_data <- dplyr::tibble( cd45 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd38 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd34 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd19 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) knn_result <- sim_data |> tof_assess_clusters_knn( cluster_col = cluster_id, num_neighbors = 10 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"function performs simplified version flowAI's statistical test detect time periods abnormal flow rates course flow cytometry experiment. Briefly, relative flow rates timestep throughout data acquisition calculated (see tof_calculate_flow_rate), outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"","code":"tof_assess_flow_rate( tof_tibble, time_col, group_cols, num_timesteps = nrow(tof_tibble)/1000, alpha_threshold = 0.01, visualize = FALSE, ..., augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. group_cols Optional. Unquoted column names indicating columns used group cells analysis. Flow rate calculation performed independently within group. Supports tidyselect helpers. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection. alpha_threshold scalar 0 1 indicating two-tailed significance level draw outlier thresholds t-distribution `num_timesteps` - 1 degrees freedom. Defaults 0.01. visualize boolean value indicating plot generated visualize timestep's relative flow rate (group) instead returning tibble directly. Defaults FALSE. ... Optional additional arguments pass facet_wrap. Ignored visualize = FALSE. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"tibble number rows `tof_tibble`. augment = FALSE (default), 3 columns: \"{time_col}\" (column `time_col`), \"timestep\" (numeric timestep cell assigned based value `time_col`), \"flagged_window\" (boolean vector indicating cell collecting timestep flagged high low flow rate). augment = TRUE, 3 columns column-bound `tof_tibble` return augmented version input dataset. (Note case, time_col duplicated). visualize = TRUE, ggplot object returned instead tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"","code":"set.seed(1000L) sim_data <- data.frame( cd4 = rnorm(n = 1000, mean = 5, sd = 0.5), cd8 = rnorm(n = 1000, mean = 0, sd = 0.1), cd33 = rnorm(n = 1000, mean = 10, sd = 0.1), file_name = c(rep(\"a\", times = 500), rep(\"b\", times = 500)), time = c( sample(1:100, size = 200, replace = TRUE), sample(100:400, size = 300, replace = TRUE), sample(1:150, size = 400, replace = TRUE), sample(1:500, size = 100, replace = TRUE) ) ) sim_data |> tof_assess_flow_rate( time_col = time, num_timesteps = 20, visualize = TRUE ) sim_data |> tof_assess_flow_rate( time_col = time, group_cols = file_name, num_timesteps = 20, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"function performs simplified version flowAI's statistical test detect time periods abnormal flow rates course flow cytometry experiment. Briefly, relative flow rates timestep throughout data acquisition calculated (see tof_calculate_flow_rate), outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"","code":"tof_assess_flow_rate_tibble( tof_tibble, time_col, num_timesteps = nrow(tof_tibble)/1000, alpha_threshold = 0.01, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection. alpha_threshold scalar 0 1 indicating two-tailed significance level draw outlier thresholds t-distribution `num_timesteps` - 1 degrees freedom. Defaults 0.01. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"tibble number rows `tof_tibble`. augment = FALSE (default), 3 columns: \"{time_col}\" (column `time_col`), \"timestep\" (numeric timestep cell assigned based value `time_col`), \"flagged_window\" (boolean vector indicating cell collecting timestep flagged high low flow rate). augment = TRUE, 3 columns column-bound `tof_tibble` return augmented version input dataset. (Note case, time_col duplicated).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"","code":"set.seed(1000L) sim_data <- data.frame( cd4 = rnorm(n = 1000, mean = 5, sd = 0.5), cd8 = rnorm(n = 1000, mean = 0, sd = 0.1), cd33 = rnorm(n = 1000, mean = 10, sd = 0.1), time = c( sample(1:100, size = 200, replace = TRUE), sample(100:400, size = 300, replace = TRUE), sample(1:150, size = 400, replace = TRUE), sample(1:500, size = 100, replace = TRUE) ) ) sim_data |> tof_assess_flow_rate( time_col = time, num_timesteps = 20, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a trained elastic net model — tof_assess_model","title":"Assess a trained elastic net model — tof_assess_model","text":"function assesses trained `tof_model`'s performance new data computing model type-specific performance measurements. new data provided, performance metrics training data provided.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a trained elastic net model — tof_assess_model","text":"","code":"tof_assess_model(tof_model, new_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a trained elastic net model — tof_assess_model","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations used evaluate `tof_model`'s performance. new_data provided, model evaluation performed using training data used fit model. Alternatively, string \"tuning\" can provided access model's performance metrics (resampled) model tuning process.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a trained elastic net model — tof_assess_model","text":"list performance metrics whose components depend model type: \"model_metrics\" tibble two columns (\"metric\" \"value\") containing standard performance metrics model type. linear models, \"mse\" (mean squared error predictions) \"mae\" (mean absolute error predictions). two-class models, \"roc_auc\" (area Receiver-Operating Curve classification), \"misclassification error\" (proportion misclassified observations), \"binomial_deviance\" (see deviance.glmnet), \"mse\" (mean squared error logit function), \"mae\" (mean absolute error logit function). multiclass models, \"roc_auc\" (area Receiver-Operating Curve classification using Hand-Till generalization ROC AUC multiclass models roc_auc), \"misclassification error\" (proportion misclassified observations), \"multinomial_deviance\" (see deviance.glmnet), \"mse\" \"mae\" . survival models, \"concordance_index\" (Harrel's C index; see deviance.glmnet) \"partial_likelihood_deviance\" (see deviance.glmnet). \"roc_curve\" Reported \"two-class\" \"multiclass\" models. , tibble provided reporting true-positive rate (tpr) false-positive rate (fpr) threshold classification use plotting receiver-operating curve. \"multiclass\" models, \".level\" column allows separating values roc_curve one ROC can plotted class. \"confusion_matrix\" Reported \"two-class\" \"multiclass\" models. , tibble provided reporting \"confusion matrix\" classification long-format. \"survival_curves\" Reported \"survival\" models. tibble indicating patient's probability survival (1 - probability(event)) timepoint dataset whether sample placed \"high\" \"low\" risk group according predicted relative risk (tof_model's optimal relative_risk cutoff training dataset).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a trained elastic net model — tof_assess_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # assess the model on new data tof_assess_model(tof_model = regression_model, new_data = new_tibble) #> $model_metrics #> # A tibble: 2 × 2 #> metric value #> #> 1 mse 0.795 #> 2 mae 0.788 #>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"Compute trained elastic net model's performance metrics using new_data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"","code":"tof_assess_model_new_data(tof_model, new_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations used evaluate `tof_model`'s performance.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"list performance metrics whose components depend model type.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":null,"dir":"Reference","previous_headings":"","what":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"Access trained elastic net model's performance metrics using tuning data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"","code":"tof_assess_model_tuning(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"tof_model `tof_model` trained using tof_train_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"list performance metrics whose components depend model type.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"function performs quantile normalization high-dimensional cytometry data tidy format using either linear rescaling quantile normalization. channel specified `channel_cols` batch corrected, `group_cols` can used break cells groups batch correction performed separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"","code":"tof_batch_correct( tof_tibble, channel_cols, group_cols, augment = TRUE, method = c(\"rescale\", \"quantile\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted. method string indicating batch correction method used. Valid options \"rescale\" linear scaling (default) \"quantile\" quantile normalization using normalize.quantiles.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"function performs quantile normalization high-dimensional cytometry data tidy format using normalize.quantiles. Optionally, groups can specified normalized separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"","code":"tof_batch_correct_quantile( tof_tibble, channel_cols, group_cols, augment = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"function performs quantile normalization high-dimensional cytometry data tidy format using normalize.quantiles.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"","code":"tof_batch_correct_quantile_tibble(tof_tibble, channel_cols, augment = TRUE)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"function performs quantile normalization high-dimensional cytometry data tidy format using linear rescaling. channel specified `channel_cols` rescaled maximum value 1 minimum value 0. `group_cols` specifies columns used break cells groups rescaling performed separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"","code":"tof_batch_correct_rescale(tof_tibble, channel_cols, group_cols, augment = TRUE)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"function takes `tibble` `tof_tibble` storing healthy cell measurements rows vector (`healthy_cell_labels`) representing cell subpopulation cell belongs. uses values calculate several values required perform \"developmental classification\" described paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"","code":"tof_build_classifier( healthy_tibble = NULL, healthy_cell_labels = NULL, classifier_markers = where(tof_is_numeric), verbose = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"healthy_tibble `tibble` `tof_tibble` containing cells healthy control samples (.e. disease samples). healthy_cell_labels character integer vector length `nrow(healthy_tibble)`. entry vector represent cell subpopulation label (cluster id) corresponding row `healthy_tibble`. classifier_markers Unquoted column names indicating columns `healthy_tibble` use developmental classification. Defaults numeric columns `healthy_tibble`. Supports tidyselect helpers. verbose boolean value indicating updates printed console classification. Defaults FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"tibble three columns: population (id healthy cell population), centroid (centroid vector cell population), covariance_matrix (covariance matrix cell population)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"Calculate relative flow rates different timepoints throughout flow mass cytometry run.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"","code":"tof_calculate_flow_rate( tof_tibble, time_col, num_timesteps = nrow(tof_tibble)/1000 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"tibble 3 columns num_timesteps rows. row represent single timestep (error thrown `num_timesteps` larger number rows `tof_tibble`). three columns follows: \"timestep\", numeric vector indicating timestep represented given row; \"time_window\", factor showing interval `time_col` \"timestep\" defined; \"num_cells\", number cells collected timestep.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"","code":"# simulate some data sim_data <- data.frame( cd4 = rnorm(n = 100, mean = 5, sd = 0.5), cd8 = rnorm(n = 100, mean = 0, sd = 0.1), cd33 = rnorm(n = 100, mean = 10, sd = 0.1), time = sample(1:300, size = 100) ) tof_calculate_flow_rate(tof_tibble = sim_data, time_col = time, num_timesteps = 20L) #> timestep time_window num_cells #> 1 1 (6.71,21.6] 4 #> 2 2 (21.6,36.2] 6 #> 3 3 (36.2,50.8] 7 #> 4 4 (50.8,65.4] 7 #> 5 5 (65.4,80] 5 #> 6 6 (80,94.6] 4 #> 7 7 (94.6,109] 5 #> 8 8 (109,124] 4 #> 9 9 (124,138] 1 #> 10 10 (138,153] 7 #> 11 11 (153,168] 5 #> 12 12 (168,182] 2 #> 13 13 (182,197] 7 #> 14 14 (197,211] 7 #> 15 15 (211,226] 5 #> 16 16 (226,241] 2 #> 17 17 (241,255] 4 #> 18 18 (255,270] 5 #> 19 19 (270,284] 7 #> 20 20 (284,299] 6"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Check argument specifications for a glmnet model. — tof_check_model_args","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"Check argument specifications glmnet model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"","code":"tof_check_model_args( split_data, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), best_model_type = c(\"best\", \"best with sparsity\"), response_col, time_col, event_col )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. Alternatively, unsplit tbl_df can provided, though recommended. model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. best_model_type Currently unused. response_col Unquoted column name indicating column data contained `split_data` used outcome \"two-class\", \"multiclass\", \"linear\" elastic net model. Must factor \"two-class\" \"multiclass\" models must numeric \"linear\" models. Ignored `model_type` \"survival\". time_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must numeric. Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". event_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must binary column - values either 0 1 (1 indicating adverse event) FALSE TRUE (TRUE indicating adverse event). Ignored `model_type` \"two-class\", \"multiclass\", \"linear\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"tibble. arguments specified correctly, tibble can used create recipe preprocessing.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":null,"dir":"Reference","previous_headings":"","what":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"function uses specified distance metric classify cell data.frame matrix (`cancer_data`) one `nrow(classifier_fit)` subpopulations based minimum distance, described paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"","code":"tof_classify_cells( classifier_fit, cancer_data, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"classifier_fit tibble produced tof_build_classifier. cancer_data matrix row corresponds cell column corresponds measured CyTOF antigen. distance_function string indicating three distance functions used calculate distances row `cancer_data` healthy developmental subpopulations corresponding row `classifier_fit`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"data.frame column represents distance cell input data healthy subpopulation cells classified .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"Rename glmnet's default model evaluation metrics make interpretable","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"","code":"tof_clean_metric_names(metric_tibble, model_type)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"metric_tibble tibble column represents glmnet model evaluation metric default name. model_type string indicating type glmnet model trained.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"tibble column represents glmnet model evaluation metric \"cleaned\" name.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster high-dimensional cytometry data. — tof_cluster","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"function wrapper around tidytof's tof_cluster_* function family. performs clustering high-dimensional cytometry data using user-specified method (5 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"","code":"tof_cluster( tof_tibble, cluster_cols = where(tof_is_numeric), group_cols = NULL, ..., augment = TRUE, method )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells clustering. Clustering performed group independently. Supports tidyselect helpers. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 500), cd38 = rnorm(n = 500), cd34 = rnorm(n = 500), cd19 = rnorm(n = 500) ) tof_cluster(tof_tibble = sim_data, method = \"kmeans\") #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 .kmeans_cluster #> #> 1 1.33 -0.447 1.50 0.436 11 #> 2 -1.20 -0.481 -0.391 -1.54 9 #> 3 -0.541 0.666 -1.68 -0.986 16 #> 4 -1.22 1.32 0.689 -0.791 10 #> 5 0.639 0.519 -1.32 -0.204 18 #> 6 -0.239 0.397 -0.780 0.372 1 #> 7 0.651 0.997 -0.665 0.805 18 #> 8 0.788 1.26 0.584 -0.953 19 #> 9 -0.344 0.388 -0.407 -0.442 13 #> 10 0.120 0.885 -2.26 0.583 17 #> # ℹ 490 more rows tof_cluster(tof_tibble = sim_data, method = \"phenograph\") #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 .phenograph_cluster #> #> 1 1.33 -0.447 1.50 0.436 2 #> 2 -1.20 -0.481 -0.391 -1.54 1 #> 3 -0.541 0.666 -1.68 -0.986 1 #> 4 -1.22 1.32 0.689 -0.791 3 #> 5 0.639 0.519 -1.32 -0.204 5 #> 6 -0.239 0.397 -0.780 0.372 5 #> 7 0.651 0.997 -0.665 0.805 4 #> 8 0.788 1.26 0.584 -0.953 8 #> 9 -0.344 0.388 -0.407 -0.442 1 #> 10 0.120 0.885 -2.26 0.583 5 #> # ℹ 490 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"function performs distance-based clustering high-dimensional cytometry data sorting cancer cells (passed function `tof_tibble`) phenotypically similar healthy cell subpopulation (passed function using `healthy_tibble`). details algorithm used perform clustering, see paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"","code":"tof_cluster_ddpr( tof_tibble, healthy_tibble, healthy_label_col, cluster_cols = where(tof_is_numeric), distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1L, parallel_cols, return_distances = FALSE, verbose = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"tof_tibble `tibble` `tof_tbl` containing cells classified nearest healthy subpopulation (generally cancer cells). healthy_tibble `tibble` `tof_tibble` containing cells healthy control samples (.e. disease samples). healthy_label_col unquoted column name indicating column `healthy_tibble` contains subpopulation label (cluster id) cell `healthy_tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing DDPR clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. distance_function string indicating distance function used perform classification. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). parallel_cols Optional. Unquoted column names indicating columns `tof_tibble` use breaking data order parallelize classification using `foreach` `doParallel` backend. Supports tidyselect helpers. return_distances boolean value indicating whether returned result include one column, cluster ids corresponding row `tof_tibble` (return_distances = FALSE, default), returned result include additional columns representing distance row `tof_tibble` healthy subpopulation centroids (return_distances = TRUE). verbose boolean value indicating whether progress updates printed developmental classification. Default FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"`return_distances = FALSE`, tibble one column named `.{distance_function}_cluster`, character vector length `nrow(tof_tibble)` indicating id developmental cluster cell (.e. row) `tof_tibble` assigned. `return_distances = TRUE`, tibble `nrow(tof_tibble)` rows `nrow(classifier_fit) + 1` columns. row represents cell `tof_tibble`, `nrow(classifier_fit)` columns represent distance cell healthy subpopulations' cluster centroids. final column represents cluster id healthy subpopulation minimum distance cell represented row. `return_distances = FALSE`, tibble one column named `.{distance_function}_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id developmental cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) healthy_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) tof_cluster_ddpr( tof_tibble = sim_data, healthy_tibble = healthy_data, healthy_label_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .mahalanobis_cluster #> #> 1 b #> 2 b #> 3 b #> 4 a #> 5 b #> 6 b #> 7 b #> 8 a #> 9 a #> 10 b #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"function performs FlowSOM clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements. mostly convenient wrapper around SOM MetaClustering.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"","code":"tof_cluster_flowsom( tof_tibble = NULL, cluster_cols = where(tof_is_numeric), som_xdim = 10, som_ydim = 10, som_distance_function = c(\"euclidean\", \"manhattan\", \"chebyshev\", \"cosine\"), perform_metaclustering = TRUE, num_metaclusters = 20, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing flowSOM clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. som_xdim width grid used self-organizing map. total number clusters returned FlowSOM som_xdim * som_ydim, adjust value affect final number clusters. Defaults 10. som_ydim height grid used self-organizing map. total number clusters returned FlowSOM som_xdim * som_ydim, adjust value affect final number clusters. Defaults 10. som_distance_function distance function used self-organizing map calculations. Options \"euclidean\" (default), \"manhattan\", \"chebyshev\", \"cosine\". perform_metaclustering boolean value indicating metaclustering performed initial clustering result returned FlowSOM. Defaults TRUE. num_metaclusters integer indicating maximum number metaclusters returned metaclustering. Defaults 20. ... Optional additional parameters can passed BuildSOM function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"tibble one column named `.flowsom_cluster` `.flowsom_metacluster` depending value `perform_metaclustering`. column contain integer vector length `nrow(tof_tibble)` indicating id flowSOM cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"additional details FlowSOM algorithm, see paper.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) tof_cluster_flowsom(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 200 × 1 #> .flowsom_metacluster #> #> 1 10 #> 2 10 #> 3 8 #> 4 5 #> 5 10 #> 6 15 #> 7 6 #> 8 8 #> 9 15 #> 10 2 #> # ℹ 190 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"function wrapper around tidytof's tof_cluster_* function family provides low-level API clustering grouped data frames. subroutine tof_cluster called directly users.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"","code":"tof_cluster_grouped(tof_tibble, group_cols, ..., augment = TRUE, method)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"tof_tibble `tof_tbl` `tibble`. group_cols unquoted column name indicating columns used group cells clustering. Clustering performed group independently. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"function performs k-means clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements. mostly convenient wrapper around kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"","code":"tof_cluster_kmeans( tof_tibble, cluster_cols = where(tof_is_numeric), num_clusters = 20, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"tof_tibble `tof_tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing k-means clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_clusters integer indicating maximum number clusters returned. Defaults 20. ... Optional additional arguments can passed kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"tibble one column named `.kmeans_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id k-means cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_cluster_kmeans(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> #> 1 8 #> 2 4 #> 3 8 #> 4 1 #> 5 2 #> 6 15 #> 7 11 #> 8 18 #> 9 19 #> 10 20 #> # ℹ 990 more rows tof_cluster_kmeans(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> #> 1 2 #> 2 1 #> 3 3 #> 4 4 #> 5 19 #> 6 4 #> 7 5 #> 8 11 #> 9 2 #> 10 20 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"function performs PhenoGraph clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"","code":"tof_cluster_phenograph( tof_tibble, cluster_cols = where(tof_is_numeric), num_neighbors = 30, distance_function = c(\"euclidean\", \"cosine\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing PhenoGraph clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_neighbors integer indicating number neighbors use constructing PhenoGraph's k-nearest-neighbor graph. Smaller values emphasize local graph structure; larger values emphasize global graph structure (add time computation). Defaults 30. distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. ... Optional additional parameters can passed tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"tibble one column named `.phenograph_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id PhenoGraph cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"additional details Phenograph algorithm, see paper.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_cluster_phenograph(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> #> 1 8 #> 2 3 #> 3 6 #> 4 4 #> 5 4 #> 6 2 #> 7 2 #> 8 9 #> 9 1 #> 10 2 #> # ℹ 990 more rows tof_cluster_phenograph(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> #> 1 4 #> 2 6 #> 3 4 #> 4 11 #> 5 4 #> 6 9 #> 7 3 #> 8 1 #> 9 10 #> 10 9 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"function wrapper around tidytof's tof_cluster_* function family provides low-level API clustering ungrouped data frames. subroutine tof_cluster called directly users.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"","code":"tof_cluster_tibble(tof_tibble, ..., augment = TRUE, method)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"tof_tibble `tof_tbl` `tibble`. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"Compute Kaplan-Meier curve sample-level survival data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"","code":"tof_compute_km_curve(survival_curves)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"survival_curves tibble Kaplan-Meier curve computed. row must represent observation must two columns named \"time_to_event\" \"event\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"tibble 3 columns: time_to_event, survival_probability, is_censored (whether event censored timepoint).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"function finding cosine distance rows numeric matrix numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"","code":"tof_cosine_dist(matrix, vector)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"matrix numeric matrix. vector numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"numeric vector distances length `nrow(matrix)` ith entry represents cosine distance ith row `matrix` `vector`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"function creates regular hyperparameter search grid (form tibble) specifying search space two hyperparameters generalized linear model using glmnet package: regularization penalty term lasso/ridge regression mixture term.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"","code":"tof_create_grid( penalty_values, mixture_values, num_penalty_values = 5, num_mixture_values = 5 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"penalty_values numeric vector unique elastic net penalty values (\"lambda\") include hyperparameter grid. unspecified, regular grid `num_penalty_values` 10^(-10) 10^(0) used. mixture_values numeric vector elastic net mixture values (\"alpha\") include hyperparameter grid. unspecified, regular grid `num_mixture_values` 0 1 used. num_penalty_values Optional. `penalty_values` supplied, `num_penalty_values` (integer) can given specify many equally-spaced penalty values 10^(-10) 1 included hyperparameter grid. method used, regular grid always returned. Defaults 5. num_mixture_values Optional. `mixture_values` supplied, `num_mixture_values` (integer) can given specify many equally-spaced penalty values 0 (ridge regression) 1 (lasso) included hyperparameter grid. method used, regular grid always returned. Defaults 5.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"tibble two numeric columns: `penalty` `mixture`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"","code":"tof_create_grid() #> # A tibble: 25 × 2 #> penalty mixture #> #> 1 0.0000000001 0 #> 2 0.0000000001 0.25 #> 3 0.0000000001 0.5 #> 4 0.0000000001 0.75 #> 5 0.0000000001 1 #> 6 0.0000000316 0 #> 7 0.0000000316 0.25 #> 8 0.0000000316 0.5 #> 9 0.0000000316 0.75 #> 10 0.0000000316 1 #> # ℹ 15 more rows tof_create_grid(num_penalty_values = 10, num_mixture_values = 5) #> # A tibble: 50 × 2 #> penalty mixture #> #> 1 0.0000000001 0 #> 2 0.0000000001 0.25 #> 3 0.0000000001 0.5 #> 4 0.0000000001 0.75 #> 5 0.0000000001 1 #> 6 0.00000000129 0 #> 7 0.00000000129 0.25 #> 8 0.00000000129 0.5 #> 9 0.00000000129 0.75 #> 10 0.00000000129 1 #> # ℹ 40 more rows tof_create_grid(penalty_values = c(0.01, 0.1, 0.5)) #> # A tibble: 15 × 2 #> penalty mixture #> #> 1 0.01 0 #> 2 0.01 0.25 #> 3 0.01 0.5 #> 4 0.01 0.75 #> 5 0.01 1 #> 6 0.1 0 #> 7 0.1 0.25 #> 8 0.1 0.5 #> 9 0.1 0.75 #> 10 0.1 1 #> 11 0.5 0 #> 12 0.5 0.25 #> 13 0.5 0.5 #> 14 0.5 0.75 #> 15 0.5 1"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"Create recipe preprocessing sample-level cytometry data elastic net model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"","code":"tof_create_recipe( feature_tibble, predictor_cols, outcome_cols, standardize_predictors = TRUE, remove_zv_predictors = FALSE, impute_missing_predictors = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"feature_tibble tibble row represents sample- patient- level observation, produced tof_extract_features. predictor_cols Unquoted column names indicating columns data contained `feature_tibble` used predictors elastic net model. Supports tidyselect helpers. outcome_cols Unquoted column names indicating columns `feature_tibble` used outcome variables elastic net model. Supports tidyselect helpers. standardize_predictors logical value indicating numeric predictor columns standardized (centered scaled) model fitting. Defaults TRUE. remove_zv_predictors logical value indicating predictor columns near-zero variance removed model fitting using step_nzv. Defaults FALSE. impute_missing_predictors logical value indicating predictor columns missing values imputed using k-nearest neighbors model fitting (see step_impute_knn). Imputation performed using observation's 5 nearest-neighbors. Defaults FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"recipe object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data. — tof_downsample","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"function downsamples number cells `tof_tbl` using one three methods (randomly sampling constant number cells, randomly sampling proportion cells, performing density-dependent downsampling per algorithm Qiu et al., (2011)).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"","code":"tof_downsample( tof_tibble, group_cols = NULL, ..., method = c(\"constant\", \"prop\", \"density\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups within downsampling performed. Supports tidyselect helpers. Defaults `NULL` (grouping). ... Additional arguments pass `tof_downsample_*` function family member corresponding chosen method. method string indicating downsampling method use: \"constant\" (default), \"prop\", \"density\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"downsampled `tof_tbl` number columns input `tof_tibble`, fewer rows. number rows result depend chosen downsampling method.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 200 cells from the input data tof_downsample( tof_tibble = sim_data, num_cells = 200L, method = \"constant\" ) #> # A tibble: 200 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -0.976 1.90 -0.238 0.441 p #> 2 -0.195 -0.474 -2.22 -0.600 p #> 3 -0.212 -0.464 -1.20 -0.891 z #> 4 1.88 0.643 1.52 -0.869 b #> 5 -0.888 -0.231 0.321 0.0759 v #> 6 0.768 -0.0448 -1.85 0.525 x #> 7 0.0491 -1.82 -0.690 -0.507 b #> 8 -0.937 0.751 -1.21 0.252 l #> 9 0.473 0.261 0.298 1.00 f #> 10 0.457 -0.547 1.71 0.00101 x #> # ℹ 190 more rows # sample 10% of all cells from the input data tof_downsample( tof_tibble = sim_data, prop_cells = 0.1, method = \"prop\" ) #> # A tibble: 100 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 0.0933 -0.218 0.840 0.164 k #> 2 0.0421 1.32 -1.24 0.163 e #> 3 1.27 -0.522 -0.135 1.48 u #> 4 -0.231 -0.223 -0.301 0.239 q #> 5 -0.721 0.722 -0.739 1.18 y #> 6 -0.0458 0.785 0.697 0.319 z #> 7 0.701 -0.605 1.84 0.861 r #> 8 -0.905 -0.783 -1.42 -0.385 s #> 9 0.867 -0.180 1.90 0.273 w #> 10 1.41 2.16 2.27 0.974 q #> # ℹ 90 more rows # sample ~10% of cells from the input data using density dependence tof_downsample( tof_tibble = sim_data, target_prop_cells = 0.1, method = \"density\" ) #> # A tibble: 96 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 1.88 0.643 1.52 -0.869 b #> 2 -0.146 0.828 1.10 -0.0467 t #> 3 -1.18 -0.259 -0.383 -0.588 e #> 4 -1.86 0.0770 1.31 0.0992 l #> 5 -0.340 0.269 -0.969 1.63 m #> 6 0.979 0.370 -1.24 1.00 d #> 7 -0.241 -0.523 0.492 1.34 n #> 8 -1.90 -1.80 -1.81 -0.999 q #> 9 -0.448 -0.294 1.46 0.830 p #> 10 -0.191 0.737 1.10 0.0106 a #> # ℹ 86 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"function downsamples number cells `tof_tbl` randomly selecting `num_cells` cells unique combination values `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"","code":"tof_downsample_constant(tof_tibble, group_cols = NULL, num_cells)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups `num_cells` downsampled. Supports tidyselect helpers. Defaults `NULL` (grouping). num_cells integer number cells sampled group defined `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. Specifically, number rows `num_cells` multiplied number unique combinations values `group_cols`. group fewer `num_cells` number cells, cells group kept.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 500 cells from the input data tof_downsample_constant( tof_tibble = sim_data, num_cells = 500L ) #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -1.37 0.119 -0.452 0.150 e #> 2 -0.806 -1.36 1.44 0.803 e #> 3 -0.167 0.366 -0.512 -0.806 c #> 4 2.13 1.18 -0.405 0.343 w #> 5 1.31 1.04 0.628 0.0154 z #> 6 1.88 -0.707 0.798 0.0972 i #> 7 -1.85 1.01 0.381 -0.256 i #> 8 -0.702 0.919 0.800 0.537 y #> 9 0.0713 1.98 -0.735 -0.220 h #> 10 -1.03 -0.344 -1.15 0.698 y #> # ℹ 490 more rows # sample 20 cells per cluster from the input data tof_downsample_constant( tof_tibble = sim_data, group_cols = cluster_id, num_cells = 20L ) #> # A tibble: 520 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -0.544 0.561 0.141 0.899 c #> 2 -0.167 0.366 -0.512 -0.806 c #> 3 2.13 1.18 -0.405 0.343 w #> 4 -0.881 -1.28 -1.08 -0.725 s #> 5 -0.299 0.119 -1.84 -0.0465 d #> 6 -1.09 -1.17 -0.0514 1.09 w #> 7 1.88 -0.707 0.798 0.0972 i #> 8 -1.85 1.01 0.381 -0.256 i #> 9 -0.420 -0.0579 1.19 -0.571 d #> 10 -0.702 0.919 0.800 0.537 y #> # ℹ 510 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"function downsamples number cells `tof_tbl` using density-dependent downsampling algorithm described Qiu et al., (2011).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"","code":"tof_downsample_density( tof_tibble, group_cols = NULL, density_cols = where(tof_is_numeric), target_num_cells, target_prop_cells, target_percentile = 0.03, outlier_percentile = 0.01, distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), density_estimation_method = c(\"mean_distance\", \"sum_distance\", \"spade\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups within downsampling performed. Supports tidyselect helpers. Defaults `NULL` (grouping). density_cols Unquoted names columns `tof_tibble` use density estimation cell. Defaults numeric columns `tof_tibble`. target_num_cells approximate constant number cells (0 1) sampled group defined `group_cols`. Slightly fewer cells may returned due density calculation performed. target_prop_cells approximate proportion cells (0 1) sampled group defined `group_cols`. Slightly fewer cells may returned due density calculation performed. Ignored `target_num_cells` specified. target_percentile local density percentile (.e. value 0 1) downsampling procedure adjust cells. short, algorithm continue remove cells input `tof_tibble` local densities remaining cells equal `target_percentile`. Lower values result cells removed. See Qiu et al., (2011) details. Defaults 0.1 (10th percentile local densities). Ignored either `target_num_cells` `target_prop_cells` specified. outlier_percentile local density percentile (.e. value 0 1) cells considered outliers (discarded). Cells local density `outlier_percentile` never selected downsampling procedure. Defaults 0.01 (cells 1st local density percentile removed). distance_function string indicating distance function use cell--cell distance calculations. Options include \"euclidean\" (default) \"cosine\" distances. density_estimation_method string indicating algorithm used calculate local density estimate cell. Options include k-nearest neighbor density estimation using mean distance cell's k-nearest neighbors (\"mean_distance\"; default), k-nearest neighbor density estimation using summed distance cell's k nearest neighbors (\"sum_distance\") counting number neighboring cells within spherical radius around cell described Qiu et al., 2011 (\"spade\"). \"spade\" often produces best results, slower knn-density estimation methods. ... Optional additional arguments pass tof_knn_density tof_spade_density.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. number rows depend chosen value `target_percentile`, fewer cells selected lower values `target_percentile`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_prop_cells = 0.5, density_estimation_method = \"spade\" ) #> # A tibble: 469 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 0.157 -0.657 -0.422 -0.171 #> 2 0.247 -2.28 -0.769 -0.640 #> 3 2.00 -0.484 -1.32 -0.375 #> 4 -1.99 -0.522 0.941 -0.574 #> 5 -0.0392 0.372 0.179 -0.188 #> 6 -1.29 0.165 0.400 -1.63 #> 7 -0.728 -0.993 1.72 0.745 #> 8 0.603 1.11 0.543 -0.284 #> 9 1.29 -1.87 -2.40 1.36 #> 10 0.233 0.323 -2.28 0.883 #> # ℹ 459 more rows tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_num_cells = 200L, density_estimation_method = \"spade\" ) #> # A tibble: 208 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -0.149 -0.134 0.965 -0.224 #> 2 1.32 1.34 0.951 -0.763 #> 3 0.104 -0.470 1.35 0.362 #> 4 1.29 -1.87 -2.40 1.36 #> 5 0.679 0.766 0.515 -0.0614 #> 6 -0.542 0.444 0.949 0.199 #> 7 0.516 1.11 0.402 0.216 #> 8 1.03 -0.134 -0.271 -0.0218 #> 9 -0.308 -0.00761 0.740 -0.209 #> 10 -0.581 0.417 -0.846 -1.68 #> # ℹ 198 more rows tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_num_cells = 200L, density_estimation_method = \"mean_distance\" ) #> # A tibble: 197 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 1.05 1.17 0.775 1.36 #> 2 -0.149 -0.134 0.965 -0.224 #> 3 0.104 -0.470 1.35 0.362 #> 4 -0.224 0.117 -0.945 -0.916 #> 5 1.50 1.71 2.40 -0.500 #> 6 -0.275 -0.375 1.50 -0.163 #> 7 0.351 0.768 -0.963 -1.20 #> 8 -0.608 -0.849 -0.496 -1.39 #> 9 1.25 0.457 0.0341 1.83 #> 10 1.71 -0.0492 0.276 -0.864 #> # ℹ 187 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"function downsamples number cells `tof_tbl` randomly selecting `prop_cells` proportion total number cells unique combination values `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"","code":"tof_downsample_prop(tof_tibble, group_cols = NULL, prop_cells)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups `prop_cells` downsampled. Supports tidyselect helpers. Defaults `NULL` (grouping). prop_cells proportion cells (0 1) sampled group defined `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. Specifically, number rows `prop_cells` times number rows input `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 10% of all cells from the input data tof_downsample_prop( tof_tibble = sim_data, prop_cells = 0.1 ) #> # A tibble: 100 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -1.86 0.177 -1.61 0.915 w #> 2 0.648 -0.110 0.861 -0.257 p #> 3 -0.736 1.22 -0.351 -1.49 l #> 4 -2.39 -1.59 0.813 0.325 y #> 5 2.05 -0.591 -0.727 -0.335 p #> 6 -0.244 -0.691 -2.46 -0.0472 w #> 7 -0.336 0.539 -0.613 -1.42 s #> 8 -0.356 -1.06 0.896 0.494 g #> 9 -1.13 2.39 0.335 -1.20 e #> 10 -0.273 -0.403 2.13 0.596 d #> # ℹ 90 more rows # sample 10% of all cells from each cluster in the input data tof_downsample_prop( tof_tibble = sim_data, group_cols = cluster_id, prop_cells = 0.1 ) #> # A tibble: 87 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 1.91 0.329 0.458 0.0340 a #> 2 -1.25 0.769 0.0705 0.923 a #> 3 -0.663 -1.04 1.23 -0.0216 a #> 4 1.33 -0.764 -1.86 -0.348 b #> 5 0.638 -2.31 -1.48 -0.371 b #> 6 -1.14 0.0955 -0.543 0.566 b #> 7 1.35 0.575 -0.346 -1.01 b #> 8 -2.01 0.769 -2.01 -0.988 c #> 9 1.88 0.748 -0.0520 0.348 c #> 10 1.06 -0.926 1.44 -1.18 c #> # ℹ 77 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"function wrapper around tidytof's tof_*_density() function family. performs local density estimation high-dimensional cytometry data using user-specified method (3 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"","code":"tof_estimate_density( tof_tibble, distance_cols = where(tof_is_numeric), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), normalize = TRUE, ..., augment = TRUE, method = c(\"mean_distance\", \"sum_distance\", \"spade\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional arguments pass `tof_*_density()` function family member corresponding chosen `method`. augment boolean value indicating output column-bind local density estimates cell new column `tof_tibble` (TRUE; default) single-column tibble including local density estimates returned (FALSE). method string indicating local density estimation method used. Valid values include \"mean_distance\", \"sum_distance\", \"spade\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding local density estimates cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding local density estimates.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # perform the density estimation tof_estimate_density(tof_tibble = sim_data, method = \"spade\") #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .spade_density #> #> 1 -0.252 0.331 -2.74 -1.08 1 #> 2 0.759 0.857 -0.277 0.966 1 #> 3 -0.241 1.15 -2.36 -2.13 1 #> 4 0.189 3.05 0.923 -1.41 1 #> 5 0.0278 0.197 1.06 0.220 1 #> 6 0.838 -1.28 1.55 -0.758 1 #> 7 0.923 -0.981 0.291 -0.286 1 #> 8 0.893 -0.143 1.51 1.54 1 #> 9 0.661 0.102 0.632 0.805 1 #> 10 0.347 0.708 -0.122 -2.55 1 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around # each cell tof_estimate_density( tof_tibble = sim_data, alpha_multiplier = 2, method = \"spade\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .spade_density #> #> 1 -0.252 0.331 -2.74 -1.08 0.1 #> 2 0.759 0.857 -0.277 0.966 1 #> 3 -0.241 1.15 -2.36 -2.13 0 #> 4 0.189 3.05 0.923 -1.41 0 #> 5 0.0278 0.197 1.06 0.220 1 #> 6 0.838 -1.28 1.55 -0.758 0.1 #> 7 0.923 -0.981 0.291 -0.286 1 #> 8 0.893 -0.143 1.51 1.54 0.7 #> 9 0.661 0.102 0.632 0.805 1 #> 10 0.347 0.708 -0.122 -2.55 0.4 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"feature extraction function calculates user-specified measurement central tendency (.e. median mode) cells cluster `tof_tibble` across user-specified selection CyTOF markers. calculations can done either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"","code":"tof_extract_central_tendency( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), stimulation_col = NULL, central_tendency_function = stats::median, format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"tof_tibble `tof_tibble` `tibble` row represents single cell column represents CyTOF measurement piece metadata (.e. cluster id, patient id, etc.) cell. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselection. stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). central_tendency_function function used calculate measurement central tendency cluster (used dependent variable linear model). Defaults median. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (central tendency given marker given cluster). names column containing cluster features obtained using following pattern: \"{marker_id}@{cluster_id}_ct\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_central_tendency( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 105 #> patient `cd45@a_ct` `cd38@a_ct` `cd34@a_ct` `cd19@a_ct` `cd45@b_ct` #> #> 1 kirby -0.216 -0.0538 -0.0502 0.429 0.175 #> 2 mario 0.350 -0.278 -0.0983 -0.289 -0.0923 #> # ℹ 99 more variables: `cd38@b_ct` , `cd34@b_ct` , `cd19@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd34@c_ct` , `cd19@c_ct` , #> # `cd45@d_ct` , `cd38@d_ct` , `cd34@d_ct` , `cd19@d_ct` , #> # `cd45@e_ct` , `cd38@e_ct` , `cd34@e_ct` , `cd19@e_ct` , #> # `cd45@f_ct` , `cd38@f_ct` , `cd34@f_ct` , `cd19@f_ct` , #> # `cd45@g_ct` , `cd38@g_ct` , `cd34@g_ct` , `cd19@g_ct` , #> # `cd45@h_ct` , `cd38@h_ct` , `cd34@h_ct` , … # extract proportion of each cluster in each patient in long format tof_extract_central_tendency( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 208 × 4 #> patient cluster_id channel values #> #> 1 kirby a cd45 -0.216 #> 2 kirby a cd38 -0.0538 #> 3 kirby a cd34 -0.0502 #> 4 kirby a cd19 0.429 #> 5 kirby b cd45 0.175 #> 6 kirby b cd38 0.144 #> 7 kirby b cd34 0.416 #> 8 kirby b cd19 0.0494 #> 9 kirby c cd45 0.113 #> 10 kirby c cd38 0.567 #> # ℹ 198 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"feature extraction function calculates earth-mover's distance (EMD) stimulated unstimulated (\"basal\") experimental conditions samples CyTOF experiment. calculation performed across user-specified selection CyTOF antigens can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"","code":"tof_extract_emd( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), emd_col, reference_level, format = c(\"wide\", \"long\"), num_bins = 100 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included earth-mover's distance calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. emd_col unquoted column name indicates column `tof_tibble` used group cells different distributions compared one another EMD calculation. example, want compare marker expression distributions across stimulation conditions, `emd_col` column `tof_tibble` containing information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). reference_level string indicating value `emd_col` corresponds \"reference\" value values `emd_col` compared. example, `emd_col` represents stimulation condition cell, reference_level might take value \"basal\" \"unstimulated\" want compare stimulation basal state. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row). num_bins Optional. number bins use dividing one-dimensional marker distributions discrete segments EMD calculation. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (EMD distribution given marker given cluster basal condition distribution marker given cluster stimulated condition). names column containing cluster features obtained using following pattern: \"{stimulation_id}_{marker_id}@{cluster_id}_emd\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract emd of each cluster in each patient (using the \"basal\" stim # condition as a reference) in wide format tof_extract_emd( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, emd_col = stim, reference_level = \"basal\" ) #> # A tibble: 2 × 105 #> patient `stim_cd45@x_emd` `stim_cd38@x_emd` `stim_cd34@x_emd` #> #> 1 mario NA NA NA #> 2 kirby NA NA NA #> # ℹ 101 more variables: `stim_cd19@x_emd` , `stim_cd45@d_emd` , #> # `stim_cd38@d_emd` , `stim_cd34@d_emd` , `stim_cd19@d_emd` , #> # `stim_cd45@i_emd` , `stim_cd38@i_emd` , `stim_cd34@i_emd` , #> # `stim_cd19@i_emd` , `stim_cd45@j_emd` , `stim_cd38@j_emd` , #> # `stim_cd34@j_emd` , `stim_cd19@j_emd` , `stim_cd45@p_emd` , #> # `stim_cd38@p_emd` , `stim_cd34@p_emd` , `stim_cd19@p_emd` , #> # `stim_cd45@m_emd` , `stim_cd38@m_emd` , … # extract emd of each cluster (using the \"basal\" stim # condition as a reference) in long format tof_extract_emd( tof_tibble = sim_data, cluster_col = cluster_id, emd_col = stim, reference_level = \"basal\", format = \"long\" ) #> # A tibble: 104 × 4 #> cluster_id marker stimulation emd #> #> 1 x cd45 stim 9.75 #> 2 x cd38 stim 5.06 #> 3 x cd34 stim 7.06 #> 4 x cd19 stim 14.9 #> 5 d cd45 stim 6.70 #> 6 d cd38 stim 8.55 #> 7 d cd34 stim 6.65 #> 8 d cd19 stim 7.10 #> 9 i cd45 stim 6.66 #> 10 i cd38 stim 6.59 #> # ℹ 94 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"function wraps members `tof_extract_*` function family extract sample-level features lineage (.e. cell surface antigen) CyTOF channels assumed stable across stimulation conditions signaling CyTOF channels assumed change across stimulation conditions. Features extracted cluster within independent sample (defined `group_cols` argument).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"","code":"tof_extract_features( tof_tibble, cluster_col, group_cols = NULL, stimulation_col = NULL, lineage_cols, signaling_cols, central_tendency_function = stats::median, signaling_method = c(\"threshold\", \"emd\", \"jsd\", \"central tendency\"), basal_level = NULL, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). lineage_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) considered lineage markers feature extraction calculation. Supports tidyselect helpers. signaling_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) considered signaling markers feature extraction calculation. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster (used dependent variable linear model). Defaults median. signaling_method string indicating feature extraction method use signaling markers (identified `signaling_cols` argument). Options \"threshold\" (default), \"emd\", \"jsd\", \"central tendency\". basal_level string indicating value `stimulation_col` corresponds basal stimulation condition (.e. \"basal\" \"unstimulated\"). ... Optional additional arguments passed tof_extract_threshold, tof_extract_emd, tof_extract_jsd.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"tibble. output tibble 1 row combination grouping variables provided `group_cols` (thus, row represent considered single \"sample\" based grouping provided). one column grouping variable one column extracted feature (\"wide\" format).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"Lineage channels specified using `lineage_cols` argument, extracted features measurements central tendency (computed user-supplied `central_tendency_function`). Signaling channels specified using `signaling_cols` argument, extracted features depend user's chosen `signaling_method`. `signaling method` == \"threshold\" (default), tof_extract_threshold used calculate proportion cells cluster signaling marker expression `threshold` stimulation condition. `signaling_method` == \"emd\" `signaling_method` == \"jsd\", tof_extract_emd tof_extract_jsd used calculate earth-mover's distance (EMD) Jensen-Shannon Distance (JSD), respectively, basal condition stimulated conditions cluster sample. Finally, none options chosen, tof_extract_central_tendency used calculate measurements central tendency. addition, tof_extract_proportion used extract proportion cells cluster computed sample. calculations can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract the following features from each cluster in each # patient/stimulation: # - proportion of each cluster # - central tendency (median) of cd45 and cd38 in each cluster # - the proportion of cells in each cluster with cd34 expression over # the default threshold (asinh(10 / 5)) tof_extract_features( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, lineage_cols = c(cd45, cd38), signaling_cols = cd34, stimulation_col = stim ) #> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.0375 0.0208 0.0458 0.0292 0.0417 0.0604 0.0208 #> 2 mario 0.0558 0.0519 0.0308 0.0442 0.0442 0.0558 0.05 #> # ℹ 123 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` , #> # `cd45@a_ct` , `cd38@a_ct` , `cd45@b_ct` , `cd38@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd45@d_ct` , … # extract the following features from each cluster in each # patient/stimulation: # - proportion of each cluster # - central tendency (mean) of cd45 and cd38 in each cluster # - the earth mover's distance between each cluster's cd34 histogram in # the \"basal\" and \"stim\" conditions tof_extract_features( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, lineage_cols = c(cd45, cd38), signaling_cols = cd34, central_tendency_function = mean, stimulation_col = stim, signaling_method = \"emd\", basal_level = \"basal\" ) #> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.0375 0.0208 0.0458 0.0292 0.0417 0.0604 0.0208 #> 2 mario 0.0558 0.0519 0.0308 0.0442 0.0442 0.0558 0.05 #> # ℹ 123 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` , #> # `cd45@a_ct` , `cd38@a_ct` , `cd45@b_ct` , `cd38@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd45@d_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"feature extraction function calculates Jensen-Shannon Distance (JSD) stimulated unstimulated (\"basal\") experimental conditions samples CyTOF experiment. calculation performed across user-specified selection CyTOF antigens can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"","code":"tof_extract_jsd( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), jsd_col, reference_level, format = c(\"wide\", \"long\"), num_bins = 100 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. jsd_col unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). reference_level string indicating value `jsd_col` corresponds basal stimulation condition (.e. \"basal\" \"unstimulated\"). format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row). num_bins Optional. number bins use dividing one-dimensional marker distributions discrete segments JSD calculation. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (JSD distribution given marker given cluster basal condition distribution marker cluster stimulated condition). names column containing cluster features obtained using following pattern: \"{stimulation_id}_{marker_id}@{cluster_id}_jsd\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract jsd of each cluster in each patient (using the \"basal\" stim # condition as a reference) in wide format tof_extract_jsd( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, jsd_col = stim, reference_level = \"basal\" ) #> # A tibble: 2 × 105 #> patient `stim_cd45@q_jsd` `stim_cd38@q_jsd` `stim_cd34@q_jsd` #> #> 1 kirby 0.844 1 0.766 #> 2 mario NA NA NA #> # ℹ 101 more variables: `stim_cd19@q_jsd` , `stim_cd45@y_jsd` , #> # `stim_cd38@y_jsd` , `stim_cd34@y_jsd` , `stim_cd19@y_jsd` , #> # `stim_cd45@x_jsd` , `stim_cd38@x_jsd` , `stim_cd34@x_jsd` , #> # `stim_cd19@x_jsd` , `stim_cd45@p_jsd` , `stim_cd38@p_jsd` , #> # `stim_cd34@p_jsd` , `stim_cd19@p_jsd` , `stim_cd45@f_jsd` , #> # `stim_cd38@f_jsd` , `stim_cd34@f_jsd` , `stim_cd19@f_jsd` , #> # `stim_cd45@c_jsd` , `stim_cd38@c_jsd` , … # extract jsd of each cluster (using the \"basal\" stim # condition as a reference) in long format tof_extract_jsd( tof_tibble = sim_data, cluster_col = cluster_id, jsd_col = stim, reference_level = \"basal\", format = \"long\" ) #> # A tibble: 104 × 4 #> cluster_id marker stimulation jsd #> #> 1 q cd45 stim 0.646 #> 2 q cd38 stim 0.842 #> 3 q cd34 stim 0.897 #> 4 q cd19 stim 0.646 #> 5 y cd45 stim 0.765 #> 6 y cd38 stim 0.836 #> 7 y cd34 stim 0.804 #> 8 y cd19 stim 0.784 #> 9 x cd45 stim 0.757 #> 10 x cd38 stim 0.687 #> # ℹ 94 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"feature extraction function allows calculate proportion cells cluster `tof_tibble` - either overall broken subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"","code":"tof_extract_proportion( tof_tibble, cluster_col, group_cols = NULL, format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). format string indicating data returned \"wide\" format (default; cluster proportion given column) \"long\" format (cluster proportion provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable well one column proportion cells cluster. names column containing cluster proportions obtained using following pattern: \"prop@{cluster_id}\". format == \"long\", tibble 1 row combination grouping variables `group_cols` cluster id (.e. level) `cluster_col`. one column grouping variable, one column cluster ids, one column (`prop`) containing cluster proportions.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 27 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.0532 0.0389 0.0327 0.0327 0.0511 0.0450 0.0511 #> 2 mario 0.0528 0.0313 0.0372 0.0274 0.0411 0.0391 0.0333 #> # ℹ 19 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` # extract proportion of each cluster in each patient in long format tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 52 × 3 #> patient cluster_id prop #> #> 1 kirby a 0.0532 #> 2 kirby b 0.0389 #> 3 kirby c 0.0327 #> 4 kirby d 0.0327 #> 5 kirby e 0.0511 #> 6 kirby f 0.0450 #> 7 kirby g 0.0511 #> 8 kirby h 0.0204 #> 9 kirby i 0.0225 #> 10 kirby j 0.0184 #> # ℹ 42 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"feature extraction function calculates proportion cells given cluster CyTOF antigen expression user-specified threshold across user-specified selection CyTOF markers. calculations can done either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"","code":"tof_extract_threshold( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), stimulation_col = NULL, threshold = asinh(10/5), format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). threshold double integer length 1 indicating threshold used. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (proportion cells given cluster marker expression values `threshold`). names column containing cluster features obtained using following pattern: \"{marker_id}@{cluster_id}_threshold\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_threshold( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 105 #> patient `cd45@a_threshold` `cd38@a_threshold` `cd34@a_threshold` #> #> 1 kirby 0.0526 0.105 0 #> 2 mario 0.0294 0.0882 0.0294 #> # ℹ 101 more variables: `cd19@a_threshold` , `cd45@b_threshold` , #> # `cd38@b_threshold` , `cd34@b_threshold` , #> # `cd19@b_threshold` , `cd45@c_threshold` , #> # `cd38@c_threshold` , `cd34@c_threshold` , #> # `cd19@c_threshold` , `cd45@d_threshold` , #> # `cd38@d_threshold` , `cd34@d_threshold` , #> # `cd19@d_threshold` , `cd45@e_threshold` , … # extract proportion of each cluster in each patient in long format tof_extract_threshold( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 208 × 4 #> patient cluster_id channel values #> #> 1 kirby a cd45 0.0526 #> 2 kirby a cd38 0.105 #> 3 kirby a cd34 0 #> 4 kirby a cd19 0.105 #> 5 kirby b cd45 0.143 #> 6 kirby b cd38 0.143 #> 7 kirby b cd34 0.0476 #> 8 kirby b cd19 0.0476 #> 9 kirby c cd45 0.0455 #> 10 kirby c cd38 0 #> # ℹ 198 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"Find optimal hyperparameters elastic net model candidate performance metrics","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"","code":"tof_find_best(performance_metrics, model_type, optimization_metric)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"performance_metrics tibble performance metrics elastic net model (wide format) model_type string indicating type glmnet model trained. optimization_metric string indicating performance metric used select optimal model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"tibble 3 columns: \"mixture\", \"penalty\", column containing chosen optimization metric. returned tibble 1 column, means 1 mixture/penalty combination yielded optimal result (.e. tuning procedure resulted tie).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"Calculate store predicted outcomes validation set observation model tuning","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"","code":"tof_find_cv_predictions( split_data, prepped_recipe, lambda, alpha, model_type, outcome_colnames )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"split_data `rsplit` object rsample package. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe trained recipe lambda single numeric value indicating penalty (lambda) value used make predictions alpha single numeric value indicating mixture (alpha) value used make predictions model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. outcome_colnames Quoted column names indicating columns data fit represent outcome variables (others assumed predictors).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"tibble containing predicted true values outcome validation observations `split_data`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"Find earth-mover's distance two numeric vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"","code":"tof_find_emd(vec_1, vec_2, num_bins = 100)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"vec_1 numeric vector. vec_2 numeric vector. num_bins integer number bins use performing kernel density estimation two vectors. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"double (length 1) representing EMD two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"Find Jensen-Shannon Divergence (JSD) two numeric vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"","code":"tof_find_jsd(vec_1, vec_2, num_bins = 100)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"vec_1 numeric vector. vec_2 numeric vector. num_bins integer number bins use binning across two vectors' combined range. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"double (length 1) representing JSD two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"Find k-nearest neighbors cell high-dimensional cytometry dataset.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"","code":"tof_find_knn( .data, k = min(10, nrow(.data)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), .query, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":".data `tof_tibble` `tibble` row represents cell column represents high-dimensional cytometry measurement. k integer indicating number nearest neighbors return cell. distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. .query set cells queried .data (.e. set cells find nearest neighbors within .data). Defaults .data , .e. finding nearest neighbors cells .data. ... Optional additional arguments pass hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"list two elements: \"neighbor_ids\" \"neighbor_distances,\" n k matrices (n number cells input `.data`. [,j]-th entry \"neighbor_ids\" represents row index j-th nearest neighbor cell -th row `.data`. [,j]-th entry \"neighbor_distances\" represents distance two cells according `distance_function`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # Find the 10 nearest neighbors of each cell in the dataset tof_find_knn( .data = sim_data, k = 10, distance_function = \"euclidean\" ) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 519 950 311 110 409 192 884 753 426 133 #> [2,] 782 386 902 938 407 955 247 667 756 284 #> [3,] 789 309 180 707 670 569 900 796 587 554 #> [4,] 891 845 413 535 37 322 298 269 195 664 #> [5,] 112 743 962 404 553 957 741 545 155 535 #> [6,] 10 232 22 765 948 522 820 724 759 368 #> [7,] 513 486 697 388 650 144 105 374 333 769 #> [8,] 577 212 951 835 525 116 115 794 210 595 #> [9,] 193 495 323 430 94 596 784 69 378 791 #> [10,] 6 232 522 22 765 820 479 27 674 368 #> [11,] 737 850 448 580 132 804 173 226 65 50 #> [12,] 602 445 162 348 610 49 375 873 571 83 #> [13,] 7 513 767 105 244 109 800 544 246 249 #> [14,] 245 100 848 600 710 883 590 754 711 920 #> [15,] 686 351 25 265 433 839 863 953 170 263 #> [16,] 870 793 578 582 872 559 812 762 918 276 #> [17,] 558 337 520 696 713 510 402 129 841 509 #> [18,] 668 742 697 95 228 551 68 474 202 892 #> [19,] 343 856 117 750 796 328 63 422 340 546 #> [20,] 496 908 127 281 749 575 892 194 318 828 #> [21,] 937 914 616 184 751 644 909 689 270 875 #> [22,] 123 332 581 820 759 948 435 227 992 319 #> [23,] 188 821 157 90 905 882 43 95 274 79 #> [24,] 615 388 161 344 545 962 745 269 340 333 #> [25,] 686 15 351 863 265 433 756 170 211 208 #> [26,] 829 182 646 598 589 249 752 668 819 716 #> [27,] 556 151 401 555 655 534 465 91 57 820 #> [28,] 586 358 470 382 32 379 299 645 572 54 #> [29,] 826 929 530 470 292 110 379 287 54 515 #> [30,] 161 700 817 739 205 957 5 545 77 388 #> [31,] 544 675 391 109 327 277 387 251 960 224 #> [32,] 470 837 367 294 299 54 28 922 887 412 #> [33,] 216 786 124 334 213 894 72 665 664 180 #> [34,] 978 694 252 612 488 949 220 720 766 304 #> [35,] 456 291 272 906 418 500 805 677 64 416 #> [36,] 541 827 75 178 933 472 152 726 502 380 #> [37,] 891 664 4 413 322 461 213 298 62 101 #> [38,] 979 492 965 314 571 302 629 476 533 966 #> [39,] 149 411 390 104 930 402 337 841 520 185 #> [40,] 861 458 505 840 594 565 404 155 403 845 #> [41,] 514 102 857 423 125 936 917 859 911 107 #> [42,] 774 648 809 851 828 720 821 598 892 643 #> [43,] 821 918 90 188 604 578 23 762 870 79 #> [44,] 81 176 695 141 941 818 89 421 768 78 #> [45,] 319 680 952 756 251 547 948 389 117 608 #> [46,] 739 957 269 597 307 592 161 436 506 404 #> [47,] 566 974 391 259 201 224 31 385 109 968 #> [48,] 127 121 815 261 370 641 676 191 289 194 #> [49,] 445 425 873 602 12 814 897 662 219 83 #> [50,] 804 132 138 737 313 324 124 11 945 703 #> [51,] 847 167 630 979 842 533 314 298 880 38 #> [52,] 874 911 893 612 949 171 304 736 766 803 #> [53,] 896 785 684 950 869 99 969 628 352 338 #> [54,] 470 922 186 292 294 477 808 32 113 758 #> [55,] 523 537 449 720 813 803 488 477 198 980 #> [56,] 116 276 951 642 241 928 525 8 490 794 #> [57,] 507 106 293 342 654 399 750 227 966 532 #> [58,] 261 191 97 960 48 675 127 575 142 815 #> [59,] 626 795 755 441 195 866 176 70 836 768 #> [60,] 830 218 304 102 864 93 190 859 776 606 #> [61,] 285 397 341 682 372 264 599 126 998 86 #> [62,] 253 461 474 664 181 685 359 383 988 882 #> [63,] 136 557 454 224 19 387 856 532 261 142 #> [64,] 209 594 492 840 629 861 418 272 373 494 #> [65,] 145 737 850 11 448 173 226 704 324 816 #> [66,] 161 788 374 858 24 615 30 961 817 246 #> [67,] 137 373 728 611 898 893 129 278 459 301 #> [68,] 960 228 392 333 551 18 315 144 202 388 #> [69,] 871 356 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757 748 270 282 408 #> [765,] 724 479 581 22 759 940 123 733 473 435 #> [766,] 107 125 911 171 79 52 949 140 612 304 #> [767,] 982 424 528 228 716 838 649 742 18 26 #> [768,] 441 560 844 971 443 141 195 44 833 231 #> [769,] 215 613 480 481 105 513 740 429 7 650 #> [770,] 158 792 614 723 970 841 639 713 509 963 #> [771,] 329 852 681 843 657 956 524 672 221 207 #> [772,] 876 438 890 164 512 238 636 118 779 153 #> [773,] 811 916 257 701 197 699 316 698 722 88 #> [774,] 42 828 648 821 987 190 851 643 892 834 #> [775,] 653 102 472 60 41 830 152 107 864 606 #> [776,] 218 643 501 80 606 864 663 190 774 926 #> [777,] 715 676 671 225 568 217 127 808 370 867 #> [778,] 942 255 919 213 654 322 992 37 891 325 #> [779,] 164 439 865 636 729 438 169 173 490 579 #> [780,] 428 942 160 894 673 213 778 72 457 70 #> [781,] 542 525 210 427 729 305 358 362 115 640 #> [782,] 2 407 225 667 938 568 671 386 955 839 #> [783,] 184 704 756 937 21 585 831 270 902 938 #> [784,] 323 886 634 947 200 166 596 9 275 378 #> [785,] 869 628 684 518 352 896 204 357 338 53 #> [786,] 124 33 350 989 62 527 939 334 253 664 #> [787,] 369 983 679 174 201 259 563 345 199 371 #> [788,] 817 961 700 161 30 242 66 77 310 635 #> [789,] 3 333 988 707 309 228 796 960 68 900 #> [790,] 484 436 741 467 196 290 349 553 592 807 #> [791,] 94 561 227 495 148 365 966 9 947 357 #> [792,] 158 126 770 723 192 509 753 970 614 341 #> [793,] 16 812 578 740 870 762 622 276 872 79 #> [794,] 212 83 885 210 525 78 870 559 872 577 #> [795,] 256 440 176 818 939 59 363 248 508 350 #> [796,] 328 117 856 19 387 422 707 229 454 247 #> [797,] 975 540 656 279 120 484 741 354 743 290 #> [798,] 662 425 749 897 462 814 548 187 854 49 #> [799,] 889 231 494 171 610 403 840 353 594 516 #> [800,] 635 246 77 731 493 994 242 315 109 455 #> [801,] 389 172 251 680 675 442 686 531 170 265 #> [802,] 990 168 153 607 512 299 529 412 924 379 #> [803,] 813 893 301 537 449 720 162 610 494 980 #> [804,] 138 50 703 132 11 737 313 850 76 145 #> [805,] 418 196 467 272 895 35 565 699 701 209 #> [806,] 910 640 408 651 317 757 538 115 683 282 #> [807,] 743 342 507 112 741 962 399 484 553 535 #> [808,] 922 294 627 339 122 113 393 777 217 715 #> [809,] 100 883 848 42 851 459 619 711 643 774 #> [810,] 881 476 111 302 413 284 516 807 548 274 #> [811,] 699 773 257 701 565 916 560 505 272 316 #> [812,] 740 79 578 793 622 762 16 582 43 215 #> [813,] 720 449 301 803 918 851 537 821 43 980 #> [814,] 897 662 268 83 602 798 49 445 425 885 #> [815,] 261 48 233 289 194 142 127 454 955 318 #> [816,] 542 355 305 362 781 65 358 703 11 737 #> [817,] 700 788 30 161 961 310 242 46 731 349 #> [818,] 440 176 695 44 81 429 661 248 89 795 #> [819,] 589 182 646 552 829 663 26 240 260 118 #> [820,] 944 319 546 401 465 22 123 332 343 435 #> [821,] 43 188 23 90 828 774 79 918 539 604 #> [822,] 498 858 96 755 59 374 66 297 866 626 #> [823,] 754 437 715 867 600 777 452 121 339 361 #> [824,] 98 827 763 788 310 961 463 493 497 91 #> [825,] 913 285 998 357 466 669 561 536 163 985 #> [826,] 29 929 530 470 110 884 292 143 515 346 #> [827,] 497 719 763 91 541 567 555 502 933 36 #> [828,] 987 892 774 648 834 821 539 42 188 598 #> [829,] 240 589 26 649 598 575 646 496 892 668 #> [830,] 60 218 93 304 102 373 190 864 859 776 #> [831,] 608 783 356 756 128 267 25 714 585 184 #> [832,] 916 721 396 773 699 131 811 316 846 906 #> [833,] 959 441 768 134 560 866 626 971 257 811 #> [834,] 987 289 504 828 641 892 202 648 191 551 #> [835,] 958 8 577 116 421 517 595 951 212 661 #> [836,] 70 306 745 615 664 891 37 269 4 900 #> [837,] 367 412 32 294 122 398 890 360 991 382 #> [838,] 424 982 649 173 865 281 725 228 908 767 #> [839,] 170 676 955 261 265 782 127 407 225 686 #> [840,] 458 594 861 403 629 965 40 64 799 889 #> [841,] 402 186 696 390 520 770 713 509 970 296 #> [842,] 314 167 630 880 51 972 443 298 468 979 #> [843,] 266 977 771 928 709 511 329 116 212 862 #> [844,] 995 423 768 89 971 936 231 171 866 155 #> [845,] 535 4 404 112 458 741 322 891 413 505 #> [846,] 895 888 698 617 701 467 196 699 899 805 #> [847,] 51 979 630 298 665 842 167 203 334 314 #> [848,] 100 883 809 14 711 963 459 920 643 390 #> [849,] 411 973 636 382 837 890 118 523 438 930 #> [850,] 173 865 11 226 580 448 65 737 145 804 #> [851,] 301 304 720 809 42 459 774 813 449 373 #> [852,] 771 329 84 321 221 262 419 396 862 672 #> [853,] 140 421 582 949 125 171 874 81 911 695 #> [854,] 579 462 897 489 798 662 169 605 761 814 #> [855,] 273 971 441 959 956 768 833 176 44 81 #> [856,] 19 117 328 750 343 399 796 229 136 340 #> [857,] 423 514 866 41 936 739 102 917 859 96 #> [858,] 374 66 513 613 564 7 650 822 24 486 #> [859,] 155 423 205 102 404 190 957 157 864 995 #> [860,] 623 877 963 970 521 469 417 74 384 713 #> [861,] 458 629 40 840 403 594 492 64 484 476 #> [862,] 143 884 826 346 843 29 932 311 929 84 #> [863,] 211 953 351 637 526 175 263 217 299 25 #> [864,] 606 501 218 205 776 80 60 190 859 830 #> [865,] 173 850 838 226 779 439 580 11 725 424 #> [866,] 626 857 936 423 844 971 833 995 768 96 #> [867,] 122 360 619 823 777 603 452 294 361 883 #> [868,] 991 361 398 341 437 367 360 614 823 770 #> [869,] 785 518 628 204 357 684 352 365 913 617 #> [870,] 16 918 578 43 762 604 83 872 582 813 #> [871,] 453 747 69 935 435 902 356 227 561 123 #> [872,] 559 582 16 870 429 695 140 78 83 71 #> [873,] 445 425 49 12 692 87 667 798 757 471 #> [874,] 52 736 949 893 911 612 879 171 803 511 #> [875,] 270 87 757 748 683 692 764 637 572 21 #> [876,] 260 772 118 646 649 238 584 890 438 982 #> [877,] 860 417 967 921 536 684 717 996 469 204 #> [878,] 951 517 931 8 835 958 241 577 56 116 #> [879,] 494 330 893 889 874 456 434 610 64 35 #> [880,] 443 630 195 842 298 51 847 314 891 257 #> [881,] 111 274 810 882 302 476 516 685 548 318 #> [882,] 274 95 881 23 685 905 111 253 90 359 #> [883,] 848 100 809 14 920 619 963 711 867 296 #> [884,] 826 29 110 1 311 519 222 530 200 929 #> [885,] 268 525 71 794 83 814 897 49 729 78 #> [886,] 634 909 644 323 308 200 784 430 475 275 #> [887,] 645 32 379 470 529 607 530 299 28 168 #> [888,] 467 846 196 895 701 899 698 151 432 617 #> [889,] 799 231 494 171 840 610 594 403 353 207 #> [890,] 438 238 412 118 837 360 652 281 122 398 #> [891,] 4 37 322 413 535 845 306 269 298 70 #> [892,] 828 668 987 598 496 648 834 504 605 188 #> [893,] 301 803 52 373 611 304 67 736 494 137 #> [894,] 673 780 428 457 33 72 216 160 213 942 #> [895,] 846 701 467 698 888 196 805 699 617 418 #> [896,] 785 352 869 53 444 617 684 518 628 591 #> [897,] 814 662 604 798 854 462 579 602 49 83 #> [898,] 67 744 677 416 135 658 456 728 373 879 #> [899,] 464 888 846 75 196 895 432 151 467 617 #> [900,] 569 554 745 457 836 664 70 306 229 988 #> [901,] 406 758 330 659 292 375 113 515 303 162 #> [902,] 386 938 937 2 616 935 453 756 751 747 #> [903,] 431 624 876 164 772 528 779 767 249 865 #> [904,] 222 570 475 764 1000 621 784 714 644 282 #> [905,] 157 237 23 882 95 359 987 274 190 188 #> [906,] 500 35 291 660 272 895 236 321 434 543 #> [907,] 574 242 493 199 310 731 259 552 224 345 #> [908,] 318 496 194 20 749 127 462 281 187 815 #> [909,] 644 430 886 634 475 751 21 937 184 748 #> [910,] 640 806 115 738 419 317 219 651 683 757 #> [911,] 52 949 612 125 766 874 107 171 853 304 #> [912,] 621 714 904 128 585 426 69 570 222 475 #> [913,] 420 285 357 561 825 998 150 628 230 223 #> [914,] 689 937 21 184 203 616 430 909 644 902 #> [915,] 671 656 225 370 476 279 326 667 568 407 #> [916,] 773 832 721 811 699 316 698 701 846 895 #> [917,] 102 957 130 404 152 859 290 423 380 502 #> [918,] 604 43 870 602 90 821 578 813 762 897 #> [919,] 255 778 722 942 197 322 257 891 160 992 #> [920,] 975 593 120 354 100 14 883 279 754 656 #> [921,] 996 326 998 466 223 723 877 600 754 158 #> [922,] 808 294 627 113 122 339 54 393 165 953 #> [923,] 925 336 455 246 976 77 315 144 182 635 #> [924,] 512 990 802 168 153 735 607 529 772 433 #> [925,] 923 606 864 501 205 336 455 218 776 30 #> [926,] 504 182 648 834 501 551 776 987 552 892 #> [927,] 707 309 952 422 387 277 670 760 796 999 #> [928,] 276 252 116 16 794 709 212 870 582 559 #> [929,] 515 29 292 826 110 287 303 659 470 54 #> [930,] 390 360 809 198 848 883 335 411 867 100 #> [931,] 517 248 577 440 951 71 241 958 989 320 #> [932,] 266 515 346 250 511 929 287 509 696 826 #> [933,] 384 469 278 472 541 623 521 583 152 502 #> [934,] 941 685 516 548 177 268 253 298 320 814 #> [935,] 747 325 386 902 871 203 937 654 966 665 #> [936,] 564 995 844 971 866 857 423 514 125 41 #> [937,] 184 616 914 21 902 430 751 909 689 644 #> [938,] 782 902 568 2 756 453 667 217 225 407 #> [939,] 941 334 176 248 320 630 934 508 440 141 #> [940,] 495 193 214 378 94 984 9 791 581 985 #> [941,] 934 268 177 78 320 71 44 685 939 516 #> [942,] 778 780 213 919 255 992 160 216 654 322 #> [943,] 410 480 366 234 76 734 490 559 762 241 #> [944,] 693 557 546 319 820 532 465 400 136 401 #> [945,] 548 132 662 685 320 425 334 798 187 665 #> [946,] 295 450 300 159 119 128 114 618 576 647 #> [947,] 596 365 415 954 698 275 94 784 634 886 #> [948,] 759 123 45 22 952 332 319 422 820 608 #> [949,] 612 911 52 874 766 853 736 140 125 582 #> [950,] 409 519 1 338 192 628 243 785 753 110 #> [951,] 878 8 517 241 525 577 931 116 56 835 #> [952,] 422 547 760 707 45 948 927 796 387 251 #> [953,] 863 211 217 351 627 263 526 715 652 122 #> [954,] 415 485 947 596 275 51 698 784 167 9 #> [955,] 247 454 261 839 170 208 815 2 782 194 #> [956,] 855 273 681 524 396 441 141 478 207 560 #> [957,] 404 739 5 46 155 553 112 741 535 859 #> [958,] 835 517 440 661 931 818 577 248 176 256 #> [959,] 833 441 768 855 866 971 560 134 273 626 #> [960,] 68 228 58 392 142 387 551 675 63 982 #> [961,] 788 817 483 655 555 161 700 763 310 66 #> [962,] 112 344 743 5 807 545 535 553 233 741 #> [963,] 600 970 296 860 728 713 883 848 14 920 #> [964,] 75 85 380 917 487 41 152 699 102 502 #> [965,] 302 516 629 840 571 492 458 38 403 476 #> [966,] 106 386 148 342 507 654 747 979 38 322 #> [967,] 536 684 877 717 451 705 414 998 921 417 #> [968,] 385 674 719 259 91 549 974 288 555 465 #> [969,] 243 950 99 338 250 708 53 409 558 135 #> [970,] 713 509 963 860 158 296 770 728 758 623 #> [971,] 844 936 768 866 995 855 81 125 44 695 #> [972,] 468 377 167 314 291 533 571 660 842 434 #> [973,] 849 153 837 367 887 382 32 411 645 398 #> [974,] 546 566 557 391 760 259 633 345 944 224 #> [975,] 593 120 279 920 797 656 706 326 540 245 #> [976,] 697 486 237 144 205 668 504 315 215 905 #> [977,] 212 709 210 287 659 794 330 317 610 381 #> [978,] 694 488 179 752 622 34 312 42 720 588 #> [979,] 38 314 847 51 167 533 148 842 571 966 #> [980,] 162 375 539 449 603 619 537 602 301 477 #> [981,] 992 450 119 935 203 473 747 942 213 914 #> [982,] 424 228 838 767 675 960 544 649 281 646 #> [983,] 679 174 369 199 437 121 710 345 452 787 #> [984,] 88 214 197 940 698 888 721 733 151 846 #> [985,] 357 503 617 495 94 536 432 940 825 869 #> [986,] 130 540 290 80 497 501 120 502 574 700 #> [987,] 834 828 892 289 504 648 774 641 202 668 #> [988,] 461 333 181 474 392 142 664 344 383 62 #> [989,] 350 248 363 786 124 320 234 440 527 138 #> [990,] 802 153 168 512 924 607 735 529 412 299 #> [991,] 868 361 341 367 398 715 823 294 360 122 #> [992,] 942 778 88 255 981 919 654 332 747 197 #> [993,] 529 139 607 175 379 299 625 530 168 637 #> [994,] 800 961 788 635 98 66 493 483 246 82 #> [995,] 844 423 936 89 171 155 125 859 359 481 #> [996,] 921 466 235 326 706 446 754 998 189 223 #> [997,] 588 312 663 978 776 218 606 336 104 752 #> [998,] 285 466 223 921 682 163 913 86 996 825 #> [999,] 340 343 19 368 707 422 856 796 569 900 #> [1000,] 727 570 904 222 485 784 746 475 275 415 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.7048266 0.8083694 0.8882599 0.9421757 1.0040249 1.0144722 1.0248596 #> [2,] 0.2646638 0.4447534 0.4508229 0.4509399 0.4834092 0.5247719 0.5497563 #> [3,] 0.5625080 0.7485735 0.8228400 0.8580177 0.9447221 1.0220488 1.0268545 #> [4,] 0.2337535 0.3869610 0.3987572 0.4311430 0.4322313 0.5076585 0.5243604 #> [5,] 0.3071985 0.4135715 0.4187607 0.4275021 0.4449949 0.4480393 0.4862072 #> [6,] 0.8162260 1.3583798 1.5773534 1.6751377 1.8243273 1.8572698 1.8800572 #> [7,] 0.6913829 0.7541327 0.8888739 0.9243094 0.9559779 0.9618115 0.9714105 #> [8,] 0.5987923 0.6929355 0.7099335 0.7234910 0.7436081 0.7586708 0.7788853 #> [9,] 0.5972976 0.6019840 0.6145234 0.6544568 0.6621461 0.7077951 0.7314612 #> [10,] 0.8162260 1.1388998 1.5691586 1.6517703 1.7160733 1.8003366 1.8312074 #> [11,] 0.4387941 0.4894021 0.5311648 0.5452097 0.6617801 0.6809391 0.7264780 #> [12,] 0.3486888 0.3676536 0.4109260 0.4335793 0.4539022 0.4627766 0.5623945 #> [13,] 1.6469234 1.7292788 1.8007623 1.8432932 1.8877758 1.9391614 1.9416047 #> [14,] 0.3999155 0.4020145 0.4489713 0.4504919 0.4615817 0.4779987 0.5029892 #> [15,] 0.2436403 0.3935127 0.4089076 0.5083646 0.5488945 0.6392918 0.6604720 #> [16,] 0.3293306 0.4893257 0.5458239 0.5741937 0.5897778 0.6447046 0.6659995 #> [17,] 0.6977377 0.8617257 1.0572980 1.1038723 1.1918617 1.2138401 1.2184243 #> [18,] 0.5040802 0.5627145 0.5672224 0.5718031 0.6170351 0.6306272 0.6928692 #> [19,] 0.2857866 0.2940478 0.5034045 0.5351798 0.5760647 0.5784438 0.5962825 #> [20,] 0.2286886 0.4625997 0.4797133 0.4798772 0.5166952 0.5390328 0.5771252 #> [21,] 0.3915093 0.4433784 0.4566071 0.4575790 0.4861910 0.5110320 0.5232558 #> [22,] 0.4862339 0.5522699 0.7315827 0.7898797 0.8151923 0.8296489 0.8669828 #> [23,] 0.3284029 0.3912126 0.4004372 0.4007780 0.4112151 0.4410145 0.4468764 #> [24,] 0.4052950 0.5732842 0.6229928 0.6652722 0.6817741 0.7026462 0.7338538 #> [25,] 0.3843435 0.4089076 0.4893418 0.6487378 0.6911578 0.7590991 0.7672499 #> [26,] 0.5589624 0.6996202 0.7212007 0.7595346 0.7693359 0.7742780 0.8258053 #> [27,] 0.5874171 0.7553784 0.8066190 0.9306648 0.9755979 1.0063360 1.0732831 #> [28,] 0.5737651 0.5889834 0.6094010 0.6560304 0.7157484 0.7175131 0.7469774 #> [29,] 0.3036663 0.5015118 0.5242928 0.6236399 0.7883728 0.8257304 0.9218299 #> [30,] 0.4241206 0.5605815 0.6033279 0.6619944 0.6658839 0.6841600 0.7411967 #> [31,] 0.7023488 0.7160217 0.8238013 0.8848219 0.9756494 0.9797639 0.9837719 #> [32,] 0.5225561 0.5611636 0.5643889 0.6709421 0.6924779 0.7095881 0.7157484 #> [33,] 0.5907024 0.5912506 0.6060134 0.6538817 0.7120533 0.8081701 0.8266657 #> [34,] 0.7235467 0.7685739 0.7745123 0.8247635 0.8285483 0.9214560 0.9243558 #> [35,] 0.4614993 0.4723733 0.4747357 0.5278493 0.5339777 0.5495823 0.6000009 #> [36,] 1.0280044 1.3326072 1.4438542 1.4950672 1.5100762 1.5186078 1.5462172 #> [37,] 0.3299522 0.3549475 0.4322313 0.4429551 0.5124888 0.5439571 0.5654315 #> [38,] 0.2847934 0.4855697 0.4862419 0.4930848 0.4956664 0.5022562 0.5031505 #> [39,] 1.2646007 1.2969284 1.3367225 1.3488551 1.4737490 1.4945992 1.5523904 #> [40,] 0.3035479 0.3186834 0.3551038 0.4236751 0.4554516 0.4722940 0.4805071 #> [41,] 0.4353166 0.5238137 0.6346238 0.7027129 0.8082479 0.8549699 0.8945612 #> [42,] 0.3158758 0.4122767 0.5134259 0.5156644 0.5397715 0.5698328 0.5908687 #> [43,] 0.3112028 0.3118351 0.3385731 0.3728544 0.4352734 0.4380835 0.4468764 #> [44,] 0.3042617 0.4165118 0.4180805 0.5900513 0.6286326 0.6854924 0.7085806 #> [45,] 0.6570087 0.6574265 0.7661296 0.7933378 0.7996498 0.8144727 0.8169656 #> [46,] 0.4739766 0.5013117 0.6193471 0.6287011 0.6384725 0.7145823 0.7185547 #> [47,] 0.8716038 1.1291670 1.2601548 1.2731740 1.4309672 1.5484044 1.6242022 #> [48,] 0.3418835 0.3449666 0.3816936 0.3847305 0.3990696 0.4634436 0.4700327 #> [49,] 0.1960542 0.3481627 0.4300678 0.4569880 0.4627766 0.5043400 0.5348194 #> [50,] 0.5048296 0.6151931 0.7414556 0.7697675 0.7865517 0.7933014 0.8135147 #> [51,] 0.3107841 0.3815946 0.4300301 0.4810555 0.4820274 0.4976498 0.5715760 #> [52,] 0.4401777 0.4851220 0.5566957 0.5749747 0.5953573 0.6111659 0.6666715 #> [53,] 0.8284443 0.9455623 1.0445572 1.1584753 1.2246511 1.2271954 1.2533244 #> [54,] 0.5165410 0.5483363 0.5577906 0.5610397 0.6063109 0.6596769 0.6916249 #> [55,] 0.3401918 0.3740531 0.5727056 0.7549157 0.8370006 0.8412854 0.8601071 #> [56,] 0.7918233 0.8732220 0.9036908 0.9597329 0.9924884 1.1504494 1.1632732 #> [57,] 0.3175616 0.3724500 0.4292543 0.4828516 0.4994282 0.4997180 0.5676278 #> [58,] 0.4791532 0.5493708 0.5503179 0.6035151 0.6610069 0.6694261 0.7053166 #> [59,] 0.6290930 0.9289136 0.9689169 0.9733285 1.0257024 1.0359829 1.0698120 #> [60,] 0.1793892 0.4433549 0.5704134 0.6528788 0.6883512 0.7304316 0.7677203 #> [61,] 0.6116325 0.6763739 0.6778514 0.6978583 0.7198049 0.7402864 0.7830806 #> [62,] 0.3478599 0.3490601 0.3556601 0.4149381 0.5310006 0.5597738 0.6224384 #> [63,] 0.3444005 0.5210476 0.5378630 0.5725214 0.5962825 0.6565383 0.6822445 #> [64,] 0.2266562 0.3956464 0.4746320 0.4763757 0.4905728 0.5245846 0.5899013 #> [65,] 0.5080052 0.7771783 0.8011727 0.8205875 0.9891684 1.0671992 1.0835386 #> [66,] 0.8743386 1.0018150 1.1016418 1.1232248 1.1248432 1.1419690 1.1658660 #> [67,] 0.3534918 0.5119499 0.5529916 0.5719831 0.5959716 0.6538127 0.7144411 #> [68,] 0.4588987 0.5010224 0.5109920 0.5944829 0.6216318 0.6928692 0.6936148 #> [69,] 0.5602409 0.6469960 0.7002396 0.7106937 0.7325301 0.8068203 0.8211363 #> [70,] 0.2020652 0.2218200 0.4595513 0.6095924 0.6650239 0.6690871 0.6998387 #> [71,] 0.3062630 0.4370194 0.4648428 0.4746678 0.5015561 0.6170349 0.6515960 #> [72,] 0.6308904 0.7111541 0.7114052 0.7174661 0.7458210 0.7520065 0.7886171 #> [73,] 0.4637712 0.7955241 0.8066526 0.8093809 1.0033759 1.0384308 1.0415756 #> [74,] 0.4819553 0.5052221 0.5255958 0.5520613 0.5833905 0.6326745 0.6415663 #> [75,] 0.7557878 0.7656519 0.7914277 0.8048466 0.8943719 0.9215540 0.9592574 #> [76,] 0.5677734 0.6241592 0.6257073 0.6401146 0.6488231 0.6922880 0.7164694 #> [77,] 0.4402712 0.4445392 0.4796866 0.5374811 0.5765453 0.7042095 0.7100582 #> [78,] 0.4369430 0.4746678 0.5246621 0.5705223 0.5949956 0.6440177 0.6726832 #> [79,] 0.4590312 0.4811924 0.4847473 0.4859729 0.4906804 0.5147732 0.5902184 #> [80,] 0.2734565 0.4826327 0.5053076 0.5955601 0.5985745 0.6196470 0.6214573 #> [81,] 0.2202836 0.3042617 0.5492871 0.6267163 0.6952081 0.7114169 0.7539298 #> [82,] 1.1691834 1.3014405 1.3326076 1.3625383 1.4254770 1.5510179 1.5517087 #> [83,] 0.3844678 0.4266016 0.4934404 0.5010035 0.5011315 0.5246621 0.5409899 #> [84,] 0.8915097 1.1654770 1.1700043 1.3160452 1.3369395 1.4012029 1.4224582 #> [85,] 0.4650003 0.5372201 0.7173710 0.7750321 0.8502619 0.8524977 0.8613864 #> [86,] 0.4552099 0.5632050 0.6270920 0.6319328 0.6451453 0.6526862 0.6801134 #> [87,] 0.2697039 0.2989971 0.3722417 0.3864791 0.3979419 0.5044755 0.5115880 #> [88,] 0.5890583 0.6303962 0.6972553 0.8012599 0.8484478 0.8800725 0.8999855 #> [89,] 0.4085959 0.6081224 0.6106471 0.6470521 0.6488359 0.6704771 0.7069878 #> [90,] 0.2682098 0.3385731 0.3977697 0.4007780 0.4088687 0.4110661 0.4334255 #> [91,] 0.2751204 0.5993097 0.7266816 0.7926226 0.9236902 1.0381235 1.0600730 #> [92,] 0.3414077 0.6613483 0.7949358 0.9309210 0.9628605 1.0047288 1.0271264 #> [93,] 0.4188806 0.4482251 0.4985003 0.5105230 0.5590794 0.6109957 0.6160324 #> [94,] 0.2915407 0.4006096 0.5662374 0.5703396 0.6104983 0.6254522 0.6571560 #> [95,] 0.3570338 0.4294050 0.4334255 0.4494573 0.4615580 0.4654187 0.4721043 #> [96,] 0.8064250 0.8357599 0.8473472 0.8582234 0.8847025 0.9628605 0.9966873 #> [97,] 0.3610341 0.3774352 0.4789762 0.4862033 0.5030681 0.5503179 0.6050880 #> [98,] 1.0499795 1.0643079 1.2747939 1.2884228 1.3809873 1.4183431 1.4441306 #> [99,] 0.8425571 1.0012882 1.0613208 1.1160400 1.2090369 1.2196290 1.2271954 #> [100,] 0.1719823 0.2005349 0.3995663 0.4020145 0.5419647 0.5490831 0.5606193 #> [101,] 0.2832737 0.4051340 0.4424304 0.5553900 0.5557919 0.5561115 0.6074579 #> [102,] 0.5238137 0.5270508 0.5384865 0.5802341 0.6482590 0.6528788 0.7393022 #> [103,] 0.7780846 0.9160502 1.0251646 1.0758883 1.2273985 1.3250280 1.3650345 #> [104,] 0.8402520 0.9372123 0.9846061 1.1511613 1.2117447 1.3488551 1.3855672 #> [105,] 0.6289333 0.8665598 0.8765106 0.9286206 0.9619865 0.9700839 0.9714105 #> [106,] 0.2661266 0.2988244 0.3085853 0.3724500 0.4407825 0.4594065 0.5107381 #> [107,] 0.4815630 0.7025629 0.7512546 0.8192894 0.8972547 0.9296012 0.9443613 #> [108,] 0.9098251 0.9994496 1.0043093 1.0074580 1.0819849 1.2459773 1.3049420 #> [109,] 0.8127954 0.8848219 1.0958495 1.1919957 1.2672147 1.2879393 1.3004464 #> [110,] 0.3765864 0.5735751 0.6231267 0.6317110 0.6976519 0.7089059 0.7372494 #> [111,] 0.2312386 0.3439918 0.3618488 0.3632050 0.3725628 0.3938001 0.4741794 #> [112,] 0.2479778 0.3071985 0.3768641 0.3914624 0.4080576 0.4565760 0.4807659 #> [113,] 0.3536279 0.4134636 0.4428247 0.4719349 0.4721957 0.5400492 0.5448039 #> [114,] 0.4448283 0.7417602 0.7560302 0.7727866 0.8194439 0.8326412 0.8550672 #> [115,] 0.3670521 0.4335027 0.4463284 0.5121596 0.6746002 0.6782024 0.7550491 #> [116,] 0.7586708 0.7918233 0.8495530 0.8584823 0.8656548 0.8673209 0.8829771 #> [117,] 0.2824080 0.3407325 0.5028377 0.5034045 0.5364504 0.5724725 0.6111820 #> [118,] 0.6011015 0.6568896 0.6970957 0.7760345 0.7873279 0.8389859 0.8612169 #> [119,] 0.4645052 0.7627281 0.7664906 0.8466935 0.8795198 0.9012175 0.9062407 #> [120,] 0.3015907 0.3781865 0.4005184 0.4426711 0.4985003 0.5100151 0.5129489 #> [121,] 0.3449666 0.4175508 0.5115338 0.5185349 0.5413713 0.5454344 0.5565225 #> [122,] 0.2218137 0.4422711 0.4834617 0.4870908 0.6026000 0.6130869 0.6203350 #> [123,] 0.3457117 0.4862339 0.4869867 0.5838292 0.5907311 0.6927150 0.7224404 #> [124,] 0.3859147 0.5860999 0.6060134 0.6827474 0.8100991 0.8135147 0.8984934 #> [125,] 0.5688090 0.5920986 0.6839222 0.7025629 0.7132284 0.7472718 0.7557377 #> [126,] 0.3214490 0.4776311 0.7167084 0.7890833 0.8072259 0.8133698 0.8167129 #> [127,] 0.2652173 0.3418835 0.4172473 0.4641082 0.4797133 0.4834878 0.5094085 #> [128,] 0.8132454 0.8566013 0.8912662 0.9567154 0.9995281 1.0529038 1.0892656 #> [129,] 0.5173034 0.5385999 0.5909574 0.6495746 0.7144411 0.7530588 0.7793916 #> [130,] 0.3016603 0.3589144 0.3798359 0.5123829 0.6620639 0.6644755 0.6849573 #> [131,] 0.7945027 0.8052348 1.0094416 1.0380981 1.1226155 1.1483449 1.1911871 #> [132,] 0.4773687 0.4896124 0.5876542 0.6151931 0.6152917 0.6525114 0.6617801 #> [133,] 0.2957885 0.5998256 0.6250829 0.6819637 0.6955587 0.7119675 0.7181709 #> [134,] 0.8466075 0.9559963 0.9653752 0.9709938 0.9928338 1.0115360 1.0325245 #> [135,] 0.1418194 0.6279938 0.6904804 0.7189922 0.7214698 0.8149124 0.8672728 #> [136,] 0.3444005 0.3513501 0.4608625 0.5105701 0.5202147 0.5617529 0.5724725 #> [137,] 0.3534918 0.3892923 0.5173034 0.5489988 0.5815853 0.6398496 0.6930631 #> [138,] 0.4773687 0.4978547 0.6620687 0.6726346 0.6922880 0.7011757 0.7414556 #> [139,] 0.4446937 0.4528535 0.5435197 0.5998256 0.6946994 0.7683952 0.7739046 #> [140,] 0.3638288 0.4680880 0.6973555 0.7250861 0.7494283 0.7619575 0.7656392 #> [141,] 0.5383358 0.5900513 0.6787596 0.7081918 0.7108781 0.7493679 0.7631740 #> [142,] 0.2109421 0.3381355 0.4371760 0.4410435 0.5464723 0.5576133 0.5636682 #> [143,] 0.9889649 1.0560097 1.1533725 1.1601190 1.2254556 1.2703172 1.3124793 #> [144,] 0.2954659 0.4437187 0.4796866 0.5504881 0.6164471 0.6256895 0.6496666 #> [145,] 0.5080052 0.8341716 0.8432496 0.9130185 0.9294930 0.9832351 0.9880707 #> [146,] 1.4565224 1.7843633 1.7996840 1.8705359 1.8760104 1.8799524 1.9060669 #> [147,] 0.9372123 1.0059526 1.1388661 1.3188446 1.3606501 1.3640721 1.5037697 #> [148,] 0.4468204 0.5485547 0.5751650 0.5758143 0.6294308 0.6353628 0.6354754 #> [149,] 0.8402520 1.0644100 1.1333596 1.2646007 1.3188446 1.3367809 1.4007968 #> [150,] 0.3891018 0.4304763 0.4883660 0.4925399 0.5255958 0.6052847 0.6073665 #> [151,] 0.3124576 0.7553784 0.7786041 0.9153356 0.9816910 0.9895036 1.0228704 #> [152,] 0.4181309 0.4396069 0.5606495 0.7003802 0.7263227 0.8679645 0.8940518 #> [153,] 0.5745431 0.6589509 0.8750195 0.8975972 0.9072083 0.9513614 0.9557934 #> [154,] 1.0687995 1.2359980 1.5919794 1.6551029 1.8572032 1.8726503 1.9143099 #> [155,] 0.3525775 0.4188517 0.4430698 0.5028352 0.5053819 0.5655088 0.5838935 #> [156,] 0.3304958 0.4964543 0.6085589 0.7224517 0.7508667 0.7620922 0.7791763 #> [157,] 0.3737895 0.3898532 0.4004372 0.4822703 0.5067166 0.5655088 0.5766819 #> [158,] 0.2298626 0.4305420 0.4738325 0.5797168 0.6287894 0.6676141 0.6826555 #> [159,] 0.5807290 0.7780846 0.8875493 1.0127100 1.1450179 1.1916313 1.2690950 #> [160,] 0.4289125 0.5011922 0.5540639 0.6948436 0.7208311 0.7520065 0.8818430 #> [161,] 0.4241206 0.6107870 0.6229928 0.6529928 0.7129272 0.7185547 0.7243970 #> [162,] 0.3859954 0.4109260 0.4555023 0.4626248 0.4633659 0.4918698 0.5110051 #> [163,] 0.4552099 0.4855611 0.5240031 0.6194519 0.7022538 0.7035018 0.7373904 #> [164,] 0.5278035 0.7871674 0.9289076 0.9783975 1.0084421 1.0511804 1.0655496 #> [165,] 0.3799644 0.3806210 0.3934462 0.4135310 0.4970086 0.5115880 0.5358688 #> [166,] 0.5511508 0.6390218 0.6504903 0.7225953 0.7955241 0.8164118 0.8319241 #> [167,] 0.2105175 0.3815946 0.3900844 0.4083693 0.4314051 0.4369799 0.4537252 #> [168,] 0.5598617 0.6075112 0.6221261 0.6616265 0.6686554 0.9067763 0.9191234 #> [169,] 0.4829863 0.5799477 0.5957137 0.5966500 0.6040105 0.6383378 0.6721866 #> [170,] 0.2656860 0.3399969 0.4277456 0.4720053 0.5953922 0.6416989 0.6518913 #> [171,] 0.4612447 0.4740072 0.4972074 0.5100298 0.5698245 0.6111659 0.6466797 #> [172,] 0.6097647 0.6132011 0.6296337 0.6743795 0.8499388 0.8502969 0.8613284 #> [173,] 0.2503394 0.2994409 0.5889596 0.6348820 0.7158948 0.7264780 0.8250275 #> [174,] 0.3672958 0.4160685 0.4725977 0.5723332 0.5830259 0.7428737 0.7919458 #> [175,] 0.3196900 0.5194182 0.5298376 0.5435197 0.5572317 0.5750432 0.5840255 #> [176,] 0.4165118 0.5049022 0.5466477 0.6267163 0.6787596 0.6830429 0.7427972 #> [177,] 0.4497791 0.5191472 0.5219601 0.5395790 0.5416235 0.6160995 0.6362140 #> [178,] 0.9989923 1.0082055 1.1357055 1.1672237 1.1891288 1.2180572 1.2978906 #> [179,] 0.4670087 0.5287582 0.5334193 0.5478272 0.5721634 0.5743282 0.6294766 #> [180,] 0.7664906 0.8228400 0.8585636 0.8969156 0.9115949 1.0017615 1.0335018 #> [181,] 0.1362916 0.3588431 0.4794574 0.5286231 0.5310006 0.5579764 0.5622382 #> [182,] 0.4579561 0.6701687 0.6773683 0.6883186 0.6996202 0.7164687 0.7429378 #> [183,] 0.7755421 0.8804326 0.9078180 1.1672237 1.1684649 1.1729379 1.2657212 #> [184,] 0.3119060 0.4224714 0.4575790 0.4683933 0.5423035 0.5515163 0.5919193 #> [185,] 0.5909574 0.6332117 0.6853056 0.7348973 0.7398255 0.7435528 0.7496748 #> [186,] 0.5069319 0.5264586 0.5577906 0.5580674 0.6443203 0.7141846 0.7309459 #> [187,] 0.3943873 0.3955992 0.4359817 0.4794574 0.4972336 0.5284220 0.5321714 #> [188,] 0.2682098 0.3284029 0.3530090 0.3678781 0.3728544 0.4075196 0.4294050 #> [189,] 0.2991949 0.3692605 0.5451733 0.5594507 0.5624723 0.6126309 0.6801919 #> [190,] 0.3898532 0.5403542 0.5727275 0.6038711 0.6124425 0.6162748 0.6253421 #> [191,] 0.4279815 0.4480237 0.4493396 0.4829012 0.4842416 0.4862033 0.4921528 #> [192,] 0.3214490 0.5980038 0.6570703 0.8534005 0.9127974 0.9277686 0.9352843 #> [193,] 0.3394844 0.3853540 0.5972976 0.6104983 0.6413421 0.6960078 0.7603939 #> [194,] 0.2752557 0.4161253 0.4172473 0.4176578 0.4396160 0.4940575 0.4972336 #> [195,] 0.2578455 0.4328642 0.6321894 0.6537618 0.6839005 0.7108781 0.7116276 #> [196,] 0.2791779 0.4024938 0.4860003 0.5316131 0.5517135 0.5688071 0.5936018 #> [197,] 0.5488358 0.5890583 0.5960984 0.6170807 0.6979232 0.7174303 0.7531728 #> [198,] 0.6311555 0.6573906 0.7012529 0.7290197 0.7398255 0.7983180 0.8183816 #> [199,] 0.3642713 0.5072499 0.5454344 0.5506138 0.5703296 0.5830259 0.6540970 #> [200,] 0.4726632 0.5489127 0.5812676 0.6390218 0.6806105 0.6894002 0.7998378 #> [201,] 0.8613284 0.8640089 0.8871901 1.0109677 1.0149250 1.0495105 1.1598583 #> [202,] 0.2164501 0.2860089 0.3381355 0.4283662 0.4480237 0.4736562 0.4737440 #> [203,] 0.3527183 0.4759184 0.5130482 0.5664733 0.5982752 0.6008386 0.6296641 #> [204,] 0.4037595 0.4106354 0.4734001 0.5075357 0.6279512 0.6381214 0.6766374 #> [205,] 0.4613472 0.5733389 0.6536408 0.6658839 0.7161500 0.7405805 0.7691944 #> [206,] 0.7557748 1.9032298 1.9909608 2.0085635 2.1575094 2.1825889 2.2086869 #> [207,] 0.5352316 0.5703028 0.6775417 0.8012966 0.8110856 0.8860435 0.8907570 #> [208,] 0.3399969 0.5087585 0.5108792 0.5812280 0.6684315 0.7307516 0.7418706 #> [209,] 0.2266562 0.4415222 0.5105230 0.5281845 0.5290100 0.5508943 0.5960563 #> [210,] 0.5144181 0.6123625 0.6639630 0.6989697 0.7102417 0.7557781 0.7588572 #> [211,] 0.2442281 0.3479301 0.4439951 0.4642895 0.4872823 0.5358688 0.5773974 #> [212,] 0.4894648 0.5949956 0.6911686 0.6929355 0.6989893 0.7356213 0.7939766 #> [213,] 0.3559836 0.4992742 0.5002217 0.5591558 0.5654315 0.5836867 0.5955450 #> [214,] 0.5745717 0.5981669 0.6085589 0.6228606 0.6447953 0.6589045 0.6828360 #> [215,] 0.4768436 0.5065259 0.5627565 0.7367339 0.7530721 0.7609732 0.7615598 #> [216,] 0.3559836 0.4424304 0.4907152 0.4921030 0.5727925 0.5907024 0.6593339 #> [217,] 0.3052188 0.4374722 0.4523524 0.4868032 0.5092464 0.5101514 0.5210982 #> [218,] 0.3974524 0.4433549 0.4991711 0.5081086 0.5470574 0.6239051 0.6253421 #> [219,] 0.4592319 0.4923259 0.4934713 0.5198035 0.5287760 0.5481299 0.5616279 #> [220,] 0.9243558 0.9301088 1.1803583 1.1868249 1.2316803 1.3025945 1.3337114 #> [221,] 0.1765483 0.6991221 0.7436371 0.8061793 0.8457712 0.8822349 0.9095427 #> [222,] 0.4114465 0.6579970 0.7081140 0.7223757 1.0008298 1.0602373 1.0841085 #> [223,] 0.4855611 0.5070293 0.5399607 0.5737582 0.5895435 0.6067046 0.6136829 #> [224,] 0.5725214 0.6602495 0.7616623 0.7667874 0.8101183 0.8179785 0.8180067 #> [225,] 0.2694920 0.2950353 0.3692134 0.3719505 0.4592028 0.4992582 0.4999803 #> [226,] 0.3343100 0.4750387 0.4832977 0.5889596 0.6089304 0.6234424 0.6648360 #> [227,] 0.4123463 0.4757520 0.5777199 0.5832221 0.6133775 0.6400521 0.6722586 #> [228,] 0.4657427 0.5010224 0.5418577 0.6170351 0.6605389 0.6997621 0.7465732 #> [229,] 0.4051340 0.4735834 0.4921030 0.5809545 0.5859734 0.6244618 0.6464568 #> [230,] 0.3891018 0.4681615 0.4966708 0.5392035 0.5647906 0.5702987 0.5720533 #> [231,] 0.2547535 0.3056966 0.4740072 0.5703028 0.5714908 0.5925200 0.5940281 #> [232,] 0.9555092 1.1303360 1.1388998 1.1594726 1.2539825 1.2605820 1.2764892 #> [233,] 0.2109421 0.2860089 0.3835569 0.4145382 0.4747144 0.5700085 0.5719874 #> [234,] 0.5355423 0.5872310 0.6488231 0.6511711 0.6514708 0.6620687 0.6808537 #> [235,] 0.3692605 0.4257540 0.5174856 0.5175952 0.5428824 0.5457228 0.6078203 #> [236,] 0.5011913 0.5154095 0.5271494 0.6122418 0.6528631 0.6848110 0.7000411 #> [237,] 0.3951226 0.4605785 0.4844073 0.4856967 0.5214682 0.5475801 0.5896545 #> [238,] 0.5555567 0.6041729 0.6486946 0.6611702 0.7187924 0.7520435 0.7555620 #> [239,] 0.4374822 0.9087339 0.9675360 1.0043093 1.0416752 1.1352041 1.2382200 #> [240,] 0.3848214 0.4431134 0.6011015 0.6048017 0.6541363 0.7072750 0.7132638 #> [241,] 0.7002668 0.8351929 0.9067863 0.9174519 0.9210116 0.9361946 0.9924884 #> [242,] 0.2230059 0.3106024 0.5786764 0.6139568 0.6573238 0.6714148 0.7042095 #> [243,] 0.3631836 0.7214698 0.7647506 0.7948385 0.8413211 0.8450892 0.8465010 #> [244,] 0.8610764 0.9358077 0.9801249 0.9993196 1.0108796 1.0252545 1.0280008 #> [245,] 0.3080751 0.3107541 0.3160396 0.3999155 0.4615110 0.5349535 0.5481820 #> [246,] 0.5765453 0.6558690 0.7005177 0.8138565 0.8658315 0.8670046 1.0029064 #> [247,] 0.3342053 0.4390372 0.4789193 0.4895538 0.5284220 0.5497563 0.5579764 #> [248,] 0.5187787 0.5655398 0.5926421 0.6029241 0.6503403 0.6525829 0.7330386 #> [249,] 0.7742780 0.8390463 0.8879565 0.8886585 0.9933613 1.0113779 1.1279124 #> [250,] 0.8413211 0.9289766 0.9943680 1.0636714 1.1219632 1.1385667 1.1523223 #> [251,] 0.4423658 0.4739314 0.6919364 0.7125685 0.7307516 0.7743503 0.7797978 #> [252,] 0.7704966 0.7745123 0.9301088 0.9765920 0.9806921 1.0033158 1.0061996 #> [253,] 0.3183395 0.3478599 0.4142914 0.4497791 0.4867866 0.5707005 0.5740675 #> [254,] 0.6927369 1.1663722 1.3124793 1.3924713 1.6727395 1.6976233 1.7778336 #> [255,] 0.1963591 0.4167413 0.5624760 0.5832461 0.6152891 0.6170807 0.6645390 #> [256,] 0.3642874 0.6713886 0.8404900 0.8498604 0.8597038 0.9465393 0.9807787 #> [257,] 0.4127776 0.4727399 0.4924958 0.6556979 0.6588958 0.6865332 0.6979232 #> [258,] 0.4670087 0.4929666 0.5014880 0.5799477 0.6056041 0.6182922 0.6302956 #> [259,] 0.4788930 0.7232206 0.7920760 0.8184894 0.8846523 0.8963857 0.9163078 #> [260,] 0.3298885 0.6568896 0.6602341 0.8319435 0.8572144 0.8684830 0.8820444 #> [261,] 0.3461297 0.3779047 0.3847305 0.4344955 0.4791532 0.5155695 0.5202147 #> [262,] 0.4019718 0.5540441 0.6682981 0.6746002 0.7254377 0.8308921 0.8470057 #> [263,] 0.4523524 0.4550712 0.4623505 0.5549466 0.5637004 0.5863843 0.5943596 #> [264,] 0.2353301 0.6801134 0.7381024 0.7402864 0.7923956 0.8268060 0.8741396 #> [265,] 0.2656860 0.4940036 0.5083646 0.5087585 0.5427427 0.6460751 0.6637709 #> [266,] 0.6714131 0.8008904 0.9622620 1.2575237 1.2897530 1.2925865 1.3800389 #> [267,] 0.7923956 0.8258942 0.8966985 0.9029819 1.0015818 1.0553813 1.1269123 #> [268,] 0.3062630 0.3782222 0.4369430 0.4465721 0.4803380 0.5010035 0.5324977 #> [269,] 0.5009687 0.5250248 0.5428234 0.5473297 0.5942332 0.5955329 0.6020509 #> [270,] 0.3694694 0.4990745 0.5044755 0.5073828 0.5221797 0.5618657 0.6075119 #> [271,] 2.1686806 2.2086869 2.2810268 2.3181527 2.3606029 2.4186803 2.4244825 #> [272,] 0.3530585 0.4734049 0.4747357 0.4937261 0.5221003 0.5710723 0.6007936 #> [273,] 0.7566811 0.9849265 1.0073145 1.0687995 1.0916150 1.2922580 1.3093768 #> [274,] 0.2474450 0.2828653 0.3439918 0.4110661 0.4494573 0.4789884 0.5098917 #> [275,] 0.4648660 0.5489127 0.5699855 0.5739238 0.5982141 0.6347572 0.6426404 #> [276,] 0.6293818 0.6993759 0.8308841 0.8464172 0.8495530 0.8732220 0.9309170 #> [277,] 0.9493566 0.9797639 1.1373486 1.1400396 1.1867739 1.1919957 1.1947125 #> [278,] 0.5676543 0.5906277 0.6551925 0.6584415 0.6663706 0.7028327 0.7198973 #> [279,] 0.3376254 0.3556077 0.4071768 0.4304381 0.4513943 0.5140644 0.5481116 #> [280,] 0.4443316 1.0295641 1.0404369 1.1279124 1.1370093 1.1961986 1.2268191 #> [281,] 0.4701398 0.4753663 0.4798772 0.5016437 0.5879416 0.6611702 0.6612903 #> [282,] 0.5963667 0.6405775 0.7198823 0.7262374 0.7851440 0.7967534 0.8175944 #> [283,] 0.4734001 0.4819553 0.5610950 0.6125552 0.6348908 0.6632266 0.7207668 #> [284,] 0.3943873 0.4328513 0.4789193 0.4850523 0.5263223 0.5286231 0.5301923 #> [285,] 0.3747992 0.5221909 0.5895435 0.6116325 0.6512875 0.6895740 0.7035018 #> [286,] 0.6183229 0.7769803 0.7859109 0.8773568 0.9343448 0.9549166 0.9764120 #> [287,] 0.6583572 0.7055782 0.7864954 0.8128896 0.8280572 0.8424534 0.8690105 #> [288,] 0.6183229 0.6824711 0.6825961 0.6918185 0.7775237 0.7884203 0.7974064 #> [289,] 0.3075964 0.3862536 0.4024244 0.4145382 0.4283662 0.4921528 0.5025113 #> [290,] 0.1998153 0.3016603 0.4811913 0.5119983 0.5129489 0.5203712 0.5852687 #> [291,] 0.3000201 0.4148677 0.4633867 0.4723733 0.4938870 0.5325850 0.5666957 #> [292,] 0.5232328 0.5400492 0.5425748 0.5460978 0.5610397 0.6117036 0.6231267 #> [293,] 0.4257540 0.4292543 0.4966009 0.5107381 0.5594507 0.6267861 0.6400521 #> [294,] 0.2218137 0.3576403 0.4037885 0.6063109 0.6104833 0.6138500 0.6443203 #> [295,] 0.5807290 0.6296239 0.7645480 0.9160502 0.9723325 1.0319325 1.1000059 #> [296,] 0.3246618 0.4604986 0.5069319 0.5312717 0.5520613 0.6287894 0.6300649 #> [297,] 1.3482078 1.4732361 1.4893027 1.5836625 1.6339643 1.6551029 1.6707667 #> [298,] 0.2296156 0.3813579 0.5184814 0.5243604 0.5431865 0.5595356 0.5634057 #> [299,] 0.3972822 0.4965400 0.5194182 0.6076082 0.6290822 0.6760092 0.6789478 #> [300,] 0.4448283 0.5734645 0.7645480 0.7941773 0.8390236 0.8422752 0.8519435 #> [301,] 0.3365770 0.4171835 0.4481301 0.4496889 0.4506818 0.4891075 0.5171006 #> [302,] 0.2569293 0.3341905 0.3618488 0.4208733 0.4286491 0.4562478 0.4609700 #> [303,] 0.3582576 0.4393601 0.4750677 0.4935474 0.5200496 0.6106381 0.6153815 #> [304,] 0.3985065 0.4481301 0.5704134 0.5758475 0.6468491 0.6484340 0.6493102 #> [305,] 0.5517353 0.6800489 0.8270838 0.8313950 0.8533623 0.8767628 0.8883322 #> [306,] 0.2020652 0.2753051 0.4636687 0.5449661 0.5473297 0.5852984 0.6668215 #> [307,] 0.3414077 0.3471195 0.6384725 0.7448607 0.7551961 0.8762055 0.9248971 #> [308,] 0.3537939 0.4440057 0.4910911 0.5114391 0.5476241 0.5530247 0.5760182 #> [309,] 0.4916895 0.5995342 0.6447800 0.7485735 0.8892563 0.9886686 0.9982018 #> [310,] 0.5312083 0.6139568 0.6994112 0.7266816 0.7379023 0.8330002 0.8425017 #> [311,] 0.6927369 0.8882599 1.0306228 1.0560097 1.2125442 1.3396713 1.3635505 #> [312,] 0.4636346 0.5320051 0.7262585 0.7761523 0.7983180 0.8087218 0.9527683 #> [313,] 0.5474971 0.5872310 0.6326847 0.6726346 0.7840795 0.7865517 0.8100991 #> [314,] 0.3160586 0.3882206 0.4023933 0.4537252 0.4826127 0.4930848 0.5560572 #> [315,] 0.2954659 0.4402712 0.6798214 0.6936148 0.7349776 0.7459878 0.7593610 #> [316,] 0.6696794 0.6865332 0.6996609 0.7618664 0.7626236 0.7792351 0.8628118 #> [317,] 0.4335027 0.4585807 0.5434027 0.5540441 0.6385622 0.6481007 0.6811298 #> [318,] 0.2752557 0.2994951 0.3710504 0.4359817 0.5036908 0.5098917 0.5263223 #> [319,] 0.5254022 0.6111820 0.6481640 0.6570087 0.6724047 0.6794556 0.7683324 #> [320,] 0.4465721 0.4648428 0.5395790 0.5984565 0.6111190 0.6136581 0.6233926 #> [321,] 0.7127578 0.7436371 0.7673176 0.8291398 0.8413863 0.8558235 1.0156060 #> [322,] 0.4470890 0.5076585 0.5124888 0.5231660 0.5373169 0.5393927 0.5440362 #> [323,] 0.4066972 0.4586935 0.6145234 0.6310264 0.6404431 0.6568517 0.6626158 #> [324,] 0.4874757 0.7017439 0.7727866 0.7933014 0.8831344 0.9190963 0.9253171 #> [325,] 0.2832737 0.4406743 0.4510282 0.4699401 0.4907152 0.5575169 0.5591558 #> [326,] 0.4557063 0.4871143 0.4931461 0.5019206 0.5051712 0.5052221 0.5481116 #> [327,] 0.7143952 0.7606851 0.7677387 0.7730754 0.7746293 0.7919458 0.8392739 #> [328,] 0.2824080 0.3459307 0.3787681 0.4735834 0.5561115 0.5666532 0.5784076 #> [329,] 1.0676613 1.5983391 1.7461672 1.8718903 1.9509921 1.9752659 1.9934865 #> [330,] 0.2656600 0.4197421 0.4314120 0.4393601 0.5153055 0.5641456 0.6239847 #> [331,] 0.8832363 0.9567154 1.2394640 1.4999657 1.5093249 1.5724634 1.6109561 #> [332,] 0.3457117 0.5522699 0.6965300 0.7437559 0.7467575 0.7628491 0.7762495 #> [333,] 0.3895432 0.4687791 0.5209139 0.5944829 0.6893626 0.7648775 0.7656143 #> [334,] 0.4374755 0.5166567 0.5836867 0.6258101 0.6296641 0.6343839 0.6538817 #> [335,] 0.2724458 0.4483847 0.4595417 0.5615556 0.5907221 0.6050880 0.6080600 #> [336,] 0.4443316 0.6424847 0.8610764 0.8815996 0.9384542 0.9568739 1.0199679 #> [337,] 0.8617257 0.8710661 0.9524282 1.0021075 1.0310961 1.0416989 1.0630547 #> [338,] 0.3631836 0.5953177 0.6319675 0.6528631 0.7359611 0.7627102 0.8105955 #> [339,] 0.4309298 0.4604986 0.4925399 0.4966708 0.4968930 0.5229099 0.5622740 #> [340,] 0.5696837 0.5945664 0.6140291 0.6607465 0.6679704 0.6915614 0.7433810 #> [341,] 0.5259341 0.6778514 0.7219848 0.7570375 0.7698148 0.8017146 0.8072259 #> [342,] 0.2217617 0.2988244 0.3202706 0.3326814 0.4828516 0.4954094 0.6143176 #> [343,] 0.2857866 0.4578845 0.5097455 0.5696837 0.7037830 0.7221809 0.7342913 #> [344,] 0.2499468 0.4080576 0.5500536 0.5505802 0.5763671 0.5945664 0.6108302 #> [345,] 0.4725977 0.5025481 0.5506138 0.6319652 0.7194182 0.7232206 0.7233274 #> [346,] 0.7372494 0.7678418 0.7828068 0.8265162 0.8465010 0.8489286 0.8980172 #> [347,] 0.4087473 0.4442985 0.4910911 0.4938870 0.4999916 0.5135788 0.5271494 #> [348,] 0.3932012 0.4194612 0.4335793 0.4401427 0.4459262 0.4721957 0.4750677 #> [349,] 0.2273583 0.3806511 0.4652016 0.4704623 0.4829322 0.5894456 0.5898104 #> [350,] 0.1983044 0.5860999 0.5923463 0.6525829 0.6622607 0.8927730 0.9317428 #> [351,] 0.3935127 0.4116965 0.4475573 0.4893418 0.5434911 0.5978571 0.6817049 #> [352,] 0.6198571 0.6364684 0.6631117 0.7236107 0.7350833 0.7603939 0.8215576 #> [353,] 0.3747890 0.4972074 0.5067166 0.5456494 0.5765121 0.5823562 0.6178335 #> [354,] 0.4482251 0.4768116 0.4777298 0.4975465 0.5205927 0.5351490 0.5767044 #> [355,] 0.5921002 0.8509328 0.8883322 1.3493029 1.4194159 1.5123797 1.5465838 #> [356,] 0.6469960 0.6535593 0.6651903 0.6765484 0.7467857 0.8138460 0.8155243 #> [357,] 0.5297576 0.5460581 0.5752158 0.6501874 0.6887962 0.6977100 0.7395676 #> [358,] 0.4777599 0.5337423 0.5889834 0.6460212 0.6903412 0.7210871 0.8240193 #> [359,] 0.4142914 0.5176881 0.5277014 0.6106471 0.6123415 0.6224384 0.6269097 #> [360,] 0.4919594 0.5453030 0.5484186 0.5550756 0.6550249 0.6896709 0.7104783 #> [361,] 0.4577316 0.5203527 0.5270669 0.5484186 0.6130869 0.6325047 0.6629479 #> [362,] 0.5048736 0.6315611 0.6460212 0.6517485 0.7300337 0.7449540 0.7578651 #> [363,] 0.6151594 0.6622607 0.9363069 0.9611949 0.9692649 0.9902827 1.0039163 #> [364,] 0.6645390 0.7595542 0.7690297 0.8238071 0.8250804 0.9323085 0.9492159 #> [365,] 0.4801832 0.5703396 0.6265655 0.6522410 0.6744898 0.6847840 0.6887962 #> [366,] 0.6241592 0.6385163 0.6849981 0.7652407 0.8636321 0.8665598 0.8747543 #> [367,] 0.4036686 0.4074138 0.5643889 0.5962516 0.6315293 0.7345245 0.7536984 #> [368,] 0.5929305 0.7351369 0.8083417 0.9341815 0.9427737 1.0242711 1.0624381 #> [369,] 0.4695051 0.4996085 0.5723332 0.6187645 0.8859597 0.8961061 0.9085786 #> [370,] 0.3556077 0.3990696 0.4175508 0.4701749 0.5415097 0.5704060 0.5748674 #> [371,] 0.5189913 0.6270920 0.7058054 0.7151584 0.7381024 0.7822497 0.8412011 #> [372,] 0.1821476 0.4550712 0.5155402 0.6817049 0.7198049 0.7698148 0.7935792 #> [373,] 0.4188806 0.5119499 0.5290100 0.5673679 0.6157251 0.6398496 0.6468491 #> [374,] 0.6158580 0.7952401 0.8465456 0.9759081 0.9771859 1.0338753 1.0490758 #> [375,] 0.3524337 0.3536279 0.3891551 0.4401427 0.4555023 0.4699200 0.5623945 #> [376,] 1.2005248 1.2138753 1.3149943 1.3170209 1.3290479 1.3774349 1.4527436 #> [377,] 0.3000201 0.3066120 0.3272235 0.3809927 0.4125014 0.4249279 0.4442985 #> [378,] 0.3853540 0.4068972 0.4964543 0.5422557 0.6198571 0.6254522 0.6384770 #> [379,] 0.3454563 0.3713510 0.4965400 0.5224370 0.6194128 0.6682066 0.7175131 #> [380,] 0.3392109 0.4181309 0.6375285 0.7202229 0.7429733 0.7588076 0.7750321 #> [381,] 0.3905628 0.4585807 0.5287760 0.6011118 0.6414148 0.6638869 0.6706265 #> [382,] 0.6376604 0.6560304 0.7972184 0.8490657 0.8542834 0.9027296 0.9437868 #> [383,] 0.1362916 0.3912933 0.3955992 0.4328513 0.4390372 0.5727553 0.6178691 #> [384,] 0.3091575 0.5322739 0.5451491 0.5872667 0.6306143 0.6480375 0.6584415 #> [385,] 0.1628608 0.7103740 1.0996304 1.1373904 1.1416821 1.1607203 1.2325702 #> [386,] 0.3784988 0.4051373 0.4406743 0.4447534 0.4783833 0.4970822 0.5196511 #> [387,] 0.4739314 0.5851950 0.6565383 0.6904380 0.7537516 0.8005908 0.8106037 #> [388,] 0.4437187 0.4687791 0.5177161 0.5509537 0.5732842 0.6291464 0.6798214 #> [389,] 0.3113292 0.4423658 0.6108399 0.6132011 0.8419828 0.8532589 0.8539395 #> [390,] 0.5042632 0.5047298 0.6332989 0.6573906 0.6640788 0.7308810 0.7348973 #> [391,] 0.7233274 0.7667874 0.7677387 0.8072730 0.8238013 0.8640089 0.8686242 #> [392,] 0.3895432 0.4814243 0.5017388 0.5109920 0.5509537 0.5559009 0.5684539 #> [393,] 0.3934241 0.4134636 0.4875948 0.4968930 0.4970086 0.5114391 0.5413453 #> [394,] 0.7262374 0.7883433 0.8249225 0.8961636 0.9529310 1.0486522 1.0517503 #> [395,] 0.1257555 0.4669204 0.4789762 0.5074462 0.5550756 0.6272511 0.6629479 #> [396,] 0.7127578 0.9761487 1.0934914 1.1792269 1.2529735 1.2647336 1.2691116 #> [397,] 0.2353301 0.5189913 0.6319328 0.6763739 0.8770099 0.9029819 0.9047004 #> [398,] 0.4036686 0.6553439 0.7098716 0.7248484 0.7313914 0.7385971 0.8750195 #> [399,] 0.3202706 0.3913705 0.4149331 0.4594065 0.4824973 0.4997180 0.5302900 #> [400,] 0.3830567 0.4335794 0.6856147 0.7246882 0.7279501 0.8605461 0.8679605 #> [401,] 0.3458029 0.6856147 0.6893942 0.7525894 0.7639661 0.7671241 0.8066190 #> [402,] 0.3184559 0.4518358 0.5047298 0.6410868 0.6615776 0.7332994 0.7435528 #> [403,] 0.3710223 0.3747890 0.3890036 0.4005514 0.4566556 0.4822703 0.4933322 #> [404,] 0.3525775 0.3633023 0.3969505 0.4275021 0.4805071 0.4826465 0.5098671 #> [405,] 0.7915908 1.0149250 1.1095494 1.1976677 1.2020657 1.2915119 1.4095128 #> [406,] 0.3285568 0.4314120 0.4459262 0.4767470 0.4802200 0.4935474 0.5290869 #> [407,] 0.3213510 0.4834092 0.5131636 0.5381790 0.5893552 0.5904987 0.6029693 #> [408,] 0.3178739 0.3953197 0.5304649 0.5385771 0.5808212 0.6046218 0.6353393 #> [409,] 0.4980218 0.5544410 0.5735751 0.6319675 0.6597138 0.7081038 0.7502911 #> [410,] 0.4111658 0.6511711 0.6553934 0.7264462 0.7492739 0.7716554 0.8001210 #> [411,] 0.6332989 0.7615951 0.8218535 0.8311973 0.9326075 0.9754550 0.9913331 #> [412,] 0.5250252 0.6174626 0.6315293 0.6413620 0.6457151 0.6603378 0.6631487 #> [413,] 0.2296156 0.3987572 0.4429551 0.4651612 0.4720911 0.5008382 0.5440362 #> [414,] 0.4051202 0.4769180 0.7755421 0.9211892 0.9332052 0.9669258 1.0150922 #> [415,] 0.1457126 0.5002871 0.5610436 0.5681428 0.5699855 0.7477874 0.7981500 #> [416,] 0.4056079 0.5185333 0.5372201 0.5717254 0.6669696 0.6682361 0.6959216 #> [417,] 0.5261482 0.5872667 0.5905954 0.6268633 0.6326371 0.6944146 0.7490721 #> [418,] 0.2546616 0.5221003 0.5281845 0.5339777 0.5899013 0.5936018 0.6222072 #> [419,] 0.5121596 0.6132402 0.7254377 0.7297429 0.7689811 0.7979053 0.8296740 #> [420,] 0.5019129 0.5067543 0.5348742 0.5720533 0.6073665 0.6227840 0.6371878 #> [421,] 0.5492871 0.6242689 0.6266138 0.7856933 0.8007903 0.8282300 0.8867929 #> [422,] 0.4955922 0.5857594 0.5938233 0.6401902 0.6480952 0.6741597 0.6915875 #> [423,] 0.4430698 0.4549399 0.4942273 0.5141040 0.5802341 0.5968755 0.6428334 #> [424,] 0.3802711 0.3884973 0.6605389 0.7177136 0.7344810 0.8601750 0.8970716 #> [425,] 0.3481627 0.3920226 0.3926587 0.4059614 0.4381897 0.4934713 0.5628062 #> [426,] 0.9883830 1.0004716 1.0509800 1.0558282 1.1139603 1.1253654 1.1421931 #> [427,] 0.4777599 0.7755872 0.8128896 0.8477208 0.8490090 0.8849198 0.9162768 #> [428,] 0.3918320 0.4289125 0.6084791 0.6263253 0.7111541 0.8128610 0.8505465 #> [429,] 0.5138186 0.6642021 0.6690111 0.6806061 0.6947922 0.7058257 0.7114169 #> [430,] 0.3688427 0.3948392 0.4609358 0.5423035 0.5549392 0.5716828 0.6080963 #> [431,] 1.0730223 1.4578942 1.4752809 1.5188541 1.5335735 1.5639044 1.5855170 #> [432,] 0.6152174 0.6268633 0.6339550 0.7537199 0.8068748 0.8384083 0.8432481 #> [433,] 0.4116965 0.5155402 0.5488945 0.5653569 0.7218092 0.7590991 0.7820999 #> [434,] 0.4125014 0.4983567 0.4999916 0.5154095 0.5325850 0.5391964 0.5422872 #> [435,] 0.4123463 0.4869867 0.5505204 0.5944104 0.6481640 0.6965300 0.7600829 #> [436,] 0.3806511 0.3838156 0.4368219 0.4897840 0.5797199 0.6907963 0.6946080 #> [437,] 0.2618901 0.4174240 0.5035822 0.5413713 0.5488100 0.5831975 0.5879309 #> [438,] 0.4959007 0.6457151 0.7176706 0.7920279 0.8368992 0.8614404 0.9128021 #> [439,] 0.4485787 0.5459737 0.5677734 0.6356550 0.6956782 0.7170209 0.7448506 #> [440,] 0.3514631 0.5187787 0.5466477 0.6713886 0.6968107 0.8028419 0.8264479 #> [441,] 0.5425146 0.7626236 0.7633689 0.7675038 0.7677695 0.7694295 0.7838109 #> [442,] 0.6296337 0.7434690 0.7822497 0.8595808 0.8613229 0.8841422 0.8859597 #> [443,] 0.2578455 0.3417088 0.5196555 0.5383358 0.6234864 0.6611541 0.6897736 #> [444,] 0.3304958 0.5745717 0.6384770 0.8066526 0.8386210 0.8411821 0.8508133 #> [445,] 0.1960542 0.3676536 0.3926587 0.3980096 0.4613971 0.5085484 0.5577556 #> [446,] 0.2991949 0.5174856 0.5902651 0.6158555 0.6207376 0.6400436 0.6540970 #> [447,] 0.5555567 0.8119474 0.9274681 0.9510667 0.9553364 0.9636199 0.9896622 #> [448,] 0.3136491 0.4379508 0.4750387 0.4872823 0.5311648 0.6108173 0.6707253 #> [449,] 0.2922924 0.2928507 0.3874939 0.4891075 0.4980564 0.5681757 0.5693549 #> [450,] 0.4645052 0.5734645 0.6892862 0.7327970 0.8550672 0.9059494 0.9226508 #> [451,] 0.3776611 0.4051202 0.7571150 0.7891617 0.8804326 0.9623114 1.1344319 #> [452,] 0.2618901 0.3390154 0.4804254 0.5135363 0.5185349 0.5615556 0.5796006 #> [453,] 0.4366422 0.5072311 0.5238620 0.5409284 0.5454542 0.7176306 0.7218152 #> [454,] 0.3352511 0.3513501 0.3779047 0.4895538 0.5026705 0.5364504 0.5378630 #> [455,] 0.6907413 0.7005177 0.9384542 1.0699758 1.0746198 1.0962976 1.1049130 #> [456,] 0.4614993 0.5081797 0.5610950 0.5666957 0.6122418 0.6170191 0.6381189 #> [457,] 0.5955406 0.6458231 0.7036961 0.7863549 0.8026338 0.8140232 0.8283215 #> [458,] 0.2289517 0.3067851 0.3186834 0.3819500 0.4005514 0.4741237 0.4938784 #> [459,] 0.5252485 0.5339298 0.5489988 0.5691434 0.6311555 0.6720264 0.6812624 #> [460,] 0.4374822 0.7195262 0.9098251 0.9343713 0.9505368 0.9764738 1.0424884 #> [461,] 0.2996762 0.3490601 0.3588431 0.3912933 0.5119789 0.5439571 0.5553900 #> [462,] 0.3660383 0.3848525 0.4423240 0.4423984 0.4764335 0.5007229 0.5412566 #> [463,] 1.1788565 1.3423411 1.4133923 1.4198957 1.4895292 1.5667001 1.6822037 #> [464,] 0.6225775 0.9153356 1.0056298 1.0156434 1.0508046 1.1443681 1.1523387 #> [465,] 0.3458029 0.3830567 0.5383580 0.6264371 0.7775237 0.7781291 0.8263970 #> [466,] 0.3757904 0.4253779 0.5240031 0.5697414 0.6078203 0.6136829 0.6402618 #> [467,] 0.2791779 0.4828984 0.4913224 0.5192799 0.5194278 0.5242562 0.6222072 #> [468,] 0.1412353 0.3066120 0.3913374 0.4314051 0.4435253 0.4633867 0.4826127 #> [469,] 0.3091575 0.5814689 0.5906277 0.5992562 0.6326371 0.6469669 0.6984444 #> [470,] 0.5165410 0.5225561 0.5504840 0.6094010 0.6236399 0.7190391 0.7255185 #> [471,] 0.3905628 0.4570374 0.4923259 0.5048736 0.5072869 0.5268461 0.5910757 #> [472,] 0.5606495 0.7588076 0.7928859 0.8473664 0.9024861 0.9034418 0.9504198 #> [473,] 0.6265314 0.7195262 0.8195750 0.9087339 0.9994496 1.0561105 1.1280151 #> [474,] 0.3556601 0.5642782 0.5707005 0.5888508 0.6033136 0.6389466 0.6528188 #> [475,] 0.4024483 0.5136618 0.6266999 0.6293683 0.6331598 0.6568448 0.7223757 #> [476,] 0.2569293 0.3657331 0.4026997 0.4276793 0.4572070 0.4812868 0.5029748 #> [477,] 0.3524337 0.5567038 0.5635622 0.5871811 0.6359616 0.6450181 0.6596769 #> [478,] 0.6504698 0.8628118 0.8934793 0.9492043 0.9711269 1.0379094 1.0430789 #> [479,] 0.7279501 0.8232968 0.8815718 0.8875119 0.9731088 1.0534073 1.0644012 #> [480,] 0.5943301 0.6619937 0.6947922 0.7085971 0.8332180 0.8358105 0.9264773 #> [481,] 0.4085959 0.4556442 0.6605592 0.6979482 0.7189606 0.7891139 0.7960983 #> [482,] 0.7915908 1.0648047 1.2543197 1.3099819 1.4000270 1.4309635 1.4379298 #> [483,] 0.5929305 0.8490390 0.9457200 1.0348564 1.0396577 1.1022982 1.1396698 #> [484,] 0.3525252 0.4063249 0.4582774 0.4732585 0.4942109 0.5168748 0.5171654 #> [485,] 0.5038661 0.5610436 0.6504698 0.7018391 0.9611765 0.9764738 1.0006022 #> [486,] 0.3358029 0.4505269 0.5504881 0.6291464 0.7264237 0.7367339 0.7541327 #> [487,] 0.5383990 0.6707741 0.7384176 0.8436659 0.9150972 0.9363727 0.9406192 #> [488,] 0.4432166 0.4470028 0.5287582 0.5528294 0.6302956 0.6820235 0.7083550 #> [489,] 0.3660383 0.4829863 0.4848390 0.5014880 0.5230184 0.5234596 0.5705091 #> [490,] 0.4485787 0.4863853 0.5623340 0.6401146 0.6553934 0.7002668 0.8068167 #> [491,] 0.6932394 0.7742510 0.8789513 0.8817063 0.9108048 0.9883042 1.0338475 #> [492,] 0.2094617 0.4414033 0.4572070 0.4609700 0.4661846 0.4746320 0.4855697 #> [493,] 0.3106024 0.4460617 0.5312083 0.6673814 0.7340519 0.8070216 0.8581231 #> [494,] 0.3301119 0.3437702 0.3669123 0.4918698 0.5940281 0.6070454 0.6111134 #> [495,] 0.3394844 0.4006096 0.4068972 0.4172339 0.6019840 0.6138800 0.6667747 #> [496,] 0.2286886 0.3686147 0.4272458 0.5016437 0.5448599 0.5484320 0.5511875 #> [497,] 0.5624785 0.5993097 0.7017688 0.7706361 0.8810112 0.9182541 0.9193467 #> [498,] 0.8064250 1.0526432 1.1023809 1.1811835 1.2005248 1.2230364 1.2752681 #> [499,] 2.4787351 2.5249703 2.6359338 2.6802160 2.6815841 2.6952508 2.7314304 #> [500,] 0.4542098 0.5011913 0.5495823 0.6193803 0.6722716 0.6991221 0.7031971 #> [501,] 0.2734565 0.4199832 0.4287305 0.4473554 0.5979192 0.6239051 0.6380812 #> [502,] 0.3392109 0.4396069 0.5123829 0.6164692 0.7387613 0.7750119 0.7871300 #> [503,] 0.5428824 0.6339550 0.6402618 0.6501874 0.6507848 0.7304828 0.7361710 #> [504,] 0.3386233 0.3930878 0.4120583 0.4747824 0.4762068 0.5085280 0.5215113 #> [505,] 0.2612405 0.3551038 0.4938784 0.5573365 0.5755479 0.5987590 0.6044852 #> [506,] 0.3083919 0.5942332 0.7722180 0.7991157 0.8329750 0.8516392 0.8547082 #> [507,] 0.2217617 0.2661266 0.3175616 0.4149331 0.4537911 0.4966009 0.5105782 #> [508,] 0.6308904 0.7466674 0.7903432 0.9064238 0.9807787 1.0042939 1.0348121 #> [509,] 0.4242651 0.4347131 0.5480419 0.6643007 0.6676141 0.7039359 0.7141846 #> [510,] 0.5904378 0.6410868 0.8222237 0.9754550 1.0094423 1.0567393 1.0831111 #> [511,] 0.4332651 0.6714131 0.7998238 0.8317753 0.8457471 0.8535646 0.8628611 #> [512,] 0.5671601 0.7282968 0.8362417 0.9513614 1.1567506 1.1991442 1.3318198 #> [513,] 0.6913829 0.8888571 0.9334695 0.9759081 0.9892210 1.0252545 1.0327843 #> [514,] 0.4353166 0.5455117 0.7431773 0.8553774 0.8819996 0.9236206 0.9959342 #> [515,] 0.4870615 0.5425748 0.6153536 0.6784835 0.6855743 0.7353062 0.7886938 #> [516,] 0.3604918 0.3632050 0.3890036 0.4383867 0.4599237 0.4645308 0.4826099 #> [517,] 0.3329671 0.5828192 0.6029241 0.7347236 0.7795816 0.8302082 0.8375110 #> [518,] 0.3944994 0.4106354 0.5612706 0.5953177 0.6034767 0.6522410 0.6632266 #> [519,] 0.3765864 0.4980218 0.7048266 0.7224488 0.7627102 0.7678418 0.8104953 #> [520,] 0.5385999 0.6332117 0.6527146 0.6615776 0.6865330 0.7093842 0.7124826 #> [521,] 0.1923351 0.5812571 0.6480375 0.6551925 0.7185370 0.7214546 0.7555407 #> [522,] 1.4254770 1.4553427 1.5319170 1.5682789 1.5691586 1.6937886 1.8572698 #> [523,] 0.3401918 0.4893371 0.5681757 0.7157430 0.7289997 0.8183816 0.8595664 #> [524,] 0.5352316 0.6435275 0.7486387 0.7818217 0.8367021 0.8470057 0.8558235 #> [525,] 0.4876600 0.5144181 0.5706278 0.6483658 0.6916804 0.7436081 0.8408944 #> [526,] 0.2442281 0.4379508 0.5443972 0.5917659 0.5943638 0.6069509 0.6195639 #> [527,] 0.5355423 0.5474971 0.5550847 0.6033136 0.6927700 0.6934277 0.7839221 #> [528,] 0.6849981 0.7236067 0.7239378 0.7559130 0.9286206 0.9476253 1.0313606 #> [529,] 0.3109425 0.3454563 0.4489389 0.6076082 0.6686554 0.7683952 0.7746529 #> [530,] 0.5224370 0.5242928 0.5504840 0.6955587 0.7028009 0.7331899 0.7756818 #> [531,] 0.2686635 0.8502969 0.8539395 0.8875681 0.9449080 0.9607605 0.9725843 #> [532,] 0.3968053 0.4608625 0.5451733 0.5693529 0.5913540 0.6207376 0.6556169 #> [533,] 0.2105175 0.2849864 0.4435253 0.4451457 0.4976498 0.5039508 0.5385771 #> [534,] 1.0056298 1.0063360 1.0228704 1.1758045 1.3061648 1.3131092 1.3539577 #> [535,] 0.1795017 0.3914624 0.4311430 0.4808685 0.5177915 0.5188129 0.5231660 #> [536,] 0.3886122 0.6940858 0.7367848 0.7859109 0.7948439 0.8092575 0.8410188 #> [537,] 0.2922924 0.3740531 0.4893371 0.5506097 0.5691912 0.5700471 0.5963322 #> [538,] 0.5304649 0.5727852 0.5982141 0.5984345 0.6064560 0.6682981 0.6768179 #> [539,] 0.3723138 0.4811191 0.4975465 0.5152524 0.5242217 0.5337690 0.5374942 #> [540,] 0.1998153 0.3781865 0.3798359 0.4114479 0.5380592 0.5418090 0.5437751 #> [541,] 0.8346431 0.9989923 1.0280044 1.2451451 1.2641855 1.3222055 1.3797486 #> [542,] 0.5150732 0.5405132 0.5517353 0.8327506 0.8509328 1.0145762 1.0188366 #> [543,] 0.6707741 0.8546885 0.8712047 0.9233843 0.9337107 0.9400282 1.0088752 #> [544,] 0.7023488 0.7678536 0.8127954 0.8866871 0.9579694 1.0152249 1.0761013 #> [545,] 0.4737440 0.4814243 0.5159818 0.5177161 0.5302001 0.5374756 0.6089900 #> [546,] 0.4570762 0.4936120 0.5217608 0.6644822 0.7165986 0.7342913 0.7596182 #> [547,] 0.2686635 0.6729536 0.8144727 0.8584639 0.8972973 0.9212723 0.9984089 #> [548,] 0.2965186 0.3938001 0.4227960 0.4627563 0.5003201 0.5122100 0.5155835 #> [549,] 0.7471292 1.0120172 1.0738716 1.0850179 1.1114957 1.1788565 1.2134379 #> [550,] 0.7557748 1.9640711 2.0158992 2.1958536 2.2025514 2.2810268 2.3287320 #> [551,] 0.2164501 0.4410435 0.4493396 0.4747144 0.4762068 0.5017388 0.5159818 #> [552,] 0.3774352 0.5874570 0.6080600 0.6632698 0.6701687 0.6766880 0.6805240 #> [553,] 0.2273583 0.3103047 0.3158317 0.4449949 0.4897840 0.5127951 0.5189196 #> [554,] 0.4215890 0.4280685 0.7515559 0.8982119 0.9799687 0.9824180 1.0017711 #> [555,] 0.2751204 0.5624785 0.8821678 0.8904358 0.9163542 0.9306648 0.9887995 #> [556,] 0.3124576 0.5874171 0.9109125 0.9132635 0.9570289 0.9756815 0.9871541 #> [557,] 0.4570762 0.4896084 0.5105701 0.5210476 0.5693529 0.6319652 0.6602495 #> [558,] 0.6977377 0.8379934 0.8425571 0.9253071 0.9524282 0.9583905 1.0047693 #> [559,] 0.2618401 0.6252074 0.6447046 0.6806061 0.6927381 0.7264462 0.7378000 #> [560,] 0.5383990 0.5511466 0.6680109 0.7092152 0.7109173 0.7745257 0.8069808 #> [561,] 0.4650941 0.5238620 0.5348742 0.5662374 0.6147478 0.6925079 0.7901603 #> [562,] 1.4131752 1.4267515 1.5172372 1.6144240 1.7843633 1.9143099 2.0157166 #> [563,] 1.0995921 1.1604199 1.2860329 1.3602894 1.4155908 1.4740372 1.5191169 #> [564,] 0.5700973 0.9030833 1.0028225 1.0354115 1.0786533 1.0800418 1.0995374 #> [565,] 0.2612405 0.3530585 0.4722940 0.5048796 0.5476488 0.5617994 0.5865857 #> [566,] 0.4788930 0.8072730 0.8456541 0.8498218 0.8716038 0.9956315 1.0053389 #> [567,] 0.6152174 0.9260792 0.9534972 0.9594619 1.0066028 1.0099486 1.0219929 #> [568,] 0.3052188 0.3113680 0.3719505 0.4422055 0.4989853 0.5074590 0.5246449 #> [569,] 0.2343413 0.4280685 0.6458231 0.6796532 0.9604054 0.9616049 1.0077803 #> [570,] 0.6254268 0.6579970 0.8277229 0.9013307 0.9356341 0.9553533 1.0004716 #> [571,] 0.2849864 0.3809927 0.3913374 0.4213421 0.4369799 0.4488936 0.4956664 #> [572,] 0.3799644 0.3981264 0.4570374 0.5037029 0.5298376 0.5905216 0.5940546 #> [573,] 0.7058054 0.9816568 1.1095494 1.1333906 1.1793584 1.2026618 1.2271717 #> [574,] 0.4938878 0.7164475 0.7231428 0.7274085 0.7840491 0.8155527 0.8425017 #> [575,] 0.3610341 0.4669204 0.4753663 0.5390328 0.5429686 0.5484320 0.5824740 #> [576,] 0.9832351 1.1916313 1.2309731 1.2885887 1.3348366 1.4022129 1.4999657 #> [577,] 0.4370194 0.5828192 0.5987923 0.6252074 0.6440177 0.7103820 0.7120553 #> [578,] 0.1907358 0.4380835 0.4398420 0.4453320 0.4968742 0.5043623 0.5147732 #> [579,] 0.3394250 0.5017704 0.5073744 0.5459737 0.5623340 0.5957137 0.6257073 #> [580,] 0.3136491 0.4832977 0.5452097 0.5469488 0.5943638 0.6684409 0.6788909 #> [581,] 0.4757520 0.5505204 0.5907311 0.7315827 0.7437559 0.8815718 0.8903385 #> [582,] 0.3638288 0.5512129 0.5698245 0.5741937 0.6482118 0.6834861 0.6927381 #> [583,] 0.7201322 0.8014768 0.9182950 0.9211684 0.9495949 0.9767439 0.9846061 #> [584,] 0.8286330 0.8392739 0.9274681 0.9415304 0.9662324 0.9756150 1.0326113 #> [585,] 0.7150264 0.8001651 0.8241293 0.8258942 0.8378235 0.8571228 0.8893262 #> [586,] 0.5337423 0.5635622 0.5737651 0.6376604 0.7493295 0.7755872 0.8280572 #> [587,] 0.9260981 0.9886686 1.0634161 1.2119385 1.3173416 1.4022129 1.4536936 #> [588,] 0.2770382 0.4636346 0.6602455 0.8192408 0.9824257 0.9941006 1.0064931 #> [589,] 0.4431134 0.4585248 0.4858187 0.5874570 0.5907221 0.6573963 0.6631667 #> [590,] 0.5029892 0.5650864 0.6707272 0.6804693 0.7201322 0.7361470 0.7548057 #> [591,] 0.4637712 0.5511508 0.7224517 0.7236107 0.7981595 0.8298472 0.8386210 #> [592,] 0.3838156 0.4704623 0.4808685 0.5406582 0.6070318 0.6076115 0.6214845 #> [593,] 0.2780944 0.2938909 0.3160396 0.4005184 0.4071768 0.4305712 0.4557063 #> [594,] 0.3080777 0.3956464 0.4415222 0.4554516 0.4652289 0.4734049 0.5048796 #> [595,] 0.4019718 0.7367865 0.7486387 0.7984211 0.8065179 0.8296740 0.8335389 #> [596,] 0.3458876 0.5025590 0.5422557 0.5644321 0.5681428 0.6667747 0.6730841 #> [597,] 0.3083919 0.5009687 0.6214845 0.6287011 0.7551961 0.7690297 0.7985806 #> [598,] 0.3176937 0.3592673 0.4412460 0.5300126 0.5439430 0.5769023 0.6025091 #> [599,] 0.1821476 0.4623505 0.5653569 0.6789478 0.6922921 0.7219848 0.7308414 #> [600,] 0.3080751 0.3136977 0.4504919 0.4725017 0.4993532 0.5012870 0.5669852 #> [601,] 0.3582576 0.4087473 0.4194612 0.4249279 0.4440057 0.4767470 0.4961666 #> [602,] 0.3486888 0.3844678 0.4569880 0.4613971 0.4621962 0.4693899 0.4812505 #> [603,] 0.2676504 0.3723138 0.5245181 0.5998886 0.6102405 0.6116010 0.6204448 #> [604,] 0.1880397 0.4352734 0.4621962 0.4660285 0.4711256 0.4978754 0.5305005 #> [605,] 0.3678781 0.4005174 0.4088687 0.4412460 0.4848390 0.4860993 0.4929666 #> [606,] 0.3445994 0.4287305 0.5211516 0.5470574 0.6196470 0.7423847 0.8015919 #> [607,] 0.3109425 0.3713510 0.3972822 0.5843780 0.6075112 0.6639107 0.7612510 #> [608,] 0.6211108 0.6765484 0.7801991 0.9374454 0.9723720 1.0410421 1.0413188 #> [609,] 0.7620922 0.7945027 0.9671000 1.0241726 1.0526528 1.0645023 1.0721897 #> [610,] 0.3301119 0.4539022 0.4626248 0.4929649 0.5011315 0.5038229 0.5371010 #> [611,] 0.3892923 0.5719831 0.6193540 0.6307219 0.6754799 0.6939600 0.7055080 #> [612,] 0.3590217 0.5622607 0.5749747 0.7050365 0.7092287 0.7328549 0.7642209 #> [613,] 0.4556442 0.5065259 0.6415368 0.7681048 0.7847803 0.7952401 0.7995223 #> [614,] 0.4841278 0.7330356 0.8625143 0.8627456 0.8871369 0.9040311 1.0021075 #> [615,] 0.4052950 0.5250248 0.5449661 0.5487680 0.5836566 0.6140291 0.7035311 #> [616,] 0.3342382 0.3528047 0.4566071 0.4588823 0.5595899 0.5650627 0.5664733 #> [617,] 0.5910692 0.6109909 0.7725971 0.7880406 0.8080487 0.8162246 0.8352604 #> [618,] 0.8995747 0.9059494 1.0124701 1.0319325 1.0337591 1.1374333 1.1429259 #> [619,] 0.2676504 0.4989698 0.5152524 0.5376920 0.5667576 0.5704259 0.5736706 #> [620,] 0.8284279 0.8684830 0.9933613 0.9954375 1.0958495 1.1693827 1.2511751 #> [621,] 0.7137572 0.7962522 0.8566013 1.1097381 1.1318377 1.1977082 1.2191116 #> [622,] 0.3788236 0.4559011 0.5616415 0.5721634 0.5902184 0.6025091 0.6110487 #> [623,] 0.1923351 0.4792305 0.5322739 0.5676543 0.5992562 0.6682089 0.7108051 #> [624,] 0.7236067 0.8886585 0.9266796 0.9953299 1.1276024 1.2146687 1.2330272 #> [625,] 0.2957885 0.4446937 0.5750432 0.5765267 0.5925585 0.6037244 0.6568023 #> [626,] 0.5509826 0.6290930 0.6537618 0.8620734 0.8626699 0.8700212 0.8840395 #> [627,] 0.3806210 0.3815088 0.3934241 0.4213650 0.4374722 0.5392035 0.5448039 #> [628,] 0.4037595 0.4367022 0.4729371 0.5086459 0.5612706 0.7159270 0.7336171 #> [629,] 0.2094617 0.3032933 0.3819500 0.3945596 0.3978667 0.4026997 0.4286491 #> [630,] 0.3813579 0.4197812 0.4300301 0.4399549 0.4519320 0.5166567 0.5815696 #> [631,] 0.8832363 1.3477283 1.3545155 1.4381920 1.4890247 1.5271940 1.5304553 #> [632,] 0.3537939 0.4304763 0.4875948 0.5019129 0.5135788 0.5934187 0.6370304 #> [633,] 0.6097647 0.8841422 0.9312233 0.9659309 0.9901034 1.0455495 1.0524537 #> [634,] 0.2411622 0.4635766 0.4648660 0.4726632 0.5298530 0.5476241 0.6141360 #> [635,] 0.4445392 0.5229273 0.6558690 0.6608258 0.6714148 0.7340519 0.7681936 #> [636,] 0.8490657 0.9090991 0.9289076 0.9717083 1.0127865 1.0738554 1.1702953 #> [637,] 0.3196900 0.3934462 0.3981264 0.4439951 0.4990745 0.5239454 0.5315435 #> [638,] 0.3856247 0.4641082 0.5074462 0.5270669 0.5824740 0.5874906 0.6256009 #> [639,] 0.4993532 0.6707272 0.6866497 0.7076178 0.7314473 0.7330356 0.7394303 #> [640,] 0.2615611 0.3670521 0.5169540 0.5198035 0.5434027 0.7077364 0.7297429 #> [641,] 0.4279815 0.4513086 0.4595417 0.4634436 0.4923078 0.5025113 0.5047042 #> [642,] 0.9597329 1.1546740 1.1975968 1.3252038 1.3591368 1.3595380 1.3768743 #> [643,] 0.4094364 0.4826327 0.4858038 0.5331247 0.5526499 0.5606193 0.5979192 #> [644,] 0.1502014 0.3948392 0.4024483 0.4363727 0.5110320 0.5298530 0.5515163 #> [645,] 0.7483794 0.7660779 0.7972184 0.8423217 0.8477208 0.9264165 0.9535310 #> [646,] 0.3298885 0.6257554 0.6793500 0.7212007 0.7591843 0.7873279 0.7969979 #> [647,] 0.6265314 0.8995747 0.9675360 1.0166948 1.0424884 1.0450260 1.1418658 #> [648,] 0.3706053 0.3744725 0.4122767 0.4483847 0.4539152 0.4645888 0.4827478 #> [649,] 0.4701398 0.5616011 0.5705091 0.6293527 0.6541363 0.6560097 0.7152830 #> [650,] 0.9559779 0.9902827 1.0017711 1.0408619 1.0470346 1.2111807 1.2288811 #> [651,] 0.5931816 0.5963667 0.6109270 0.7269927 0.7596997 0.7883433 0.8504473 #> [652,] 0.3343100 0.5469488 0.6108173 0.6170841 0.6329588 0.6413620 0.6597046 #> [653,] 0.8118327 1.0229776 1.2321380 1.3246079 1.3556170 1.3676437 1.4039970 #> [654,] 0.4407825 0.4994282 0.5063334 0.5119253 0.5206700 0.5721931 0.5740050 #> [655,] 0.3471195 0.6613483 0.7012632 0.7894252 0.7945076 0.8148472 0.8189127 #> [656,] 0.4028254 0.4151472 0.4276793 0.4414033 0.4663869 0.4732585 0.4777298 #> [657,] 0.8341872 0.8865120 0.9124553 0.9776484 0.9869563 0.9919096 1.0184556 #> [658,] 0.5081797 0.5225944 0.5464530 0.5717254 0.6125552 0.6259600 0.6279938 #> [659,] 0.2656600 0.3891551 0.3932012 0.4618651 0.4633659 0.4802200 0.5200496 #> [660,] 0.4583481 0.4983567 0.5065117 0.5727852 0.5739238 0.6249346 0.6435275 #> [661,] 0.7064498 0.7354697 0.7449229 0.8980629 0.9261128 0.9273094 0.9797472 #> [662,] 0.2599636 0.3310614 0.3920226 0.4318124 0.4627563 0.5120180 0.5462467 #> [663,] 0.6329682 0.6602455 0.6773683 0.7154039 0.7537751 0.7591667 0.7627073 #> [664,] 0.2996762 0.3549475 0.4149381 0.6040388 0.6074579 0.6244618 0.6274167 #> [665,] 0.3527183 0.4374755 0.4699401 0.5557919 0.5650627 0.5727925 0.5955450 #> [666,] 0.8355623 1.1958227 1.3173416 1.3620397 1.3930836 1.4061571 1.5639044 #> [667,] 0.2694920 0.4093251 0.4940575 0.5246449 0.5301315 0.5522417 0.5525923 #> [668,] 0.3176937 0.3298639 0.4615580 0.4781089 0.4860993 0.5040802 0.5085280 #> [669,] 0.8076283 0.8741396 0.9529027 1.0189253 1.1386040 1.1676503 1.2302778 #> [670,] 0.4916895 0.8355623 0.9260981 0.9447221 0.9853316 1.0694053 1.1113730 #> [671,] 0.2950353 0.3113680 0.4127848 0.4388591 0.4701749 0.5131636 0.5181448 #> [672,] 0.1765483 0.6193803 0.7609701 0.7924176 0.8291398 0.8341872 0.8631459 #> [673,] 0.1882941 0.6084791 0.6591222 0.8985321 0.9114107 0.9670695 0.9727846 #> [674,] 0.7103740 0.7996086 1.1310247 1.1396698 1.1415995 1.1594726 1.2106312 #> [675,] 0.6694261 0.7091689 0.7125685 0.7160217 0.7290825 0.7359664 0.7543159 #> [676,] 0.2652173 0.3591586 0.3856247 0.4302680 0.4700327 0.5284957 0.5455016 #> [677,] 0.4056079 0.4650003 0.6410389 0.6644238 0.7346686 0.7799487 0.7987587 #> [678,] 0.7652407 1.0127818 1.1077200 1.1214226 1.1279028 1.2002891 1.2068199 #> [679,] 0.3086478 0.3642713 0.3672958 0.5025481 0.6187645 0.7493980 0.7529638 #> [680,] 0.6108399 0.6574265 0.6846185 0.7434690 0.7667026 0.7743503 0.8051438 #> [681,] 0.9406192 0.9900879 1.0073145 1.0223147 1.0868001 1.1268332 1.1792269 #> [682,] 0.5070293 0.5632050 0.6182612 0.6978583 0.7107505 0.7151584 0.7312178 #> [683,] 0.4334864 0.4357644 0.5221797 0.6035912 0.6405775 0.6628211 0.6794570 #> [684,] 0.5086459 0.5793263 0.5985444 0.6381214 0.6499291 0.7309148 0.8410188 #> [685,] 0.2965186 0.3183395 0.3937665 0.4383867 0.4429527 0.4651612 0.4741794 #> [686,] 0.2436403 0.3843435 0.4940036 0.5953922 0.5978571 0.6161363 0.6846185 #> [687,] 0.6109270 0.7466674 0.8328029 0.8611830 0.9437925 0.9840271 0.9916199 #> [688,] 1.1609350 1.1780794 1.2971786 1.2993309 1.3046307 1.3752090 1.4155252 #> [689,] 0.3227764 0.5130482 0.5373405 0.5434537 0.5549392 0.6665069 0.7139751 #> [690,] 0.6298396 0.9606412 0.9806925 1.0191859 1.0786642 1.0977456 1.1067650 #> [691,] 1.1912198 1.2343525 1.3290869 1.3660693 1.3753267 1.5181986 1.6123363 #> [692,] 0.2697039 0.3892588 0.4135310 0.4528358 0.5037029 0.5203917 0.5268461 #> [693,] 0.4335794 0.4556055 0.5383580 0.6724047 0.7737363 0.7917263 0.8150843 #> [694,] 0.3496784 0.4470028 0.5743282 0.7685739 0.8087218 0.8201508 0.9122154 #> [695,] 0.2202836 0.4180805 0.5138186 0.6213372 0.6242689 0.6470521 0.6813480 #> [696,] 0.3184559 0.5904378 0.6486461 0.6865330 0.7496748 0.7809224 0.8325244 #> [697,] 0.3358029 0.3893338 0.4844073 0.5672224 0.6164471 0.6320225 0.6561801 #> [698,] 0.5025590 0.5278623 0.5865902 0.5981669 0.6182001 0.6365512 0.6947839 #> [699,] 0.3784456 0.4625917 0.6006478 0.6319259 0.6326365 0.6393936 0.6485100 #> [700,] 0.4097627 0.5605815 0.5786764 0.6970361 0.6994112 0.7243970 0.7910807 #> [701,] 0.4532875 0.4625917 0.4769697 0.4860003 0.4913224 0.5144246 0.5899088 #> [702,] 1.0514347 1.1351614 1.1604199 1.2314421 1.2675142 1.2719898 1.2757552 #> [703,] 0.6039762 0.8073952 0.8313950 0.8410883 0.8428926 0.8934076 0.8941275 #> [704,] 0.4874757 0.5073828 0.6075559 0.6104783 0.6204127 0.6582852 0.7073790 #> [705,] 0.3776611 0.4769180 0.5101209 0.8239215 0.9078180 0.9702416 1.0187160 #> [706,] 0.2938909 0.4308291 0.4513943 0.4871143 0.5175952 0.5349535 0.5568170 #> [707,] 0.4360400 0.5857594 0.6447800 0.6451698 0.7482271 0.8122301 0.8444847 #> [708,] 0.9514395 1.0606779 1.0636714 1.1426683 1.2643485 1.2781764 1.2947481 #> [709,] 0.6583572 0.6639630 0.7268477 0.9004944 0.9162768 0.9609299 1.0435224 #> [710,] 0.3390154 0.4174240 0.4615110 0.4615817 0.4617750 0.5644427 0.5650864 #> [711,] 0.2724458 0.4804254 0.4858038 0.4923078 0.5294087 0.5417924 0.5419647 #> [712,] 0.1257555 0.5030681 0.5135363 0.5429686 0.5453030 0.5831975 0.5874906 #> [713,] 0.2316066 0.4242651 0.5503053 0.5668125 0.6527146 0.6826380 0.7104982 #> [714,] 0.7137572 0.8132454 0.8288236 0.8378235 0.9917530 1.0033330 1.1027254 #> [715,] 0.3445357 0.5203527 0.5210982 0.5302128 0.5474013 0.5504232 0.5537372 #> [716,] 0.6289333 0.7239378 0.7605889 0.8364011 0.8404026 0.8441677 0.8458550 #> [717,] 0.5101209 0.7502501 0.7571150 0.7914278 0.9332052 0.9764120 1.0077118 #> [718,] 0.5185333 0.5451491 0.5464530 0.6375285 0.7150237 0.7173710 0.7490721 #> [719,] 0.7471292 0.7926226 0.9163542 0.9244381 0.9594619 0.9813975 0.9902617 #> [720,] 0.2798545 0.2928507 0.4171835 0.4241446 0.5506097 0.5528294 0.5698328 #> [721,] 0.5810662 0.6228606 0.6947839 0.6994219 0.8052348 0.8134730 0.8163599 #> [722,] 0.4924958 0.4981826 0.5540639 0.6878055 0.6996609 0.7174303 0.7634109 #> [723,] 0.2298626 0.4681615 0.4883660 0.5622740 0.6198420 0.6236002 0.6291796 #> [724,] 0.2985586 1.2498559 1.3073808 1.3259615 1.4278049 1.4933361 1.5392211 #> [725,] 0.7177136 0.7769373 0.8442932 0.9119135 0.9680577 0.9692823 1.0137121 #> [726,] 1.6054625 2.0848020 2.2737335 2.3440355 2.4777165 2.4800381 2.5488145 #> [727,] 0.6776881 0.7018391 0.7596997 0.8934793 0.8953289 0.9017456 0.9556454 #> [728,] 0.5503053 0.5529916 0.5644517 0.5815853 0.6495746 0.6587434 0.6663706 #> [729,] 0.4863853 0.5017704 0.5706278 0.6356550 0.6714550 0.7973914 0.8065675 #> [730,] 0.6298396 0.8941275 1.0586703 1.1020911 1.1238132 1.1308614 1.1605411 #> [731,] 0.2230059 0.4460617 0.5229273 0.5374811 0.6970361 0.7349776 0.7393258 #> [732,] 0.9529027 1.0492829 1.0509800 1.2951662 1.3222785 1.3498134 1.4098043 #> [733,] 0.9483649 0.9880734 1.0074580 1.0216102 1.1741660 1.2103866 1.2133149 #> [734,] 0.6690111 0.7064498 0.7085971 0.8331035 0.8896609 0.9334063 0.9730671 #> [735,] 0.6221261 0.8075866 0.9425628 1.0028363 1.0798067 1.1136847 1.1192310 #> [736,] 0.4332651 0.5409303 0.6307219 0.6624071 0.7050365 0.7191833 0.7874377 #> [737,] 0.4387941 0.6204127 0.6707253 0.6965939 0.6972482 0.7017439 0.7697675 #> [738,] 0.5950760 0.7354020 0.7369217 0.7641021 0.8088560 0.8308253 0.8328029 #> [739,] 0.4441841 0.4739766 0.6529928 0.6619944 0.7024932 0.7527359 0.7571572 #> [740,] 0.3433086 0.4559011 0.4768436 0.6148437 0.6968936 0.7092597 0.7328150 #> [741,] 0.3158317 0.3165580 0.3969505 0.4063249 0.4579166 0.4631483 0.4714148 #> [742,] 0.5550847 0.5627145 0.6103244 0.6326847 0.6514708 0.7510046 0.7562212 #> [743,] 0.3056034 0.3103047 0.3165580 0.3768641 0.4135715 0.4179349 0.4652016 #> [744,] 0.1418194 0.5225944 0.6063061 0.6682361 0.7811779 0.7920934 0.7948385 #> [745,] 0.3113118 0.4595513 0.4636687 0.5151912 0.5487680 0.6796532 0.7036961 #> [746,] 1.2094194 1.2693452 1.2838527 1.2862101 1.5435401 1.6377179 1.6807627 #> [747,] 0.1921174 0.4963909 0.4970822 0.5206700 0.5575169 0.5603175 0.5684175 #> [748,] 0.2709125 0.2989971 0.3178739 0.3892588 0.3984968 0.5257014 0.6096382 #> [749,] 0.3710504 0.4176578 0.4423984 0.4646938 0.4921990 0.5166952 0.5442631 #> [750,] 0.3913705 0.3968053 0.4414737 0.5097455 0.5351798 0.5617529 0.5676278 #> [751,] 0.3342382 0.3864791 0.4802595 0.4861910 0.5138721 0.5257014 0.5507399 #> [752,] 0.3788236 0.5478272 0.5989136 0.6381861 0.7703288 0.8092654 0.8158994 #> [753,] 0.5702987 0.5980038 0.6198420 0.6559528 0.6597138 0.7089059 0.7139787 #> [754,] 0.3107541 0.3136977 0.4305712 0.4617750 0.4785309 0.4931461 0.5282038 #> [755,] 0.6690871 0.6860968 0.7725749 0.8556933 0.8632565 0.9030162 0.9208716 #> [756,] 0.4915624 0.5409284 0.5495761 0.5752191 0.6137225 0.7168790 0.7279401 #> [757,] 0.2709125 0.3940243 0.3953197 0.3979419 0.4528358 0.4592319 0.5072869 #> [758,] 0.3246618 0.4099349 0.5264586 0.5480419 0.5910737 0.6117036 0.6689205 #> [759,] 0.5578534 0.5838292 0.6651903 0.7801991 0.8151923 0.8256253 0.8408133 #> [760,] 0.4936120 0.7140313 0.8417973 0.8946692 0.9161463 0.9171797 0.9212723 #> [761,] 0.4423240 0.4711256 0.5230184 0.5773746 0.5822046 0.5966500 0.6572810 #> [762,] 0.1907358 0.4005174 0.4075196 0.4639866 0.4674898 0.5261269 0.5539596 #> [763,] 0.9182541 1.0350864 1.1025945 1.1219863 1.1424055 1.1723399 1.1851861 #> [764,] 0.4357644 0.5396707 0.7081140 0.7458473 0.7559599 0.7662393 0.7817104 #> [765,] 0.2985586 1.2673941 1.3162858 1.3490841 1.3812083 1.4572480 1.4857447 #> [766,] 0.4815630 0.5688090 0.6190446 0.7048870 0.7183197 0.7194659 0.7259945 #> [767,] 0.7216168 0.7344810 0.7559130 0.8101955 0.8404026 0.8766603 0.8847948 #> [768,] 0.5425146 0.5511466 0.6194563 0.6747291 0.6897736 0.7081918 0.7287542 #> [769,] 0.5627565 0.6415368 0.6619937 0.7960983 0.8765106 0.8888571 0.8970740 #> [770,] 0.4305420 0.4811124 0.4841278 0.6360874 0.6567670 0.7184861 0.7732483 #> [771,] 1.0676613 1.4296416 1.4939067 1.5614253 1.5694705 1.7011895 1.7708172 #> [772,] 0.7779108 0.8614404 1.0133924 1.0792046 1.1991442 1.2035680 1.2053993 #> [773,] 0.3852675 0.4621747 0.4727399 0.5144246 0.5488358 0.6393936 0.6696794 #> [774,] 0.3158758 0.3585868 0.3706053 0.4252485 0.4941148 0.5403542 0.5470744 #> [775,] 0.8118327 0.8399439 0.8473664 0.8689506 0.9741627 0.9763009 0.9807302 #> [776,] 0.3974524 0.4094364 0.4199832 0.5053076 0.5211516 0.6099352 0.6329682 #> [777,] 0.3445357 0.3591586 0.4127848 0.4592028 0.4989853 0.5092464 0.5094085 #> [778,] 0.3027772 0.4167413 0.4696109 0.5002217 0.5063334 0.5373169 0.6704545 #> [779,] 0.5278035 0.6956782 0.8427137 0.9090991 0.9683068 0.9759108 0.9917875 #> [780,] 0.3918320 0.4896400 0.5011922 0.6041616 0.6591222 0.7215146 0.7815183 #> [781,] 0.5405132 0.6916804 0.6989697 0.8490090 0.8606617 0.8767628 0.9370817 #> [782,] 0.2646638 0.3213510 0.3692134 0.4093251 0.4185317 0.5074590 0.5181448 #> [783,] 0.4224714 0.6104783 0.6137225 0.6729815 0.7095745 0.7150264 0.7208141 #> [784,] 0.4066972 0.6110123 0.6401066 0.6787280 0.6894002 0.7225953 0.7293708 #> [785,] 0.2995764 0.4729371 0.5985444 0.6034767 0.6364684 0.6724590 0.6766374 #> [786,] 0.3859147 0.5912506 0.5923463 0.6539348 0.7856543 0.8075600 0.8499710 #> [787,] 0.4695051 0.8063933 0.8487997 0.9466795 1.0495105 1.0972245 1.0995921 #> [788,] 0.5394996 0.7051774 0.8265935 0.8760955 0.8869731 0.9950556 1.0018150 #> [789,] 0.5625080 0.8329584 0.8397169 0.8863304 0.8892563 0.8977614 0.9131567 #> [790,] 0.3525252 0.4368219 0.4714148 0.5242562 0.5316131 0.5881955 0.5894456 #> [791,] 0.2915407 0.4650941 0.5777199 0.6138800 0.6294308 0.6847840 0.7266085 #> [792,] 0.4738325 0.4776311 0.4811124 0.6236002 0.6570703 0.7039359 0.7526110 #> [793,] 0.4893257 0.5173880 0.6514329 0.7328150 0.7466487 0.7937440 0.7984116 #> [794,] 0.4894648 0.4934404 0.5353879 0.6123625 0.6483658 0.6927912 0.7029612 #> [795,] 0.3642874 0.6968107 0.7434605 0.8517166 0.9269132 0.9289136 1.0205662 #> [796,] 0.3459307 0.5028377 0.5434625 0.5760647 0.5851950 0.5938233 0.6451698 #> [797,] 0.3974026 0.4114479 0.4151472 0.4304381 0.4426711 0.4582774 0.4631483 #> [798,] 0.2599636 0.4381897 0.4646938 0.4704474 0.4764335 0.4929968 0.5003201 #> [799,] 0.1603362 0.3056966 0.3669123 0.4612447 0.4929649 0.5254921 0.5291550 #> [800,] 0.6608258 1.0429812 1.0439467 1.1099186 1.1286047 1.2167245 1.2207269 #> [801,] 0.3113292 0.6743795 0.6919364 0.8051438 0.8565539 0.8595808 0.8784011 #> [802,] 0.3474395 0.5598617 0.6589509 0.7612510 0.8362417 0.9492731 0.9708233 #> [803,] 0.4604155 0.4981237 0.5171006 0.5691912 0.5693549 0.6212383 0.6379603 #> [804,] 0.4978547 0.5048296 0.6039762 0.6525114 0.6809391 0.8173460 0.8338392 #> [805,] 0.2546616 0.4024938 0.4828984 0.4937261 0.5813304 0.6000009 0.6202705 #> [806,] 0.4080902 0.5169540 0.5808212 0.5931816 0.6385622 0.6709518 0.6768179 #> [807,] 0.3056034 0.3326814 0.4537911 0.4565760 0.4579166 0.4781016 0.4824973 #> [808,] 0.2136091 0.4037885 0.4213650 0.4309298 0.4422711 0.4428247 0.5413453 #> [809,] 0.3995663 0.4340247 0.4436905 0.5134259 0.5156347 0.5339298 0.5704259 #> [810,] 0.3092599 0.3657331 0.3725628 0.4208733 0.4720911 0.4850523 0.5555859 #> [811,] 0.3784456 0.3852675 0.4127776 0.4532875 0.6210650 0.6525644 0.6680109 #> [812,] 0.3433086 0.4590312 0.4968742 0.5173880 0.5616415 0.6270975 0.6659995 #> [813,] 0.2798545 0.3874939 0.4496889 0.4604155 0.5485144 0.5682557 0.5700471 #> [814,] 0.3082097 0.3310614 0.3782222 0.4266016 0.4693899 0.4929968 0.5043400 #> [815,] 0.3461297 0.3816936 0.3835569 0.4024244 0.4161253 0.4371760 0.4834878 #> [816,] 0.5150732 0.5921002 0.6800489 0.9387221 1.0449819 1.1255987 1.2031884 #> [817,] 0.4097627 0.5394996 0.6033279 0.6107870 0.7756786 0.8485907 0.8577339 #> [818,] 0.3514631 0.5049022 0.6213372 0.6854924 0.6952081 0.7058257 0.7449229 #> [819,] 0.4858187 0.7164687 0.7591843 0.8094750 0.8311792 0.8322227 0.8456569 #> [820,] 0.5556390 0.6794556 0.7165986 0.7671241 0.7781291 0.7898797 0.8203556 #> [821,] 0.3112028 0.3530090 0.3912126 0.3977697 0.4197831 0.4252485 0.4859729 #> [822,] 1.1811835 1.4155497 1.6946680 1.7408778 1.7963002 1.9419126 2.0003276 #> [823,] 0.4785309 0.5035822 0.5504232 0.5581019 0.5920239 0.6022480 0.6114413 #> [824,] 1.4441306 1.9944252 2.0704969 2.2442786 2.2650094 2.3348776 2.3617711 #> [825,] 0.6218947 0.6512875 0.6732712 0.7395676 0.7976003 0.8076283 0.8764037 #> [826,] 0.3036663 0.6271310 0.7331899 0.8259215 0.9237299 0.9609905 0.9817945 #> [827,] 0.9193467 1.0772629 1.1219863 1.2334469 1.2451451 1.2538902 1.3005034 #> [828,] 0.2696111 0.2853528 0.3585868 0.3744725 0.4054881 0.4197831 0.5242217 #> [829,] 0.3848214 0.4585248 0.5589624 0.5616011 0.5769023 0.6223452 0.6257554 #> [830,] 0.1793892 0.4991711 0.5590794 0.5758475 0.6482590 0.6809989 0.6897370 #> [831,] 0.6211108 0.7208141 0.7467857 0.7814715 0.8912662 0.8966985 0.9665416 #> [832,] 0.5439116 0.6994219 0.9761487 0.9915919 1.0092772 1.0094416 1.0673060 #> [833,] 0.3097309 0.7838109 0.8271271 0.8466075 0.8590657 0.8687430 1.0163926 #> [834,] 0.1657857 0.3075964 0.3930878 0.4054881 0.4513086 0.4665484 0.4736562 #> [835,] 0.6569257 0.7234910 0.8336439 0.8673209 0.9341469 0.9458068 0.9533946 #> [836,] 0.2218200 0.2753051 0.3113118 0.5836566 0.6286956 0.6558379 0.6649380 #> [837,] 0.4074138 0.5250252 0.5611636 0.6104833 0.6594435 0.7098716 0.7220400 #> [838,] 0.3802711 0.5821115 0.6293527 0.6348820 0.6427768 0.6612903 0.7769373 #> [839,] 0.4277456 0.4302680 0.4351358 0.5361542 0.5427427 0.5441774 0.5844532 #> [840,] 0.3067851 0.3080777 0.3261829 0.3710223 0.3945596 0.4150834 0.4236751 #> [841,] 0.4518358 0.5580674 0.6486461 0.6640788 0.7124826 0.7184861 0.7256895 #> [842,] 0.3160586 0.4083693 0.4399549 0.4689018 0.4820274 0.5174262 0.5196555 #> [843,] 1.5311992 1.5536753 1.5614253 1.6258930 1.7115210 1.7434298 1.7461672 #> [844,] 0.2972695 0.5968755 0.6194563 0.6488359 0.6492446 0.6546223 0.7091232 #> [845,] 0.1795017 0.3869610 0.4826465 0.4837839 0.4985066 0.5214377 0.5393927 #> [846,] 0.2536980 0.5370576 0.5865902 0.5910692 0.6302841 0.6917589 0.7002640 #> [847,] 0.3107841 0.4453761 0.4519320 0.5431865 0.6136417 0.6244927 0.6254025 #> [848,] 0.1719823 0.1765733 0.4436905 0.4489713 0.6157921 0.6186984 0.6720264 #> [849,] 0.8311973 0.8747926 0.9717083 1.0013779 1.0122960 1.0163732 1.2066618 #> [850,] 0.2994409 0.4768582 0.4894021 0.6089304 0.6684409 0.7042698 0.8011727 #> [851,] 0.3365770 0.3985065 0.4241446 0.5156347 0.5156644 0.5252485 0.5470744 #> [852,] 1.4296416 1.5983391 1.9137894 2.0242065 2.0456072 2.0512273 2.0535124 #> [853,] 0.4680880 0.6266138 0.6834861 0.7391465 0.7472718 0.8071015 0.8707201 #> [854,] 0.3394250 0.3848525 0.4798373 0.5234596 0.5509819 0.6162140 0.6383378 #> [855,] 0.7566811 0.8401697 0.8518542 0.9411251 0.9457062 0.9563972 1.1295818 #> [856,] 0.2940478 0.3407325 0.3787681 0.4414737 0.4578845 0.5302900 0.5434625 #> [857,] 0.4942273 0.5455117 0.6028087 0.6346238 0.6826033 0.7024932 0.7393022 #> [858,] 0.6158580 1.1232248 1.2848476 1.3117549 1.3278298 1.3405774 1.3982930 #> [859,] 0.4188517 0.4549399 0.4613472 0.5384865 0.6145878 0.6387264 0.6612431 #> [860,] 0.4792305 0.5141196 0.5410854 0.5430704 0.5812571 0.5814689 0.5905954 #> [861,] 0.2289517 0.3032933 0.3035479 0.3261829 0.4566556 0.4652289 0.4926226 #> [862,] 1.3375979 1.5159084 1.7651675 1.7951556 1.8873251 1.9282501 1.9311304 #> [863,] 0.3479301 0.4158467 0.4475573 0.5239454 0.5443972 0.5572317 0.5943596 #> [864,] 0.3445994 0.4473554 0.5081086 0.5733389 0.6099352 0.6214573 0.6883512 #> [865,] 0.2503394 0.4768582 0.6427768 0.7949227 0.8427137 0.8543760 0.8600206 #> [866,] 0.5509826 0.6028087 0.6743603 0.7374324 0.7572010 0.8214049 0.8687430 #> [867,] 0.4870908 0.4919594 0.4989698 0.5581019 0.5980778 0.5998886 0.6074706 #> [868,] 0.4299956 0.6966550 0.7385971 0.7570375 0.8153590 0.8393366 0.8576094 #> [869,] 0.2995764 0.3944994 0.4367022 0.5075357 0.5297576 0.6499291 0.6631117 #> [870,] 0.3293306 0.3850889 0.4398420 0.4766956 0.5261269 0.5305005 0.6059146 #> [871,] 0.4366422 0.4963909 0.5602409 0.5644773 0.5944104 0.6285445 0.6535593 #> [872,] 0.2618401 0.5512129 0.5897778 0.6207546 0.6642021 0.6813480 0.6973555 #> [873,] 0.3980096 0.4059614 0.4300678 0.5654316 0.5859741 0.5926003 0.6126095 #> [874,] 0.4401777 0.5409303 0.6310690 0.6985039 0.7151776 0.7328549 0.7661509 #> [875,] 0.3694694 0.3722417 0.3940243 0.3984968 0.4334864 0.5203917 0.5396707 #> [876,] 0.6602341 0.7779108 0.8882337 0.9693348 1.0649957 1.0676211 1.1084016 #> [877,] 0.5141196 0.5261482 0.5798880 0.6404703 0.6940858 0.7309148 0.7914278 #> [878,] 0.6389136 0.9553313 1.0718025 1.1883763 1.2044469 1.2161923 1.2191719 #> [879,] 0.6111134 0.6330021 0.7264062 0.7466766 0.7661509 0.7662488 0.7706794 #> [880,] 0.3417088 0.4197812 0.4328642 0.4689018 0.5634057 0.6811357 0.6952935 #> [881,] 0.2312386 0.2828653 0.3092599 0.3927840 0.4562478 0.4812868 0.4826099 #> [882,] 0.2474450 0.3570338 0.3927840 0.4410145 0.4429527 0.4597591 0.4845243 #> [883,] 0.1765733 0.2005349 0.4340247 0.4779987 0.5640750 0.5933102 0.5941373 #> [884,] 0.9609905 0.9755831 1.0158348 1.0248596 1.0306228 1.0421030 1.0602373 #> [885,] 0.4803380 0.4876600 0.5015561 0.5353879 0.5473466 0.5765421 0.6334912 #> [886,] 0.2411622 0.3859884 0.4363727 0.4586935 0.5530247 0.5812676 0.6110123 #> [887,] 0.7483794 0.7858414 0.7975968 0.8328092 0.8531193 0.9108383 0.9761211 #> [888,] 0.5192799 0.5370576 0.5517135 0.5649657 0.5899088 0.6580907 0.6966668 #> [889,] 0.1603362 0.2547535 0.3437702 0.5100298 0.5368799 0.5371010 0.5595756 #> [890,] 0.4959007 0.6041729 0.6174626 0.6970957 0.7220400 0.7628530 0.9100871 #> [891,] 0.2337535 0.3299522 0.4470890 0.5008382 0.5391656 0.5486938 0.5852984 #> [892,] 0.2853528 0.3298639 0.3561649 0.3592673 0.4272458 0.4645888 0.4665484 #> [893,] 0.4506818 0.4981237 0.5566957 0.5673679 0.6193540 0.6484340 0.6538127 #> [894,] 0.1882941 0.6041616 0.6263253 0.8026338 0.8081701 0.9258872 0.9768949 #> [895,] 0.2536980 0.4769697 0.5194278 0.5278623 0.5649657 0.5688071 0.5813304 #> [896,] 0.6724590 0.7350833 0.8214040 0.8284443 0.9983396 1.0588523 1.0601857 #> [897,] 0.3082097 0.4318124 0.4660285 0.4704474 0.4798373 0.5007229 0.5073744 #> [898,] 0.5959716 0.6063061 0.6644238 0.6669696 0.6904804 0.7117599 0.7358067 #> [899,] 0.6225775 0.6580907 0.7458233 0.7656519 0.8636642 0.9055562 0.9134872 #> [900,] 0.2343413 0.4215890 0.5151912 0.5955406 0.7681511 0.8515844 0.8669462 #> [901,] 0.3285568 0.4099349 0.4197421 0.4618651 0.5232328 0.5740169 0.6015343 #> [902,] 0.3784988 0.4220740 0.4358854 0.4508229 0.4588823 0.5052489 0.5454542 #> [903,] 2.0556048 2.1108382 2.1577122 2.1925334 2.2083299 2.2801831 2.5363151 #> [904,] 0.4114465 0.6254268 0.8252143 1.0101165 1.0390967 1.1097381 1.1741516 #> [905,] 0.3737895 0.3951226 0.4112151 0.4597591 0.4654187 0.5176881 0.5691951 #> [906,] 0.4542098 0.5278493 0.6860703 0.7970234 0.8091093 0.8225765 0.8265693 #> [907,] 0.4938878 0.6573238 0.6673814 0.7260920 0.7379023 0.7393258 0.7920760 #> [908,] 0.2994951 0.3686147 0.4396160 0.4625997 0.4921990 0.5790150 0.5847977 #> [909,] 0.1502014 0.3688427 0.3859884 0.4635766 0.5136618 0.5138721 0.5232558 #> [910,] 0.2615611 0.4080902 0.4463284 0.5950760 0.6132402 0.6481007 0.6598114 #> [911,] 0.4851220 0.5057987 0.5622607 0.5920986 0.6190446 0.7151776 0.7512546 #> [912,] 0.7962522 1.1027254 1.3672287 1.3807148 1.4204162 1.4331734 1.6037038 #> [913,] 0.5067543 0.5221909 0.5752158 0.6147478 0.6218947 0.6348818 0.6790847 #> [914,] 0.3227764 0.3627995 0.4433784 0.4683933 0.4759184 0.5595899 0.5716828 #> [915,] 0.4388591 0.4663869 0.4992582 0.5415097 0.5598902 0.5853602 0.5982363 #> [916,] 0.4621747 0.5439116 0.5810662 0.6525644 0.7261151 0.7618664 0.7742697 #> [917,] 0.5270508 0.6896589 0.6949101 0.6962610 0.7263227 0.7282629 0.7321677 #> [918,] 0.1880397 0.3118351 0.3850889 0.4812505 0.4999104 0.5074146 0.5335917 #> [919,] 0.1963591 0.4696109 0.4981826 0.5449325 0.5960984 0.6329302 0.6556979 #> [920,] 0.3811083 0.4901664 0.5100151 0.5351490 0.5490831 0.5580313 0.5640750 #> [921,] 0.3875109 0.5019206 0.5601844 0.5697414 0.5737582 0.6291796 0.6404703 #> [922,] 0.2136091 0.3576403 0.3815088 0.4719349 0.4834617 0.5229099 0.5483363 #> [923,] 0.6154598 0.6424847 0.6907413 0.8670046 0.8818672 0.8885824 0.8975248 #> [924,] 0.5671601 0.8629524 1.0250429 1.0556881 1.2399047 1.2926398 1.5970988 #> [925,] 0.6154598 0.7423847 0.8346140 1.0411140 1.0660808 1.0823524 1.1049130 #> [926,] 0.3386233 0.4579561 0.6172867 0.6309674 0.6380812 0.6801436 0.6812344 #> [927,] 0.4360400 0.5995342 0.8487750 0.8763325 0.9250326 0.9493566 0.9853316 #> [928,] 0.6293818 0.7704966 0.8845181 0.9375603 0.9499213 0.9609299 1.0233418 #> [929,] 0.4870615 0.5015118 0.5460978 0.6271310 0.6976519 0.7055782 0.8089936 #> [930,] 0.5042632 0.6896709 0.7113964 0.7290197 0.7380223 0.7713337 0.7809407 #> [931,] 0.3329671 0.5655398 0.7402701 0.8392612 0.8733825 0.8951593 0.9067863 #> [932,] 0.8008904 0.8786278 0.8980172 0.9943680 1.0081010 1.0912510 1.3251528 #> [933,] 0.6306143 0.6469669 0.7280660 0.7928859 0.8346431 0.8725721 0.8731730 #> [934,] 0.2743603 0.3937665 0.4599237 0.5155835 0.5191472 0.5414595 0.5740675 #> [935,] 0.1921174 0.4510282 0.4783833 0.5052489 0.5644773 0.6008386 0.6455745 #> [936,] 0.5700973 0.6075683 0.6546223 0.6574239 0.6743603 0.6826033 0.6853944 #> [937,] 0.3119060 0.3528047 0.3627995 0.3915093 0.4358854 0.4609358 0.4802595 #> [938,] 0.4185317 0.4220740 0.4422055 0.4509399 0.4915624 0.5072311 0.5301315 #> [939,] 0.6726428 0.7354445 0.7427972 0.7515865 0.7576142 0.7620362 0.7784925 #> [940,] 0.4172339 0.6413421 0.6954323 0.7064193 0.7385654 0.8145048 0.8370775 #> [941,] 0.2743603 0.5324977 0.5416235 0.5705223 0.5984565 0.6170349 0.6286326 #> [942,] 0.3027772 0.4896400 0.4992742 0.5449325 0.5624760 0.6325971 0.6948436 #> [943,] 0.4111658 0.5943301 0.6385163 0.6979967 0.8446133 0.8896609 0.9001202 #> [944,] 0.4556055 0.4896084 0.5217608 0.5254022 0.5556390 0.5913540 0.6264371 #> [945,] 0.4227960 0.4896124 0.5120180 0.5913042 0.6111190 0.6146042 0.6258101 #> [946,] 0.6296239 0.6892862 0.8390236 0.8875493 0.9461592 1.0529038 1.1175567 #> [947,] 0.3458876 0.4801832 0.5002871 0.5415791 0.6182001 0.6493688 0.6571560 #> [948,] 0.5578534 0.7378963 0.8169656 0.8296489 0.8388325 0.8670486 0.8909451 #> [949,] 0.3590217 0.5057987 0.5953573 0.6310690 0.7259945 0.7391465 0.8038318 #> [950,] 0.5544410 0.7224488 0.8083694 0.8105955 0.8534005 0.8792767 0.8823584 #> [951,] 0.6389136 0.7099335 0.7347236 0.8351929 0.8554912 0.8708675 0.8733825 #> [952,] 0.4955922 0.6729536 0.7140313 0.7482271 0.7661296 0.8388325 0.8487750 #> [953,] 0.4158467 0.4642895 0.4868032 0.5434911 0.5755035 0.5863843 0.5917659 #> [954,] 0.1457126 0.5038661 0.5415791 0.5644321 0.6778771 0.7278628 0.8170445 #> [955,] 0.3342053 0.3352511 0.4344955 0.4351358 0.4720053 0.5108792 0.5189029 #> [956,] 0.9457062 0.9849265 1.0223147 1.0483017 1.0934914 1.0951222 1.1603080 #> [957,] 0.3633023 0.4441841 0.4480393 0.5013117 0.5053819 0.5627093 0.6093778 #> [958,] 0.6569257 0.8302082 0.8922407 0.8980629 0.9490880 0.9769237 1.0046860 #> [959,] 0.3097309 0.7675038 0.9328969 0.9411251 1.0235728 1.0326252 1.0389430 #> [960,] 0.4588987 0.4657427 0.6035151 0.6461901 0.6718601 0.6904380 0.7021603 #> [961,] 0.7051774 0.7756786 0.9457200 1.0963949 1.1187557 1.1253954 1.1275522 #> [962,] 0.2479778 0.2499468 0.4179349 0.4187607 0.4781016 0.5302001 0.5350829 #> [963,] 0.5012870 0.5258843 0.5312717 0.5410854 0.5644517 0.5668125 0.5941373 #> [964,] 0.7557878 0.8613864 0.9285815 0.9622023 1.0018294 1.0047035 1.0277976 #> [965,] 0.3341905 0.3604918 0.3978667 0.4150834 0.4213421 0.4661846 0.4741237 #> [966,] 0.3085853 0.4051373 0.4468204 0.4954094 0.5105782 0.5119253 0.5684175 #> [967,] 0.3886122 0.5793263 0.5798880 0.7502501 0.7891617 0.8239215 0.9211892 #> [968,] 0.1628608 0.7996086 0.9813975 1.0457239 1.0600730 1.0738716 1.1739681 #> [969,] 0.8815454 0.9994666 1.0613208 1.1361912 1.1385667 1.1426683 1.2533244 #> [970,] 0.2316066 0.4347131 0.5258843 0.5430704 0.5797168 0.6455426 0.6567670 #> [971,] 0.6492446 0.6574239 0.6747291 0.8214049 0.8362544 0.8401697 0.8707394 #> [972,] 0.1412353 0.3272235 0.3900844 0.4023933 0.4148677 0.4451457 0.4488936 #> [973,] 0.8747926 1.0726503 1.1068952 1.1648686 1.2204814 1.2386833 1.2633442 #> [974,] 0.8193688 0.8456541 0.8527007 0.8686242 0.9171797 0.9520110 0.9659309 #> [975,] 0.2780944 0.3015907 0.3376254 0.3811083 0.3974026 0.4028254 0.4308291 #> [976,] 0.3893338 0.4505269 0.4605785 0.6256895 0.6536408 0.7331622 0.7586668 #> [977,] 0.6911686 0.7268477 0.8231290 0.8424534 0.9000961 0.9033437 0.9233767 #> [978,] 0.3496784 0.4432166 0.5334193 0.5989136 0.7096008 0.7235467 0.7262585 #> [979,] 0.2847934 0.3882206 0.4453761 0.4810555 0.5301634 0.5475485 0.5485547 #> [980,] 0.3859954 0.4699200 0.4811191 0.4980564 0.5245181 0.5667576 0.5963322 #> [981,] 0.7278976 0.7327970 0.7627281 0.7788747 0.7951149 0.8195750 0.8378108 #> [982,] 0.3884973 0.5418577 0.5821115 0.7216168 0.7290825 0.7671997 0.7678536 #> [983,] 0.3086478 0.4160685 0.4996085 0.5072499 0.5879309 0.7115002 0.7117846 #> [984,] 0.6303962 0.6589045 0.8001376 0.8145048 0.8338672 0.9112486 0.9150192 #> [985,] 0.5460581 0.6507848 0.8080487 0.8499740 0.8614721 0.8856987 0.8991177 #> [986,] 0.3589144 0.5418090 0.5852687 0.5985745 0.7017688 0.7516601 0.7699749 #> [987,] 0.1657857 0.2696111 0.3561649 0.3862536 0.4120583 0.4539152 0.4941148 #> [988,] 0.5119789 0.5209139 0.5622382 0.5642782 0.5787497 0.5996913 0.6040388 #> [989,] 0.1983044 0.5926421 0.6151594 0.6539348 0.6827474 0.8271119 0.8582468 #> [990,] 0.3474395 0.5745431 0.6616265 0.7282968 0.8629524 1.0278892 1.1192310 #> [991,] 0.4299956 0.4577316 0.5259341 0.5962516 0.6553439 0.7172299 0.7221793 #> [992,] 0.6325971 0.6704545 0.6972553 0.7056987 0.7278976 0.7571621 0.7676339 #> [993,] 0.4489389 0.4528535 0.5843780 0.6116490 0.6194128 0.7036732 0.8328092 #> [994,] 1.2167245 1.4022221 1.4315399 1.4493745 1.4979496 1.5439557 1.6053293 #> [995,] 0.2972695 0.5141040 0.6075683 0.6081224 0.6466797 0.6477152 0.6839222 #> [996,] 0.3875109 0.4253779 0.5457228 0.5545367 0.5935204 0.6400436 0.6516956 #> [997,] 0.2770382 0.5320051 0.7591667 1.0625637 1.1118740 1.1502454 1.1741015 #> [998,] 0.3747992 0.3757904 0.5399607 0.5601844 0.6182612 0.6194519 0.6348818 #> [999,] 0.6915614 0.7037830 0.7320299 0.8083417 0.8444847 0.8632591 0.9026419 #> [1000,] 0.6776881 0.9553533 1.0390967 1.1113306 1.1381286 1.1394759 1.2094194 #> [,8] [,9] [,10] #> [1,] 1.0948143 1.1253654 1.1294703 #> [2,] 0.5525923 0.5752191 0.5948832 #> [3,] 1.0433555 1.0634161 1.0639414 #> [4,] 0.6020509 0.6321894 0.6900302 #> [5,] 0.5374756 0.5838935 0.6076137 #> [6,] 1.9607194 1.9707421 1.9894195 #> [7,] 0.9771859 1.0105006 1.0358824 #> [8,] 0.7905344 0.7948463 0.8065179 #> [9,] 0.7325301 0.7529760 0.7644297 #> [10,] 1.8648328 1.8954785 1.9155196 #> [11,] 0.7809546 0.8205875 0.8244164 #> [12,] 0.5654316 0.5838902 0.5863865 #> [13,] 1.9906122 2.0133325 2.0347324 #> [14,] 0.5282038 0.5294087 0.5580313 #> [15,] 0.6695476 0.6720961 0.7860751 #> [16,] 0.6848587 0.6942770 0.6993759 #> [17,] 1.2454930 1.2909212 1.3323128 #> [18,] 0.7167810 0.7366055 0.7377562 #> [19,] 0.6401902 0.6607465 0.6644822 #> [20,] 0.5855031 0.5910505 0.6080686 #> [21,] 0.5373405 0.5618657 0.5968875 #> [22,] 1.0337314 1.1039228 1.1077311 #> [23,] 0.4721043 0.4789884 0.4811924 #> [24,] 0.7415321 0.7484844 0.7656143 #> [25,] 0.8201046 0.8592889 0.8610294 #> [26,] 0.8449646 0.8456569 0.8807989 #> [27,] 1.1437594 1.1803415 1.2018161 #> [28,] 0.7660779 0.7776805 0.8061198 #> [29,] 0.9298050 0.9307238 0.9379767 #> [30,] 0.7981114 0.8339729 0.8374160 #> [31,] 0.9943953 1.0235233 1.0283907 #> [32,] 0.7459984 0.7858414 0.7963569 #> [33,] 0.8277493 0.8512057 0.8969156 #> [34,] 0.9271726 0.9673539 0.9850058 #> [35,] 0.6410389 0.6600794 0.6959216 #> [36,] 1.6054625 1.6865838 1.6911640 #> [37,] 0.5849180 0.6452064 0.6531977 #> [38,] 0.5227291 0.5905755 0.6106738 #> [39,] 1.5553362 1.6244928 1.6247010 #> [40,] 0.5028352 0.5363079 0.5814556 #> [41,] 0.9051968 0.9132779 0.9296012 #> [42,] 0.6478459 0.6729798 0.6803337 #> [43,] 0.4674898 0.4766956 0.4847473 #> [44,] 0.7856933 0.8096945 0.8112277 #> [45,] 0.8419828 0.8642496 0.9374454 #> [46,] 0.7466119 0.7722180 0.7869417 #> [47,] 1.6690312 1.6896383 1.6958277 #> [48,] 0.4829012 0.5538597 0.5852644 #> [49,] 0.5462467 0.5481299 0.5585343 #> [50,] 0.8244164 0.8805266 0.9358046 #> [51,] 0.6613715 0.6811357 0.7124117 #> [52,] 0.7191833 0.7194659 0.7649574 #> [53,] 1.2564305 1.3031879 1.3709338 #> [54,] 0.7095881 0.7101002 0.7457393 #> [55,] 0.8632450 0.9452734 0.9582362 #> [56,] 1.2330285 1.2860053 1.2878207 #> [57,] 0.6133775 0.6570875 0.6647266 #> [58,] 0.7066226 0.7161633 0.7200286 #> [59,] 1.1455786 1.1821034 1.2087668 #> [60,] 0.7898192 0.7939243 0.8015919 #> [61,] 0.8167129 0.8387293 0.8398155 #> [62,] 0.6275538 0.6390093 0.6403610 #> [63,] 0.6823798 0.6855014 0.6864298 #> [64,] 0.6007936 0.6157251 0.6158978 #> [65,] 1.0931949 1.1069983 1.1255987 #> [66,] 1.1747662 1.1762794 1.1793043 #> [67,] 0.7198973 0.8448278 0.8528679 #> [68,] 0.7744335 0.8008949 0.8113809 #> [69,] 0.8337477 0.8546016 0.8666153 #> [70,] 0.7035311 0.7185677 0.7657909 #> [71,] 0.6959320 0.7141490 0.7330386 #> [72,] 0.8266657 0.8555373 0.8824980 #> [73,] 1.0689941 1.0765964 1.0970276 #> [74,] 0.6422858 0.6753351 0.6794117 #> [75,] 0.9881594 1.0049256 1.0092698 #> [76,] 0.7840795 0.8029412 0.8446133 #> [77,] 0.8339729 0.8852561 0.8885824 #> [78,] 0.6776343 0.6783473 0.6927912 #> [79,] 0.5990214 0.6148437 0.6178335 #> [80,] 0.6261561 0.6705603 0.6917049 #> [81,] 0.8183042 0.8248413 0.8452508 #> [82,] 1.5711966 1.5921858 1.6799638 #> [83,] 0.5473466 0.5577556 0.5585343 #> [84,] 1.4575733 1.4748398 1.5450134 #> [85,] 0.9101200 0.9215540 0.9400282 #> [86,] 0.6895740 0.6964980 0.8398155 #> [87,] 0.5315435 0.5926003 0.5940546 #> [88,] 0.9466164 0.9505368 0.9819191 #> [89,] 0.7085806 0.7539298 0.7576612 #> [90,] 0.4639866 0.4886340 0.4978754 #> [91,] 1.0661671 1.0703207 1.0850179 #> [92,] 1.0350864 1.1246410 1.1410353 #> [93,] 0.6273846 0.6396846 0.6729936 #> [94,] 0.6621461 0.6730841 0.6755372 #> [95,] 0.5589076 0.5718031 0.5726976 #> [96,] 1.0967401 1.1063678 1.1619855 #> [97,] 0.6227062 0.6631667 0.7028310 #> [98,] 1.4545619 1.4804709 1.4940935 #> [99,] 1.2369132 1.2409701 1.2629280 #> [100,] 0.6414267 0.6420026 0.6812624 #> [101,] 0.6256541 0.6270762 0.6340315 #> [102,] 0.8030102 0.8399439 0.8470966 #> [103,] 1.3906688 1.4321209 1.4531103 #> [104,] 1.4258069 1.4359298 1.4368440 #> [105,] 0.9993196 1.0286461 1.0327843 #> [106,] 0.5507339 0.5758143 0.6218453 #> [107,] 0.9608068 0.9634866 0.9682306 #> [108,] 1.3341929 1.3603603 1.4866068 #> [109,] 1.3107763 1.3393935 1.3667934 #> [110,] 0.8175726 0.8257304 0.8447845 #> [111,] 0.4845243 0.5029748 0.5270942 #> [112,] 0.4837839 0.5098671 0.5127951 #> [113,] 0.5567038 0.5651490 0.5693620 #> [114,] 0.8781318 0.9038817 0.9218376 #> [115,] 0.7588572 0.7636821 0.7788853 #> [116,] 0.8845181 0.9186031 0.9341500 #> [117,] 0.6133688 0.6480952 0.6680145 #> [118,] 0.8748226 0.8857262 0.8882337 #> [119,] 0.9168692 0.9190963 0.9461592 #> [120,] 0.5529531 0.5568170 0.5956290 #> [121,] 0.5807087 0.5853281 0.6225125 #> [122,] 0.6371422 0.6550249 0.6594435 #> [123,] 0.7378963 0.8014425 0.8158279 #> [124,] 0.9562657 0.9765916 0.9771331 #> [125,] 0.7866932 0.7870167 0.8082479 #> [126,] 0.8753202 0.9776262 0.9876411 #> [127,] 0.5155695 0.5517497 0.5578328 #> [128,] 1.1372116 1.2638671 1.2847125 #> [129,] 0.8325244 0.8735306 0.8902842 #> [130,] 0.6917049 0.6949101 0.7003802 #> [131,] 1.2206230 1.2382200 1.2576681 #> [132,] 0.6918563 0.6972482 0.6981607 #> [133,] 0.7293444 0.7451747 0.7648917 #> [134,] 1.0767381 1.1098807 1.1120624 #> [135,] 0.8793499 0.9069460 0.9134570 #> [136,] 0.6395725 0.6667419 0.6688439 #> [137,] 0.7184914 0.7327227 0.7652532 #> [138,] 0.7553037 0.8062697 0.8073952 #> [139,] 0.7791502 0.8055212 0.8210332 #> [140,] 0.7756952 0.7866932 0.8007903 #> [141,] 0.7677695 0.7754018 0.7818217 #> [142,] 0.5684539 0.5996913 0.6007714 #> [143,] 1.3375979 1.4037835 1.4188600 #> [144,] 0.7343907 0.7648775 0.7744335 #> [145,] 1.0005149 1.0839008 1.1359603 #> [146,] 1.9247666 2.0528754 2.1450529 #> [147,] 1.5396802 1.5606996 1.5799396 #> [148,] 0.6387329 0.6418468 0.7399409 #> [149,] 1.4024336 1.4556944 1.4722021 #> [150,] 0.6279512 0.6348908 0.6559528 #> [151,] 1.0326327 1.0389256 1.0914895 #> [152,] 0.9378915 0.9794508 0.9807302 #> [153,] 1.0243505 1.0493163 1.0726503 #> [154,] 1.9435902 2.0558332 2.0685179 #> [155,] 0.6269097 0.6477152 0.6494768 #> [156,] 0.8093809 0.8215576 0.8361150 #> [157,] 0.5778372 0.5838077 0.5843167 #> [158,] 0.6856160 0.6878116 0.6889679 #> [159,] 1.3000231 1.3299072 1.4954100 #> [160,] 0.9060245 0.9248323 0.9391637 #> [161,] 0.7344118 0.7827488 0.8158820 #> [162,] 0.5290869 0.5333304 0.5641456 #> [163,] 0.7405819 0.7453283 0.7493836 #> [164,] 1.0792046 1.1108133 1.1561070 #> [165,] 0.5784775 0.5840255 0.5874667 #> [166,] 0.8497432 0.8526097 0.8793478 #> [167,] 0.5301634 0.5773118 0.5815696 #> [168,] 0.9205566 1.0556881 1.0748362 #> [169,] 0.6847138 0.6932394 0.7051132 #> [170,] 0.6720961 0.6875287 0.7077823 #> [171,] 0.6625082 0.6636391 0.7048870 #> [172,] 0.9430009 0.9596114 1.0043056 #> [173,] 0.8368947 0.8442932 0.9303547 #> [174,] 0.8347588 0.8516636 0.8723330 #> [175,] 0.6116490 0.6363949 0.6639107 #> [176,] 0.7434605 0.7671118 0.8219937 #> [177,] 0.6475037 0.6693719 0.6704771 #> [178,] 1.3141811 1.3641529 1.3654928 #> [179,] 0.7288562 0.7513864 0.8175451 #> [180,] 1.0607921 1.0778866 1.0892902 #> [181,] 0.5993173 0.6505006 0.6528188 #> [182,] 0.7856517 0.8236180 0.8815996 #> [183,] 1.2932255 1.2943257 1.3099257 #> [184,] 0.5943086 0.6065965 0.6508316 #> [185,] 0.8558937 1.0087461 1.0173097 #> [186,] 0.7332994 0.7524957 0.7620211 #> [187,] 0.5388128 0.5724169 0.5871660 #> [188,] 0.4453320 0.4781089 0.4906804 #> [189,] 0.6825961 0.7315208 0.7662661 #> [190,] 0.6261561 0.6322562 0.6387264 #> [191,] 0.5431800 0.5493708 0.5842524 #> [192,] 0.9366650 1.0115112 1.0139018 #> [193,] 0.7791763 0.7877686 0.8267271 #> [194,] 0.4999803 0.5224957 0.5284957 #> [195,] 0.7287542 0.7338708 0.7382701 #> [196,] 0.6793757 0.7002640 0.7527591 #> [197,] 0.8001376 0.8348291 0.8683633 #> [198,] 0.8281828 0.8604421 0.8701024 #> [199,] 0.7081871 0.7107376 0.7260920 #> [200,] 0.8064651 0.8132218 0.8245745 #> [201,] 1.1696744 1.1757446 1.1955097 #> [202,] 0.5215113 0.5506487 0.5559009 #> [203,] 0.6368902 0.6431490 0.6779259 #> [204,] 0.7081038 0.7217274 0.7359611 #> [205,] 0.7783967 0.7897575 0.8099471 #> [206,] 2.2261040 2.2775297 2.3224374 #> [207,] 0.9126229 0.9135974 0.9346452 #> [208,] 0.7500319 0.7543159 0.8307144 #> [209,] 0.5990616 0.6094973 0.6518840 #> [210,] 0.7939766 0.7948463 0.8174748 #> [211,] 0.6363949 0.6399279 0.6597046 #> [212,] 0.7984211 0.8140872 0.8308921 #> [213,] 0.6526012 0.6779259 0.7076093 #> [214,] 0.6954323 0.7328516 0.8023101 #> [215,] 0.7891139 0.8550059 0.8793124 #> [216,] 0.6767596 0.6819420 0.7056023 #> [217,] 0.5446488 0.5520749 0.5598840 #> [218,] 0.6493102 0.6569956 0.7001344 #> [219,] 0.6517485 0.6598114 0.6637741 #> [220,] 1.3616232 1.3623973 1.3651631 #> [221,] 0.9490913 0.9776484 1.0237415 #> [222,] 1.0844905 1.0989884 1.1113306 #> [223,] 0.6221887 0.6253999 0.6451453 #> [224,] 0.8194787 0.8278785 0.8498218 #> [225,] 0.5101514 0.5381790 0.5455016 #> [226,] 0.7723712 0.7773210 0.7809546 #> [227,] 0.6798027 0.6873260 0.6927150 #> [228,] 0.7488809 0.7905154 0.8101955 #> [229,] 0.6734346 0.6924004 0.7396594 #> [230,] 0.6067046 0.6370304 0.6404394 #> [231,] 0.6918033 0.7091232 0.7606984 #> [232,] 1.2896599 1.3014405 1.3156883 #> [233,] 0.5900934 0.6004548 0.6081852 #> [234,] 0.6822182 0.6979967 0.7666046 #> [235,] 0.6520138 0.6824711 0.6960982 #> [236,] 0.7053574 0.7241683 0.7403172 #> [237,] 0.5986689 0.5990214 0.6377405 #> [238,] 0.7920279 0.8213955 0.8286330 #> [239,] 1.2819247 1.2855510 1.3117287 #> [240,] 0.7161184 0.7414333 0.7443708 #> [241,] 1.0238656 1.0239953 1.0652112 #> [242,] 0.7274085 0.8577339 0.9286956 #> [243,] 0.8815454 0.8823584 0.9279361 #> [244,] 1.0295641 1.0425783 1.0451755 #> [245,] 0.5792627 0.5956290 0.5963923 #> [246,] 1.0161725 1.0426072 1.0429812 #> [247,] 0.5784076 0.5812280 0.6161878 #> [248,] 0.7515865 0.8088560 0.8094516 #> [249,] 1.1293264 1.1461501 1.1804159 #> [250,] 1.1818696 1.1882331 1.1978149 #> [251,] 0.7825243 0.7996498 0.8301877 #> [252,] 1.0085870 1.0653633 1.0755396 #> [253,] 0.5808555 0.5864900 0.6044754 #> [254,] 1.7971424 1.8032660 1.8215755 #> [255,] 0.6878055 0.7056987 0.7316238 #> [256,] 0.9891129 1.0039163 1.0513837 #> [257,] 0.7224331 0.7338708 0.7404499 #> [258,] 0.6437494 0.6840457 0.6865839 #> [259,] 0.9520110 1.0119328 1.0333963 #> [260,] 0.8879565 0.8959714 0.9304029 #> [261,] 0.5361542 0.5636682 0.5842524 #> [262,] 0.8899253 0.8908305 0.9057293 #> [263,] 0.6037244 0.6114538 0.6404394 #> [264,] 1.0037048 1.0046564 1.0069419 #> [265,] 0.6911578 0.7127932 0.7773210 #> [266,] 1.4057024 1.4076882 1.4225578 #> [267,] 1.1279720 1.1550765 1.1581750 #> [268,] 0.5414595 0.5564536 0.6357178 #> [269,] 0.6076115 0.6193471 0.6276841 #> [270,] 0.6143492 0.6556173 0.6836048 #> [271,] 2.4496041 2.5344793 2.5391836 #> [272,] 0.6044852 0.6194560 0.6485100 #> [273,] 1.3404538 1.4055332 1.4131752 #> [274,] 0.5337690 0.5394863 0.5442631 #> [275,] 0.6493688 0.6504903 0.6757886 #> [276,] 0.9664330 1.0029752 1.0502675 #> [277,] 1.2916295 1.3270275 1.3620397 #> [278,] 0.7264431 0.7280660 0.7652532 #> [279,] 0.5512399 0.5529531 0.5653469 #> [280,] 1.2674576 1.2947856 1.3007113 #> [281,] 0.6846347 0.6856328 0.7447192 #> [282,] 0.8194439 0.8902137 0.9103053 #> [283,] 0.7289972 0.7363352 0.7464540 #> [284,] 0.5464866 0.5698526 0.5700085 #> [285,] 0.7070283 0.7107505 0.7586351 #> [286,] 0.9786823 0.9902617 1.0043523 #> [287,] 0.8835670 0.9166792 0.9298050 #> [288,] 0.8211376 0.8313677 0.8479914 #> [289,] 0.5036908 0.5224957 0.5538597 #> [290,] 0.5881955 0.5898104 0.6164692 #> [291,] 0.5928864 0.6194560 0.6233224 #> [292,] 0.6303293 0.6526242 0.6651987 #> [293,] 0.6756108 0.6893942 0.7012269 #> [294,] 0.6567613 0.6709421 0.6779583 #> [295,] 1.1418658 1.3219935 1.3361117 #> [296,] 0.6455426 0.6471754 0.6570015 #> [297,] 1.6822661 1.7729263 1.7731140 #> [298,] 0.5762717 0.5778650 0.5849180 #> [299,] 0.6902308 0.6924779 0.6946994 #> [300,] 0.9062407 0.9221555 1.0124701 #> [301,] 0.5691434 0.6206144 0.6520764 #> [302,] 0.4645308 0.5022562 0.5374942 #> [303,] 0.6195858 0.6303293 0.6317110 #> [304,] 0.6666715 0.6754799 0.6776307 #> [305,] 0.9657700 1.0268108 1.0743191 #> [306,] 0.6860968 0.7290304 0.7363749 #> [307,] 0.9330724 1.0323207 1.0533723 #> [308,] 0.6227840 0.6391521 0.6412936 #> [309,] 1.0081923 1.0499037 1.1157758 #> [310,] 0.8485907 0.8904358 1.0119328 #> [311,] 1.4585826 1.5491043 1.5699872 #> [312,] 1.0084942 1.0480537 1.1049122 #> [313,] 0.8173120 0.8294177 0.8338392 #> [314,] 0.5715760 0.5716476 0.5847812 #> [315,] 0.7681936 0.7818949 0.7836927 #> [316,] 0.8939884 0.9396375 0.9799018 #> [317,] 0.6936951 0.7488810 0.7905965 #> [318,] 0.5284969 0.5448599 0.5563110 #> [319,] 0.7708202 0.7891957 0.7910154 #> [320,] 0.6448727 0.6503403 0.6822182 #> [321,] 1.0162726 1.0430789 1.0781098 #> [322,] 0.5721931 0.5832461 0.6130888 #> [323,] 0.6755372 0.6960078 0.7344727 #> [324,] 0.9294930 0.9347328 0.9469777 #> [325,] 0.5809545 0.6368902 0.6649969 #> [326,] 0.5545367 0.5669852 0.5982363 #> [327,] 0.8570095 0.8741171 0.9018141 #> [328,] 0.5784438 0.6741597 0.6947247 #> [329,] 2.1946260 2.1992391 2.2446966 #> [330,] 0.6299160 0.6330021 0.6784835 #> [331,] 1.6457078 1.6651054 1.7061774 #> [332,] 0.8167660 0.8280890 0.8350104 #> [333,] 0.7697932 0.7825019 0.8039611 #> [334,] 0.6594180 0.6819420 0.7174661 #> [335,] 0.6489326 0.6538819 0.6575023 #> [336,] 1.0346965 1.0823524 1.1103062 #> [337,] 1.1231594 1.1492845 1.1676271 #> [338,] 0.8149124 0.8261038 0.8263901 #> [339,] 0.5833905 0.5875335 0.5910737 #> [340,] 0.7484844 0.7489488 0.8072147 #> [341,] 0.8174948 0.8685083 0.8690115 #> [342,] 0.6255115 0.6255987 0.6267861 #> [343,] 0.7351369 0.7598897 0.7924194 #> [344,] 0.6258388 0.6274167 0.6378654 #> [345,] 0.7616623 0.7817998 0.7882697 #> [346,] 0.9191435 0.9289766 0.9628716 #> [347,] 0.6106381 0.6141360 0.6265655 #> [348,] 0.5110051 0.5153055 0.5965174 #> [349,] 0.6150592 0.6544731 0.6558452 #> [350,] 0.9379206 0.9586946 0.9891129 #> [351,] 0.6922921 0.7232509 0.7310245 #> [352,] 0.8284646 0.8411821 0.8562097 #> [353,] 0.6232940 0.6251972 0.6336534 #> [354,] 0.5802153 0.5900908 0.5905871 #> [355,] 1.6016927 1.6183683 1.6376652 #> [356,] 0.8466935 0.8662803 0.8833108 #> [357,] 0.7460089 0.7497731 0.7529493 #> [358,] 0.8835670 0.9017609 0.9370817 #> [359,] 0.6362140 0.6389466 0.6555353 #> [360,] 0.7257879 0.7571024 0.7628530 #> [361,] 0.6661770 0.6735804 0.6826631 #> [362,] 0.7604367 0.7631046 0.7719649 #> [363,] 1.0205662 1.0823203 1.1001316 #> [364,] 0.9601521 0.9658617 0.9756815 #> [365,] 0.7093827 0.7349957 0.7540449 #> [366,] 0.9733015 0.9780837 0.9795397 #> [367,] 0.8142208 0.8174948 0.8393366 #> [368,] 1.1288409 1.1303360 1.1309967 #> [369,] 0.9086849 0.9438448 1.0109677 #> [370,] 0.5769253 0.5848354 0.5854538 #> [371,] 0.9085786 1.0613554 1.0737194 #> [372,] 0.8003550 0.8014943 0.8155339 #> [373,] 0.6520764 0.6809989 0.6925582 #> [374,] 1.0800418 1.0875153 1.1016418 #> [375,] 0.5740169 0.5790913 0.6239847 #> [376,] 1.4694834 1.4839979 1.5690349 #> [377,] 0.5039508 0.5773118 0.6093329 #> [378,] 0.6447953 0.7064193 0.7456880 #> [379,] 0.7255185 0.7739046 0.7975968 #> [380,] 0.7766619 0.7914277 0.7957228 #> [381,] 0.6988580 0.7077364 0.7449540 #> [382,] 0.9485530 0.9536875 0.9786161 #> [383,] 0.6256541 0.6275538 0.6362574 #> [384,] 0.6720682 0.7787870 0.7957228 #> [385,] 1.2687172 1.2802805 1.3047140 #> [386,] 0.5507339 0.6064328 0.6108049 #> [387,] 0.8122301 0.8179785 0.8191682 #> [388,] 0.7100582 0.7123172 0.7344118 #> [389,] 0.8584639 0.8613229 0.8715004 #> [390,] 0.7809224 0.7943283 0.8036352 #> [391,] 0.8789989 0.8846523 0.9430009 #> [392,] 0.5787497 0.6434306 0.6461901 #> [393,] 0.5508534 0.5647906 0.5816181 #> [394,] 1.0615136 1.0758883 1.1293451 #> [395,] 0.6867323 0.7006231 0.7072118 #> [396,] 1.3040997 1.3157568 1.3392170 #> [397,] 0.9589776 0.9816568 1.0123912 #> [398,] 0.8932279 0.9187887 0.9317281 #> [399,] 0.5505802 0.6370801 0.6504363 #> [400,] 0.9190036 0.9236847 0.9552591 #> [401,] 0.8119278 0.8150843 0.8183023 #> [402,] 0.7615951 0.9273662 0.9407729 #> [403,] 0.5254921 0.5363079 0.5819930 #> [404,] 0.5177915 0.5189196 0.5980605 #> [405,] 1.4713607 1.4878362 1.5052571 #> [406,] 0.5693620 0.5790913 0.6271961 #> [407,] 0.6229257 0.6454792 0.6617013 #> [408,] 0.6365288 0.6380717 0.6629856 #> [409,] 0.7639416 0.7647506 0.7828068 #> [410,] 0.8029412 0.8332180 0.8366698 #> [411,] 1.0092043 1.1255428 1.1476501 #> [412,] 0.7288516 0.7313914 0.7520435 #> [413,] 0.5735514 0.5819185 0.5864900 #> [414,] 1.1381939 1.2571642 1.2603551 #> [415,] 0.8042587 0.8070106 0.8208756 #> [416,] 0.7189922 0.7228040 0.7890711 #> [417,] 0.8023364 0.8728391 0.8812638 #> [418,] 0.6259600 0.6381189 0.6573023 #> [419,] 0.8915097 0.9321882 0.9529310 #> [420,] 0.6882820 0.7178746 0.7298191 #> [421,] 0.8980328 0.9007722 0.9341469 #> [422,] 0.7858526 0.8211451 0.8417973 #> [423,] 0.6611121 0.6853944 0.7027129 #> [424,] 0.9034129 0.9303547 0.9338346 #> [425,] 0.6103830 0.6146042 0.6152917 #> [426,] 1.2741930 1.3048889 1.3166447 #> [427,] 0.9660454 0.9786161 1.0160631 #> [428,] 0.9027740 0.9208716 0.9272575 #> [429,] 0.7189606 0.7354697 0.8051510 #> [430,] 0.6149044 0.6544568 0.6568448 #> [431,] 1.6014659 1.6124795 1.6464965 #> [432,] 0.8645507 0.8864328 0.8991177 #> [433,] 0.7941475 0.7968045 0.8960429 #> [434,] 0.5584686 0.6195858 0.6347572 #> [435,] 0.7867618 0.7902651 0.8077762 #> [436,] 0.7012632 0.7241992 0.7251975 #> [437,] 0.6840500 0.6843970 0.6867323 #> [438,] 0.9685332 0.9759108 0.9967674 #> [439,] 0.7871674 0.8543760 0.9412158 #> [440,] 0.8392612 0.8716437 0.8721196 #> [441,] 0.8069808 0.8518542 0.8620734 #> [442,] 0.8871901 0.9332700 1.0400032 #> [443,] 0.7402080 0.7404499 0.7449671 #> [444,] 0.9053295 0.9441010 0.9513810 #> [445,] 0.5589335 0.5616279 0.6127602 #> [446,] 0.6576721 0.6804033 0.7033662 #> [447,] 0.9927727 0.9967219 1.0073378 #> [448,] 0.7042698 0.7459097 0.7554355 #> [449,] 0.5727056 0.5983884 0.7005419 #> [450,] 0.9723325 0.9768080 1.0335018 #> [451,] 1.1559923 1.2082916 1.3320827 #> [452,] 0.5845112 0.6074706 0.6114413 #> [453,] 0.7398085 0.7490561 0.7493836 #> [454,] 0.5464723 0.5666532 0.6183374 #> [455,] 1.1370093 1.2190428 1.2428198 #> [456,] 0.6722716 0.6847392 0.6861309 #> [457,] 0.8434437 0.8982119 0.9272575 #> [458,] 0.4985066 0.5338072 0.5382786 #> [459,] 0.6853056 0.6895883 0.7055080 #> [460,] 1.0526528 1.0920031 1.1344009 #> [461,] 0.5698526 0.5888508 0.6044754 #> [462,] 0.5562461 0.5621462 0.5847977 #> [463,] 1.7375101 1.8041639 1.8503639 #> [464,] 1.2224570 1.2429012 1.2841455 #> [465,] 0.8482913 0.8697166 0.8788324 #> [466,] 0.6964980 0.7070283 0.7537934 #> [467,] 0.6418468 0.6671150 0.6917589 #> [468,] 0.5065117 0.5391964 0.5705462 #> [469,] 0.7010090 0.7214546 0.7667716 #> [470,] 0.7275787 0.8099892 0.8246746 #> [471,] 0.6374177 0.6419992 0.6515068 #> [472,] 0.9687647 0.9824862 1.0229776 #> [473,] 1.1807701 1.2286957 1.2921542 #> [474,] 0.6557559 0.6844510 0.7066562 #> [475,] 0.7262108 0.7448291 0.7559599 #> [476,] 0.5164314 0.5171654 0.5205927 #> [477,] 0.6980515 0.7317562 0.7372731 #> [478,] 1.0509450 1.0538652 1.0635850 #> [479,] 1.1200334 1.1386040 1.1417755 #> [480,] 0.9911805 1.0084563 1.0218342 #> [481,] 0.7985012 0.8332130 0.8358105 #> [482,] 1.4960626 1.5213869 1.5779222 #> [483,] 1.1499770 1.1650330 1.1797567 #> [484,] 0.5254051 0.5810606 0.5872490 #> [485,] 1.0248897 1.0299032 1.0416752 #> [486,] 0.7847803 0.8099471 0.8112365 #> [487,] 0.9444033 0.9586442 0.9673000 #> [488,] 0.7103499 0.7157430 0.8285483 #> [489,] 0.6194508 0.6614908 0.6766100 #> [490,] 0.8434020 0.8588308 0.9001202 #> [491,] 1.0468715 1.0511804 1.0698476 #> [492,] 0.4926226 0.5391086 0.5810606 #> [493,] 0.9021870 1.0212893 1.0643079 #> [494,] 0.6158978 0.6229634 0.6336534 #> [495,] 0.7328516 0.8080500 0.8320779 #> [496,] 0.5578328 0.5877514 0.5907850 #> [497,] 0.9729825 0.9912929 1.0054113 #> [498,] 1.3147718 1.3647384 1.3662988 #> [499,] 2.7978178 2.8484729 2.9330105 #> [500,] 0.7264557 0.8516435 0.8679878 #> [501,] 0.6613612 0.7231428 0.7516601 #> [502,] 0.8481883 0.8585328 0.8599669 #> [503,] 0.7367848 0.7974064 0.8334096 #> [504,] 0.5542075 0.5820559 0.5893635 #> [505,] 0.6116252 0.6465721 0.7169874 #> [506,] 0.8556933 0.8736286 0.9100260 #> [507,] 0.5740050 0.6339344 0.6344504 #> [508,] 1.0379094 1.0471588 1.0521241 #> [509,] 0.7351449 0.7733922 0.7906739 #> [510,] 1.1501040 1.1827899 1.2138401 #> [511,] 1.0081010 1.0269155 1.0403183 #> [512,] 1.5049288 1.5205302 1.5471490 #> [513,] 1.1290831 1.1957300 1.2288811 #> [514,] 1.0214357 1.0770711 1.1021957 #> [515,] 0.8460879 0.8489286 0.8553952 #> [516,] 0.5122100 0.5338072 0.5555859 #> [517,] 0.8566329 0.8780640 0.9458068 #> [518,] 0.7053574 0.7396663 0.7529493 #> [519,] 0.8132218 0.8619528 0.8794552 #> [520,] 0.7932397 0.8036352 0.8371247 #> [521,] 0.7600011 0.8030624 0.8731730 #> [522,] 1.9339702 1.9723446 2.0921342 #> [523,] 0.8728979 0.8798903 0.9108048 #> [524,] 0.8602468 0.8898735 0.9355186 #> [525,] 0.8434020 0.8554912 0.8611303 #> [526,] 0.6234424 0.6281006 0.6329588 #> [527,] 0.8075600 0.8575611 0.8953935 #> [528,] 1.0731621 1.1108133 1.1127074 #> [529,] 0.8029918 0.8075866 0.8531193 #> [530,] 0.7844424 0.8029918 0.8055212 #> [531,] 0.9918341 1.0455495 1.2020781 #> [532,] 0.6647266 0.6756108 0.6823798 #> [533,] 0.5475485 0.5560572 0.5752051 #> [534,] 1.4061900 1.4145051 1.4499503 #> [535,] 0.5350829 0.5391656 0.5428234 #> [536,] 0.8666887 0.8838251 0.8856987 #> [537,] 0.6450181 0.6989626 0.7645513 #> [538,] 0.6936951 0.6942788 0.7069361 #> [539,] 0.5448491 0.5483102 0.5591494 #> [540,] 0.5762100 0.5955601 0.6073795 #> [541,] 1.3941351 1.4337750 1.4711072 #> [542,] 1.1020501 1.1086187 1.1164041 #> [543,] 1.0664033 1.0783751 1.0789541 #> [544,] 1.1373486 1.1421385 1.1896530 #> [545,] 0.6098957 0.6264674 0.6398529 #> [546,] 0.7891117 0.7891957 0.8193688 #> [547,] 1.0043056 1.0920505 1.1169794 #> [548,] 0.5197251 0.5388128 0.5394863 #> [549,] 1.2325702 1.3233724 1.3253074 #> [550,] 2.3698032 2.4459672 2.4605241 #> [551,] 0.5694467 0.5983994 0.6216318 #> [552,] 0.7072118 0.7211100 0.7239720 #> [553,] 0.5203712 0.5367804 0.5406582 #> [554,] 1.0371558 1.0512924 1.0639414 #> [555,] 0.9910236 1.0217891 1.0661520 #> [556,] 1.0388253 1.0509622 1.0759924 #> [557,] 0.7511114 0.7523335 0.7737363 #> [558,] 1.0436830 1.0595302 1.1315020 #> [559,] 0.7485221 0.7555543 0.7562221 #> [560,] 0.8110856 0.8442247 0.8590657 #> [561,] 0.7910970 0.8125575 0.8269329 #> [562,] 2.0329060 2.0464947 2.1051615 #> [563,] 1.5614089 1.5792095 1.6496802 #> [564,] 1.1087357 1.1345552 1.1487033 #> [565,] 0.6202705 0.6210650 0.6326365 #> [566,] 1.0882388 1.1474096 1.1573929 #> [567,] 1.0228863 1.0319975 1.0508046 #> [568,] 0.5302128 0.5637004 0.5904987 #> [569,] 1.0220488 1.0539673 1.0574935 #> [570,] 1.0401324 1.0888612 1.1436175 #> [571,] 0.4961666 0.5716476 0.5838902 #> [572,] 0.6315611 0.6399279 0.6997841 #> [573,] 1.2314421 1.2539958 1.2860329 #> [574,] 0.8483180 0.8519278 0.8581231 #> [575,] 0.6048017 0.6223452 0.6331574 #> [576,] 1.5010928 1.5378015 1.5387295 #> [577,] 0.7322894 0.7402701 0.7722186 #> [578,] 0.5335917 0.5458239 0.5537864 #> [579,] 0.6568820 0.7080392 0.7319985 #> [580,] 0.6873511 0.7011420 0.7050837 #> [581,] 0.8928800 0.9238549 0.9298428 #> [582,] 0.7118597 0.7422491 0.7729236 #> [583,] 0.9892560 1.0059526 1.0644100 #> [584,] 1.0792311 1.0849133 1.1084016 #> [585,] 0.8916000 0.9396140 0.9467054 #> [586,] 0.8400040 0.8639699 0.9180399 #> [587,] 1.4627325 1.4824280 1.5210347 #> [588,] 1.0141649 1.1053538 1.1212797 #> [589,] 0.6793500 0.6883186 0.7481124 #> [590,] 0.7648148 0.7906054 0.7945961 #> [591,] 0.9025918 0.9716701 1.0721897 #> [592,] 0.6423844 0.6449319 0.6724115 #> [593,] 0.4725017 0.4901664 0.6232102 #> [594,] 0.5382786 0.5595756 0.5714908 #> [595,] 0.8342513 0.8361073 0.9158839 #> [596,] 0.6828360 0.7077951 0.7093827 #> [597,] 0.8045615 0.8189127 0.8202734 #> [598,] 0.6056041 0.6122406 0.6294766 #> [599,] 0.7319659 0.7665120 0.7830806 #> [600,] 0.5793024 0.5920239 0.5944527 #> [601,] 0.5422872 0.5934187 0.6225972 #> [602,] 0.5038229 0.5088133 0.5333304 #> [603,] 0.6339928 0.6344587 0.6415068 #> [604,] 0.5621462 0.5822668 0.5928315 #> [605,] 0.5043623 0.5152282 0.5412566 #> [606,] 0.8514101 0.8581030 0.8976943 #> [607,] 0.8099237 0.8210332 0.8644987 #> [608,] 1.0553813 1.0852216 1.0892656 #> [609,] 1.0876542 1.0967448 1.0970276 #> [610,] 0.6575762 0.6783473 0.6829099 #> [611,] 0.7092287 0.7793916 0.8176927 #> [612,] 0.7845856 0.8247635 0.8874659 #> [613,] 0.8388916 0.8632137 0.9030833 #> [614,] 1.0231618 1.0235836 1.0425666 #> [615,] 0.7166701 0.7827488 0.8202734 #> [616,] 0.6064328 0.6065965 0.6075559 #> [617,] 0.8378213 0.8425124 0.8486141 #> [618,] 1.2220645 1.3184867 1.3757140 #> [619,] 0.5756904 0.5858072 0.5933102 #> [620,] 1.2608402 1.2822373 1.2869762 #> [621,] 1.2739743 1.3099188 1.3250280 #> [622,] 0.6619830 0.6699510 0.7000377 #> [623,] 0.7164536 0.7257358 0.8023364 #> [624,] 1.2374534 1.2635508 1.2643068 #> [625,] 0.6902308 0.7028009 0.7319659 #> [626,] 0.8958902 0.9030162 0.9093301 #> [627,] 0.5549466 0.5755035 0.5765267 #> [628,] 0.7460089 0.7639416 0.8313206 #> [629,] 0.4905728 0.4942109 0.5031505 #> [630,] 0.6007005 0.6234864 0.6677731 #> [631,] 1.5964418 1.6594767 1.6659284 #> [632,] 0.6744898 0.6937754 0.6943448 #> [633,] 1.0761527 1.0898412 1.1706041 #> [634,] 0.6242899 0.6293683 0.6310264 #> [635,] 0.8992914 1.0140728 1.0649797 #> [636,] 1.1882590 1.2053993 1.2229430 #> [637,] 0.5477302 0.6005423 0.6195639 #> [638,] 0.6460751 0.6486946 0.6554590 #> [639,] 0.7732483 0.7848387 0.8093511 #> [640,] 0.7354020 0.7401168 0.7578651 #> [641,] 0.5115338 0.5376920 0.5490150 #> [642,] 1.3809669 1.3931236 1.3963649 #> [643,] 0.6162748 0.6355525 0.6373189 #> [644,] 0.5609127 0.5977554 0.6404431 #> [645,] 0.9621100 1.0284746 1.0561041 #> [646,] 0.8011934 0.8153516 0.8284279 #> [647,] 1.1757033 1.2322491 1.3049420 #> [648,] 0.5047042 0.5331247 0.5417924 #> [649,] 0.7390150 0.7774246 0.7941222 #> [650,] 1.2468155 1.2588606 1.2648697 #> [651,] 0.8700253 0.9101938 0.9348567 #> [652,] 0.7176706 0.7422686 0.7525945 #> [653,] 1.4647242 1.4649795 1.4841694 #> [654,] 0.5751650 0.5832221 0.6152891 #> [655,] 0.8821678 0.9729825 0.9755979 #> [656,] 0.5068300 0.5140644 0.5700511 #> [657,] 1.0711251 1.1204842 1.1268332 #> [658,] 0.6944146 0.7117599 0.7210043 #> [659,] 0.5651490 0.5871811 0.6401649 #> [660,] 0.6530109 0.6705030 0.7392660 #> [661,] 0.9938395 1.0084563 1.0115921 #> [662,] 0.5564536 0.5589335 0.5876542 #> [663,] 0.7698231 0.7761523 0.8092654 #> [664,] 0.6286956 0.6359375 0.6481956 #> [665,] 0.6136417 0.6709636 0.6793446 #> [666,] 1.5772505 1.5959408 1.7235659 #> [667,] 0.5598840 0.5724169 0.5938387 #> [668,] 0.5214682 0.5598422 0.5650591 #> [669,] 1.2304458 1.3551871 1.3687091 #> [670,] 1.1947125 1.2882108 1.2936240 #> [671,] 0.5446488 0.5522417 0.5537372 #> [672,] 0.8751083 0.8829570 1.0368283 #> [673,] 1.0337591 1.0884658 1.0925525 #> [674,] 1.3091289 1.4285975 1.4426094 #> [675,] 0.7962729 0.8106037 0.8532589 #> [676,] 0.5474013 0.5836521 0.6108524 #> [677,] 0.8050155 0.8197892 0.8250720 #> [678,] 1.2499746 1.2588606 1.2623633 #> [679,] 0.7672906 0.8184894 0.8372153 #> [680,] 0.8309062 0.8499388 0.8522157 #> [681,] 1.2357405 1.2813162 1.2881833 #> [682,] 0.7512256 0.7537934 0.8017146 #> [683,] 0.6924338 0.7604367 0.7621253 #> [684,] 0.8923240 0.9105257 0.9758638 #> [685,] 0.5165238 0.5184814 0.5219601 #> [686,] 0.7218092 0.7418706 0.8362253 #> [687,] 1.0000655 1.0190872 1.0434870 #> [688,] 1.4733664 1.4897250 1.5071121 #> [689,] 0.7361053 0.7372937 0.7560528 #> [690,] 1.1736718 1.2135157 1.2144153 #> [691,] 1.6315990 1.6820170 1.6940504 #> [692,] 0.5508534 0.5760182 0.5859741 #> [693,] 0.8191035 0.8410250 0.8522157 #> [694,] 0.9218645 0.9487543 0.9883042 #> [695,] 0.6830429 0.7555543 0.7700034 #> [696,] 0.8328559 0.8566730 0.8788406 #> [697,] 0.7060284 0.7123172 0.7189718 #> [698,] 0.6966668 0.7095384 0.7583497 #> [699,] 0.6793757 0.7066244 0.7092152 #> [700,] 0.8070216 0.8265935 0.8519278 #> [701,] 0.6302841 0.6342402 0.6365512 #> [702,] 1.2890853 1.2964382 1.3010610 #> [703,] 0.9358046 1.1144320 1.1393480 #> [704,] 0.7081493 0.7151052 0.7405949 #> [705,] 1.0195369 1.2259891 1.2269391 #> [706,] 0.5624723 0.5935204 0.6081886 #> [707,] 0.8580177 0.8863304 0.9252928 #> [708,] 1.3400658 1.3529517 1.3630672 #> [709,] 1.0605147 1.0743078 1.0866625 #> [710,] 0.5703296 0.5793024 0.5853281 #> [711,] 0.5644427 0.6095891 0.6157921 #> [712,] 0.6489326 0.6562908 0.6735804 #> [713,] 0.7164536 0.7185370 0.7198287 #> [714,] 1.1301325 1.1696451 1.1861796 #> [715,] 0.6106142 0.6114538 0.6166628 #> [716,] 0.8472093 0.8533833 0.8807989 #> [717,] 1.0511908 1.0913276 1.0950373 #> [718,] 0.7813662 0.7920934 0.8048466 #> [719,] 1.0322897 1.0772629 1.0996304 #> [720,] 0.6212383 0.6707692 0.6776307 #> [721,] 0.8476778 0.8508133 0.9056069 #> [722,] 0.7818596 0.7991157 0.8047440 #> [723,] 0.6360874 0.6570015 0.6794117 #> [724,] 1.5425863 1.5770401 1.5775648 #> [725,] 1.0469513 1.0730223 1.1365501 #> [726,] 2.5499001 2.5877972 2.5973348 #> [727,] 0.9649681 1.0000655 1.0105031 #> [728,] 0.6682089 0.6689205 0.6925582 #> [729,] 0.8289745 0.8606617 0.8995628 #> [730,] 1.1909792 1.2032655 1.2214736 #> [731,] 0.8155527 0.8330002 0.8910643 #> [732,] 1.4129700 1.4257205 1.4269882 #> [733,] 1.2326746 1.2666659 1.2807269 #> [734,] 0.9759457 1.0941410 1.1131137 #> [735,] 1.2926398 1.2943778 1.3189782 #> [736,] 0.8038318 0.9191734 0.9259836 #> [737,] 0.7771783 0.8173460 0.8183498 #> [738,] 0.8342751 0.8375110 0.8468821 #> [739,] 0.7669882 0.7769399 0.7783967 #> [740,] 0.7676540 0.7703288 0.7959924 #> [741,] 0.4807659 0.4811913 0.4829322 #> [742,] 0.7628195 0.7722913 0.8143765 #> [743,] 0.5038506 0.5872490 0.5902789 #> [744,] 0.8199415 0.8279275 0.8672934 #> [745,] 0.7083628 0.7338538 0.7515559 #> [746,] 1.7624131 1.7831930 1.8529676 #> [747,] 0.6527544 0.6722586 0.7173171 #> [748,] 0.6165924 0.6242899 0.6525633 #> [749,] 0.5591494 0.5773746 0.5871660 #> [750,] 0.6133688 0.6143176 0.6344504 #> [751,] 0.5977554 0.6075119 0.6081672 #> [752,] 0.8201508 0.8217113 0.8258053 #> [753,] 0.7158419 0.7167084 0.7526110 #> [754,] 0.5488100 0.5512399 0.5565225 #> [755,] 0.9689169 0.9702371 0.9763988 #> [756,] 0.7403582 0.7672499 0.7689766 #> [757,] 0.6011118 0.6035912 0.6143492 #> [758,] 0.6690084 0.6855743 0.6954385 #> [759,] 0.8650603 0.9300395 1.0290951 #> [760,] 0.9413051 0.9449080 0.9541890 #> [761,] 0.6814680 0.6873511 0.6933264 #> [762,] 0.6146394 0.6270975 0.6384403 #> [763,] 1.2295889 1.2969550 1.3012819 #> [764,] 0.7912666 0.8175944 0.8652332 #> [765,] 1.5165658 1.5632683 1.6220706 #> [766,] 0.7619575 0.7845856 0.7903842 #> [767,] 0.9105072 0.9167320 0.9350408 #> [768,] 0.8096945 0.8271271 0.8438052 #> [769,] 1.0189938 1.0358824 1.0408619 #> [770,] 0.7793938 0.7906739 0.8376128 #> [771,] 1.7807055 1.7927447 1.7961446 #> [772,] 1.2081725 1.2300703 1.2320259 #> [773,] 0.7095384 0.7634109 0.8012599 #> [774,] 0.5526499 0.5690779 0.5941848 #> [775,] 1.0034701 1.0198180 1.0597253 #> [776,] 0.6628386 0.6747995 0.6812344 #> [777,] 0.5489606 0.5848354 0.5980778 #> [778,] 0.6729456 0.6753574 0.6895843 #> [779,] 0.9989262 1.0340941 1.0549130 #> [780,] 0.7886171 0.8140232 0.8596395 #> [781,] 0.9398637 1.0138404 1.0257756 #> [782,] 0.5196511 0.5300860 0.5441774 #> [783,] 0.7712708 0.7785486 0.7933427 #> [784,] 0.7314612 0.7390954 0.7903991 #> [785,] 0.7497731 0.8423679 0.9455623 #> [786,] 0.8584332 0.8779746 0.8886868 #> [787,] 1.1090116 1.1598086 1.1838323 #> [788,] 1.0667274 1.0709461 1.0753148 #> [789,] 0.9512250 1.0026992 1.0055707 #> [790,] 0.6047498 0.6423844 0.6672514 #> [791,] 0.7644297 0.7823320 0.8289601 #> [792,] 0.7533340 0.8625143 0.8690115 #> [793,] 0.8308841 0.8392308 0.8533834 #> [794,] 0.7378000 0.7576315 0.7722186 #> [795,] 1.0377217 1.0471588 1.1430132 #> [796,] 0.6464568 0.6722230 0.7071145 #> [797,] 0.4768116 0.5038506 0.5119983 #> [798,] 0.5321714 0.5509819 0.5825465 #> [799,] 0.5765121 0.6117266 0.6334817 #> [800,] 1.2780696 1.2879393 1.3291877 #> [801,] 0.9607605 0.9730312 0.9790967 #> [802,] 0.9759251 1.0250429 1.0486243 #> [803,] 0.6575762 0.6844912 0.6890709 #> [804,] 0.8679566 0.9108230 1.0005149 #> [805,] 0.6319259 0.6342402 0.7315519 #> [806,] 0.6782024 0.6794570 0.7198823 #> [807,] 0.5168748 0.5367804 0.5435968 #> [808,] 0.5489606 0.6098179 0.6106142 #> [809,] 0.6365382 0.6373189 0.6435149 #> [810,] 0.5801760 0.5845530 0.5859639 #> [811,] 0.7177089 0.7441591 0.7792351 #> [812,] 0.7422491 0.7606548 0.7615598 #> [813,] 0.5879726 0.5909601 0.6122462 #> [814,] 0.5085484 0.5628062 0.5765421 #> [815,] 0.5026705 0.5189029 0.5284969 #> [816,] 1.2496203 1.2682469 1.2889422 #> [817,] 0.9497079 0.9783047 0.9847914 #> [818,] 0.8094516 0.8416187 0.8517166 #> [819,] 0.8513099 0.9304029 0.9474305 #> [820,] 0.8280890 0.8349683 0.8424210 #> [821,] 0.5074146 0.5448491 0.5822668 #> [822,] 2.0027772 2.0190620 2.0273821 #> [823,] 0.6307234 0.6482195 0.6661770 #> [824,] 2.3750614 2.3903208 2.3941222 #> [825,] 0.8838251 0.9023417 0.9142908 #> [826,] 0.9889649 1.0167606 1.0442092 #> [827,] 1.3254673 1.3315739 1.3326072 #> [828,] 0.5397715 0.5425306 0.5439430 #> [829,] 0.6322275 0.6484510 0.7294171 #> [830,] 0.7303706 0.7766673 0.7895184 #> [831,] 0.9917530 1.0002187 1.0179955 #> [832,] 1.0934972 1.0939917 1.1230953 #> [833,] 1.0198930 1.0874022 1.1050338 #> [834,] 0.4827478 0.4842416 0.5694467 #> [835,] 0.9877948 1.1083030 1.1223708 #> [836,] 0.6776030 0.7596441 0.7681511 #> [837,] 0.8056446 0.8448444 0.8542834 #> [838,] 0.8195514 0.8665423 0.8766603 #> [839,] 0.5893552 0.6070066 0.6161363 #> [840,] 0.4763757 0.5291550 0.5368799 #> [841,] 0.7351449 0.8048950 0.8117724 #> [842,] 0.5595356 0.5705462 0.5735618 #> [843,] 1.8124457 1.8751240 1.8873251 #> [844,] 0.7224461 0.7572010 0.7808384 #> [845,] 0.5486938 0.5735514 0.5755479 #> [846,] 0.7066244 0.7458233 0.7714272 #> [847,] 0.6431490 0.6594180 0.6620760 #> [848,] 0.6721738 0.7166255 0.7308810 #> [849,] 1.2402478 1.2403056 1.2798592 #> [850,] 0.8183498 0.8341716 0.8679566 #> [851,] 0.5682557 0.5983884 0.7436629 #> [852,] 2.0901283 2.1193404 2.1453606 #> [853,] 0.8889776 0.9101522 0.9120216 #> [854,] 0.6801136 0.6814680 0.7064047 #> [855,] 1.1362938 1.1583177 1.1843568 #> [856,] 0.5859734 0.6395725 0.6679704 #> [857,] 0.7928113 0.8106336 0.8582234 #> [858,] 1.4155497 1.4261048 1.4362454 #> [859,] 0.6932967 0.7067968 0.7267149 #> [860,] 0.6422858 0.6720682 0.6826380 #> [861,] 0.5245846 0.5254051 0.5499494 #> [862,] 1.9490004 1.9533986 1.9915554 #> [863,] 0.6024946 0.6290822 0.6487378 #> [864,] 0.7039406 0.7067968 0.7303706 #> [865,] 0.8614348 0.9119135 0.9338346 #> [866,] 0.8723443 0.8807200 0.8847025 #> [867,] 0.6138500 0.6325047 0.6444264 #> [868,] 0.8627456 0.9297663 0.9506089 #> [869,] 0.7957403 0.8158396 0.8162246 #> [870,] 0.6207546 0.6482118 0.6815168 #> [871,] 0.6873260 0.6925079 0.7224404 #> [872,] 0.7004519 0.7115287 0.7141490 #> [873,] 0.6206424 0.6294411 0.6419992 #> [874,] 0.7751067 0.7927907 0.8317753 #> [875,] 0.5477302 0.5905216 0.5968875 #> [876,] 1.1172828 1.1568073 1.1668856 #> [877,] 0.8027620 0.8289667 0.8584660 #> [878,] 1.3446773 1.3871050 1.4087059 #> [879,] 0.7947447 0.8026697 0.8187020 #> [880,] 0.7095780 0.7120531 0.7224331 #> [881,] 0.5165238 0.5197251 0.5563110 #> [882,] 0.4867866 0.4886340 0.5277014 #> [883,] 0.6095891 0.6444264 0.6471754 #> [884,] 1.1103547 1.1321855 1.1509306 #> [885,] 0.6683102 0.6714550 0.6776343 #> [886,] 0.6149044 0.6266999 0.6792860 #> [887,] 1.0134636 1.0375347 1.0748362 #> [888,] 0.7786041 0.8384083 0.8425124 #> [889,] 0.6276169 0.6734215 0.6775417 #> [890,] 0.9176432 0.9291135 0.9400309 #> [891,] 0.5955329 0.6026508 0.6095924 #> [892,] 0.4747824 0.5152282 0.5298413 #> [893,] 0.6624071 0.6869585 0.6930631 #> [894,] 0.9902618 0.9946636 1.0059933 #> [895,] 0.6006478 0.6109909 0.7827485 #> [896,] 1.0739924 1.1353441 1.1362353 #> [897,] 0.5088133 0.5348194 0.5409899 #> [898,] 0.8149680 0.8505206 0.8677531 #> [899,] 0.9816910 0.9847571 0.9914863 #> [900,] 0.9081888 0.9153452 0.9188084 #> [901,] 0.6153536 0.6153815 0.6182721 #> [902,] 0.5495761 0.5507399 0.5603175 #> [903,] 2.5840580 2.5929933 2.6234895 #> [904,] 1.1861796 1.1973393 1.2292255 #> [905,] 0.5724835 0.6038711 0.6115347 #> [906,] 0.8413863 0.8465449 0.8546885 #> [907,] 0.8071671 0.8180067 0.8939851 #> [908,] 0.5879416 0.6358956 0.6381506 #> [909,] 0.5240560 0.5919193 0.6096382 #> [910,] 0.7269927 0.8232129 0.8276135 #> [911,] 0.7996571 0.9101522 0.9129058 #> [912,] 1.6359869 1.7033136 1.7092960 #> [913,] 0.7159270 0.7256828 0.7342817 #> [914,] 0.7425863 0.7431353 0.7449256 #> [915,] 0.6427402 0.6471053 0.6617013 #> [916,] 0.7987813 0.8538310 0.8605647 #> [917,] 0.7536898 0.7766619 0.7871300 #> [918,] 0.5485144 0.5539596 0.5594923 #> [919,] 0.6752461 0.7208311 0.7571621 #> [920,] 0.5653469 0.5664138 0.5700511 #> [921,] 0.6455285 0.6785113 0.6878116 #> [922,] 0.5816181 0.6417378 0.6576109 #> [923,] 0.9541905 0.9633048 1.0140728 #> [924,] 1.7120622 1.7506400 1.7786597 #> [925,] 1.1356111 1.1501145 1.1861868 #> [926,] 0.6844323 0.7211100 0.7277595 #> [927,] 0.9919870 1.0046539 1.0585988 #> [928,] 1.0909418 1.0970285 1.1137077 #> [929,] 0.8215322 0.8246746 0.8262601 #> [930,] 0.8218535 0.8307444 0.8410435 #> [931,] 0.9490880 0.9559149 1.0106206 #> [932,] 1.3404173 1.3431972 1.3540991 #> [933,] 0.9182950 0.9930687 1.0316881 #> [934,] 0.5778650 0.6233926 0.6552800 #> [935,] 0.6513649 0.6688012 0.6800487 #> [936,] 0.7431773 0.7557377 0.8549699 #> [937,] 0.5240560 0.5434537 0.5609127 #> [938,] 0.5520749 0.5642003 0.6029693 #> [939,] 0.7903432 0.8028419 0.8341896 #> [940,] 0.8409575 0.8928800 0.9019933 #> [941,] 0.6343338 0.6726428 0.6845662 #> [942,] 0.7056023 0.7745546 0.8093433 #> [943,] 0.9929385 0.9997025 1.0238656 #> [944,] 0.7246882 0.7409949 0.7639661 #> [945,] 0.6295790 0.6592961 0.6793446 #> [946,] 1.1429259 1.2309731 1.2322491 #> [947,] 0.6787280 0.6887799 0.7126490 #> [948,] 0.9650437 0.9661742 0.9723720 #> [949,] 0.8346578 0.8391675 0.8997573 #> [950,] 0.9614591 0.9675984 0.9725935 #> [951,] 0.8829771 0.9036908 0.9877948 #> [952,] 0.8512426 0.8532802 0.8848484 #> [953,] 0.6166628 0.6170841 0.6371422 #> [954,] 0.8329126 0.8530069 0.8645209 #> [955,] 0.5247719 0.5300860 0.6102380 #> [956,] 1.1619952 1.2036054 1.2282273 #> [957,] 0.6391615 0.6557125 0.6612431 #> [958,] 1.0725884 1.0784070 1.1014701 #> [959,] 1.0767381 1.0916150 1.1107009 #> [960,] 0.7359664 0.7421168 0.7671997 #> [961,] 1.1424055 1.1488222 1.1747662 #> [962,] 0.5546416 0.5719874 0.6326870 #> [963,] 0.6186984 0.6290006 0.6319184 #> [964,] 1.0452503 1.0807821 1.0947882 #> [965,] 0.4862419 0.4933322 0.5164314 #> [966,] 0.6015028 0.6106738 0.6130888 #> [967,] 0.9397593 0.9456987 0.9475875 #> [968,] 1.1989367 1.2039149 1.2430222 #> [969,] 1.2733077 1.2977592 1.3079020 #> [970,] 0.6587434 0.7021421 0.7257358 #> [971,] 0.9298788 0.9332233 0.9786370 #> [972,] 0.4583481 0.5174262 0.5584686 #> [973,] 1.2784206 1.3214717 1.3237718 #> [974,] 0.9892116 0.9895480 1.0097542 #> [975,] 0.5051712 0.5437751 0.5481820 #> [976,] 0.7593610 0.7609732 0.7633887 #> [977,] 0.9492200 0.9543498 0.9551139 #> [978,] 0.7632394 0.7974951 0.8192408 #> [979,] 0.5735618 0.5908839 0.6015028 #> [980,] 0.6197546 0.6206144 0.6359616 #> [981,] 0.8684410 0.8772932 0.9226718 #> [982,] 0.7774246 0.7999180 0.8602760 #> [983,] 0.7194182 0.7592156 0.8063933 #> [984,] 0.9483649 0.9895036 1.0640046 #> [985,] 0.9019933 0.9142908 0.9483463 #> [986,] 0.7750119 0.7840491 0.7910807 #> [987,] 0.5490150 0.5614692 0.5650591 #> [988,] 0.6108302 0.6178691 0.6390093 #> [989,] 0.8721196 0.8953935 0.9019052 #> [990,] 1.1754947 1.2030136 1.2309854 #> [991,] 0.7230304 0.7697567 0.7910246 #> [992,] 0.8350104 0.8966890 0.9137141 #> [993,] 0.8924814 0.9205566 0.9220425 #> [994,] 1.6178327 1.6278497 1.6799638 #> [995,] 0.7267149 0.7846327 0.7985012 #> [996,] 0.6584006 0.6801919 0.6883392 #> [997,] 1.1804192 1.2117447 1.2190261 #> [998,] 0.6526862 0.6584006 0.6732712 #> [999,] 0.9596488 0.9604054 1.0200492 #> [1000,] 1.2215756 1.2249653 1.2474735 #> # Find the 10 approximate nearest neighbors tof_find_knn( .data = sim_data, k = 10, distance_function = \"euclidean\", ) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 519 950 311 110 409 192 884 753 426 133 #> [2,] 782 386 902 938 407 955 247 667 756 284 #> [3,] 789 309 180 707 670 569 900 796 587 554 #> [4,] 891 845 413 535 37 322 298 269 195 664 #> [5,] 112 743 962 404 553 957 741 545 155 535 #> [6,] 10 232 22 765 948 522 820 724 759 368 #> [7,] 513 486 697 388 650 144 105 374 333 769 #> [8,] 577 212 951 835 525 116 115 794 210 595 #> [9,] 193 495 323 430 94 596 784 69 378 791 #> [10,] 6 232 522 22 765 820 479 27 674 368 #> [11,] 737 850 448 580 132 804 173 226 65 50 #> [12,] 602 445 162 348 610 49 375 873 571 83 #> [13,] 7 513 767 105 244 109 800 544 246 249 #> [14,] 245 100 848 600 710 883 590 754 711 920 #> [15,] 686 351 25 265 433 839 863 953 170 263 #> [16,] 870 793 578 582 872 559 812 762 918 276 #> [17,] 558 337 520 696 713 510 402 129 841 509 #> [18,] 668 742 697 95 228 551 68 474 202 892 #> [19,] 343 856 117 750 796 328 63 422 340 546 #> [20,] 496 908 127 281 749 575 892 194 318 828 #> [21,] 937 914 616 184 751 644 909 689 270 875 #> [22,] 123 332 581 820 759 948 435 227 992 319 #> [23,] 188 821 157 90 905 882 43 95 274 79 #> [24,] 615 388 161 344 545 962 745 269 340 333 #> [25,] 686 15 351 863 265 433 756 170 211 208 #> [26,] 829 182 646 598 589 249 752 668 819 716 #> [27,] 556 151 401 555 655 534 465 91 57 820 #> [28,] 586 358 470 382 32 379 299 645 572 54 #> [29,] 826 929 530 470 292 110 379 287 54 515 #> [30,] 161 700 817 739 205 957 5 545 77 388 #> [31,] 544 675 391 109 327 277 387 251 960 224 #> [32,] 470 837 367 294 299 54 28 922 887 412 #> [33,] 216 786 124 334 213 894 72 665 664 180 #> [34,] 978 694 252 612 488 949 220 720 766 304 #> [35,] 456 291 272 906 418 500 805 677 64 416 #> [36,] 541 827 75 178 933 472 152 726 502 380 #> [37,] 891 664 4 413 322 461 213 298 62 101 #> [38,] 979 492 965 314 571 302 629 476 533 966 #> [39,] 149 411 390 104 930 402 337 841 520 185 #> [40,] 861 458 505 840 594 565 404 155 403 845 #> [41,] 514 102 857 423 125 936 917 859 911 107 #> [42,] 774 648 809 851 828 720 821 598 892 643 #> [43,] 821 918 90 188 604 578 23 762 870 79 #> [44,] 81 176 695 141 941 818 89 421 768 78 #> [45,] 319 680 952 756 251 547 948 389 117 608 #> [46,] 739 957 269 597 307 592 161 436 506 404 #> [47,] 566 974 391 259 201 224 31 385 109 968 #> [48,] 127 121 815 261 370 641 676 191 289 194 #> [49,] 445 425 873 602 12 814 897 662 219 83 #> [50,] 804 132 138 737 313 324 124 11 945 703 #> [51,] 847 167 630 979 842 533 314 298 880 38 #> [52,] 874 911 893 612 949 171 304 736 766 803 #> [53,] 896 785 684 950 869 99 969 628 352 338 #> [54,] 470 922 186 292 294 477 808 32 113 758 #> [55,] 523 537 449 720 813 803 488 477 198 980 #> [56,] 116 276 951 642 241 928 525 8 490 794 #> [57,] 507 106 293 342 654 399 750 227 966 532 #> [58,] 261 191 97 960 48 675 127 575 142 815 #> [59,] 626 795 755 441 195 866 176 70 836 768 #> [60,] 830 218 304 102 864 93 190 859 776 606 #> [61,] 285 397 341 682 372 264 599 126 998 86 #> [62,] 253 461 474 664 181 685 359 383 988 882 #> [63,] 136 557 454 224 19 387 856 532 261 142 #> [64,] 209 594 492 840 629 861 418 272 373 494 #> [65,] 145 737 850 11 448 173 226 704 324 816 #> [66,] 161 788 374 858 24 615 30 961 817 246 #> [67,] 137 373 728 611 898 893 129 278 459 301 #> [68,] 960 228 392 333 551 18 315 144 202 388 #> [69,] 871 356 430 184 9 453 937 747 561 914 #> [70,] 306 836 745 891 37 755 269 615 664 4 #> [71,] 268 577 320 78 885 941 814 83 872 248 #> [72,] 508 428 213 334 880 160 780 33 630 939 #> [73,] 591 166 444 156 721 906 352 596 378 609 #> [74,] 283 326 150 296 339 920 963 860 656 723 #> [75,] 964 899 380 718 502 85 196 805 152 416 #> [76,] 439 366 579 490 234 138 854 313 410 943 #> [77,] 315 635 144 731 246 242 388 30 700 923 #> [78,] 268 71 83 941 212 577 934 885 610 794 #> [79,] 812 23 43 821 188 578 622 237 740 353 #> [80,] 501 643 776 540 986 606 864 190 120 130 #> [81,] 695 44 421 176 818 429 89 941 844 872 #> [82,] 368 232 483 760 522 974 546 999 674 994 #> [83,] 602 814 794 268 610 78 897 885 445 49 #> [84,] 419 115 394 910 640 806 317 262 305 8 #> [85,] 677 416 718 380 209 418 964 805 75 543 #> [86,] 163 682 371 397 223 998 264 285 466 61 #> [87,] 692 748 875 751 757 270 165 637 873 572 #> [88,] 197 984 992 773 698 919 255 916 460 721 #> [89,] 481 995 359 695 844 177 941 44 81 253 #> [90,] 188 43 821 23 605 274 95 762 882 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833 #> [561,] 791 453 420 94 913 871 163 227 430 909 #> [562,] 273 396 956 681 146 154 832 855 771 321 #> [563,] 787 702 573 369 371 286 717 682 614 983 #> [564,] 936 613 971 866 995 374 857 514 125 481 #> [565,] 505 272 40 594 840 458 861 805 811 699 #> [566,] 259 391 974 224 47 345 907 493 557 310 #> [567,] 432 502 288 719 497 417 286 503 899 464 #> [568,] 217 671 225 938 777 782 667 715 263 407 #> [569,] 900 554 457 745 999 836 789 3 70 988 #> [570,] 904 222 784 200 475 1000 426 323 166 634 #> [571,] 533 377 468 965 167 972 38 601 314 12 #> [572,] 165 637 471 692 175 875 87 362 211 270 #> [573,] 371 397 405 442 61 264 369 702 682 563 #> [574,] 907 80 501 242 986 731 310 926 700 493 #> [575,] 97 395 281 20 712 496 638 240 829 127 #> [576,] 145 159 946 65 324 587 331 295 114 737 #> [577,] 71 517 8 559 78 885 268 872 931 794 #> [578,] 762 43 870 188 812 605 79 918 16 90 #> [579,] 854 729 897 439 490 169 76 462 662 489 #> [580,] 448 226 11 652 526 850 425 761 798 211 #> [581,] 227 435 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439 276 779 491 525 #> [643,] 776 80 711 648 774 100 501 190 14 809 #> [644,] 909 430 475 886 21 634 184 937 751 323 #> [645,] 887 28 382 470 427 379 358 29 32 826 #> [646,] 260 829 589 26 819 118 240 575 649 620 #> [647,] 473 618 239 981 460 450 295 300 946 108 #> [648,] 774 828 42 335 987 892 834 641 643 711 #> [649,] 281 829 489 838 240 496 20 575 982 462 #> [650,] 7 363 554 769 527 480 513 989 678 786 #> [651,] 806 282 687 910 727 394 738 408 538 689 #> [652,] 226 580 448 953 526 412 211 438 761 281 #> [653,] 775 472 60 606 830 218 107 152 864 997 #> [654,] 106 57 778 966 747 322 507 148 227 255 #> [655,] 307 92 436 592 46 349 597 555 497 27 #> [656,] 975 797 476 492 915 484 354 629 279 920 #> [657,] 672 879 677 221 874 500 207 898 736 681 #> [658,] 456 744 718 416 283 418 135 417 898 35 #> [659,] 330 375 348 901 162 406 303 113 477 12 #> [660,] 972 434 468 538 275 377 524 167 291 533 #> [661,] 734 429 818 958 695 440 559 421 480 81 #> [662,] 798 814 425 897 548 945 49 268 445 132 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738 145 #> [704,] 324 270 616 783 737 21 184 751 937 526 #> [705,] 451 414 717 967 183 877 286 536 684 417 #> [706,] 593 975 279 326 235 245 120 189 996 754 #> [707,] 927 422 309 796 952 387 999 3 789 19 #> [708,] 243 338 250 969 221 346 73 672 591 166 #> [709,] 287 210 977 794 427 928 212 525 55 781 #> [710,] 452 437 245 14 754 711 590 199 600 121 #> [711,] 335 452 643 641 14 648 100 710 883 848 #> [712,] 395 97 452 575 360 437 638 335 121 361 #> [713,] 970 509 728 963 520 860 296 623 521 758 #> [714,] 621 128 783 585 831 184 912 475 267 904 #> [715,] 777 361 217 568 676 823 671 808 263 953 #> [716,] 105 528 622 179 767 605 742 752 598 26 #> [717,] 705 967 451 877 414 286 536 639 860 684 #> [718,] 416 384 658 380 418 85 417 469 744 75 #> [719,] 549 91 555 288 567 968 286 497 827 385 #> [720,] 813 449 301 851 537 488 42 803 980 304 #> [721,] 916 214 698 832 131 596 846 156 444 773 #> [722,] 257 919 160 255 316 197 773 880 506 891 #> [723,] 158 230 150 339 753 792 921 770 296 74 #> 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283 718 243 623 456 338 #> [745,] 836 70 306 900 615 569 457 664 24 554 #> [746,] 1000 904 570 222 884 394 84 727 764 691 #> [747,] 935 871 386 654 325 902 966 106 227 937 #> [748,] 757 87 408 692 875 751 909 21 634 308 #> [749,] 318 194 462 798 908 20 274 539 761 187 #> [750,] 399 532 856 343 19 136 57 117 342 507 #> [751,] 616 87 937 21 909 748 902 644 270 692 #> [752,] 622 179 978 598 740 663 668 694 976 26 #> [753,] 230 192 723 150 409 110 158 292 126 792 #> [754,] 245 600 593 710 823 326 14 437 279 121 #> [755,] 70 306 836 506 745 626 428 59 269 160 #> [756,] 938 453 902 2 783 782 871 184 25 704 #> [757,] 748 875 408 87 692 219 471 381 683 270 #> [758,] 296 901 186 509 339 292 728 406 515 74 #> [759,] 948 123 356 608 22 871 332 435 69 581 #> [760,] 546 952 422 387 557 974 547 944 531 19 #> [761,] 462 604 489 749 897 169 918 854 580 602 #> [762,] 578 605 188 90 43 870 918 604 812 79 #> [763,] 497 92 307 827 961 655 555 817 91 788 #> [764,] 683 875 222 806 475 757 748 270 282 408 #> 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518 352 896 204 357 338 53 #> [786,] 124 33 350 989 62 527 939 334 253 664 #> [787,] 369 983 679 174 201 259 563 345 199 371 #> [788,] 817 961 700 161 30 242 66 77 310 635 #> [789,] 3 333 988 707 309 228 796 960 68 900 #> [790,] 484 436 741 467 196 290 349 553 592 807 #> [791,] 94 561 227 495 148 365 966 9 947 357 #> [792,] 158 126 770 723 192 509 753 970 614 341 #> [793,] 16 812 578 740 870 762 622 276 872 79 #> [794,] 212 83 885 210 525 78 870 559 872 577 #> [795,] 256 440 176 818 939 59 363 248 508 350 #> [796,] 328 117 856 19 387 422 707 229 454 247 #> [797,] 975 540 656 279 120 484 741 354 743 290 #> [798,] 662 425 749 897 462 814 548 187 854 49 #> [799,] 889 231 494 171 610 403 840 353 594 516 #> [800,] 635 246 77 731 493 994 242 315 109 455 #> [801,] 389 172 251 680 675 442 686 531 170 265 #> [802,] 990 168 153 607 512 299 529 412 924 379 #> [803,] 813 893 301 537 449 720 162 610 494 980 #> [804,] 138 50 703 132 11 737 313 850 76 145 #> [805,] 418 196 467 272 895 35 565 699 701 209 #> [806,] 910 640 408 651 317 757 538 115 683 282 #> [807,] 743 342 507 112 741 962 399 484 553 535 #> [808,] 922 294 627 339 122 113 393 777 217 715 #> [809,] 100 883 848 42 851 459 619 711 643 774 #> [810,] 881 476 111 302 413 284 516 807 548 274 #> [811,] 699 773 257 701 565 916 560 505 272 316 #> [812,] 740 79 578 793 622 762 16 582 43 215 #> [813,] 720 449 301 803 918 851 537 821 43 980 #> [814,] 897 662 268 83 602 798 49 445 425 885 #> [815,] 261 48 233 289 194 142 127 454 955 318 #> [816,] 542 355 305 362 781 65 358 703 11 737 #> [817,] 700 788 30 161 961 310 242 46 731 349 #> [818,] 440 176 695 44 81 429 661 248 89 795 #> [819,] 589 182 646 552 829 663 26 240 260 118 #> [820,] 944 319 546 401 465 22 123 332 343 435 #> [821,] 43 188 23 90 828 774 79 918 539 604 #> [822,] 498 858 96 755 59 374 66 297 866 626 #> [823,] 754 437 715 867 600 777 452 121 339 361 #> [824,] 98 827 763 788 310 961 463 493 497 91 #> [825,] 913 285 998 357 466 669 561 536 163 985 #> [826,] 29 929 530 470 110 884 292 143 515 346 #> [827,] 497 719 763 91 541 567 555 502 933 36 #> [828,] 987 892 774 648 834 821 539 42 188 598 #> [829,] 240 589 26 649 598 575 646 496 892 668 #> [830,] 60 218 93 304 102 373 190 864 859 776 #> [831,] 608 783 356 756 128 267 25 714 585 184 #> [832,] 916 721 396 773 699 131 811 316 846 906 #> [833,] 959 441 768 134 560 866 626 971 257 811 #> [834,] 987 289 504 828 641 892 202 648 191 551 #> [835,] 958 8 577 116 421 517 595 951 212 661 #> [836,] 70 306 745 615 664 891 37 269 4 900 #> [837,] 367 412 32 294 122 398 890 360 991 382 #> [838,] 424 982 649 173 865 281 725 228 908 767 #> [839,] 170 676 955 261 265 782 127 407 225 686 #> [840,] 458 594 861 403 629 965 40 64 799 889 #> [841,] 402 186 696 390 520 770 713 509 970 296 #> [842,] 314 167 630 880 51 972 443 298 468 979 #> [843,] 266 977 771 928 709 511 329 116 212 862 #> [844,] 995 423 768 89 971 936 231 171 866 155 #> [845,] 535 4 404 112 458 741 322 891 413 505 #> [846,] 895 888 698 617 701 467 196 699 899 805 #> [847,] 51 979 630 298 665 842 167 203 334 314 #> [848,] 100 883 809 14 711 963 459 920 643 390 #> [849,] 411 973 636 382 837 890 118 523 438 930 #> [850,] 173 865 11 226 580 448 65 737 145 804 #> [851,] 301 304 720 809 42 459 774 813 449 373 #> [852,] 771 329 84 321 221 262 419 396 862 672 #> [853,] 140 421 582 949 125 171 874 81 911 695 #> [854,] 579 462 897 489 798 662 169 605 761 814 #> [855,] 273 971 441 959 956 768 833 176 44 81 #> [856,] 19 117 328 750 343 399 796 229 136 340 #> [857,] 423 514 866 41 936 739 102 917 859 96 #> [858,] 374 66 513 613 564 7 650 822 24 486 #> [859,] 155 423 205 102 404 190 957 157 864 995 #> [860,] 623 877 963 970 521 469 417 74 384 713 #> [861,] 458 629 40 840 403 594 492 64 484 476 #> [862,] 143 884 826 346 843 29 932 311 929 84 #> [863,] 211 953 351 637 526 175 263 217 299 25 #> [864,] 606 501 218 205 776 80 60 190 859 830 #> [865,] 173 850 838 226 779 439 580 11 725 424 #> [866,] 626 857 936 423 844 971 833 995 768 96 #> [867,] 122 360 619 823 777 603 452 294 361 883 #> [868,] 991 361 398 341 437 367 360 614 823 770 #> [869,] 785 518 628 204 357 684 352 365 913 617 #> [870,] 16 918 578 43 762 604 83 872 582 813 #> [871,] 453 747 69 935 435 902 356 227 561 123 #> [872,] 559 582 16 870 429 695 140 78 83 71 #> [873,] 445 425 49 12 692 87 667 798 757 471 #> [874,] 52 736 949 893 911 612 879 171 803 511 #> [875,] 270 87 757 748 683 692 764 637 572 21 #> [876,] 260 772 118 646 649 238 584 890 438 982 #> [877,] 860 417 967 921 536 684 717 996 469 204 #> [878,] 951 517 931 8 835 958 241 577 56 116 #> [879,] 494 330 893 889 874 456 434 610 64 35 #> [880,] 443 630 195 842 298 51 847 314 891 257 #> [881,] 111 274 810 882 302 476 516 685 548 318 #> [882,] 274 95 881 23 685 905 111 253 90 359 #> [883,] 848 100 809 14 920 619 963 711 867 296 #> [884,] 826 29 110 1 311 519 222 530 200 929 #> [885,] 268 525 71 794 83 814 897 49 729 78 #> [886,] 634 909 644 323 308 200 784 430 475 275 #> [887,] 645 32 379 470 529 607 530 299 28 168 #> [888,] 467 846 196 895 701 899 698 151 432 617 #> [889,] 799 231 494 171 840 610 594 403 353 207 #> [890,] 438 238 412 118 837 360 652 281 122 398 #> [891,] 4 37 322 413 535 845 306 269 298 70 #> [892,] 828 668 987 598 496 648 834 504 605 188 #> [893,] 301 803 52 373 611 304 67 736 494 137 #> [894,] 673 780 428 457 33 72 216 160 213 942 #> [895,] 846 701 467 698 888 196 805 699 617 418 #> [896,] 785 352 869 53 444 617 684 518 628 591 #> [897,] 814 662 604 798 854 462 579 602 49 83 #> [898,] 67 744 677 416 135 658 456 728 373 879 #> [899,] 464 888 846 75 196 895 432 151 467 617 #> [900,] 569 554 745 457 836 664 70 306 229 988 #> [901,] 406 758 330 659 292 375 113 515 303 162 #> [902,] 386 938 937 2 616 935 453 756 751 747 #> [903,] 431 624 876 164 772 528 779 767 249 865 #> [904,] 222 570 475 764 1000 621 784 714 644 282 #> [905,] 157 237 23 882 95 359 987 274 190 188 #> [906,] 500 35 291 660 272 895 236 321 434 543 #> [907,] 574 242 493 199 310 731 259 552 224 345 #> [908,] 318 496 194 20 749 127 462 281 187 815 #> [909,] 644 430 886 634 475 751 21 937 184 748 #> [910,] 640 806 115 738 419 317 219 651 683 757 #> [911,] 52 949 612 125 766 874 107 171 853 304 #> [912,] 621 714 904 128 585 426 69 570 222 475 #> [913,] 420 285 357 561 825 998 150 628 230 223 #> [914,] 689 937 21 184 203 616 430 909 644 902 #> [915,] 671 656 225 370 476 279 326 667 568 407 #> [916,] 773 832 721 811 699 316 698 701 846 895 #> [917,] 102 957 130 404 152 859 290 423 380 502 #> [918,] 604 43 870 602 90 821 578 813 762 897 #> [919,] 255 778 722 942 197 322 257 891 160 992 #> [920,] 975 593 120 354 100 14 883 279 754 656 #> [921,] 996 326 998 466 223 723 877 600 754 158 #> [922,] 808 294 627 113 122 339 54 393 165 953 #> [923,] 925 336 455 246 976 77 315 144 182 635 #> [924,] 512 990 802 168 153 735 607 529 772 433 #> [925,] 923 606 864 501 205 336 455 218 776 30 #> [926,] 504 182 648 834 501 551 776 987 552 892 #> [927,] 707 309 952 422 387 277 670 760 796 999 #> [928,] 276 252 116 16 794 709 212 870 582 559 #> [929,] 515 29 292 826 110 287 303 659 470 54 #> [930,] 390 360 809 198 848 883 335 411 867 100 #> [931,] 517 248 577 440 951 71 241 958 989 320 #> [932,] 266 515 346 250 511 929 287 509 696 826 #> [933,] 384 469 278 472 541 623 521 583 152 502 #> [934,] 941 685 516 548 177 268 253 298 320 814 #> [935,] 747 325 386 902 871 203 937 654 966 665 #> [936,] 564 995 844 971 866 857 423 514 125 41 #> [937,] 184 616 914 21 902 430 751 909 689 644 #> [938,] 782 902 568 2 756 453 667 217 225 407 #> [939,] 941 334 176 248 320 630 934 508 440 141 #> [940,] 495 193 214 378 94 984 9 791 581 985 #> [941,] 934 268 177 78 320 71 44 685 939 516 #> [942,] 778 780 213 919 255 992 160 216 654 322 #> [943,] 410 480 366 234 76 734 490 559 762 241 #> [944,] 693 557 546 319 820 532 465 400 136 401 #> [945,] 548 132 662 685 320 425 334 798 187 665 #> [946,] 295 450 300 159 119 128 114 618 576 647 #> [947,] 596 365 415 954 698 275 94 784 634 886 #> [948,] 759 123 45 22 952 332 319 422 820 608 #> [949,] 612 911 52 874 766 853 736 140 125 582 #> [950,] 409 519 1 338 192 628 243 785 753 110 #> [951,] 878 8 517 241 525 577 931 116 56 835 #> [952,] 422 547 760 707 45 948 927 796 387 251 #> [953,] 863 211 217 351 627 263 526 715 652 122 #> [954,] 415 485 947 596 275 51 698 784 167 9 #> [955,] 247 454 261 839 170 208 815 2 782 194 #> [956,] 855 273 681 524 396 441 141 478 207 560 #> [957,] 404 739 5 46 155 553 112 741 535 859 #> [958,] 835 517 440 661 931 818 577 248 176 256 #> [959,] 833 441 768 855 866 971 560 134 273 626 #> [960,] 68 228 58 392 142 387 551 675 63 982 #> [961,] 788 817 483 655 555 161 700 763 310 66 #> [962,] 112 344 743 5 807 545 535 553 233 741 #> [963,] 600 970 296 860 728 713 883 848 14 920 #> [964,] 75 85 380 917 487 41 152 699 102 502 #> [965,] 302 516 629 840 571 492 458 38 403 476 #> [966,] 106 386 148 342 507 654 747 979 38 322 #> [967,] 536 684 877 717 451 705 414 998 921 417 #> [968,] 385 674 719 259 91 549 974 288 555 465 #> [969,] 243 950 99 338 250 708 53 409 558 135 #> [970,] 713 509 963 860 158 296 770 728 758 623 #> [971,] 844 936 768 866 995 855 81 125 44 695 #> [972,] 468 377 167 314 291 533 571 660 842 434 #> [973,] 849 153 837 367 887 382 32 411 645 398 #> [974,] 546 566 557 391 760 259 633 345 944 224 #> [975,] 593 120 279 920 797 656 706 326 540 245 #> [976,] 697 486 237 144 205 668 504 315 215 905 #> [977,] 212 709 210 287 659 794 330 317 610 381 #> [978,] 694 488 179 752 622 34 312 42 720 588 #> [979,] 38 314 847 51 167 533 148 842 571 966 #> [980,] 162 375 539 449 603 619 537 602 301 477 #> [981,] 992 450 119 935 203 473 747 942 213 914 #> [982,] 424 228 838 767 675 960 544 649 281 646 #> [983,] 679 174 369 199 437 121 710 345 452 787 #> [984,] 88 214 197 940 698 888 721 733 151 846 #> [985,] 357 503 617 495 94 536 432 940 825 869 #> [986,] 130 540 290 80 497 501 120 502 574 700 #> [987,] 834 828 892 289 504 648 774 641 202 668 #> [988,] 461 333 181 474 392 142 664 344 383 62 #> [989,] 350 248 363 786 124 320 234 440 527 138 #> [990,] 802 153 168 512 924 607 735 529 412 299 #> [991,] 868 361 341 367 398 715 823 294 360 122 #> [992,] 942 778 88 255 981 919 654 332 747 197 #> [993,] 529 139 607 175 379 299 625 530 168 637 #> [994,] 800 961 788 635 98 66 493 483 246 82 #> [995,] 844 423 936 89 171 155 125 859 359 481 #> [996,] 921 466 235 326 706 446 754 998 189 223 #> [997,] 588 312 663 978 776 218 606 336 104 752 #> [998,] 285 466 223 921 682 163 913 86 996 825 #> [999,] 340 343 19 368 707 422 856 796 569 900 #> [1000,] 727 570 904 222 485 784 746 475 275 415 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.7048266 0.8083694 0.8882599 0.9421757 1.0040249 1.0144722 1.0248596 #> [2,] 0.2646638 0.4447534 0.4508229 0.4509399 0.4834092 0.5247719 0.5497563 #> [3,] 0.5625080 0.7485735 0.8228400 0.8580177 0.9447221 1.0220488 1.0268545 #> [4,] 0.2337535 0.3869610 0.3987572 0.4311430 0.4322313 0.5076585 0.5243604 #> [5,] 0.3071985 0.4135715 0.4187607 0.4275021 0.4449949 0.4480393 0.4862072 #> [6,] 0.8162260 1.3583798 1.5773534 1.6751377 1.8243273 1.8572698 1.8800572 #> [7,] 0.6913829 0.7541327 0.8888739 0.9243094 0.9559779 0.9618115 0.9714105 #> [8,] 0.5987923 0.6929355 0.7099335 0.7234910 0.7436081 0.7586708 0.7788853 #> [9,] 0.5972976 0.6019840 0.6145234 0.6544568 0.6621461 0.7077951 0.7314612 #> [10,] 0.8162260 1.1388998 1.5691586 1.6517703 1.7160733 1.8003366 1.8312074 #> [11,] 0.4387941 0.4894021 0.5311648 0.5452097 0.6617801 0.6809391 0.7264780 #> [12,] 0.3486888 0.3676536 0.4109260 0.4335793 0.4539022 0.4627766 0.5623945 #> [13,] 1.6469234 1.7292788 1.8007623 1.8432932 1.8877758 1.9391614 1.9416047 #> [14,] 0.3999155 0.4020145 0.4489713 0.4504919 0.4615817 0.4779987 0.5029892 #> [15,] 0.2436403 0.3935127 0.4089076 0.5083646 0.5488945 0.6392918 0.6604720 #> [16,] 0.3293306 0.4893257 0.5458239 0.5741937 0.5897778 0.6447046 0.6659995 #> [17,] 0.6977377 0.8617257 1.0572980 1.1038723 1.1918617 1.2138401 1.2184243 #> [18,] 0.5040802 0.5627145 0.5672224 0.5718031 0.6170351 0.6306272 0.6928692 #> [19,] 0.2857866 0.2940478 0.5034045 0.5351798 0.5760647 0.5784438 0.5962825 #> [20,] 0.2286886 0.4625997 0.4797133 0.4798772 0.5166952 0.5390328 0.5771252 #> [21,] 0.3915093 0.4433784 0.4566071 0.4575790 0.4861910 0.5110320 0.5232558 #> [22,] 0.4862339 0.5522699 0.7315827 0.7898797 0.8151923 0.8296489 0.8669828 #> [23,] 0.3284029 0.3912126 0.4004372 0.4007780 0.4112151 0.4410145 0.4468764 #> [24,] 0.4052950 0.5732842 0.6229928 0.6652722 0.6817741 0.7026462 0.7338538 #> [25,] 0.3843435 0.4089076 0.4893418 0.6487378 0.6911578 0.7590991 0.7672499 #> [26,] 0.5589624 0.6996202 0.7212007 0.7595346 0.7693359 0.7742780 0.8258053 #> [27,] 0.5874171 0.7553784 0.8066190 0.9306648 0.9755979 1.0063360 1.0732831 #> [28,] 0.5737651 0.5889834 0.6094010 0.6560304 0.7157484 0.7175131 0.7469774 #> [29,] 0.3036663 0.5015118 0.5242928 0.6236399 0.7883728 0.8257304 0.9218299 #> [30,] 0.4241206 0.5605815 0.6033279 0.6619944 0.6658839 0.6841600 0.7411967 #> [31,] 0.7023488 0.7160217 0.8238013 0.8848219 0.9756494 0.9797639 0.9837719 #> [32,] 0.5225561 0.5611636 0.5643889 0.6709421 0.6924779 0.7095881 0.7157484 #> [33,] 0.5907024 0.5912506 0.6060134 0.6538817 0.7120533 0.8081701 0.8266657 #> [34,] 0.7235467 0.7685739 0.7745123 0.8247635 0.8285483 0.9214560 0.9243558 #> [35,] 0.4614993 0.4723733 0.4747357 0.5278493 0.5339777 0.5495823 0.6000009 #> [36,] 1.0280044 1.3326072 1.4438542 1.4950672 1.5100762 1.5186078 1.5462172 #> [37,] 0.3299522 0.3549475 0.4322313 0.4429551 0.5124888 0.5439571 0.5654315 #> [38,] 0.2847934 0.4855697 0.4862419 0.4930848 0.4956664 0.5022562 0.5031505 #> [39,] 1.2646007 1.2969284 1.3367225 1.3488551 1.4737490 1.4945992 1.5523904 #> [40,] 0.3035479 0.3186834 0.3551038 0.4236751 0.4554516 0.4722940 0.4805071 #> [41,] 0.4353166 0.5238137 0.6346238 0.7027129 0.8082479 0.8549699 0.8945612 #> [42,] 0.3158758 0.4122767 0.5134259 0.5156644 0.5397715 0.5698328 0.5908687 #> [43,] 0.3112028 0.3118351 0.3385731 0.3728544 0.4352734 0.4380835 0.4468764 #> [44,] 0.3042617 0.4165118 0.4180805 0.5900513 0.6286326 0.6854924 0.7085806 #> [45,] 0.6570087 0.6574265 0.7661296 0.7933378 0.7996498 0.8144727 0.8169656 #> [46,] 0.4739766 0.5013117 0.6193471 0.6287011 0.6384725 0.7145823 0.7185547 #> [47,] 0.8716038 1.1291670 1.2601548 1.2731740 1.4309672 1.5484044 1.6242022 #> [48,] 0.3418835 0.3449666 0.3816936 0.3847305 0.3990696 0.4634436 0.4700327 #> [49,] 0.1960542 0.3481627 0.4300678 0.4569880 0.4627766 0.5043400 0.5348194 #> [50,] 0.5048296 0.6151931 0.7414556 0.7697675 0.7865517 0.7933014 0.8135147 #> [51,] 0.3107841 0.3815946 0.4300301 0.4810555 0.4820274 0.4976498 0.5715760 #> [52,] 0.4401777 0.4851220 0.5566957 0.5749747 0.5953573 0.6111659 0.6666715 #> [53,] 0.8284443 0.9455623 1.0445572 1.1584753 1.2246511 1.2271954 1.2533244 #> [54,] 0.5165410 0.5483363 0.5577906 0.5610397 0.6063109 0.6596769 0.6916249 #> [55,] 0.3401918 0.3740531 0.5727056 0.7549157 0.8370006 0.8412854 0.8601071 #> [56,] 0.7918233 0.8732220 0.9036908 0.9597329 0.9924884 1.1504494 1.1632732 #> [57,] 0.3175616 0.3724500 0.4292543 0.4828516 0.4994282 0.4997180 0.5676278 #> [58,] 0.4791532 0.5493708 0.5503179 0.6035151 0.6610069 0.6694261 0.7053166 #> [59,] 0.6290930 0.9289136 0.9689169 0.9733285 1.0257024 1.0359829 1.0698120 #> [60,] 0.1793892 0.4433549 0.5704134 0.6528788 0.6883512 0.7304316 0.7677203 #> [61,] 0.6116325 0.6763739 0.6778514 0.6978583 0.7198049 0.7402864 0.7830806 #> [62,] 0.3478599 0.3490601 0.3556601 0.4149381 0.5310006 0.5597738 0.6224384 #> [63,] 0.3444005 0.5210476 0.5378630 0.5725214 0.5962825 0.6565383 0.6822445 #> [64,] 0.2266562 0.3956464 0.4746320 0.4763757 0.4905728 0.5245846 0.5899013 #> [65,] 0.5080052 0.7771783 0.8011727 0.8205875 0.9891684 1.0671992 1.0835386 #> [66,] 0.8743386 1.0018150 1.1016418 1.1232248 1.1248432 1.1419690 1.1658660 #> [67,] 0.3534918 0.5119499 0.5529916 0.5719831 0.5959716 0.6538127 0.7144411 #> [68,] 0.4588987 0.5010224 0.5109920 0.5944829 0.6216318 0.6928692 0.6936148 #> [69,] 0.5602409 0.6469960 0.7002396 0.7106937 0.7325301 0.8068203 0.8211363 #> [70,] 0.2020652 0.2218200 0.4595513 0.6095924 0.6650239 0.6690871 0.6998387 #> [71,] 0.3062630 0.4370194 0.4648428 0.4746678 0.5015561 0.6170349 0.6515960 #> [72,] 0.6308904 0.7111541 0.7114052 0.7174661 0.7458210 0.7520065 0.7886171 #> [73,] 0.4637712 0.7955241 0.8066526 0.8093809 1.0033759 1.0384308 1.0415756 #> [74,] 0.4819553 0.5052221 0.5255958 0.5520613 0.5833905 0.6326745 0.6415663 #> [75,] 0.7557878 0.7656519 0.7914277 0.8048466 0.8943719 0.9215540 0.9592574 #> [76,] 0.5677734 0.6241592 0.6257073 0.6401146 0.6488231 0.6922880 0.7164694 #> [77,] 0.4402712 0.4445392 0.4796866 0.5374811 0.5765453 0.7042095 0.7100582 #> [78,] 0.4369430 0.4746678 0.5246621 0.5705223 0.5949956 0.6440177 0.6726832 #> [79,] 0.4590312 0.4811924 0.4847473 0.4859729 0.4906804 0.5147732 0.5902184 #> [80,] 0.2734565 0.4826327 0.5053076 0.5955601 0.5985745 0.6196470 0.6214573 #> [81,] 0.2202836 0.3042617 0.5492871 0.6267163 0.6952081 0.7114169 0.7539298 #> [82,] 1.1691834 1.3014405 1.3326076 1.3625383 1.4254770 1.5510179 1.5517087 #> [83,] 0.3844678 0.4266016 0.4934404 0.5010035 0.5011315 0.5246621 0.5409899 #> [84,] 0.8915097 1.1654770 1.1700043 1.3160452 1.3369395 1.4012029 1.4224582 #> [85,] 0.4650003 0.5372201 0.7173710 0.7750321 0.8502619 0.8524977 0.8613864 #> [86,] 0.4552099 0.5632050 0.6270920 0.6319328 0.6451453 0.6526862 0.6801134 #> [87,] 0.2697039 0.2989971 0.3722417 0.3864791 0.3979419 0.5044755 0.5115880 #> [88,] 0.5890583 0.6303962 0.6972553 0.8012599 0.8484478 0.8800725 0.8999855 #> [89,] 0.4085959 0.6081224 0.6106471 0.6470521 0.6488359 0.6704771 0.7069878 #> [90,] 0.2682098 0.3385731 0.3977697 0.4007780 0.4088687 0.4110661 0.4334255 #> [91,] 0.2751204 0.5993097 0.7266816 0.7926226 0.9236902 1.0381235 1.0600730 #> [92,] 0.3414077 0.6613483 0.7949358 0.9309210 0.9628605 1.0047288 1.0271264 #> [93,] 0.4188806 0.4482251 0.4985003 0.5105230 0.5590794 0.6109957 0.6160324 #> [94,] 0.2915407 0.4006096 0.5662374 0.5703396 0.6104983 0.6254522 0.6571560 #> [95,] 0.3570338 0.4294050 0.4334255 0.4494573 0.4615580 0.4654187 0.4721043 #> [96,] 0.8064250 0.8357599 0.8473472 0.8582234 0.8847025 0.9628605 0.9966873 #> [97,] 0.3610341 0.3774352 0.4789762 0.4862033 0.5030681 0.5503179 0.6050880 #> [98,] 1.0499795 1.0643079 1.2747939 1.2884228 1.3809873 1.4183431 1.4441306 #> [99,] 0.8425571 1.0012882 1.0613208 1.1160400 1.2090369 1.2196290 1.2271954 #> [100,] 0.1719823 0.2005349 0.3995663 0.4020145 0.5419647 0.5490831 0.5606193 #> [101,] 0.2832737 0.4051340 0.4424304 0.5553900 0.5557919 0.5561115 0.6074579 #> [102,] 0.5238137 0.5270508 0.5384865 0.5802341 0.6482590 0.6528788 0.7393022 #> [103,] 0.7780846 0.9160502 1.0251646 1.0758883 1.2273985 1.3250280 1.3650345 #> [104,] 0.8402520 0.9372123 0.9846061 1.1511613 1.2117447 1.3488551 1.3855672 #> [105,] 0.6289333 0.8665598 0.8765106 0.9286206 0.9619865 0.9700839 0.9714105 #> [106,] 0.2661266 0.2988244 0.3085853 0.3724500 0.4407825 0.4594065 0.5107381 #> [107,] 0.4815630 0.7025629 0.7512546 0.8192894 0.8972547 0.9296012 0.9443613 #> [108,] 0.9098251 0.9994496 1.0043093 1.0074580 1.0819849 1.2459773 1.3049420 #> [109,] 0.8127954 0.8848219 1.0958495 1.1919957 1.2672147 1.2879393 1.3004464 #> [110,] 0.3765864 0.5735751 0.6231267 0.6317110 0.6976519 0.7089059 0.7372494 #> [111,] 0.2312386 0.3439918 0.3618488 0.3632050 0.3725628 0.3938001 0.4741794 #> [112,] 0.2479778 0.3071985 0.3768641 0.3914624 0.4080576 0.4565760 0.4807659 #> [113,] 0.3536279 0.4134636 0.4428247 0.4719349 0.4721957 0.5400492 0.5448039 #> [114,] 0.4448283 0.7417602 0.7560302 0.7727866 0.8194439 0.8326412 0.8550672 #> [115,] 0.3670521 0.4335027 0.4463284 0.5121596 0.6746002 0.6782024 0.7550491 #> [116,] 0.7586708 0.7918233 0.8495530 0.8584823 0.8656548 0.8673209 0.8829771 #> [117,] 0.2824080 0.3407325 0.5028377 0.5034045 0.5364504 0.5724725 0.6111820 #> [118,] 0.6011015 0.6568896 0.6970957 0.7760345 0.7873279 0.8389859 0.8612169 #> [119,] 0.4645052 0.7627281 0.7664906 0.8466935 0.8795198 0.9012175 0.9062407 #> [120,] 0.3015907 0.3781865 0.4005184 0.4426711 0.4985003 0.5100151 0.5129489 #> [121,] 0.3449666 0.4175508 0.5115338 0.5185349 0.5413713 0.5454344 0.5565225 #> [122,] 0.2218137 0.4422711 0.4834617 0.4870908 0.6026000 0.6130869 0.6203350 #> [123,] 0.3457117 0.4862339 0.4869867 0.5838292 0.5907311 0.6927150 0.7224404 #> [124,] 0.3859147 0.5860999 0.6060134 0.6827474 0.8100991 0.8135147 0.8984934 #> [125,] 0.5688090 0.5920986 0.6839222 0.7025629 0.7132284 0.7472718 0.7557377 #> [126,] 0.3214490 0.4776311 0.7167084 0.7890833 0.8072259 0.8133698 0.8167129 #> [127,] 0.2652173 0.3418835 0.4172473 0.4641082 0.4797133 0.4834878 0.5094085 #> [128,] 0.8132454 0.8566013 0.8912662 0.9567154 0.9995281 1.0529038 1.0892656 #> [129,] 0.5173034 0.5385999 0.5909574 0.6495746 0.7144411 0.7530588 0.7793916 #> [130,] 0.3016603 0.3589144 0.3798359 0.5123829 0.6620639 0.6644755 0.6849573 #> [131,] 0.7945027 0.8052348 1.0094416 1.0380981 1.1226155 1.1483449 1.1911871 #> [132,] 0.4773687 0.4896124 0.5876542 0.6151931 0.6152917 0.6525114 0.6617801 #> [133,] 0.2957885 0.5998256 0.6250829 0.6819637 0.6955587 0.7119675 0.7181709 #> [134,] 0.8466075 0.9559963 0.9653752 0.9709938 0.9928338 1.0115360 1.0325245 #> [135,] 0.1418194 0.6279938 0.6904804 0.7189922 0.7214698 0.8149124 0.8672728 #> [136,] 0.3444005 0.3513501 0.4608625 0.5105701 0.5202147 0.5617529 0.5724725 #> [137,] 0.3534918 0.3892923 0.5173034 0.5489988 0.5815853 0.6398496 0.6930631 #> [138,] 0.4773687 0.4978547 0.6620687 0.6726346 0.6922880 0.7011757 0.7414556 #> [139,] 0.4446937 0.4528535 0.5435197 0.5998256 0.6946994 0.7683952 0.7739046 #> [140,] 0.3638288 0.4680880 0.6973555 0.7250861 0.7494283 0.7619575 0.7656392 #> [141,] 0.5383358 0.5900513 0.6787596 0.7081918 0.7108781 0.7493679 0.7631740 #> [142,] 0.2109421 0.3381355 0.4371760 0.4410435 0.5464723 0.5576133 0.5636682 #> [143,] 0.9889649 1.0560097 1.1533725 1.1601190 1.2254556 1.2703172 1.3124793 #> [144,] 0.2954659 0.4437187 0.4796866 0.5504881 0.6164471 0.6256895 0.6496666 #> [145,] 0.5080052 0.8341716 0.8432496 0.9130185 0.9294930 0.9832351 0.9880707 #> [146,] 1.4565224 1.7843633 1.7996840 1.8705359 1.8760104 1.8799524 1.9060669 #> [147,] 0.9372123 1.0059526 1.1388661 1.3188446 1.3606501 1.3640721 1.5037697 #> [148,] 0.4468204 0.5485547 0.5751650 0.5758143 0.6294308 0.6353628 0.6354754 #> [149,] 0.8402520 1.0644100 1.1333596 1.2646007 1.3188446 1.3367809 1.4007968 #> [150,] 0.3891018 0.4304763 0.4883660 0.4925399 0.5255958 0.6052847 0.6073665 #> [151,] 0.3124576 0.7553784 0.7786041 0.9153356 0.9816910 0.9895036 1.0228704 #> [152,] 0.4181309 0.4396069 0.5606495 0.7003802 0.7263227 0.8679645 0.8940518 #> [153,] 0.5745431 0.6589509 0.8750195 0.8975972 0.9072083 0.9513614 0.9557934 #> [154,] 1.0687995 1.2359980 1.5919794 1.6551029 1.8572032 1.8726503 1.9143099 #> [155,] 0.3525775 0.4188517 0.4430698 0.5028352 0.5053819 0.5655088 0.5838935 #> [156,] 0.3304958 0.4964543 0.6085589 0.7224517 0.7508667 0.7620922 0.7791763 #> [157,] 0.3737895 0.3898532 0.4004372 0.4822703 0.5067166 0.5655088 0.5766819 #> [158,] 0.2298626 0.4305420 0.4738325 0.5797168 0.6287894 0.6676141 0.6826555 #> [159,] 0.5807290 0.7780846 0.8875493 1.0127100 1.1450179 1.1916313 1.2690950 #> [160,] 0.4289125 0.5011922 0.5540639 0.6948436 0.7208311 0.7520065 0.8818430 #> [161,] 0.4241206 0.6107870 0.6229928 0.6529928 0.7129272 0.7185547 0.7243970 #> [162,] 0.3859954 0.4109260 0.4555023 0.4626248 0.4633659 0.4918698 0.5110051 #> [163,] 0.4552099 0.4855611 0.5240031 0.6194519 0.7022538 0.7035018 0.7373904 #> [164,] 0.5278035 0.7871674 0.9289076 0.9783975 1.0084421 1.0511804 1.0655496 #> [165,] 0.3799644 0.3806210 0.3934462 0.4135310 0.4970086 0.5115880 0.5358688 #> [166,] 0.5511508 0.6390218 0.6504903 0.7225953 0.7955241 0.8164118 0.8319241 #> [167,] 0.2105175 0.3815946 0.3900844 0.4083693 0.4314051 0.4369799 0.4537252 #> [168,] 0.5598617 0.6075112 0.6221261 0.6616265 0.6686554 0.9067763 0.9191234 #> [169,] 0.4829863 0.5799477 0.5957137 0.5966500 0.6040105 0.6383378 0.6721866 #> [170,] 0.2656860 0.3399969 0.4277456 0.4720053 0.5953922 0.6416989 0.6518913 #> [171,] 0.4612447 0.4740072 0.4972074 0.5100298 0.5698245 0.6111659 0.6466797 #> [172,] 0.6097647 0.6132011 0.6296337 0.6743795 0.8499388 0.8502969 0.8613284 #> [173,] 0.2503394 0.2994409 0.5889596 0.6348820 0.7158948 0.7264780 0.8250275 #> [174,] 0.3672958 0.4160685 0.4725977 0.5723332 0.5830259 0.7428737 0.7919458 #> [175,] 0.3196900 0.5194182 0.5298376 0.5435197 0.5572317 0.5750432 0.5840255 #> [176,] 0.4165118 0.5049022 0.5466477 0.6267163 0.6787596 0.6830429 0.7427972 #> [177,] 0.4497791 0.5191472 0.5219601 0.5395790 0.5416235 0.6160995 0.6362140 #> [178,] 0.9989923 1.0082055 1.1357055 1.1672237 1.1891288 1.2180572 1.2978906 #> [179,] 0.4670087 0.5287582 0.5334193 0.5478272 0.5721634 0.5743282 0.6294766 #> [180,] 0.7664906 0.8228400 0.8585636 0.8969156 0.9115949 1.0017615 1.0335018 #> [181,] 0.1362916 0.3588431 0.4794574 0.5286231 0.5310006 0.5579764 0.5622382 #> [182,] 0.4579561 0.6701687 0.6773683 0.6883186 0.6996202 0.7164687 0.7429378 #> [183,] 0.7755421 0.8804326 0.9078180 1.1672237 1.1684649 1.1729379 1.2657212 #> [184,] 0.3119060 0.4224714 0.4575790 0.4683933 0.5423035 0.5515163 0.5919193 #> [185,] 0.5909574 0.6332117 0.6853056 0.7348973 0.7398255 0.7435528 0.7496748 #> [186,] 0.5069319 0.5264586 0.5577906 0.5580674 0.6443203 0.7141846 0.7309459 #> [187,] 0.3943873 0.3955992 0.4359817 0.4794574 0.4972336 0.5284220 0.5321714 #> [188,] 0.2682098 0.3284029 0.3530090 0.3678781 0.3728544 0.4075196 0.4294050 #> [189,] 0.2991949 0.3692605 0.5451733 0.5594507 0.5624723 0.6126309 0.6801919 #> [190,] 0.3898532 0.5403542 0.5727275 0.6038711 0.6124425 0.6162748 0.6253421 #> [191,] 0.4279815 0.4480237 0.4493396 0.4829012 0.4842416 0.4862033 0.4921528 #> [192,] 0.3214490 0.5980038 0.6570703 0.8534005 0.9127974 0.9277686 0.9352843 #> [193,] 0.3394844 0.3853540 0.5972976 0.6104983 0.6413421 0.6960078 0.7603939 #> [194,] 0.2752557 0.4161253 0.4172473 0.4176578 0.4396160 0.4940575 0.4972336 #> [195,] 0.2578455 0.4328642 0.6321894 0.6537618 0.6839005 0.7108781 0.7116276 #> [196,] 0.2791779 0.4024938 0.4860003 0.5316131 0.5517135 0.5688071 0.5936018 #> [197,] 0.5488358 0.5890583 0.5960984 0.6170807 0.6979232 0.7174303 0.7531728 #> [198,] 0.6311555 0.6573906 0.7012529 0.7290197 0.7398255 0.7983180 0.8183816 #> [199,] 0.3642713 0.5072499 0.5454344 0.5506138 0.5703296 0.5830259 0.6540970 #> [200,] 0.4726632 0.5489127 0.5812676 0.6390218 0.6806105 0.6894002 0.7998378 #> [201,] 0.8613284 0.8640089 0.8871901 1.0109677 1.0149250 1.0495105 1.1598583 #> [202,] 0.2164501 0.2860089 0.3381355 0.4283662 0.4480237 0.4736562 0.4737440 #> [203,] 0.3527183 0.4759184 0.5130482 0.5664733 0.5982752 0.6008386 0.6296641 #> [204,] 0.4037595 0.4106354 0.4734001 0.5075357 0.6279512 0.6381214 0.6766374 #> [205,] 0.4613472 0.5733389 0.6536408 0.6658839 0.7161500 0.7405805 0.7691944 #> [206,] 0.7557748 1.9032298 1.9909608 2.0085635 2.1575094 2.1825889 2.2086869 #> [207,] 0.5352316 0.5703028 0.6775417 0.8012966 0.8110856 0.8860435 0.8907570 #> [208,] 0.3399969 0.5087585 0.5108792 0.5812280 0.6684315 0.7307516 0.7418706 #> [209,] 0.2266562 0.4415222 0.5105230 0.5281845 0.5290100 0.5508943 0.5960563 #> [210,] 0.5144181 0.6123625 0.6639630 0.6989697 0.7102417 0.7557781 0.7588572 #> [211,] 0.2442281 0.3479301 0.4439951 0.4642895 0.4872823 0.5358688 0.5773974 #> [212,] 0.4894648 0.5949956 0.6911686 0.6929355 0.6989893 0.7356213 0.7939766 #> [213,] 0.3559836 0.4992742 0.5002217 0.5591558 0.5654315 0.5836867 0.5955450 #> [214,] 0.5745717 0.5981669 0.6085589 0.6228606 0.6447953 0.6589045 0.6828360 #> [215,] 0.4768436 0.5065259 0.5627565 0.7367339 0.7530721 0.7609732 0.7615598 #> [216,] 0.3559836 0.4424304 0.4907152 0.4921030 0.5727925 0.5907024 0.6593339 #> [217,] 0.3052188 0.4374722 0.4523524 0.4868032 0.5092464 0.5101514 0.5210982 #> [218,] 0.3974524 0.4433549 0.4991711 0.5081086 0.5470574 0.6239051 0.6253421 #> [219,] 0.4592319 0.4923259 0.4934713 0.5198035 0.5287760 0.5481299 0.5616279 #> [220,] 0.9243558 0.9301088 1.1803583 1.1868249 1.2316803 1.3025945 1.3337114 #> [221,] 0.1765483 0.6991221 0.7436371 0.8061793 0.8457712 0.8822349 0.9095427 #> [222,] 0.4114465 0.6579970 0.7081140 0.7223757 1.0008298 1.0602373 1.0841085 #> [223,] 0.4855611 0.5070293 0.5399607 0.5737582 0.5895435 0.6067046 0.6136829 #> [224,] 0.5725214 0.6602495 0.7616623 0.7667874 0.8101183 0.8179785 0.8180067 #> [225,] 0.2694920 0.2950353 0.3692134 0.3719505 0.4592028 0.4992582 0.4999803 #> [226,] 0.3343100 0.4750387 0.4832977 0.5889596 0.6089304 0.6234424 0.6648360 #> [227,] 0.4123463 0.4757520 0.5777199 0.5832221 0.6133775 0.6400521 0.6722586 #> [228,] 0.4657427 0.5010224 0.5418577 0.6170351 0.6605389 0.6997621 0.7465732 #> [229,] 0.4051340 0.4735834 0.4921030 0.5809545 0.5859734 0.6244618 0.6464568 #> [230,] 0.3891018 0.4681615 0.4966708 0.5392035 0.5647906 0.5702987 0.5720533 #> [231,] 0.2547535 0.3056966 0.4740072 0.5703028 0.5714908 0.5925200 0.5940281 #> [232,] 0.9555092 1.1303360 1.1388998 1.1594726 1.2539825 1.2605820 1.2764892 #> [233,] 0.2109421 0.2860089 0.3835569 0.4145382 0.4747144 0.5700085 0.5719874 #> [234,] 0.5355423 0.5872310 0.6488231 0.6511711 0.6514708 0.6620687 0.6808537 #> [235,] 0.3692605 0.4257540 0.5174856 0.5175952 0.5428824 0.5457228 0.6078203 #> [236,] 0.5011913 0.5154095 0.5271494 0.6122418 0.6528631 0.6848110 0.7000411 #> [237,] 0.3951226 0.4605785 0.4844073 0.4856967 0.5214682 0.5475801 0.5896545 #> [238,] 0.5555567 0.6041729 0.6486946 0.6611702 0.7187924 0.7520435 0.7555620 #> [239,] 0.4374822 0.9087339 0.9675360 1.0043093 1.0416752 1.1352041 1.2382200 #> [240,] 0.3848214 0.4431134 0.6011015 0.6048017 0.6541363 0.7072750 0.7132638 #> [241,] 0.7002668 0.8351929 0.9067863 0.9174519 0.9210116 0.9361946 0.9924884 #> [242,] 0.2230059 0.3106024 0.5786764 0.6139568 0.6573238 0.6714148 0.7042095 #> [243,] 0.3631836 0.7214698 0.7647506 0.7948385 0.8413211 0.8450892 0.8465010 #> [244,] 0.8610764 0.9358077 0.9801249 0.9993196 1.0108796 1.0252545 1.0280008 #> [245,] 0.3080751 0.3107541 0.3160396 0.3999155 0.4615110 0.5349535 0.5481820 #> [246,] 0.5765453 0.6558690 0.7005177 0.8138565 0.8658315 0.8670046 1.0029064 #> [247,] 0.3342053 0.4390372 0.4789193 0.4895538 0.5284220 0.5497563 0.5579764 #> [248,] 0.5187787 0.5655398 0.5926421 0.6029241 0.6503403 0.6525829 0.7330386 #> [249,] 0.7742780 0.8390463 0.8879565 0.8886585 0.9933613 1.0113779 1.1279124 #> [250,] 0.8413211 0.9289766 0.9943680 1.0636714 1.1219632 1.1385667 1.1523223 #> [251,] 0.4423658 0.4739314 0.6919364 0.7125685 0.7307516 0.7743503 0.7797978 #> [252,] 0.7704966 0.7745123 0.9301088 0.9765920 0.9806921 1.0033158 1.0061996 #> [253,] 0.3183395 0.3478599 0.4142914 0.4497791 0.4867866 0.5707005 0.5740675 #> [254,] 0.6927369 1.1663722 1.3124793 1.3924713 1.6727395 1.6976233 1.7778336 #> [255,] 0.1963591 0.4167413 0.5624760 0.5832461 0.6152891 0.6170807 0.6645390 #> [256,] 0.3642874 0.6713886 0.8404900 0.8498604 0.8597038 0.9465393 0.9807787 #> [257,] 0.4127776 0.4727399 0.4924958 0.6556979 0.6588958 0.6865332 0.6979232 #> [258,] 0.4670087 0.4929666 0.5014880 0.5799477 0.6056041 0.6182922 0.6302956 #> [259,] 0.4788930 0.7232206 0.7920760 0.8184894 0.8846523 0.8963857 0.9163078 #> [260,] 0.3298885 0.6568896 0.6602341 0.8319435 0.8572144 0.8684830 0.8820444 #> [261,] 0.3461297 0.3779047 0.3847305 0.4344955 0.4791532 0.5155695 0.5202147 #> [262,] 0.4019718 0.5540441 0.6682981 0.6746002 0.7254377 0.8308921 0.8470057 #> [263,] 0.4523524 0.4550712 0.4623505 0.5549466 0.5637004 0.5863843 0.5943596 #> [264,] 0.2353301 0.6801134 0.7381024 0.7402864 0.7923956 0.8268060 0.8741396 #> [265,] 0.2656860 0.4940036 0.5083646 0.5087585 0.5427427 0.6460751 0.6637709 #> [266,] 0.6714131 0.8008904 0.9622620 1.2575237 1.2897530 1.2925865 1.3800389 #> [267,] 0.7923956 0.8258942 0.8966985 0.9029819 1.0015818 1.0553813 1.1269123 #> [268,] 0.3062630 0.3782222 0.4369430 0.4465721 0.4803380 0.5010035 0.5324977 #> [269,] 0.5009687 0.5250248 0.5428234 0.5473297 0.5942332 0.5955329 0.6020509 #> [270,] 0.3694694 0.4990745 0.5044755 0.5073828 0.5221797 0.5618657 0.6075119 #> [271,] 2.1686806 2.2086869 2.2810268 2.3181527 2.3606029 2.4186803 2.4244825 #> [272,] 0.3530585 0.4734049 0.4747357 0.4937261 0.5221003 0.5710723 0.6007936 #> [273,] 0.7566811 0.9849265 1.0073145 1.0687995 1.0916150 1.2922580 1.3093768 #> [274,] 0.2474450 0.2828653 0.3439918 0.4110661 0.4494573 0.4789884 0.5098917 #> [275,] 0.4648660 0.5489127 0.5699855 0.5739238 0.5982141 0.6347572 0.6426404 #> [276,] 0.6293818 0.6993759 0.8308841 0.8464172 0.8495530 0.8732220 0.9309170 #> [277,] 0.9493566 0.9797639 1.1373486 1.1400396 1.1867739 1.1919957 1.1947125 #> [278,] 0.5676543 0.5906277 0.6551925 0.6584415 0.6663706 0.7028327 0.7198973 #> [279,] 0.3376254 0.3556077 0.4071768 0.4304381 0.4513943 0.5140644 0.5481116 #> [280,] 0.4443316 1.0295641 1.0404369 1.1279124 1.1370093 1.1961986 1.2268191 #> [281,] 0.4701398 0.4753663 0.4798772 0.5016437 0.5879416 0.6611702 0.6612903 #> [282,] 0.5963667 0.6405775 0.7198823 0.7262374 0.7851440 0.7967534 0.8175944 #> [283,] 0.4734001 0.4819553 0.5610950 0.6125552 0.6348908 0.6632266 0.7207668 #> [284,] 0.3943873 0.4328513 0.4789193 0.4850523 0.5263223 0.5286231 0.5301923 #> [285,] 0.3747992 0.5221909 0.5895435 0.6116325 0.6512875 0.6895740 0.7035018 #> [286,] 0.6183229 0.7769803 0.7859109 0.8773568 0.9343448 0.9549166 0.9764120 #> [287,] 0.6583572 0.7055782 0.7864954 0.8128896 0.8280572 0.8424534 0.8690105 #> [288,] 0.6183229 0.6824711 0.6825961 0.6918185 0.7775237 0.7884203 0.7974064 #> [289,] 0.3075964 0.3862536 0.4024244 0.4145382 0.4283662 0.4921528 0.5025113 #> [290,] 0.1998153 0.3016603 0.4811913 0.5119983 0.5129489 0.5203712 0.5852687 #> [291,] 0.3000201 0.4148677 0.4633867 0.4723733 0.4938870 0.5325850 0.5666957 #> [292,] 0.5232328 0.5400492 0.5425748 0.5460978 0.5610397 0.6117036 0.6231267 #> [293,] 0.4257540 0.4292543 0.4966009 0.5107381 0.5594507 0.6267861 0.6400521 #> [294,] 0.2218137 0.3576403 0.4037885 0.6063109 0.6104833 0.6138500 0.6443203 #> [295,] 0.5807290 0.6296239 0.7645480 0.9160502 0.9723325 1.0319325 1.1000059 #> [296,] 0.3246618 0.4604986 0.5069319 0.5312717 0.5520613 0.6287894 0.6300649 #> [297,] 1.3482078 1.4732361 1.4893027 1.5836625 1.6339643 1.6551029 1.6707667 #> [298,] 0.2296156 0.3813579 0.5184814 0.5243604 0.5431865 0.5595356 0.5634057 #> [299,] 0.3972822 0.4965400 0.5194182 0.6076082 0.6290822 0.6760092 0.6789478 #> [300,] 0.4448283 0.5734645 0.7645480 0.7941773 0.8390236 0.8422752 0.8519435 #> [301,] 0.3365770 0.4171835 0.4481301 0.4496889 0.4506818 0.4891075 0.5171006 #> [302,] 0.2569293 0.3341905 0.3618488 0.4208733 0.4286491 0.4562478 0.4609700 #> [303,] 0.3582576 0.4393601 0.4750677 0.4935474 0.5200496 0.6106381 0.6153815 #> [304,] 0.3985065 0.4481301 0.5704134 0.5758475 0.6468491 0.6484340 0.6493102 #> [305,] 0.5517353 0.6800489 0.8270838 0.8313950 0.8533623 0.8767628 0.8883322 #> [306,] 0.2020652 0.2753051 0.4636687 0.5449661 0.5473297 0.5852984 0.6668215 #> [307,] 0.3414077 0.3471195 0.6384725 0.7448607 0.7551961 0.8762055 0.9248971 #> [308,] 0.3537939 0.4440057 0.4910911 0.5114391 0.5476241 0.5530247 0.5760182 #> [309,] 0.4916895 0.5995342 0.6447800 0.7485735 0.8892563 0.9886686 0.9982018 #> [310,] 0.5312083 0.6139568 0.6994112 0.7266816 0.7379023 0.8330002 0.8425017 #> [311,] 0.6927369 0.8882599 1.0306228 1.0560097 1.2125442 1.3396713 1.3635505 #> [312,] 0.4636346 0.5320051 0.7262585 0.7761523 0.7983180 0.8087218 0.9527683 #> [313,] 0.5474971 0.5872310 0.6326847 0.6726346 0.7840795 0.7865517 0.8100991 #> [314,] 0.3160586 0.3882206 0.4023933 0.4537252 0.4826127 0.4930848 0.5560572 #> [315,] 0.2954659 0.4402712 0.6798214 0.6936148 0.7349776 0.7459878 0.7593610 #> [316,] 0.6696794 0.6865332 0.6996609 0.7618664 0.7626236 0.7792351 0.8628118 #> [317,] 0.4335027 0.4585807 0.5434027 0.5540441 0.6385622 0.6481007 0.6811298 #> [318,] 0.2752557 0.2994951 0.3710504 0.4359817 0.5036908 0.5098917 0.5263223 #> [319,] 0.5254022 0.6111820 0.6481640 0.6570087 0.6724047 0.6794556 0.7683324 #> [320,] 0.4465721 0.4648428 0.5395790 0.5984565 0.6111190 0.6136581 0.6233926 #> [321,] 0.7127578 0.7436371 0.7673176 0.8291398 0.8413863 0.8558235 1.0156060 #> [322,] 0.4470890 0.5076585 0.5124888 0.5231660 0.5373169 0.5393927 0.5440362 #> [323,] 0.4066972 0.4586935 0.6145234 0.6310264 0.6404431 0.6568517 0.6626158 #> [324,] 0.4874757 0.7017439 0.7727866 0.7933014 0.8831344 0.9190963 0.9253171 #> [325,] 0.2832737 0.4406743 0.4510282 0.4699401 0.4907152 0.5575169 0.5591558 #> [326,] 0.4557063 0.4871143 0.4931461 0.5019206 0.5051712 0.5052221 0.5481116 #> [327,] 0.7143952 0.7606851 0.7677387 0.7730754 0.7746293 0.7919458 0.8392739 #> [328,] 0.2824080 0.3459307 0.3787681 0.4735834 0.5561115 0.5666532 0.5784076 #> [329,] 1.0676613 1.5983391 1.7461672 1.8718903 1.9509921 1.9752659 1.9934865 #> [330,] 0.2656600 0.4197421 0.4314120 0.4393601 0.5153055 0.5641456 0.6239847 #> [331,] 0.8832363 0.9567154 1.2394640 1.4999657 1.5093249 1.5724634 1.6109561 #> [332,] 0.3457117 0.5522699 0.6965300 0.7437559 0.7467575 0.7628491 0.7762495 #> [333,] 0.3895432 0.4687791 0.5209139 0.5944829 0.6893626 0.7648775 0.7656143 #> [334,] 0.4374755 0.5166567 0.5836867 0.6258101 0.6296641 0.6343839 0.6538817 #> [335,] 0.2724458 0.4483847 0.4595417 0.5615556 0.5907221 0.6050880 0.6080600 #> [336,] 0.4443316 0.6424847 0.8610764 0.8815996 0.9384542 0.9568739 1.0199679 #> [337,] 0.8617257 0.8710661 0.9524282 1.0021075 1.0310961 1.0416989 1.0630547 #> [338,] 0.3631836 0.5953177 0.6319675 0.6528631 0.7359611 0.7627102 0.8105955 #> [339,] 0.4309298 0.4604986 0.4925399 0.4966708 0.4968930 0.5229099 0.5622740 #> [340,] 0.5696837 0.5945664 0.6140291 0.6607465 0.6679704 0.6915614 0.7433810 #> [341,] 0.5259341 0.6778514 0.7219848 0.7570375 0.7698148 0.8017146 0.8072259 #> [342,] 0.2217617 0.2988244 0.3202706 0.3326814 0.4828516 0.4954094 0.6143176 #> [343,] 0.2857866 0.4578845 0.5097455 0.5696837 0.7037830 0.7221809 0.7342913 #> [344,] 0.2499468 0.4080576 0.5500536 0.5505802 0.5763671 0.5945664 0.6108302 #> [345,] 0.4725977 0.5025481 0.5506138 0.6319652 0.7194182 0.7232206 0.7233274 #> [346,] 0.7372494 0.7678418 0.7828068 0.8265162 0.8465010 0.8489286 0.8980172 #> [347,] 0.4087473 0.4442985 0.4910911 0.4938870 0.4999916 0.5135788 0.5271494 #> [348,] 0.3932012 0.4194612 0.4335793 0.4401427 0.4459262 0.4721957 0.4750677 #> [349,] 0.2273583 0.3806511 0.4652016 0.4704623 0.4829322 0.5894456 0.5898104 #> [350,] 0.1983044 0.5860999 0.5923463 0.6525829 0.6622607 0.8927730 0.9317428 #> [351,] 0.3935127 0.4116965 0.4475573 0.4893418 0.5434911 0.5978571 0.6817049 #> [352,] 0.6198571 0.6364684 0.6631117 0.7236107 0.7350833 0.7603939 0.8215576 #> [353,] 0.3747890 0.4972074 0.5067166 0.5456494 0.5765121 0.5823562 0.6178335 #> [354,] 0.4482251 0.4768116 0.4777298 0.4975465 0.5205927 0.5351490 0.5767044 #> [355,] 0.5921002 0.8509328 0.8883322 1.3493029 1.4194159 1.5123797 1.5465838 #> [356,] 0.6469960 0.6535593 0.6651903 0.6765484 0.7467857 0.8138460 0.8155243 #> [357,] 0.5297576 0.5460581 0.5752158 0.6501874 0.6887962 0.6977100 0.7395676 #> [358,] 0.4777599 0.5337423 0.5889834 0.6460212 0.6903412 0.7210871 0.8240193 #> [359,] 0.4142914 0.5176881 0.5277014 0.6106471 0.6123415 0.6224384 0.6269097 #> [360,] 0.4919594 0.5453030 0.5484186 0.5550756 0.6550249 0.6896709 0.7104783 #> [361,] 0.4577316 0.5203527 0.5270669 0.5484186 0.6130869 0.6325047 0.6629479 #> [362,] 0.5048736 0.6315611 0.6460212 0.6517485 0.7300337 0.7449540 0.7578651 #> [363,] 0.6151594 0.6622607 0.9363069 0.9611949 0.9692649 0.9902827 1.0039163 #> [364,] 0.6645390 0.7595542 0.7690297 0.8238071 0.8250804 0.9323085 0.9492159 #> [365,] 0.4801832 0.5703396 0.6265655 0.6522410 0.6744898 0.6847840 0.6887962 #> [366,] 0.6241592 0.6385163 0.6849981 0.7652407 0.8636321 0.8665598 0.8747543 #> [367,] 0.4036686 0.4074138 0.5643889 0.5962516 0.6315293 0.7345245 0.7536984 #> [368,] 0.5929305 0.7351369 0.8083417 0.9341815 0.9427737 1.0242711 1.0624381 #> [369,] 0.4695051 0.4996085 0.5723332 0.6187645 0.8859597 0.8961061 0.9085786 #> [370,] 0.3556077 0.3990696 0.4175508 0.4701749 0.5415097 0.5704060 0.5748674 #> [371,] 0.5189913 0.6270920 0.7058054 0.7151584 0.7381024 0.7822497 0.8412011 #> [372,] 0.1821476 0.4550712 0.5155402 0.6817049 0.7198049 0.7698148 0.7935792 #> [373,] 0.4188806 0.5119499 0.5290100 0.5673679 0.6157251 0.6398496 0.6468491 #> [374,] 0.6158580 0.7952401 0.8465456 0.9759081 0.9771859 1.0338753 1.0490758 #> [375,] 0.3524337 0.3536279 0.3891551 0.4401427 0.4555023 0.4699200 0.5623945 #> [376,] 1.2005248 1.2138753 1.3149943 1.3170209 1.3290479 1.3774349 1.4527436 #> [377,] 0.3000201 0.3066120 0.3272235 0.3809927 0.4125014 0.4249279 0.4442985 #> [378,] 0.3853540 0.4068972 0.4964543 0.5422557 0.6198571 0.6254522 0.6384770 #> [379,] 0.3454563 0.3713510 0.4965400 0.5224370 0.6194128 0.6682066 0.7175131 #> [380,] 0.3392109 0.4181309 0.6375285 0.7202229 0.7429733 0.7588076 0.7750321 #> [381,] 0.3905628 0.4585807 0.5287760 0.6011118 0.6414148 0.6638869 0.6706265 #> [382,] 0.6376604 0.6560304 0.7972184 0.8490657 0.8542834 0.9027296 0.9437868 #> [383,] 0.1362916 0.3912933 0.3955992 0.4328513 0.4390372 0.5727553 0.6178691 #> [384,] 0.3091575 0.5322739 0.5451491 0.5872667 0.6306143 0.6480375 0.6584415 #> [385,] 0.1628608 0.7103740 1.0996304 1.1373904 1.1416821 1.1607203 1.2325702 #> [386,] 0.3784988 0.4051373 0.4406743 0.4447534 0.4783833 0.4970822 0.5196511 #> [387,] 0.4739314 0.5851950 0.6565383 0.6904380 0.7537516 0.8005908 0.8106037 #> [388,] 0.4437187 0.4687791 0.5177161 0.5509537 0.5732842 0.6291464 0.6798214 #> [389,] 0.3113292 0.4423658 0.6108399 0.6132011 0.8419828 0.8532589 0.8539395 #> [390,] 0.5042632 0.5047298 0.6332989 0.6573906 0.6640788 0.7308810 0.7348973 #> [391,] 0.7233274 0.7667874 0.7677387 0.8072730 0.8238013 0.8640089 0.8686242 #> [392,] 0.3895432 0.4814243 0.5017388 0.5109920 0.5509537 0.5559009 0.5684539 #> [393,] 0.3934241 0.4134636 0.4875948 0.4968930 0.4970086 0.5114391 0.5413453 #> [394,] 0.7262374 0.7883433 0.8249225 0.8961636 0.9529310 1.0486522 1.0517503 #> [395,] 0.1257555 0.4669204 0.4789762 0.5074462 0.5550756 0.6272511 0.6629479 #> [396,] 0.7127578 0.9761487 1.0934914 1.1792269 1.2529735 1.2647336 1.2691116 #> [397,] 0.2353301 0.5189913 0.6319328 0.6763739 0.8770099 0.9029819 0.9047004 #> [398,] 0.4036686 0.6553439 0.7098716 0.7248484 0.7313914 0.7385971 0.8750195 #> [399,] 0.3202706 0.3913705 0.4149331 0.4594065 0.4824973 0.4997180 0.5302900 #> [400,] 0.3830567 0.4335794 0.6856147 0.7246882 0.7279501 0.8605461 0.8679605 #> [401,] 0.3458029 0.6856147 0.6893942 0.7525894 0.7639661 0.7671241 0.8066190 #> [402,] 0.3184559 0.4518358 0.5047298 0.6410868 0.6615776 0.7332994 0.7435528 #> [403,] 0.3710223 0.3747890 0.3890036 0.4005514 0.4566556 0.4822703 0.4933322 #> [404,] 0.3525775 0.3633023 0.3969505 0.4275021 0.4805071 0.4826465 0.5098671 #> [405,] 0.7915908 1.0149250 1.1095494 1.1976677 1.2020657 1.2915119 1.4095128 #> [406,] 0.3285568 0.4314120 0.4459262 0.4767470 0.4802200 0.4935474 0.5290869 #> [407,] 0.3213510 0.4834092 0.5131636 0.5381790 0.5893552 0.5904987 0.6029693 #> [408,] 0.3178739 0.3953197 0.5304649 0.5385771 0.5808212 0.6046218 0.6353393 #> [409,] 0.4980218 0.5544410 0.5735751 0.6319675 0.6597138 0.7081038 0.7502911 #> [410,] 0.4111658 0.6511711 0.6553934 0.7264462 0.7492739 0.7716554 0.8001210 #> [411,] 0.6332989 0.7615951 0.8218535 0.8311973 0.9326075 0.9754550 0.9913331 #> [412,] 0.5250252 0.6174626 0.6315293 0.6413620 0.6457151 0.6603378 0.6631487 #> [413,] 0.2296156 0.3987572 0.4429551 0.4651612 0.4720911 0.5008382 0.5440362 #> [414,] 0.4051202 0.4769180 0.7755421 0.9211892 0.9332052 0.9669258 1.0150922 #> [415,] 0.1457126 0.5002871 0.5610436 0.5681428 0.5699855 0.7477874 0.7981500 #> [416,] 0.4056079 0.5185333 0.5372201 0.5717254 0.6669696 0.6682361 0.6959216 #> [417,] 0.5261482 0.5872667 0.5905954 0.6268633 0.6326371 0.6944146 0.7490721 #> [418,] 0.2546616 0.5221003 0.5281845 0.5339777 0.5899013 0.5936018 0.6222072 #> [419,] 0.5121596 0.6132402 0.7254377 0.7297429 0.7689811 0.7979053 0.8296740 #> [420,] 0.5019129 0.5067543 0.5348742 0.5720533 0.6073665 0.6227840 0.6371878 #> [421,] 0.5492871 0.6242689 0.6266138 0.7856933 0.8007903 0.8282300 0.8867929 #> [422,] 0.4955922 0.5857594 0.5938233 0.6401902 0.6480952 0.6741597 0.6915875 #> [423,] 0.4430698 0.4549399 0.4942273 0.5141040 0.5802341 0.5968755 0.6428334 #> [424,] 0.3802711 0.3884973 0.6605389 0.7177136 0.7344810 0.8601750 0.8970716 #> [425,] 0.3481627 0.3920226 0.3926587 0.4059614 0.4381897 0.4934713 0.5628062 #> [426,] 0.9883830 1.0004716 1.0509800 1.0558282 1.1139603 1.1253654 1.1421931 #> [427,] 0.4777599 0.7755872 0.8128896 0.8477208 0.8490090 0.8849198 0.9162768 #> [428,] 0.3918320 0.4289125 0.6084791 0.6263253 0.7111541 0.8128610 0.8505465 #> [429,] 0.5138186 0.6642021 0.6690111 0.6806061 0.6947922 0.7058257 0.7114169 #> [430,] 0.3688427 0.3948392 0.4609358 0.5423035 0.5549392 0.5716828 0.6080963 #> [431,] 1.0730223 1.4578942 1.4752809 1.5188541 1.5335735 1.5639044 1.5855170 #> [432,] 0.6152174 0.6268633 0.6339550 0.7537199 0.8068748 0.8384083 0.8432481 #> [433,] 0.4116965 0.5155402 0.5488945 0.5653569 0.7218092 0.7590991 0.7820999 #> [434,] 0.4125014 0.4983567 0.4999916 0.5154095 0.5325850 0.5391964 0.5422872 #> [435,] 0.4123463 0.4869867 0.5505204 0.5944104 0.6481640 0.6965300 0.7600829 #> [436,] 0.3806511 0.3838156 0.4368219 0.4897840 0.5797199 0.6907963 0.6946080 #> [437,] 0.2618901 0.4174240 0.5035822 0.5413713 0.5488100 0.5831975 0.5879309 #> [438,] 0.4959007 0.6457151 0.7176706 0.7920279 0.8368992 0.8614404 0.9128021 #> [439,] 0.4485787 0.5459737 0.5677734 0.6356550 0.6956782 0.7170209 0.7448506 #> [440,] 0.3514631 0.5187787 0.5466477 0.6713886 0.6968107 0.8028419 0.8264479 #> [441,] 0.5425146 0.7626236 0.7633689 0.7675038 0.7677695 0.7694295 0.7838109 #> [442,] 0.6296337 0.7434690 0.7822497 0.8595808 0.8613229 0.8841422 0.8859597 #> [443,] 0.2578455 0.3417088 0.5196555 0.5383358 0.6234864 0.6611541 0.6897736 #> [444,] 0.3304958 0.5745717 0.6384770 0.8066526 0.8386210 0.8411821 0.8508133 #> [445,] 0.1960542 0.3676536 0.3926587 0.3980096 0.4613971 0.5085484 0.5577556 #> [446,] 0.2991949 0.5174856 0.5902651 0.6158555 0.6207376 0.6400436 0.6540970 #> [447,] 0.5555567 0.8119474 0.9274681 0.9510667 0.9553364 0.9636199 0.9896622 #> [448,] 0.3136491 0.4379508 0.4750387 0.4872823 0.5311648 0.6108173 0.6707253 #> [449,] 0.2922924 0.2928507 0.3874939 0.4891075 0.4980564 0.5681757 0.5693549 #> [450,] 0.4645052 0.5734645 0.6892862 0.7327970 0.8550672 0.9059494 0.9226508 #> [451,] 0.3776611 0.4051202 0.7571150 0.7891617 0.8804326 0.9623114 1.1344319 #> [452,] 0.2618901 0.3390154 0.4804254 0.5135363 0.5185349 0.5615556 0.5796006 #> [453,] 0.4366422 0.5072311 0.5238620 0.5409284 0.5454542 0.7176306 0.7218152 #> [454,] 0.3352511 0.3513501 0.3779047 0.4895538 0.5026705 0.5364504 0.5378630 #> [455,] 0.6907413 0.7005177 0.9384542 1.0699758 1.0746198 1.0962976 1.1049130 #> [456,] 0.4614993 0.5081797 0.5610950 0.5666957 0.6122418 0.6170191 0.6381189 #> [457,] 0.5955406 0.6458231 0.7036961 0.7863549 0.8026338 0.8140232 0.8283215 #> [458,] 0.2289517 0.3067851 0.3186834 0.3819500 0.4005514 0.4741237 0.4938784 #> [459,] 0.5252485 0.5339298 0.5489988 0.5691434 0.6311555 0.6720264 0.6812624 #> [460,] 0.4374822 0.7195262 0.9098251 0.9343713 0.9505368 0.9764738 1.0424884 #> [461,] 0.2996762 0.3490601 0.3588431 0.3912933 0.5119789 0.5439571 0.5553900 #> [462,] 0.3660383 0.3848525 0.4423240 0.4423984 0.4764335 0.5007229 0.5412566 #> [463,] 1.1788565 1.3423411 1.4133923 1.4198957 1.4895292 1.5667001 1.6822037 #> [464,] 0.6225775 0.9153356 1.0056298 1.0156434 1.0508046 1.1443681 1.1523387 #> [465,] 0.3458029 0.3830567 0.5383580 0.6264371 0.7775237 0.7781291 0.8263970 #> [466,] 0.3757904 0.4253779 0.5240031 0.5697414 0.6078203 0.6136829 0.6402618 #> [467,] 0.2791779 0.4828984 0.4913224 0.5192799 0.5194278 0.5242562 0.6222072 #> [468,] 0.1412353 0.3066120 0.3913374 0.4314051 0.4435253 0.4633867 0.4826127 #> [469,] 0.3091575 0.5814689 0.5906277 0.5992562 0.6326371 0.6469669 0.6984444 #> [470,] 0.5165410 0.5225561 0.5504840 0.6094010 0.6236399 0.7190391 0.7255185 #> [471,] 0.3905628 0.4570374 0.4923259 0.5048736 0.5072869 0.5268461 0.5910757 #> [472,] 0.5606495 0.7588076 0.7928859 0.8473664 0.9024861 0.9034418 0.9504198 #> [473,] 0.6265314 0.7195262 0.8195750 0.9087339 0.9994496 1.0561105 1.1280151 #> [474,] 0.3556601 0.5642782 0.5707005 0.5888508 0.6033136 0.6389466 0.6528188 #> [475,] 0.4024483 0.5136618 0.6266999 0.6293683 0.6331598 0.6568448 0.7223757 #> [476,] 0.2569293 0.3657331 0.4026997 0.4276793 0.4572070 0.4812868 0.5029748 #> [477,] 0.3524337 0.5567038 0.5635622 0.5871811 0.6359616 0.6450181 0.6596769 #> [478,] 0.6504698 0.8628118 0.8934793 0.9492043 0.9711269 1.0379094 1.0430789 #> [479,] 0.7279501 0.8232968 0.8815718 0.8875119 0.9731088 1.0534073 1.0644012 #> [480,] 0.5943301 0.6619937 0.6947922 0.7085971 0.8332180 0.8358105 0.9264773 #> [481,] 0.4085959 0.4556442 0.6605592 0.6979482 0.7189606 0.7891139 0.7960983 #> [482,] 0.7915908 1.0648047 1.2543197 1.3099819 1.4000270 1.4309635 1.4379298 #> [483,] 0.5929305 0.8490390 0.9457200 1.0348564 1.0396577 1.1022982 1.1396698 #> [484,] 0.3525252 0.4063249 0.4582774 0.4732585 0.4942109 0.5168748 0.5171654 #> [485,] 0.5038661 0.5610436 0.6504698 0.7018391 0.9611765 0.9764738 1.0006022 #> [486,] 0.3358029 0.4505269 0.5504881 0.6291464 0.7264237 0.7367339 0.7541327 #> [487,] 0.5383990 0.6707741 0.7384176 0.8436659 0.9150972 0.9363727 0.9406192 #> [488,] 0.4432166 0.4470028 0.5287582 0.5528294 0.6302956 0.6820235 0.7083550 #> [489,] 0.3660383 0.4829863 0.4848390 0.5014880 0.5230184 0.5234596 0.5705091 #> [490,] 0.4485787 0.4863853 0.5623340 0.6401146 0.6553934 0.7002668 0.8068167 #> [491,] 0.6932394 0.7742510 0.8789513 0.8817063 0.9108048 0.9883042 1.0338475 #> [492,] 0.2094617 0.4414033 0.4572070 0.4609700 0.4661846 0.4746320 0.4855697 #> [493,] 0.3106024 0.4460617 0.5312083 0.6673814 0.7340519 0.8070216 0.8581231 #> [494,] 0.3301119 0.3437702 0.3669123 0.4918698 0.5940281 0.6070454 0.6111134 #> [495,] 0.3394844 0.4006096 0.4068972 0.4172339 0.6019840 0.6138800 0.6667747 #> [496,] 0.2286886 0.3686147 0.4272458 0.5016437 0.5448599 0.5484320 0.5511875 #> [497,] 0.5624785 0.5993097 0.7017688 0.7706361 0.8810112 0.9182541 0.9193467 #> [498,] 0.8064250 1.0526432 1.1023809 1.1811835 1.2005248 1.2230364 1.2752681 #> [499,] 2.4787351 2.5249703 2.6359338 2.6802160 2.6815841 2.6952508 2.7314304 #> [500,] 0.4542098 0.5011913 0.5495823 0.6193803 0.6722716 0.6991221 0.7031971 #> [501,] 0.2734565 0.4199832 0.4287305 0.4473554 0.5979192 0.6239051 0.6380812 #> [502,] 0.3392109 0.4396069 0.5123829 0.6164692 0.7387613 0.7750119 0.7871300 #> [503,] 0.5428824 0.6339550 0.6402618 0.6501874 0.6507848 0.7304828 0.7361710 #> [504,] 0.3386233 0.3930878 0.4120583 0.4747824 0.4762068 0.5085280 0.5215113 #> [505,] 0.2612405 0.3551038 0.4938784 0.5573365 0.5755479 0.5987590 0.6044852 #> [506,] 0.3083919 0.5942332 0.7722180 0.7991157 0.8329750 0.8516392 0.8547082 #> [507,] 0.2217617 0.2661266 0.3175616 0.4149331 0.4537911 0.4966009 0.5105782 #> [508,] 0.6308904 0.7466674 0.7903432 0.9064238 0.9807787 1.0042939 1.0348121 #> [509,] 0.4242651 0.4347131 0.5480419 0.6643007 0.6676141 0.7039359 0.7141846 #> [510,] 0.5904378 0.6410868 0.8222237 0.9754550 1.0094423 1.0567393 1.0831111 #> [511,] 0.4332651 0.6714131 0.7998238 0.8317753 0.8457471 0.8535646 0.8628611 #> [512,] 0.5671601 0.7282968 0.8362417 0.9513614 1.1567506 1.1991442 1.3318198 #> [513,] 0.6913829 0.8888571 0.9334695 0.9759081 0.9892210 1.0252545 1.0327843 #> [514,] 0.4353166 0.5455117 0.7431773 0.8553774 0.8819996 0.9236206 0.9959342 #> [515,] 0.4870615 0.5425748 0.6153536 0.6784835 0.6855743 0.7353062 0.7886938 #> [516,] 0.3604918 0.3632050 0.3890036 0.4383867 0.4599237 0.4645308 0.4826099 #> [517,] 0.3329671 0.5828192 0.6029241 0.7347236 0.7795816 0.8302082 0.8375110 #> [518,] 0.3944994 0.4106354 0.5612706 0.5953177 0.6034767 0.6522410 0.6632266 #> [519,] 0.3765864 0.4980218 0.7048266 0.7224488 0.7627102 0.7678418 0.8104953 #> [520,] 0.5385999 0.6332117 0.6527146 0.6615776 0.6865330 0.7093842 0.7124826 #> [521,] 0.1923351 0.5812571 0.6480375 0.6551925 0.7185370 0.7214546 0.7555407 #> [522,] 1.4254770 1.4553427 1.5319170 1.5682789 1.5691586 1.6937886 1.8572698 #> [523,] 0.3401918 0.4893371 0.5681757 0.7157430 0.7289997 0.8183816 0.8595664 #> [524,] 0.5352316 0.6435275 0.7486387 0.7818217 0.8367021 0.8470057 0.8558235 #> [525,] 0.4876600 0.5144181 0.5706278 0.6483658 0.6916804 0.7436081 0.8408944 #> [526,] 0.2442281 0.4379508 0.5443972 0.5917659 0.5943638 0.6069509 0.6195639 #> [527,] 0.5355423 0.5474971 0.5550847 0.6033136 0.6927700 0.6934277 0.7839221 #> [528,] 0.6849981 0.7236067 0.7239378 0.7559130 0.9286206 0.9476253 1.0313606 #> [529,] 0.3109425 0.3454563 0.4489389 0.6076082 0.6686554 0.7683952 0.7746529 #> [530,] 0.5224370 0.5242928 0.5504840 0.6955587 0.7028009 0.7331899 0.7756818 #> [531,] 0.2686635 0.8502969 0.8539395 0.8875681 0.9449080 0.9607605 0.9725843 #> [532,] 0.3968053 0.4608625 0.5451733 0.5693529 0.5913540 0.6207376 0.6556169 #> [533,] 0.2105175 0.2849864 0.4435253 0.4451457 0.4976498 0.5039508 0.5385771 #> [534,] 1.0056298 1.0063360 1.0228704 1.1758045 1.3061648 1.3131092 1.3539577 #> [535,] 0.1795017 0.3914624 0.4311430 0.4808685 0.5177915 0.5188129 0.5231660 #> [536,] 0.3886122 0.6940858 0.7367848 0.7859109 0.7948439 0.8092575 0.8410188 #> [537,] 0.2922924 0.3740531 0.4893371 0.5506097 0.5691912 0.5700471 0.5963322 #> [538,] 0.5304649 0.5727852 0.5982141 0.5984345 0.6064560 0.6682981 0.6768179 #> [539,] 0.3723138 0.4811191 0.4975465 0.5152524 0.5242217 0.5337690 0.5374942 #> [540,] 0.1998153 0.3781865 0.3798359 0.4114479 0.5380592 0.5418090 0.5437751 #> [541,] 0.8346431 0.9989923 1.0280044 1.2451451 1.2641855 1.3222055 1.3797486 #> [542,] 0.5150732 0.5405132 0.5517353 0.8327506 0.8509328 1.0145762 1.0188366 #> [543,] 0.6707741 0.8546885 0.8712047 0.9233843 0.9337107 0.9400282 1.0088752 #> [544,] 0.7023488 0.7678536 0.8127954 0.8866871 0.9579694 1.0152249 1.0761013 #> [545,] 0.4737440 0.4814243 0.5159818 0.5177161 0.5302001 0.5374756 0.6089900 #> [546,] 0.4570762 0.4936120 0.5217608 0.6644822 0.7165986 0.7342913 0.7596182 #> [547,] 0.2686635 0.6729536 0.8144727 0.8584639 0.8972973 0.9212723 0.9984089 #> [548,] 0.2965186 0.3938001 0.4227960 0.4627563 0.5003201 0.5122100 0.5155835 #> [549,] 0.7471292 1.0120172 1.0738716 1.0850179 1.1114957 1.1788565 1.2134379 #> [550,] 0.7557748 1.9640711 2.0158992 2.1958536 2.2025514 2.2810268 2.3287320 #> [551,] 0.2164501 0.4410435 0.4493396 0.4747144 0.4762068 0.5017388 0.5159818 #> [552,] 0.3774352 0.5874570 0.6080600 0.6632698 0.6701687 0.6766880 0.6805240 #> [553,] 0.2273583 0.3103047 0.3158317 0.4449949 0.4897840 0.5127951 0.5189196 #> [554,] 0.4215890 0.4280685 0.7515559 0.8982119 0.9799687 0.9824180 1.0017711 #> [555,] 0.2751204 0.5624785 0.8821678 0.8904358 0.9163542 0.9306648 0.9887995 #> [556,] 0.3124576 0.5874171 0.9109125 0.9132635 0.9570289 0.9756815 0.9871541 #> [557,] 0.4570762 0.4896084 0.5105701 0.5210476 0.5693529 0.6319652 0.6602495 #> [558,] 0.6977377 0.8379934 0.8425571 0.9253071 0.9524282 0.9583905 1.0047693 #> [559,] 0.2618401 0.6252074 0.6447046 0.6806061 0.6927381 0.7264462 0.7378000 #> [560,] 0.5383990 0.5511466 0.6680109 0.7092152 0.7109173 0.7745257 0.8069808 #> [561,] 0.4650941 0.5238620 0.5348742 0.5662374 0.6147478 0.6925079 0.7901603 #> [562,] 1.4131752 1.4267515 1.5172372 1.6144240 1.7843633 1.9143099 2.0157166 #> [563,] 1.0995921 1.1604199 1.2860329 1.3602894 1.4155908 1.4740372 1.5191169 #> [564,] 0.5700973 0.9030833 1.0028225 1.0354115 1.0786533 1.0800418 1.0995374 #> [565,] 0.2612405 0.3530585 0.4722940 0.5048796 0.5476488 0.5617994 0.5865857 #> [566,] 0.4788930 0.8072730 0.8456541 0.8498218 0.8716038 0.9956315 1.0053389 #> [567,] 0.6152174 0.9260792 0.9534972 0.9594619 1.0066028 1.0099486 1.0219929 #> [568,] 0.3052188 0.3113680 0.3719505 0.4422055 0.4989853 0.5074590 0.5246449 #> [569,] 0.2343413 0.4280685 0.6458231 0.6796532 0.9604054 0.9616049 1.0077803 #> [570,] 0.6254268 0.6579970 0.8277229 0.9013307 0.9356341 0.9553533 1.0004716 #> [571,] 0.2849864 0.3809927 0.3913374 0.4213421 0.4369799 0.4488936 0.4956664 #> [572,] 0.3799644 0.3981264 0.4570374 0.5037029 0.5298376 0.5905216 0.5940546 #> [573,] 0.7058054 0.9816568 1.1095494 1.1333906 1.1793584 1.2026618 1.2271717 #> [574,] 0.4938878 0.7164475 0.7231428 0.7274085 0.7840491 0.8155527 0.8425017 #> [575,] 0.3610341 0.4669204 0.4753663 0.5390328 0.5429686 0.5484320 0.5824740 #> [576,] 0.9832351 1.1916313 1.2309731 1.2885887 1.3348366 1.4022129 1.4999657 #> [577,] 0.4370194 0.5828192 0.5987923 0.6252074 0.6440177 0.7103820 0.7120553 #> [578,] 0.1907358 0.4380835 0.4398420 0.4453320 0.4968742 0.5043623 0.5147732 #> [579,] 0.3394250 0.5017704 0.5073744 0.5459737 0.5623340 0.5957137 0.6257073 #> [580,] 0.3136491 0.4832977 0.5452097 0.5469488 0.5943638 0.6684409 0.6788909 #> [581,] 0.4757520 0.5505204 0.5907311 0.7315827 0.7437559 0.8815718 0.8903385 #> [582,] 0.3638288 0.5512129 0.5698245 0.5741937 0.6482118 0.6834861 0.6927381 #> [583,] 0.7201322 0.8014768 0.9182950 0.9211684 0.9495949 0.9767439 0.9846061 #> [584,] 0.8286330 0.8392739 0.9274681 0.9415304 0.9662324 0.9756150 1.0326113 #> [585,] 0.7150264 0.8001651 0.8241293 0.8258942 0.8378235 0.8571228 0.8893262 #> [586,] 0.5337423 0.5635622 0.5737651 0.6376604 0.7493295 0.7755872 0.8280572 #> [587,] 0.9260981 0.9886686 1.0634161 1.2119385 1.3173416 1.4022129 1.4536936 #> [588,] 0.2770382 0.4636346 0.6602455 0.8192408 0.9824257 0.9941006 1.0064931 #> [589,] 0.4431134 0.4585248 0.4858187 0.5874570 0.5907221 0.6573963 0.6631667 #> [590,] 0.5029892 0.5650864 0.6707272 0.6804693 0.7201322 0.7361470 0.7548057 #> [591,] 0.4637712 0.5511508 0.7224517 0.7236107 0.7981595 0.8298472 0.8386210 #> [592,] 0.3838156 0.4704623 0.4808685 0.5406582 0.6070318 0.6076115 0.6214845 #> [593,] 0.2780944 0.2938909 0.3160396 0.4005184 0.4071768 0.4305712 0.4557063 #> [594,] 0.3080777 0.3956464 0.4415222 0.4554516 0.4652289 0.4734049 0.5048796 #> [595,] 0.4019718 0.7367865 0.7486387 0.7984211 0.8065179 0.8296740 0.8335389 #> [596,] 0.3458876 0.5025590 0.5422557 0.5644321 0.5681428 0.6667747 0.6730841 #> [597,] 0.3083919 0.5009687 0.6214845 0.6287011 0.7551961 0.7690297 0.7985806 #> [598,] 0.3176937 0.3592673 0.4412460 0.5300126 0.5439430 0.5769023 0.6025091 #> [599,] 0.1821476 0.4623505 0.5653569 0.6789478 0.6922921 0.7219848 0.7308414 #> [600,] 0.3080751 0.3136977 0.4504919 0.4725017 0.4993532 0.5012870 0.5669852 #> [601,] 0.3582576 0.4087473 0.4194612 0.4249279 0.4440057 0.4767470 0.4961666 #> [602,] 0.3486888 0.3844678 0.4569880 0.4613971 0.4621962 0.4693899 0.4812505 #> [603,] 0.2676504 0.3723138 0.5245181 0.5998886 0.6102405 0.6116010 0.6204448 #> [604,] 0.1880397 0.4352734 0.4621962 0.4660285 0.4711256 0.4978754 0.5305005 #> [605,] 0.3678781 0.4005174 0.4088687 0.4412460 0.4848390 0.4860993 0.4929666 #> [606,] 0.3445994 0.4287305 0.5211516 0.5470574 0.6196470 0.7423847 0.8015919 #> [607,] 0.3109425 0.3713510 0.3972822 0.5843780 0.6075112 0.6639107 0.7612510 #> [608,] 0.6211108 0.6765484 0.7801991 0.9374454 0.9723720 1.0410421 1.0413188 #> [609,] 0.7620922 0.7945027 0.9671000 1.0241726 1.0526528 1.0645023 1.0721897 #> [610,] 0.3301119 0.4539022 0.4626248 0.4929649 0.5011315 0.5038229 0.5371010 #> [611,] 0.3892923 0.5719831 0.6193540 0.6307219 0.6754799 0.6939600 0.7055080 #> [612,] 0.3590217 0.5622607 0.5749747 0.7050365 0.7092287 0.7328549 0.7642209 #> [613,] 0.4556442 0.5065259 0.6415368 0.7681048 0.7847803 0.7952401 0.7995223 #> [614,] 0.4841278 0.7330356 0.8625143 0.8627456 0.8871369 0.9040311 1.0021075 #> [615,] 0.4052950 0.5250248 0.5449661 0.5487680 0.5836566 0.6140291 0.7035311 #> [616,] 0.3342382 0.3528047 0.4566071 0.4588823 0.5595899 0.5650627 0.5664733 #> [617,] 0.5910692 0.6109909 0.7725971 0.7880406 0.8080487 0.8162246 0.8352604 #> [618,] 0.8995747 0.9059494 1.0124701 1.0319325 1.0337591 1.1374333 1.1429259 #> [619,] 0.2676504 0.4989698 0.5152524 0.5376920 0.5667576 0.5704259 0.5736706 #> [620,] 0.8284279 0.8684830 0.9933613 0.9954375 1.0958495 1.1693827 1.2511751 #> [621,] 0.7137572 0.7962522 0.8566013 1.1097381 1.1318377 1.1977082 1.2191116 #> [622,] 0.3788236 0.4559011 0.5616415 0.5721634 0.5902184 0.6025091 0.6110487 #> [623,] 0.1923351 0.4792305 0.5322739 0.5676543 0.5992562 0.6682089 0.7108051 #> [624,] 0.7236067 0.8886585 0.9266796 0.9953299 1.1276024 1.2146687 1.2330272 #> [625,] 0.2957885 0.4446937 0.5750432 0.5765267 0.5925585 0.6037244 0.6568023 #> [626,] 0.5509826 0.6290930 0.6537618 0.8620734 0.8626699 0.8700212 0.8840395 #> [627,] 0.3806210 0.3815088 0.3934241 0.4213650 0.4374722 0.5392035 0.5448039 #> [628,] 0.4037595 0.4367022 0.4729371 0.5086459 0.5612706 0.7159270 0.7336171 #> [629,] 0.2094617 0.3032933 0.3819500 0.3945596 0.3978667 0.4026997 0.4286491 #> [630,] 0.3813579 0.4197812 0.4300301 0.4399549 0.4519320 0.5166567 0.5815696 #> [631,] 0.8832363 1.3477283 1.3545155 1.4381920 1.4890247 1.5271940 1.5304553 #> [632,] 0.3537939 0.4304763 0.4875948 0.5019129 0.5135788 0.5934187 0.6370304 #> [633,] 0.6097647 0.8841422 0.9312233 0.9659309 0.9901034 1.0455495 1.0524537 #> [634,] 0.2411622 0.4635766 0.4648660 0.4726632 0.5298530 0.5476241 0.6141360 #> [635,] 0.4445392 0.5229273 0.6558690 0.6608258 0.6714148 0.7340519 0.7681936 #> [636,] 0.8490657 0.9090991 0.9289076 0.9717083 1.0127865 1.0738554 1.1702953 #> [637,] 0.3196900 0.3934462 0.3981264 0.4439951 0.4990745 0.5239454 0.5315435 #> [638,] 0.3856247 0.4641082 0.5074462 0.5270669 0.5824740 0.5874906 0.6256009 #> [639,] 0.4993532 0.6707272 0.6866497 0.7076178 0.7314473 0.7330356 0.7394303 #> [640,] 0.2615611 0.3670521 0.5169540 0.5198035 0.5434027 0.7077364 0.7297429 #> [641,] 0.4279815 0.4513086 0.4595417 0.4634436 0.4923078 0.5025113 0.5047042 #> [642,] 0.9597329 1.1546740 1.1975968 1.3252038 1.3591368 1.3595380 1.3768743 #> [643,] 0.4094364 0.4826327 0.4858038 0.5331247 0.5526499 0.5606193 0.5979192 #> [644,] 0.1502014 0.3948392 0.4024483 0.4363727 0.5110320 0.5298530 0.5515163 #> [645,] 0.7483794 0.7660779 0.7972184 0.8423217 0.8477208 0.9264165 0.9535310 #> [646,] 0.3298885 0.6257554 0.6793500 0.7212007 0.7591843 0.7873279 0.7969979 #> [647,] 0.6265314 0.8995747 0.9675360 1.0166948 1.0424884 1.0450260 1.1418658 #> [648,] 0.3706053 0.3744725 0.4122767 0.4483847 0.4539152 0.4645888 0.4827478 #> [649,] 0.4701398 0.5616011 0.5705091 0.6293527 0.6541363 0.6560097 0.7152830 #> [650,] 0.9559779 0.9902827 1.0017711 1.0408619 1.0470346 1.2111807 1.2288811 #> [651,] 0.5931816 0.5963667 0.6109270 0.7269927 0.7596997 0.7883433 0.8504473 #> [652,] 0.3343100 0.5469488 0.6108173 0.6170841 0.6329588 0.6413620 0.6597046 #> [653,] 0.8118327 1.0229776 1.2321380 1.3246079 1.3556170 1.3676437 1.4039970 #> [654,] 0.4407825 0.4994282 0.5063334 0.5119253 0.5206700 0.5721931 0.5740050 #> [655,] 0.3471195 0.6613483 0.7012632 0.7894252 0.7945076 0.8148472 0.8189127 #> [656,] 0.4028254 0.4151472 0.4276793 0.4414033 0.4663869 0.4732585 0.4777298 #> [657,] 0.8341872 0.8865120 0.9124553 0.9776484 0.9869563 0.9919096 1.0184556 #> [658,] 0.5081797 0.5225944 0.5464530 0.5717254 0.6125552 0.6259600 0.6279938 #> [659,] 0.2656600 0.3891551 0.3932012 0.4618651 0.4633659 0.4802200 0.5200496 #> [660,] 0.4583481 0.4983567 0.5065117 0.5727852 0.5739238 0.6249346 0.6435275 #> [661,] 0.7064498 0.7354697 0.7449229 0.8980629 0.9261128 0.9273094 0.9797472 #> [662,] 0.2599636 0.3310614 0.3920226 0.4318124 0.4627563 0.5120180 0.5462467 #> [663,] 0.6329682 0.6602455 0.6773683 0.7154039 0.7537751 0.7591667 0.7627073 #> [664,] 0.2996762 0.3549475 0.4149381 0.6040388 0.6074579 0.6244618 0.6274167 #> [665,] 0.3527183 0.4374755 0.4699401 0.5557919 0.5650627 0.5727925 0.5955450 #> [666,] 0.8355623 1.1958227 1.3173416 1.3620397 1.3930836 1.4061571 1.5639044 #> [667,] 0.2694920 0.4093251 0.4940575 0.5246449 0.5301315 0.5522417 0.5525923 #> [668,] 0.3176937 0.3298639 0.4615580 0.4781089 0.4860993 0.5040802 0.5085280 #> [669,] 0.8076283 0.8741396 0.9529027 1.0189253 1.1386040 1.1676503 1.2302778 #> [670,] 0.4916895 0.8355623 0.9260981 0.9447221 0.9853316 1.0694053 1.1113730 #> [671,] 0.2950353 0.3113680 0.4127848 0.4388591 0.4701749 0.5131636 0.5181448 #> [672,] 0.1765483 0.6193803 0.7609701 0.7924176 0.8291398 0.8341872 0.8631459 #> [673,] 0.1882941 0.6084791 0.6591222 0.8985321 0.9114107 0.9670695 0.9727846 #> [674,] 0.7103740 0.7996086 1.1310247 1.1396698 1.1415995 1.1594726 1.2106312 #> [675,] 0.6694261 0.7091689 0.7125685 0.7160217 0.7290825 0.7359664 0.7543159 #> [676,] 0.2652173 0.3591586 0.3856247 0.4302680 0.4700327 0.5284957 0.5455016 #> [677,] 0.4056079 0.4650003 0.6410389 0.6644238 0.7346686 0.7799487 0.7987587 #> [678,] 0.7652407 1.0127818 1.1077200 1.1214226 1.1279028 1.2002891 1.2068199 #> [679,] 0.3086478 0.3642713 0.3672958 0.5025481 0.6187645 0.7493980 0.7529638 #> [680,] 0.6108399 0.6574265 0.6846185 0.7434690 0.7667026 0.7743503 0.8051438 #> [681,] 0.9406192 0.9900879 1.0073145 1.0223147 1.0868001 1.1268332 1.1792269 #> [682,] 0.5070293 0.5632050 0.6182612 0.6978583 0.7107505 0.7151584 0.7312178 #> [683,] 0.4334864 0.4357644 0.5221797 0.6035912 0.6405775 0.6628211 0.6794570 #> [684,] 0.5086459 0.5793263 0.5985444 0.6381214 0.6499291 0.7309148 0.8410188 #> [685,] 0.2965186 0.3183395 0.3937665 0.4383867 0.4429527 0.4651612 0.4741794 #> [686,] 0.2436403 0.3843435 0.4940036 0.5953922 0.5978571 0.6161363 0.6846185 #> [687,] 0.6109270 0.7466674 0.8328029 0.8611830 0.9437925 0.9840271 0.9916199 #> [688,] 1.1609350 1.1780794 1.2971786 1.2993309 1.3046307 1.3752090 1.4155252 #> [689,] 0.3227764 0.5130482 0.5373405 0.5434537 0.5549392 0.6665069 0.7139751 #> [690,] 0.6298396 0.9606412 0.9806925 1.0191859 1.0786642 1.0977456 1.1067650 #> [691,] 1.1912198 1.2343525 1.3290869 1.3660693 1.3753267 1.5181986 1.6123363 #> [692,] 0.2697039 0.3892588 0.4135310 0.4528358 0.5037029 0.5203917 0.5268461 #> [693,] 0.4335794 0.4556055 0.5383580 0.6724047 0.7737363 0.7917263 0.8150843 #> [694,] 0.3496784 0.4470028 0.5743282 0.7685739 0.8087218 0.8201508 0.9122154 #> [695,] 0.2202836 0.4180805 0.5138186 0.6213372 0.6242689 0.6470521 0.6813480 #> [696,] 0.3184559 0.5904378 0.6486461 0.6865330 0.7496748 0.7809224 0.8325244 #> [697,] 0.3358029 0.3893338 0.4844073 0.5672224 0.6164471 0.6320225 0.6561801 #> [698,] 0.5025590 0.5278623 0.5865902 0.5981669 0.6182001 0.6365512 0.6947839 #> [699,] 0.3784456 0.4625917 0.6006478 0.6319259 0.6326365 0.6393936 0.6485100 #> [700,] 0.4097627 0.5605815 0.5786764 0.6970361 0.6994112 0.7243970 0.7910807 #> [701,] 0.4532875 0.4625917 0.4769697 0.4860003 0.4913224 0.5144246 0.5899088 #> [702,] 1.0514347 1.1351614 1.1604199 1.2314421 1.2675142 1.2719898 1.2757552 #> [703,] 0.6039762 0.8073952 0.8313950 0.8410883 0.8428926 0.8934076 0.8941275 #> [704,] 0.4874757 0.5073828 0.6075559 0.6104783 0.6204127 0.6582852 0.7073790 #> [705,] 0.3776611 0.4769180 0.5101209 0.8239215 0.9078180 0.9702416 1.0187160 #> [706,] 0.2938909 0.4308291 0.4513943 0.4871143 0.5175952 0.5349535 0.5568170 #> [707,] 0.4360400 0.5857594 0.6447800 0.6451698 0.7482271 0.8122301 0.8444847 #> [708,] 0.9514395 1.0606779 1.0636714 1.1426683 1.2643485 1.2781764 1.2947481 #> [709,] 0.6583572 0.6639630 0.7268477 0.9004944 0.9162768 0.9609299 1.0435224 #> [710,] 0.3390154 0.4174240 0.4615110 0.4615817 0.4617750 0.5644427 0.5650864 #> [711,] 0.2724458 0.4804254 0.4858038 0.4923078 0.5294087 0.5417924 0.5419647 #> [712,] 0.1257555 0.5030681 0.5135363 0.5429686 0.5453030 0.5831975 0.5874906 #> [713,] 0.2316066 0.4242651 0.5503053 0.5668125 0.6527146 0.6826380 0.7104982 #> [714,] 0.7137572 0.8132454 0.8288236 0.8378235 0.9917530 1.0033330 1.1027254 #> [715,] 0.3445357 0.5203527 0.5210982 0.5302128 0.5474013 0.5504232 0.5537372 #> [716,] 0.6289333 0.7239378 0.7605889 0.8364011 0.8404026 0.8441677 0.8458550 #> [717,] 0.5101209 0.7502501 0.7571150 0.7914278 0.9332052 0.9764120 1.0077118 #> [718,] 0.5185333 0.5451491 0.5464530 0.6375285 0.7150237 0.7173710 0.7490721 #> [719,] 0.7471292 0.7926226 0.9163542 0.9244381 0.9594619 0.9813975 0.9902617 #> [720,] 0.2798545 0.2928507 0.4171835 0.4241446 0.5506097 0.5528294 0.5698328 #> [721,] 0.5810662 0.6228606 0.6947839 0.6994219 0.8052348 0.8134730 0.8163599 #> [722,] 0.4924958 0.4981826 0.5540639 0.6878055 0.6996609 0.7174303 0.7634109 #> [723,] 0.2298626 0.4681615 0.4883660 0.5622740 0.6198420 0.6236002 0.6291796 #> [724,] 0.2985586 1.2498559 1.3073808 1.3259615 1.4278049 1.4933361 1.5392211 #> [725,] 0.7177136 0.7769373 0.8442932 0.9119135 0.9680577 0.9692823 1.0137121 #> [726,] 1.6054625 2.0848020 2.2737335 2.3440355 2.4777165 2.4800381 2.5488145 #> [727,] 0.6776881 0.7018391 0.7596997 0.8934793 0.8953289 0.9017456 0.9556454 #> [728,] 0.5503053 0.5529916 0.5644517 0.5815853 0.6495746 0.6587434 0.6663706 #> [729,] 0.4863853 0.5017704 0.5706278 0.6356550 0.6714550 0.7973914 0.8065675 #> [730,] 0.6298396 0.8941275 1.0586703 1.1020911 1.1238132 1.1308614 1.1605411 #> [731,] 0.2230059 0.4460617 0.5229273 0.5374811 0.6970361 0.7349776 0.7393258 #> [732,] 0.9529027 1.0492829 1.0509800 1.2951662 1.3222785 1.3498134 1.4098043 #> [733,] 0.9483649 0.9880734 1.0074580 1.0216102 1.1741660 1.2103866 1.2133149 #> [734,] 0.6690111 0.7064498 0.7085971 0.8331035 0.8896609 0.9334063 0.9730671 #> [735,] 0.6221261 0.8075866 0.9425628 1.0028363 1.0798067 1.1136847 1.1192310 #> [736,] 0.4332651 0.5409303 0.6307219 0.6624071 0.7050365 0.7191833 0.7874377 #> [737,] 0.4387941 0.6204127 0.6707253 0.6965939 0.6972482 0.7017439 0.7697675 #> [738,] 0.5950760 0.7354020 0.7369217 0.7641021 0.8088560 0.8308253 0.8328029 #> [739,] 0.4441841 0.4739766 0.6529928 0.6619944 0.7024932 0.7527359 0.7571572 #> [740,] 0.3433086 0.4559011 0.4768436 0.6148437 0.6968936 0.7092597 0.7328150 #> [741,] 0.3158317 0.3165580 0.3969505 0.4063249 0.4579166 0.4631483 0.4714148 #> [742,] 0.5550847 0.5627145 0.6103244 0.6326847 0.6514708 0.7510046 0.7562212 #> [743,] 0.3056034 0.3103047 0.3165580 0.3768641 0.4135715 0.4179349 0.4652016 #> [744,] 0.1418194 0.5225944 0.6063061 0.6682361 0.7811779 0.7920934 0.7948385 #> [745,] 0.3113118 0.4595513 0.4636687 0.5151912 0.5487680 0.6796532 0.7036961 #> [746,] 1.2094194 1.2693452 1.2838527 1.2862101 1.5435401 1.6377179 1.6807627 #> [747,] 0.1921174 0.4963909 0.4970822 0.5206700 0.5575169 0.5603175 0.5684175 #> [748,] 0.2709125 0.2989971 0.3178739 0.3892588 0.3984968 0.5257014 0.6096382 #> [749,] 0.3710504 0.4176578 0.4423984 0.4646938 0.4921990 0.5166952 0.5442631 #> [750,] 0.3913705 0.3968053 0.4414737 0.5097455 0.5351798 0.5617529 0.5676278 #> [751,] 0.3342382 0.3864791 0.4802595 0.4861910 0.5138721 0.5257014 0.5507399 #> [752,] 0.3788236 0.5478272 0.5989136 0.6381861 0.7703288 0.8092654 0.8158994 #> [753,] 0.5702987 0.5980038 0.6198420 0.6559528 0.6597138 0.7089059 0.7139787 #> [754,] 0.3107541 0.3136977 0.4305712 0.4617750 0.4785309 0.4931461 0.5282038 #> [755,] 0.6690871 0.6860968 0.7725749 0.8556933 0.8632565 0.9030162 0.9208716 #> [756,] 0.4915624 0.5409284 0.5495761 0.5752191 0.6137225 0.7168790 0.7279401 #> [757,] 0.2709125 0.3940243 0.3953197 0.3979419 0.4528358 0.4592319 0.5072869 #> [758,] 0.3246618 0.4099349 0.5264586 0.5480419 0.5910737 0.6117036 0.6689205 #> [759,] 0.5578534 0.5838292 0.6651903 0.7801991 0.8151923 0.8256253 0.8408133 #> [760,] 0.4936120 0.7140313 0.8417973 0.8946692 0.9161463 0.9171797 0.9212723 #> [761,] 0.4423240 0.4711256 0.5230184 0.5773746 0.5822046 0.5966500 0.6572810 #> [762,] 0.1907358 0.4005174 0.4075196 0.4639866 0.4674898 0.5261269 0.5539596 #> [763,] 0.9182541 1.0350864 1.1025945 1.1219863 1.1424055 1.1723399 1.1851861 #> [764,] 0.4357644 0.5396707 0.7081140 0.7458473 0.7559599 0.7662393 0.7817104 #> [765,] 0.2985586 1.2673941 1.3162858 1.3490841 1.3812083 1.4572480 1.4857447 #> [766,] 0.4815630 0.5688090 0.6190446 0.7048870 0.7183197 0.7194659 0.7259945 #> [767,] 0.7216168 0.7344810 0.7559130 0.8101955 0.8404026 0.8766603 0.8847948 #> [768,] 0.5425146 0.5511466 0.6194563 0.6747291 0.6897736 0.7081918 0.7287542 #> [769,] 0.5627565 0.6415368 0.6619937 0.7960983 0.8765106 0.8888571 0.8970740 #> [770,] 0.4305420 0.4811124 0.4841278 0.6360874 0.6567670 0.7184861 0.7732483 #> [771,] 1.0676613 1.4296416 1.4939067 1.5614253 1.5694705 1.7011895 1.7708172 #> [772,] 0.7779108 0.8614404 1.0133924 1.0792046 1.1991442 1.2035680 1.2053993 #> [773,] 0.3852675 0.4621747 0.4727399 0.5144246 0.5488358 0.6393936 0.6696794 #> [774,] 0.3158758 0.3585868 0.3706053 0.4252485 0.4941148 0.5403542 0.5470744 #> [775,] 0.8118327 0.8399439 0.8473664 0.8689506 0.9741627 0.9763009 0.9807302 #> [776,] 0.3974524 0.4094364 0.4199832 0.5053076 0.5211516 0.6099352 0.6329682 #> [777,] 0.3445357 0.3591586 0.4127848 0.4592028 0.4989853 0.5092464 0.5094085 #> [778,] 0.3027772 0.4167413 0.4696109 0.5002217 0.5063334 0.5373169 0.6704545 #> [779,] 0.5278035 0.6956782 0.8427137 0.9090991 0.9683068 0.9759108 0.9917875 #> [780,] 0.3918320 0.4896400 0.5011922 0.6041616 0.6591222 0.7215146 0.7815183 #> [781,] 0.5405132 0.6916804 0.6989697 0.8490090 0.8606617 0.8767628 0.9370817 #> [782,] 0.2646638 0.3213510 0.3692134 0.4093251 0.4185317 0.5074590 0.5181448 #> [783,] 0.4224714 0.6104783 0.6137225 0.6729815 0.7095745 0.7150264 0.7208141 #> [784,] 0.4066972 0.6110123 0.6401066 0.6787280 0.6894002 0.7225953 0.7293708 #> [785,] 0.2995764 0.4729371 0.5985444 0.6034767 0.6364684 0.6724590 0.6766374 #> [786,] 0.3859147 0.5912506 0.5923463 0.6539348 0.7856543 0.8075600 0.8499710 #> [787,] 0.4695051 0.8063933 0.8487997 0.9466795 1.0495105 1.0972245 1.0995921 #> [788,] 0.5394996 0.7051774 0.8265935 0.8760955 0.8869731 0.9950556 1.0018150 #> [789,] 0.5625080 0.8329584 0.8397169 0.8863304 0.8892563 0.8977614 0.9131567 #> [790,] 0.3525252 0.4368219 0.4714148 0.5242562 0.5316131 0.5881955 0.5894456 #> [791,] 0.2915407 0.4650941 0.5777199 0.6138800 0.6294308 0.6847840 0.7266085 #> [792,] 0.4738325 0.4776311 0.4811124 0.6236002 0.6570703 0.7039359 0.7526110 #> [793,] 0.4893257 0.5173880 0.6514329 0.7328150 0.7466487 0.7937440 0.7984116 #> [794,] 0.4894648 0.4934404 0.5353879 0.6123625 0.6483658 0.6927912 0.7029612 #> [795,] 0.3642874 0.6968107 0.7434605 0.8517166 0.9269132 0.9289136 1.0205662 #> [796,] 0.3459307 0.5028377 0.5434625 0.5760647 0.5851950 0.5938233 0.6451698 #> [797,] 0.3974026 0.4114479 0.4151472 0.4304381 0.4426711 0.4582774 0.4631483 #> [798,] 0.2599636 0.4381897 0.4646938 0.4704474 0.4764335 0.4929968 0.5003201 #> [799,] 0.1603362 0.3056966 0.3669123 0.4612447 0.4929649 0.5254921 0.5291550 #> [800,] 0.6608258 1.0429812 1.0439467 1.1099186 1.1286047 1.2167245 1.2207269 #> [801,] 0.3113292 0.6743795 0.6919364 0.8051438 0.8565539 0.8595808 0.8784011 #> [802,] 0.3474395 0.5598617 0.6589509 0.7612510 0.8362417 0.9492731 0.9708233 #> [803,] 0.4604155 0.4981237 0.5171006 0.5691912 0.5693549 0.6212383 0.6379603 #> [804,] 0.4978547 0.5048296 0.6039762 0.6525114 0.6809391 0.8173460 0.8338392 #> [805,] 0.2546616 0.4024938 0.4828984 0.4937261 0.5813304 0.6000009 0.6202705 #> [806,] 0.4080902 0.5169540 0.5808212 0.5931816 0.6385622 0.6709518 0.6768179 #> [807,] 0.3056034 0.3326814 0.4537911 0.4565760 0.4579166 0.4781016 0.4824973 #> [808,] 0.2136091 0.4037885 0.4213650 0.4309298 0.4422711 0.4428247 0.5413453 #> [809,] 0.3995663 0.4340247 0.4436905 0.5134259 0.5156347 0.5339298 0.5704259 #> [810,] 0.3092599 0.3657331 0.3725628 0.4208733 0.4720911 0.4850523 0.5555859 #> [811,] 0.3784456 0.3852675 0.4127776 0.4532875 0.6210650 0.6525644 0.6680109 #> [812,] 0.3433086 0.4590312 0.4968742 0.5173880 0.5616415 0.6270975 0.6659995 #> [813,] 0.2798545 0.3874939 0.4496889 0.4604155 0.5485144 0.5682557 0.5700471 #> [814,] 0.3082097 0.3310614 0.3782222 0.4266016 0.4693899 0.4929968 0.5043400 #> [815,] 0.3461297 0.3816936 0.3835569 0.4024244 0.4161253 0.4371760 0.4834878 #> [816,] 0.5150732 0.5921002 0.6800489 0.9387221 1.0449819 1.1255987 1.2031884 #> [817,] 0.4097627 0.5394996 0.6033279 0.6107870 0.7756786 0.8485907 0.8577339 #> [818,] 0.3514631 0.5049022 0.6213372 0.6854924 0.6952081 0.7058257 0.7449229 #> [819,] 0.4858187 0.7164687 0.7591843 0.8094750 0.8311792 0.8322227 0.8456569 #> [820,] 0.5556390 0.6794556 0.7165986 0.7671241 0.7781291 0.7898797 0.8203556 #> [821,] 0.3112028 0.3530090 0.3912126 0.3977697 0.4197831 0.4252485 0.4859729 #> [822,] 1.1811835 1.4155497 1.6946680 1.7408778 1.7963002 1.9419126 2.0003276 #> [823,] 0.4785309 0.5035822 0.5504232 0.5581019 0.5920239 0.6022480 0.6114413 #> [824,] 1.4441306 1.9944252 2.0704969 2.2442786 2.2650094 2.3348776 2.3617711 #> [825,] 0.6218947 0.6512875 0.6732712 0.7395676 0.7976003 0.8076283 0.8764037 #> [826,] 0.3036663 0.6271310 0.7331899 0.8259215 0.9237299 0.9609905 0.9817945 #> [827,] 0.9193467 1.0772629 1.1219863 1.2334469 1.2451451 1.2538902 1.3005034 #> [828,] 0.2696111 0.2853528 0.3585868 0.3744725 0.4054881 0.4197831 0.5242217 #> [829,] 0.3848214 0.4585248 0.5589624 0.5616011 0.5769023 0.6223452 0.6257554 #> [830,] 0.1793892 0.4991711 0.5590794 0.5758475 0.6482590 0.6809989 0.6897370 #> [831,] 0.6211108 0.7208141 0.7467857 0.7814715 0.8912662 0.8966985 0.9665416 #> [832,] 0.5439116 0.6994219 0.9761487 0.9915919 1.0092772 1.0094416 1.0673060 #> [833,] 0.3097309 0.7838109 0.8271271 0.8466075 0.8590657 0.8687430 1.0163926 #> [834,] 0.1657857 0.3075964 0.3930878 0.4054881 0.4513086 0.4665484 0.4736562 #> [835,] 0.6569257 0.7234910 0.8336439 0.8673209 0.9341469 0.9458068 0.9533946 #> [836,] 0.2218200 0.2753051 0.3113118 0.5836566 0.6286956 0.6558379 0.6649380 #> [837,] 0.4074138 0.5250252 0.5611636 0.6104833 0.6594435 0.7098716 0.7220400 #> [838,] 0.3802711 0.5821115 0.6293527 0.6348820 0.6427768 0.6612903 0.7769373 #> [839,] 0.4277456 0.4302680 0.4351358 0.5361542 0.5427427 0.5441774 0.5844532 #> [840,] 0.3067851 0.3080777 0.3261829 0.3710223 0.3945596 0.4150834 0.4236751 #> [841,] 0.4518358 0.5580674 0.6486461 0.6640788 0.7124826 0.7184861 0.7256895 #> [842,] 0.3160586 0.4083693 0.4399549 0.4689018 0.4820274 0.5174262 0.5196555 #> [843,] 1.5311992 1.5536753 1.5614253 1.6258930 1.7115210 1.7434298 1.7461672 #> [844,] 0.2972695 0.5968755 0.6194563 0.6488359 0.6492446 0.6546223 0.7091232 #> [845,] 0.1795017 0.3869610 0.4826465 0.4837839 0.4985066 0.5214377 0.5393927 #> [846,] 0.2536980 0.5370576 0.5865902 0.5910692 0.6302841 0.6917589 0.7002640 #> [847,] 0.3107841 0.4453761 0.4519320 0.5431865 0.6136417 0.6244927 0.6254025 #> [848,] 0.1719823 0.1765733 0.4436905 0.4489713 0.6157921 0.6186984 0.6720264 #> [849,] 0.8311973 0.8747926 0.9717083 1.0013779 1.0122960 1.0163732 1.2066618 #> [850,] 0.2994409 0.4768582 0.4894021 0.6089304 0.6684409 0.7042698 0.8011727 #> [851,] 0.3365770 0.3985065 0.4241446 0.5156347 0.5156644 0.5252485 0.5470744 #> [852,] 1.4296416 1.5983391 1.9137894 2.0242065 2.0456072 2.0512273 2.0535124 #> [853,] 0.4680880 0.6266138 0.6834861 0.7391465 0.7472718 0.8071015 0.8707201 #> [854,] 0.3394250 0.3848525 0.4798373 0.5234596 0.5509819 0.6162140 0.6383378 #> [855,] 0.7566811 0.8401697 0.8518542 0.9411251 0.9457062 0.9563972 1.1295818 #> [856,] 0.2940478 0.3407325 0.3787681 0.4414737 0.4578845 0.5302900 0.5434625 #> [857,] 0.4942273 0.5455117 0.6028087 0.6346238 0.6826033 0.7024932 0.7393022 #> [858,] 0.6158580 1.1232248 1.2848476 1.3117549 1.3278298 1.3405774 1.3982930 #> [859,] 0.4188517 0.4549399 0.4613472 0.5384865 0.6145878 0.6387264 0.6612431 #> [860,] 0.4792305 0.5141196 0.5410854 0.5430704 0.5812571 0.5814689 0.5905954 #> [861,] 0.2289517 0.3032933 0.3035479 0.3261829 0.4566556 0.4652289 0.4926226 #> [862,] 1.3375979 1.5159084 1.7651675 1.7951556 1.8873251 1.9282501 1.9311304 #> [863,] 0.3479301 0.4158467 0.4475573 0.5239454 0.5443972 0.5572317 0.5943596 #> [864,] 0.3445994 0.4473554 0.5081086 0.5733389 0.6099352 0.6214573 0.6883512 #> [865,] 0.2503394 0.4768582 0.6427768 0.7949227 0.8427137 0.8543760 0.8600206 #> [866,] 0.5509826 0.6028087 0.6743603 0.7374324 0.7572010 0.8214049 0.8687430 #> [867,] 0.4870908 0.4919594 0.4989698 0.5581019 0.5980778 0.5998886 0.6074706 #> [868,] 0.4299956 0.6966550 0.7385971 0.7570375 0.8153590 0.8393366 0.8576094 #> [869,] 0.2995764 0.3944994 0.4367022 0.5075357 0.5297576 0.6499291 0.6631117 #> [870,] 0.3293306 0.3850889 0.4398420 0.4766956 0.5261269 0.5305005 0.6059146 #> [871,] 0.4366422 0.4963909 0.5602409 0.5644773 0.5944104 0.6285445 0.6535593 #> [872,] 0.2618401 0.5512129 0.5897778 0.6207546 0.6642021 0.6813480 0.6973555 #> [873,] 0.3980096 0.4059614 0.4300678 0.5654316 0.5859741 0.5926003 0.6126095 #> [874,] 0.4401777 0.5409303 0.6310690 0.6985039 0.7151776 0.7328549 0.7661509 #> [875,] 0.3694694 0.3722417 0.3940243 0.3984968 0.4334864 0.5203917 0.5396707 #> [876,] 0.6602341 0.7779108 0.8882337 0.9693348 1.0649957 1.0676211 1.1084016 #> [877,] 0.5141196 0.5261482 0.5798880 0.6404703 0.6940858 0.7309148 0.7914278 #> [878,] 0.6389136 0.9553313 1.0718025 1.1883763 1.2044469 1.2161923 1.2191719 #> [879,] 0.6111134 0.6330021 0.7264062 0.7466766 0.7661509 0.7662488 0.7706794 #> [880,] 0.3417088 0.4197812 0.4328642 0.4689018 0.5634057 0.6811357 0.6952935 #> [881,] 0.2312386 0.2828653 0.3092599 0.3927840 0.4562478 0.4812868 0.4826099 #> [882,] 0.2474450 0.3570338 0.3927840 0.4410145 0.4429527 0.4597591 0.4845243 #> [883,] 0.1765733 0.2005349 0.4340247 0.4779987 0.5640750 0.5933102 0.5941373 #> [884,] 0.9609905 0.9755831 1.0158348 1.0248596 1.0306228 1.0421030 1.0602373 #> [885,] 0.4803380 0.4876600 0.5015561 0.5353879 0.5473466 0.5765421 0.6334912 #> [886,] 0.2411622 0.3859884 0.4363727 0.4586935 0.5530247 0.5812676 0.6110123 #> [887,] 0.7483794 0.7858414 0.7975968 0.8328092 0.8531193 0.9108383 0.9761211 #> [888,] 0.5192799 0.5370576 0.5517135 0.5649657 0.5899088 0.6580907 0.6966668 #> [889,] 0.1603362 0.2547535 0.3437702 0.5100298 0.5368799 0.5371010 0.5595756 #> [890,] 0.4959007 0.6041729 0.6174626 0.6970957 0.7220400 0.7628530 0.9100871 #> [891,] 0.2337535 0.3299522 0.4470890 0.5008382 0.5391656 0.5486938 0.5852984 #> [892,] 0.2853528 0.3298639 0.3561649 0.3592673 0.4272458 0.4645888 0.4665484 #> [893,] 0.4506818 0.4981237 0.5566957 0.5673679 0.6193540 0.6484340 0.6538127 #> [894,] 0.1882941 0.6041616 0.6263253 0.8026338 0.8081701 0.9258872 0.9768949 #> [895,] 0.2536980 0.4769697 0.5194278 0.5278623 0.5649657 0.5688071 0.5813304 #> [896,] 0.6724590 0.7350833 0.8214040 0.8284443 0.9983396 1.0588523 1.0601857 #> [897,] 0.3082097 0.4318124 0.4660285 0.4704474 0.4798373 0.5007229 0.5073744 #> [898,] 0.5959716 0.6063061 0.6644238 0.6669696 0.6904804 0.7117599 0.7358067 #> [899,] 0.6225775 0.6580907 0.7458233 0.7656519 0.8636642 0.9055562 0.9134872 #> [900,] 0.2343413 0.4215890 0.5151912 0.5955406 0.7681511 0.8515844 0.8669462 #> [901,] 0.3285568 0.4099349 0.4197421 0.4618651 0.5232328 0.5740169 0.6015343 #> [902,] 0.3784988 0.4220740 0.4358854 0.4508229 0.4588823 0.5052489 0.5454542 #> [903,] 2.0556048 2.1108382 2.1577122 2.1925334 2.2083299 2.2801831 2.5363151 #> [904,] 0.4114465 0.6254268 0.8252143 1.0101165 1.0390967 1.1097381 1.1741516 #> [905,] 0.3737895 0.3951226 0.4112151 0.4597591 0.4654187 0.5176881 0.5691951 #> [906,] 0.4542098 0.5278493 0.6860703 0.7970234 0.8091093 0.8225765 0.8265693 #> [907,] 0.4938878 0.6573238 0.6673814 0.7260920 0.7379023 0.7393258 0.7920760 #> [908,] 0.2994951 0.3686147 0.4396160 0.4625997 0.4921990 0.5790150 0.5847977 #> [909,] 0.1502014 0.3688427 0.3859884 0.4635766 0.5136618 0.5138721 0.5232558 #> [910,] 0.2615611 0.4080902 0.4463284 0.5950760 0.6132402 0.6481007 0.6598114 #> [911,] 0.4851220 0.5057987 0.5622607 0.5920986 0.6190446 0.7151776 0.7512546 #> [912,] 0.7962522 1.1027254 1.3672287 1.3807148 1.4204162 1.4331734 1.6037038 #> [913,] 0.5067543 0.5221909 0.5752158 0.6147478 0.6218947 0.6348818 0.6790847 #> [914,] 0.3227764 0.3627995 0.4433784 0.4683933 0.4759184 0.5595899 0.5716828 #> [915,] 0.4388591 0.4663869 0.4992582 0.5415097 0.5598902 0.5853602 0.5982363 #> [916,] 0.4621747 0.5439116 0.5810662 0.6525644 0.7261151 0.7618664 0.7742697 #> [917,] 0.5270508 0.6896589 0.6949101 0.6962610 0.7263227 0.7282629 0.7321677 #> [918,] 0.1880397 0.3118351 0.3850889 0.4812505 0.4999104 0.5074146 0.5335917 #> [919,] 0.1963591 0.4696109 0.4981826 0.5449325 0.5960984 0.6329302 0.6556979 #> [920,] 0.3811083 0.4901664 0.5100151 0.5351490 0.5490831 0.5580313 0.5640750 #> [921,] 0.3875109 0.5019206 0.5601844 0.5697414 0.5737582 0.6291796 0.6404703 #> [922,] 0.2136091 0.3576403 0.3815088 0.4719349 0.4834617 0.5229099 0.5483363 #> [923,] 0.6154598 0.6424847 0.6907413 0.8670046 0.8818672 0.8885824 0.8975248 #> [924,] 0.5671601 0.8629524 1.0250429 1.0556881 1.2399047 1.2926398 1.5970988 #> [925,] 0.6154598 0.7423847 0.8346140 1.0411140 1.0660808 1.0823524 1.1049130 #> [926,] 0.3386233 0.4579561 0.6172867 0.6309674 0.6380812 0.6801436 0.6812344 #> [927,] 0.4360400 0.5995342 0.8487750 0.8763325 0.9250326 0.9493566 0.9853316 #> [928,] 0.6293818 0.7704966 0.8845181 0.9375603 0.9499213 0.9609299 1.0233418 #> [929,] 0.4870615 0.5015118 0.5460978 0.6271310 0.6976519 0.7055782 0.8089936 #> [930,] 0.5042632 0.6896709 0.7113964 0.7290197 0.7380223 0.7713337 0.7809407 #> [931,] 0.3329671 0.5655398 0.7402701 0.8392612 0.8733825 0.8951593 0.9067863 #> [932,] 0.8008904 0.8786278 0.8980172 0.9943680 1.0081010 1.0912510 1.3251528 #> [933,] 0.6306143 0.6469669 0.7280660 0.7928859 0.8346431 0.8725721 0.8731730 #> [934,] 0.2743603 0.3937665 0.4599237 0.5155835 0.5191472 0.5414595 0.5740675 #> [935,] 0.1921174 0.4510282 0.4783833 0.5052489 0.5644773 0.6008386 0.6455745 #> [936,] 0.5700973 0.6075683 0.6546223 0.6574239 0.6743603 0.6826033 0.6853944 #> [937,] 0.3119060 0.3528047 0.3627995 0.3915093 0.4358854 0.4609358 0.4802595 #> [938,] 0.4185317 0.4220740 0.4422055 0.4509399 0.4915624 0.5072311 0.5301315 #> [939,] 0.6726428 0.7354445 0.7427972 0.7515865 0.7576142 0.7620362 0.7784925 #> [940,] 0.4172339 0.6413421 0.6954323 0.7064193 0.7385654 0.8145048 0.8370775 #> [941,] 0.2743603 0.5324977 0.5416235 0.5705223 0.5984565 0.6170349 0.6286326 #> [942,] 0.3027772 0.4896400 0.4992742 0.5449325 0.5624760 0.6325971 0.6948436 #> [943,] 0.4111658 0.5943301 0.6385163 0.6979967 0.8446133 0.8896609 0.9001202 #> [944,] 0.4556055 0.4896084 0.5217608 0.5254022 0.5556390 0.5913540 0.6264371 #> [945,] 0.4227960 0.4896124 0.5120180 0.5913042 0.6111190 0.6146042 0.6258101 #> [946,] 0.6296239 0.6892862 0.8390236 0.8875493 0.9461592 1.0529038 1.1175567 #> [947,] 0.3458876 0.4801832 0.5002871 0.5415791 0.6182001 0.6493688 0.6571560 #> [948,] 0.5578534 0.7378963 0.8169656 0.8296489 0.8388325 0.8670486 0.8909451 #> [949,] 0.3590217 0.5057987 0.5953573 0.6310690 0.7259945 0.7391465 0.8038318 #> [950,] 0.5544410 0.7224488 0.8083694 0.8105955 0.8534005 0.8792767 0.8823584 #> [951,] 0.6389136 0.7099335 0.7347236 0.8351929 0.8554912 0.8708675 0.8733825 #> [952,] 0.4955922 0.6729536 0.7140313 0.7482271 0.7661296 0.8388325 0.8487750 #> [953,] 0.4158467 0.4642895 0.4868032 0.5434911 0.5755035 0.5863843 0.5917659 #> [954,] 0.1457126 0.5038661 0.5415791 0.5644321 0.6778771 0.7278628 0.8170445 #> [955,] 0.3342053 0.3352511 0.4344955 0.4351358 0.4720053 0.5108792 0.5189029 #> [956,] 0.9457062 0.9849265 1.0223147 1.0483017 1.0934914 1.0951222 1.1603080 #> [957,] 0.3633023 0.4441841 0.4480393 0.5013117 0.5053819 0.5627093 0.6093778 #> [958,] 0.6569257 0.8302082 0.8922407 0.8980629 0.9490880 0.9769237 1.0046860 #> [959,] 0.3097309 0.7675038 0.9328969 0.9411251 1.0235728 1.0326252 1.0389430 #> [960,] 0.4588987 0.4657427 0.6035151 0.6461901 0.6718601 0.6904380 0.7021603 #> [961,] 0.7051774 0.7756786 0.9457200 1.0963949 1.1187557 1.1253954 1.1275522 #> [962,] 0.2479778 0.2499468 0.4179349 0.4187607 0.4781016 0.5302001 0.5350829 #> [963,] 0.5012870 0.5258843 0.5312717 0.5410854 0.5644517 0.5668125 0.5941373 #> [964,] 0.7557878 0.8613864 0.9285815 0.9622023 1.0018294 1.0047035 1.0277976 #> [965,] 0.3341905 0.3604918 0.3978667 0.4150834 0.4213421 0.4661846 0.4741237 #> [966,] 0.3085853 0.4051373 0.4468204 0.4954094 0.5105782 0.5119253 0.5684175 #> [967,] 0.3886122 0.5793263 0.5798880 0.7502501 0.7891617 0.8239215 0.9211892 #> [968,] 0.1628608 0.7996086 0.9813975 1.0457239 1.0600730 1.0738716 1.1739681 #> [969,] 0.8815454 0.9994666 1.0613208 1.1361912 1.1385667 1.1426683 1.2533244 #> [970,] 0.2316066 0.4347131 0.5258843 0.5430704 0.5797168 0.6455426 0.6567670 #> [971,] 0.6492446 0.6574239 0.6747291 0.8214049 0.8362544 0.8401697 0.8707394 #> [972,] 0.1412353 0.3272235 0.3900844 0.4023933 0.4148677 0.4451457 0.4488936 #> [973,] 0.8747926 1.0726503 1.1068952 1.1648686 1.2204814 1.2386833 1.2633442 #> [974,] 0.8193688 0.8456541 0.8527007 0.8686242 0.9171797 0.9520110 0.9659309 #> [975,] 0.2780944 0.3015907 0.3376254 0.3811083 0.3974026 0.4028254 0.4308291 #> [976,] 0.3893338 0.4505269 0.4605785 0.6256895 0.6536408 0.7331622 0.7586668 #> [977,] 0.6911686 0.7268477 0.8231290 0.8424534 0.9000961 0.9033437 0.9233767 #> [978,] 0.3496784 0.4432166 0.5334193 0.5989136 0.7096008 0.7235467 0.7262585 #> [979,] 0.2847934 0.3882206 0.4453761 0.4810555 0.5301634 0.5475485 0.5485547 #> [980,] 0.3859954 0.4699200 0.4811191 0.4980564 0.5245181 0.5667576 0.5963322 #> [981,] 0.7278976 0.7327970 0.7627281 0.7788747 0.7951149 0.8195750 0.8378108 #> [982,] 0.3884973 0.5418577 0.5821115 0.7216168 0.7290825 0.7671997 0.7678536 #> [983,] 0.3086478 0.4160685 0.4996085 0.5072499 0.5879309 0.7115002 0.7117846 #> [984,] 0.6303962 0.6589045 0.8001376 0.8145048 0.8338672 0.9112486 0.9150192 #> [985,] 0.5460581 0.6507848 0.8080487 0.8499740 0.8614721 0.8856987 0.8991177 #> [986,] 0.3589144 0.5418090 0.5852687 0.5985745 0.7017688 0.7516601 0.7699749 #> [987,] 0.1657857 0.2696111 0.3561649 0.3862536 0.4120583 0.4539152 0.4941148 #> [988,] 0.5119789 0.5209139 0.5622382 0.5642782 0.5787497 0.5996913 0.6040388 #> [989,] 0.1983044 0.5926421 0.6151594 0.6539348 0.6827474 0.8271119 0.8582468 #> [990,] 0.3474395 0.5745431 0.6616265 0.7282968 0.8629524 1.0278892 1.1192310 #> [991,] 0.4299956 0.4577316 0.5259341 0.5962516 0.6553439 0.7172299 0.7221793 #> [992,] 0.6325971 0.6704545 0.6972553 0.7056987 0.7278976 0.7571621 0.7676339 #> [993,] 0.4489389 0.4528535 0.5843780 0.6116490 0.6194128 0.7036732 0.8328092 #> [994,] 1.2167245 1.4022221 1.4315399 1.4493745 1.4979496 1.5439557 1.6053293 #> [995,] 0.2972695 0.5141040 0.6075683 0.6081224 0.6466797 0.6477152 0.6839222 #> [996,] 0.3875109 0.4253779 0.5457228 0.5545367 0.5935204 0.6400436 0.6516956 #> [997,] 0.2770382 0.5320051 0.7591667 1.0625637 1.1118740 1.1502454 1.1741015 #> [998,] 0.3747992 0.3757904 0.5399607 0.5601844 0.6182612 0.6194519 0.6348818 #> [999,] 0.6915614 0.7037830 0.7320299 0.8083417 0.8444847 0.8632591 0.9026419 #> [1000,] 0.6776881 0.9553533 1.0390967 1.1113306 1.1381286 1.1394759 1.2094194 #> [,8] [,9] [,10] #> [1,] 1.0948143 1.1253654 1.1294703 #> [2,] 0.5525923 0.5752191 0.5948832 #> [3,] 1.0433555 1.0634161 1.0639414 #> [4,] 0.6020509 0.6321894 0.6900302 #> [5,] 0.5374756 0.5838935 0.6076137 #> [6,] 1.9607194 1.9707421 1.9894195 #> [7,] 0.9771859 1.0105006 1.0358824 #> [8,] 0.7905344 0.7948463 0.8065179 #> [9,] 0.7325301 0.7529760 0.7644297 #> [10,] 1.8648328 1.8954785 1.9155196 #> [11,] 0.7809546 0.8205875 0.8244164 #> [12,] 0.5654316 0.5838902 0.5863865 #> [13,] 1.9906122 2.0133325 2.0347324 #> [14,] 0.5282038 0.5294087 0.5580313 #> [15,] 0.6695476 0.6720961 0.7860751 #> [16,] 0.6848587 0.6942770 0.6993759 #> [17,] 1.2454930 1.2909212 1.3323128 #> [18,] 0.7167810 0.7366055 0.7377562 #> [19,] 0.6401902 0.6607465 0.6644822 #> [20,] 0.5855031 0.5910505 0.6080686 #> [21,] 0.5373405 0.5618657 0.5968875 #> [22,] 1.0337314 1.1039228 1.1077311 #> [23,] 0.4721043 0.4789884 0.4811924 #> [24,] 0.7415321 0.7484844 0.7656143 #> [25,] 0.8201046 0.8592889 0.8610294 #> [26,] 0.8449646 0.8456569 0.8807989 #> [27,] 1.1437594 1.1803415 1.2018161 #> [28,] 0.7660779 0.7776805 0.8061198 #> [29,] 0.9298050 0.9307238 0.9379767 #> [30,] 0.7981114 0.8339729 0.8374160 #> [31,] 0.9943953 1.0235233 1.0283907 #> [32,] 0.7459984 0.7858414 0.7963569 #> [33,] 0.8277493 0.8512057 0.8969156 #> [34,] 0.9271726 0.9673539 0.9850058 #> [35,] 0.6410389 0.6600794 0.6959216 #> [36,] 1.6054625 1.6865838 1.6911640 #> [37,] 0.5849180 0.6452064 0.6531977 #> [38,] 0.5227291 0.5905755 0.6106738 #> [39,] 1.5553362 1.6244928 1.6247010 #> [40,] 0.5028352 0.5363079 0.5814556 #> [41,] 0.9051968 0.9132779 0.9296012 #> [42,] 0.6478459 0.6729798 0.6803337 #> [43,] 0.4674898 0.4766956 0.4847473 #> [44,] 0.7856933 0.8096945 0.8112277 #> [45,] 0.8419828 0.8642496 0.9374454 #> [46,] 0.7466119 0.7722180 0.7869417 #> [47,] 1.6690312 1.6896383 1.6958277 #> [48,] 0.4829012 0.5538597 0.5852644 #> [49,] 0.5462467 0.5481299 0.5585343 #> [50,] 0.8244164 0.8805266 0.9358046 #> [51,] 0.6613715 0.6811357 0.7124117 #> [52,] 0.7191833 0.7194659 0.7649574 #> [53,] 1.2564305 1.3031879 1.3709338 #> [54,] 0.7095881 0.7101002 0.7457393 #> [55,] 0.8632450 0.9452734 0.9582362 #> [56,] 1.2330285 1.2860053 1.2878207 #> [57,] 0.6133775 0.6570875 0.6647266 #> [58,] 0.7066226 0.7161633 0.7200286 #> [59,] 1.1455786 1.1821034 1.2087668 #> [60,] 0.7898192 0.7939243 0.8015919 #> [61,] 0.8167129 0.8387293 0.8398155 #> [62,] 0.6275538 0.6390093 0.6403610 #> [63,] 0.6823798 0.6855014 0.6864298 #> [64,] 0.6007936 0.6157251 0.6158978 #> [65,] 1.0931949 1.1069983 1.1255987 #> [66,] 1.1747662 1.1762794 1.1793043 #> [67,] 0.7198973 0.8448278 0.8528679 #> [68,] 0.7744335 0.8008949 0.8113809 #> [69,] 0.8337477 0.8546016 0.8666153 #> [70,] 0.7035311 0.7185677 0.7657909 #> [71,] 0.6959320 0.7141490 0.7330386 #> [72,] 0.8266657 0.8555373 0.8824980 #> [73,] 1.0689941 1.0765964 1.0970276 #> [74,] 0.6422858 0.6753351 0.6794117 #> [75,] 0.9881594 1.0049256 1.0092698 #> [76,] 0.7840795 0.8029412 0.8446133 #> [77,] 0.8339729 0.8852561 0.8885824 #> [78,] 0.6776343 0.6783473 0.6927912 #> [79,] 0.5990214 0.6148437 0.6178335 #> [80,] 0.6261561 0.6705603 0.6917049 #> [81,] 0.8183042 0.8248413 0.8452508 #> [82,] 1.5711966 1.5921858 1.6799638 #> [83,] 0.5473466 0.5577556 0.5585343 #> [84,] 1.4575733 1.4748398 1.5450134 #> [85,] 0.9101200 0.9215540 0.9400282 #> [86,] 0.6895740 0.6964980 0.8398155 #> [87,] 0.5315435 0.5926003 0.5940546 #> [88,] 0.9466164 0.9505368 0.9819191 #> [89,] 0.7085806 0.7539298 0.7576612 #> [90,] 0.4639866 0.4886340 0.4978754 #> [91,] 1.0661671 1.0703207 1.0850179 #> [92,] 1.0350864 1.1246410 1.1410353 #> [93,] 0.6273846 0.6396846 0.6729936 #> [94,] 0.6621461 0.6730841 0.6755372 #> [95,] 0.5589076 0.5718031 0.5726976 #> [96,] 1.0967401 1.1063678 1.1619855 #> [97,] 0.6227062 0.6631667 0.7028310 #> [98,] 1.4545619 1.4804709 1.4940935 #> [99,] 1.2369132 1.2409701 1.2629280 #> [100,] 0.6414267 0.6420026 0.6812624 #> [101,] 0.6256541 0.6270762 0.6340315 #> [102,] 0.8030102 0.8399439 0.8470966 #> [103,] 1.3906688 1.4321209 1.4531103 #> [104,] 1.4258069 1.4359298 1.4368440 #> [105,] 0.9993196 1.0286461 1.0327843 #> [106,] 0.5507339 0.5758143 0.6218453 #> [107,] 0.9608068 0.9634866 0.9682306 #> [108,] 1.3341929 1.3603603 1.4866068 #> [109,] 1.3107763 1.3393935 1.3667934 #> [110,] 0.8175726 0.8257304 0.8447845 #> [111,] 0.4845243 0.5029748 0.5270942 #> [112,] 0.4837839 0.5098671 0.5127951 #> [113,] 0.5567038 0.5651490 0.5693620 #> [114,] 0.8781318 0.9038817 0.9218376 #> [115,] 0.7588572 0.7636821 0.7788853 #> [116,] 0.8845181 0.9186031 0.9341500 #> [117,] 0.6133688 0.6480952 0.6680145 #> [118,] 0.8748226 0.8857262 0.8882337 #> [119,] 0.9168692 0.9190963 0.9461592 #> [120,] 0.5529531 0.5568170 0.5956290 #> [121,] 0.5807087 0.5853281 0.6225125 #> [122,] 0.6371422 0.6550249 0.6594435 #> [123,] 0.7378963 0.8014425 0.8158279 #> [124,] 0.9562657 0.9765916 0.9771331 #> [125,] 0.7866932 0.7870167 0.8082479 #> [126,] 0.8753202 0.9776262 0.9876411 #> [127,] 0.5155695 0.5517497 0.5578328 #> [128,] 1.1372116 1.2638671 1.2847125 #> [129,] 0.8325244 0.8735306 0.8902842 #> [130,] 0.6917049 0.6949101 0.7003802 #> [131,] 1.2206230 1.2382200 1.2576681 #> [132,] 0.6918563 0.6972482 0.6981607 #> [133,] 0.7293444 0.7451747 0.7648917 #> [134,] 1.0767381 1.1098807 1.1120624 #> [135,] 0.8793499 0.9069460 0.9134570 #> [136,] 0.6395725 0.6667419 0.6688439 #> [137,] 0.7184914 0.7327227 0.7652532 #> [138,] 0.7553037 0.8062697 0.8073952 #> [139,] 0.7791502 0.8055212 0.8210332 #> [140,] 0.7756952 0.7866932 0.8007903 #> [141,] 0.7677695 0.7754018 0.7818217 #> [142,] 0.5684539 0.5996913 0.6007714 #> [143,] 1.3375979 1.4037835 1.4188600 #> [144,] 0.7343907 0.7648775 0.7744335 #> [145,] 1.0005149 1.0839008 1.1359603 #> [146,] 1.9247666 2.0528754 2.1450529 #> [147,] 1.5396802 1.5606996 1.5799396 #> [148,] 0.6387329 0.6418468 0.7399409 #> [149,] 1.4024336 1.4556944 1.4722021 #> [150,] 0.6279512 0.6348908 0.6559528 #> [151,] 1.0326327 1.0389256 1.0914895 #> [152,] 0.9378915 0.9794508 0.9807302 #> [153,] 1.0243505 1.0493163 1.0726503 #> [154,] 1.9435902 2.0558332 2.0685179 #> [155,] 0.6269097 0.6477152 0.6494768 #> [156,] 0.8093809 0.8215576 0.8361150 #> [157,] 0.5778372 0.5838077 0.5843167 #> [158,] 0.6856160 0.6878116 0.6889679 #> [159,] 1.3000231 1.3299072 1.4954100 #> [160,] 0.9060245 0.9248323 0.9391637 #> [161,] 0.7344118 0.7827488 0.8158820 #> [162,] 0.5290869 0.5333304 0.5641456 #> [163,] 0.7405819 0.7453283 0.7493836 #> [164,] 1.0792046 1.1108133 1.1561070 #> [165,] 0.5784775 0.5840255 0.5874667 #> [166,] 0.8497432 0.8526097 0.8793478 #> [167,] 0.5301634 0.5773118 0.5815696 #> [168,] 0.9205566 1.0556881 1.0748362 #> [169,] 0.6847138 0.6932394 0.7051132 #> [170,] 0.6720961 0.6875287 0.7077823 #> [171,] 0.6625082 0.6636391 0.7048870 #> [172,] 0.9430009 0.9596114 1.0043056 #> [173,] 0.8368947 0.8442932 0.9303547 #> [174,] 0.8347588 0.8516636 0.8723330 #> [175,] 0.6116490 0.6363949 0.6639107 #> [176,] 0.7434605 0.7671118 0.8219937 #> [177,] 0.6475037 0.6693719 0.6704771 #> [178,] 1.3141811 1.3641529 1.3654928 #> [179,] 0.7288562 0.7513864 0.8175451 #> [180,] 1.0607921 1.0778866 1.0892902 #> [181,] 0.5993173 0.6505006 0.6528188 #> [182,] 0.7856517 0.8236180 0.8815996 #> [183,] 1.2932255 1.2943257 1.3099257 #> [184,] 0.5943086 0.6065965 0.6508316 #> [185,] 0.8558937 1.0087461 1.0173097 #> [186,] 0.7332994 0.7524957 0.7620211 #> [187,] 0.5388128 0.5724169 0.5871660 #> [188,] 0.4453320 0.4781089 0.4906804 #> [189,] 0.6825961 0.7315208 0.7662661 #> [190,] 0.6261561 0.6322562 0.6387264 #> [191,] 0.5431800 0.5493708 0.5842524 #> [192,] 0.9366650 1.0115112 1.0139018 #> [193,] 0.7791763 0.7877686 0.8267271 #> [194,] 0.4999803 0.5224957 0.5284957 #> [195,] 0.7287542 0.7338708 0.7382701 #> [196,] 0.6793757 0.7002640 0.7527591 #> [197,] 0.8001376 0.8348291 0.8683633 #> [198,] 0.8281828 0.8604421 0.8701024 #> [199,] 0.7081871 0.7107376 0.7260920 #> [200,] 0.8064651 0.8132218 0.8245745 #> [201,] 1.1696744 1.1757446 1.1955097 #> [202,] 0.5215113 0.5506487 0.5559009 #> [203,] 0.6368902 0.6431490 0.6779259 #> [204,] 0.7081038 0.7217274 0.7359611 #> [205,] 0.7783967 0.7897575 0.8099471 #> [206,] 2.2261040 2.2775297 2.3224374 #> [207,] 0.9126229 0.9135974 0.9346452 #> [208,] 0.7500319 0.7543159 0.8307144 #> [209,] 0.5990616 0.6094973 0.6518840 #> [210,] 0.7939766 0.7948463 0.8174748 #> [211,] 0.6363949 0.6399279 0.6597046 #> [212,] 0.7984211 0.8140872 0.8308921 #> [213,] 0.6526012 0.6779259 0.7076093 #> [214,] 0.6954323 0.7328516 0.8023101 #> [215,] 0.7891139 0.8550059 0.8793124 #> [216,] 0.6767596 0.6819420 0.7056023 #> [217,] 0.5446488 0.5520749 0.5598840 #> [218,] 0.6493102 0.6569956 0.7001344 #> [219,] 0.6517485 0.6598114 0.6637741 #> [220,] 1.3616232 1.3623973 1.3651631 #> [221,] 0.9490913 0.9776484 1.0237415 #> [222,] 1.0844905 1.0989884 1.1113306 #> [223,] 0.6221887 0.6253999 0.6451453 #> [224,] 0.8194787 0.8278785 0.8498218 #> [225,] 0.5101514 0.5381790 0.5455016 #> [226,] 0.7723712 0.7773210 0.7809546 #> [227,] 0.6798027 0.6873260 0.6927150 #> [228,] 0.7488809 0.7905154 0.8101955 #> [229,] 0.6734346 0.6924004 0.7396594 #> [230,] 0.6067046 0.6370304 0.6404394 #> [231,] 0.6918033 0.7091232 0.7606984 #> [232,] 1.2896599 1.3014405 1.3156883 #> [233,] 0.5900934 0.6004548 0.6081852 #> [234,] 0.6822182 0.6979967 0.7666046 #> [235,] 0.6520138 0.6824711 0.6960982 #> [236,] 0.7053574 0.7241683 0.7403172 #> [237,] 0.5986689 0.5990214 0.6377405 #> [238,] 0.7920279 0.8213955 0.8286330 #> [239,] 1.2819247 1.2855510 1.3117287 #> [240,] 0.7161184 0.7414333 0.7443708 #> [241,] 1.0238656 1.0239953 1.0652112 #> [242,] 0.7274085 0.8577339 0.9286956 #> [243,] 0.8815454 0.8823584 0.9279361 #> [244,] 1.0295641 1.0425783 1.0451755 #> [245,] 0.5792627 0.5956290 0.5963923 #> [246,] 1.0161725 1.0426072 1.0429812 #> [247,] 0.5784076 0.5812280 0.6161878 #> [248,] 0.7515865 0.8088560 0.8094516 #> [249,] 1.1293264 1.1461501 1.1804159 #> [250,] 1.1818696 1.1882331 1.1978149 #> [251,] 0.7825243 0.7996498 0.8301877 #> [252,] 1.0085870 1.0653633 1.0755396 #> [253,] 0.5808555 0.5864900 0.6044754 #> [254,] 1.7971424 1.8032660 1.8215755 #> [255,] 0.6878055 0.7056987 0.7316238 #> [256,] 0.9891129 1.0039163 1.0513837 #> [257,] 0.7224331 0.7338708 0.7404499 #> [258,] 0.6437494 0.6840457 0.6865839 #> [259,] 0.9520110 1.0119328 1.0333963 #> [260,] 0.8879565 0.8959714 0.9304029 #> [261,] 0.5361542 0.5636682 0.5842524 #> [262,] 0.8899253 0.8908305 0.9057293 #> [263,] 0.6037244 0.6114538 0.6404394 #> [264,] 1.0037048 1.0046564 1.0069419 #> [265,] 0.6911578 0.7127932 0.7773210 #> [266,] 1.4057024 1.4076882 1.4225578 #> [267,] 1.1279720 1.1550765 1.1581750 #> [268,] 0.5414595 0.5564536 0.6357178 #> [269,] 0.6076115 0.6193471 0.6276841 #> [270,] 0.6143492 0.6556173 0.6836048 #> [271,] 2.4496041 2.5344793 2.5391836 #> [272,] 0.6044852 0.6194560 0.6485100 #> [273,] 1.3404538 1.4055332 1.4131752 #> [274,] 0.5337690 0.5394863 0.5442631 #> [275,] 0.6493688 0.6504903 0.6757886 #> [276,] 0.9664330 1.0029752 1.0502675 #> [277,] 1.2916295 1.3270275 1.3620397 #> [278,] 0.7264431 0.7280660 0.7652532 #> [279,] 0.5512399 0.5529531 0.5653469 #> [280,] 1.2674576 1.2947856 1.3007113 #> [281,] 0.6846347 0.6856328 0.7447192 #> [282,] 0.8194439 0.8902137 0.9103053 #> [283,] 0.7289972 0.7363352 0.7464540 #> [284,] 0.5464866 0.5698526 0.5700085 #> [285,] 0.7070283 0.7107505 0.7586351 #> [286,] 0.9786823 0.9902617 1.0043523 #> [287,] 0.8835670 0.9166792 0.9298050 #> [288,] 0.8211376 0.8313677 0.8479914 #> [289,] 0.5036908 0.5224957 0.5538597 #> [290,] 0.5881955 0.5898104 0.6164692 #> [291,] 0.5928864 0.6194560 0.6233224 #> [292,] 0.6303293 0.6526242 0.6651987 #> [293,] 0.6756108 0.6893942 0.7012269 #> [294,] 0.6567613 0.6709421 0.6779583 #> [295,] 1.1418658 1.3219935 1.3361117 #> [296,] 0.6455426 0.6471754 0.6570015 #> [297,] 1.6822661 1.7729263 1.7731140 #> [298,] 0.5762717 0.5778650 0.5849180 #> [299,] 0.6902308 0.6924779 0.6946994 #> [300,] 0.9062407 0.9221555 1.0124701 #> [301,] 0.5691434 0.6206144 0.6520764 #> [302,] 0.4645308 0.5022562 0.5374942 #> [303,] 0.6195858 0.6303293 0.6317110 #> [304,] 0.6666715 0.6754799 0.6776307 #> [305,] 0.9657700 1.0268108 1.0743191 #> [306,] 0.6860968 0.7290304 0.7363749 #> [307,] 0.9330724 1.0323207 1.0533723 #> [308,] 0.6227840 0.6391521 0.6412936 #> [309,] 1.0081923 1.0499037 1.1157758 #> [310,] 0.8485907 0.8904358 1.0119328 #> [311,] 1.4585826 1.5491043 1.5699872 #> [312,] 1.0084942 1.0480537 1.1049122 #> [313,] 0.8173120 0.8294177 0.8338392 #> [314,] 0.5715760 0.5716476 0.5847812 #> [315,] 0.7681936 0.7818949 0.7836927 #> [316,] 0.8939884 0.9396375 0.9799018 #> [317,] 0.6936951 0.7488810 0.7905965 #> [318,] 0.5284969 0.5448599 0.5563110 #> [319,] 0.7708202 0.7891957 0.7910154 #> [320,] 0.6448727 0.6503403 0.6822182 #> [321,] 1.0162726 1.0430789 1.0781098 #> [322,] 0.5721931 0.5832461 0.6130888 #> [323,] 0.6755372 0.6960078 0.7344727 #> [324,] 0.9294930 0.9347328 0.9469777 #> [325,] 0.5809545 0.6368902 0.6649969 #> [326,] 0.5545367 0.5669852 0.5982363 #> [327,] 0.8570095 0.8741171 0.9018141 #> [328,] 0.5784438 0.6741597 0.6947247 #> [329,] 2.1946260 2.1992391 2.2446966 #> [330,] 0.6299160 0.6330021 0.6784835 #> [331,] 1.6457078 1.6651054 1.7061774 #> [332,] 0.8167660 0.8280890 0.8350104 #> [333,] 0.7697932 0.7825019 0.8039611 #> [334,] 0.6594180 0.6819420 0.7174661 #> [335,] 0.6489326 0.6538819 0.6575023 #> [336,] 1.0346965 1.0823524 1.1103062 #> [337,] 1.1231594 1.1492845 1.1676271 #> [338,] 0.8149124 0.8261038 0.8263901 #> [339,] 0.5833905 0.5875335 0.5910737 #> [340,] 0.7484844 0.7489488 0.8072147 #> [341,] 0.8174948 0.8685083 0.8690115 #> [342,] 0.6255115 0.6255987 0.6267861 #> [343,] 0.7351369 0.7598897 0.7924194 #> [344,] 0.6258388 0.6274167 0.6378654 #> [345,] 0.7616623 0.7817998 0.7882697 #> [346,] 0.9191435 0.9289766 0.9628716 #> [347,] 0.6106381 0.6141360 0.6265655 #> [348,] 0.5110051 0.5153055 0.5965174 #> [349,] 0.6150592 0.6544731 0.6558452 #> [350,] 0.9379206 0.9586946 0.9891129 #> [351,] 0.6922921 0.7232509 0.7310245 #> [352,] 0.8284646 0.8411821 0.8562097 #> [353,] 0.6232940 0.6251972 0.6336534 #> [354,] 0.5802153 0.5900908 0.5905871 #> [355,] 1.6016927 1.6183683 1.6376652 #> [356,] 0.8466935 0.8662803 0.8833108 #> [357,] 0.7460089 0.7497731 0.7529493 #> [358,] 0.8835670 0.9017609 0.9370817 #> [359,] 0.6362140 0.6389466 0.6555353 #> [360,] 0.7257879 0.7571024 0.7628530 #> [361,] 0.6661770 0.6735804 0.6826631 #> [362,] 0.7604367 0.7631046 0.7719649 #> [363,] 1.0205662 1.0823203 1.1001316 #> [364,] 0.9601521 0.9658617 0.9756815 #> [365,] 0.7093827 0.7349957 0.7540449 #> [366,] 0.9733015 0.9780837 0.9795397 #> [367,] 0.8142208 0.8174948 0.8393366 #> [368,] 1.1288409 1.1303360 1.1309967 #> [369,] 0.9086849 0.9438448 1.0109677 #> [370,] 0.5769253 0.5848354 0.5854538 #> [371,] 0.9085786 1.0613554 1.0737194 #> [372,] 0.8003550 0.8014943 0.8155339 #> [373,] 0.6520764 0.6809989 0.6925582 #> [374,] 1.0800418 1.0875153 1.1016418 #> [375,] 0.5740169 0.5790913 0.6239847 #> [376,] 1.4694834 1.4839979 1.5690349 #> [377,] 0.5039508 0.5773118 0.6093329 #> [378,] 0.6447953 0.7064193 0.7456880 #> [379,] 0.7255185 0.7739046 0.7975968 #> [380,] 0.7766619 0.7914277 0.7957228 #> [381,] 0.6988580 0.7077364 0.7449540 #> [382,] 0.9485530 0.9536875 0.9786161 #> [383,] 0.6256541 0.6275538 0.6362574 #> [384,] 0.6720682 0.7787870 0.7957228 #> [385,] 1.2687172 1.2802805 1.3047140 #> [386,] 0.5507339 0.6064328 0.6108049 #> [387,] 0.8122301 0.8179785 0.8191682 #> [388,] 0.7100582 0.7123172 0.7344118 #> [389,] 0.8584639 0.8613229 0.8715004 #> [390,] 0.7809224 0.7943283 0.8036352 #> [391,] 0.8789989 0.8846523 0.9430009 #> [392,] 0.5787497 0.6434306 0.6461901 #> [393,] 0.5508534 0.5647906 0.5816181 #> [394,] 1.0615136 1.0758883 1.1293451 #> [395,] 0.6867323 0.7006231 0.7072118 #> [396,] 1.3040997 1.3157568 1.3392170 #> [397,] 0.9589776 0.9816568 1.0123912 #> [398,] 0.8932279 0.9187887 0.9317281 #> [399,] 0.5505802 0.6370801 0.6504363 #> [400,] 0.9190036 0.9236847 0.9552591 #> [401,] 0.8119278 0.8150843 0.8183023 #> [402,] 0.7615951 0.9273662 0.9407729 #> [403,] 0.5254921 0.5363079 0.5819930 #> [404,] 0.5177915 0.5189196 0.5980605 #> [405,] 1.4713607 1.4878362 1.5052571 #> [406,] 0.5693620 0.5790913 0.6271961 #> [407,] 0.6229257 0.6454792 0.6617013 #> [408,] 0.6365288 0.6380717 0.6629856 #> [409,] 0.7639416 0.7647506 0.7828068 #> [410,] 0.8029412 0.8332180 0.8366698 #> [411,] 1.0092043 1.1255428 1.1476501 #> [412,] 0.7288516 0.7313914 0.7520435 #> [413,] 0.5735514 0.5819185 0.5864900 #> [414,] 1.1381939 1.2571642 1.2603551 #> [415,] 0.8042587 0.8070106 0.8208756 #> [416,] 0.7189922 0.7228040 0.7890711 #> [417,] 0.8023364 0.8728391 0.8812638 #> [418,] 0.6259600 0.6381189 0.6573023 #> [419,] 0.8915097 0.9321882 0.9529310 #> [420,] 0.6882820 0.7178746 0.7298191 #> [421,] 0.8980328 0.9007722 0.9341469 #> [422,] 0.7858526 0.8211451 0.8417973 #> [423,] 0.6611121 0.6853944 0.7027129 #> [424,] 0.9034129 0.9303547 0.9338346 #> [425,] 0.6103830 0.6146042 0.6152917 #> [426,] 1.2741930 1.3048889 1.3166447 #> [427,] 0.9660454 0.9786161 1.0160631 #> [428,] 0.9027740 0.9208716 0.9272575 #> [429,] 0.7189606 0.7354697 0.8051510 #> [430,] 0.6149044 0.6544568 0.6568448 #> [431,] 1.6014659 1.6124795 1.6464965 #> [432,] 0.8645507 0.8864328 0.8991177 #> [433,] 0.7941475 0.7968045 0.8960429 #> [434,] 0.5584686 0.6195858 0.6347572 #> [435,] 0.7867618 0.7902651 0.8077762 #> [436,] 0.7012632 0.7241992 0.7251975 #> [437,] 0.6840500 0.6843970 0.6867323 #> [438,] 0.9685332 0.9759108 0.9967674 #> [439,] 0.7871674 0.8543760 0.9412158 #> [440,] 0.8392612 0.8716437 0.8721196 #> [441,] 0.8069808 0.8518542 0.8620734 #> [442,] 0.8871901 0.9332700 1.0400032 #> [443,] 0.7402080 0.7404499 0.7449671 #> [444,] 0.9053295 0.9441010 0.9513810 #> [445,] 0.5589335 0.5616279 0.6127602 #> [446,] 0.6576721 0.6804033 0.7033662 #> [447,] 0.9927727 0.9967219 1.0073378 #> [448,] 0.7042698 0.7459097 0.7554355 #> [449,] 0.5727056 0.5983884 0.7005419 #> [450,] 0.9723325 0.9768080 1.0335018 #> [451,] 1.1559923 1.2082916 1.3320827 #> [452,] 0.5845112 0.6074706 0.6114413 #> [453,] 0.7398085 0.7490561 0.7493836 #> [454,] 0.5464723 0.5666532 0.6183374 #> [455,] 1.1370093 1.2190428 1.2428198 #> [456,] 0.6722716 0.6847392 0.6861309 #> [457,] 0.8434437 0.8982119 0.9272575 #> [458,] 0.4985066 0.5338072 0.5382786 #> [459,] 0.6853056 0.6895883 0.7055080 #> [460,] 1.0526528 1.0920031 1.1344009 #> [461,] 0.5698526 0.5888508 0.6044754 #> [462,] 0.5562461 0.5621462 0.5847977 #> [463,] 1.7375101 1.8041639 1.8503639 #> [464,] 1.2224570 1.2429012 1.2841455 #> [465,] 0.8482913 0.8697166 0.8788324 #> [466,] 0.6964980 0.7070283 0.7537934 #> [467,] 0.6418468 0.6671150 0.6917589 #> [468,] 0.5065117 0.5391964 0.5705462 #> [469,] 0.7010090 0.7214546 0.7667716 #> [470,] 0.7275787 0.8099892 0.8246746 #> [471,] 0.6374177 0.6419992 0.6515068 #> [472,] 0.9687647 0.9824862 1.0229776 #> [473,] 1.1807701 1.2286957 1.2921542 #> [474,] 0.6557559 0.6844510 0.7066562 #> [475,] 0.7262108 0.7448291 0.7559599 #> [476,] 0.5164314 0.5171654 0.5205927 #> [477,] 0.6980515 0.7317562 0.7372731 #> [478,] 1.0509450 1.0538652 1.0635850 #> [479,] 1.1200334 1.1386040 1.1417755 #> [480,] 0.9911805 1.0084563 1.0218342 #> [481,] 0.7985012 0.8332130 0.8358105 #> [482,] 1.4960626 1.5213869 1.5779222 #> [483,] 1.1499770 1.1650330 1.1797567 #> [484,] 0.5254051 0.5810606 0.5872490 #> [485,] 1.0248897 1.0299032 1.0416752 #> [486,] 0.7847803 0.8099471 0.8112365 #> [487,] 0.9444033 0.9586442 0.9673000 #> [488,] 0.7103499 0.7157430 0.8285483 #> [489,] 0.6194508 0.6614908 0.6766100 #> [490,] 0.8434020 0.8588308 0.9001202 #> [491,] 1.0468715 1.0511804 1.0698476 #> [492,] 0.4926226 0.5391086 0.5810606 #> [493,] 0.9021870 1.0212893 1.0643079 #> [494,] 0.6158978 0.6229634 0.6336534 #> [495,] 0.7328516 0.8080500 0.8320779 #> [496,] 0.5578328 0.5877514 0.5907850 #> [497,] 0.9729825 0.9912929 1.0054113 #> [498,] 1.3147718 1.3647384 1.3662988 #> [499,] 2.7978178 2.8484729 2.9330105 #> [500,] 0.7264557 0.8516435 0.8679878 #> [501,] 0.6613612 0.7231428 0.7516601 #> [502,] 0.8481883 0.8585328 0.8599669 #> [503,] 0.7367848 0.7974064 0.8334096 #> [504,] 0.5542075 0.5820559 0.5893635 #> [505,] 0.6116252 0.6465721 0.7169874 #> [506,] 0.8556933 0.8736286 0.9100260 #> [507,] 0.5740050 0.6339344 0.6344504 #> [508,] 1.0379094 1.0471588 1.0521241 #> [509,] 0.7351449 0.7733922 0.7906739 #> [510,] 1.1501040 1.1827899 1.2138401 #> [511,] 1.0081010 1.0269155 1.0403183 #> [512,] 1.5049288 1.5205302 1.5471490 #> [513,] 1.1290831 1.1957300 1.2288811 #> [514,] 1.0214357 1.0770711 1.1021957 #> [515,] 0.8460879 0.8489286 0.8553952 #> [516,] 0.5122100 0.5338072 0.5555859 #> [517,] 0.8566329 0.8780640 0.9458068 #> [518,] 0.7053574 0.7396663 0.7529493 #> [519,] 0.8132218 0.8619528 0.8794552 #> [520,] 0.7932397 0.8036352 0.8371247 #> [521,] 0.7600011 0.8030624 0.8731730 #> [522,] 1.9339702 1.9723446 2.0921342 #> [523,] 0.8728979 0.8798903 0.9108048 #> [524,] 0.8602468 0.8898735 0.9355186 #> [525,] 0.8434020 0.8554912 0.8611303 #> [526,] 0.6234424 0.6281006 0.6329588 #> [527,] 0.8075600 0.8575611 0.8953935 #> [528,] 1.0731621 1.1108133 1.1127074 #> [529,] 0.8029918 0.8075866 0.8531193 #> [530,] 0.7844424 0.8029918 0.8055212 #> [531,] 0.9918341 1.0455495 1.2020781 #> [532,] 0.6647266 0.6756108 0.6823798 #> [533,] 0.5475485 0.5560572 0.5752051 #> [534,] 1.4061900 1.4145051 1.4499503 #> [535,] 0.5350829 0.5391656 0.5428234 #> [536,] 0.8666887 0.8838251 0.8856987 #> [537,] 0.6450181 0.6989626 0.7645513 #> [538,] 0.6936951 0.6942788 0.7069361 #> [539,] 0.5448491 0.5483102 0.5591494 #> [540,] 0.5762100 0.5955601 0.6073795 #> [541,] 1.3941351 1.4337750 1.4711072 #> [542,] 1.1020501 1.1086187 1.1164041 #> [543,] 1.0664033 1.0783751 1.0789541 #> [544,] 1.1373486 1.1421385 1.1896530 #> [545,] 0.6098957 0.6264674 0.6398529 #> [546,] 0.7891117 0.7891957 0.8193688 #> [547,] 1.0043056 1.0920505 1.1169794 #> [548,] 0.5197251 0.5388128 0.5394863 #> [549,] 1.2325702 1.3233724 1.3253074 #> [550,] 2.3698032 2.4459672 2.4605241 #> [551,] 0.5694467 0.5983994 0.6216318 #> [552,] 0.7072118 0.7211100 0.7239720 #> [553,] 0.5203712 0.5367804 0.5406582 #> [554,] 1.0371558 1.0512924 1.0639414 #> [555,] 0.9910236 1.0217891 1.0661520 #> [556,] 1.0388253 1.0509622 1.0759924 #> [557,] 0.7511114 0.7523335 0.7737363 #> [558,] 1.0436830 1.0595302 1.1315020 #> [559,] 0.7485221 0.7555543 0.7562221 #> [560,] 0.8110856 0.8442247 0.8590657 #> [561,] 0.7910970 0.8125575 0.8269329 #> [562,] 2.0329060 2.0464947 2.1051615 #> [563,] 1.5614089 1.5792095 1.6496802 #> [564,] 1.1087357 1.1345552 1.1487033 #> [565,] 0.6202705 0.6210650 0.6326365 #> [566,] 1.0882388 1.1474096 1.1573929 #> [567,] 1.0228863 1.0319975 1.0508046 #> [568,] 0.5302128 0.5637004 0.5904987 #> [569,] 1.0220488 1.0539673 1.0574935 #> [570,] 1.0401324 1.0888612 1.1436175 #> [571,] 0.4961666 0.5716476 0.5838902 #> [572,] 0.6315611 0.6399279 0.6997841 #> [573,] 1.2314421 1.2539958 1.2860329 #> [574,] 0.8483180 0.8519278 0.8581231 #> [575,] 0.6048017 0.6223452 0.6331574 #> [576,] 1.5010928 1.5378015 1.5387295 #> [577,] 0.7322894 0.7402701 0.7722186 #> [578,] 0.5335917 0.5458239 0.5537864 #> [579,] 0.6568820 0.7080392 0.7319985 #> [580,] 0.6873511 0.7011420 0.7050837 #> [581,] 0.8928800 0.9238549 0.9298428 #> [582,] 0.7118597 0.7422491 0.7729236 #> [583,] 0.9892560 1.0059526 1.0644100 #> [584,] 1.0792311 1.0849133 1.1084016 #> [585,] 0.8916000 0.9396140 0.9467054 #> [586,] 0.8400040 0.8639699 0.9180399 #> [587,] 1.4627325 1.4824280 1.5210347 #> [588,] 1.0141649 1.1053538 1.1212797 #> [589,] 0.6793500 0.6883186 0.7481124 #> [590,] 0.7648148 0.7906054 0.7945961 #> [591,] 0.9025918 0.9716701 1.0721897 #> [592,] 0.6423844 0.6449319 0.6724115 #> [593,] 0.4725017 0.4901664 0.6232102 #> [594,] 0.5382786 0.5595756 0.5714908 #> [595,] 0.8342513 0.8361073 0.9158839 #> [596,] 0.6828360 0.7077951 0.7093827 #> [597,] 0.8045615 0.8189127 0.8202734 #> [598,] 0.6056041 0.6122406 0.6294766 #> [599,] 0.7319659 0.7665120 0.7830806 #> [600,] 0.5793024 0.5920239 0.5944527 #> [601,] 0.5422872 0.5934187 0.6225972 #> [602,] 0.5038229 0.5088133 0.5333304 #> [603,] 0.6339928 0.6344587 0.6415068 #> [604,] 0.5621462 0.5822668 0.5928315 #> [605,] 0.5043623 0.5152282 0.5412566 #> [606,] 0.8514101 0.8581030 0.8976943 #> [607,] 0.8099237 0.8210332 0.8644987 #> [608,] 1.0553813 1.0852216 1.0892656 #> [609,] 1.0876542 1.0967448 1.0970276 #> [610,] 0.6575762 0.6783473 0.6829099 #> [611,] 0.7092287 0.7793916 0.8176927 #> [612,] 0.7845856 0.8247635 0.8874659 #> [613,] 0.8388916 0.8632137 0.9030833 #> [614,] 1.0231618 1.0235836 1.0425666 #> [615,] 0.7166701 0.7827488 0.8202734 #> [616,] 0.6064328 0.6065965 0.6075559 #> [617,] 0.8378213 0.8425124 0.8486141 #> [618,] 1.2220645 1.3184867 1.3757140 #> [619,] 0.5756904 0.5858072 0.5933102 #> [620,] 1.2608402 1.2822373 1.2869762 #> [621,] 1.2739743 1.3099188 1.3250280 #> [622,] 0.6619830 0.6699510 0.7000377 #> [623,] 0.7164536 0.7257358 0.8023364 #> [624,] 1.2374534 1.2635508 1.2643068 #> [625,] 0.6902308 0.7028009 0.7319659 #> [626,] 0.8958902 0.9030162 0.9093301 #> [627,] 0.5549466 0.5755035 0.5765267 #> [628,] 0.7460089 0.7639416 0.8313206 #> [629,] 0.4905728 0.4942109 0.5031505 #> [630,] 0.6007005 0.6234864 0.6677731 #> [631,] 1.5964418 1.6594767 1.6659284 #> [632,] 0.6744898 0.6937754 0.6943448 #> [633,] 1.0761527 1.0898412 1.1706041 #> [634,] 0.6242899 0.6293683 0.6310264 #> [635,] 0.8992914 1.0140728 1.0649797 #> [636,] 1.1882590 1.2053993 1.2229430 #> [637,] 0.5477302 0.6005423 0.6195639 #> [638,] 0.6460751 0.6486946 0.6554590 #> [639,] 0.7732483 0.7848387 0.8093511 #> [640,] 0.7354020 0.7401168 0.7578651 #> [641,] 0.5115338 0.5376920 0.5490150 #> [642,] 1.3809669 1.3931236 1.3963649 #> [643,] 0.6162748 0.6355525 0.6373189 #> [644,] 0.5609127 0.5977554 0.6404431 #> [645,] 0.9621100 1.0284746 1.0561041 #> [646,] 0.8011934 0.8153516 0.8284279 #> [647,] 1.1757033 1.2322491 1.3049420 #> [648,] 0.5047042 0.5331247 0.5417924 #> [649,] 0.7390150 0.7774246 0.7941222 #> [650,] 1.2468155 1.2588606 1.2648697 #> [651,] 0.8700253 0.9101938 0.9348567 #> [652,] 0.7176706 0.7422686 0.7525945 #> [653,] 1.4647242 1.4649795 1.4841694 #> [654,] 0.5751650 0.5832221 0.6152891 #> [655,] 0.8821678 0.9729825 0.9755979 #> [656,] 0.5068300 0.5140644 0.5700511 #> [657,] 1.0711251 1.1204842 1.1268332 #> [658,] 0.6944146 0.7117599 0.7210043 #> [659,] 0.5651490 0.5871811 0.6401649 #> [660,] 0.6530109 0.6705030 0.7392660 #> [661,] 0.9938395 1.0084563 1.0115921 #> [662,] 0.5564536 0.5589335 0.5876542 #> [663,] 0.7698231 0.7761523 0.8092654 #> [664,] 0.6286956 0.6359375 0.6481956 #> [665,] 0.6136417 0.6709636 0.6793446 #> [666,] 1.5772505 1.5959408 1.7235659 #> [667,] 0.5598840 0.5724169 0.5938387 #> [668,] 0.5214682 0.5598422 0.5650591 #> [669,] 1.2304458 1.3551871 1.3687091 #> [670,] 1.1947125 1.2882108 1.2936240 #> [671,] 0.5446488 0.5522417 0.5537372 #> [672,] 0.8751083 0.8829570 1.0368283 #> [673,] 1.0337591 1.0884658 1.0925525 #> [674,] 1.3091289 1.4285975 1.4426094 #> [675,] 0.7962729 0.8106037 0.8532589 #> [676,] 0.5474013 0.5836521 0.6108524 #> [677,] 0.8050155 0.8197892 0.8250720 #> [678,] 1.2499746 1.2588606 1.2623633 #> [679,] 0.7672906 0.8184894 0.8372153 #> [680,] 0.8309062 0.8499388 0.8522157 #> [681,] 1.2357405 1.2813162 1.2881833 #> [682,] 0.7512256 0.7537934 0.8017146 #> [683,] 0.6924338 0.7604367 0.7621253 #> [684,] 0.8923240 0.9105257 0.9758638 #> [685,] 0.5165238 0.5184814 0.5219601 #> [686,] 0.7218092 0.7418706 0.8362253 #> [687,] 1.0000655 1.0190872 1.0434870 #> [688,] 1.4733664 1.4897250 1.5071121 #> [689,] 0.7361053 0.7372937 0.7560528 #> [690,] 1.1736718 1.2135157 1.2144153 #> [691,] 1.6315990 1.6820170 1.6940504 #> [692,] 0.5508534 0.5760182 0.5859741 #> [693,] 0.8191035 0.8410250 0.8522157 #> [694,] 0.9218645 0.9487543 0.9883042 #> [695,] 0.6830429 0.7555543 0.7700034 #> [696,] 0.8328559 0.8566730 0.8788406 #> [697,] 0.7060284 0.7123172 0.7189718 #> [698,] 0.6966668 0.7095384 0.7583497 #> [699,] 0.6793757 0.7066244 0.7092152 #> [700,] 0.8070216 0.8265935 0.8519278 #> [701,] 0.6302841 0.6342402 0.6365512 #> [702,] 1.2890853 1.2964382 1.3010610 #> [703,] 0.9358046 1.1144320 1.1393480 #> [704,] 0.7081493 0.7151052 0.7405949 #> [705,] 1.0195369 1.2259891 1.2269391 #> [706,] 0.5624723 0.5935204 0.6081886 #> [707,] 0.8580177 0.8863304 0.9252928 #> [708,] 1.3400658 1.3529517 1.3630672 #> [709,] 1.0605147 1.0743078 1.0866625 #> [710,] 0.5703296 0.5793024 0.5853281 #> [711,] 0.5644427 0.6095891 0.6157921 #> [712,] 0.6489326 0.6562908 0.6735804 #> [713,] 0.7164536 0.7185370 0.7198287 #> [714,] 1.1301325 1.1696451 1.1861796 #> [715,] 0.6106142 0.6114538 0.6166628 #> [716,] 0.8472093 0.8533833 0.8807989 #> [717,] 1.0511908 1.0913276 1.0950373 #> [718,] 0.7813662 0.7920934 0.8048466 #> [719,] 1.0322897 1.0772629 1.0996304 #> [720,] 0.6212383 0.6707692 0.6776307 #> [721,] 0.8476778 0.8508133 0.9056069 #> [722,] 0.7818596 0.7991157 0.8047440 #> [723,] 0.6360874 0.6570015 0.6794117 #> [724,] 1.5425863 1.5770401 1.5775648 #> [725,] 1.0469513 1.0730223 1.1365501 #> [726,] 2.5499001 2.5877972 2.5973348 #> [727,] 0.9649681 1.0000655 1.0105031 #> [728,] 0.6682089 0.6689205 0.6925582 #> [729,] 0.8289745 0.8606617 0.8995628 #> [730,] 1.1909792 1.2032655 1.2214736 #> [731,] 0.8155527 0.8330002 0.8910643 #> [732,] 1.4129700 1.4257205 1.4269882 #> [733,] 1.2326746 1.2666659 1.2807269 #> [734,] 0.9759457 1.0941410 1.1131137 #> [735,] 1.2926398 1.2943778 1.3189782 #> [736,] 0.8038318 0.9191734 0.9259836 #> [737,] 0.7771783 0.8173460 0.8183498 #> [738,] 0.8342751 0.8375110 0.8468821 #> [739,] 0.7669882 0.7769399 0.7783967 #> [740,] 0.7676540 0.7703288 0.7959924 #> [741,] 0.4807659 0.4811913 0.4829322 #> [742,] 0.7628195 0.7722913 0.8143765 #> [743,] 0.5038506 0.5872490 0.5902789 #> [744,] 0.8199415 0.8279275 0.8672934 #> [745,] 0.7083628 0.7338538 0.7515559 #> [746,] 1.7624131 1.7831930 1.8529676 #> [747,] 0.6527544 0.6722586 0.7173171 #> [748,] 0.6165924 0.6242899 0.6525633 #> [749,] 0.5591494 0.5773746 0.5871660 #> [750,] 0.6133688 0.6143176 0.6344504 #> [751,] 0.5977554 0.6075119 0.6081672 #> [752,] 0.8201508 0.8217113 0.8258053 #> [753,] 0.7158419 0.7167084 0.7526110 #> [754,] 0.5488100 0.5512399 0.5565225 #> [755,] 0.9689169 0.9702371 0.9763988 #> [756,] 0.7403582 0.7672499 0.7689766 #> [757,] 0.6011118 0.6035912 0.6143492 #> [758,] 0.6690084 0.6855743 0.6954385 #> [759,] 0.8650603 0.9300395 1.0290951 #> [760,] 0.9413051 0.9449080 0.9541890 #> [761,] 0.6814680 0.6873511 0.6933264 #> [762,] 0.6146394 0.6270975 0.6384403 #> [763,] 1.2295889 1.2969550 1.3012819 #> [764,] 0.7912666 0.8175944 0.8652332 #> [765,] 1.5165658 1.5632683 1.6220706 #> [766,] 0.7619575 0.7845856 0.7903842 #> [767,] 0.9105072 0.9167320 0.9350408 #> [768,] 0.8096945 0.8271271 0.8438052 #> [769,] 1.0189938 1.0358824 1.0408619 #> [770,] 0.7793938 0.7906739 0.8376128 #> [771,] 1.7807055 1.7927447 1.7961446 #> [772,] 1.2081725 1.2300703 1.2320259 #> [773,] 0.7095384 0.7634109 0.8012599 #> [774,] 0.5526499 0.5690779 0.5941848 #> [775,] 1.0034701 1.0198180 1.0597253 #> [776,] 0.6628386 0.6747995 0.6812344 #> [777,] 0.5489606 0.5848354 0.5980778 #> [778,] 0.6729456 0.6753574 0.6895843 #> [779,] 0.9989262 1.0340941 1.0549130 #> [780,] 0.7886171 0.8140232 0.8596395 #> [781,] 0.9398637 1.0138404 1.0257756 #> [782,] 0.5196511 0.5300860 0.5441774 #> [783,] 0.7712708 0.7785486 0.7933427 #> [784,] 0.7314612 0.7390954 0.7903991 #> [785,] 0.7497731 0.8423679 0.9455623 #> [786,] 0.8584332 0.8779746 0.8886868 #> [787,] 1.1090116 1.1598086 1.1838323 #> [788,] 1.0667274 1.0709461 1.0753148 #> [789,] 0.9512250 1.0026992 1.0055707 #> [790,] 0.6047498 0.6423844 0.6672514 #> [791,] 0.7644297 0.7823320 0.8289601 #> [792,] 0.7533340 0.8625143 0.8690115 #> [793,] 0.8308841 0.8392308 0.8533834 #> [794,] 0.7378000 0.7576315 0.7722186 #> [795,] 1.0377217 1.0471588 1.1430132 #> [796,] 0.6464568 0.6722230 0.7071145 #> [797,] 0.4768116 0.5038506 0.5119983 #> [798,] 0.5321714 0.5509819 0.5825465 #> [799,] 0.5765121 0.6117266 0.6334817 #> [800,] 1.2780696 1.2879393 1.3291877 #> [801,] 0.9607605 0.9730312 0.9790967 #> [802,] 0.9759251 1.0250429 1.0486243 #> [803,] 0.6575762 0.6844912 0.6890709 #> [804,] 0.8679566 0.9108230 1.0005149 #> [805,] 0.6319259 0.6342402 0.7315519 #> [806,] 0.6782024 0.6794570 0.7198823 #> [807,] 0.5168748 0.5367804 0.5435968 #> [808,] 0.5489606 0.6098179 0.6106142 #> [809,] 0.6365382 0.6373189 0.6435149 #> [810,] 0.5801760 0.5845530 0.5859639 #> [811,] 0.7177089 0.7441591 0.7792351 #> [812,] 0.7422491 0.7606548 0.7615598 #> [813,] 0.5879726 0.5909601 0.6122462 #> [814,] 0.5085484 0.5628062 0.5765421 #> [815,] 0.5026705 0.5189029 0.5284969 #> [816,] 1.2496203 1.2682469 1.2889422 #> [817,] 0.9497079 0.9783047 0.9847914 #> [818,] 0.8094516 0.8416187 0.8517166 #> [819,] 0.8513099 0.9304029 0.9474305 #> [820,] 0.8280890 0.8349683 0.8424210 #> [821,] 0.5074146 0.5448491 0.5822668 #> [822,] 2.0027772 2.0190620 2.0273821 #> [823,] 0.6307234 0.6482195 0.6661770 #> [824,] 2.3750614 2.3903208 2.3941222 #> [825,] 0.8838251 0.9023417 0.9142908 #> [826,] 0.9889649 1.0167606 1.0442092 #> [827,] 1.3254673 1.3315739 1.3326072 #> [828,] 0.5397715 0.5425306 0.5439430 #> [829,] 0.6322275 0.6484510 0.7294171 #> [830,] 0.7303706 0.7766673 0.7895184 #> [831,] 0.9917530 1.0002187 1.0179955 #> [832,] 1.0934972 1.0939917 1.1230953 #> [833,] 1.0198930 1.0874022 1.1050338 #> [834,] 0.4827478 0.4842416 0.5694467 #> [835,] 0.9877948 1.1083030 1.1223708 #> [836,] 0.6776030 0.7596441 0.7681511 #> [837,] 0.8056446 0.8448444 0.8542834 #> [838,] 0.8195514 0.8665423 0.8766603 #> [839,] 0.5893552 0.6070066 0.6161363 #> [840,] 0.4763757 0.5291550 0.5368799 #> [841,] 0.7351449 0.8048950 0.8117724 #> [842,] 0.5595356 0.5705462 0.5735618 #> [843,] 1.8124457 1.8751240 1.8873251 #> [844,] 0.7224461 0.7572010 0.7808384 #> [845,] 0.5486938 0.5735514 0.5755479 #> [846,] 0.7066244 0.7458233 0.7714272 #> [847,] 0.6431490 0.6594180 0.6620760 #> [848,] 0.6721738 0.7166255 0.7308810 #> [849,] 1.2402478 1.2403056 1.2798592 #> [850,] 0.8183498 0.8341716 0.8679566 #> [851,] 0.5682557 0.5983884 0.7436629 #> [852,] 2.0901283 2.1193404 2.1453606 #> [853,] 0.8889776 0.9101522 0.9120216 #> [854,] 0.6801136 0.6814680 0.7064047 #> [855,] 1.1362938 1.1583177 1.1843568 #> [856,] 0.5859734 0.6395725 0.6679704 #> [857,] 0.7928113 0.8106336 0.8582234 #> [858,] 1.4155497 1.4261048 1.4362454 #> [859,] 0.6932967 0.7067968 0.7267149 #> [860,] 0.6422858 0.6720682 0.6826380 #> [861,] 0.5245846 0.5254051 0.5499494 #> [862,] 1.9490004 1.9533986 1.9915554 #> [863,] 0.6024946 0.6290822 0.6487378 #> [864,] 0.7039406 0.7067968 0.7303706 #> [865,] 0.8614348 0.9119135 0.9338346 #> [866,] 0.8723443 0.8807200 0.8847025 #> [867,] 0.6138500 0.6325047 0.6444264 #> [868,] 0.8627456 0.9297663 0.9506089 #> [869,] 0.7957403 0.8158396 0.8162246 #> [870,] 0.6207546 0.6482118 0.6815168 #> [871,] 0.6873260 0.6925079 0.7224404 #> [872,] 0.7004519 0.7115287 0.7141490 #> [873,] 0.6206424 0.6294411 0.6419992 #> [874,] 0.7751067 0.7927907 0.8317753 #> [875,] 0.5477302 0.5905216 0.5968875 #> [876,] 1.1172828 1.1568073 1.1668856 #> [877,] 0.8027620 0.8289667 0.8584660 #> [878,] 1.3446773 1.3871050 1.4087059 #> [879,] 0.7947447 0.8026697 0.8187020 #> [880,] 0.7095780 0.7120531 0.7224331 #> [881,] 0.5165238 0.5197251 0.5563110 #> [882,] 0.4867866 0.4886340 0.5277014 #> [883,] 0.6095891 0.6444264 0.6471754 #> [884,] 1.1103547 1.1321855 1.1509306 #> [885,] 0.6683102 0.6714550 0.6776343 #> [886,] 0.6149044 0.6266999 0.6792860 #> [887,] 1.0134636 1.0375347 1.0748362 #> [888,] 0.7786041 0.8384083 0.8425124 #> [889,] 0.6276169 0.6734215 0.6775417 #> [890,] 0.9176432 0.9291135 0.9400309 #> [891,] 0.5955329 0.6026508 0.6095924 #> [892,] 0.4747824 0.5152282 0.5298413 #> [893,] 0.6624071 0.6869585 0.6930631 #> [894,] 0.9902618 0.9946636 1.0059933 #> [895,] 0.6006478 0.6109909 0.7827485 #> [896,] 1.0739924 1.1353441 1.1362353 #> [897,] 0.5088133 0.5348194 0.5409899 #> [898,] 0.8149680 0.8505206 0.8677531 #> [899,] 0.9816910 0.9847571 0.9914863 #> [900,] 0.9081888 0.9153452 0.9188084 #> [901,] 0.6153536 0.6153815 0.6182721 #> [902,] 0.5495761 0.5507399 0.5603175 #> [903,] 2.5840580 2.5929933 2.6234895 #> [904,] 1.1861796 1.1973393 1.2292255 #> [905,] 0.5724835 0.6038711 0.6115347 #> [906,] 0.8413863 0.8465449 0.8546885 #> [907,] 0.8071671 0.8180067 0.8939851 #> [908,] 0.5879416 0.6358956 0.6381506 #> [909,] 0.5240560 0.5919193 0.6096382 #> [910,] 0.7269927 0.8232129 0.8276135 #> [911,] 0.7996571 0.9101522 0.9129058 #> [912,] 1.6359869 1.7033136 1.7092960 #> [913,] 0.7159270 0.7256828 0.7342817 #> [914,] 0.7425863 0.7431353 0.7449256 #> [915,] 0.6427402 0.6471053 0.6617013 #> [916,] 0.7987813 0.8538310 0.8605647 #> [917,] 0.7536898 0.7766619 0.7871300 #> [918,] 0.5485144 0.5539596 0.5594923 #> [919,] 0.6752461 0.7208311 0.7571621 #> [920,] 0.5653469 0.5664138 0.5700511 #> [921,] 0.6455285 0.6785113 0.6878116 #> [922,] 0.5816181 0.6417378 0.6576109 #> [923,] 0.9541905 0.9633048 1.0140728 #> [924,] 1.7120622 1.7506400 1.7786597 #> [925,] 1.1356111 1.1501145 1.1861868 #> [926,] 0.6844323 0.7211100 0.7277595 #> [927,] 0.9919870 1.0046539 1.0585988 #> [928,] 1.0909418 1.0970285 1.1137077 #> [929,] 0.8215322 0.8246746 0.8262601 #> [930,] 0.8218535 0.8307444 0.8410435 #> [931,] 0.9490880 0.9559149 1.0106206 #> [932,] 1.3404173 1.3431972 1.3540991 #> [933,] 0.9182950 0.9930687 1.0316881 #> [934,] 0.5778650 0.6233926 0.6552800 #> [935,] 0.6513649 0.6688012 0.6800487 #> [936,] 0.7431773 0.7557377 0.8549699 #> [937,] 0.5240560 0.5434537 0.5609127 #> [938,] 0.5520749 0.5642003 0.6029693 #> [939,] 0.7903432 0.8028419 0.8341896 #> [940,] 0.8409575 0.8928800 0.9019933 #> [941,] 0.6343338 0.6726428 0.6845662 #> [942,] 0.7056023 0.7745546 0.8093433 #> [943,] 0.9929385 0.9997025 1.0238656 #> [944,] 0.7246882 0.7409949 0.7639661 #> [945,] 0.6295790 0.6592961 0.6793446 #> [946,] 1.1429259 1.2309731 1.2322491 #> [947,] 0.6787280 0.6887799 0.7126490 #> [948,] 0.9650437 0.9661742 0.9723720 #> [949,] 0.8346578 0.8391675 0.8997573 #> [950,] 0.9614591 0.9675984 0.9725935 #> [951,] 0.8829771 0.9036908 0.9877948 #> [952,] 0.8512426 0.8532802 0.8848484 #> [953,] 0.6166628 0.6170841 0.6371422 #> [954,] 0.8329126 0.8530069 0.8645209 #> [955,] 0.5247719 0.5300860 0.6102380 #> [956,] 1.1619952 1.2036054 1.2282273 #> [957,] 0.6391615 0.6557125 0.6612431 #> [958,] 1.0725884 1.0784070 1.1014701 #> [959,] 1.0767381 1.0916150 1.1107009 #> [960,] 0.7359664 0.7421168 0.7671997 #> [961,] 1.1424055 1.1488222 1.1747662 #> [962,] 0.5546416 0.5719874 0.6326870 #> [963,] 0.6186984 0.6290006 0.6319184 #> [964,] 1.0452503 1.0807821 1.0947882 #> [965,] 0.4862419 0.4933322 0.5164314 #> [966,] 0.6015028 0.6106738 0.6130888 #> [967,] 0.9397593 0.9456987 0.9475875 #> [968,] 1.1989367 1.2039149 1.2430222 #> [969,] 1.2733077 1.2977592 1.3079020 #> [970,] 0.6587434 0.7021421 0.7257358 #> [971,] 0.9298788 0.9332233 0.9786370 #> [972,] 0.4583481 0.5174262 0.5584686 #> [973,] 1.2784206 1.3214717 1.3237718 #> [974,] 0.9892116 0.9895480 1.0097542 #> [975,] 0.5051712 0.5437751 0.5481820 #> [976,] 0.7593610 0.7609732 0.7633887 #> [977,] 0.9492200 0.9543498 0.9551139 #> [978,] 0.7632394 0.7974951 0.8192408 #> [979,] 0.5735618 0.5908839 0.6015028 #> [980,] 0.6197546 0.6206144 0.6359616 #> [981,] 0.8684410 0.8772932 0.9226718 #> [982,] 0.7774246 0.7999180 0.8602760 #> [983,] 0.7194182 0.7592156 0.8063933 #> [984,] 0.9483649 0.9895036 1.0640046 #> [985,] 0.9019933 0.9142908 0.9483463 #> [986,] 0.7750119 0.7840491 0.7910807 #> [987,] 0.5490150 0.5614692 0.5650591 #> [988,] 0.6108302 0.6178691 0.6390093 #> [989,] 0.8721196 0.8953935 0.9019052 #> [990,] 1.1754947 1.2030136 1.2309854 #> [991,] 0.7230304 0.7697567 0.7910246 #> [992,] 0.8350104 0.8966890 0.9137141 #> [993,] 0.8924814 0.9205566 0.9220425 #> [994,] 1.6178327 1.6278497 1.6799638 #> [995,] 0.7267149 0.7846327 0.7985012 #> [996,] 0.6584006 0.6801919 0.6883392 #> [997,] 1.1804192 1.2117447 1.2190261 #> [998,] 0.6526862 0.6584006 0.6732712 #> [999,] 0.9596488 0.9604054 1.0200492 #> [1000,] 1.2215756 1.2249653 1.2474735 #>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"Compute log-rank test p-value difference two survival curves obtained splitting dataset \"low\" \"high\" risk group using possible relative-risk thresholds.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"tof_find_log_rank_threshold(input_data, relative_risk_col, time_col, event_col)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"input_data tbl_df data.frame observation row. relative_risk_col unquote column name indicating column contains relative-risk estimates observation. time_col unquoted column name indicating column contains true time--event information observation. event_col unquoted column name indicating column contains outcome (event censorship). Must binary column - values either 0 1 (1 indicating adverse event 0 indicating censorship) FALSE TRUE (TRUE indicating adverse event FALSE indicating censorship).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"tibble 3 columns: \"candidate_thresholds\" (relative-risk threshold used log-rank test), \"log_rank_p_val\" (p-values log-rank tests) \"is_best\" (logical value indicating candidate threshold gave optimal, .e. smallest, p-value).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"Using character vectors obtained `name` `desc` columns parameters data flowFrame, figure high-dimensional cytometry panel used collect data return tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"","code":"tof_find_panel_info(input_flowFrame)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"input_flowFrame raw flowFrame (just read .fcs file) high-dimensional cytometry panel extracted","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"tibble 2 columns (`metals` `antigens`) correspond metals antigens high-dimensional cytometry panel used data acquisition.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"function trains glmnet model training set rsplit object, calculates performance metrics model validation/holdout set combinations mixture penalty hyperparameters provided hyperparameter grid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"","code":"tof_fit_split( split_data, prepped_recipe, hyperparameter_grid, model_type, outcome_colnames )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"split_data `rsplit` object rsample package. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe trained recipe hyperparameter_grid tibble containing hyperparameter values tune. Can created using tof_create_grid model_type string representing type glmnet model fit. outcome_colnames Quoted column names indicating columns data fit represent outcome variables (others assumed predictors).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"tibble number rows input hyperparameter grid. row represents combination mixture penalty, column contains performance metric fitted glmnet model `split_data`'s holdout set. specific performance metrics depend type model fit: \"linear\" mean-squared error (`mse`) mean absolute error (`mae`) \"two-class\" binomial deviance (`binomial_deviance`); misclassification error rate `misclassification_error`; area receiver-operating curve (`roc_auc`); `mse` `mse` \"multiclass\" multinomial deviance (`multinomial_deviance`); misclassification error rate `misclassification_error`; area receiver-operating curve (`roc_auc`) computed using Hand-Till method roc_auc; `mse` `mse` \"survival\" negative log2-transformed partial likelihood (`neg_log_partial_likelihood`) Harrel's concordance index (often simply called \"C\"; `concordance_index`)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"Harrel Jr, F. E. Lee, K. L. Mark, D. B. (1996) Tutorial biostatistics: multivariable prognostic models: issues developing models, evaluating assumptions adequacy, measuring reducing error, Statistics Medicine, 15, pages 361–387.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a color palette using tidytof. — tof_generate_palette","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"function generates color palette based color palette author's favorite pokemon.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"","code":"tof_generate_palette(num_colors)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"num_colors integer specifying number colors like generate.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"character vector hex codes specifying colors palette.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"","code":"tof_generate_palette(num_colors = 5L) #> [1] \"#D86020\" \"#28A8B8\" \"#F89040\" \"#D0D0D0\" \"#903000\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"Get `tof_model`'s optimal mixture (alpha) value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"","code":"tof_get_model_mixture(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"numeric value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_mixture(regression_model) #> [1] 1"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"Get `tof_model`'s outcome variable name(s)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"","code":"tof_get_model_outcomes(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"character vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_outcomes(regression_model) #> [1] \"outcome\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"Get `tof_model`'s optimal penalty (lambda) value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"","code":"tof_get_model_penalty(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"numeric value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_penalty(regression_model) #> [1] 1e-10"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s training data — tof_get_model_training_data","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"Get `tof_model`'s training data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"","code":"tof_get_model_training_data(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"tibble (non-preprocessed) training data used fit model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_training_data(regression_model) #> # A tibble: 100 × 9 #> sample cd45 pstat5 cd34 outcome class multiclass event time_to_event #> #> 1 65 0.822 0.619 0.815 5.72 class1 class3 1 7.41 #> 2 84 0.597 0.767 0.260 6.34 class1 class3 1 12.5 #> 3 17 0.902 0.723 0.749 2.97 class2 class1 0 12.4 #> 4 24 0.667 0.0417 0.801 3.15 class2 class1 0 10.2 #> 5 3 0.293 0.230 0.443 2.70 class2 class1 0 11.1 #> 6 49 0.374 0.951 0.286 4.82 class1 class2 1 11.0 #> 7 9 0.280 0.0778 0.509 1.96 class2 class1 0 10.3 #> 8 87 0.295 0.651 0.226 4.22 class1 class3 1 8.02 #> 9 53 0.231 0.793 0.335 5.00 class1 class2 1 8.03 #> 10 70 0.917 0.183 0.255 2.38 class2 class3 1 12.7 #> # ℹ 90 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s model type — tof_get_model_type","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"Get `tof_model`'s model type","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"","code":"tof_get_model_type(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"string","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_type(regression_model) #> [1] \"linear\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"Get `tof_model`'s processed predictor matrix (glmnet)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"","code":"tof_get_model_x(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"x value formatted glmnet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_x(regression_model) #> cd45 pstat5 cd34 #> [1,] 0.097430219 0.82910106 -0.94376493 #> [2,] -1.311838342 -0.83388574 2.00234953 #> [3,] 0.608982549 0.25588910 -0.45198780 #> [4,] -0.302489013 0.63513275 -0.78947643 #> [5,] 0.814591515 1.35424932 -0.06893559 #> [6,] -1.490715046 -0.60767688 0.20873417 #> [7,] -1.068171324 -0.98809671 -0.92545271 #> [8,] 1.720644052 1.03349182 -0.61031206 #> [9,] 0.344938617 1.45622241 -0.58876885 #> [10,] -0.344691402 -1.64191753 1.46032139 #> [11,] -0.316963867 -0.29841413 -0.19475752 #> [12,] -1.364896702 -0.21680095 1.06028628 #> [13,] -0.788463388 1.56784234 1.34496699 #> [14,] 1.465392231 -0.02392575 -0.38715496 #> [15,] -0.983790203 1.22733473 -0.57337552 #> [16,] -1.351760250 -1.48437184 -1.45299400 #> [17,] -1.021288982 -0.99886311 -0.84663647 #> [18,] -1.389544154 0.53088546 1.88944653 #> [19,] -1.218894884 -0.25109334 1.20528647 #> [20,] 0.238289453 -0.60142456 -0.79626050 #> [21,] 0.989845642 -0.56050792 1.00459207 #> [22,] -1.449966131 -1.21051222 -0.26514832 #> [23,] -1.482798231 0.09343527 -0.39684951 #> [24,] 1.463977818 1.26356640 1.72149337 #> [25,] 1.405791893 0.17131523 -1.38922709 #> [26,] -0.620979465 1.69803167 0.91600303 #> [27,] -0.629277673 -0.27514976 1.12814102 #> [28,] 0.500584250 1.06722334 0.30449975 #> [29,] -1.043556082 1.27808977 0.77334193 #> [30,] -1.024425108 -0.97478798 0.84206340 #> [31,] 1.554943783 1.06852314 -0.35906208 #> [32,] 1.169379658 0.08050339 -0.16781256 #> [33,] -1.177645648 -0.13092913 -0.02108945 #> [34,] -0.172509694 -1.46016260 0.06155719 #> [35,] -0.748479429 -1.13536404 -0.67258770 #> [36,] -1.402700354 1.34348410 -0.05211684 #> [37,] -1.062300509 -0.75774393 1.81266694 #> [38,] -1.282857700 0.41580356 1.22121612 #> [39,] 0.875433929 1.68290377 -1.39538817 #> [40,] -1.164910939 0.56025763 -1.40528210 #> [41,] 0.668840929 1.63436095 -0.41448278 #> [42,] -0.002996884 -1.64653309 -0.59163196 #> [43,] 0.172427732 -0.26530729 -0.91681641 #> [44,] 1.468934435 -0.49443278 1.26750344 #> [45,] -0.924013752 -0.96065024 -1.41537556 #> [46,] 0.181290611 0.45321197 -0.55039255 #> [47,] 0.274956688 -0.03118150 0.48177377 #> [48,] -0.282324875 1.57603526 0.23943288 #> [49,] 1.147478504 -1.24884927 0.19638588 #> [50,] 1.010348840 0.61371718 -0.76901762 #> [51,] 1.110491475 1.29781210 -1.04943965 #> [52,] 1.078293933 0.56541751 -0.10336833 #> [53,] -0.176056382 -0.71155325 -1.09782905 #> [54,] 0.621151974 -1.64261801 -0.69870671 #> [55,] -1.656946890 -0.83395761 -0.06055877 #> [56,] -0.305423555 0.57731463 -1.08112087 #> [57,] 0.702750034 0.06664733 1.73234041 #> [58,] -0.513923518 -1.59376747 1.88020934 #> [59,] 0.401774984 0.90797477 1.00688706 #> [60,] -1.251357033 0.27391588 -1.47560501 #> [61,] 0.249423289 0.54384053 -0.57632363 #> [62,] -0.563953322 0.18670903 -0.87890035 #> [63,] -0.134333347 -1.47950062 0.52142003 #> [64,] -0.447376307 -1.63419639 -1.17733125 #> [65,] 1.056123410 -0.77591397 1.55592024 #> [66,] -0.649333161 -1.60929699 0.36112138 #> [67,] -0.314440154 -0.15318593 -1.57390336 #> [68,] -1.574189375 1.36296254 0.43201458 #> [69,] -1.236690266 0.39641178 1.61239377 #> [70,] 1.059948573 -0.91541842 -1.36370039 #> [71,] 1.660572266 -0.70944415 1.05667214 #> [72,] -1.493476736 -0.49845220 -1.63788023 #> [73,] 1.655897949 -0.39093657 0.29411358 #> [74,] 0.451123291 0.98262889 1.98757738 #> [75,] -0.414424253 -1.07530968 0.31514068 #> [76,] 0.453726423 0.85686567 1.01130980 #> [77,] 0.999958797 -1.57167820 -1.63538826 #> [78,] 1.423268538 1.18211735 0.61977295 #> [79,] 0.599291206 -0.11074027 -1.43890299 #> [80,] -0.283921239 -0.69847066 1.61055618 #> [81,] 1.233219158 0.37126964 1.08175076 #> [82,] 0.803109507 1.66628276 -0.70233496 #> [83,] 0.694205481 1.36237235 0.33442099 #> [84,] -0.227147094 -0.07495812 -1.03451647 #> [85,] 0.316764480 -1.28795998 -0.58610671 #> [86,] 1.566441362 0.32637161 -0.12839293 #> [87,] 0.442802106 -0.54085031 -0.07227478 #> [88,] 1.197063319 0.26925504 -1.00240896 #> [89,] -0.135714814 -1.60973478 -0.74200950 #> [90,] -1.478638693 0.60936144 0.52842898 #> [91,] 0.264647737 0.60063557 -1.04296196 #> [92,] 1.531517096 -1.16694306 -0.11886303 #> [93,] 1.526609706 0.46163629 0.26221660 #> [94,] -1.248391947 0.26145114 -1.14140504 #> [95,] -0.116980597 0.41800529 -0.30383907 #> [96,] -1.150374900 -0.89023524 0.88019565 #> [97,] 1.376741715 1.42083375 0.32368321 #> [98,] -0.065986285 -0.07541222 0.28225801 #> [99,] 0.551185862 -1.35040975 0.34747512 #> [100,] -0.338277123 1.64082156 0.95628936"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"Get `tof_model`'s processed outcome variable matrix (glmnet)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"","code":"tof_get_model_y(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"y value formatted glmnet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_y(regression_model) #> [1] 5.7621216 2.2316713 0.6277567 3.8226163 3.4438041 3.2022874 #> [7] 4.6158093 2.9033474 3.6741484 3.1352677 2.4676448 5.1249311 #> [13] 4.7087302 6.2543310 1.1120282 4.7324417 5.9082607 2.0386707 #> [19] 5.4174583 3.5122708 4.8392255 0.8281352 5.2922102 5.3983678 #> [25] 3.5386303 4.1805624 3.7113123 0.8101075 2.2566154 7.1559778 #> [31] 5.1942710 4.4812019 3.2366547 1.4674999 0.8180363 2.3338306 #> [37] 0.8810946 3.6455296 3.3840711 5.5595769 4.7384607 4.4243745 #> [43] 4.7496679 2.0974114 2.7392128 4.5135190 2.7730292 3.1200798 #> [49] 2.7923983 2.4138013 4.5699212 4.5719286 0.7985220 1.0197147 #> [55] 3.5951921 2.8275037 4.5476347 3.3280893 3.6705805 2.8785254 #> [61] 6.7006236 1.5374820 1.2341384 2.6680193 2.7916082 5.3360892 #> [67] -0.2466384 5.0882917 1.9183955 2.9068708 5.0615714 5.6398780 #> [73] 1.1183529 1.6471065 5.3397798 3.8092044 3.4540926 1.2882773 #> [79] 1.9441636 2.3991209 5.9633846 5.1459914 3.5421044 6.5684994 #> [85] 1.6638710 2.6644894 3.1831984 2.6593735 2.0910804 4.8734370 #> [91] 1.8225112 4.0398150 2.8298920 3.1629248 2.0121100 3.8578668 #> [97] 0.8822519 3.6211117 4.9356237 6.4689079"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":null,"dir":"Reference","previous_headings":"","what":"Get panel information from a tof_tibble — tof_get_panel","title":"Get panel information from a tof_tibble — tof_get_panel","text":"Get panel information tof_tibble","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get panel information from a tof_tibble — tof_get_panel","text":"","code":"tof_get_panel(tof_tibble)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get panel information from a tof_tibble — tof_get_panel","text":"tof_tibble `tof_tbl`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get panel information from a tof_tibble — tof_get_panel","text":"tibble containing information CyTOF panel used data acquisition data contained `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get panel information from a tof_tibble — tof_get_panel","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) tof_get_panel(tof_tibble) #> # A tibble: 59 × 2 #> metals antigens #> #> 1 Time Time #> 2 Event_length Event_length #> 3 Y89 CD45 #> 4 Pd102 empty #> 5 Pd104 empty #> 6 Pd105 empty #> 7 Pd106 empty #> 8 Pd108 empty #> 9 Pd110 empty #> 10 In113 CD61 #> # ℹ 49 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Find if a vector is numeric — tof_is_numeric","title":"Find if a vector is numeric — tof_is_numeric","text":"function takes input vector `.vec` checks either integer double (.e. type vector might encode high-dimensional cytometry measurements).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find if a vector is numeric — tof_is_numeric","text":"","code":"tof_is_numeric(.vec)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find if a vector is numeric — tof_is_numeric","text":".vec vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find if a vector is numeric — tof_is_numeric","text":"boolean value indicating .vec type integer double.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"function uses distances cell K nearest neighbors estimate local density cell `tof_tbl` `tibble` containing high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"","code":"tof_knn_density( tof_tibble, distance_cols = where(tof_is_numeric), num_neighbors = min(15L, nrow(tof_tibble)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), estimation_method = c(\"mean_distance\", \"sum_distance\"), normalize = TRUE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. num_neighbors integer indicating number nearest neighbors use estimating local density cell. Defaults minimum 15 number rows `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". estimation_method string indicating relative density cell calculated distances k nearest neighbors. Options \"mean_distance\" (default; estimates relative density cell's neighborhood taking negative average distances nearest neighbors) \"sum_distance\" (estimates relative density cell's neighborhood taking negative sum distances nearest neighbors). normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional optional arguments pass tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"tibble single column named \".knn_density\" containing local density estimates input cell `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"Compute log-rank test p-value difference two survival curves obtained splitting dataset \"low\" \"high\" risk group using given relative-risk threshold.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"tof_log_rank_test( input_data, relative_risk_col, time_col, event_col, threshold )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"input_data tbl_df data.frame observation row. relative_risk_col unquote column name indicating column contains relative-risk estimates observation. time_col unquoted column name indicating column contains true time--event information observation. event_col unquoted column name indicating column contains outcome (event censorship). Must binary column - values either 0 1 (1 indicating adverse event 0 indicating censorship) FALSE TRUE (TRUE indicating adverse event FALSE indicating censorship). threshold numeric value indicating relative-risk threshold used split observations low- high-risk groups.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"numeric value <1, p-value log-rank test.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Title — tof_make_knn_graph","title":"Title — tof_make_knn_graph","text":"Title","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Title — tof_make_knn_graph","text":"","code":"tof_make_knn_graph( tof_tibble, knn_cols, num_neighbors, distance_function = c(\"euclidean\", \"cosine\"), graph_type = c(\"weighted\", \"unweighted\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Title — tof_make_knn_graph","text":"tof_tibble tibble tof_tbl. knn_cols Unquoted column names indicating columns tof_tibble used KNN calculation. num_neighbors integer number neighbors find cell ( including ). distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. graph_type string indicating graph's edges weights (\"weighted\"; default) (\"unweighted\"). ... Optional additional arguments pass tof_find_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Title — tof_make_knn_graph","text":"tbl_graph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Title — tof_make_knn_graph","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"Compute receiver-operating curve (ROC) two-class multiclass dataset","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"","code":"tof_make_roc_curve(input_data, truth_col, prob_cols)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"input_data tof_tbl, tbl_df, data.frame row observation. truth_col unquoted column name indicating column `input_data` contains true class labels observation. Must factor. prob_cols Unquoted column names indicating columns `input_data` contain probability estimates class `truth_col`. columns must specified order factor levels `truth_col`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"tibble can used plot ROC classification task. candidate probability threshold, following reported: specificity, sensitivity, true-positive rate (tpr), false-positive rate (fpr).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a logistic regression classifier log_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) # make predictions predictions <- tof_predict( log_model, new_data = feature_tibble, prediction_type = \"response\" ) prediction_tibble <- dplyr::tibble( truth = feature_tibble$class, prediction = predictions$.pred ) # make ROC curve tof_make_roc_curve( input_data = prediction_tibble, truth_col = truth, prob_cols = prediction )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data. — tof_metacluster","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"function wrapper around tidytof's tof_metacluster_* function family. performs metaclustering CyTOF data using user-specified method (5 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"","code":"tof_metacluster( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, ..., augment = TRUE, method = c(\"consensus\", \"hierarchical\", \"kmeans\", \"phenograph\", \"flowsom\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. ... Additional arguments pass `tof_metacluster_*` function family member corresponding chosen `method`. augment boolean value indicating output column-bind metacluster ids cell new column `tof_tibble` (TRUE; default) single-column tibble including metacluster ids returned (FALSE). method string indicating clustering method used. Valid values include \"consensus\", \"hierarchical\", \"kmeans\", \"phenograph\", \"flowsom\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding metacluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding metacluster ids.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"consensus\", method = \"flowsom\" ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster #> #> 1 -0.443 1.22 -0.322 0.477 z 1 #> 2 0.342 0.282 0.616 1.37 z 1 #> 3 -1.67 -1.54 0.615 -0.475 l 2 #> 4 -1.53 -0.707 0.280 -1.11 a 1 #> 5 -0.539 -0.923 -0.239 0.145 w 1 #> 6 1.19 -0.295 -0.661 0.493 f 3 #> 7 -0.418 -0.110 -0.557 -1.10 z 1 #> 8 -0.315 0.803 -2.72 0.0648 m 4 #> 9 -0.846 0.127 0.117 -0.162 f 3 #> 10 -0.669 2.21 -0.608 -0.494 o 1 #> # ℹ 990 more rows tof_metacluster( tof_tibble = sim_data, cluster_col = cluster_id, method = \"phenograph\" ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster #> #> 1 -0.443 1.22 -0.322 0.477 z 1 #> 2 0.342 0.282 0.616 1.37 z 1 #> 3 -1.67 -1.54 0.615 -0.475 l 3 #> 4 -1.53 -0.707 0.280 -1.11 a 1 #> 5 -0.539 -0.923 -0.239 0.145 w 2 #> 6 1.19 -0.295 -0.661 0.493 f 4 #> 7 -0.418 -0.110 -0.557 -1.10 z 1 #> 8 -0.315 0.803 -2.72 0.0648 m 3 #> 9 -0.846 0.127 0.117 -0.162 f 4 #> 10 -0.669 2.21 -0.608 -0.494 o 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"function performs consensus metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See ConsensusClusterPlus additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"","code":"tof_metacluster_consensus( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, proportion_clusters = 0.9, proportion_features = 1, num_reps = 20L, clustering_algorithm = c(\"hierarchical\", \"pam\", \"kmeans\"), distance_function = c(\"euclidean\", \"minkowski\", \"pearson\", \"spearman\", \"maximum\", \"binary\", \"canberra\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. proportion_clusters numeric value 0 1 indicating proportion clusters subsample (total number clusters `cluster_col`) iteration consensus clustering. Defaults 0.9 proportion_features numeric value 0 1 indicating proportion features (.e. proportion columns specified `metacluster_cols`) subsample iteration consensus clustering. Defaults 1 (features included). num_reps integer indicating many subsampled replicates run consensus clustering. Defaults 20. clustering_algorithm string indicating clustering algorithm ConsensusClusterPlus use metacluster subsampled clusters resampling. Options \"hierarchical\" (default), \"pam\" (partitioning around medoids), \"kmeans\". distance_function string indicating distance function used compute distances clusters consensus clustering. Options \"euclidean\" (default), \"manhattan\", \"minkowski\", \"pearson\", \"spearman\", \"maximum\", \"binary\", \"canberra\". See ConsensusClusterPlus. ... Optional additional arguments pass ConsensusClusterPlus.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"tibble single column (`.consensus_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_consensus(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .consensus_metacluster #> #> 1 1 #> 2 1 #> 3 5 #> 4 2 #> 5 1 #> 6 1 #> 7 1 #> 8 5 #> 9 2 #> 10 7 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"function performs metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. takes advantage FlowSOM package's built-functionality automatically detecting number metaclusters can use several strategies adapted FlowSOM team: consensus metaclustering, hierarchical metaclustering, k-means metaclustering, metaclustering using FlowSOM algorithm . See MetaClustering additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"","code":"tof_metacluster_flowsom( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, clustering_algorithm = c(\"consensus\", \"hierarchical\", \"kmeans\", \"som\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating maximum number clusters returned. Defaults 10. Note function, output may provide small number metaclusters requested. MetaClustering uses \"Elbow method\" automatically detect optimal number metaclusters. clustering_algorithm string indicating clustering algorithm MetaClustering use perform metaclustering. Options \"consensus\" (default), \"hierarchical\", \"kmeans\", \"som\" (.e. self-organizing map; FlowSOM algorithm ). ... Optional additional arguments pass MetaClustering.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"tibble single column (`.flowsom_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_flowsom( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"consensus\" ) #> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> #> 1 3 #> 2 4 #> 3 3 #> 4 4 #> 5 4 #> 6 3 #> 7 4 #> 8 2 #> 9 4 #> 10 1 #> # ℹ 990 more rows tof_metacluster_flowsom( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"som\" ) #> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> #> 1 2 #> 2 1 #> 3 1 #> 4 1 #> 5 1 #> 6 2 #> 7 1 #> 8 4 #> 9 1 #> 10 2 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"function performs hierarchical metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See hclust.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"","code":"tof_metacluster_hierarchical( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, distance_function = c(\"euclidean\", \"manhattan\", \"minkowski\", \"maximum\", \"canberra\", \"binary\"), agglomeration_method = c(\"complete\", \"single\", \"average\", \"median\", \"centroid\", \"ward.D\", \"ward.D2\", \"mcquitty\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. distance_function string indicating distance function used compute distances clusters hierarchical metaclustering. Options \"euclidean\" (default), \"manhattan\", \"minkowski\", \"maximum\", \"canberra\", \"binary\". See dist additional details. agglomeration_method string indicating agglomeration algorithm used hierarchical cluster combination. Options \"complete\" (default), \"single\", \"average\", \"median\", \"centroid\", \"ward.D\", \"ward.D2\", \"mcquitty\". See hclust details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"tibble single column (`.hierarchical_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_hierarchical(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .hierarchical_metacluster #> #> 1 10 #> 2 5 #> 3 3 #> 4 6 #> 5 8 #> 6 7 #> 7 7 #> 8 7 #> 9 2 #> 10 3 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"function performs k-means metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See hclust.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"","code":"tof_metacluster_kmeans( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. ... Optional additional method specifications pass tof_cluster_kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"tibble single column (`.kmeans_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_kmeans(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .kmeans_metacluster #> #> 1 3 #> 2 8 #> 3 9 #> 4 2 #> 5 5 #> 6 3 #> 7 8 #> 8 2 #> 9 4 #> 10 7 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"function performs PhenoGraph metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements. number metaclusters automatically detected PhenoGraph algorithm. See tof_cluster_phenograph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"","code":"tof_metacluster_phenograph( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_neighbors = 5L, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_neighbors integer indicating number neighbors use constructing PhenoGraph's k-nearest-neighbor graph. Smaller values emphasize local graph structure; larger values emphasize global graph structure (add time computation). Defaults 5. ... Optional additional method specifications pass tof_cluster_phenograph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"tibble single column (`.phenograph_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_phenograph(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .phenograph_metacluster #> #> 1 2 #> 2 1 #> 3 3 #> 4 1 #> 5 1 #> 6 2 #> 7 1 #> 8 1 #> 9 3 #> 10 2 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot marker expression density plots — tof_plot_cells_density","title":"Plot marker expression density plots — tof_plot_cells_density","text":"function plots marker expression density plots user-specified column tof_tbl. Optionally, cells can grouped plot multiple vertically-arranged density plots","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot marker expression density plots — tof_plot_cells_density","text":"","code":"tof_plot_cells_density( tof_tibble, marker_col, group_col, num_points = 512, theme = ggplot2::theme_bw(), use_ggridges = FALSE, scale = 1, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot marker expression density plots — tof_plot_cells_density","text":"tof_tibble `tof_tbl` `tibble`. marker_col unquoted column name representing column `tof_tibble` (.e. CyTOF protein measurement) included feature extraction calculation. group_col Unquoted column names representing column `tof_tibble` used break rows `tof_tibble` subgroups plotted separate histograms. Defaults plotting without subgroups. num_points number points along full range `marker_col` density calculated theme ggplot2 theme plot. Defaults theme_bw use_ggridges boolean value indicting geom_ridgeline used plot overlain histograms. Defaults FALSE. TRUE, ggridges package must installed. scale Use set `scale` argument geom_ridgeline, controls far apart (vertically) density plots arranged along y-axis. Defaults 1. ... Additional optional arguments send geom_ridgeline.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot marker expression density plots — tof_plot_cells_density","text":"ggplot object","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot marker expression density plots — tof_plot_cells_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(c(\"a\", \"b\"), size = 1000, replace = TRUE) ) density_plot <- tof_plot_cells_density( tof_tibble = sim_data, marker_col = cd45, group_col = cluster_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"function makes scatterplots using single-cell data embedded low-dimensional space (generated tof_reduce_dimensions, point colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"","code":"tof_plot_cells_embedding( tof_tibble, embedding_cols, color_col, facet_cols, compute_embedding_cols = where(tof_is_numeric), embedding_method = c(\"pca\", \"tsne\", \"umap\"), embedding_args = list(), theme = ggplot2::theme_bw(), ..., method = c(\"ggplot2\", \"scattermore\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"tof_tibble `tof_tbl` `tibble`. embedding_cols Unquoted column names indicating columns `tof_tibble` used x y axes scatterplot. Supports tidyselect helpers. Must select exactly 2 columns. provided, feature embedding can computed scratch using method provided using `embedding_method` argument tof_reduce_dimensions arguments passed `embedding_args`. color_col unquoted column name specifying column `tof_tibble` used color point scatterplot. facet_cols unquoted column name specifying column `tof_tibble` used break scatterplot facets using facet_wrap. compute_embedding_cols Unquoted column names indicating columns 'tof_tibble' use computing embeddings method specified `embedding_method`. Defaults numeric columns 'tof_tibble'. Supports tidyselect helpers. embedding_method string indicating method used feature embedding (`embedding_cols` provided). Options (passed tof_reduce_dimensions) \"pca\" (default), \"tsne\", \"umap\". embedding_args Optional additional arguments pass tof_reduce_dimensions. example, `method = \"tsne\"`, might include `num_comp`, `perplexity`, `theta`. theme ggplot2 theme apply scatterplot. Defaults theme_bw. ... Optional additional arguments pass tof_plot_cells_scatter. method string indicating plotting engine used. Valid values include \"ggplot2\" (default) \"scattermore\" (recommended 100K cells plotted). Note method = \"scattermore\" requires scattermore package installed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # embed with pca pca_plot <- tof_plot_cells_embedding( tof_tibble = sim_data, color_col = cd38, embedding_method = \"pca\", compute_embedding_cols = starts_with(\"cd\") ) # embed with tsne tsne_plot <- tof_plot_cells_embedding( tof_tibble = sim_data, color_col = cluster_id, embedding_method = \"tsne\", compute_embedding_cols = starts_with(\"cd\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"function makes force-directed layouts using single-cell data embedded 2-dimensional space representing k-nearest-neighbor graph constructed using cell--cell similarities. node force-directed layout represents single cell colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"","code":"tof_plot_cells_layout( tof_tibble, knn_cols = where(tof_is_numeric), color_col, facet_cols, num_neighbors = 5, graph_type = c(\"weighted\", \"unweighted\"), graph_layout = \"fr\", distance_function = c(\"euclidean\", \"cosine\"), edge_alpha = 0.25, node_size = 2, theme = ggplot2::theme_void(), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"tof_tibble `tof_tbl` `tibble`. knn_cols Unquoted column names indicating columns `tof_tibble` used compute cell--cell distances used construct k-nearest-neighbor graph. Supports tidyselect helpers. Defaults numeric columns. color_col Unquoted column name indicating column `tof_tibble` used color nodes force-directed layout. facet_cols Unquoted column names indicating columns `tof_tibble` used separate nodes different force-directed layouts. num_neighbors integer specifying many neighbors used construct k-nearest neighbor graph. graph_type string specifying k-nearest neighbor graph \"weighted\" (default) \"unweighted\". graph_layout string specifying algorithm used compute force-directed layout. Passed ggraph. Defaults \"fr\", Fruchterman-Reingold algorithm. examples include \"nicely\", \"gem\", \"kk\", many others. See layout_tbl_graph_igraph examples. distance_function string indicating distance function use computing cell--cell distances. Valid options include \"euclidean\" (default) \"cosine\". edge_alpha numeric value 0 1 specifying transparency edges drawn force-directed layout. Defaults 0.25. node_size numeric value specifying size nodes force-directed layout. Defaults 2. theme ggplot2 theme apply force-directed layout. Defaults theme_void ... hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"ggraph/ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # make a layout colored by a marker layout_cd38 <- tof_plot_cells_layout( tof_tibble = sim_data, color_col = cd38 ) # make a layout colored by cluster id layout_cluster <- tof_plot_cells_layout( tof_tibble = sim_data, color_col = cluster_id, )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"function makes scatterplots single-cell data using user-specified x- y-axes. Additionally, point scatterplot can colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"","code":"tof_plot_cells_scatter( tof_tibble, x_col, y_col, color_col, facet_cols, theme = ggplot2::theme_bw(), ..., method = c(\"ggplot2\", \"scattermore\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"tof_tibble `tof_tbl` `tibble`. x_col unquoted column name specifying column `tof_tibble` used x-axis. y_col unquoted column name specifying column `tof_tibble` used y-axis. color_col unquoted column name specifying column `tof_tibble` used color point scatterplot. facet_cols unquoted column name specifying column `tof_tibble` used break scatterplot facets using facet_wrap. theme ggplot2 theme apply scatterplot. Defaults theme_bw. ... Optional additional arguments pass geom_point method = \"ggplot2\" geom_scattermore method = \"scattermore\". method string indicating plotting engine used. Valid values include \"ggplot2\" (default) \"scattermore\" (recommended 100K cells plotted). Note method = \"scattermore\" requires scattermore package installed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"function makes heatmap cluster--cluster marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap cluster IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"","code":"tof_plot_clusters_heatmap( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_clusterwise = FALSE, cluster_markers = TRUE, cluster_clusters = TRUE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_clusterwise boolean value indicating heatmap rescale rows heatmap maximum value cluster 1 minimum value 0. Defaults FALSE. cluster_markers boolean value indicating heatmap order columns (.e. markers) using hierarchical clustering. Defaults TRUE. cluster_clusters boolean value indicating heatmap order rows (.e. clusters) using hierarchical clustering. Defaults TRUE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) heatmap <- tof_plot_clusters_heatmap( tof_tibble = sim_data, cluster_col = cluster_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"function plots minimum-spanning tree using clustered single-cell data order summarize cluster-level characteristics. node MST represents single cluster colored using user-specified variable (either continuous discrete).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"","code":"tof_plot_clusters_mst( tof_tibble, cluster_col, knn_cols = where(tof_is_numeric), color_col, num_neighbors = 5L, graph_type = c(\"unweighted\", \"weighted\"), graph_layout = \"nicely\", central_tendency_function = stats::median, distance_function = c(\"euclidean\", \"cosine\"), edge_alpha = 0.4, node_size = \"cluster_size\", theme = ggplot2::theme_void(), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. knn_cols Unquoted column names indicating columns `tof_tibble` used compute cluster--cluster distances used construct k-nearest-neighbor graph. Supports tidyselect helpers. Defaults numeric columns. color_col Unquoted column name indicating column `tof_tibble` used color nodes MST. num_neighbors integer specifying many neighbors used construct k-nearest neighbor graph. graph_type string specifying k-nearest neighbor graph \"weighted\" (default) \"unweighted\". graph_layout argument specifies layout MST one two ways. Option 1: Provide string specifying algorithm used compute force-directed layout. Passed ggraph. Defaults \"nicely\", tries automatically select visually-appealing layout. examples include \"fr\", \"gem\", \"kk\", many others. See layout_tbl_graph_igraph examples. Option 2: Provide ggraph object previously generated function. layout used plot ggraph object used template new plot. Using option, number clusters (labels) must identical template. option useful want make multiple plots tof_tibble colored different protein markers, example. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. distance_function string indicating distance function use computing cluster--clusters distances constructing MST. Valid options include \"euclidean\" (default) \"cosine\". edge_alpha numeric value 0 1 specifying transparency edges drawn force-directed layout. Defaults 0.25. node_size Either numeric value specifying size nodes MST string \"cluster_size\", case size node representing cluster scaled according number cells cluster (default). theme ggplot2 theme apply force-directed layout. Defaults theme_void ... Optional additional arguments hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"ggraph/ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # make a layout colored by a marker layout_cd38 <- tof_plot_clusters_mst( tof_tibble = sim_data, cluster_col = cluster_id, color_col = cd38 ) # use the same layout as the plot above to color the same # tree using a different marker layout_cd45 <- tof_plot_clusters_mst( tof_tibble = sim_data, cluster_col = cluster_id, color_col = cd45, graph_layout = layout_cd38 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"function makes volcano plot using results differential expression analysis (DEA) produced one `tof_dea_*` verbs. point volcano plot represents single cluster-marker pair, colored significance level direction marker expression difference.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"","code":"tof_plot_clusters_volcano( dea_result, num_top_pairs = 10L, alpha = 0.05, point_size = 2, label_size = 3, nudge_x = 0, nudge_y = 0.25, increase_color = \"#207394\", decrease_color = \"#cd5241\", insignificant_color = \"#cdcdcd\", use_ggrepel = FALSE, theme = ggplot2::theme_bw() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"dea_result tibble containing differential expression analysis (DEA) results produced one members `tof_dea_*` function family. num_top_pairs integer representing number significant cluster-marker pairs labeled volcano plot. alpha numeric value 0 1 representing significance level p-value considered statistically significant. Defaults 0.05. point_size numeric value specifying size points volcano plot. label_size numeric value specifying size text labeling cluster-marker pairs. nudge_x numeric value specifying far cluster-marker pair labels adjusted left (`nudge_x` negative) right (`nudge_x` positive) avoid overlap plotted points. Passed geom_text, ignored `use_ggrepel` = TRUE. Defaults 0. nudge_y numeric value specifying far cluster-marker pair labels adjusted downwards (`nudge_y` negative) upwards (`nudge_y` positive) avoid overlap plotted points. Passed geom_text, ignored `use_ggrepel` = TRUE. Defaults 0.25. increase_color hex code specifying fill color used points corresponding cluster-marker pairs significant increases detected. decrease_color hex code specifying fill color used points corresponding cluster-marker pairs significant decreases detected. insignificant_color hex code specifying fill color used points corresponding cluster-marker pairs significant differences detected. use_ggrepel boolean value indicting geom_text_repel used plot labels cluster-marker pairs. Defaults FALSE. TRUE, ggrepel package must installed. theme ggplot2 theme apply volcano plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"","code":"# create a mock differential expression analysis result sim_dea_result <- dplyr::tibble( cluster_id = rep(letters, 2), marker = rep(c(\"cd45\", \"cd34\"), times = length(letters)), p_adj = runif(n = 2 * length(letters), min = 0, max = 0.5), mean_fc = runif(n = 2 * length(letters), min = 0.01, max = 10), significant = dplyr::if_else(p_adj < 0.05, \"*\", \"\") ) attr(sim_dea_result, which = \"dea_method\") <- \"t_unpaired\" # create the volcano plot volcano <- tof_plot_clusters_volcano(dea_result = sim_dea_result)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"function makes heatmap group--group marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap groups plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"","code":"tof_plot_heatmap( tof_tibble, y_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_ywise = FALSE, cluster_markers = TRUE, cluster_groups = TRUE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"tof_tibble `tof_tbl` `tibble`. y_col unquoted column name indicating column `tof_tibble` stores ids group cell belongs. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_ywise boolean value indicating heatmap rescale rows heatmap maximum value group 1 minimum value 0. Defaults FALSE. cluster_markers boolean value indicating heatmap order columns (.e. markers) using hierarchical clustering. Defaults TRUE. cluster_groups boolean value indicating heatmap order rows (.e. groups) using hierarchical clustering. Defaults TRUE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"Plot results glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"","code":"tof_plot_model(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"ggplot object. `tof_model` linear model, scatterplot predicted outcome vs. true outcome returned. `tof_model` two-class model, ROC curve returned. `tof_model` multiclass model, one-versus-ROC curve returned class. `tof_model` survival model, Kaplan-Meier curve returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # make the plot plot_1 <- tof_plot_model(tof_model = regression_model, new_data = new_tibble) # train a logistic regression classifier logistic_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) # make the plot plot_2 <- tof_plot_model(tof_model = logistic_model, new_data = new_tibble)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"Plot results linear glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"","code":"tof_plot_model_linear(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"ggplot object. Specifically, scatterplot predicted outcome vs. true outcome returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"Plot results two-class glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"","code":"tof_plot_model_logistic(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"ggplot object. Specifically, ROC curve..","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"Plot results multiclass glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"","code":"tof_plot_model_multinomial(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot. Defaults theme_bw.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"ggplot object. Specifically, one-versus-ROC curve (one class).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"Plot results survival glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"","code":"tof_plot_model_survival( tof_model, new_data, censor_size = 2.5, theme = ggplot2::theme_bw() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. censor_size numeric value indicating large plot tick marks representing censored values Kaplan-Meier curve. theme ggplot2 theme apply plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"ggplot object. Specifically, Kaplan-Meier curve.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"function makes heatmap sample--sample marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap sample IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"","code":"tof_plot_sample_features( feature_tibble, sample_col, feature_cols = where(tof_is_numeric), scale_featurewise = FALSE, scale_samplewise = FALSE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"feature_tibble tbl_df data.frame aggregated sample-level features, generated tof_extract_features. sample_col unquoted column name indicating column `tof_tibble` stores IDs sample. sample IDs present, numeric ID assigned row `feature_tibble` based row index. feature_cols Unquoted column names indicating column `feature_tibble` interpreted features plotted along x-axis heatmap. Supports tidyselect helpers. scale_featurewise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_samplewise boolean value indicating heatmap rescale rows heatmap maximum value sample 1 minimum value 0. Defaults FALSE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), sample_id = sample(paste0(\"sample\", 1:5), size = 1000, replace = TRUE) ) # extract cluster proportions in each simulated patient feature_data <- tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = sample_id ) # plot the heatmap heatmap <- tof_plot_sample_features(feature_tibble = feature_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"function makes heatmap sample--sample marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap sample IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"","code":"tof_plot_sample_heatmap( tof_tibble, sample_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_samplewise = FALSE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` stores ids sample cell belongs. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated sample cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_samplewise boolean value indicating heatmap rescale rows heatmap maximum value sample 1 minimum value 0. Defaults FALSE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), sample_id = sample(paste0(\"sample\", 1:5), size = 1000, replace = TRUE) ) heatmap <- tof_plot_sample_heatmap( tof_tibble = sim_data, sample_col = sample_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":null,"dir":"Reference","previous_headings":"","what":"Post-process transformed CyTOF data. — tof_postprocess","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"function transforms `tof_tibble` transformed ion counts mass cytometer back something looks like .fcs file Fluidigm software generates.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"","code":"tof_postprocess( tof_tibble = NULL, channel_cols = where(tof_is_numeric), redo_noise = FALSE, transform_fun = function(x) rev_asinh(x, shift_factor = 0, scale_factor = 0.2) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"tof_tibble `tof_tibble` `tibble`. channel_cols vector non-quoted column names indicating columns `tof_tibble` contain protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. redo_noise boolean value indicating whether add uniform noise CyTOF measurement aesthetic visualization purposes. See paper. Defaults FALSE transform_fun vectorized function apply column specified `channel_cols` post-processing. Defaults rev_asinh transformation (cofactor 5).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun` (noise added removed depending `redo_noise`).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 preprocessed_tof_tibble <- tof_preprocess(tof_tibble) # postprocess all numeric columns to reverse the preprocessing tof_postprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 Inf 22215276. 6.00e224 8.82 1411688. Inf #> 2 Inf 446205752. Inf 135. 859679. Inf #> 3 Inf 22215276. Inf 2634. 321. 8.51e277 #> 4 Inf 22215276. 2.24e254 3.47 1383. 8.29e254 #> 5 Inf 164149923. Inf 127. 38726. 3.70e280 #> 6 Inf 446205752. 1.14e211 163. 116. 3.29e272 #> 7 Inf 22215276. 2.62e219 79.1 175. 1.82e246 #> 8 Inf 164149923. 5.35e246 1876. 291228. 5.64e305 #> 9 Inf 446205752. Inf 55.3 24727. Inf #> 10 Inf 22215276. 2.38e236 5.05 2010273. Inf #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Use a trained elastic net model to predict fitted values from new data — tof_predict","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"function uses trained `tof_model` make predictions new data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"","code":"tof_predict( tof_model, new_data, prediction_type = c(\"response\", \"class\", \"link\", \"survival curve\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations predictions made. new_data provided, predictions made training data used fit model. prediction_type string indicating type prediction provided model: \"response\" (default) \"linear\" models, predicted response observation. \"two-class\" \"multiclass\" models, fitted probabilities class observation. \"survival\" models, fitted relative-risk observation. \"class\" applies \"two-class\" \"multiclass\" models. , class label corresponding class maximum fitted probability. \"link\" linear predictions model (output link function model family.) \"survival curve\" applies \"survival\" models. Returns tibble indicating patient's probability survival (1 - probability(event)) timepoint dataset. Obtained using survfit function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"tibble single column (`.pred`) containing predictions , multiclass models `prediction_type` == \"response\", tibble one column class. row output corresponds row `new_data` ( , `new_data` provided, row `tof_model`'s training data). latter case, sure check `tof_model$training_data` confirm order observations, resampling procedure can change ordering relative original input data.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # apply the model to new data tof_predict(tof_model = regression_model, new_data = new_tibble) #> # A tibble: 20 × 1 #> .pred #> #> 1 3.57 #> 2 2.35 #> 3 4.91 #> 4 4.72 #> 5 2.93 #> 6 1.97 #> 7 3.57 #> 8 3.52 #> 9 5.53 #> 10 2.48 #> 11 2.21 #> 12 3.35 #> 13 2.38 #> 14 2.91 #> 15 3.89 #> 16 3.45 #> 17 5.20 #> 18 5.53 #> 19 3.45 #> 20 3.69"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"Train recipe list recipes preprocessing sample-level cytometry data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"","code":"tof_prep_recipe(split_data, unprepped_recipe)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. easiest way generate use tof_split_data. Alternatively, unsplit tbl_df, though recommended. unprepped_recipe recipe object (`split_data` `rsplit` object `tbl_df`) list recipes (`split_data` `rset` object).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"split_data \"rsplit\" \"tbl_df\" object, return single prepped recipe. split_data \"rset\" object, return list prepped recipes specific fold resampling procedure.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"function transforms `tof_tbl` raw ion counts, reads, fluorescence intensity units directly measured cytometer using user-provided function. can used perform standard pre-processing steps (.e. arcsinh transformation) cytometry data analysis.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"","code":"tof_preprocess( tof_tibble = NULL, channel_cols = where(tof_is_numeric), undo_noise = FALSE, transform_fun = function(x) asinh(x/5) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. undo_noise boolean value indicating whether remove uniform noise Fluidigm software adds CyTOF measurements aesthetic visualization purposes. See paper. Defaults FALSE. transform_fun vectorized function apply protein value variance stabilization. Defaults asinh transformation (co-factor 5).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun` (noise removed removed depending `undo_noise`).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 tof_preprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 15.3 1.88 5.33 0.263 1.70 5.85 #> 2 14.9 2.05 5.83 0.731 1.67 5.71 #> 3 15.2 1.88 5.70 1.13 0.861 5.54 #> 4 13.7 1.88 5.45 0.129 1.06 5.46 #> 5 15.2 1.99 5.73 0.721 1.41 5.55 #> 6 14.4 2.05 5.27 0.760 0.708 5.52 #> 7 13.9 1.88 5.31 0.645 0.771 5.42 #> 8 14.2 1.99 5.42 1.09 1.58 5.64 #> 9 15.6 2.05 6.03 0.586 1.37 5.83 #> 10 9.75 1.88 5.38 0.177 1.73 5.78 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , … # preprocess all numeric columns using the log base 10 tranformation tof_preprocess(tof_tibble, transform_fun = log10) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 7.04 1.20 2.71 0.125 1.12 2.94 #> 2 6.85 1.28 2.93 0.601 1.11 2.88 #> 3 6.99 1.20 2.87 0.843 0.686 2.81 #> 4 6.36 1.20 2.77 -0.189 0.800 2.77 #> 5 6.98 1.26 2.89 0.594 0.984 2.81 #> 6 6.65 1.28 2.69 0.621 0.584 2.80 #> 7 6.44 1.20 2.70 0.539 0.628 2.75 #> 8 6.57 1.26 2.75 0.821 1.07 2.85 #> 9 7.18 1.28 3.02 0.491 0.964 2.93 #> 10 4.63 1.20 2.74 -0.0515 1.13 2.91 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"Read high-dimensional cytometry data .csv file tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"","code":"tof_read_csv(file_path = NULL, panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"file_path file path single .csv file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition). panel information obvious data read .csv file, information must provided manually user (unlike `tof_read_fcs`).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"Read data .fcs/.csv file directory .fcs/.csv files.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"","code":"tof_read_data(path = NULL, sep = \"|\", panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"path file path single file directory files. valid file types .fcs files .csv files containing high-dimensional cytometry data. sep Optional. string use separate antigen name associated metal column names output tibble. Defaults \"|\". used input file .fcs file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\". used input file .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"[c m+1] tibble row represents single cell (c total dataset) column represents high-dimensional cytometry measurement (m total dataset). one .fcs read , last column tibble (`file_name`) represent file name .fcs file cell read.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_read_data(input_file) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 11021370 16 517. 1.33 13.2 865. #> 2 7112446. 19 850. 3.99 12.7 756. #> 3 9722098 16 747. 6.96 4.85 639. #> 4 2267279. 16 585. 0.648 6.32 586. #> 5 9624729 18 773. 3.93 9.65 645. #> 6 4439897 19 485. 4.18 3.84 627. #> 7 2762526. 16 504. 3.46 4.25 566. #> 8 3746682. 18 567. 6.62 11.7 703. #> 9 15214280 19 1043. 3.10 9.20 853. #> 10 42699. 16 543. 0.888 13.6 813. #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"function reads high-dimensional cytometry data single .fcs file tidy data structure called `tof_tbl` (\"tof_tibble\"). tof_tibbles identical normal tibbles except additional attribute (\"panel\") stores information high-dimensional cytometry panel used data acquisition.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"","code":"tof_read_fcs(file_path = NULL, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"file_path file path single .fcs file. sep string use separate antigen name associated metal column names output tibble. Defaults \"|\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"Read high-dimensional cytometry data single .fcs .csv file tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"","code":"tof_read_file(file_path = NULL, sep = \"|\", panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"file_path file path single .fcs .csv file. sep string use separate antigen name associated metal column names output tibble. Defaults \"|\". used input file .fcs file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\". used input file .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition). panel information obvious data read .csv file, information must provided manually user.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"function wrapper around tidytof's tof_reduce_* function family. performs dimensionality reduction single-cell data using user-specified method (3 choices) method's corresponding input parameters","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"","code":"tof_reduce_dimensions( tof_tibble, ..., augment = TRUE, method = c(\"pca\", \"tsne\", \"umap\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"tof_tibble `tof_tbl` `tibble`. ... Arguments passed tof_reduce_* function corresponding embedding method. See tof_reduce_pca, tof_reduce_tsne, tof_reduce_umap. augment boolean value indicating output column-bind dimensionality-reduced embedding vectors cell new column `tof_tibble` (TRUE, default) tibble including low-dimensionality embeddings returned (FALSE). method method dimensionality reduction. Currently, PCA, tSNE, UMAP embedding supported.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated embedding space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 100), cd38 = rnorm(n = 100), cd34 = rnorm(n = 100), cd19 = rnorm(n = 100) ) # calculate pca tof_reduce_dimensions(tof_tibble = sim_data, method = \"pca\") #> # A tibble: 100 × 8 #> cd45 cd38 cd34 cd19 .pc1 .pc2 .pc3 .pc4 #> #> 1 -0.435 -1.61 0.636 0.405 1.04 0.0683 -1.27 0.666 #> 2 -1.43 -1.12 0.532 0.673 0.138 -0.144 -1.95 0.503 #> 3 -0.506 -0.435 -0.170 -0.587 -0.203 0.702 -0.453 0.0480 #> 4 1.38 -0.210 0.971 -0.444 1.41 -0.0798 0.697 -0.790 #> 5 -0.755 0.0102 -0.913 -1.88 -1.05 1.85 -0.0182 -0.250 #> 6 1.45 -0.217 0.0959 1.06 0.835 -1.07 0.949 0.387 #> 7 -0.164 0.0759 0.638 0.248 0.172 -0.426 -0.459 -0.510 #> 8 -0.0347 -1.10 -1.07 1.47 -0.198 -0.565 -0.135 1.90 #> 9 -0.528 0.557 -3.12 0.487 -2.77 0.297 1.04 1.80 #> 10 0.520 -0.244 0.676 -2.51 0.837 1.83 0.352 -1.38 #> # ℹ 90 more rows # calculate tsne tof_reduce_dimensions(tof_tibble = sim_data, method = \"tsne\") #> # A tibble: 100 × 6 #> cd45 cd38 cd34 cd19 .tsne1 .tsne2 #> #> 1 -0.435 -1.61 0.636 0.405 -2.95 3.44 #> 2 -1.43 -1.12 0.532 0.673 -2.36 4.03 #> 3 -0.506 -0.435 -0.170 -0.587 -0.234 2.25 #> 4 1.38 -0.210 0.971 -0.444 -3.05 -0.949 #> 5 -0.755 0.0102 -0.913 -1.88 0.443 3.37 #> 6 1.45 -0.217 0.0959 1.06 2.10 -3.11 #> 7 -0.164 0.0759 0.638 0.248 -0.676 -0.522 #> 8 -0.0347 -1.10 -1.07 1.47 0.142 -3.74 #> 9 -0.528 0.557 -3.12 0.487 4.06 1.46 #> 10 0.520 -0.244 0.676 -2.51 -3.85 1.09 #> # ℹ 90 more rows # calculate umap tof_reduce_dimensions(tof_tibble = sim_data, method = \"umap\") #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 100 × 6 #> cd45 cd38 cd34 cd19 .umap1 .umap2 #> #> 1 -0.435 -1.61 0.636 0.405 2.01 4.48 #> 2 -1.43 -1.12 0.532 0.673 2.85 4.72 #> 3 -0.506 -0.435 -0.170 -0.587 1.76 -1.98 #> 4 1.38 -0.210 0.971 -0.444 2.21 2.25 #> 5 -0.755 0.0102 -0.913 -1.88 2.20 -2.82 #> 6 1.45 -0.217 0.0959 1.06 -3.10 -1.22 #> 7 -0.164 0.0759 0.638 0.248 0.942 -0.870 #> 8 -0.0347 -1.10 -1.07 1.47 -4.01 -0.354 #> 9 -0.528 0.557 -3.12 0.487 -0.849 -0.123 #> 10 0.520 -0.244 0.676 -2.51 2.54 0.216 #> # ℹ 90 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform principal component analysis on single-cell data — tof_reduce_pca","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"function calculates principal components using single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"","code":"tof_reduce_pca( tof_tibble, pca_cols = where(tof_is_numeric), num_comp = 5, threshold = NA, center = TRUE, scale = TRUE, return_recipe = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"tof_tibble `tof_tbl` `tibble`. pca_cols Unquoted column names indicating columns `tof_tibble` use computing principal components. Defaults numeric columns. Supports tidyselect helpers. num_comp number PCA components calculate. Defaults 5. See step_pca. threshold double 0 1 representing fraction total variance covered components returned output. See step_pca. center boolean value indicating column centered mean 0 PCA analysis. Defaults TRUE. scale boolean value indicating column scaled standard deviation = 1 PCA analysis. Defaults TRUE. return_recipe boolean value indicating instead UMAP result, prepped recipe object containing PCA embedding returned. Set option TRUE want create PCA embedding using one dataset also want project new observations onto embedding space later.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated principal component space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) new_data <- dplyr::tibble( cd45 = rnorm(n = 50), cd38 = rnorm(n = 50), cd34 = rnorm(n = 50), cd19 = rnorm(n = 50) ) # calculate pca tof_reduce_pca(tof_tibble = sim_data, num_comp = 2) #> # A tibble: 200 × 2 #> .pc1 .pc2 #> #> 1 -0.489 0.432 #> 2 -0.486 -0.514 #> 3 0.422 -0.325 #> 4 -0.565 1.28 #> 5 1.55 -2.16 #> 6 0.664 0.406 #> 7 -1.20 -0.136 #> 8 0.518 0.481 #> 9 -0.0566 0.370 #> 10 0.739 -1.34 #> # ℹ 190 more rows # return recipe instead of embeddings pca_recipe <- tof_reduce_pca(tof_tibble = sim_data, return_recipe = TRUE) # apply recipe to new data recipes::bake(pca_recipe, new_data = new_data) #> # A tibble: 50 × 4 #> PC1 PC2 PC3 PC4 #> #> 1 -0.737 0.656 -0.752 -0.199 #> 2 -0.938 0.936 -0.101 -0.158 #> 3 0.290 -0.395 -0.00400 -0.517 #> 4 -0.157 -0.421 -0.593 1.12 #> 5 -1.31 1.01 0.753 -0.205 #> 6 -0.617 -2.82 0.273 -0.925 #> 7 -0.404 -0.134 0.271 0.228 #> 8 0.929 -0.0137 0.839 0.000129 #> 9 0.312 2.42 1.67 -0.0232 #> 10 -1.89 2.14 -1.09 0.0440 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"function calculates tSNE embedding using single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"","code":"tof_reduce_tsne( tof_tibble, tsne_cols = where(tof_is_numeric), num_comp = 2, perplexity = 30, theta = 0.5, max_iterations = 1000, verbose = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"tof_tibble `tof_tbl` `tibble`. tsne_cols Unquoted column names indicating columns `tof_tibble` use computing tSNE embedding. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_comp number tSNE components calculate embedding. Defaults 2. perplexity positive numeric value represents represents rough balance input data’s local global structure emphasized embedding. Smaller values emphasize local structure; larger values emphasize global structure. recommended range generally 5-50. Defaults 30. theta numeric value representing speed/accuracy tradeoff embedding. Set 0 exact tSNE; increase faster approximation. Defaults 0.5 max_iterations integer number iterations use embedding calculation. Defaults 1000. verbose boolean value indicating whether progress updates printed embedding calculation. Default FALSE. ... Additional arguments pass Rtsne.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated tSNE space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) # calculate tsne tof_reduce_tsne(tof_tibble = sim_data) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 0.491 4.86 #> 2 5.76 1.73 #> 3 3.00 -4.96 #> 4 -2.56 -4.14 #> 5 -4.99 -4.90 #> 6 2.02 -3.28 #> 7 -1.76 -7.35 #> 8 1.36 -6.55 #> 9 -5.08 4.51 #> 10 4.82 7.52 #> # ℹ 190 more rows # calculate tsne with only 2 columns tof_reduce_tsne(tof_tibble = sim_data, tsne_cols = c(cd34, cd38)) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 3.22 -5.14 #> 2 5.77 -8.41 #> 3 0.381 2.88 #> 4 -6.70 3.60 #> 5 -5.71 7.82 #> 6 -3.06 0.412 #> 7 -7.00 6.05 #> 8 1.10 3.51 #> 9 4.94 2.74 #> 10 -5.72 -5.85 #> # ℹ 190 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"function calculates UMAP embedding single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"","code":"tof_reduce_umap( tof_tibble, umap_cols = where(tof_is_numeric), num_comp = 2, neighbors = 5, min_dist = 0.01, learn_rate = 1, epochs = NULL, verbose = FALSE, n_threads = 1, return_recipe = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"tof_tibble `tof_tbl` `tibble`. umap_cols Unquoted column names indicating columns `tof_tibble` use computing UMAP embedding. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_comp integer number UMAP components. neighbors integer number nearest neighbors used construct target simplicial set. min_dist effective minimum distance embedded points. learn_rate Positive number learning rate optimization process. epochs Number iterations neighbor optimization. See umap details. verbose boolean indicating run details logged console. Defaults FALSE. n_threads Number threads use UMAP calculation. Defaults 1. return_recipe boolean value indicating instead UMAP result, prepped recipe object containing UMAP embedding returned. Set option TRUE want create UMAP embedding using one dataset also want project new observations onto embedding space later. ... Optional. options passed arguments umap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated UMAP space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) new_data <- dplyr::tibble( cd45 = rnorm(n = 50), cd38 = rnorm(n = 50), cd34 = rnorm(n = 50), cd19 = rnorm(n = 50) ) # calculate umap tof_reduce_umap(tof_tibble = sim_data) #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 200 × 2 #> .umap1 .umap2 #> #> 1 -2.83 -2.74 #> 2 0.581 -1.52 #> 3 3.81 0.0325 #> 4 -3.04 0.595 #> 5 0.267 -0.993 #> 6 -3.85 -0.836 #> 7 -1.89 1.52 #> 8 5.78 -0.180 #> 9 3.51 -1.97 #> 10 -2.14 0.703 #> # ℹ 190 more rows # calculate umap with only 2 columns tof_reduce_tsne(tof_tibble = sim_data, umap_cols = c(cd34, cd38)) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 -7.59 -1.06 #> 2 -1.97 5.26 #> 3 -1.46 -4.92 #> 4 -4.09 0.256 #> 5 -2.76 4.66 #> 6 -5.98 -1.23 #> 7 3.30 -0.344 #> 8 7.05 2.68 #> 9 1.88 7.55 #> 10 -3.34 1.99 #> # ℹ 190 more rows # return recipe umap_recipe <- tof_reduce_umap(tof_tibble = sim_data, return_recipe = TRUE) #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ # apply recipe to new data recipes::bake(umap_recipe, new_data = new_data) #> # A tibble: 50 × 2 #> UMAP1 UMAP2 #> #> 1 -0.114 3.45 #> 2 2.39 -2.68 #> 3 2.72 -0.965 #> 4 1.07 3.15 #> 5 2.33 1.42 #> 6 -4.80 -1.13 #> 7 -5.03 -0.379 #> 8 -0.114 2.93 #> 9 -0.00782 -3.22 #> 10 2.49 2.88 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":null,"dir":"Reference","previous_headings":"","what":"Set panel information from a tof_tibble — tof_set_panel","title":"Set panel information from a tof_tibble — tof_set_panel","text":"Set panel information tof_tibble","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set panel information from a tof_tibble — tof_set_panel","text":"","code":"tof_set_panel(tof_tibble, panel)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set panel information from a tof_tibble — tof_set_panel","text":"tof_tibble `tof_tbl`. panel tibble containing two columns (`metals` `antigens`) representing information panel","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set panel information from a tof_tibble — tof_set_panel","text":"`tof_tibble` containing information CyTOF panel used data acquisition data contained input `tof_tibble`. Two columns required: \"metals\" \"antigens\".","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set panel information from a tof_tibble — tof_set_panel","text":"","code":"# get current panel from an .fcs file input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) current_panel <- tof_get_panel(tof_tibble) # create a new panel (remove empty channels) new_panel <- dplyr::filter(current_panel, antigens != \"empty\") tof_set_panel(tof_tibble = tof_tibble, panel = new_panel) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 11021370 16 517. 1.33 13.2 865. #> 2 7112446. 19 850. 3.99 12.7 756. #> 3 9722098 16 747. 6.96 4.85 639. #> 4 2267279. 16 585. 0.648 6.32 586. #> 5 9624729 18 773. 3.93 9.65 645. #> 6 4439897 19 485. 4.18 3.84 627. #> 7 2762526. 16 504. 3.46 4.25 566. #> 8 3746682. 18 567. 6.62 11.7 703. #> 9 15214280 19 1043. 3.10 9.20 853. #> 10 42699. 16 543. 0.888 13.6 813. #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"function uses algorithm described Qiu et al., (2011) estimate local density cell `tof_tbl` `tibble` containing high-dimensional cytometry data. Briefly, algorithm involves counting number neighboring cells within sphere radius alpha surrounding cell. , using nn2 function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"","code":"tof_spade_density( tof_tibble, distance_cols = where(tof_is_numeric), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), num_alpha_cells = 2000L, alpha_multiplier = 5, max_neighbors = round(0.01 * nrow(tof_tibble)), normalize = TRUE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". num_alpha_cells integer indicating many cells `tof_tibble` randomly sampled `tof_tibble` order estimate `alpha`, radius sphere constructed around cell local density estimation. Alpha calculated taking median nearest-neighbor distance `num_alpha_cells` randomly-sampled cells multiplying `alpha_multiplier`. Defaults 2000. alpha_multiplier numeric value indicating multiplier used calculating `alpha`, radius sphere constructed around cell local density estimation. Alpha calculated taking median nearest-neighbor distance `num_alpha_cells` cells randomly-sampled `tof_tibble` multiplying `alpha_multiplier`. Defaults 5. max_neighbors integer indicating maximum number neighbors can counted within sphere surrounding given cell. Implemented reduce density estimation procedure's speed memory requirements. Defaults 1% number rows `tof_tibble`. normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional optional arguments pass tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"tibble single column named \".spade_density\" containing local density estimates input cell `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # perform the density estimation tof_spade_density(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 1 #> 2 1 #> 3 1 #> 4 1 #> 5 1 #> 6 1 #> 7 1 #> 8 1 #> 9 0.6 #> 10 1 #> # ℹ 990 more rows # perform the density estimation using cosine distance tof_spade_density( tof_tibble = sim_data, distance_function = \"cosine\", alpha_multiplier = 2 ) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 0.375 #> 2 0.125 #> 3 0.125 #> 4 0.375 #> 5 0.25 #> 6 0.75 #> 7 0.25 #> 8 0.25 #> 9 0.25 #> 10 0.375 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around # each cell tof_spade_density( tof_tibble = sim_data, alpha_multiplier = 2 ) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 0.9 #> 2 0.3 #> 3 0 #> 4 1 #> 5 0.1 #> 6 1 #> 7 0.4 #> 8 0.1 #> 9 0 #> 10 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split high-dimensional cytometry data into a training and test set — tof_split_data","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"Split high-dimensional cytometry data training test set","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"","code":"tof_split_data( feature_tibble, split_method = c(\"k-fold\", \"bootstrap\", \"simple\"), split_col, simple_prop = 3/4, num_cv_folds = 10, num_cv_repeats = 1L, num_bootstraps = 10, strata = NULL, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"feature_tibble tibble row represents sample- patient- level observation, produced tof_extract_features. split_method Either string logical vector specifying perform split. string, valid options include k-fold cross validation (\"k-fold\"; default), bootstrapping (\"bootstrap\"), single binary split (\"simple\"). logical vector, contain one entry row `feature_tibble` indicating row included training set (TRUE) excluded validation/test set (FALSE). Ignored entirely `split_col` specified. split_col unquoted column name logical column `feature_tibble` indicating row included training set (TRUE) excluded validation/test set (FALSE). simple_prop numeric value 0 1 indicating proportion data used training. Defaults 3/4. Ignored split_method \"simple\". num_cv_folds integer indicating many cross-validation folds used. Defaults 10. Ignored split_method \"k-fold\". num_cv_repeats integer indicating many independent cross-validation replicates used (.e. many num_cv_fold splits performed). Defaults 1. Ignored split_method \"k-fold\". num_bootstraps integer indicating many independent bootstrap replicates used. Defaults 25. Ignored split_method \"bootstrap\". strata unquoted column name representing column feature_tibble used stratify data splitting. Defaults NULL (stratification). ... Optional additional arguments pass vfold_cv k-fold cross validation, bootstraps bootstrapping, initial_split simple splitting.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"k-fold cross validation bootstrapping, \"rset\" object; simple splitting, \"rsplit\" object. details, see rsample.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 50), rep(1, times = 50)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) # split the dataset into 10 CV folds tof_split_data( feature_tibble = feature_tibble, split_method = \"k-fold\" ) #> # 10-fold cross-validation #> # A tibble: 10 × 2 #> splits id #> #> 1 Fold01 #> 2 Fold02 #> 3 Fold03 #> 4 Fold04 #> 5 Fold05 #> 6 Fold06 #> 7 Fold07 #> 8 Fold08 #> 9 Fold09 #> 10 Fold10 # split the dataset into 10 bootstrap resamplings tof_split_data( feature_tibble = feature_tibble, split_method = \"bootstrap\" ) #> # Bootstrap sampling #> # A tibble: 10 × 2 #> splits id #> #> 1 Bootstrap01 #> 2 Bootstrap02 #> 3 Bootstrap03 #> 4 Bootstrap04 #> 5 Bootstrap05 #> 6 Bootstrap06 #> 7 Bootstrap07 #> 8 Bootstrap08 #> 9 Bootstrap09 #> 10 Bootstrap10 # split the dataset into a single training/test set # stratified by the \"class\" column tof_split_data( feature_tibble = feature_tibble, split_method = \"simple\", strata = class ) #> #> <74/26/100>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"Split dimensionality reduction data tidytof combines SingleCellExperiment conversion","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"","code":"tof_split_tidytof_reduced_dimensions(sce)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"sce SingleCellExperiment entry named \"tidytof_reduced_dimensions\" reducedDims slot.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"SingleCellExperiment separate entries named \"tidytof_pca\", \"tidytof_umap\", \"tidytof_tsne\" reducedDims slots (one dimensionality reduction methods tidytof native support).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"function uses training set/test set paradigm tune fit elastic net model using variety user-specified details. Tuning can performed using either simple training vs. test set split, k-fold cross-validation, bootstrapping, multiple preprocessing options available.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"","code":"tof_train_model( split_data, unsplit_data, predictor_cols, response_col = NULL, time_col = NULL, event_col = NULL, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), hyperparameter_grid = tof_create_grid(), standardize_predictors = TRUE, remove_zv_predictors = FALSE, impute_missing_predictors = FALSE, optimization_metric = \"tidytof_default\", best_model_type = c(\"best\", \"best with sparsity\"), num_cores = 1 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. easiest way generate use tof_split_data. unsplit_data tibble containing sample-level data use modeling without resampling. using resampling method advised, argument provides interface fit model without using cross-validation bootstrap resampling. Ignored split_data provided. predictor_cols Unquoted column names indicating columns data contained `split_data` used predictors elastic net model. Supports tidyselect helpers. response_col Unquoted column name indicating column data contained `split_data` used outcome \"two-class\", \"multiclass\", \"linear\" elastic net model. Must factor \"two-class\" \"multiclass\" models must numeric \"linear\" models. Ignored `model_type` \"survival\". time_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must numeric. Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". event_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must binary column - values either 0 1 (1 indicating adverse event) FALSE TRUE (TRUE indicating adverse event). Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. hyperparameter_grid hyperparameter grid indicating values elastic net penalty (lambda) elastic net mixture (alpha) hyperparamters used model tuning. Generate grid using tof_create_grid. standardize_predictors logical value indicating numeric predictor columns standardized (centered scaled) model fitting, standard practice elastic net regularization. Defaults TRUE. remove_zv_predictors logical value indicating predictor columns near-zero variance removed model fitting using step_nzv. Defaults FALSE. impute_missing_predictors logical value indicating predictor columns missing values imputed using k-nearest neighbors model fitting (see step_impute_knn). Imputation performed using observation's 5 nearest-neighbors. Defaults FALSE. optimization_metric string indicating optimization metric used hyperparameter selection model tuning. Valid values depend model_type. \"linear\" models, choices \"mse\" (mean squared error predictions; default) \"mae\" (mean absolute error predictions). \"two-class\" models, choices \"roc_auc\" (area Receiver-Operating Curve classification; default), \"misclassification error\" (proportion misclassified observations), \"binomial_deviance\" (see deviance.glmnet), \"mse\" (mean squared error logit function), \"mae\" (mean absolute error logit function). \"multiclass\" models, choices \"roc_auc\" (area Receiver-Operating Curve classification using Hand-Till generalization ROC AUC multiclass models roc_auc; default), \"misclassification error\" (proportion misclassified observations), \"multinomial_deviance\" (see deviance.glmnet), \"mse\" \"mae\" . \"survival\" models, choices \"concordance_index\" (Harrel's C index; see deviance.glmnet) \"partial_likelihood_deviance\" (see deviance.glmnet). best_model_type Currently unused. num_cores Integer indicating many cores used parallel processing fitting multiple models. Defaults 1. Overhead separate models across multiple cores can high, significant speedup unlikely observed unless many large models fit.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"`tof_model`, S3 class includes elastic net model best performance (assessed via cross-validation, bootstrapping, simple splitting depending `split_data`) across tested hyperparameter value combinations. `tof_models` store following information: model final elastic net (\"glmnet\") model, chosen selecting elastic net hyperparameters best `optimization_metric` performance validation sets resample used train model (average) recipe recipe used data preprocessing mixture optimal mixture hyperparameter (alpha) glmnet model penalty optimal penalty hyperparameter (lambda) glmnet model model_type string indicating type glmnet model fit outcome_colnames character vector representing names columns training data modeled outcome variables training_data tibble containing (preprocessed) data used train model tuning_metrics tibble containing validation set performance metrics (model predictions) resample fold model tuning. log_rank_thresholds survival models , tibble containing information relative-risk thresholds can used split training data 2 risk groups (low- high-risk) based final model's predictions. relative-risk threshold, log-rank test p-value indicator threshold gives significant separation provided. best_log_rank_threshold survival models , numeric value representing relative-risk threshold yields significant log-rank test separating training data low- high-risk groups.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) #> A linear `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 4 × 2 #> feature coefficient #> #> 1 (Intercept) 3.61 #> 2 pstat5 1.16 #> 3 cd45 0.749 #> 4 cd34 0.0891 # train a logistic regression classifier tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) #> A two-class `tof_model` with a mixture parameter (alpha) of 0.25 and a penalty parameter (lambda) of 1e+00 #> # A tibble: 2 × 2 #> feature coefficient #> #> 1 pstat5 -0.0512 #> 2 (Intercept) 0.00000244 # train a cox regression survival model tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), time_col = time_to_event, event_col = event, model_type = \"survival\" ) #> A survival `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 3 × 2 #> feature coefficient #> #> 1 cd45 -0.314 #> 2 cd34 0.238 #> 3 pstat5 -0.145"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform raw high-dimensional cytometry data. — tof_transform","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"function transforms `tof_tbl` raw ion counts, reads, fluorescence intensity units directly measured cytometer using user-provided function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"","code":"tof_transform( tof_tibble = NULL, channel_cols = where(tof_is_numeric), transform_fun )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. transform_fun vectorized function apply protein value variance stabilization.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 tof_preprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 15.3 1.88 5.33 0.263 1.70 5.85 #> 2 14.9 2.05 5.83 0.731 1.67 5.71 #> 3 15.2 1.88 5.70 1.13 0.861 5.54 #> 4 13.7 1.88 5.45 0.129 1.06 5.46 #> 5 15.2 1.99 5.73 0.721 1.41 5.55 #> 6 14.4 2.05 5.27 0.760 0.708 5.52 #> 7 13.9 1.88 5.31 0.645 0.771 5.42 #> 8 14.2 1.99 5.42 1.09 1.58 5.64 #> 9 15.6 2.05 6.03 0.586 1.37 5.83 #> 10 9.75 1.88 5.38 0.177 1.73 5.78 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , … # preprocess all numeric columns using the log base 10 tranformation tof_preprocess(tof_tibble, transform_fun = log10) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 7.04 1.20 2.71 0.125 1.12 2.94 #> 2 6.85 1.28 2.93 0.601 1.11 2.88 #> 3 6.99 1.20 2.87 0.843 0.686 2.81 #> 4 6.36 1.20 2.77 -0.189 0.800 2.77 #> 5 6.98 1.26 2.89 0.594 0.984 2.81 #> 6 6.65 1.28 2.69 0.621 0.584 2.80 #> 7 6.44 1.20 2.70 0.539 0.628 2.75 #> 8 6.57 1.26 2.75 0.821 1.07 2.85 #> 9 7.18 1.28 3.02 0.491 0.964 2.93 #> 10 4.63 1.20 2.74 -0.0515 1.13 2.91 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":null,"dir":"Reference","previous_headings":"","what":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"Tune elastic net model's hyperparameters multiple resamples","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"","code":"tof_tune_glmnet( split_data, prepped_recipe, hyperparameter_grid, model_type, outcome_cols, optimization_metric = \"tidytof_default\", num_cores = 1 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"split_data `rsplit` `rset` object rsample package. easiest way generate use tof_split_data. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe Either single recipe object (`split_data` `rsplit` object `tbl_df`) list recipes (`split_data` `rset` object) entry list corresponds resample `split_data`. hyperparameter_grid hyperparameter grid indicating values elastic net penalty (lambda) elastic net mixture (alpha) hyperparameters used model tuning. Generate grid using tof_create_grid. model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. outcome_cols Unquoted column name(s) indicating column(s) data contained `split_data` used outcome elastic net model. survival models, two columns selected; others, one column selected. optimization_metric string indicating optimization metric used hyperparameter selection model tuning. Valid values depend model_type. num_cores Integer indicating many cores used parallel processing fitting multiple models. Defaults 1. Overhead separate models across multiple cores can high, significant speedup unlikely observed unless many large models fit.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"tibble containing summary model's performance resampling iteration across hyperparameter combinations. contain 3 columns: \"splits\" (list-col containing resampling iteration's `rsplit` object), \"id\" (name resampling iteration), \"performance_metrics\" (list-col containing performance metrics resampling iteration. row \"performance_metrics\" tibble columns \"mixture\" \"penalty\" several additional columns containing performance metrics model mixture/penalty combination). See tof_fit_split additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"function performs distance-based upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). calculating distance (either mahalanobis, cosine, pearson) cell `tof_tibble` centroid cluster `reference_tibble`, sorting cells cluster corresponding closest centroid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"","code":"tof_upsample( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), ..., augment = TRUE, method = c(\"distance\", \"neighbor\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. ... Additional arguments pass `tof_upsample_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"distance\" (default) \"neighbor\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding upsampled cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using distance to cluster centroids tof_upsample( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, method = \"distance\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .upsample_cluster #> #> 1 -0.814 0.0914 0.0564 -0.667 a #> 2 -0.450 -1.16 0.0757 1.25 a #> 3 -0.459 0.707 -0.119 1.27 a #> 4 -0.973 -0.875 -0.715 0.809 a #> 5 -0.994 1.14 -0.691 -1.24 b #> 6 0.471 -1.62 0.389 0.621 b #> 7 0.490 -0.331 1.02 -0.268 a #> 8 0.395 0.234 -0.0729 1.65 b #> 9 -0.789 0.679 0.464 -1.01 b #> 10 0.199 -1.54 -0.116 0.434 b #> # ℹ 990 more rows # upsample using distance to nearest neighbor tof_upsample( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, method = \"neighbor\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .upsample_cluster #> #> 1 -0.814 0.0914 0.0564 -0.667 a #> 2 -0.450 -1.16 0.0757 1.25 b #> 3 -0.459 0.707 -0.119 1.27 b #> 4 -0.973 -0.875 -0.715 0.809 a #> 5 -0.994 1.14 -0.691 -1.24 a #> 6 0.471 -1.62 0.389 0.621 b #> 7 0.490 -0.331 1.02 -0.268 b #> 8 0.395 0.234 -0.0729 1.65 b #> 9 -0.789 0.679 0.464 -1.01 b #> 10 0.199 -1.54 -0.116 0.434 a #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"function performs distance-based upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). calculating distance (either mahalanobis, cosine, pearson) cell `tof_tibble` centroid cluster `reference_tibble`, sorting cells cluster corresponding closest centroid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"","code":"tof_upsample_distance( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), parallel_cols, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1L, return_distances = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. parallel_cols Optional. Unquoted column names indicating columns `tof_tibble` use breaking data order parallelize upsampling using `foreach` `doParallel` backend. Supports tidyselect helpers. distance_function string indicating distance function used perform upsampling. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). return_distances boolean value indicating whether returned result include one column, cluster ids corresponding row `tof_tibble` (return_distances = FALSE, default), returned result include additional columns representing distance row `tof_tibble` reference subpopulation centroids (return_distances = TRUE).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"`return_distances = FALSE`, tibble one column named `.upsample_cluster`, character vector length `nrow(tof_tibble)` indicating id reference cluster cell (.e. row) `tof_tibble` assigned. `return_distances = TRUE`, tibble `nrow(tof_tibble)` rows num_clusters + 1 columns, num_clusters number clusters `reference_tibble`. row represents cell `tof_tibble`, num_clusters columns represent distance cell reference subpopulations' cluster centroids. final column represents cluster id reference subpopulation minimum distance cell represented row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using mahalanobis distance tof_upsample_distance( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 b #> 2 b #> 3 b #> 4 b #> 5 b #> 6 b #> 7 a #> 8 b #> 9 b #> 10 b #> # ℹ 990 more rows # upsample using cosine distance tof_upsample_distance( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, distance_function = \"cosine\" ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 a #> 2 a #> 3 b #> 4 b #> 5 a #> 6 b #> 7 a #> 8 b #> 9 b #> 10 b #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"function performs upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). finding cell `tof_tibble`'s nearest neighbor `reference_tibble` assigning cluster nearest neighbor belongs. nearest neighbor calculation can performed either euclidean cosine distance.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"","code":"tof_upsample_neighbor( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), num_neighbors = 1L, distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_neighbors integer indicating many neighbors used nearest neighbor calculation. Clusters assigned based majority vote. distance_function string indicating distance function used perform upsampling. Options \"euclidean\" (default) \"cosine\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"tibble one column named `.upsample_cluster`, character vector length `nrow(tof_tibble)` indicating id reference cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using euclidean distance tof_upsample_neighbor( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 a #> 2 a #> 3 a #> 4 a #> 5 a #> 6 b #> 7 a #> 8 b #> 9 a #> 10 b #> # ℹ 990 more rows # upsample using cosine distance tof_upsample_neighbor( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, distance_function = \"cosine\" ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 a #> 2 a #> 3 a #> 4 a #> 5 b #> 6 b #> 7 b #> 8 b #> 9 b #> 10 a #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Write a series of .csv files from a tof_tbl — tof_write_csv","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"function takes given `tof_tbl` writes single-cell data contains .csv files within directory located `out_path`. `group_cols` argument specifies rows `tof_tbl` (cell) broken separate .csv files","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"","code":"tof_write_csv(tof_tibble, group_cols, out_path, sep = \"_\", file_name)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"tof_tibble `tof_tbl` `tibble`. group_cols Optional. Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults NULL (cells written single file). out_path system path indicating directory output .csv files saved. directory exist, created. sep Delimiter used values `group_cols` create output .csv file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"function return anything. Instead, side-effect saving .csv files `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"Write data (form `tof_tbl`) either .csv .fcs file storage.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"","code":"tof_write_data( tof_tibble = NULL, group_cols, out_path = NULL, format = c(\"fcs\", \"csv\"), sep = \"_\", file_name )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"tof_tibble `tof_tbl` `tibble`. group_cols Optional. Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults grouping (cells written single file). out_path Path directory output files saved. format format files written. Currently supports .csv .fcs files sep Delimiter used values `group_cols` create output .csv/.fcs file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"function explicitly return values. Instead, writes .csv /.fcs files specified `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"function takes given `tof_tbl` writes single-cell data contains .fcs files within directory located `out_path`. `group_cols` argument specifies rows `tof_tbl` (cell) broken separate .fcs files","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"","code":"tof_write_fcs(tof_tibble, group_cols, out_path, sep = \"_\", file_name)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults NULL (cells written single file). out_path system path indicating directory output .csv files saved. directory exist, created. sep Delimiter used values `group_cols` create output .fcs file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"function return anything. Instead, side-effect saving .fcs files `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":null,"dir":"Reference","previous_headings":"","what":"Select variables with a function — where","title":"Select variables with a function — where","text":"copy , selection helper selects variables predicate function returns TRUE. See language details tidyselection.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select variables with a function — where","text":"","code":"where(fn)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select variables with a function — where","text":"fn function returns TRUE FALSE (technically, predicate function). Can also purrr-like formula.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select variables with a function — where","text":"predicate can used select columns data.frame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Select variables with a function — where","text":"help file replicated verbatim tidyselect-package.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Select variables with a function — where","text":"Lionel Henry Hadley Wickham (2021). tidyselect: Select Set Strings. R package version 1.1.1. https://CRAN.R-project.org/package=tidyselect","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Select variables with a function — where","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0995","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.5","title":"tidytof 0.99.5","text":"NEW FEATURES Update dependencies. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0994","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.4","title":"tidytof 0.99.4","text":"NEW FEATURES Update tests. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0993","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.3","title":"tidytof 0.99.3","text":"NEW FEATURES Update docs. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0992","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.2","title":"tidytof 0.99.2","text":"NEW FEATURES Update docs. SIGNIFICANT USER-VISIBLE CHANGES None BUG FIXES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0992-1","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.2","title":"tidytof 0.99.2","text":"NEW FEATURES Removed unnecessary files caused build fail. SIGNIFICANT USER-VISIBLE CHANGES None BUG FIXES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0990","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.0","title":"tidytof 0.99.0","text":"NEW FEATURES Added NEWS.md file track changes package. SIGNIFICANT USER-VISIBLE CHANGES Submitted Bioconductor BUG FIXES None","code":""}] +[{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":null,"dir":"","previous_headings":"","what":"Contributing to tidytof","title":"Contributing to tidytof","text":"outlines propose change tidytof. detailed info contributing , tidyverse packages, please see development contributing guide.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"fixing-typos","dir":"","previous_headings":"","what":"Fixing typos","title":"Contributing to tidytof","text":"can fix typos, spelling mistakes, grammatical errors documentation directly using GitHub web interface, long changes made source file. generally means ’ll need edit roxygen2 comments .R, .Rd file. can find .R file generates .Rd reading comment first line.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"bigger-changes","dir":"","previous_headings":"","what":"Bigger changes","title":"Contributing to tidytof","text":"want make bigger change, ’s good idea first file issue make sure someone team agrees ’s needed. ’ve found bug, please file issue illustrates bug minimal reprex (also help write unit test, needed).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"pull-request-process","dir":"","previous_headings":"Bigger changes","what":"Pull request process","title":"Contributing to tidytof","text":"Fork package clone onto computer. haven’t done , recommend using usethis::create_from_github(\"keyes-timothy/tidytof\", fork = TRUE). Install development dependencies devtools::install_dev_deps(), make sure package passes R CMD check running devtools::check(). R CMD check doesn’t pass cleanly, ’s good idea ask help continuing. Create Git branch pull request (PR). recommend using usethis::pr_init(\"brief-description--change\"). Make changes, commit git, create PR running usethis::pr_push(), following prompts browser. title PR briefly describe change. body PR contain Fixes #issue-number. user-facing changes, add bullet top NEWS.md (.e. just first header). Follow style described https://style.tidyverse.org/news.html.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"code-style","dir":"","previous_headings":"Bigger changes","what":"Code style","title":"Contributing to tidytof","text":"New code follow tidyverse style guide. can use styler package apply styles, please don’t restyle code nothing PR. use roxygen2, Markdown syntax, documentation. use testthat unit tests. Contributions test cases included easier accept.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/CONTRIBUTING.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Contributing to tidytof","text":"Please note tidytof project released Contributor Code Conduct. contributing project agree abide terms.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2020 Timothy Keyes Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/clustering.html","id":"clustering-with-tof_cluster","dir":"Articles","previous_headings":"","what":"Clustering with tof_cluster()","title":"Clustering and metaclustering","text":"demonstrate, can apply PhenoGraph clustering algorithm tidytof’s built-phenograph_data. Note phenograph_data contains 3000 total cells (1000 3 clusters identified original PhenoGraph publication). demonstration purposes, also metacluster PhenoGraph clusters using k-means clustering. outputs tof_cluster() tof_metacluster() tof_tbl identical input tibble, now addition additional column (case, “.phenograph_cluster” “.kmeans_metacluster”) encodes cluster id cell input tof_tbl. Note output columns added tibble tof_tbl tidytof begin full-stop (“.”) reduce likelihood collisions existing column names. output tof_cluster tof_tbl, can use dplyr’s count method assess accuracy clustering procedure compared original clustering PhenoGraph paper. , can see clustering procedure groups cells PhenoGraph cluster one another (small number mistakes). change clustering algorithm tof_cluster uses, alter method flag. change columns used compute clusters, change cluster_cols flag. finally, want return one-column tibble includes cluster labels (opposed cluster labels added new column input tof_tbl), set augment FALSE.","code":"data(phenograph_data) set.seed(203L) phenograph_clusters <- phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = starts_with(\"cd\"), num_neighbors = 50L, distance_function = \"cosine\", method = \"phenograph\" ) |> tof_metacluster( cluster_col = .phenograph_cluster, metacluster_cols = starts_with(\"cd\"), num_metaclusters = 3L, method = \"kmeans\" ) phenograph_clusters |> dplyr::select(sample_name, .phenograph_cluster, .kmeans_metacluster) |> head() #> # A tibble: 6 × 3 #> sample_name .phenograph_cluster .kmeans_metacluster #> #> 1 H1_PhenoGraph_cluster1 6 2 #> 2 H1_PhenoGraph_cluster1 1 2 #> 3 H1_PhenoGraph_cluster1 6 2 #> 4 H1_PhenoGraph_cluster1 6 2 #> 5 H1_PhenoGraph_cluster1 6 2 #> 6 H1_PhenoGraph_cluster1 6 2 phenograph_clusters |> dplyr::count(phenograph_cluster, .kmeans_metacluster, sort = TRUE) #> # A tibble: 4 × 3 #> phenograph_cluster .kmeans_metacluster n #> #> 1 cluster2 3 1000 #> 2 cluster3 1 1000 #> 3 cluster1 2 995 #> 4 cluster1 1 5 # use the kmeans algorithm phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"kmeans\" ) # use the flowsom algorithm phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"flowsom\" ) # will result in a tibble with only 1 column (the cluster labels) phenograph_data |> tof_preprocess() |> tof_cluster( cluster_cols = contains(\"cd\"), method = \"kmeans\", augment = FALSE ) |> head() #> # A tibble: 6 × 1 #> .kmeans_cluster #> #> 1 2 #> 2 1 #> 3 19 #> 4 9 #> 5 2 #> 6 9"},{"path":"https://keyes-timothy.github.io/tidytof/articles/clustering.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Clustering and metaclustering","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.1 viridisLite_0.4.2 timeDate_4032.109 #> [4] farver_2.1.1 viridis_0.6.5 ggraph_2.2.1 #> [7] fastmap_1.1.1 tweenr_2.0.3 rpart_4.1.23 #> [10] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.1 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.9 #> [19] tools_4.3.3 igraph_2.0.3 utf8_1.2.4 #> [22] yaml_2.3.8 data.table_1.15.2 knitr_1.45 #> [25] graphlayouts_1.1.1 withr_3.0.0 purrr_1.0.2 #> [28] RProtoBufLib_2.14.1 BiocGenerics_0.48.1 desc_1.4.3 #> [31] nnet_7.3-19 grid_4.3.3 polyclip_1.10-6 #> [34] stats4_4.3.3 fansi_1.0.6 RcppHNSW_0.6.0 #> [37] future_1.33.1 colorspace_2.1-0 ggplot2_3.5.0 #> [40] globals_0.16.3 scales_1.3.0 iterators_1.0.14 #> [43] MASS_7.3-60.0.1 cli_3.6.2 rmarkdown_2.26 #> [46] ragg_1.3.0 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.2 #> [52] ggforce_0.4.2 stringr_1.5.1 splines_4.3.3 #> [55] parallel_4.3.3 matrixStats_1.2.0 vctrs_0.6.5 #> [58] hardhat_1.3.1 glmnet_4.1-8 Matrix_1.6-5 #> [61] jsonlite_1.8.8 cytolib_2.14.1 hms_1.1.3 #> [64] S4Vectors_0.40.2 ggrepel_0.9.5 listenv_0.9.1 #> [67] systemfonts_1.0.6 foreach_1.5.2 gower_1.0.1 #> [70] tidyr_1.3.1 jquerylib_0.1.4 recipes_1.0.10 #> [73] parallelly_1.37.1 glue_1.7.0 pkgdown_2.0.7 #> [76] codetools_0.2-19 stringi_1.8.3 lubridate_1.9.3 #> [79] gtable_0.3.4 shape_1.4.6.1 munsell_0.5.0 #> [82] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.7 #> [85] ipred_0.9-14 lava_1.8.0 R6_2.5.1 #> [88] textshaping_0.3.7 doParallel_1.0.17 tidygraph_1.3.1 #> [91] evaluate_0.23 Biobase_2.62.0 lattice_0.22-5 #> [94] readr_2.1.5 memoise_2.0.1 bslib_0.6.2 #> [97] class_7.3-22 Rcpp_1.0.12 prodlim_2023.08.28 #> [100] gridExtra_2.3 xfun_0.42 fs_1.6.3 #> [103] pkgconfig_2.0.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"general-guidelines","dir":"Articles","previous_headings":"","what":"General Guidelines","title":"How to contribute code","text":"extend tidytof include new algorithm - example, one ’ve just developed - can take 1 2 general strategies (cases, may take !). first write tidytof-style verb algorithm can included standalone package. case, benefit writing tidytof-style verb algorithm taking advantage tidytof’s design schema make algorithm easy users access without learning much () new syntax still allowing maintain code base independently team. second approach write tidytof-style function ’d like team add tidytof next release. case, code review process take bit time, also allow teams collaborate provide greater degree critical feedback one another well share burden code maintenance future. either case, ’re welcome contact tidytof team review code via pull request /issue tidytof GitHub page. tutorial may helpful don’t lot experience collaborating programmers via GitHub. open request, can submit code team reviewed. Whether want method incorporated tidytof ’re simply looking external code review/feedback team, please mention request.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"code-style","dir":"Articles","previous_headings":"","what":"Code style","title":"How to contribute code","text":"tidytof uses tidyverse style guide. Adhering tidyverse style something team expect code incorporated tidytof, ’s also something encourage functions write analysis packages. experience, best code written just executed, also read humans! also many tools can use lint automatically style R code, {lintr} {styler} packages.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"testing","dir":"Articles","previous_headings":"","what":"Testing","title":"How to contribute code","text":"addition written well-styled code, encourage write unit tests every function write. common practice software engineering world, common probably (!) bioinformatics community. tidytof team uses {testthat} package unit tests, ’s great tutorial .","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"general-principles","dir":"Articles","previous_headings":"How to contribute","what":"General principles","title":"How to contribute code","text":"important part writing function extends tidytof adhere tidytof verb syntax. exceptions, tidytof functions follow specific, shared syntax involves 3 types arguments always occur order. argument types follows: almost tidytof functions, first argument data frame (tibble). enables use pipe (|>) multi-step calculations, means first argument functions implicit (passed previous function using pipe). second group arguments called column specifications, end suffix _col _cols. Column specifications unquoted column names tell tidytof verb columns compute particular operation. example, cluster_cols argument tof_cluster allows user specify column input data frames used perform clustering. Regardless verb requires , column specifications support tidyselect helpers follow rules tidyselection tidyverse verbs like dplyr::select() tidyr::pivot_longer(). Finally, third group arguments tidytof verb called method specifications, ’re comprised every argument isn’t input data frame column specification. Whereas column specifications represent columns used perform operation, method specifications represent details operation performed. example, tof_cluster_phenograph() function requires method specification num_neighbors, specifies many nearest neighbors used construct PhenoGraph algorithm’s k-nearest-neighbor graph. exceptions, tidytof extension include 3 argument types (order). addition, functions extend tidytof name starts prefix tof_. make easier users find tidytof functions using text completion functionality included development environments.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"contributing-a-new-method-to-an-existing-tidytof-verb","dir":"Articles","previous_headings":"How to contribute","what":"Contributing a new method to an existing {tidytof} verb","title":"How to contribute code","text":"tidytof currently includes multiple verbs perform fundamental single-cell data manipulation tasks. Currently, tidytof’s extensible verbs following: tof_analyze_abundance: Perform differential cluster abundance analysis tof_analyze_expression: Perform differential marker expression analysis tof_annotate_clusters: Annotate clusters manual IDs tof_batch_correct: Perform batch correction tof_cluster: Cluster cells subpopulations tof_downsample: Subsample dataset smaller number cells tof_extract: Calculate sample-level summary statistics tof_metacluster: Metacluster clusters smaller number subpopulations tof_plot_cells: Plot cell-level data tof_plot_clusters: Plot cluster-level data tof_plot_model: Plot results sample-level model tof_read_data: Read data memory disk tof_reduce_dimensions: Perform dimensionality reduction tof_transform: Transform marker expression values vectorized fashion tof_upsample: Assign new cells existing clusters (defined downsample dataset) tof_write_data: Write data memory disk tidytof verb wraps family related functions perform basic task. example, tof_cluster verb wrapper following functions: tof_cluster_ddpr, tof_cluster_flowsom, tof_cluster_kmeans, tof_cluster_phenograph. functions implement different clustering algorithm, share underlying logic standardized tof_cluster abstraction. practice, means users can apply DDPR, FlowSOM, K-means, PhenoGraph clustering algorithms datasets either calling one tof_cluster_* functions directly, calling tof_cluster method argument set appropriate value (“ddpr”, “flowsom”, “kmeans”, “phenograph”, respectively). extend existing tidytof verb, write function whose name fits pattern tof_{verb name}_*, “*” represents name algorithm used perform computation. function definition, try share many arguments possible tidytof verb ’re extending, return output object described “Value” heading help file verb extended. example, suppose wanted write tidytof-style interface new clustering algorithm “supercluster”, performs k-means clustering dataset twice outputs final cluster assignment equal two k-means cluster assignments spliced together. add supercluster algorithm tidytof, might write function like : example , note tof_cluster_supercluster named using tof_{verb name}_* style, function definition uses tof_tibble cluster_cols arguments tof_cluster, returned output object tof_tbl single column encoding cluster ids cells tof_tibble.","code":"#' Perform superclustering on high-dimensional cytometry data. #' #' This function applies the silly, hypothetical clustering algorithm #' \"supercluster\" to high-dimensional cytometry data using user-specified #' input variables/cytometry measurements. #' #' @param tof_tibble A `tof_tbl` or `tibble`. #' #' @param cluster_cols Unquoted column names indicating which columns in #' `tof_tibble` to use in computing the supercluster clusters. #' Supports tidyselect helpers. #' #' @param num_kmeans_clusters An integer indicating how many clusters should be #' used for the two k-means clustering steps. #' #' @param sep A string to use when splicing the 2 k-means clustering assignments #' to one another. #' #' @param ... Optional additional parameters to pass to #' \\code{\\link[tidytof]{tof_cluster_kmeans}} #' #' @return A tibble with one column named `.supercluster_cluster` containing #' a character vector of length `nrow(tof_tibble)` indicating the id of the #' supercluster cluster to which each cell (i.e. each row) in `tof_tibble` was #' assigned. #' #' @importFrom dplyr tibble #' tof_cluster_supercluster <- function(tof_tibble, cluster_cols, num_kmeans_clusters = 10L, sep = \"_\", ...) { kmeans_1 <- tof_tibble |> tof_cluster_kmeans( cluster_cols = {{ cluster_cols }}, num_clusters = num_kmeans_clusters, ... ) kmeans_2 <- tof_tibble |> tof_cluster_kmeans( cluster_cols = {{ cluster_cols }}, num_clusters = num_kmeans_clusters, ... ) final_result <- dplyr::tibble( .supercluster_cluster = paste(kmeans_1$.kmeans_cluster, kmeans_2$.kmeans_cluster, sep = sep) ) return(final_result) }"},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"creating-a-new-tidytof-verb","dir":"Articles","previous_headings":"How to contribute","what":"Creating a new {tidytof} verb","title":"How to contribute code","text":"want contribute function tidytof represents new operation encompassed existing verbs , include suggestion create new verb pull request tidytof team. case, ’ll considerably flexibility define interface tidytof use implement new verb, tidytof team happy work figure makes sense (least brainstorm together).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"a-note-about-modeling-functions","dir":"Articles","previous_headings":"How to contribute","what":"A note about modeling functions","title":"How to contribute code","text":"point development, don’t recommend extending tidytof’s modeling functionality, likely abstracted standalone package (emphasis interoperability tidymodels ecosystem) point future.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"contact-us","dir":"Articles","previous_headings":"","what":"Contact us","title":"How to contribute code","text":"general questions/comments/concerns tidytof, feel free reach team GitHub .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/contributing-to-tidytof.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"How to contribute code","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> loaded via a namespace (and not attached): #> [1] digest_0.6.35 desc_1.4.3 R6_2.5.1 fastmap_1.1.1 #> [5] xfun_0.42 magrittr_2.0.3 cachem_1.0.8 knitr_1.45 #> [9] memoise_2.0.1 htmltools_0.5.7 rmarkdown_2.26 lifecycle_1.0.4 #> [13] cli_3.6.2 vctrs_0.6.5 sass_0.4.9 pkgdown_2.0.7 #> [17] textshaping_0.3.7 jquerylib_0.1.4 systemfonts_1.0.6 compiler_4.3.3 #> [21] purrr_1.0.2 tools_4.3.3 ragg_1.3.0 bslib_0.6.2 #> [25] evaluate_0.23 yaml_2.3.8 jsonlite_1.8.8 rlang_1.1.3 #> [29] fs_1.6.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Differential discovery analysis","text":"demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = 1:length(file_name), patient = stringr::str_extract(file_name, \"patient[:digit:]\"), stimulation = stringr::str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"differential-abundance-analysis-using-tof_analyze_abundance","dir":"Articles","previous_headings":"","what":"Differential abundance analysis using tof_analyze_abundance()","title":"Differential discovery analysis","text":"might wonder certain clusters expand deplete within patients two stimulation conditions described - question requires differential abundance analysis (DAA). tidytof’s tof_analyze_abundance() verb supports use 3 statistical approaches performing DAA: diffcyt, generalized-linear mixed modeling (GLMMs), simple t-tests. setup described uses paired design 2 experimental conditions interest (Basal vs. BCR-XL), can use paired t-test method: Based output, can see 6 8 clusters statistically different abundance two stimulation conditions. Using tidytof easy integration tidyverse packages, can use result visualize fold-changes cluster (within patient) BCR-XL condition compared Basal condition using ggplot2: Importantly, output tof_analyze_abundance depends slightly underlying statistical method used, details can found documentation tof_analyze_abundance_* function family member: tof_analyze_abundance_diffcyt tof_analyze_abundance_glmm tof_analyze_abundance_ttest","code":"daa_result <- citrus_data |> tof_analyze_abundance( cluster_col = population_id, effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) daa_result #> # A tibble: 8 × 8 #> population_id p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 0.000924 0.00535 \"*\" -5.48 7 -0.00743 0.644 #> 2 2 0.00623 0.0166 \"*\" -3.86 7 -0.0156 0.674 #> 3 3 0.0235 0.0314 \"*\" -2.88 7 -0.0638 0.849 #> 4 4 0.0235 0.0314 \"*\" 2.88 7 0.0832 1.38 #> 5 5 0.0116 0.0232 \"*\" 3.39 7 0.00246 1.08 #> 6 6 0.371 0.371 \"\" -0.955 7 -0.0168 0.919 #> 7 7 0.00134 0.00535 \"*\" 5.14 7 0.0202 1.14 #> 8 8 0.236 0.270 \"\" -1.30 7 -0.00228 0.901 plot_data <- citrus_data |> mutate(population_id = as.character(population_id)) |> left_join( select(daa_result, population_id, significant, mean_fc), by = \"population_id\" ) |> dplyr::count(patient, stimulation, population_id, significant, mean_fc, name = \"n\") |> group_by(patient, stimulation) |> mutate(prop = n / sum(n)) |> ungroup() |> pivot_wider( names_from = stimulation, values_from = c(prop, n), ) |> mutate( diff = `prop_BCR-XL` - prop_Basal, fc = `prop_BCR-XL` / prop_Basal, population_id = fct_reorder(population_id, diff), direction = case_when( mean_fc > 1 & significant == \"*\" ~ \"increase\", mean_fc < 1 & significant == \"*\" ~ \"decrease\", TRUE ~ NA_character_ ) ) significance_data <- plot_data |> group_by(population_id, significant, direction) |> summarize(diff = max(diff), fc = max(fc)) |> ungroup() plot_data |> ggplot(aes(x = population_id, y = fc, fill = direction)) + geom_violin(trim = FALSE) + geom_hline(yintercept = 1, color = \"red\", linetype = \"dotted\", size = 0.5) + geom_point() + geom_text( aes(x = population_id, y = fc, label = significant), data = significance_data, size = 8, nudge_x = 0.2, nudge_y = 0.06 ) + scale_x_discrete(labels = function(x) str_c(\"cluster \", x)) + scale_fill_manual( values = c(\"decrease\" = \"#cd5241\", \"increase\" = \"#207394\"), na.translate = FALSE ) + labs( x = NULL, y = \"Abundance fold-change (stimulated / basal)\", fill = \"Effect\", caption = \"Asterisks indicate significance at an adjusted p-value of 0.05\" ) #> Warning: Using `size` aesthetic for lines was deprecated in ggplot2 3.4.0. #> ℹ Please use `linewidth` instead. #> This warning is displayed once every 8 hours. #> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was #> generated."},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"differential-expression-analysis-with-tof_analyze_expression","dir":"Articles","previous_headings":"","what":"Differential expression analysis with tof_analyze_expression()","title":"Differential discovery analysis","text":"Similarly, suppose ’re interested intracellular signaling proteins change expression levels two stimulation conditions clusters. Differential Expression Analysis (DEA) can performed using tidytof’s tof_analyze_expression verb. , can use paired t-tests multiple-hypothesis correction test significant differences cluster’s expression signaling markers stimulation conditions. output tof_analyze_expression() also depends underlying test used, can see result looks relatively similar output tof_analyze_abundance(). , output tibble row represents differential expression results single cluster-marker pair - example, first row represents difference expression pS6 cluster 1 BCR-XL Basal conditions. row includes raw p-value multiple-hypothesis-corrected p-value cluster-marker pair. result can used make volcano plot visualize results cluster-marker pairs: , details can found documentation tof_analyze_expression_* function family member: tof_analyze_expression_diffcyt tof_analyze_expression_lmm tof_analyze_expression_ttest","code":"signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) dea_result <- citrus_data |> tof_preprocess(channel_cols = any_of(signaling_markers)) |> tof_analyze_expression( method = \"ttest\", cluster_col = population_id, marker_cols = any_of(signaling_markers), effect_col = stimulation, group_cols = patient, test_type = \"paired\" ) dea_result |> head() #> # A tibble: 6 × 9 #> population_id marker p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 pS6_Y… 7.58e-8 2.12e-6 * 22.9 7 2.56 4.31 #> 2 2 pS6_Y… 1.16e-7 2.12e-6 * 21.6 7 2.13 2.49 #> 3 3 pBtk_… 1.32e-7 2.12e-6 * -21.2 7 -0.475 0.289 #> 4 7 pBtk_… 1.18e-7 2.12e-6 * -21.5 7 -0.518 0.286 #> 5 8 pBtk_… 1.30e-7 2.12e-6 * -21.2 7 -0.516 0.324 #> 6 4 pBtk_… 7.85e-7 1.05e-5 * -16.3 7 -0.462 0.296 volcano_data <- dea_result |> mutate( log2_fc = log(mean_fc, base = 2), log_p = -log(p_adj), significance = case_when( p_adj < 0.05 & mean_fc > 1 ~ \"increased\", p_adj < 0.05 & mean_fc < 1 ~ \"decreased\", TRUE ~ NA_character_ ), marker = str_extract(marker, \".+_\") |> str_remove(\"_\"), pair = str_c(marker, str_c(\"cluster \", population_id), sep = \"@\") ) volcano_data |> ggplot(aes(x = log2_fc, y = log_p, fill = significance)) + geom_vline(xintercept = 0, linetype = \"dashed\", color = \"gray50\") + geom_hline(yintercept = -log(0.05), linetype = \"dashed\", color = \"red\") + geom_point(shape = 21, size = 2) + ggrepel::geom_text_repel( aes(label = pair), data = slice_head(volcano_data, n = 10L), size = 2 ) + scale_fill_manual( values = c(\"decreased\" = \"#cd5241\", \"increased\" = \"#207394\"), na.value = \"#cdcdcd\" ) + labs( x = \"log2(Fold-change)\", y = \"-log10(p-value)\", fill = NULL, caption = \"Labels indicate the 10 most significant marker-cluster pairs\" )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/differential-discovery-analysis.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Differential discovery analysis","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] tidyr_1.3.1 ggplot2_3.5.0 #> [19] stringr_1.5.1 dplyr_1.1.4 #> [21] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 labeling_0.4.3 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] highr_0.10 ggforce_0.4.2 #> [55] MASS_7.3-60.0.1 lava_1.8.0 #> [57] rappdirs_0.3.3 DelayedArray_0.28.0 #> [59] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [61] httpuv_1.6.14 future.apply_1.11.1 #> [63] nnet_7.3-19 glue_1.7.0 #> [65] promises_1.2.1 grid_4.3.3 #> [67] generics_0.1.3 recipes_1.0.10 #> [69] gtable_0.3.4 tzdb_0.4.0 #> [71] class_7.3-22 data.table_1.15.2 #> [73] hms_1.1.3 tidygraph_1.3.1 #> [75] utf8_1.2.4 XVector_0.42.0 #> [77] ggrepel_0.9.5 BiocVersion_3.18.1 #> [79] foreach_1.5.2 pillar_1.9.0 #> [81] RcppHNSW_0.6.0 later_1.3.2 #> [83] splines_4.3.3 tweenr_2.0.3 #> [85] lattice_0.22-5 survival_3.5-8 #> [87] bit_4.0.5 RProtoBufLib_2.14.1 #> [89] tidyselect_1.2.1 Biostrings_2.70.3 #> [91] knitr_1.45 gridExtra_2.3 #> [93] xfun_0.42 graphlayouts_1.1.1 #> [95] hardhat_1.3.1 timeDate_4032.109 #> [97] stringi_1.8.3 yaml_2.3.8 #> [99] evaluate_0.23 codetools_0.2-19 #> [101] ggraph_2.2.1 tibble_3.2.1 #> [103] BiocManager_1.30.22 cli_3.6.2 #> [105] rpart_4.1.23 xtable_1.8-4 #> [107] systemfonts_1.0.6 munsell_0.5.0 #> [109] jquerylib_0.1.4 Rcpp_1.0.12 #> [111] globals_0.16.3 png_0.1-8 #> [113] parallel_4.3.3 ellipsis_0.3.2 #> [115] pkgdown_2.0.7 gower_1.0.1 #> [117] readr_2.1.5 blob_1.2.4 #> [119] bitops_1.0-7 listenv_0.9.1 #> [121] glmnet_4.1-8 viridisLite_0.4.2 #> [123] ipred_0.9-14 scales_1.3.0 #> [125] prodlim_2023.08.28 purrr_1.0.2 #> [127] crayon_1.5.2 rlang_1.1.3 #> [129] KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"dimensionality-reduction-with-tof_reduce_dimensions-","dir":"Articles","previous_headings":"","what":"Dimensionality reduction with tof_reduce_dimensions().","title":"Dimensionality reduction","text":"example call tof_reduce_dimensions() use tSNE visualize data tidytof’s built-phenograph_data dataset. default, tof_reduce_dimensions add reduced-dimension feature embeddings input tof_tbl return augmented tof_tbl (, tof_tbl new columns embedding dimension) result. return features embeddings , set augment FALSE (tof_cluster). Changing method argument results different low-dimensional embeddings:","code":"data(phenograph_data) # perform the dimensionality reduction phenograph_tsne <- phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\") #> Loading required namespace: Rtsne # select only the tsne embedding columns phenograph_tsne |> select(contains(\"tsne\")) |> head() #> # A tibble: 6 × 2 #> .tsne1 .tsne2 #> #> 1 5.75 -16.0 #> 2 -1.53 -7.52 #> 3 -23.2 -15.1 #> 4 -8.86 -16.9 #> 5 2.17 -17.9 #> 6 -8.98 -24.1 phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .tsne1 .tsne2 #> #> 1 -13.1 -12.2 #> 2 -10.9 -0.332 #> 3 -32.5 10.2 #> 4 -18.5 0.134 #> 5 -17.9 -9.29 #> 6 -21.1 7.89 #> 7 -11.2 1.36 #> 8 -25.7 8.52 #> 9 -18.5 3.61 #> 10 -6.88 -7.10 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"umap\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .umap1 .umap2 #> #> 1 -9.77 4.05 #> 2 -8.71 3.49 #> 3 -7.10 2.62 #> 4 -2.22 -2.59 #> 5 -9.86 3.80 #> 6 0.174 -3.20 #> 7 -9.85 3.23 #> 8 -1.38 -1.78 #> 9 -6.17 1.24 #> 10 -8.62 5.78 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"pca\", augment = FALSE) #> # A tibble: 3,000 × 5 #> .pc1 .pc2 .pc3 .pc4 .pc5 #> #> 1 -2.77 1.23 -0.868 0.978 3.49 #> 2 -0.969 -1.02 -0.787 1.22 0.329 #> 3 -2.36 2.54 -1.95 -0.882 -1.30 #> 4 -3.68 -0.00565 0.962 0.410 0.788 #> 5 -4.03 2.07 -0.829 1.59 5.39 #> 6 -2.59 -0.108 1.32 -1.41 -1.24 #> 7 -1.55 -0.651 -0.233 1.08 0.129 #> 8 -1.18 -0.446 0.134 -0.771 -0.932 #> 9 -2.00 -0.485 0.593 -0.0416 -0.658 #> 10 -0.0356 -0.924 -0.692 1.45 0.270 #> # ℹ 2,990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"method-specifications-for-tof_reduce_-functions","dir":"Articles","previous_headings":"","what":"Method specifications for tof_reduce_*() functions","title":"Dimensionality reduction","text":"tof_reduce_dimensions() provides high-level API three lower-level functions: tof_reduce_pca(), tof_reduce_umap(), tof_reduce_tsne(). help files functions provide details algorithm-specific method specifications associated dimensionality reduction approaches. example, tof_reduce_pca takes num_comp argument determine many principal components returned: see ?tof_reduce_pca, ?tof_reduce_umap, ?tof_reduce_tsne additional details.","code":"# 2 principal components phenograph_data |> tof_reduce_pca(num_comp = 2) #> # A tibble: 3,000 × 2 #> .pc1 .pc2 #> #> 1 -2.77 1.23 #> 2 -0.969 -1.02 #> 3 -2.36 2.54 #> 4 -3.68 -0.00565 #> 5 -4.03 2.07 #> 6 -2.59 -0.108 #> 7 -1.55 -0.651 #> 8 -1.18 -0.446 #> 9 -2.00 -0.485 #> 10 -0.0356 -0.924 #> # ℹ 2,990 more rows # 3 principal components phenograph_data |> tof_reduce_pca(num_comp = 3) #> # A tibble: 3,000 × 3 #> .pc1 .pc2 .pc3 #> #> 1 -2.77 1.23 -0.868 #> 2 -0.969 -1.02 -0.787 #> 3 -2.36 2.54 -1.95 #> 4 -3.68 -0.00565 0.962 #> 5 -4.03 2.07 -0.829 #> 6 -2.59 -0.108 1.32 #> 7 -1.55 -0.651 -0.233 #> 8 -1.18 -0.446 0.134 #> 9 -2.00 -0.485 0.593 #> 10 -0.0356 -0.924 -0.692 #> # ℹ 2,990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"visualization-using-tof_plot_cells_embedding","dir":"Articles","previous_headings":"","what":"Visualization using tof_plot_cells_embedding()","title":"Dimensionality reduction","text":"Regardless method used, reduced-dimension feature embeddings can visualized using ggplot2 (graphics package). tidytof also provides helper functions easily generating dimensionality reduction plots tof_tbl tibble columns representing embedding dimensions: visualizations can helpful qualitatively describing phenotypic differences clusters dataset. example, example , can see one clusters high CD11b expression, whereas others lower CD11b expression.","code":"# plot the tsne embeddings using color to distinguish between clusters phenograph_tsne |> tof_plot_cells_embedding( embedding_cols = contains(\".tsne\"), color_col = phenograph_cluster ) # plot the tsne embeddings using color to represent CD11b expression phenograph_tsne |> tof_plot_cells_embedding( embedding_cols = contains(\".tsne\"), color_col = cd11b ) + ggplot2::scale_fill_viridis_c()"},{"path":"https://keyes-timothy.github.io/tidytof/articles/dimensionality-reduction.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Dimensionality reduction","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] ggplot2_3.5.0 dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] RcppAnnoy_0.0.22 matrixStats_1.2.0 compiler_4.3.3 #> [7] systemfonts_1.0.6 vctrs_0.6.5 stringr_1.5.1 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ellipsis_0.3.2 labeling_0.4.3 ggraph_2.2.1 #> [16] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [19] tzdb_0.4.0 ragg_1.3.0 purrr_1.0.2 #> [22] xfun_0.42 glmnet_4.1-8 cachem_1.0.8 #> [25] jsonlite_1.8.8 recipes_1.0.10 highr_0.10 #> [28] tweenr_2.0.3 irlba_2.3.5.1 parallel_4.3.3 #> [31] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [34] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [37] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [40] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [43] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [46] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [49] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [52] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [55] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [58] Biobase_2.62.0 withr_3.0.0 evaluate_0.23 #> [61] Rtsne_0.17 future_1.33.1 desc_1.4.3 #> [64] survival_3.5-8 polyclip_1.10-6 embed_1.1.4 #> [67] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [70] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [73] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [76] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [79] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [82] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [85] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [88] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [91] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [94] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [97] lava_1.8.0 uwot_0.1.16 gtable_0.3.4 #> [100] sass_0.4.9 digest_0.6.35 BiocGenerics_0.48.1 #> [103] ggrepel_0.9.5 farver_2.1.1 memoise_2.0.1 #> [106] htmltools_0.5.7 pkgdown_2.0.7 lifecycle_1.0.4 #> [109] hardhat_1.3.1 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"downsampling-with-tof_downsample","dir":"Articles","previous_headings":"","what":"Downsampling with tof_downsample()","title":"Downsampling","text":"Using tidytof’s built-dataset phenograph_data, can see original size dataset 1000 cells per cluster, 3000 cells total: randomly sample 200 cells per cluster, can use tof_downsample() using “constant” method: Alternatively, wanted sample 50% cells cluster, use “prop” method: finally, might also interested taking slightly different approach downsampling reduces number cells fixed constant proportion, fixed density phenotypic space. example, following scatterplot demonstrates certain areas phenotypic density phenograph_data contain cells others along cd34/cd38 axes: reduce number cells dataset local density around cell dataset relatively constant, can use “density” method tof_downsample: Thus, can see density downsampling uniform (though exactly uniform) across range cd34/cd38 values phenograph_data.","code":"data(phenograph_data) phenograph_data |> dplyr::count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 1000 #> 2 cluster2 1000 #> 3 cluster3 1000 phenograph_data |> # downsample tof_downsample( group_cols = phenograph_cluster, method = \"constant\", num_cells = 200 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 200 #> 2 cluster2 200 #> 3 cluster3 200 phenograph_data |> # downsample tof_downsample( group_cols = phenograph_cluster, method = \"prop\", prop_cells = 0.5 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 500 #> 2 cluster2 500 #> 3 cluster3 500 rescale_max <- function(x, to = c(0, 1), from = range(x, na.rm = TRUE)) { x / from[2] * to[2] } phenograph_data |> # preprocess all numeric columns in the dataset tof_preprocess(undo_noise = FALSE) |> # plot ggplot(aes(x = cd34, y = cd38)) + geom_hex() + coord_fixed(ratio = 0.4) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + scale_fill_viridis_c( labels = function(x) round(rescale_max(x), 2) ) + labs( fill = \"relative density\" ) phenograph_data |> tof_preprocess(undo_noise = FALSE) |> tof_downsample(method = \"density\", density_cols = c(cd34, cd38)) |> # plot ggplot(aes(x = cd34, y = cd38)) + geom_hex() + coord_fixed(ratio = 0.4) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + scale_fill_viridis_c( labels = function(x) round(rescale_max(x), 2) ) + labs( fill = \"relative density\" )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"additional-documentation","dir":"Articles","previous_headings":"","what":"Additional documentation","title":"Downsampling","text":"details, check documentation 3 underlying members tof_downsample_* function family (wrapped tof_downsample): tof_downsample_constant tof_downsample_prop tof_downsample_density","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/downsampling.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Downsampling","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] ggplot2_3.5.0 dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [10] shape_1.4.6.1 fastmap_1.1.1 ggraph_2.2.1 #> [13] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [16] tzdb_0.4.0 ragg_1.3.0 purrr_1.0.2 #> [19] xfun_0.42 glmnet_4.1-8 cachem_1.0.8 #> [22] jsonlite_1.8.8 recipes_1.0.10 highr_0.10 #> [25] tweenr_2.0.3 parallel_4.3.3 R6_2.5.1 #> [28] bslib_0.6.2 stringi_1.8.3 parallelly_1.37.1 #> [31] rpart_4.1.23 lubridate_1.9.3 jquerylib_0.1.4 #> [34] Rcpp_1.0.12 iterators_1.0.14 knitr_1.45 #> [37] future.apply_1.11.1 readr_2.1.5 flowCore_2.14.2 #> [40] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [43] igraph_2.0.3 timechange_0.3.0 tidyselect_1.2.1 #> [46] yaml_2.3.8 viridis_0.6.5 timeDate_4032.109 #> [49] doParallel_1.0.17 codetools_0.2-19 listenv_0.9.1 #> [52] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [55] withr_3.0.0 evaluate_0.23 future_1.33.1 #> [58] desc_1.4.3 survival_3.5-8 polyclip_1.10-6 #> [61] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [64] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [67] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [70] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [73] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [76] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [79] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [82] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [85] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [88] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [91] lava_1.8.0 gtable_0.3.4 sass_0.4.9 #> [94] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [97] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [100] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [103] MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Feature extraction","text":"demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = 1:length(file_name), patient = stringr::str_extract(file_name, \"patient[:digit:]\"), stimulation = stringr::str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-cluster-proportions-using-tof_extract_proportion","dir":"Articles","previous_headings":"","what":"Calculating cluster proportions using tof_extract_proportion()","title":"Feature extraction","text":"First, tof_extract_proportion(), extracts proportion cells cluster within sample (samples defined using group_cols argument): Like members tof_extract_* function family, tof_extract_proportion() returns one row sample (defined unique combination values columns specified group_cols) one column extracted feature (, one column proportion 8 clusters citrus_data). values can also returned “long” format changing format argument:","code":"# preprocess the numeric columns in the citrus dataset citrus_data <- citrus_data |> mutate(cluster = str_c(\"cluster\", population_id)) |> tof_preprocess() citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation) ) |> head() #> # A tibble: 6 × 10 #> patient stimulation `prop@cluster1` `prop@cluster2` `prop@cluster3` #> #> 1 patient1 Basal 0.0190 0.0482 0.447 #> 2 patient1 BCR-XL 0.0109 0.0395 0.268 #> 3 patient2 Basal 0.0130 0.0280 0.491 #> 4 patient2 BCR-XL 0.0101 0.0143 0.358 #> 5 patient3 Basal 0.0326 0.0830 0.397 #> 6 patient3 BCR-XL 0.0200 0.0412 0.323 #> # ℹ 5 more variables: `prop@cluster4` , `prop@cluster5` , #> # `prop@cluster6` , `prop@cluster7` , `prop@cluster8` citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation), format = \"long\" ) |> head() #> # A tibble: 6 × 4 #> patient stimulation cluster prop #> #> 1 patient1 Basal cluster1 0.0190 #> 2 patient1 Basal cluster2 0.0482 #> 3 patient1 Basal cluster3 0.447 #> 4 patient1 Basal cluster4 0.237 #> 5 patient1 Basal cluster5 0.00219 #> 6 patient1 Basal cluster6 0.0759"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-cluster-marker-expression-measures-using-tof_extract_central_tendency","dir":"Articles","previous_headings":"","what":"Calculating cluster marker expression measures using tof_extract_central_tendency()","title":"Feature extraction","text":"Another member tof_extract_*() function family, tof_extract_central_tendency(), computes central tendency (e.g. mean median) user-specified markers cluster. argument central_tendency_function can used compute summary statistic. example, following choice central_tendency_function compute 75th percentile marker-cluster pair citrus_data:","code":"citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = mean ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 4.80 0.0967 #> 2 patient1 Basal 4.68 0.765 #> 3 patient2 BCR-XL 5.00 -0.0579 #> 4 patient2 Basal 4.88 0.808 #> 5 patient3 BCR-XL 5.04 -0.0432 #> 6 patient3 Basal 4.98 0.745 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , … citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = function(x) quantile(x = x, probs = 0.75) ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 5.30 -0.0186 #> 2 patient1 Basal 5.18 1.32 #> 3 patient2 BCR-XL 5.41 -0.0201 #> 4 patient2 Basal 5.28 1.39 #> 5 patient3 BCR-XL 5.42 -0.0362 #> 6 patient3 Basal 5.41 1.27 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-the-proportion-of-cells-with-marker-expression-above-a-threshold-using-tof_extract_proportion","dir":"Articles","previous_headings":"","what":"Calculating the proportion of cells with marker expression above a threshold using tof_extract_proportion()","title":"Feature extraction","text":"tof_extract_threshold() similar tof_extract_central_tendency(), calculates proportion cells user-specified expression value marker instead measure central tendency:","code":"citrus_data |> tof_extract_threshold( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), threshold = 5 ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_threshold` CD4_Nd145@cluster1_thre…¹ #> #> 1 patient1 BCR-XL 0.516 0 #> 2 patient1 Basal 0.365 0 #> 3 patient2 BCR-XL 0.554 0 #> 4 patient2 Basal 0.452 0 #> 5 patient3 BCR-XL 0.547 0 #> 6 patient3 Basal 0.549 0 #> # ℹ abbreviated name: ¹​`CD4_Nd145@cluster1_threshold` #> # ℹ 22 more variables: `CD20_Sm147@cluster1_threshold` , #> # `CD45_In115@cluster2_threshold` , #> # `CD4_Nd145@cluster2_threshold` , #> # `CD20_Sm147@cluster2_threshold` , #> # `CD45_In115@cluster3_threshold` , #> # `CD4_Nd145@cluster3_threshold` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"calculating-differences-in-marker-distributions-using-tof_extract_emd-and-tof_extract_jsd","dir":"Articles","previous_headings":"","what":"Calculating differences in marker distributions using tof_extract_emd() and tof_extract_jsd()","title":"Feature extraction","text":"two final members tof_extract_* function family – tof_extract_emd tof_extract_jsd – designed specifically comparing distributions marker expression stimulation conditions. , must given stimulation column (using emd_col jsd_col argument) identifies stimulation condition cell , reference_level specifies reference (.e. unstimulated) condition within emd_col jsd_col. additional arguments, tof_extract_emd computes Earth-mover’s distance marker’s distribution stimulation conditions (within cluster) basal condition; similarly, tof_extract_jsd computes Jensen-Shannon divergence index distributions. values ways compare different 2 distributions one another computationally expensive (also higher-resolution) simply comparing measures central tendency.","code":"# Earth-mover's distance citrus_data |> tof_extract_emd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), emd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.864 2.47 13.0 #> 2 patient2 1.11 7.05 10.8 #> 3 patient3 0.670 6.23 10.5 #> 4 patient4 2.64 5.86 9.90 #> 5 patient5 0.594 7.56 8.13 #> 6 patient6 0.661 4.77 7.97 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_emd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_emd`, ³​`BCR-XL_CD20_Sm147@cluster3_emd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_emd` , #> # `BCR-XL_CD4_Nd145@cluster7_emd` , #> # `BCR-XL_CD20_Sm147@cluster7_emd` , #> # `BCR-XL_CD45_In115@cluster4_emd` , #> # `BCR-XL_CD4_Nd145@cluster4_emd` , … # Jensen-Shannon Divergence citrus_data |> tof_extract_jsd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), jsd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.0367 0.0513 0.347 #> 2 patient2 0.00831 0.168 0.401 #> 3 patient3 0.0104 0.115 0.357 #> 4 patient4 0.0301 0.135 0.205 #> 5 patient5 0.00911 0.0789 0.274 #> 6 patient6 0.00972 0.0346 0.214 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_jsd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_jsd`, ³​`BCR-XL_CD20_Sm147@cluster3_jsd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_jsd` , #> # `BCR-XL_CD4_Nd145@cluster7_jsd` , #> # `BCR-XL_CD20_Sm147@cluster7_jsd` , #> # `BCR-XL_CD45_In115@cluster4_jsd` , #> # `BCR-XL_CD4_Nd145@cluster4_jsd` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"putting-it-all-together-with-tof_extract_features","dir":"Articles","previous_headings":"","what":"Putting it all together with tof_extract_features()","title":"Feature extraction","text":"Finally, tof_extract_features() verb provides wrapper members function family, allowing users extract multiple features types . example, following code extracts proportion cluster, median several markers cluster, EMD basal condition stimulated condition cluster patients citrus_data.","code":"signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) citrus_data |> tof_extract_features( cluster_col = cluster, group_cols = patient, stimulation_col = stimulation, lineage_cols = any_of(c(\"CD45_In115\", \"CD20_Sm147\", \"CD33_Nd148\")), signaling_cols = any_of(signaling_markers), signaling_method = \"emd\", basal_level = \"Basal\" ) |> head() #> # A tibble: 6 × 193 #> patient `prop@cluster1` `prop@cluster2` `prop@cluster3` `prop@cluster4` #> #> 1 patient1 0.0149 0.0438 0.356 0.351 #> 2 patient2 0.0115 0.0212 0.425 0.323 #> 3 patient3 0.0255 0.0594 0.355 0.217 #> 4 patient4 0.0127 0.0418 0.320 0.223 #> 5 patient5 0.0207 0.0423 0.377 0.269 #> 6 patient6 0.0183 0.0493 0.459 0.250 #> # ℹ 188 more variables: `prop@cluster5` , `prop@cluster6` , #> # `prop@cluster7` , `prop@cluster8` , #> # `CD45_In115@cluster1_ct` , `CD20_Sm147@cluster1_ct` , #> # `CD33_Nd148@cluster1_ct` , `CD45_In115@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD33_Nd148@cluster2_ct` , #> # `CD45_In115@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD33_Nd148@cluster3_ct` , `CD45_In115@cluster4_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/articles/feature-extraction.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Feature extraction","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 stringr_1.5.1 #> [17] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 philentropy_0.8.0 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] ggforce_0.4.2 MASS_7.3-60.0.1 #> [55] lava_1.8.0 rappdirs_0.3.3 #> [57] DelayedArray_0.28.0 tools_4.3.3 #> [59] interactiveDisplayBase_1.40.0 httpuv_1.6.14 #> [61] future.apply_1.11.1 nnet_7.3-19 #> [63] glue_1.7.0 promises_1.2.1 #> [65] grid_4.3.3 generics_0.1.3 #> [67] recipes_1.0.10 gtable_0.3.4 #> [69] tzdb_0.4.0 class_7.3-22 #> [71] tidyr_1.3.1 data.table_1.15.2 #> [73] hms_1.1.3 tidygraph_1.3.1 #> [75] utf8_1.2.4 XVector_0.42.0 #> [77] ggrepel_0.9.5 BiocVersion_3.18.1 #> [79] foreach_1.5.2 pillar_1.9.0 #> [81] RcppHNSW_0.6.0 later_1.3.2 #> [83] splines_4.3.3 tweenr_2.0.3 #> [85] lattice_0.22-5 survival_3.5-8 #> [87] bit_4.0.5 emdist_0.3-3 #> [89] RProtoBufLib_2.14.1 tidyselect_1.2.1 #> [91] Biostrings_2.70.3 knitr_1.45 #> [93] gridExtra_2.3 xfun_0.42 #> [95] graphlayouts_1.1.1 hardhat_1.3.1 #> [97] timeDate_4032.109 stringi_1.8.3 #> [99] yaml_2.3.8 evaluate_0.23 #> [101] codetools_0.2-19 ggraph_2.2.1 #> [103] tibble_3.2.1 BiocManager_1.30.22 #> [105] cli_3.6.2 rpart_4.1.23 #> [107] xtable_1.8-4 systemfonts_1.0.6 #> [109] munsell_0.5.0 jquerylib_0.1.4 #> [111] Rcpp_1.0.12 globals_0.16.3 #> [113] png_0.1-8 parallel_4.3.3 #> [115] ellipsis_0.3.2 pkgdown_2.0.7 #> [117] gower_1.0.1 ggplot2_3.5.0 #> [119] readr_2.1.5 blob_1.2.4 #> [121] bitops_1.0-7 listenv_0.9.1 #> [123] glmnet_4.1-8 viridisLite_0.4.2 #> [125] ipred_0.9-14 scales_1.3.0 #> [127] prodlim_2023.08.28 purrr_1.0.2 #> [129] crayon_1.5.2 rlang_1.1.3 #> [131] KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Building predictive models","text":"illustrate work, first download patient-level data paper combine sample-level clinical annotations one tidytof’s built-datasets (ddpr_metadata). data processing steps result tibble called ddpr_patients. numeric columns ddpr_patients represent aggregated cell population features sample (see Supplementary Table 5 paper details). non-numeric columns represent clinical metadata sample (run ?ddpr_metadata information). metadata columns, important ones indicate patient develop refractory disease (“relapse”), /happen. information stored relapse_status time_to_relapse columns, respectively. also preprocessing steps might want perform now save us headaches ’re fitting models later. next part vignette, ’ll use patient-level data build predictive models using resampling procedures like k-fold cross-validation bootstrapping.","code":"data(ddpr_metadata) # link for downloading the sample-level data from the Nature Medicine website data_link <- \"https://static-content.springer.com/esm/art%3A10.1038%2Fnm.4505/MediaObjects/41591_2018_BFnm4505_MOESM3_ESM.csv\" # download the data and combine it with clinical annotations ddpr_patients <- readr::read_csv(data_link, skip = 2L, n_max = 78L, show_col_types = FALSE) |> dplyr::rename(patient_id = Patient_ID) |> dplyr::left_join(ddpr_metadata, by = \"patient_id\") |> dplyr::filter(!str_detect(patient_id, \"Healthy\")) # preview only the metadata (i.e. non-numeric) columns ddpr_patients |> dplyr::select(where(~ !is.numeric(.x))) |> head() #> # A tibble: 6 × 8 #> patient_id gender mrd_risk nci_rome_risk relapse_status type_of_relapse cohort #> #> 1 UPN1 Male Interme… Standard Yes Early Train… #> 2 UPN1-Rx Male Interme… Standard Yes Early Train… #> 3 UPN2 Male Interme… Standard No NA Train… #> 4 UPN3 Female Standard Standard No NA Train… #> 5 UPN4 Male Standard Standard No NA Valid… #> 6 UPN5 Female Standard High No NA Valid… #> # ℹ 1 more variable: ddpr_risk ddpr_patients <- ddpr_patients |> # convert the relapse_status variable to a factor # and create the time_to_event and event columns for survival modeling dplyr::mutate( relapse_status = as.factor(relapse_status), time_to_event = dplyr::if_else(relapse_status == \"Yes\", time_to_relapse, ccr), event = dplyr::if_else(relapse_status == \"Yes\", 1, 0) )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"building-a-classifier-using-elastic-net-regularized-logistic-regression","dir":"Articles","previous_headings":"","what":"Building a classifier using elastic net-regularized logistic regression","title":"Building predictive models","text":"First, can build elastic net classifier predict patients relapse patients won’t (ignoring time--event data now). , can use relapse_status column ddpr_patients outcome variable: can see samples annotated, can throw away samples don’t clinical outcome associated . original DDPR paper, 10-fold cross-validation used tune glmnet model estimate error model new datasets. , can use tof_split_data() function split cohort training test set either 10 times using k-fold cross-validation bootstrapping. Reading documentation tof_split_data() demonstrates use resampling methods (like bootstrapping). output tof_split_data() varies depending split_method used. cross-validation, result rset object rsample package. rset objects type tibble two columns: splits - column entry rsplit object (contains single resample full dataset) id - character column entry represents name fold entry splits belongs . can inspect one resamples splits column see contain: Note can use rsample::training rsample::testing return training test observations resampling: , can feed training_split tof_train_model function tune logistic regression model predicts relapse_status leukemia patient. sure check tof_create_grid documentation learn make hyperparameter search grid model tuning (case, limit mixture parameter value 1, fits sparse lasso model). Also note , case, illustrative purposes ’re incorporating features one populations interest (population 2) model, whereas original model incorporated features 12 populations (likely required quite bit computational power result). output tof_train_model tof_model, object containing information trained model (can passed tof_predict tof_assess_model verbs). tof_model printed, information optimal hyperparamters printed, table nonzero model coefficients model. training model, might interested seeing performs. One way assess classification model see well works applied directly back data trained (model’s “training data”). , can use tof_assess_model() function arguments: tof_assess_model() returns list several model assessment metrics differ depending kind tof_model trained. two-class classifier models, among useful confusion_matrix, shows classifier classified observation relative true class assignment. case, can see model performed perfectly training data (expected, model optimized using data !). can also visualize model’s performance using tof_plot_model() verb, case two-class model give us Receiver-Operating Characteristic (ROC) curve: shown , tof_plot_model() return receiver-operating curve two-class model. ’s unusual get AUC 1 machine learning world, can note case, classification problem wasn’t particularly difficult (lot input features work ). training model, generally isn’t sufficient evaluate model performs training data alone, provide overly-optimistic representation model perform data ’s never seen (problem often called “overfitting” model training data). get fairer estimate model’s performance new datasets, can also evaluate cross-validation error calling tof_assess_model() tof_plot_model() new_data argument set “tuning”. case, plot ROC Curve using predictions observation excluded model training cross-validation, approach gives accurate estimate model’s performance new data simple evaluation training dataset.","code":"# find how many of each outcome we have in our cohort ddpr_patients |> dplyr::count(relapse_status) #> # A tibble: 3 × 2 #> relapse_status n #> #> 1 No 37 #> 2 Yes 24 #> 3 NA 12 ddpr_patients_unannotated <- ddpr_patients |> dplyr::filter(is.na(relapse_status)) ddpr_patients <- ddpr_patients |> dplyr::filter(!is.na(relapse_status)) set.seed(3000L) training_split <- ddpr_patients |> tof_split_data( split_method = \"k-fold\", num_cv_folds = 10, strata = relapse_status ) training_split #> # 10-fold cross-validation using stratification #> # A tibble: 10 × 2 #> splits id #> #> 1 Fold01 #> 2 Fold02 #> 3 Fold03 #> 4 Fold04 #> 5 Fold05 #> 6 Fold06 #> 7 Fold07 #> 8 Fold08 #> 9 Fold09 #> 10 Fold10 my_resample <- training_split$splits[[1]] print(my_resample) #> #> <54/7/61> my_resample |> rsample::training() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN1 3.06 0.583 0.00449 0.164 1.94 0.416 #> 2 UPN1-Rx 0.0395 0.618 0.0634 0.572 2.93 0.944 #> 3 UPN2 0.139 0.0662 0.0221 0.0825 2.25 0.454 #> 4 UPN3 0.633 0.0234 0.0165 0.0327 2.25 0.226 #> 5 UPN4 0.0443 0.129 0.0447 0.232 2.47 0.336 #> 6 UPN5 0.0647 0.0577 0.0163 0.162 2.89 0.406 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … my_resample |> rsample::testing() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN6 5.62 0.550 0.00374 0.622 2.86 0.342 #> 2 UPN10-Rx 0.00240 0.167 0.203 0.802 2.57 0.822 #> 3 UPN13 0.0634 0.0300 0.0219 0.109 2.34 0.314 #> 4 UPN22-Rx 0.0643 1.68 0.0804 1.56 3.06 0.529 #> 5 UPN58 0.00546 0.00918 0.0168 0.480 2.70 0.112 #> 6 UPN95 0.300 0.389 0.00454 0.697 2.45 0.247 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … hyperparams <- tof_create_grid(mixture_values = 1) class_mod <- training_split |> tof_train_model( predictor_cols = c(contains(\"Pop2\")), response_col = relapse_status, model_type = \"two-class\", hyperparameter_grid = hyperparams, impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(class_mod) #> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e-10 #> # A tibble: 28 × 2 #> feature coefficient #> #> 1 p4EBP1_dP_IL7_Pop2 -3.10 #> 2 pCreb_dP_PVO4_Pop2 -2.66 #> 3 TSLPr_Pop2 2.07 #> 4 CD43_Pop2 2.00 #> 5 pSTAT5_FC_PVO4_Pop2 -1.80 #> 6 pS6_dP_IL7_Pop2 1.56 #> 7 pPLCg1_2_dP_PVO4_Pop2 1.44 #> 8 (Intercept) -1.43 #> 9 pSTAT5_FC_BCR_Pop2 1.24 #> 10 pErk_dP_IL7_Pop2 -1.23 #> # ℹ 18 more rows training_classifier_metrics <- class_mod |> tof_assess_model() training_classifier_metrics$confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 37 #> 2 No Yes 0 #> 3 Yes No 0 #> 4 Yes Yes 24 class_mod |> tof_plot_model() cv_classifier_metrics <- class_mod |> tof_assess_model(new_data = \"tuning\") class_mod |> tof_plot_model(new_data = \"tuning\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"building-a-survival-model-using-elastic-net-regularized-cox-regression","dir":"Articles","previous_headings":"","what":"Building a survival model using elastic net-regularized cox regression","title":"Building predictive models","text":"Building ideas , sophisticated way model data simply predict relapse won’t, build time--event model estimates patients’ probabilities relapse function time since diagnosis. approach called “survival modeling” (specifically, case use Cox-proportional hazards modeling) takes account patients adverse events different times course disease (.e. everyone relapses time). build survival model using tidytof, use tof_train_model() verb setting model_type flag “survival”. addition, need provide two outcome columns. first columns (event_col) indicates patient relapsed (.e. experienced event--interest) censored certain amount follow-time. second (time_col) indicates much time took patient relapse censored analysis. survival model trained, can used predict patient’s probability event--interest different times post-diagnosis. However, common way survival models applied practice use patient’s predicted relative risk event--interest divide patients low- high-risk subgroups. tidytof can automatically according optimal split obtained using log-rank test possible split points dataset tof_assess_model(). addition, return predicted survival curve patient time: survival models, tof_plot_model() plots average survival curves low- high-risk groups:","code":"hyperparams <- tof_create_grid(mixture_values = 1) survival_mod <- training_split |> tof_train_model( predictor_cols = c(contains(\"Pop2\")), time_col = time_to_event, event_col = event, model_type = \"survival\", hyperparameter_grid = hyperparams, impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(survival_mod) #> A survival `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 40 × 2 #> feature coefficient #> #> 1 pErk_dP_TSLP_Pop2 -7.03 #> 2 pCreb_dP_PVO4_Pop2 -5.47 #> 3 CD19_Pop2 -3.73 #> 4 CD34_Pop2 3.63 #> 5 pSTAT5_FC_BCR_Pop2 3.40 #> 6 HLADR_Pop2 -3.38 #> 7 pPLCg1_2_dP_IL7_Pop2 3.33 #> 8 pPLCg1_2_dP_PVO4_Pop2 3.14 #> 9 pSyk_dP_TSLP_Pop2 2.88 #> 10 CD123_Pop2 2.77 #> # ℹ 30 more rows survival_metrics <- survival_mod |> tof_assess_model() survival_metrics #> $model_metrics #> # A tibble: 3 × 2 #> metric value #> #> 1 neg_log_partial_likelihood 1.76e+ 1 #> 2 concordance_index 1 e+ 0 #> 3 log_rank_p_value 1.47e-22 #> #> $survival_curves #> # A tibble: 61 × 6 #> row_index survival_curve relative_risk time_to_event event risk_group #> #> 1 1 2.83e+3 1043 1 low #> 2 2 2.61e+3 1043 1 low #> 3 3 1.58e-8 5406 0 low #> 4 4 2.09e-4 4917 0 low #> 5 5 9.98e-3 4538 0 low #> 6 6 6.62e-1 4490 0 low #> 7 7 4.09e+9 136 1 high #> 8 8 2.57e+8 364 1 high #> 9 9 1.27e+9 237 1 high #> 10 10 2.31e+4 886 1 low #> # ℹ 51 more rows survival_mod |> tof_plot_model() cv_survival_metrics <- survival_mod |> tof_assess_model(new_data = \"tuning\") survival_mod |> tof_plot_model(new_data = \"tuning\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/modeling.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Building predictive models","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] stringr_1.5.1 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] furrr_0.3.1 matrixStats_1.2.0 compiler_4.3.3 #> [7] systemfonts_1.0.6 vctrs_0.6.5 crayon_1.5.2 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ellipsis_0.3.2 labeling_0.4.3 ggraph_2.2.1 #> [16] utf8_1.2.4 rmarkdown_2.26 prodlim_2023.08.28 #> [19] tzdb_0.4.0 ragg_1.3.0 bit_4.0.5 #> [22] purrr_1.0.2 xfun_0.42 glmnet_4.1-8 #> [25] cachem_1.0.8 jsonlite_1.8.8 recipes_1.0.10 #> [28] highr_0.10 tweenr_2.0.3 parallel_4.3.3 #> [31] R6_2.5.1 rsample_1.2.1 bslib_0.6.2 #> [34] stringi_1.8.3 parallelly_1.37.1 rpart_4.1.23 #> [37] lubridate_1.9.3 jquerylib_0.1.4 Rcpp_1.0.12 #> [40] iterators_1.0.14 knitr_1.45 future.apply_1.11.1 #> [43] readr_2.1.5 flowCore_2.14.2 Matrix_1.6-5 #> [46] splines_4.3.3 nnet_7.3-19 igraph_2.0.3 #> [49] timechange_0.3.0 tidyselect_1.2.1 yaml_2.3.8 #> [52] viridis_0.6.5 timeDate_4032.109 doParallel_1.0.17 #> [55] codetools_0.2-19 curl_5.2.1 listenv_0.9.1 #> [58] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [61] withr_3.0.0 evaluate_0.23 future_1.33.1 #> [64] desc_1.4.3 survival_3.5-8 polyclip_1.10-6 #> [67] pillar_1.9.0 foreach_1.5.2 stats4_4.3.3 #> [70] generics_0.1.3 vroom_1.6.5 RcppHNSW_0.6.0 #> [73] S4Vectors_0.40.2 hms_1.1.3 ggplot2_3.5.0 #> [76] munsell_0.5.0 scales_1.3.0 globals_0.16.3 #> [79] class_7.3-22 glue_1.7.0 tools_4.3.3 #> [82] data.table_1.15.2 gower_1.0.1 fs_1.6.3 #> [85] graphlayouts_1.1.1 tidygraph_1.3.1 grid_4.3.3 #> [88] yardstick_1.3.1 tidyr_1.3.1 RProtoBufLib_2.14.1 #> [91] ipred_0.9-14 colorspace_2.1-0 ggforce_0.4.2 #> [94] cli_3.6.2 textshaping_0.3.7 fansi_1.0.6 #> [97] cytolib_2.14.1 viridisLite_0.4.2 lava_1.8.0 #> [100] dplyr_1.1.4 gtable_0.3.4 sass_0.4.9 #> [103] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [106] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [109] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [112] bit64_4.0.5 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"preprocessing-with-tof_preprocess","dir":"Articles","previous_headings":"","what":"Preprocessing with tof_preprocess","title":"Preprocessing","text":"Generally speaking, raw ion counts measured analyte mass cytometer (content raw FCS files obtained directly mass cytometer) need transformed CyTOF data analysis. Common preprocessing steps may include variance-stabilizing transformations - hyperbolic arcsine (arcsinh) transformation log transformation - scaling/centering, /denoising. perform standard preprocessing tasks tidytof, use tof_preprocess. tof_preprocess’s default behavior apply arcsinh transformation (cofactor 5) numeric column input tof_tibble well remove gaussian noise Fluidigm software adds ion count (noise added visualization purposes, analyses, removing recommended). example, can preprocess tidytof’s built-phenograph_data tof_tibble see first measurements change . alter tof_preprocess’s default behavior, change channel_cols argument specify columns tof_tibble transformed. Alter transform_fun argument specify vector-valued function used transform channel_cols. example, suppose want center scale numeric columns instead arcsinh-transforming : keep gaussian noise added Fluidigm software (working dataset noise), set undo_noise argument FALSE.","code":"data(phenograph_data) # before preprocessing phenograph_data %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 131. 3.23 1.51 #> 2 230. -0.582 11.4 #> 3 293. 5.20 1.84 #> 4 431. 0.363 13.3 #> # ℹ 2 more rows phenograph_data %>% # perform preprocessing tof_preprocess() %>% # inspect new values select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 3.96 0.608 0.298 #> 2 4.52 -0.116 1.56 #> 3 4.76 0.909 0.360 #> 4 5.15 0.0725 1.70 #> # ℹ 2 more rows phenograph_data %>% # preprocess tof_preprocess(transform_fun = scale) %>% # inspect new values select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45[,1] cd34[,1] cd38[,1] #> #> 1 -1.40 1.01 -0.437 #> 2 -1.15 -0.911 0.0316 #> 3 -0.999 2.00 -0.422 #> 4 -0.661 -0.436 0.120 #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"postprocessing-with-tof_postprocess","dir":"Articles","previous_headings":"","what":"Postprocessing with tof_postprocess","title":"Preprocessing","text":"final note, note built-function tof_postprocess works nearly identically tof_preprocess, provides different default behavior (namely, applying reverse arcsinh transformation cofactor 5 numeric columns. See ?tof_postprocess details).","code":"print(phenograph_data) %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 3,000 × 25 #> sample_name phenograph_cluster cd19 cd11b cd34 cd45 cd123 cd33 cd47 #> #> 1 H1_PhenoGra… cluster1 -0.168 29.0 3.23 131. -0.609 1.21 13.0 #> 2 H1_PhenoGra… cluster1 1.65 4.83 -0.582 230. 2.53 -0.507 12.9 #> 3 H1_PhenoGra… cluster1 2.79 36.1 5.20 293. -0.265 3.67 27.1 #> 4 H1_PhenoGra… cluster1 0.0816 48.8 0.363 431. 2.04 9.40 41.0 #> # ℹ 2,996 more rows #> # ℹ 16 more variables: cd7 , cd44 , cd38 , cd3 , #> # cd117 , cd64 , cd41 , pstat3 , pstat5 , #> # pampk , p4ebp1 , ps6 , pcreb , `pzap70-syk` , #> # prb , `perk1-2` #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 131. 3.23 1.51 #> 2 230. -0.582 11.4 #> 3 293. 5.20 1.84 #> 4 431. 0.363 13.3 #> # ℹ 2 more rows # after preprocessing and post-processing, the data are the same # except that the re-added noise component is different for each value phenograph_data %>% tof_preprocess() %>% tof_postprocess(redo_noise = TRUE) %>% select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 #> cd45 cd34 cd38 #> #> 1 130. 2.62 0.535 #> 2 230. -0.918 11.0 #> 3 293. 4.91 1.10 #> 4 430. 0.318 13.3 #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/preprocessing.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Preprocessing","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.1 viridisLite_0.4.2 timeDate_4032.109 #> [4] farver_2.1.1 viridis_0.6.5 ggraph_2.2.1 #> [7] fastmap_1.1.1 tweenr_2.0.3 rpart_4.1.23 #> [10] digest_0.6.35 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.1 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.9 #> [19] tools_4.3.3 igraph_2.0.3 utf8_1.2.4 #> [22] yaml_2.3.8 data.table_1.15.2 knitr_1.45 #> [25] graphlayouts_1.1.1 withr_3.0.0 purrr_1.0.2 #> [28] RProtoBufLib_2.14.1 BiocGenerics_0.48.1 desc_1.4.3 #> [31] nnet_7.3-19 grid_4.3.3 polyclip_1.10-6 #> [34] stats4_4.3.3 fansi_1.0.6 RcppHNSW_0.6.0 #> [37] future_1.33.1 colorspace_2.1-0 ggplot2_3.5.0 #> [40] globals_0.16.3 scales_1.3.0 iterators_1.0.14 #> [43] MASS_7.3-60.0.1 cli_3.6.2 rmarkdown_2.26 #> [46] ragg_1.3.0 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.2 #> [52] ggforce_0.4.2 stringr_1.5.1 splines_4.3.3 #> [55] parallel_4.3.3 matrixStats_1.2.0 vctrs_0.6.5 #> [58] hardhat_1.3.1 glmnet_4.1-8 Matrix_1.6-5 #> [61] jsonlite_1.8.8 cytolib_2.14.1 hms_1.1.3 #> [64] S4Vectors_0.40.2 ggrepel_0.9.5 listenv_0.9.1 #> [67] systemfonts_1.0.6 foreach_1.5.2 gower_1.0.1 #> [70] tidyr_1.3.1 jquerylib_0.1.4 recipes_1.0.10 #> [73] parallelly_1.37.1 glue_1.7.0 pkgdown_2.0.7 #> [76] codetools_0.2-19 stringi_1.8.3 lubridate_1.9.3 #> [79] gtable_0.3.4 shape_1.4.6.1 munsell_0.5.0 #> [82] tibble_3.2.1 pillar_1.9.0 htmltools_0.5.7 #> [85] ipred_0.9-14 lava_1.8.0 R6_2.5.1 #> [88] textshaping_0.3.7 doParallel_1.0.17 tidygraph_1.3.1 #> [91] evaluate_0.23 Biobase_2.62.0 lattice_0.22-5 #> [94] readr_2.1.5 memoise_2.0.1 bslib_0.6.2 #> [97] class_7.3-22 Rcpp_1.0.12 prodlim_2023.08.28 #> [100] gridExtra_2.3 xfun_0.42 fs_1.6.3 #> [103] pkgconfig_2.0.3"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Quality control","text":"demonstrate use {tidytof}’s quality control verbs, use combination simulated real data vignette. Simulated data generated --fly sections , walk download real dataset section. want download dataset originally collected development PhenoGraph algorithm. data built HDCytoData package, available Bioconductor can downloaded following command: load PhenoGraph data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s. also add lines dplyr code clean column names perform standard arcsinh transformation. Thus, can see levine tof_tbl 265627 cells (one row) 40 pieces information cell (one column).","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") levine <- HDCytoData::Levine_32dim_flowSet() |> as_tof_tbl() |> # a bit of data cleaning dplyr::mutate(population_id = as.character(population_id)) |> dplyr::rename_with( .fn = \\(x) stringr::str_to_lower(stringr::str_remove(x, \"\\\\|.+\")) ) |> dplyr::mutate(dplyr::across(c(file_number, population_id), as.character)) |> # arcsinh transformation tof_preprocess( channel_cols = c(-time, -cell_length, -event_number, -file_number, -population_id) )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"detect-low-expression-i-e--potentially-failed-channels-with-tof_assess_channels","dir":"Articles","previous_headings":"","what":"Detect low-expression (i.e. potentially failed) channels with tof_assess_channels()","title":"Quality control","text":"data collection, might wonder channels include downstream analyses. particular, might want exclude channels positive values, situation indicates antibody may failed, signal particular channel may weak detected. , can use tof_assess_channels(), verb calculates many cells negative (.e. use-specified threshold, negative_threshold) channel flags channels user-specified proportion negative cells (negative_proportion_flag). levine dataset, look markers 97.5% cells threshold 5 ion counts: can see two channels 97.5% cells dataset 5 counts. given experiment, might expected (.e. marker expressed rare cell population) unexpected (marker expressed many cells). case, can visualize marker negative cells manually inspect (recommend flagged channels). case, looks like small population cells slightly positive cd14, simply measurement noise/nonspecific binding antibody. user whether include cd14 downstream analyses.","code":"# convert 5 counts to asinh value with a cofactor of 5 threshold <- asinh(5 / 5) levine |> tof_assess_channels( negative_threshold = threshold, negative_proportion_flag = 0.975 ) #> # A tibble: 38 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd14 0.988 TRUE #> 2 cd133 0.975 TRUE #> 3 cd117 0.969 FALSE #> 4 cd16 0.967 FALSE #> 5 flt3 0.960 FALSE #> 6 cd15 0.940 FALSE #> 7 cd41 0.923 FALSE #> 8 cd34 0.909 FALSE #> 9 cd61 0.890 FALSE #> 10 cd33 0.885 FALSE #> # ℹ 28 more rows levine |> tof_plot_cells_density(marker_col = cd14)"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"identify-time-periods-of-abnormally-low-or-high-flow-rate-during-data-acquisition-using-tof_assess_flow_rate","dir":"Articles","previous_headings":"","what":"Identify time periods of abnormally low or high flow rate during data acquisition using tof_assess_flow_rate()","title":"Quality control","text":"Large changes flow rate cytometer can impact quality signal acquired data collection: example, abnormally low flow rates can caused partial occlusions cytometer’s flow cell, leading debris air infiltration cytometer’s microfluidics system. Thus, can useful perform quality control step explicitly interrogates flow rate course cytometry experiment order flag cells collected unusually high low rates acquisition. , {tidytof} provides tof_assess_flow_rate(), function implements simplified version FlowAI’s flow rate analysis. short, relative flow rates timestep cytometry experiment calculated, outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged. size timesteps user-defined parameter calculation, significance level (0 1) within t-distribution determines anomalous time step’s flow rate must cells flagged. can apply tof_assess_flow_rate() levine dataset , using result , can see last several timesteps flagged potentially low flow rates. decision include exclude cells analyses left user. group_cols argument can also used analyze flow rates samples, patients, mass cytometry barcode plates, etc. separately.","code":"levine |> tof_assess_flow_rate( time_col = time, num_timesteps = 200, # flag timepoints in which flow rates are high or low at a signicance level # of p = 0.01 alpha_threshold = 0.01, # plot the number of cells in each timestep, and whether or not the # rates were flagged as too high or too low visualize = TRUE ) levine |> tof_assess_flow_rate( time_col = time, # analyze two files in the levine dataset separately group_cols = file_number, alpha_threshold = 0.01, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"flag-cells-that-are-unusually-far-from-the-centroid-of-a-cluster-to-which-they-have-been-assigned-with-tof_assess_clusters_distance","dir":"Articles","previous_headings":"","what":"Flag cells that are unusually far from the centroid of a cluster to which they have been assigned with tof_assess_clusters_distance()","title":"Quality control","text":"using favorite clustering algorithm define cell subpopulations (example, using tof_cluster()), may wonder well clustering procedure worked. example, may clusters contain outliers - , cells less similar cells cluster typical. detect cells, tidytof provides tof_assess_clusters_distance() verb, computes Mahalanobis distance cell centroid cluster assigned. , computes z-score mahalanobis distances cells cluster flags cells z-score user-specified threshold. Altogether, procedure flags cells unusually far cluster centroid - .e. candidate outliers. demonstrate use tof_assess_clusters_distance() simulated data. simulate data 3 clusters, large population cells “truly” belong cluster well small population outliers cells erroneously assigned cluster. following plots visualize simulated data described : Using dataset, see tof_assess_clusters_distance() can successfully flag majority outlier cells cluster.","code":"set.seed(2020L) # simulate large population of cells that truly belong in their assigned cluster sim_data_base <- dplyr::tibble( cd45 = c(rnorm(n = 600), rnorm(n = 500, mean = -4)), cd38 = c( rnorm(n = 100, sd = 0.5), rnorm(n = 500, mean = -3), rnorm(n = 500, mean = 8) ), cd34 = c( rnorm(n = 100, sd = 0.2, mean = -10), rnorm(n = 500, mean = 4), rnorm(n = 500, mean = 60) ), cd19 = c(rnorm(n = 100, sd = 0.3, mean = 10), rnorm(n = 1000)), cluster_id = c(rep(\"a\", 100), rep(\"b\", 500), rep(\"c\", 500)), dataset = \"non-outlier\" ) # simulate outlier cells that do not belong in their assigned cluster sim_data_outlier <- dplyr::tibble( cd45 = c(rnorm(n = 10), rnorm(50, mean = 3), rnorm(n = 50, mean = -12)), cd38 = c( rnorm(n = 10, sd = 0.5), rnorm(n = 50, mean = -10), rnorm(n = 50, mean = 10) ), cd34 = c( rnorm(n = 10, sd = 0.2, mean = -15), rnorm(n = 50, mean = 15), rnorm(n = 50, mean = 70) ), cd19 = c(rnorm(n = 10, sd = 0.3, mean = 19), rnorm(n = 100)), cluster_id = c(rep(\"a\", 10), rep(\"b\", 50), rep(\"c\", 50)), dataset = \"outlier\" ) # bind simulated data together sim_data <- bind_rows(sim_data_base, sim_data_outlier) sim_data |> tof_plot_cells_embedding(color_col = cluster_id) sim_data |> tof_plot_cells_embedding(color_col = dataset) sim_data |> tof_assess_clusters_distance( cluster_col = cluster_id, # flag cells with a mahalanobis distance z-score over 3 z_threshold = 3, augment = TRUE ) |> # visualize result as above dplyr::select(-dplyr::starts_with(\".mahala\"), -z_score) |> dplyr::mutate(flagged_cell = as.character(flagged_cell)) |> tof_plot_cells_embedding(color_col = flagged_cell) + scale_fill_manual(values = tof_generate_palette(num_colors = 2))"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"flag-cells-whose-cluster-assignment-is-ambiguous-with-tof_assess_clusters_entropy","dir":"Articles","previous_headings":"","what":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","title":"Quality control","text":"may also wish evaluate clustering result based cell’s absolute distance centroid cluster assigned, based relative distances cell cluster centroids. , order confident cell’s cluster assignment, ideally want cell close centroid cluster assigned, relatively distant clusters. contrasts scenario cell might similarly close centroids 2-3 clusters, case might think cell “ambiguous” phenotype, phenotype intermediate clusters clustering algorithm identified. flag “ambiguous” clusters, {tidytof} provides tof_assess_clusters_entropy() verb. tof_assess_clusters_entropy() computes entropy L1-scaled mahalanobis distance vector (.e. mahalanobis distance cell centroids clusters dataset) - entropy low (close 0) confident cell’s cluster assignment, high (near 1) equally close multiple cluster centroids. demonstrate use function simulated data levine dataset .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"simulated-data","dir":"Articles","previous_headings":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","what":"Simulated data","title":"Quality control","text":"First, simulate fake dataset 3000 cells 4 channels. simulated dataset, two well-defined clusters (“b” “c”) dispersed cluster intermediate others (“”). data visualized : dataset, can imagine first analysis approach might involve clustering cells 2 distinct clusters. data simulated, already know number clusters small - can calculating entropy cells resulting clusters help us realize without prior knowledge? check, can use tof_assess_clusters_entropy(): plots , can see cells middle 2 k-means clusters (correspond well ground-truth clusters “b” “c” ) high entropy values, whereas cells closer one centroids low entropy values. can also see tof_assess_clusters_entropy() flags cells potentially anomalous (.e. intermediate phenotype two clusters identified) entropy values 75th percentile (user-specified parameter) entropy values dataset. user wish recluster dataset, filter anomalous cells, processing steps. can see , expected, intermediate cluster (“”) higher entropies either distinct clusters (“b” “c”).","code":"sim_data <- dplyr::tibble( cd45 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd38 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd34 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cd19 = c( rnorm(n = 1000, sd = 2), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2) ), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) sim_data |> tof_reduce_dimensions(method = \"pca\") |> tof_plot_cells_embedding( embedding_cols = c(.pc1, .pc2), color_col = cluster_id ) set.seed(17L) entropy_result <- sim_data |> # cluster into 2 clusters tof_cluster( num_clusters = 2, method = \"kmeans\" ) |> # calculate the entropy of all cells tof_assess_clusters_entropy( cluster_col = .kmeans_cluster, marker_cols = starts_with(\"cd\"), entropy_quantile = 0.8, augment = TRUE ) # plot the clusters in PCA space entropy_result |> select(-starts_with(\".mahala\"), -flagged_cell) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = .kmeans_cluster) # show the entropy values for each cell entropy_result |> select(-starts_with(\".mahala\"), -flagged_cell) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = entropy) + scale_fill_viridis_c() entropy_result |> select(-starts_with(\".mahala\")) |> tof_reduce_dimensions(pca_cols = starts_with(\"cd\"), method = \"pca\") |> tof_plot_cells_embedding(embedding_cols = c(.pc1, .pc2), color_col = flagged_cell) + scale_fill_viridis_d() entropy_result |> ggplot(aes(x = entropy, fill = cluster_id)) + geom_density(alpha = 0.4) + theme_bw()"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"real-data","dir":"Articles","previous_headings":"Flag cells whose cluster assignment is ambiguous with tof_assess_clusters_entropy()","what":"Real data","title":"Quality control","text":"can also apply tof_assess_clusters_entropy() dataset derived levine data. Suppose take 5 largest clusters levine save result small_levine tof_tbl. , can perturb dataset replacing labels cells smallest cluster (cluster 9) random labels. effectively creates population cells dataset whose “true” cluster label absent. scenario, expect cells perturbed cluster relatively distant remaining clusters, whereas unperturbed cells relatively close cluster centroid (correct centroid). Thus, can test tof_assess_clusters_entropy() successfully flags cells perturbed cluster relative others: plot , can see cells cluster 9 larger entropy values cells clusters, expected. Similarly, can see majority cells 9 successfully flagged tof_assess_clusters_entropy() using entropy quantile threshold 0.9. Conversely, cells cluster (cluster 15) flagged.","code":"clusters_to_keep <- levine |> dplyr::count(population_id) |> dplyr::slice_max(order_by = n, n = 5L) |> dplyr::arrange(n) |> pull(population_id) smallest_cluster <- clusters_to_keep[1] largest_cluster <- clusters_to_keep[[length(clusters_to_keep)]] small_levine <- levine |> dplyr::filter(population_id %in% clusters_to_keep) # perform the perturbation small_levine <- small_levine |> dplyr::mutate( new_population_id = dplyr::if_else( population_id %in% smallest_cluster, sample( clusters_to_keep[-which(clusters_to_keep %in% smallest_cluster)], size = nrow(small_levine), replace = TRUE ), population_id ) ) # perform the entropy assessment entropy_levine <- small_levine |> tof_assess_clusters_entropy( cluster_col = new_population_id, marker_cols = starts_with(\"cd\"), augment = TRUE ) entropy_levine |> mutate(population_id = fct_reorder(population_id, entropy)) |> tof_plot_cells_density( marker_col = entropy, group_col = population_id, use_ggridges = TRUE, scale = 0.1 ) + ggplot2::theme(legend.position = \"none\") + ggplot2::labs(x = \"Entropy\", y = \"Cluster ID\") entropy_levine |> mutate(flagged_cell = entropy > quantile(entropy, prob = 0.9)) |> dplyr::count(population_id, flagged_cell) |> group_by(population_id) |> mutate(prop = n / sum(n)) |> ungroup() |> dplyr::filter(flagged_cell) #> # A tibble: 2 × 4 #> population_id flagged_cell n prop #> #> 1 15 TRUE 12888 0.0798 #> 2 9 TRUE 11666 0.706"},{"path":"https://keyes-timothy.github.io/tidytof/articles/quality-control.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Quality control","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats4 stats graphics grDevices utils datasets methods #> [8] base #> #> other attached packages: #> [1] HDCytoData_1.22.1 flowCore_2.14.2 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.8 #> [7] IRanges_2.36.0 S4Vectors_0.40.2 #> [9] MatrixGenerics_1.14.0 matrixStats_1.2.0 #> [11] ExperimentHub_2.10.0 AnnotationHub_3.10.0 #> [13] BiocFileCache_2.10.1 dbplyr_2.5.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] ggplot2_3.5.0 dplyr_1.1.4 #> [19] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] jsonlite_1.8.8 shape_1.4.6.1 #> [3] magrittr_2.0.3 farver_2.1.1 #> [5] rmarkdown_2.26 fs_1.6.3 #> [7] zlibbioc_1.48.2 ragg_1.3.0 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] RCurl_1.98-1.14 htmltools_0.5.7 #> [13] S4Arrays_1.2.1 curl_5.2.1 #> [15] SparseArray_1.2.4 sass_0.4.9 #> [17] parallelly_1.37.1 bslib_0.6.2 #> [19] desc_1.4.3 lubridate_1.9.3 #> [21] cachem_1.0.8 igraph_2.0.3 #> [23] mime_0.12 lifecycle_1.0.4 #> [25] iterators_1.0.14 pkgconfig_2.0.3 #> [27] Matrix_1.6-5 R6_2.5.1 #> [29] fastmap_1.1.1 shiny_1.8.0 #> [31] GenomeInfoDbData_1.2.11 future_1.33.1 #> [33] digest_0.6.35 colorspace_2.1-0 #> [35] AnnotationDbi_1.64.1 textshaping_0.3.7 #> [37] RSQLite_2.3.5 labeling_0.4.3 #> [39] filelock_1.0.3 cytolib_2.14.1 #> [41] fansi_1.0.6 yardstick_1.3.1 #> [43] timechange_0.3.0 httr_1.4.7 #> [45] polyclip_1.10-6 abind_1.4-5 #> [47] compiler_4.3.3 bit64_4.0.5 #> [49] withr_3.0.0 doParallel_1.0.17 #> [51] viridis_0.6.5 DBI_1.2.2 #> [53] highr_0.10 ggforce_0.4.2 #> [55] MASS_7.3-60.0.1 lava_1.8.0 #> [57] rappdirs_0.3.3 DelayedArray_0.28.0 #> [59] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [61] httpuv_1.6.14 future.apply_1.11.1 #> [63] nnet_7.3-19 glue_1.7.0 #> [65] promises_1.2.1 grid_4.3.3 #> [67] generics_0.1.3 recipes_1.0.10 #> [69] gtable_0.3.4 tzdb_0.4.0 #> [71] class_7.3-22 tidyr_1.3.1 #> [73] data.table_1.15.2 hms_1.1.3 #> [75] tidygraph_1.3.1 utf8_1.2.4 #> [77] XVector_0.42.0 ggrepel_0.9.5 #> [79] BiocVersion_3.18.1 foreach_1.5.2 #> [81] pillar_1.9.0 stringr_1.5.1 #> [83] RcppHNSW_0.6.0 later_1.3.2 #> [85] splines_4.3.3 tweenr_2.0.3 #> [87] lattice_0.22-5 survival_3.5-8 #> [89] bit_4.0.5 RProtoBufLib_2.14.1 #> [91] tidyselect_1.2.1 Biostrings_2.70.3 #> [93] knitr_1.45 gridExtra_2.3 #> [95] xfun_0.42 graphlayouts_1.1.1 #> [97] hardhat_1.3.1 timeDate_4032.109 #> [99] stringi_1.8.3 yaml_2.3.8 #> [101] evaluate_0.23 codetools_0.2-19 #> [103] ggraph_2.2.1 tibble_3.2.1 #> [105] BiocManager_1.30.22 cli_3.6.2 #> [107] rpart_4.1.23 xtable_1.8-4 #> [109] systemfonts_1.0.6 munsell_0.5.0 #> [111] jquerylib_0.1.4 Rcpp_1.0.12 #> [113] globals_0.16.3 png_0.1-8 #> [115] parallel_4.3.3 ellipsis_0.3.2 #> [117] pkgdown_2.0.7 gower_1.0.1 #> [119] readr_2.1.5 blob_1.2.4 #> [121] bitops_1.0-7 listenv_0.9.1 #> [123] glmnet_4.1-8 viridisLite_0.4.2 #> [125] ipred_0.9-14 ggridges_0.5.6 #> [127] scales_1.3.0 prodlim_2023.08.28 #> [129] purrr_1.0.2 crayon_1.5.2 #> [131] rlang_1.1.3 KEGGREST_1.42.0"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"accessing-the-data-for-this-vignette","dir":"Articles","previous_headings":"","what":"Accessing the data for this vignette","title":"Reading and writing data","text":"tidytof comes bundled several example mass cytometry datasets. access raw FCS CSV files containing data, use tidytof_example_data function. called arguments, tidytof_example_data return character vector naming datasets contained tidytof: details datasets contained directories isn’t particularly important, basic information follows: aml - one FCS file containing myeloid cells healthy bone marrow one FCS file containing myeloid cells AML patient bone marrow ddpr - two FCS files containing B-cell lineage cells paper mix - two FCS files different CyTOF antigen panels (one FCS file “aml” directory one “phenograph” directory) mix2 - three files different CyTOF antigen panels different file extensions (one FCS file “aml” directory two CSV files “phenograph_csv directory) phenograph - three FCS files containing AML cells paper phenograph_csv - cells “phenograph” directory, stored CSV files scaffold - three FCS files paper statistical_scaffold - three FCS files paper surgery - three FCS files paper obtain file path directory containing dataset, call tidytof_example_data one dataset names argument. example, obtain directory phenograph data, use following command:","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"surgery\" tidytof_example_data(\"phenograph\") #> [1] \"/home/runner/work/_temp/Library/tidytof/extdata/phenograph\""},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"reading-data-with-tof_read_data","dir":"Articles","previous_headings":"","what":"Reading Data with tof_read_data","title":"Reading and writing data","text":"Using one directories (directory containing CyTOF data local machine), can use tof_read_data read CyTOF data raw files. Acceptable formats include FCS files CSV files. Importantly, tof_read_data smart enough read single FCS/CSV files multiple FCS/CSV files depending whether first argument (path) leads single file directory files. , can use tof_read_data read FCS files “phenograph” example dataset bundled tidytof store phenograph variable. Regardless input data file type, tidytof reads data extended tibble class called tof_tbl (pronounced “tof tibble”). tof tibbles S3 class identical tbl_df, one additional attribute (“panel”). tidytof stores additional attribute tof_tbls , addition analyzing CyTOF data individual experiments, CyTOF users often want compare panels experiments find common markers compare metals associated particular markers across panels. retrieve panel information tof_tbl, use tof_get_panel: additional notes tof_tbls: tof_tbls contains one cell per row one CyTOF channel per column (provide data “tidy” format). tof_read_data adds additional column output tof_tbl encoding name file cell read (“file_name” column). tof_tbls inherit tbl_df class, methods available tibbles also available tof_tbls.","code":"phenograph <- tidytof_example_data(\"phenograph\") %>% tof_read_data() phenograph %>% class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph %>% tof_get_panel() #> # A tibble: 44 × 2 #> metals antigens #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 Ir191 DNA1 #> 4 Ir193 DNA2 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"using-tibble-methods-with-tidytof-tibbles","dir":"Articles","previous_headings":"","what":"Using tibble methods with {tidytof} tibbles","title":"Reading and writing data","text":"extension tbl_df class, tof_tbls get access dplyr tidyr free. can useful performing variety common operations. example, phenograph object two columns - PhenoGraph Condition - encode categorical variables numeric codes. might interested converting types columns strings make sure don’t accidentally perform quantitative operations later. Thus, dplyr’s useful mutate method can applied phenograph convert two columns character vectors. note tof_tbl class preserved even transformations. Importantly, tof_read_data uses opinionated heuristic mine different keyword slots input FCS file(s) guess metals antigens used data acquisition. Thus, CSV files read using tof_read_data, recommended use panel_info argument provide panel manually (CSV files, unlike FCS files, provide built-metadata columns contain).","code":"phenograph <- phenograph %>% # mutate the input tof_tbl mutate( PhenoGraph = as.character(PhenoGraph), Condition = as.character(Condition) ) phenograph %>% # use dplyr's select method to show # that the columns have been changed select(where(is.character)) #> # A tibble: 300 × 3 #> file_name PhenoGraph Condition #> #> 1 H1_PhenoGraph_cluster1.fcs 7 7 #> 2 H1_PhenoGraph_cluster1.fcs 6 6 #> 3 H1_PhenoGraph_cluster1.fcs 9 9 #> 4 H1_PhenoGraph_cluster1.fcs 2 2 #> # ℹ 296 more rows phenograph %>% class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" # when csv files are read, the tof_tibble's \"panel\" # attribute will be empty by default tidytof_example_data(\"phenograph_csv\") %>% tof_read_data() %>% tof_get_panel() #> # A tibble: 0 × 0 # to add a panel manually, provide it as a tibble # to tof_read_data phenograph_panel <- phenograph %>% tof_get_panel() tidytof_example_data(\"phenograph_csv\") %>% tof_read_data(panel_info = phenograph_panel) %>% tof_get_panel() #> # A tibble: 44 × 2 #> antigens metals #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 DNA1 Ir191 #> 4 DNA2 Ir193 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"writing-data-from-a-tof_tbl-to-disk","dir":"Articles","previous_headings":"","what":"Writing data from a tof_tbl to disk","title":"Reading and writing data","text":"Users may wish store CyTOF data FCS CSV files transformation, concatenation, filtering, data processing. write single-cell data tof_tbl FCS CSV files, use tof_write_data. illustrate use verb, use tidytof’s built-phenograph_data dataset. tof_write_data’s trickiest argument group_cols, argument used specify columns tof_tibble used group cells (rows tof_tibble) separate FCS CSV files. Simply put, argument allows tof_write_data create single FCS CSV file unique combination values group_cols columns specified user. example , cells grouped 3 output FCS files - one 3 clusters encoded phenograph_cluster column phenograph_data. files following names (derived values phenograph_cluster column): cluster1.fcs cluster2.fcs cluster3.fcs Note file names match distinct values group_cols column (phenograph_cluster): However, suppose wanted write multiple files cluster breaking cells two groups: express high levels pstat5 express low levels pstat5. can use dplyr::mutate create new column phenograph_data breaks cells high- low-pstat5 expression groups, add column group_cols specification: write 6 files following names (derived values phenograph_cluster expression_group). cluster1_low.fcs cluster1_high.fcs cluster2_low.fcs cluster2_high.fcs cluster3_low.fcs cluster3_high.fcs , note file names match distinct values group_cols columns (phenograph_cluster expression_group): useful feature tof_write_data automatically concatenate cells single FCS CSV files based specified group_cols regardless many unique files cells came . allows easy concatenation FCS CSV files containing data single sample acquired multiple CyTOF runs, example.","code":"data(phenograph_data) print(phenograph_data) #> # A tibble: 3,000 × 25 #> sample_name phenograph_cluster cd19 cd11b cd34 cd45 cd123 cd33 cd47 #> #> 1 H1_PhenoGra… cluster1 -0.168 29.0 3.23 131. -0.609 1.21 13.0 #> 2 H1_PhenoGra… cluster1 1.65 4.83 -0.582 230. 2.53 -0.507 12.9 #> 3 H1_PhenoGra… cluster1 2.79 36.1 5.20 293. -0.265 3.67 27.1 #> 4 H1_PhenoGra… cluster1 0.0816 48.8 0.363 431. 2.04 9.40 41.0 #> # ℹ 2,996 more rows #> # ℹ 16 more variables: cd7 , cd44 , cd38 , cd3 , #> # cd117 , cd64 , cd41 , pstat3 , pstat5 , #> # pampk , p4ebp1 , ps6 , pcreb , `pzap70-syk` , #> # prb , `perk1-2` # when copying and pasting this code, feel free to change this path # to wherever you'd like to save your output files my_path <- file.path(\"~\", \"Desktop\", \"tidytof_vignette_files\") phenograph_data %>% tof_write_data( group_cols = phenograph_cluster, out_path = my_path, format = \"fcs\" ) phenograph_data %>% distinct(phenograph_cluster) #> # A tibble: 3 × 1 #> phenograph_cluster #> #> 1 cluster1 #> 2 cluster2 #> 3 cluster3 phenograph_data %>% # create a variable representing if a cell is above or below # the median expression level of pstat5 mutate( expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\") ) %>% tof_write_data( group_cols = c(phenograph_cluster, expression_group), out_path = my_path, format = \"fcs\" ) phenograph_data %>% mutate( expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\") ) %>% distinct(phenograph_cluster, expression_group) #> # A tibble: 6 × 2 #> phenograph_cluster expression_group #> #> 1 cluster1 low #> 2 cluster1 high #> 3 cluster2 low #> 4 cluster2 high #> # ℹ 2 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/articles/reading-and-writing-data.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Reading and writing data","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] dplyr_1.1.4 tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 crayon_1.5.2 #> [10] pkgconfig_2.0.3 shape_1.4.6.1 fastmap_1.1.1 #> [13] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [16] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.3.0 #> [19] bit_4.0.5 purrr_1.0.2 xfun_0.42 #> [22] glmnet_4.1-8 cachem_1.0.8 jsonlite_1.8.8 #> [25] recipes_1.0.10 tweenr_2.0.3 parallel_4.3.3 #> [28] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [31] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [34] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [37] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [40] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [46] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [49] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [52] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [55] Biobase_2.62.0 withr_3.0.0 evaluate_0.23 #> [58] future_1.33.1 desc_1.4.3 survival_3.5-8 #> [61] polyclip_1.10-6 pillar_1.9.0 foreach_1.5.2 #> [64] stats4_4.3.3 generics_0.1.3 vroom_1.6.5 #> [67] RcppHNSW_0.6.0 S4Vectors_0.40.2 hms_1.1.3 #> [70] ggplot2_3.5.0 munsell_0.5.0 scales_1.3.0 #> [73] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [76] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [79] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [82] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [88] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [91] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [94] lava_1.8.0 gtable_0.3.4 sass_0.4.9 #> [97] digest_0.6.35 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [100] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [103] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [106] bit64_4.0.5 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"prerequisites","dir":"Articles","previous_headings":"","what":"Prerequisites","title":"Getting started with tidytof","text":"tidytof makes heavy use two concepts may unfamiliar R beginners. first pipe (|>), can read . second “grouping” data data.frame tibble using dplyr::group_by, can read . tidytof users also benefit relatively -depth understanding dplyr package, wonderful introductory vignette : Everything else self-explanatory beginner advanced R users, though zero background running R code, read chapter R Data Science Hadley Wickham.","code":"vignette(\"dplyr\")"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"workflow-basics","dir":"Articles","previous_headings":"","what":"Workflow basics","title":"Getting started with tidytof","text":"Broadly speaking, tidytof’s functionality organized support 3 levels analysis inherent single-cell data described : Reading, writing, preprocessing, visualizing data level individual cells Identifying describing cell subpopulations clusters Building models (inference prediction) level patients samples tidytof provides functions (“verbs”) operate levels analysis: Cell-level data: tof_read_data() reads single-cell data FCS CSV files disk tidy data frame called tof_tbl. tof_tbls represent cell row protein measurement (piece information associated given cell) column. tof_preprocess() transforms protein expression values using user-provided function (.e. log-transformation, centering, scaling) tof_downsample() reduces number cells tof_tibble via subsampling. tof_reduce_dimensions() performs dimensionality reduction (across columns) tof_write_data writes single-cell data tof_tibble back disk form FCS CSV file. Cluster-level data: tof_cluster() clusters cells using one several algorithms commonly applied high-dimensional cytometry data tof_metacluster() agglomerates clusters smaller number metaclusters tof_analyze_abundance() performs differential abundance analysis (DAA) clusters metaclusters across experimental groups tof_analyze_expression() performs differential expression analysis (DEA) clusters’ metaclusters’ marker expression levels across experimental groups tof_extract_features() computes summary statistics (mean marker expression) cluster. Also (optionally) pivots summary statistics sample-level tidy data frame row represents sample column represents cluster-level summary statistic. Sample-level data: tof_split_data() splits sample-level data training test set predictive modeling tof_create_grid() creates elastic net hyperparameter search grid model tuning tof_train_model() trains sample-level elastic net model saves tof_model object tof_predict() Applies trained tof_model new data predict sample-level outcomes tof_assess_model() calculates performance metrics trained tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"tidytof-verb-syntax","dir":"Articles","previous_headings":"","what":"{tidytof} verb syntax","title":"Getting started with tidytof","text":"exceptions, tidytof functions follow specific, shared syntax involves 3 types arguments always occur order. argument types follows: almost tidytof functions, first argument data frame (tibble). enables use pipe (|>) multi-step calculations, means first argument functions implicit (passed previous function using pipe). also means tidytof functions -called “single-table verbs,” exception tof_cluster_ddpr, “two-table verb” (details use tof_cluster_ddpr, see “clustering--metaclustering” vignette). second group arguments called column specifications, end suffix _col _cols. Column specifications unquoted column names tell tidytof verb columns compute particular operation. example, cluster_cols argument tof_cluster allows user specify column input data frames used perform clustering. Regardless verb requires , column specifications support tidyselect helpers follow rules tidyselection tidyverse verbs like dplyr::select() tidyr::pivot_longer(). Finally, third group arguments tidytof verb called method specifications, ’re comprised every argument isn’t input data frame column specification. Whereas column specifications represent columns used perform operation, method specifications represent details operation performed. example, tof_cluster_phenograph() function requires method specification num_neighbors, specifies many nearest neighbors used construct PhenoGraph algorithm’s k-nearest-neighbor graph. cases, tidytof sets reasonable defaults verb’s particular method specifications, workflows can also customized experimenting non-default values. following code demonstrates tidytof verb syntax looks practice, column method specifications explicitly pointed :","code":"data(ddpr_data) set.seed(777L) ddpr_data |> tof_preprocess() |> tof_cluster( cluster_cols = starts_with(\"cd\"), # column specification method = \"phenograph\", # method specification, ) |> tof_metacluster( cluster_col = .phenograph_cluster, # column specification num_metaclusters = 4, # method specification method = \"kmeans\" # method specification ) |> tof_downsample( group_cols = .kmeans_metacluster, # column specification num_cells = 200, # method specification method = \"constant\" # method specification ) |> tof_plot_cells_layout( knn_cols = starts_with(\"cd\"), # column specification color_col = .kmeans_metacluster, # column specification num_neighbors = 7L, # method specification node_size = 2L # method specification )"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"pipelines","dir":"Articles","previous_headings":"","what":"Pipelines","title":"Getting started with tidytof","text":"tidytof verbs can used combination one another using pipe (|>) operator. example, multistep “pipeline” takes built-tidytof dataset performs following analytical steps: Arcsinh-transform column protein measurements (default behavior tof_preprocess verb Cluster cells based surface markers panel Downsample dataset 100 random cells picked cluster Perform dimensionality reduction downsampled dataset using tSNE Visualize clusters using low-dimensional tSNE embedding","code":"ddpr_data |> # step 1 tof_preprocess() |> # step 2 tof_cluster( cluster_cols = starts_with(\"cd\"), method = \"phenograph\", # num_metaclusters = 4L, seed = 2020L ) |> # step 3 tof_downsample( group_cols = .phenograph_cluster, method = \"constant\", num_cells = 400 ) |> # step 4 tof_reduce_dimensions(method = \"tsne\") |> # step 5 tof_plot_cells_embedding( embedding_cols = contains(\"tsne\"), color_col = .phenograph_cluster ) + ggplot2::theme(legend.position = \"none\") #> Loading required namespace: Rtsne"},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"other-tips","dir":"Articles","previous_headings":"","what":"Other tips","title":"Getting started with tidytof","text":"tidytof designed multidisciplinary team wet-lab biologists, bioinformaticians, physician-scientists analyze high-dimensional cytometry kinds single-cell data solve variety problems. result, tidytof’s high-level API designed great care mirror tidyverse - , human-centered, consistent, composable, inclusive wide userbase. Practically speaking, means things using tidytof. First, means tidytof designed quality--life features mind. example, may notice tidytof functions begin prefix tof_. intentional, allow use development environment’s code-completing software search tidytof functions easily (even can’t remember specific function name). reason, recommend using tidytof within RStudio development environment; however, many code editors predictive text functionality serves similar function. general, tidytof verbs organized way IDE’s code-completion tools also allow search (compare) related functions relative ease. (instance, tof_cluster_ prefix used clustering functions, tof_downsample_ prefix used downsampling functions). Second, means tidytof functions relatively intuitive use due shared logic - words, understand use one tidytof function, understand use others. example shared logic across tidytof functions argument group_cols, shows multiple verbs (tof_downsample, tof_cluster, tof_daa, tof_dea, tof_extract_features, tof_write_data). case, group_cols works way: accepts unquoted vector column names (specified manually using tidyselection) used group cells operation performed. idea generalizes throughout tidytof: see argument one place, behave identically (least similarly) wherever else encounter . Finally, means tidytof optimized first ease--use, performance. humans computers interact data differently, always trade-choosing data representation intuitive human user vs. choosing data representation optimized computational speed memory efficiency. design choices conflict one another, team tends err side choosing representation easy--understand users even expense small performance costs. Ultimately, means tidytof may optimal tool every high-dimensional cytometry analysis, though hopefully general framework provide users useful functionality.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/articles/tidytof.html","id":"session-info","dir":"Articles","previous_headings":"","what":"Session info","title":"Getting started with tidytof","text":"","code":"sessionInfo() #> R version 4.3.3 (2024-02-29) #> Platform: x86_64-pc-linux-gnu (64-bit) #> Running under: Ubuntu 22.04.4 LTS #> #> Matrix products: default #> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 #> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so; LAPACK version 3.10.0 #> #> locale: #> [1] LC_CTYPE=C.UTF-8 LC_NUMERIC=C LC_TIME=C.UTF-8 #> [4] LC_COLLATE=C.UTF-8 LC_MONETARY=C.UTF-8 LC_MESSAGES=C.UTF-8 #> [7] LC_PAPER=C.UTF-8 LC_NAME=C LC_ADDRESS=C #> [10] LC_TELEPHONE=C LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C #> #> time zone: UTC #> tzcode source: system (glibc) #> #> attached base packages: #> [1] stats graphics grDevices utils datasets methods base #> #> other attached packages: #> [1] tidytof_0.99.5 #> #> loaded via a namespace (and not attached): #> [1] gridExtra_2.3 rlang_1.1.3 magrittr_2.0.3 #> [4] matrixStats_1.2.0 compiler_4.3.3 systemfonts_1.0.6 #> [7] vctrs_0.6.5 stringr_1.5.1 pkgconfig_2.0.3 #> [10] shape_1.4.6.1 fastmap_1.1.1 labeling_0.4.3 #> [13] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [16] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.3.0 #> [19] purrr_1.0.2 xfun_0.42 glmnet_4.1-8 #> [22] cachem_1.0.8 jsonlite_1.8.8 recipes_1.0.10 #> [25] highr_0.10 tweenr_2.0.3 parallel_4.3.3 #> [28] R6_2.5.1 bslib_0.6.2 stringi_1.8.3 #> [31] parallelly_1.37.1 rpart_4.1.23 lubridate_1.9.3 #> [34] jquerylib_0.1.4 Rcpp_1.0.12 iterators_1.0.14 #> [37] knitr_1.45 future.apply_1.11.1 readr_2.1.5 #> [40] flowCore_2.14.2 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.3 timechange_0.3.0 #> [46] tidyselect_1.2.1 yaml_2.3.8 viridis_0.6.5 #> [49] timeDate_4032.109 doParallel_1.0.17 codetools_0.2-19 #> [52] listenv_0.9.1 lattice_0.22-5 tibble_3.2.1 #> [55] Biobase_2.62.0 withr_3.0.0 Rtsne_0.17 #> [58] evaluate_0.23 future_1.33.1 desc_1.4.3 #> [61] survival_3.5-8 polyclip_1.10-6 pillar_1.9.0 #> [64] foreach_1.5.2 stats4_4.3.3 generics_0.1.3 #> [67] RcppHNSW_0.6.0 S4Vectors_0.40.2 hms_1.1.3 #> [70] ggplot2_3.5.0 munsell_0.5.0 scales_1.3.0 #> [73] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [76] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [79] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [82] grid_4.3.3 yardstick_1.3.1 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.1 ipred_0.9-14 colorspace_2.1-0 #> [88] ggforce_0.4.2 cli_3.6.2 textshaping_0.3.7 #> [91] fansi_1.0.6 cytolib_2.14.1 viridisLite_0.4.2 #> [94] lava_1.8.0 dplyr_1.1.4 gtable_0.3.4 #> [97] sass_0.4.9 digest_0.6.35 BiocGenerics_0.48.1 #> [100] ggrepel_0.9.5 farver_2.1.1 memoise_2.0.1 #> [103] htmltools_0.5.7 pkgdown_2.0.7 lifecycle_1.0.4 #> [106] hardhat_1.3.1 MASS_7.3-60.0.1"},{"path":"https://keyes-timothy.github.io/tidytof/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Timothy Keyes. Maintainer. Kara Davis. Research team head, owner. Garry Nolan. Research team head, owner.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Keyes T, Davis K, Nolan G (2024). tidytof: user-friendly framework scalable reproducible high-dimensional cytometry data analysis. doi:10.18129/B9.bioc.tidytof, https://github.com/keyes-timothy/tidytof/tidytof - R package version 0.99.5, http://www.bioconductor.org/packages/tidytof. Keyes, J. T, Koladiya, Abhishek, Lo, Yu-Chen, Nolan, P. G, Davis, L. K (2023). “tidytof: user-friendly framework scalable reproducible high-dimensional cytometry data analysis.” Bioinformatics Advances, 3(1). doi:10.1093/bioadv/vbad071, https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad071/7192984.","code":"@Manual{, title = {tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis}, author = {Timothy Keyes and Kara Davis and Garry Nolan}, year = {2024}, url = {http://www.bioconductor.org/packages/tidytof}, note = {https://github.com/keyes-timothy/tidytof/tidytof - R package version 0.99.5}, doi = {10.18129/B9.bioc.tidytof}, } @Article{, title = {tidytof: a user-friendly framework for scalable and reproducible high-dimensional cytometry data analysis}, author = {{Keyes} and Timothy J. and {Koladiya} and {Abhishek} and {Lo} and {Yu-Chen} and {Nolan} and Garry P. and {Davis} and Kara L.}, year = {2023}, journal = {Bioinformatics Advances}, volume = {3}, number = {1}, doi = {10.1093/bioadv/vbad071}, url = {https://academic.oup.com/bioinformaticsadvances/article/3/1/vbad071/7192984}, }"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"tidytof-a-user-friendly-framework-for-interactive-and-reproducible-cytometry-data-analysis-","dir":"","previous_headings":"","what":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof R package implements open-source, integrated “grammar” single-cell data analysis high-dimensional cytometry data (.e. mass cytometry, full-spectrum flow cytometry, sequence-based cytometry). Specifically, tidytof provides easy--use pipeline handling high-dimensional cytometry data multiple levels observation - single-cell level, cell subpopulation (cluster) level, whole-sample level - automating many common data-processing tasks common “tidy data” interface. extension tidyverse ecosystem data manipulation tools R, tidytof’s functions developed internally consistent, human-centered set design principles. means using tidytof equally intuitive among scientists wide range coding experience (including beginners).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"prerequisites","dir":"","previous_headings":"Getting started","what":"Prerequisites","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof makes heavy use two concepts R beginners may unfamiliar . first pipe (|>), can read . second “grouping” data data.frame tibble using dplyr::group_by, can read . Everything else self-explanatory beginner advanced R users, though zero background running R code, read chapter R Data Science Hadley Wickham.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"package-structure","dir":"","previous_headings":"Getting started","what":"Package structure","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Broadly speaking, tidytof’s functionality organized support 3 levels analysis inherent single-cell data: Reading, writing, preprocessing, visualizing data level single cells Identifying describing cell subpopulations clusters Building models (inference prediction) level patients samples use tidytof levels cytometry data analysis detailed “Usage” section .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"can install development version tidytof GitHub following command: tidytof installed, can attach current R session using following code: addition, can install load packages need vignette:","code":"if (!require(devtools)) install.packages(\"devtools\") devtools::install_github(\"keyes-timothy/tidytof\") library(tidytof) if (!require(FlowSOM)) BiocManager::install(\"FlowSOM\") library(FlowSOM) if (!require(tidyverse)) install.packages(\"tidyverse\") library(tidyverse)"},{"path":[]},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"reading-data-with-tof_read_data","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Reading data with tof_read_data","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof comes bundled several example mass cytometry datasets. access raw .fcs .csv files containing data, use tidytof_example_data function. called arguments, tidytof_example_data return character vector naming datasets contained tidytof: obtain file path directory containing dataset, call tidytof_example_data one dataset names argument. example, obtain directory phenograph data, use following command: Using one directories (directory containing cytometry data local machine), can use tof_read_data read cytometry data raw files. Acceptable formats include .fcs files .csv files. Importantly, tof_read_data smart enough read single .fcs/.csv files multiple .fcs/.csv files depending whether first argument (path) leads single file directory files. , can use tof_read_data read .fcs files “phenograph” example dataset bundled tidytof store phenograph variable. Regardless input format, tidytof reads data extended tibble called tof_tbl (pronounced “tof tibble”), S3 class identical tbl_df, one additional attribute (“panel”). tidytof stores additional attribute tof_tbls , addition analyzing cytometry data individual experiments, cytometry users often want compare panels experiments find common markers compare metals associated particular markers across panels. notes tof_tbls: tof_tbls contains one cell per row one cytometry channel per column (provide data “tidy” format). tof_read_data adds additional column output tof_tbl encoding name file cell read (“file_name” column). tof_tbls inherit tbl_df class, methods available tibbles also available tof_tbls. example, dplyr’s useful mutate method can applied tof_tbl named phenograph convert columns encoding phenograph cluster ID stimulation condition cell belongs character vectors (instead original numeric codes uncleaned dataset). tof_tbl class preserved even transformations. Finally, retrieve panel information tof_tbl, use tof_get_panel: Importantly, tof_read_data uses opinionated heuristic mine different keyword slots input .fcs file(s) guess metals antigens used data collection. Thus, .csv files read using tof_read_data, recommended use panel_info argument provide panel manually (.csv files, unlike .fcs files, provide built-metadata columns contain).","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"scaffold\" \"statistical_scaffold\" #> [10] \"surgery\" tidytof_example_data(\"phenograph\") #> [1] \"/Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/library/tidytof/extdata/phenograph\" phenograph <- tidytof_example_data(\"phenograph\") |> tof_read_data() phenograph |> class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph <- phenograph |> # mutate the input tof_tbl mutate( PhenoGraph = as.character(PhenoGraph), Condition = as.character(Condition) ) phenograph |> # use dplyr's select method to show that the columns have been changed select(where(is.character)) |> head() #> # A tibble: 6 × 3 #> file_name PhenoGraph Condition #> #> 1 H1_PhenoGraph_cluster1.fcs 7 7 #> 2 H1_PhenoGraph_cluster1.fcs 6 6 #> 3 H1_PhenoGraph_cluster1.fcs 9 9 #> 4 H1_PhenoGraph_cluster1.fcs 2 2 #> 5 H1_PhenoGraph_cluster1.fcs 15 15 #> 6 H1_PhenoGraph_cluster1.fcs 12 12 phenograph |> class() #> [1] \"tof_tbl\" \"tbl_df\" \"tbl\" \"data.frame\" phenograph |> tof_get_panel() |> head() #> # A tibble: 6 × 2 #> metals antigens #> #> 1 Time Time #> 2 Cell_length Cell_length #> 3 Ir191 DNA1 #> 4 Ir193 DNA2 #> 5 Pd104 BC1 #> 6 Pd106 BC2"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"pre-processing-with-tof_preprocess","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Pre-processing with tof_preprocess","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Generally, raw ion counts analyte measured mass cytometer need transformed cytometry data analysis. Common preprocessing steps may include variance-stabilizing transformations - hyperbolic arcsine (arcsinh) transformation log transformation - scaling/centering, /denoising. perform standard preprocessing tasks tidytof, use tof_preprocess. tof_preprocess’s default behavior apply arcsinh transformation (cofactor 5) numeric column input tof_tibble well remove gaussian noise Fluidigm software adds ion count (noise added visualization purposes, analyses, removing recommended). example, can preprocess phenograph tof_tibble see first measurements change . alter tof_preprocess’s default behavior, change channel_cols argument (specify columns tof_tibble transformed) transform_fun argument (specify vector-valued function used transform channel_cols). keep gaussian noise added Fluidigm software (working dataset noise), set undo_noise argument FALSE. Finally, note built-function tof_postprocess works nearly identically tof_preprocess, provides different default behavior (namely, applying reverse arcsinh transformation cofactor 5 numeric columns. See ?tof_postprocess details).","code":"# before preprocessing phenograph |> select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> head() #> # A tibble: 6 × 3 #> `CD45|Sm154` `CD34|Nd148` `CD38|Er167` #> #> 1 440. 0.256 18.7 #> 2 705. 1.96 41.2 #> 3 383. -0.302 6.51 #> 4 44.4 2.74 27.2 #> 5 892. 4.08 24.5 #> 6 448. 2.69 11.1 # perform preprocessing phenograph <- phenograph |> tof_preprocess() # inspect new values phenograph |> select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> head() #> # A tibble: 6 × 3 #> `CD45|Sm154` `CD34|Nd148` `CD38|Er167` #> #> 1 5.17 0.0512 2.03 #> 2 5.64 0.382 2.81 #> 3 5.03 -0.0603 1.08 #> 4 2.88 0.524 2.40 #> 5 5.88 0.746 2.29 #> 6 5.19 0.515 1.54"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"downsampling-with-tof_downsample","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Downsampling with tof_downsample","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Often, cytometry experiments collect tens hundreds millions cells total, can useful downsample smaller, computationally tractable number cells - either final analysis developing code. , tidytof implements tof_downsample verb, allows downsampling using 3 methods. Using tidytof’s built-dataset phenograph_data (smaller version dataset read ), can see original size dataset 1000 cells per cluster, 3000 cells total: randomly sample 200 cells per cluster, can use tof_downsample using “constant” method: Alternatively, wanted sample 50% cells cluster, use “prop” method: finally, might also interested taking slightly different approach downsampling downsamples number cells fixed constant proportion, fixed density phenotypic space. example, following scatterplot demonstrates certain areas phenotypic density phenograph_data contain cells others along cd34/cd38 axes: reduce number cells dataset local density around cell dataset relatively constant, can use “density” method tof_downsample: details, check documentation 3 underlying members tof_downsample_* function family (wrapped tof_downsample): tof_downsample_constant tof_downsample_prop tof_downsample_density","code":"data(phenograph_data) phenograph_data |> count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 1000 #> 2 cluster2 1000 #> 3 cluster3 1000 phenograph_data |> # downsample tof_downsample( method = \"constant\", group_cols = phenograph_cluster, num_cells = 200 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 200 #> 2 cluster2 200 #> 3 cluster3 200 phenograph_data |> # downsample tof_downsample( method = \"prop\", group_cols = phenograph_cluster, prop_cells = 0.5 ) |> # count the number of downsampled cells in each cluster count(phenograph_cluster) #> # A tibble: 3 × 2 #> phenograph_cluster n #> #> 1 cluster1 500 #> 2 cluster2 500 #> 3 cluster3 500 phenograph_data |> # preprocess all numeric columns in the dataset tof_preprocess(undo_noise = FALSE) |> # make a scatterplot ggplot(aes(x = cd34, y = cd38)) + geom_point(alpha = 0.5) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + theme_bw() phenograph_data |> tof_preprocess(undo_noise = FALSE) |> tof_downsample( density_cols = c(cd34, cd38), target_prop_cells = 0.25, method = \"density\", ) |> ggplot(aes(x = cd34, y = cd38)) + geom_point(alpha = 0.5) + scale_x_continuous(limits = c(NA, 1.5)) + scale_y_continuous(limits = c(NA, 4)) + theme_bw()"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"writing-data-with-tof_write_data","dir":"","previous_headings":"Usage > Analyzing data at the single-cell level","what":"Writing data with tof_write_data","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"Finally, users may wish store single-cell data .fcs .csv files transformation, concatenation, filtering, data processing steps dimensionality reduction /clustering (see ). write single-cell data tof_tbl .fcs .csv files, use tof_write_data. tof_write_data’s trickiest argument group_cols, argument used specify columns tof_tibble used group cells (.e. rows tof_tibble) separate .fcs .csv files. Simply put, argument allows tof_write_data create single .fcs .csv file unique combination values columns specified user. example , cells grouped 3 output .fcs files - one 3 clusters encoded phenograph_cluster column phenograph_data. files following names (derived values phenograph_cluster column): cluster1.fcs cluster2.fcs cluster3.fcs However, suppose wanted write multiple files cluster breaking cells two groups: express high levels pstat5 express low levels pstat5. can use dplyr::mutate create new column phenograph_data breaks cells high- low-pstat5 expression groups, add column group_cols specification: write 6 files following names (derived values phenograph_cluster expression_group). cluster1_low.fcs cluster1_high.fcs cluster2_low.fcs cluster2_high.fcs cluster3_low.fcs cluster3_high.fcs useful feature tof_write_data automatically concatenate cells single .fcs .csv files based specified group_cols regardless many unique files cells came , allowing easy concatenation .fcs .csv files containing data single sample acquired multiple cytometry runs.","code":"# when copying and pasting this code, feel free to change this path # to wherever you'd like to save your output files my_path <- file.path(\"~\", \"Desktop\", \"tidytof_vignette_files\") phenograph_data |> tof_write_data( group_cols = phenograph_cluster, out_path = my_path, format = \"fcs\" ) phenograph_data |> # create a variable representing if a cell is above or below the median # expression level of pstat5 mutate(expression_group = if_else(pstat5 > median(pstat5), \"high\", \"low\")) |> tof_write_data( group_cols = c(phenograph_cluster, expression_group), out_path = my_path, format = \"fcs\" )"},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"identifying-clusters-with-tof_cluster","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Identifying clusters with tof_cluster","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"input files read tabular format preprocessed/downsampled, might interested clustering data define communities cells shared characteristics. , can use tof_cluster verb. Several clustering methods implemented tidytof, including FlowSOM, PhenoGraph, k-means, others. demonstrate, can apply FlowSOM clustering algorithm phenograph_data . Note phenograph_data contains 6000 total cells (2000 3 clusters identified original PhenoGraph publication). output tof_cluster tof_tbl identical input tibble, now addition additional column (“.flowsom_metacluster”) encodes cluster id cell input tof_tbl. Note output columns added tibble tof_tbl tidytof begin full-stop (“.”) reduce likelihood collisions existing column names. output tof_cluster tof_tbl, can use dplyr’s count method assess accuracy FlowSOM clustering compared original clustering PhenoGraph paper. , can see FlowSOM algorithm groups cells PhenoGraph cluster one another (small number mistakes per PhenoGraph cluster). change clustering algorithm tof_cluster uses, alter method flag; change columns used compute clusters, change cluster_cols flag. finally, want return tibble includes cluster labels (cluster labels added new column input tof_tbl), set augment FALSE.","code":"phenograph_clusters <- phenograph_data |> tof_preprocess() |> tof_cluster(method = \"flowsom\", cluster_cols = contains(\"cd\")) phenograph_clusters |> select(sample_name, .flowsom_metacluster, everything()) |> head() #> # A tibble: 6 × 26 #> sample_name .flowsom_metacluster phenograph_cluster cd19 cd11b cd34 #> #> 1 H1_PhenoGraph_c… 13 cluster1 -0.0336 2.46 0.608 #> 2 H1_PhenoGraph_c… 18 cluster1 0.324 0.856 -0.116 #> 3 H1_PhenoGraph_c… 10 cluster1 0.532 2.67 0.909 #> 4 H1_PhenoGraph_c… 8 cluster1 0.0163 2.97 0.0725 #> 5 H1_PhenoGraph_c… 13 cluster1 0.144 2.98 0.128 #> 6 H1_PhenoGraph_c… 8 cluster1 0.742 3.41 0.336 #> # ℹ 20 more variables: cd45 , cd123 , cd33 , cd47 , #> # cd7 , cd44 , cd38 , cd3 , cd117 , cd64 , #> # cd41 , pstat3 , pstat5 , pampk , p4ebp1 , #> # ps6 , pcreb , `pzap70-syk` , prb , `perk1-2` phenograph_clusters |> count(phenograph_cluster, .flowsom_metacluster, sort = TRUE) #> # A tibble: 23 × 3 #> phenograph_cluster .flowsom_metacluster n #> #> 1 cluster3 12 323 #> 2 cluster3 15 318 #> 3 cluster2 3 309 #> 4 cluster1 17 234 #> 5 cluster2 2 218 #> 6 cluster2 4 206 #> 7 cluster1 8 182 #> 8 cluster1 18 167 #> 9 cluster1 9 162 #> 10 cluster3 20 162 #> # ℹ 13 more rows # will result in a tibble with only 1 column (the cluster labels) phenograph_data |> tof_preprocess() |> tof_cluster(method = \"flowsom\", cluster_cols = contains(\"cd\"), augment = FALSE) |> head() #> # A tibble: 6 × 1 #> .flowsom_metacluster #> #> 1 13 #> 2 3 #> 3 10 #> 4 11 #> 5 10 #> 6 11"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"dimensionality-reduction-with-tof_reduce_dimensions","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Dimensionality reduction with tof_reduce_dimensions()","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"clusters identified, useful tool visualizing dimensionality reduction, form unsupervised machine learning used represent high-dimensional datasets smaller, easier--visualize number dimensions. tidytof includes several algorithms commonly used biologists dimensionality reduction: Principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), uniform manifold approximation projection (UMAP). apply dataset, use tof_reduce_dimensions: default, tof_reduce_dimensions add reduced-dimension feature embeddings input tof_tbl return augmented tof_tbl (, tof_tbl new columns embedding dimension) result. return features embeddings , set augment FALSE (tof_cluster). Regardless method used, reduced-dimension feature embeddings can visualized using ggplot2 (graphics package): visualizations can helpful qualitatively describing phenotypic differences clusters dataset. example, example , can see one clusters high CD11b expression, whereas others lower CD11b expression.","code":"# perform the dimensionality reduction phenograph_tsne <- phenograph_clusters |> tof_reduce_dimensions(method = \"tsne\") # select only the tsne embedding columns using a tidyselect helper (contains) phenograph_tsne |> select(contains(\"tsne\")) |> head() #> # A tibble: 6 × 2 #> .tsne1 .tsne2 #> #> 1 -8.41 17.2 #> 2 1.91 13.6 #> 3 23.9 20.1 #> 4 4.79 22.3 #> 5 -4.99 22.4 #> 6 11.0 20.2 # plot the tsne embeddings using color to distinguish between clusters phenograph_tsne |> ggplot(aes(x = .tsne1, y = .tsne2, fill = phenograph_cluster)) + geom_point(shape = 21) + theme_bw() + labs(fill = NULL) # plot the tsne embeddings using color to represent CD11b expression phenograph_tsne |> ggplot(aes(x = .tsne1, y = .tsne2, fill = cd11b)) + geom_point(shape = 21) + scale_fill_viridis_c() + theme_bw() + labs(fill = \"CD11b expression\")"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"differential-discovery-analysis-with-tof_analyze_abundance-and-tof_analyze_expression","dir":"","previous_headings":"Usage > Analyzing data at the cluster-level","what":"Differential discovery analysis with tof_analyze_abundance and tof_analyze_expression","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"dimensionality reduction can used visualize clustering result, many cytometry users also want use statistical tools rigorously quantify clusters(s) dataset associate particular experimental clinical variable. analyses often grouped umbrella term differential discovery analysis include comparing relative size clusters experimental conditions (differential abundance analysis; DAA) well comparing marker expression patterns clusters experimental conditions (differential expression analysis; DEA). tidytof provides tof_analyze_abundance tof_analyze_expression verbs differential abundance differential expression analyses, respectively. demonstrate use verbs, ’ll first download dataset originally collected development CITRUS algorithm. data available HDCytoData package, available Bioconductor can downloaded following command: load CITRUS data current R session, can call function HDCytoData, provide us format {flowCore} package (called “flowSet”). convert tidy tibble, can use tidytof built-method converting flowCore objects tof_tbl’s . Thus, can see citrus_data tof_tbl 172791 cells (one row) 39 pieces information cell (one column). can also extract metadata raw data join single-cell data using functions tidyverse: Thus, now sample-level information patient sample collected stimulation condition (“Basal” “BCR-XL”) sample exposed data acquisition. Finally, can join metadata single-cell tof_tbl obtain cleaned dataset. data cleaning steps, now citrus_data, tof_tbl containing cells collected 8 patients. Specifically, 2 samples taken patient: one cells’ B-cell receptors stimulated (BCR-XL) one (Basal). citrus_data, cell’s patient origin stored patient column, cell’s stimulation condition stored stimulation column. addition, population_id column stores information cluster labels applied cell using combination FlowSOM clustering manual merging (details, run ?HDCytoData::Bodenmiller_BCR_XL R console). might wonder certain clusters expand deplete within patients two stimulation conditions described - question requires differential abundance analysis (DAA). tidytof’s tof_analyze_abundance verb supports use 3 statistical approaches performing DAA: diffcyt, generalized-linear mixed modeling (GLMMs), simple t-tests. setup described uses paired design 2 experimental conditions interest (Basal vs. BCR-XL), can use paired t-test method: Based output, can see 6 8 clusters statistically different abundance two stimulation conditions. Using tidytof easy integration tidyverse packages, can use result visualize fold-changes cluster (within patient) BCR-XL condition compared Basal condition using ggplot2: Importantly, output tof_analyze_abundance depends slightly underlying statistical method used, details can found documentation tof_analyze_abundance_* function family member: tof_analyze_abundance_diffcyt tof_analyze_abundance_glmm tof_analyze_abundance_ttest Similarly, suppose ’re interested intracellular signaling proteins change expression levels two stimulation conditions clusters. Differential Expression Analysis (DEA) can performed using tidytof’s tof_analyze_expression verb. , can use paired t-tests multiple-hypothesis correction test significant differences cluster’s expression signaling markers stimulation conditions. output tof_analyze_expression() also depends underlying test used, can see result looks relatively similar output tof_analyze_abundance(). , output tibble row represents differential expression results single cluster-marker pair - example, first row represents difference expression pS6 cluster 1 BCR-XL Basal conditions. row includes raw p-value multiple-hypothesis-corrected p-value cluster-marker pair. result can used make volcano plot visualize results cluster-marker pairs:","code":"if (!requireNamespace(\"BiocManager\", quietly = TRUE)) { install.packages(\"BiocManager\") } BiocManager::install(\"HDCytoData\") citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet() citrus_data <- citrus_raw |> as_tof_tbl(sep = \"_\") citrus_metadata <- tibble( file_name = as.character(flowCore::pData(citrus_raw)[[1]]), sample_id = seq_along(file_name), patient = str_extract(file_name, \"patient[:digit:]\"), stimulation = str_extract(file_name, \"(BCR-XL)|Reference\") ) |> mutate( stimulation = if_else(stimulation == \"Reference\", \"Basal\", stimulation) ) citrus_metadata |> head() #> # A tibble: 6 × 4 #> file_name sample_id patient stimulation #> #> 1 PBMC8_30min_patient1_BCR-XL.fcs 1 patient1 BCR-XL #> 2 PBMC8_30min_patient1_Reference.fcs 2 patient1 Basal #> 3 PBMC8_30min_patient2_BCR-XL.fcs 3 patient2 BCR-XL #> 4 PBMC8_30min_patient2_Reference.fcs 4 patient2 Basal #> 5 PBMC8_30min_patient3_BCR-XL.fcs 5 patient3 BCR-XL #> 6 PBMC8_30min_patient3_Reference.fcs 6 patient3 Basal citrus_data <- citrus_data |> left_join(citrus_metadata, by = \"sample_id\") daa_result <- citrus_data |> tof_analyze_abundance( cluster_col = population_id, effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) daa_result #> # A tibble: 8 × 8 #> population_id p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 0.000924 0.00535 \"*\" -5.48 7 -0.00743 0.644 #> 2 2 0.00623 0.0166 \"*\" -3.86 7 -0.0156 0.674 #> 3 3 0.0235 0.0314 \"*\" -2.88 7 -0.0638 0.849 #> 4 4 0.0235 0.0314 \"*\" 2.88 7 0.0832 1.38 #> 5 5 0.0116 0.0232 \"*\" 3.39 7 0.00246 1.08 #> 6 6 0.371 0.371 \"\" -0.955 7 -0.0168 0.919 #> 7 7 0.00134 0.00535 \"*\" 5.14 7 0.0202 1.14 #> 8 8 0.236 0.270 \"\" -1.30 7 -0.00228 0.901 plot_data <- citrus_data |> mutate(population_id = as.character(population_id)) |> left_join( select(daa_result, population_id, significant, mean_fc), by = \"population_id\" ) |> dplyr::count(patient, stimulation, population_id, significant, mean_fc, name = \"n\") |> group_by(patient, stimulation) |> mutate(prop = n / sum(n)) |> ungroup() |> pivot_wider( names_from = stimulation, values_from = c(prop, n), ) |> mutate( diff = `prop_BCR-XL` - prop_Basal, fc = `prop_BCR-XL` / prop_Basal, population_id = fct_reorder(population_id, diff), direction = case_when( mean_fc > 1 & significant == \"*\" ~ \"increase\", mean_fc < 1 & significant == \"*\" ~ \"decrease\", TRUE ~ NA_character_ ) ) significance_data <- plot_data |> group_by(population_id, significant, direction) |> summarize(diff = max(diff), fc = max(fc)) |> ungroup() plot_data |> ggplot(aes(x = population_id, y = fc, fill = direction)) + geom_violin(trim = FALSE) + geom_hline(yintercept = 1, color = \"red\", linetype = \"dotted\", size = 0.5) + geom_point() + geom_text( aes(x = population_id, y = fc, label = significant), data = significance_data, size = 8, nudge_x = 0.2, nudge_y = 0.06 ) + scale_x_discrete(labels = function(x) str_c(\"cluster \", x)) + scale_fill_manual( values = c(\"decrease\" = \"#cd5241\", \"increase\" = \"#207394\"), na.translate = FALSE ) + labs( x = NULL, y = \"Abundance fold-change (stimulated / basal)\", fill = \"Effect\", caption = \"Asterisks indicate significance at an adjusted p-value of 0.05\" ) signaling_markers <- c( \"pNFkB_Nd142\", \"pStat5_Nd150\", \"pAkt_Sm152\", \"pStat1_Eu153\", \"pStat3_Gd158\", \"pSlp76_Dy164\", \"pBtk_Er166\", \"pErk_Er168\", \"pS6_Yb172\", \"pZap70_Gd156\" ) dea_result <- citrus_data |> tof_preprocess(channel_cols = any_of(signaling_markers)) |> tof_analyze_expression( cluster_col = population_id, marker_cols = any_of(signaling_markers), effect_col = stimulation, group_cols = patient, test_type = \"paired\", method = \"ttest\" ) dea_result |> head() #> # A tibble: 6 × 9 #> population_id marker p_val p_adj significant t df mean_diff mean_fc #> #> 1 1 pS6_Y… 7.58e-8 2.12e-6 * 22.9 7 2.56 4.31 #> 2 2 pS6_Y… 1.16e-7 2.12e-6 * 21.6 7 2.13 2.49 #> 3 3 pBtk_… 1.32e-7 2.12e-6 * -21.2 7 -0.475 0.289 #> 4 7 pBtk_… 1.18e-7 2.12e-6 * -21.5 7 -0.518 0.286 #> 5 8 pBtk_… 1.30e-7 2.12e-6 * -21.2 7 -0.516 0.324 #> 6 4 pBtk_… 7.85e-7 1.05e-5 * -16.3 7 -0.462 0.296 volcano_plot <- dea_result |> tof_plot_clusters_volcano( use_ggrepel = TRUE ) volcano_plot"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"analyzing-data-at-the-patient--and-sample-level","dir":"","previous_headings":"Usage","what":"Analyzing data at the patient- and sample-level","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"addition verbs operate single-cell data directly, tidytof implements functions aggregating single-cell measurements cluster- sample-level summary statistics can analyzed using variety statistical models.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"feature-extraction-with-tof_extract_features","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level","what":"Feature extraction with tof_extract_features","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"addition functions analyzing visualizing cytometry data single-cell cluster levels, tidytof’s tof_extract_features verb allows users aggregate single-cell cluster-level information order summarize whole-samples (whole-patients) cells collected. features can useful visualizing differences patients samples different experimental conditions building machine learning models. understand tof_extract_features verb works, ’s easiest look subroutines (members tof_extract_* function family) independently. First, tof_extract_proportion, extracts proportion cells cluster within sample (samples defined using group_cols argument): Like members tof_extract_* function family, tof_extract_proportion() returns one row sample (defined unique combination values group_cols) one column extracted feature (, one column proportion 8 clusters citrus_data). values can also returned “long” format changing format argument: Another member function family, tof_extract_central_tendency, computes central tendency (e.g. mean median) user-specified markers cluster. tof_extract_threshold similar tof_extract_central_tendency, calculates proportion cells user-specified expression value marker instead measure central tendency: two final members tof_extract_* function family – tof_extract_emd tof_extract_jsd designed specifically comparing distributions marker expression stimulation conditions. , must given stimulation_col identifies stimulation condition cell , basal_level specifies reference (.e. unstimulated) condition within stimulation_col. additional arguments, tof_extract_emd computes Earth-mover’s distance marker’s distribution stimulation conditions (within cluster) basal condition; similarly, tof_extract_jsd computes Jensen-Shannon divergence index distributions. values ways compare different 2 distributions one another computationally expensive (also higher-resolution) simply comparing measures central tendency. Finally, tof_extract_features verb provides wrapper members function family, allowing users extract multiple features types . example, following code extracts proportion cluster, median several markers cluster, EMD basal condition stimulated condition cluster patients citrus_data.","code":"# preprocess the numeric columns in the citrus dataset citrus_data <- citrus_data |> mutate(cluster = str_c(\"cluster\", population_id)) |> tof_preprocess() citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation) ) |> head() #> # A tibble: 6 × 10 #> patient stimulation `prop@cluster1` `prop@cluster2` `prop@cluster3` #> #> 1 patient1 Basal 0.0190 0.0482 0.447 #> 2 patient1 BCR-XL 0.0109 0.0395 0.268 #> 3 patient2 Basal 0.0130 0.0280 0.491 #> 4 patient2 BCR-XL 0.0101 0.0143 0.358 #> 5 patient3 Basal 0.0326 0.0830 0.397 #> 6 patient3 BCR-XL 0.0200 0.0412 0.323 #> # ℹ 5 more variables: `prop@cluster4` , `prop@cluster5` , #> # `prop@cluster6` , `prop@cluster7` , `prop@cluster8` citrus_data |> tof_extract_proportion( cluster_col = cluster, group_cols = c(patient, stimulation), format = \"long\" ) |> head() #> # A tibble: 6 × 4 #> patient stimulation cluster prop #> #> 1 patient1 Basal cluster1 0.0190 #> 2 patient1 Basal cluster2 0.0482 #> 3 patient1 Basal cluster3 0.447 #> 4 patient1 Basal cluster4 0.237 #> 5 patient1 Basal cluster5 0.00219 #> 6 patient1 Basal cluster6 0.0759 citrus_data |> tof_extract_central_tendency( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), central_tendency_function = mean ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_ct` `CD4_Nd145@cluster1_ct` #> #> 1 patient1 BCR-XL 4.80 0.0967 #> 2 patient1 Basal 4.68 0.765 #> 3 patient2 BCR-XL 5.00 -0.0579 #> 4 patient2 Basal 4.88 0.808 #> 5 patient3 BCR-XL 5.04 -0.0432 #> 6 patient3 Basal 4.98 0.745 #> # ℹ 22 more variables: `CD20_Sm147@cluster1_ct` , #> # `CD45_In115@cluster2_ct` , `CD4_Nd145@cluster2_ct` , #> # `CD20_Sm147@cluster2_ct` , `CD45_In115@cluster3_ct` , #> # `CD4_Nd145@cluster3_ct` , `CD20_Sm147@cluster3_ct` , #> # `CD45_In115@cluster4_ct` , `CD4_Nd145@cluster4_ct` , #> # `CD20_Sm147@cluster4_ct` , `CD45_In115@cluster5_ct` , #> # `CD4_Nd145@cluster5_ct` , `CD20_Sm147@cluster5_ct` , … citrus_data |> tof_extract_threshold( cluster_col = cluster, group_cols = c(patient, stimulation), marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), threshold = 5 ) |> head() #> # A tibble: 6 × 26 #> patient stimulation `CD45_In115@cluster1_threshold` CD4_Nd145@cluster1_thre…¹ #> #> 1 patient1 BCR-XL 0.516 0 #> 2 patient1 Basal 0.365 0 #> 3 patient2 BCR-XL 0.554 0 #> 4 patient2 Basal 0.452 0 #> 5 patient3 BCR-XL 0.547 0 #> 6 patient3 Basal 0.549 0 #> # ℹ abbreviated name: ¹​`CD4_Nd145@cluster1_threshold` #> # ℹ 22 more variables: `CD20_Sm147@cluster1_threshold` , #> # `CD45_In115@cluster2_threshold` , #> # `CD4_Nd145@cluster2_threshold` , #> # `CD20_Sm147@cluster2_threshold` , #> # `CD45_In115@cluster3_threshold` , #> # `CD4_Nd145@cluster3_threshold` , … # Earth-mover's distance citrus_data |> tof_extract_emd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), emd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.864 2.47 13.0 #> 2 patient2 1.11 7.05 10.8 #> 3 patient3 0.670 6.23 10.5 #> 4 patient4 2.64 5.86 9.90 #> 5 patient5 0.594 7.56 8.13 #> 6 patient6 0.661 4.77 7.97 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_emd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_emd`, ³​`BCR-XL_CD20_Sm147@cluster3_emd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_emd` , #> # `BCR-XL_CD4_Nd145@cluster7_emd` , #> # `BCR-XL_CD20_Sm147@cluster7_emd` , #> # `BCR-XL_CD45_In115@cluster4_emd` , #> # `BCR-XL_CD4_Nd145@cluster4_emd` , … # Jensen-Shannon Divergence citrus_data |> tof_extract_jsd( cluster_col = cluster, group_cols = patient, marker_cols = any_of(c(\"CD45_In115\", \"CD4_Nd145\", \"CD20_Sm147\")), jsd_col = stimulation, reference_level = \"Basal\" ) |> head() #> # A tibble: 6 × 25 #> patient BCR-XL_CD45_In115@clu…¹ BCR-XL_CD4_Nd145@clu…² BCR-XL_CD20_Sm147@cl…³ #> #> 1 patient1 0.0367 0.0513 0.347 #> 2 patient2 0.00831 0.168 0.401 #> 3 patient3 0.0104 0.115 0.357 #> 4 patient4 0.0301 0.135 0.205 #> 5 patient5 0.00911 0.0789 0.274 #> 6 patient6 0.00972 0.0346 0.214 #> # ℹ abbreviated names: ¹​`BCR-XL_CD45_In115@cluster3_jsd`, #> # ²​`BCR-XL_CD4_Nd145@cluster3_jsd`, ³​`BCR-XL_CD20_Sm147@cluster3_jsd` #> # ℹ 21 more variables: `BCR-XL_CD45_In115@cluster7_jsd` , #> # `BCR-XL_CD4_Nd145@cluster7_jsd` , #> # `BCR-XL_CD20_Sm147@cluster7_jsd` , #> # `BCR-XL_CD45_In115@cluster4_jsd` , #> # `BCR-XL_CD4_Nd145@cluster4_jsd` , … citrus_data |> tof_extract_features( cluster_col = cluster, group_cols = patient, stimulation_col = stimulation, lineage_cols = any_of(c(\"CD45_In115\", \"CD20_Sm147\", \"CD33_Nd148\")), signaling_cols = any_of(signaling_markers), signaling_method = \"emd\", basal_level = \"Basal\" ) |> head()"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"outcomes-modeling-with-tof_model","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level","what":"Outcomes modeling with tof_model","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"[brief intro building predictive models might motivated .] tidytof implements several functions building predictive models using sample- patient-level data. illustrate work, first download patient-level data paper combining sample-level clinical annotations one tidytof’s built-data objects (ddpr_metadata). data processing steps result ddpr_patients tibble. numeric columns ddpr_patients represent aggregated cell population features sample (see Supplementary Table 5 paper details). non-numeric columns represent clinical metadata sample (run ?ddpr_metadata information). also preprocessing steps might want perform now save us headaches ’re fitting models later.","code":"data(ddpr_metadata) # link for downloading the sample-level data from the Nature Medicine website data_link <- \"https://static-content.springer.com/esm/art%3A10.1038%2Fnm.4505/MediaObjects/41591_2018_BFnm4505_MOESM3_ESM.csv\" # downloading the data and combining it with clinical annotations ddpr_patients <- readr::read_csv(data_link, skip = 2L, n_max = 78L, show_col_types = FALSE) |> dplyr::rename(patient_id = Patient_ID) |> left_join(ddpr_metadata, by = \"patient_id\") |> dplyr::filter(!str_detect(patient_id, \"Healthy\")) ddpr_patients |> select(where(~ !is.numeric(.x))) |> head() #> # A tibble: 6 × 8 #> patient_id gender mrd_risk nci_rome_risk relapse_status type_of_relapse cohort #> #> 1 UPN1 Male Interme… Standard Yes Early Train… #> 2 UPN1-Rx Male Interme… Standard Yes Early Train… #> 3 UPN2 Male Interme… Standard No Train… #> 4 UPN3 Female Standard Standard No Train… #> 5 UPN4 Male Standard Standard No Valid… #> 6 UPN5 Female Standard High No Valid… #> # ℹ 1 more variable: ddpr_risk ddpr_patients <- ddpr_patients |> # convert the relapse_status variable to a factor first, # which is something we'll want for fitting the model later # and create the time_to_event and event columns for survival modeling mutate( relapse_status = as.factor(relapse_status), time_to_event = if_else(relapse_status == \"Yes\", time_to_relapse, ccr), event = if_else(relapse_status == \"Yes\", 1, 0) )"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"separating-the-training-and-validation-cohorts","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level > Outcomes modeling with tof_model","what":"Separating the training and validation cohorts","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"original DDPR paper, patients used fit model rest used assess model tuned. can separate training validation cohorts using cohort variable ddpr_patients","code":"ddpr_training <- ddpr_patients |> dplyr::filter(cohort == \"Training\") ddpr_validation <- ddpr_patients |> dplyr::filter(cohort == \"Validation\") nrow(ddpr_training) #> [1] 49 nrow(ddpr_validation) #> [1] 12"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"building-a-classifier-using-logistic-regression","dir":"","previous_headings":"Usage > Analyzing data at the patient- and sample-level > Outcomes modeling with tof_model","what":"Building a classifier using logistic regression","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"First, can build elastic net classifier predict patients relapse patients won’t (ignoring time--event data now). , can use relapse_status column ddpr_training outcome variable: Specifically, can use tof_split_data function split cohort training test set either (“simple” split) multiple times (using either k-fold cross-validation bootstrapping). case, use 5-fold cross-validation, reading documentation tof_split_data demonstrates use methods. output tof_split_data varies depending split_method used. cross-validation, result rset object rsample package. rset objects type tibble two columns: splits - column entry rsplit object (contains single resample full dataset) id - character column entry represents name fold entry splits belongs . can inspect one resamples splits column see contain: Note can use rsample::training rsample::testing return training test obeservations resampling: , can feed training_split tof_train_model function tune logistic regression model predicts relapse_status leukemia patient. sure check tof_create_grid documentation learn make hyperparameter search grid model tuning (case, limit mixture parameter value 1, fits sparse lasso model). Also note demonstration purposes, include features come one cell population (“Population 2”) original dataset, means probably shouldn’t expect model perform well one original paper (select many features). output tof_train_model tof_model, object containing information trained model (can passed tof_predict tof_assess_model verbs). tof_model printed, information optimal hyperparamters printed, table nonzero model coefficients model. can use trained model make predictions validation data set aside earlier: can see model gets (!) predictions correct validation set set aside. can also assess model directly using tof_assess_model can make ROC curve using metrics: can assess model validation data…","code":"# find how many of each outcome we have in our cohort ddpr_training |> dplyr::count(relapse_status) #> # A tibble: 2 × 2 #> relapse_status n #> #> 1 No 31 #> 2 Yes 18 training_split <- ddpr_training |> tof_split_data( split_method = \"k-fold\", num_cv_folds = 5, strata = relapse_status ) training_split #> # 5-fold cross-validation using stratification #> # A tibble: 5 × 2 #> splits id #> #> 1 Fold1 #> 2 Fold2 #> 3 Fold3 #> 4 Fold4 #> 5 Fold5 my_resample <- training_split$splits[[1]] print(my_resample) #> #> <38/11/49> class(my_resample) #> [1] \"vfold_split\" \"rsplit\" my_resample |> rsample::training() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN1 3.06 0.583 0.00449 0.164 1.94 0.416 #> 2 UPN1-Rx 0.0395 0.618 0.0634 0.572 2.93 0.944 #> 3 UPN3 0.633 0.0234 0.0165 0.0327 2.25 0.226 #> 4 UPN8 0.951 0.958 0.161 0.556 3.18 0.556 #> 5 UPN10 0.00374 0.761 0.000696 0.829 3.19 0.886 #> 6 UPN10-Rx 0.00240 0.167 0.203 0.802 2.57 0.822 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … my_resample |> rsample::testing() |> head() #> # A tibble: 6 × 1,854 #> patient_id Pop_P_Pop1 CD19_Pop1 CD20_Pop1 CD24_Pop1 CD34_Pop1 CD38_Pop1 #> #> 1 UPN2 0.139 0.0662 0.0221 0.0825 2.25 0.454 #> 2 UPN6 5.62 0.550 0.00374 0.622 2.86 0.342 #> 3 UPN7 0.474 0.966 0.124 1.24 2.59 0.243 #> 4 UPN9 15.6 0.446 0.0445 0.163 2.86 0.434 #> 5 UPN12 0.0565 0.185 0.0115 0.142 2.49 0.254 #> 6 UPN17 1.40 1.52 0.0128 0.284 3.46 0.656 #> # ℹ 1,847 more variables: CD127_Pop1 , CD179a_Pop1 , #> # CD179b_Pop1 , IgMi_Pop1 , IgMs_Pop1 , TdT_Pop1 , #> # CD22_Pop1 , tIkaros_Pop1 , CD79b_Pop1 , Ki67_Pop1 , #> # TSLPr_Pop1 , RAG1_Pop1 , CD123_Pop1 , CD45_Pop1 , #> # CD10_Pop1 , Pax5_Pop1 , CD43_Pop1 , CD58_Pop1 , #> # HLADR_Pop1 , p4EBP1_FC_Basal_Pop1 , pSTAT5_FC_Basal_Pop1 , #> # pPLCg1_2_FC_Basal_Pop1 , pAkt_FC_Basal_Pop1 , … class_mod <- training_split |> tof_train_model( predictor_cols = contains(\"Pop2\"), response_col = relapse_status, model_type = \"two-class\", hyperparameter_grid = tof_create_grid(mixture_values = 1), impute_missing_predictors = TRUE, remove_zv_predictors = TRUE # often a smart decision ) print(class_mod) #> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e-05 #> # A tibble: 25 × 2 #> feature coefficient #> #> 1 p4EBP1_dP_IL7_Pop2 -2.59 #> 2 CD58_Pop2 2.23 #> 3 (Intercept) -1.83 #> 4 pSTAT5_dP_TSLP_Pop2 1.69 #> 5 p4EBP1_FC_IL7_Pop2 1.46 #> 6 CD43_Pop2 1.37 #> 7 HLADR_Pop2 -1.32 #> 8 pSyk_dP_TSLP_Pop2 1.08 #> 9 pErk_dP_IL7_Pop2 -1.05 #> 10 Ki67_Pop2 -1.05 #> # ℹ 15 more rows class_predictions <- class_mod |> tof_predict(new_data = ddpr_validation, prediction_type = \"class\") class_predictions |> dplyr::mutate( truth = ddpr_validation$relapse_status ) #> # A tibble: 12 × 2 #> .pred truth #> #> 1 Yes No #> 2 No No #> 3 No Yes #> 4 No No #> 5 No No #> 6 Yes Yes #> 7 Yes Yes #> 8 No No #> 9 No No #> 10 No Yes #> 11 No Yes #> 12 No Yes # calling the function with no new_data evaluates the # the nodel using its training data training_assessment <- class_mod |> tof_assess_model() training_assessment #> $model_metrics #> # A tibble: 6 × 2 #> metric value #> #> 1 binomial_deviance 0.0291 #> 2 misclassification_error 0 #> 3 roc_auc 1 #> 4 mse 0.00119 #> 5 mae 0.0285 #> 6 accuracy 1 #> #> $roc_curve #> # A tibble: 51 × 5 #> .threshold specificity sensitivity tpr fpr #> #> 1 -Inf 0 1 1 1 #> 2 0.00000114 0 1 1 1 #> 3 0.0000955 0.0323 1 1 0.968 #> 4 0.000160 0.0645 1 1 0.935 #> 5 0.000190 0.0968 1 1 0.903 #> 6 0.000612 0.129 1 1 0.871 #> 7 0.000896 0.161 1 1 0.839 #> 8 0.00135 0.194 1 1 0.806 #> 9 0.00142 0.226 1 1 0.774 #> 10 0.00194 0.258 1 1 0.742 #> # ℹ 41 more rows #> #> $confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 31 #> 2 No Yes 0 #> 3 Yes No 0 #> 4 Yes Yes 18 class_mod |> tof_plot_model() + labs(subtitle = \"ROC Curve (Training data)\") validation_assessment <- class_mod |> tof_assess_model(new_data = ddpr_validation) validation_assessment #> $model_metrics #> # A tibble: 6 × 2 #> metric value #> #> 1 binomial_deviance 4.75 #> 2 misclassification_error 0.417 #> 3 roc_auc 0.639 #> 4 mse 0.759 #> 5 mae 0.879 #> 6 accuracy 0.583 #> #> $roc_curve #> # A tibble: 14 × 5 #> .threshold specificity sensitivity tpr fpr #> #> 1 -Inf 0 1 1 1 #> 2 0.000240 0 1 1 1 #> 3 0.00105 0.167 1 1 0.833 #> 4 0.00195 0.167 0.833 0.833 0.833 #> 5 0.00230 0.333 0.833 0.833 0.667 #> 6 0.00472 0.5 0.833 0.833 0.5 #> 7 0.00618 0.667 0.833 0.833 0.333 #> 8 0.0464 0.667 0.667 0.667 0.333 #> 9 0.273 0.667 0.5 0.5 0.333 #> 10 0.286 0.667 0.333 0.333 0.333 #> 11 0.844 0.833 0.333 0.333 0.167 #> 12 0.852 0.833 0.167 0.167 0.167 #> 13 1.00 0.833 0 0 0.167 #> 14 Inf 1 0 0 0 #> #> $confusion_matrix #> # A tibble: 4 × 3 #> true_outcome predicted_outcome num_observations #> #> 1 No No 5 #> 2 No Yes 1 #> 3 Yes No 4 #> 4 Yes Yes 2 class_mod |> tof_plot_model(new_data = ddpr_validation) + labs(subtitle = \"ROC Curve (Validation data)\")"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"tidytofs-design-principles-and-some-tips","dir":"","previous_headings":"","what":"{tidytof}’s Design Principles (and some tips)","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"{tidytof} designed multidisciplinary team wet-lab biologists, bioinformaticians, physician-scientists analyze cytometry kinds single-cell data solve variety problems. result, tidytof’s high-level API designed great care mirror tidyverse - , human-centered, consistent, composable, inclusive wide userbase. section, describe miscellaneous design decisions tips using tidytof may help enthusiastic user.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_1-use-the-tof_-prefix-to-your-advantage","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"1. Use the tof_ prefix to your advantage.","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"may notice tidytof functions begin prefix tof_. intentional, allow use development environment’s code-completing software search functions easily (even don’t remember function name). reason, recommend using tidytof within RStudio development environment; however, many code editors predictive text functionality serves similar function. general, tidytof verbs organized way IDE’s code-completion tools also allow search (compare) related functions relative ease. (instance, tof_cluster_ prefix used clustering functions, tof_downsample_ prefix used downsampling functions).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_2-tidytof-functions-use-2-kinds-of-arguments","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"2. {tidytof} functions use 2 kinds of arguments","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof functions optimized working “tidy” data form tibbles data.frames. means tidytof functions share basic design principles terms arguments work. details design principles, check Getting Started tidytof vignette","code":""},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_3-use-tidytof-to-write-human-readable-pipelines","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"3. Use {tidytof} to write human-readable pipelines","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"real “magic” tidytof derives ability simplify multistep data-processing tasks simple readable chunk code. example, suppose just acquired .fcs files mass cytometer want perform following analysis: Read .fcs files R session Arcsinh-transform column protein measurements Cluster cells based surface markers panel Downsample dataset 100 random cells picked cluster Perform dimensionality reduction downsampled dataset using tSNE Visualize clusters using low-dimensional tSNE embedding using appropriate tidytof verbs step analysis, can easily write code function call corresponds exactly one step pipeline: shown , stringing together tidytof verbs creates pipeline can read easily left--right top--bottom – means relatively easy return code later (modify , write methods section next high-impact manuscript!) , perhaps importantly, one colleagues return later want recreate analysis.","code":"input_path <- tidytof_example_data(\"phenograph\") set.seed(0012) input_path |> # step 1 tof_read_data() |> # step 2 tof_preprocess() |> # step 3 tof_cluster(method = \"phenograph\") |> # step 4 tof_downsample( group_cols = .phenograph_cluster, num_cells = 100, method = \"constant\" ) |> # step 5 tof_reduce_dimensions(perplexity = 50, method = \"tsne\") |> # step 6 tof_plot_cells_embedding( embedding_cols = starts_with(\".tsne\"), color_col = .phenograph_cluster )"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"id_4-additional-resources","dir":"","previous_headings":"{tidytof}’s Design Principles (and some tips)","what":"4. Additional resources","title":"Analyze High-dimensional Cytometry Data Using Tidy Data Principles","text":"tidytof built top tidyverse family R packages. result, users tidytof benefit substantially spending hours dplyr, ggplot2, tidyr package vignettes learn many useful functions packages provide. access recommended list package vignettes, run following lines R code console:","code":"# dplyr vignette(topic = \"dplyr\", package = \"dplyr\") vignette(topic = \"grouping\", package = \"dplyr\") vignette(topic = \"colwise\", package = \"dplyr\") # ggplot2 vignette(topic = \"ggplot2-specs\", package = \"ggplot2\") # tidyr vignette(topic = \"tidy-data\", package = \"tidyr\") vignette(topic = \"nest\", package = \"tidyr\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"Coerce object SingleCellExperiment Coerce tof_tbl SingleCellExperiment","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"","code":"as_SingleCellExperiment(x, ...) # S3 method for tof_tbl as_SingleCellExperiment( x, channel_cols = where(tof_is_numeric), reduced_dimensions_cols, metadata_cols = where(function(.x) !tof_is_numeric(.x)), split_reduced_dimensions = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"x tof_tbl ... Unused. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default numeric columns. reduced_dimensions_cols Unquoted column names representing columns contain dimensionality reduction embeddings, tSNE UMAP embeddings. Supports tidyselect helpers. metadata_cols Unquoted column names representing columns contain metadata samples cell collected. nothing specified, default non-numeric columns. split_reduced_dimensions boolean value indicating whether dimensionality results x split separate slots resulting SingleCellExperiment. FALSE (default), split performed reducedDims slot result single entry (\"tidytof_reduced_dimensions\"). TRUE, split performed reducedDims slot result 1-4 entries depending dimensionality reduction results present x (\"tidytof_pca\", \"tidytof_tsne\", \"tidytof_umap\", \"tidytof_reduced_dimensions\"). Note \"tidytof_reduced_dimensions\" include dimensionality reduction results named according tidytof's pca, umap, tsne conventions.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"SingleCellExperiment SingleCellExperiment.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_SingleCellExperiment.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a SingleCellExperiment — as_SingleCellExperiment","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a flowFrame — as_flowFrame","title":"Coerce an object into a flowFrame — as_flowFrame","text":"Coerce object flowFrame Coerce tof_tbl flowFrame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a flowFrame — as_flowFrame","text":"","code":"as_flowFrame(x, ...) # S3 method for tof_tbl as_flowFrame(x, ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a flowFrame — as_flowFrame","text":"x tof_tbl. ... Unused.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a flowFrame — as_flowFrame","text":"flowFrame flowFrame. Note non-numeric columns `x` removed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowFrame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a flowFrame — as_flowFrame","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a flowSet — as_flowSet","title":"Coerce an object into a flowSet — as_flowSet","text":"Coerce object flowSet Coerce tof_tbl flowSet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a flowSet — as_flowSet","text":"","code":"as_flowSet(x, ...) # S3 method for tof_tbl as_flowSet(x, group_cols, ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a flowSet — as_flowSet","text":"x tof_tbl. ... Unused. group_cols Unquoted names columns `x` used group cells separate flowFrames. Supports tidyselect helpers. Defaults NULL (cells written single flowFrame).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a flowSet — as_flowSet","text":"flowSet flowSet. Note non-numeric columns `x` removed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_flowSet.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a flowSet — as_flowSet","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce an object into a SeuratObject — as_seurat","title":"Coerce an object into a SeuratObject — as_seurat","text":"Coerce object SeuratObject Coerce tof_tbl SeuratObject","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce an object into a SeuratObject — as_seurat","text":"","code":"as_seurat(x, ...) # S3 method for tof_tbl as_seurat( x, channel_cols = where(tof_is_numeric), reduced_dimensions_cols, metadata_cols = where(function(.x) !tof_is_numeric(.x)), split_reduced_dimensions = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce an object into a SeuratObject — as_seurat","text":"x tof_tbl ... Unused. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default numeric columns. reduced_dimensions_cols Unquoted column names representing columns contain dimensionality reduction embeddings, tSNE UMAP embeddings. Supports tidyselect helpers. metadata_cols Unquoted column names representing columns contain metadata samples cell collected. nothing specified, default non-numeric columns. split_reduced_dimensions boolean value indicating whether dimensionality results x split separate slots resulting SingleCellExperiment. FALSE (default), split performed reducedDims slot result single entry (\"tidytof_reduced_dimensions\"). TRUE, split performed reducedDims slot result 1-4 entries depending dimensionality reduction results present x (\"tidytof_pca\", \"tidytof_tsne\", \"tidytof_umap\", \"tidytof_reduced_dimensions\"). Note \"tidytof_reduced_dimensions\" include dimensionality reduction results named according tidytof's pca, umap, tsne conventions.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce an object into a SeuratObject — as_seurat","text":"SeuratObject SeuratObject.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_seurat.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce an object into a SeuratObject — as_seurat","text":"","code":"NULL #> NULL NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":null,"dir":"Reference","previous_headings":"","what":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"Convert object tof_tbl","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"","code":"# S3 method for flowSet as_tof_tbl(flow_data, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"flow_data FlowSet sep string use separate antigen name associated metal column names output tibble. Defaults \"|\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.flowSet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Convert an object into a tof_tbl — as_tof_tbl.flowSet","text":"`tof_tbl`","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":null,"dir":"Reference","previous_headings":"","what":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"Coerce flowFrames flowSets tof_tbl's.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"","code":"as_tof_tbl(flow_data, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"flow_data flowFrame flowSet sep string indicating symbol used separate antigen names metal names columns output tof_tbl.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"tof_tbl.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/as_tof_tbl.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Coerce flowFrames or flowSets into tof_tbl's. — as_tof_tbl","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] input_flowframe <- flowCore::read.FCS(input_file) tof_tibble <- as_tof_tbl(input_flowframe)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the cosine similarity between two vectors — cosine_similarity","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"Find cosine similarity two vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"","code":"cosine_similarity(x, y)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"x numeric vector y numeric vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/cosine_similarity.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the cosine similarity between two vectors — cosine_similarity","text":"scalar value representing cosine similarity x y","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":null,"dir":"Reference","previous_headings":"","what":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"dataset containing CyTOF measurements immune cells originally studied following paper: Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"","code":"data(ddpr_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"data frame 10000 rows 24 variables: sample_name name sample data read cd45 CyTOF measurement raw ion counts cd19 CyTOF measurement raw ion counts cd22 CyTOF measurement raw ion counts cd79b CyTOF measurement raw ion counts cd20 CyTOF measurement raw ion counts cd34 CyTOF measurement raw ion counts cd123 CyTOF measurement raw ion counts cd10 CyTOF measurement raw ion counts cd24 CyTOF measurement raw ion counts cd127 CyTOF measurement raw ion counts cd43 CyTOF measurement raw ion counts cd38 CyTOF measurement raw ion counts cd58 CyTOF measurement raw ion counts psyk CyTOF measurement raw ion counts p4ebp1 CyTOF measurement raw ion counts pstat5 CyTOF measurement raw ion counts pakt CyTOF measurement raw ion counts ps6 CyTOF measurement raw ion counts perk CyTOF measurement raw ion counts pcreb CyTOF measurement raw ion counts","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"https://github.com/kara-davis-lab/DDPR","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CyTOF data from two samples: 5,000 B-cell lineage cells from a healthy\npatient and 5,000 B-cell lineage cells from a B-cell precursor Acute\nLymphoblastic Leukemia (BCP-ALL) patient. — ddpr_data","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":null,"dir":"Reference","previous_headings":"","what":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"dataset containing patient-level clinical metadata samples originally studied following paper: Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"","code":"data(ddpr_metadata)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"data frame 10000 rows 12 variables: patient_id Name sample data read gender Gender patient sample collected age_at_diagnosis Age (years) patient sample collected wbc_count diagnostic White Blood Cell (WBC) count patient sample collected mrd_risk Risk stratification category patient using minimal residual disease (MRD) criteria nci_rome_risk Risk stratification category patient using National Cancer Institute (NCI) criteria relapse_status string representing whether patient relapsed time_to_relapse time (days) took patient relapse. Patients relapse value NA type_of_relapse string representing timing relapse patient. \"early\" relapses occurred less 18 months diagnosis; \"Early\" relapses occurred 18 months 32 months diagnosis; \"Late\" relapses occurred later 32 months diagnosis. ccr number documented days continuous complete remission (CCR) patients relapse. patients relapsed value NA. cohort string representing sample used \"Training\" \"Validation\" cohort original study ddpr_risk risk category (\"Low\" \"High\") assigned sample using original paper's risk-stratification algorithm","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"Good Z, Sarno J, et al. Single-cell developmental classification B cell precursor acute lymphoblastic leukemia diagnosis reveals predictors relapse. Nat Med. 2018 May;24(4):474-483. doi: 10.1038/nm.4505. Epub 2018 Mar 5. PMID: 29505032; PMCID: PMC5953207. Supplementary Table 1.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/ddpr_metadata.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Clinical metadata for each patient sample in Good & Sarno et al. (2018). — ddpr_metadata","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the dot product between two vectors. — dot","title":"Find the dot product between two vectors. — dot","text":"Find dot product two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the dot product between two vectors. — dot","text":"","code":"dot(x, y)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the dot product between two vectors. — dot","text":"x numeric vector. y numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/dot.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the dot product between two vectors. — dot","text":"dot product x y.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the extension for a file — get_extension","title":"Find the extension for a file — get_extension","text":"Find extension file","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the extension for a file — get_extension","text":"","code":"get_extension(filename)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the extension for a file — get_extension","text":"filename string representing name file local 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l2_normalize","text":"","code":"l2_normalize(x)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"x numeric vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"vector length length(x) magnitude 1","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the magnitude of a vector. — magnitude","title":"Find the magnitude of a vector. — magnitude","text":"Find magnitude vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the magnitude of a vector. — magnitude","text":"","code":"magnitude(x)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the magnitude of a vector. — magnitude","text":"x numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/magnitude.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the magnitude of a vector. — magnitude","text":"scalar value (magnitude x).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":null,"dir":"Reference","previous_headings":"","what":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"Make AnnotatedDataFrame needed flowFrame class","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"","code":"make_flowcore_annotated_data_frame(maxes_and_mins)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"maxes_and_mins data.frame containing information max min values channel saved flowFrame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"AnnotatedDataFrame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/make_flowcore_annotated_data_frame.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make the AnnotatedDataFrame needed for the flowFrame class — make_flowcore_annotated_data_frame","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":null,"dir":"Reference","previous_headings":"","what":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"character vector used `tof_read_fcs` `tof_read_data` detect parse CyTOF metals correspond channel input .fcs file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"","code":"data(metal_masterlist)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"character vector entry pattern tidytof searches every CyTOF channel input .fcs files. patterns amalgamate example .fcs files sampled studies linked .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"https://github.com/kara-davis-lab/DDPR https://cytobank.org/nolanlab/reports/Levine2015.html https://cytobank.org/nolanlab/reports/Spitzer2015.html https://cytobank.org/nolanlab/reports/Spitzer2017.html https://community.cytobank.org/cytobank/projects/609","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/metal_masterlist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A character vector of metal name patterns supported by tidytof. — metal_masterlist","text":"named character vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for a tof_model. — new_tof_model","title":"Constructor for a tof_model. — new_tof_model","text":"Constructor tof_model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for a tof_model. — new_tof_model","text":"","code":"new_tof_model( model, recipe, penalty, mixture, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), outcome_colnames, training_data )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for a tof_model. — new_tof_model","text":"model glmnet model. recipe prepped recipe object. penalty double indicating lambda value used within glmnet path. mixture double indicating alpha value used fit glmnet model. model_type string indicating type glmnet model fit. outcome_colnames training_data ","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for a tof_model. — new_tof_model","text":"`tof_model`, S3 class includes trained glmnet model recipe used perform associated preprocessing.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Constructor for a tof_tibble. — new_tof_tibble","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"Constructor tof_tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"","code":"new_tof_tibble(x = dplyr::tibble(), panel = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"x data.frame tibble containing single-cell mass cytometry data rows cells columns CyTOF measurements. panel data.frame tibble containing information panel mass cytometry data x.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/new_tof_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Constructor for a tof_tibble. — new_tof_tibble","text":"`tof_tbl`, tibble extension tracks attributes useful CyTOF data analysis.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":null,"dir":"Reference","previous_headings":"","what":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"dataset containing CyTOF measurements healthy control cells originally studied following paper: Levine JH, Simonds EF, et al. Data-Driven Phenotypic Dissection AML Reveals Progenitor-like Cells Correlate Prognosis. Cell. 2015 Jul 2;162(1):184-97. doi: 10.1016/j.cell.2015.05.047. Epub 2015 Jun 18. PMID: 26095251; PMCID: PMC4508757.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"","code":"data(phenograph_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"format","dir":"Reference","previous_headings":"","what":"Format","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"data frame 6000 rows 26 variables: sample_name Name sample data read phenograph_cluster Numeric ID cluster assignment row cd19 CyTOF measurement raw ion counts cd11b CyTOF measurement raw ion counts cd34 CyTOF measurement raw ion counts cd45 CyTOF measurement raw ion counts cd123 CyTOF measurement raw ion counts cd33 CyTOF measurement raw ion counts cd47 CyTOF measurement raw ion counts cd7 CyTOF measurement raw ion counts cd44 CyTOF measurement raw ion counts cd38 CyTOF measurement raw ion counts cd3 CyTOF measurement raw ion counts cd117 CyTOF measurement raw ion counts cd64 CyTOF measurement raw ion counts cd41 CyTOF measurement raw ion counts pstat3 CyTOF measurement raw ion counts pstat5 CyTOF measurement raw ion counts pampk CyTOF measurement raw ion counts p4ebp1 CyTOF measurement raw ion counts ps6 CyTOF measurement raw ion counts pcreb CyTOF measurement raw ion counts pzap70-syk CyTOF measurement raw ion counts prb CyTOF measurement raw ion counts perk1-2 CyTOF measurement raw ion counts","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"source","dir":"Reference","previous_headings":"","what":"Source","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"https://cytobank.org/nolanlab/reports/Levine2015.html","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"data.frame","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/phenograph_data.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"CyTOF data from 6,000 healthy immune cells from a single patient. — phenograph_data","text":"2000 cells 3 clusters identified original paper sampled.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":null,"dir":"Reference","previous_headings":"","what":"Objects exported from other packages — reexports","title":"Objects exported from other packages — reexports","text":"objects imported packages. Follow links see documentation. dplyr %>% rlang :=, .data tidyselect all_of, any_of, contains, ends_with, everything, last_col, matches, num_range, starts_with","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Objects exported from other packages — reexports","text":"See documentation object's original package.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/reexports.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Objects exported from other packages — reexports","text":"","code":"# See examples in each object's original package NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":null,"dir":"Reference","previous_headings":"","what":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"Reverses arcsinh transformation cofactor `scale_factor` shift `shift_factor`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"","code":"rev_asinh(x, shift_factor, scale_factor)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"x numeric vector. shift_factor scalar value `` following equation used transform high-dimensional cytometry raw data ion counts using hyperbolic arcsinh function: `new_x <- asinh(+ b * x)`. scale_factor scalar value `b` following equation used transform high-dimensional cytometry raw data ion counts using hyperbolic arcsinh function: `new_x <- asinh(+ b * x)`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"numeric vector undergoing reverse arcsinh transformation","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/rev_asinh.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Reverses arcsinh transformation with cofactor `scale_factor` and a\nshift of `shift_factor`. — rev_asinh","text":"","code":"shift_factor <- 0 scale_factor <- 1 / 5 input_value <- 20 asinh_value <- asinh(shift_factor + input_value * scale_factor) restored_value <- rev_asinh(asinh_value, shift_factor, scale_factor)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get paths to tidytof example data — tidytof_example_data","title":"Get paths to tidytof example data — tidytof_example_data","text":"tidytof comes bundled number sample .fcs files inst/extdata directory. function makes easy access.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get paths to tidytof example data — tidytof_example_data","text":"","code":"tidytof_example_data(dataset_name = NULL)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get paths to tidytof example data — tidytof_example_data","text":"dataset_name Name dataset want access. NULL, names datasets (different study) listed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get paths to tidytof example data — tidytof_example_data","text":"character vector file paths requested .fcs files located. `dataset_name` NULL, character vector dataset names (can used values `dataset_name`) returned instead.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tidytof_example_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get paths to tidytof example data — tidytof_example_data","text":"","code":"tidytof_example_data() #> [1] \"aml\" \"ddpr\" \"ddpr_metadata.csv\" #> [4] \"mix\" \"mix2\" \"phenograph\" #> [7] \"phenograph_csv\" \"surgery\" tidytof_example_data(dataset_name = \"phenograph\") #> [1] \"/home/runner/work/_temp/Library/tidytof/extdata/phenograph\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using one three methods (\"diffcyt\", \"glmm\", \"ttest\"). wraps members `tof_analyze_abundance_*` function family: tof_analyze_abundance_diffcyt, tof_analyze_abundance_glmm, tof_analyze_abundance_ttest.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"","code":"tof_analyze_abundance(tof_tibble, method = c(\"diffcyt\", \"glmm\", \"ttest\"), ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"tof_tibble `tof_tbl` `tibble`. method string indicating statistical method used. Valid values include \"diffcyt\", \"glmm\", \"ttest\". ... Additional arguments pass onto `tof_analyze_abundance_*` function family member corresponding chosen method.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"tibble nested tibble containing differential abundance results chosen method. See tof_analyze_abundance_diffcyt, tof_analyze_abundance_glmm, tof_analyze_abundance_ttest details.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform Differential Abundance Analysis (DAA) on high-dimensional cytometry data — tof_analyze_abundance","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using one three methods implemented diffcyt package differential discovery analysis high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"","code":"tof_analyze_abundance_diffcyt( tof_tibble, sample_col, cluster_col, fixed_effect_cols, random_effect_cols, diffcyt_method = c(\"glmm\", \"edgeR\", \"voom\"), include_observation_level_random_effects = FALSE, min_cells = 3, min_samples = 5, alpha = 0.05, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential abundance analysis. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Optional. Unquoted column names representing columns `tof_tibble` used model random effects differential abundance analysis. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without multiple samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. diffcyt_method string indicating diffcyt method used differential abundance analysis. Valid methods include \"glmm\" (default), \"edgeR\", \"voom\". include_observation_level_random_effects boolean value indicating \"observation-level random effects\" (OLREs) included random effect terms \"glmm\" differential abundance model. details OLREs , see diffcyt paper. \"glmm\" method can model observation-level random effects, values ignore argument (throw warning set TRUE). Defaults FALSE. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. ... Optional additional arguments pass --hood diffcyt function used perform differential abundance analysis. See testDA_GLMM, testDA_edgeR, testDA_voom details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"nested tibble two columns: `tested_effect` `daa_results`. first column, `tested_effect` character vector indicating term differential abundance model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named `fixed_effect` levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential abundance model represent difference cluster abundances samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `daa_results` list tibbles entry gives differential abundance results tested_effect. Within entry `daa_results`, find several columns including following: * `p_val`, p-value associated tested effect input cluster * `p_adj`, multiple-comparison adjusted p-value (using p.adjust function) * values associated underlying method used perform differential abundance analysis (log-fold change cluster abundance levels compared). details, see glmFit, voom, topTable, testDA_GLMM.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"three methods based generalized linear mixed models (\"glmm\"), edgeR (\"edgeR\"), voom (\"voom\"). \"glmm\" \"voom\" methods can model fixed effects random effects, \"edgeR\" method can model fixed effects.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_diffcyt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with diffcyt — tof_analyze_abundance_diffcyt","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using generalized linear mixed-models. Users specify columns represent sample, cluster, fixed effect, random effect information, (mixed) binomial regression model fit using either glmer glm.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"","code":"tof_analyze_abundance_glmm( tof_tibble, sample_col, cluster_col, fixed_effect_cols, random_effect_cols, min_cells = 3, min_samples = 5, alpha = 0.05 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential abundance analysis. Supports tidyselect helpers. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Unquoted column names representing columns `tof_tibble` used model random effects differential abundance analysis. Supports tidyselection. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without many samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"nested tibble two columns: `tested_effect` `daa_results`. first column, `tested_effect`, character vector indicating term differential abundance model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential abundance model represent difference cluster abundances samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `daa_results`, list tibbles entry gives differential abundance results tested_effect. Within entry `daa_results`, find `p_value`, p-value associated tested effect input cluster; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential abundance analysis (log-fold change cluster abundance levels compared).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_glmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with generalized linear mixed-models (GLMMs) — tof_analyze_abundance_glmm","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"function performs differential abundance analysis cell clusters contained within `tof_tbl` using simple t-tests. Users specify columns represent sample, cluster, effect information, either paired unpaired t-test (one per cluster) used detect significant differences sample types.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"","code":"tof_analyze_abundance_ttest( tof_tibble, cluster_col, effect_col, group_cols, test_type = c(\"unpaired\", \"paired\"), min_cells = 3, min_samples = 5, alpha = 0.05, quiet = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. effect_col Unquoted column name representing column `tof_tibble` used break samples groups t-test. 2 unique values. group_cols Unquoted names columns `effect_col` used group cells independent observations. Fills similar role `sample_col` `tof_analyze_abundance_*` functions. example, experiment involves analyzing samples taken multiple patients two timepoints (`effect_col = timepoint`), group_cols name column representing patient IDs. test_type string indicating whether t-test \"unpaired\" (default) \"paired\". min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. quiet boolean value indicating whether warnings printed. Defaults `TRUE`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"tibble 7 columns: {cluster_col} name/ID cluster tested. entry column match unique value input {cluster_col}. t t-statistic computed cluster. df degrees freedom used t-test cluster. p_val (unadjusted) p-value t-test cluster. p_adj p.adjust-adjusted p-value t-test cluster. significant character vector \"*\" clusters p_adj < alpha \"\" otherwise. mean_diff unpaired t-test, difference average proportions cluster two levels `effect_col`. paired t-test, average difference proportions cluster two levels `effect_col` within given patient. mean_fc unpaired t-test, ratio average proportions cluster two levels `effect_col`. paired t-test, average ratio proportions cluster two levels `effect_col` within given patient. 0.001 added denominator ratio avoid divide--zero errors. \"levels\" attribute result indicates order different levels `effect_col` considered. `mean_diff` value row output computed subtracting second level first level, `mean_fc` value row computed dividing first level second level.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_abundance_ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Abundance Analysis (DAA) with t-tests — tof_analyze_abundance_ttest","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using one three methods (\"diffcyt\", \"glmm\", \"ttest\"). wraps members `tof_analyze_expression_*` function family: tof_analyze_expression_diffcyt, tof_analyze_expression_lmm, tof_analyze_expression_ttest.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"","code":"tof_analyze_expression(tof_tibble, method = c(\"diffcyt\", \"glmm\", \"ttest\"), ...)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"tof_tibble `tof_tbl` `tibble`. method string indicating statistical method used. Valid values include \"diffcyt\", \"lmm\", \"ttest\". ... Additional arguments pass onto `tof_analyze_expression_*` function family member corresponding chosen method.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"tibble nested tibble containing differential abundance results chosen method. See tof_analyze_expression_diffcyt, tof_analyze_expression_lmm, tof_analyze_expression_ttest details.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform Differential Expression Analysis (DEA) on high-dimensional cytometry data — tof_analyze_expression","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using one two methods implemented diffcyt package differential discovery analysis high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"","code":"tof_analyze_expression_diffcyt( tof_tibble, sample_col, cluster_col, marker_cols = where(tof_is_numeric), fixed_effect_cols, random_effect_cols, diffcyt_method = c(\"lmm\", \"limma\"), include_observation_level_random_effects = FALSE, min_cells = 3, min_samples = 5, alpha = 0.05, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) tested differential expression levels `fixed_effect_cols`. Defaults numeric (integer double) columns. Supports tidyselect helpers. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential expression analysis. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Unquoted column names representing columns `tof_tibble` used model random effects differential expression analysis. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. Note without many samples level random effect variables, can easy overfit mixed models. high-dimensional cytometry experiments, 2 fewer (often 0) random effect variables appropriate. diffcyt_method string indicating diffcyt method used differential expression analysis. Valid methods include \"lmm\" (default) \"limma\". include_observation_level_random_effects boolean value indicating \"observation-level random effects\" (OLREs) included random effect terms \"lmm\" differential expression model. details OLREs , see diffcyt paper. Defaults FALSE. min_cells integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. ... Optional additional arguments pass --hood diffcyt function used perform differential expression analysis. See testDS_LMM testDS_limma details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"nested tibble two columns: `tested_effect` `dea_results`. first column, `tested_effect` character vector indicating term differential expression model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential expression model represent difference cluster median expression values marker samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `dea_results` list tibbles entry gives differential expression results tested_effect. Within entry `dea_results`, find `p_val`, p-value associated tested effect input cluster/marker pair; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential expression analysis (log-fold change clusters' median marker expression values conditions compared). tibble `dea_results` also two columns representing cluster marker corresponding p-value row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"two methods based linear mixed models (\"lmm\") limma (\"limma\"). \"lmm\" \"limma\" methods can model fixed effects random effects.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_diffcyt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with diffcyt — tof_analyze_expression_diffcyt","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using linear mixed-models. Users specify columns represent sample, cluster, marker, fixed effect, random effect information, (mixed) linear regression model fit using either lmer glm.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"","code":"tof_analyze_expression_lmm( tof_tibble, sample_col, cluster_col, marker_cols = where(tof_is_numeric), fixed_effect_cols, random_effect_cols, central_tendency_function = median, min_cells = 3, min_samples = 5, alpha = 0.05 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` represents id sample cell collected. `sample_col` serve unique identifier sample collected data acquisition - cells value `sample_col` treated part observational unit. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) included differential discovery analysis. Defaults numeric (integer double) columns. Supports tidyselection. fixed_effect_cols Unquoted column names representing columns `tof_tibble` used model fixed effects differential expression analysis. Supports tidyselection. Generally speaking, fixed effects represent comparisons biological interest (often variables manipulated experiments), treated vs. non-treated, -treatment vs. -treatment, healthy vs. non-healthy. random_effect_cols Optional. Unquoted column names representing columns `tof_tibble` used model random effects differential expression analysis. Supports tidyselection. Generally speaking, random effects represent variables researcher wants control/account , necessarily biological interest. Example random effect variables might include batch id, patient id (paired design), patient age. analyses include random effects. central_tendency_function function used calculate measurement central tendency cluster/marker pair (used dependent variable linear model). Defaults median. min_cells integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential expression analysis. Clusters included differential expression testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"nested tibble two columns: `tested_effect` `dea_results`. first column, `tested_effect` character vector indicating term differential expression model used significance testing. values row obtained pasting together column names fixed effect variable values. example, fixed effect column named fixed_effect levels \"\", \"b\", \"c\" two terms `tested_effect`: \"fixed_effectb\" \"fixed_effectc\" (note level \"\" fixed_effect set reference level dummy coding). values correspond terms differential expression model represent difference cluster median expression values marker samples fixed_effect = \"b\" fixed_effect = \"\" samples fixed_effect = \"c\" fixed_effect = \"\", respectively. addition, note first row `tested_effect` always represent \"omnibus\" test, test significant differences levels fixed effect variable model. second column, `dea_results` list tibbles entry gives differential expression results tested_effect. Within entry `daa_results`, find `p_val`, p-value associated tested effect input cluster/marker pair; `p_adj`, multiple-comparison adjusted p-value (using p.adjust function), values associated underlying method used perform differential expression analysis (log-fold change clusters' median marker expression values levels compared).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"Specifically, one linear model fit cluster/marker pair. cluster/marker pair, user-supplied measurement central tendency (`central_tendency_function`), mean median, calculated across cells cluster sample--sample basis. , central tendency value used dependent variable linear model `fixed_effect_cols` fixed effects predictors `random_effect_cols` random effects predictors. models (one per cluster/marker pair) fit, p-values coefficient model multiple-comparisons adjusted using p.adjust function.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_lmm.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with linear mixed-models (LMMs) — tof_analyze_expression_lmm","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":null,"dir":"Reference","previous_headings":"","what":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"function performs differential expression analysis cell clusters contained within `tof_tbl` using simple t-tests. Specifically, either unpaired paired t-test compare samples' marker expression distributions (two conditions) within cluster using user-specified summary function (.e. mean median). One t-test conducted per cluster/marker pair significant differences sample types detected multiple-hypothesis correction.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"","code":"tof_analyze_expression_ttest( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), effect_col, group_cols, test_type = c(\"unpaired\", \"paired\"), summary_function = mean, min_cells = 3, min_samples = 5, alpha = 0.05, quiet = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names representing columns `tof_tibble` (.e. high-dimensional cytometry protein measurements) tested differential expression levels `effect_col`. Defaults numeric (integer double) columns. Supports tidyselect helpers. effect_col Unquoted column name representing column `tof_tibble` used break samples groups t-test. 2 unique values. group_cols Unquoted names columns `effect_col` used group cells independent observations. Fills similar role `sample_col` `tof_analyze_abundance_*` functions. example, experiment involves analyzing samples taken multiple patients two timepoints (`effect_col = timepoint`), group_cols name column representing patient IDs. test_type string indicating whether t-test \"unpaired\" (default) \"paired\". summary_function vector-valued function used summarize distribution marker cluster (within sample, grouped `group_cols`). Defaults `mean`. min_cells integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 3. min_samples integer value used filter clusters differential abundance analysis. Clusters included differential abundance testing least `min_cells` least `min_samples` samples. Defaults 5. alpha numeric value 0 1 indicating significance level applied multiple-comparison adjusted p-values differential abundance analysis. Defaults 0.05. quiet boolean value indicating whether warnings printed. Defaults `TRUE`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"tibble 7 columns: {cluster_col} name/ID cluster cluster/marker pair tested. entry column match unique value input {cluster_col}. marker name marker cluster/marker pair tested. t t-statistic computed cluster. df degrees freedom used t-test cluster. p_val (unadjusted) p-value t-test cluster. p_adj p.adjust-adjusted p-value t-test cluster. significant character vector \"*\" clusters p_adj < alpha \"\" otherwise. mean_diff unpaired t-test, difference average proportions cluster two levels `effect_col`. paired t-test, average difference proportions cluster two levels `effect_col` within given patient. mean_fc unpaired t-test, ratio average proportions cluster two levels `effect_col`. paired t-test, average ratio proportions cluster two levels `effect_col` within given patient. 0.001 added denominator ratio avoid divide--zero errors. \"levels\" attribute result indicates order different levels `effect_col` considered. `mean_diff` value row output computed subtracting second level first level, `mean_fc` value row computed dividing first level second level.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_analyze_expression_ttest.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Differential Expression Analysis (DEA) with t-tests — tof_analyze_expression_ttest","text":"","code":"# For differential discovery examples, please see the package vignettes NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":null,"dir":"Reference","previous_headings":"","what":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"function adds additional column `tibble` `tof_tbl` allow users incorporate manual cell type labels clusters identified using unsupervised algorithms.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"","code":"tof_annotate_clusters(tof_tibble, cluster_col, annotations)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` contains ids unsupervised cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. annotations data structure indicating annotate cluster id `cluster_col`. `annotations` can provided data.frame two columns (first name `cluster_col` contain unique cluster id; second can name contain character vector indicating manual annotation matched cluster id first column). `annotations` can also provided named character vector; case, entry `annotations` unique cluster id, names entry corresponding manual cluster annotation. See examples.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"`tof_tbl` number rows `tof_tibble` one additional column containing manual cluster annotations cell (character vector). `annotations` provided data.frame, new column name column containing cluster annotations `annotations`. `annotations` provided named character vector, new column named `{cluster_col}_annotation`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_annotate_clusters.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Manually annotate tidytof-computed clusters using user-specified labels — tof_annotate_clusters","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # using named character vector sim_data |> tof_annotate_clusters( cluster_col = cluster_id, annotations = c(\"macrophage\" = \"a\", \"dendritic cell\" = \"b\") ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id cluster_id_annotation #> #> 1 -1.40 -0.337 -0.166 1.12 a macrophage #> 2 0.255 -0.216 0.120 0.400 a macrophage #> 3 -2.44 0.621 -0.662 -0.985 a macrophage #> 4 -0.00557 -1.28 -0.531 -0.503 a macrophage #> 5 0.622 -1.30 -0.301 0.987 a macrophage #> 6 1.15 -0.377 -0.602 2.19 a macrophage #> 7 -1.82 0.104 -0.318 -0.165 a macrophage #> 8 -0.247 -0.704 0.308 -0.686 a macrophage #> 9 -0.244 1.50 0.799 0.941 a macrophage #> 10 -0.283 -0.303 1.75 -0.164 a macrophage #> # ℹ 990 more rows # using two-column data.frame annotation_data_frame <- data.frame( cluster_id = c(\"a\", \"b\"), cluster_annotation = c(\"macrophage\", \"dendritic cell\") ) sim_data |> tof_annotate_clusters( cluster_col = cluster_id, annotations = annotation_data_frame ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id cluster_annotation #> #> 1 -1.40 -0.337 -0.166 1.12 a macrophage #> 2 0.255 -0.216 0.120 0.400 a macrophage #> 3 -2.44 0.621 -0.662 -0.985 a macrophage #> 4 -0.00557 -1.28 -0.531 -0.503 a macrophage #> 5 0.622 -1.30 -0.301 0.987 a macrophage #> 6 1.15 -0.377 -0.602 2.19 a macrophage #> 7 -1.82 0.104 -0.318 -0.165 a macrophage #> 8 -0.247 -0.704 0.308 -0.686 a macrophage #> 9 -0.244 1.50 0.799 0.941 a macrophage #> 10 -0.283 -0.303 1.75 -0.164 a macrophage #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"Perform developmental clustering CyTOF data using pre-fit classifier","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"","code":"tof_apply_classifier( cancer_tibble = NULL, classifier_fit = NULL, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1, parallel_vars )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"cancer_tibble `tibble` `tof_tibble` containing cells classified nearest healthy subpopulation (generally cancer cells). classifier_fit nested `tibble` produced `tof_build_classifier` row represents healthy cell subpopulation cells `cancer_tibble` classified using minimum distance. distance_function string indicating distance function used perform classification. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). parallel_vars Unquoted column names indicating columns `cancer_tibble` use breaking data order parallelize classification. Defaults NULL. Supports tidyselect helpers.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"tibble `nrow(cancer_tibble)` rows `nrow(classifier_fit) + 1` columns. row represents cell `cancer_tibble`, `nrow(classifier_fit)` columns represent distance cell healthy subpopulations' cluster centroids. final column represents cluster id healthy subpopulation minimum distance cell represented row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_apply_classifier.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform developmental clustering on CyTOF data using a pre-fit classifier — tof_apply_classifier","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"Detect low-expression (.e. potentially failed) channels high-dimensional cytometry data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"","code":"tof_assess_channels( tof_tibble, channel_cols = where(tof_is_numeric), negative_threshold = asinh(10/5), negative_proportion_flag = 0.95 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"tof_tibble `tof_tbl` `tibble`. channel_cols vector unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default analyze numeric columns. negative_threshold scalar indicating threshold measurement considered negative. Defaults hyperbolic arcsine transformation 10 counts. negative_proportion_flag scalar 0 1 indicating proportion cells tof_tibble need `negative_threshold` given marker order marker flagged. Defaults 0.95.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"tibble 3 columns number rows equal number columns `tof_tibble` chosen `channel_cols`. three columns \"channel\", character vector channel names, \"negative_proportion\", numeric vector values 0 1 indicating many cells `tof_tibble` `negative_threshold` channel, `flagged_channel`, boolean vector indicating whether channel flagged potentially failed (TRUE means channel large number cells `negative_threshold`).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_channels.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect low-expression (i.e. potentially failed) channels in high-dimensional cytometry data — tof_assess_channels","text":"","code":"# simulate some data sim_data <- data.frame( cd4 = rnorm(n = 100, mean = 5, sd = 0.5), cd8 = rnorm(n = 100, mean = 0, sd = 0.1), cd33 = rnorm(n = 100, mean = 10, sd = 0.1) ) tof_assess_channels(tof_tibble = sim_data) #> # A tibble: 3 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE #> 3 cd33 0 FALSE tof_assess_channels(tof_tibble = sim_data, channel_cols = c(cd4, cd8)) #> # A tibble: 2 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE tof_assess_channels(tof_tibble = sim_data, negative_threshold = 2) #> # A tibble: 3 × 3 #> channel negative_proportion flagged_channel #> #> 1 cd8 1 TRUE #> 2 cd4 0 FALSE #> 3 cd33 0 FALSE"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"function evaluates result clustering procedure comparing mahalanobis distance cell centroid cluster assigned among cells given cluster. cells mahalanobis-distance z-score user-specified threshold flagged potentially anomalous. Note z-score calculated using modified formula minimize effect outliers (Z = x - median(x) / mad(x)).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"","code":"tof_assess_clusters_distance( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), z_threshold = 3, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. z_threshold scalar indicating distance z-score threshold cell considered anomalous. Defaults 3. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"augment = FALSE (default), tibble 3 columns: \".mahalanobis_distance\" (mahalanobis distance cell centroid tits assigned cluster), \"z_score\" (modified z-score cell's mahalanobis distance relative cells dataset), \"flagged_cell\" (boolean indicating whether cell flagged z-score z_threshold). augment = TRUE, 3 columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating the z-score of each cell's\nmahalanobis distance to its cluster centroid and flagging outliers. — tof_assess_clusters_distance","text":"","code":"# simulate data sim_data_inner <- dplyr::tibble( cd45 = c(rnorm(n = 600), rnorm(n = 500, mean = -4)), cd38 = c( rnorm(n = 100, sd = 0.5), rnorm(n = 500, mean = -3), rnorm(n = 500, mean = 8) ), cd34 = c( rnorm(n = 100, sd = 0.2, mean = -10), rnorm(n = 500, mean = 4), rnorm(n = 500, mean = 60) ), cd19 = c(rnorm(n = 100, sd = 0.3, mean = 10), rnorm(n = 1000)), cluster_id = c(rep(\"a\", 100), rep(\"b\", 500), rep(\"c\", 500)), dataset = \"inner\" ) sim_data_outer <- dplyr::tibble( cd45 = c(rnorm(n = 10), rnorm(50, mean = 3), rnorm(n = 50, mean = -12)), cd38 = c( rnorm(n = 10, sd = 0.5), rnorm(n = 50, mean = -10), rnorm(n = 50, mean = 10) ), cd34 = c( rnorm(n = 10, sd = 0.2, mean = -15), rnorm(n = 50, mean = 15), rnorm(n = 50, mean = 70) ), cd19 = c(rnorm(n = 10, sd = 0.3, mean = 19), rnorm(n = 100)), cluster_id = c(rep(\"a\", 10), rep(\"b\", 50), rep(\"c\", 50)), dataset = \"outer\" ) sim_data <- rbind(sim_data_inner, sim_data_outer) # detect anomalous cells (in this case, the \"outer\" dataset contains small # clusters that get lumped into the larger clusters in the \"inner\" dataset) z_result <- sim_data |> tof_assess_clusters_distance(cluster_col = cluster_id, z_threshold = 2.5)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"function evaluates result clustering procedure calculating mahalanobis distance cell centroids clusters dataset finding shannon entropy resulting vector distances. cells entropy threshold user-specified threshold flagged potentially anomalous. Entropy minimized (0) cell close one (small number) clusters, far rest . cell close multiple cluster centroids (.e. ambiguous phenotype), entropy large.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"","code":"tof_assess_clusters_entropy( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), entropy_threshold, entropy_quantile = 0.9, num_closest_clusters, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. entropy_threshold scalar indicating entropy threshold cell considered anomalous. unspecified, threshold computed using `entropy_quantile` (see ). (Note: Entropy often 0 1, can larger many classes/clusters). entropy_quantile scalar 0 1 indicating entropy quantile cell considered anomalous. Defaults 0.9, means cells entropy 90th percentile flagged. Ignored entropy_threshold specified directly. num_closest_clusters integer indicating many cell's closest cluster centroids mahalanobis distance included entropy calculation. Playing argument allow ignore distances clusters far away cell (thus may distort result, many distant centroids large distances can artificially inflate cells' entropy value; said, rarely issue empirically). Defaults clusters tof_tibble. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"augment = FALSE (default), tibble 2 + NUM_CLUSTERS columns. NUM_CLUSTERS number unique clusters cluster_col. Two columns \"entropy\" (entropy value cell) \"flagged_cell\" (boolean value indicating cell entropy value entropy_threshold). NUM_CLUSTERS columns contain mahalanobis distances cell clusters cluster_col (named \".mahalanobis_{cluster_name}\"). augment = TRUE, 2 + NUM_CLUSTERS columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_entropy.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating the shannon entropy of each cell's\nmahalanobis distance to all cluster centroids and flagging outliers. — tof_assess_clusters_entropy","text":"","code":"# simulate data sim_data <- dplyr::tibble( cd45 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd38 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd34 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd19 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) # imagine a \"reference\" dataset in which \"cluster a\" isn't present sim_data_reference <- sim_data |> dplyr::filter(cluster_id %in% c(\"b\", \"c\")) # if we cluster into the reference dataset, we will force all cells in # cluster a into a population where they don't fit very well sim_data <- sim_data |> tof_cluster( healthy_tibble = sim_data_reference, healthy_label_col = cluster_id, method = \"ddpr\" ) # we can evaluate the clustering quality by calculating by the entropy of the # mahalanobis distance vector for each cell to all cluster centroids entropy_result <- sim_data |> tof_assess_clusters_entropy( cluster_col = .mahalanobis_cluster, marker_cols = starts_with(\"cd\"), entropy_quantile = 0.8, augment = TRUE ) # most cells in \"cluster a\" are flagged, and few cells in the other clusters are flagged_cluster_proportions <- entropy_result |> dplyr::group_by(cluster_id) |> dplyr::summarize( prop_flagged = mean(flagged_cell) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"function evaluates result clustering procedure finding cell's K nearest neighbors, determining cluster majority assigned , checking matches cell's cluster assignment. cluster assignment majority cell's nearest neighbors match cell's cluster assignment, cell flagged potentially anomalous.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"","code":"tof_assess_clusters_knn( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), num_neighbors = min(10, nrow(tof_tibble)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers used mahalanobis distance calculation. Defaults numeric columns. Supports tidyselection. num_neighbors integer indicating many neighbors found nearest neighbor calculation. distance_function string indicating distance function used perform k nearest neighbor calculation. Options \"euclidean\" (default) \"cosine\". augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"augment = FALSE (default), tibble 2 columns: \".knn_cluster\" (character vector indicating cluster received majority vote cell's k nearest neighbors) \"flagged_cell\" (boolean value indicating cell's cluster assignment matched majority vote (TRUE) (FALSE)). augment = TRUE, 2 columns column-bound tof_tibble, resulting tibble returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_clusters_knn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a clustering result by calculating a cell's cluster assignment to that\nof its K nearest neighbors. — tof_assess_clusters_knn","text":"","code":"sim_data <- dplyr::tibble( cd45 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd38 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd34 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cd19 = c(rnorm(n = 1000, sd = 1.5), rnorm(n = 1000, mean = 2), rnorm(n = 1000, mean = -2)), cluster_id = c(rep(\"a\", 1000), rep(\"b\", 1000), rep(\"c\", 1000)) ) knn_result <- sim_data |> tof_assess_clusters_knn( cluster_col = cluster_id, num_neighbors = 10 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"function performs simplified version flowAI's statistical test detect time periods abnormal flow rates course flow cytometry experiment. Briefly, relative flow rates timestep throughout data acquisition calculated (see tof_calculate_flow_rate), outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"","code":"tof_assess_flow_rate( tof_tibble, time_col, group_cols, num_timesteps = nrow(tof_tibble)/1000, alpha_threshold = 0.01, visualize = FALSE, ..., augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. group_cols Optional. Unquoted column names indicating columns used group cells analysis. Flow rate calculation performed independently within group. Supports tidyselect helpers. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection. alpha_threshold scalar 0 1 indicating two-tailed significance level draw outlier thresholds t-distribution `num_timesteps` - 1 degrees freedom. Defaults 0.01. visualize boolean value indicating plot generated visualize timestep's relative flow rate (group) instead returning tibble directly. Defaults FALSE. ... Optional additional arguments pass facet_wrap. Ignored visualize = FALSE. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"tibble number rows `tof_tibble`. augment = FALSE (default), 3 columns: \"{time_col}\" (column `time_col`), \"timestep\" (numeric timestep cell assigned based value `time_col`), \"flagged_window\" (boolean vector indicating cell collecting timestep flagged high low flow rate). augment = TRUE, 3 columns column-bound `tof_tibble` return augmented version input dataset. (Note case, time_col duplicated). visualize = TRUE, ggplot object returned instead tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect flow rate abnormalities in high-dimensional cytometry data — tof_assess_flow_rate","text":"","code":"set.seed(1000L) sim_data <- data.frame( cd4 = rnorm(n = 1000, mean = 5, sd = 0.5), cd8 = rnorm(n = 1000, mean = 0, sd = 0.1), cd33 = rnorm(n = 1000, mean = 10, sd = 0.1), file_name = c(rep(\"a\", times = 500), rep(\"b\", times = 500)), time = c( sample(1:100, size = 200, replace = TRUE), sample(100:400, size = 300, replace = TRUE), sample(1:150, size = 400, replace = TRUE), sample(1:500, size = 100, replace = TRUE) ) ) sim_data |> tof_assess_flow_rate( time_col = time, num_timesteps = 20, visualize = TRUE ) sim_data |> tof_assess_flow_rate( time_col = time, group_cols = file_name, num_timesteps = 20, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"function performs simplified version flowAI's statistical test detect time periods abnormal flow rates course flow cytometry experiment. Briefly, relative flow rates timestep throughout data acquisition calculated (see tof_calculate_flow_rate), outlier timepoints particularly high low flow rates (.e. beyond extreme values t-distribution across timesteps) flagged.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"","code":"tof_assess_flow_rate_tibble( tof_tibble, time_col, num_timesteps = nrow(tof_tibble)/1000, alpha_threshold = 0.01, augment = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection. alpha_threshold scalar 0 1 indicating two-tailed significance level draw outlier thresholds t-distribution `num_timesteps` - 1 degrees freedom. Defaults 0.01. augment boolean value indicating output column-bind computed flags cell (see ) new columns `tof_tibble` (TRUE) tibble including computed flags returned (FALSE, default).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"tibble number rows `tof_tibble`. augment = FALSE (default), 3 columns: \"{time_col}\" (column `time_col`), \"timestep\" (numeric timestep cell assigned based value `time_col`), \"flagged_window\" (boolean vector indicating cell collecting timestep flagged high low flow rate). augment = TRUE, 3 columns column-bound `tof_tibble` return augmented version input dataset. (Note case, time_col duplicated).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_flow_rate_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Detect flow rate abnormalities in high-dimensional cytometry data (stored in a\nsingle data.frame) — tof_assess_flow_rate_tibble","text":"","code":"set.seed(1000L) sim_data <- data.frame( cd4 = rnorm(n = 1000, mean = 5, sd = 0.5), cd8 = rnorm(n = 1000, mean = 0, sd = 0.1), cd33 = rnorm(n = 1000, mean = 10, sd = 0.1), time = c( sample(1:100, size = 200, replace = TRUE), sample(100:400, size = 300, replace = TRUE), sample(1:150, size = 400, replace = TRUE), sample(1:500, size = 100, replace = TRUE) ) ) sim_data |> tof_assess_flow_rate( time_col = time, num_timesteps = 20, visualize = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Assess a trained elastic net model — tof_assess_model","title":"Assess a trained elastic net model — tof_assess_model","text":"function assesses trained `tof_model`'s performance new data computing model type-specific performance measurements. new data provided, performance metrics training data provided.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Assess a trained elastic net model — tof_assess_model","text":"","code":"tof_assess_model(tof_model, new_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Assess a trained elastic net model — tof_assess_model","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations used evaluate `tof_model`'s performance. new_data provided, model evaluation performed using training data used fit model. Alternatively, string \"tuning\" can provided access model's performance metrics (resampled) model tuning process.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Assess a trained elastic net model — tof_assess_model","text":"list performance metrics whose components depend model type: \"model_metrics\" tibble two columns (\"metric\" \"value\") containing standard performance metrics model type. linear models, \"mse\" (mean squared error predictions) \"mae\" (mean absolute error predictions). two-class models, \"roc_auc\" (area Receiver-Operating Curve classification), \"misclassification error\" (proportion misclassified observations), \"binomial_deviance\" (see deviance.glmnet), \"mse\" (mean squared error logit function), \"mae\" (mean absolute error logit function). multiclass models, \"roc_auc\" (area Receiver-Operating Curve classification using Hand-Till generalization ROC AUC multiclass models roc_auc), \"misclassification error\" (proportion misclassified observations), \"multinomial_deviance\" (see deviance.glmnet), \"mse\" \"mae\" . survival models, \"concordance_index\" (Harrel's C index; see deviance.glmnet) \"partial_likelihood_deviance\" (see deviance.glmnet). \"roc_curve\" Reported \"two-class\" \"multiclass\" models. , tibble provided reporting true-positive rate (tpr) false-positive rate (fpr) threshold classification use plotting receiver-operating curve. \"multiclass\" models, \".level\" column allows separating values roc_curve one ROC can plotted class. \"confusion_matrix\" Reported \"two-class\" \"multiclass\" models. , tibble provided reporting \"confusion matrix\" classification long-format. \"survival_curves\" Reported \"survival\" models. tibble indicating patient's probability survival (1 - probability(event)) timepoint dataset whether sample placed \"high\" \"low\" risk group according predicted relative risk (tof_model's optimal relative_risk cutoff training dataset).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Assess a trained elastic net model — tof_assess_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # assess the model on new data tof_assess_model(tof_model = regression_model, new_data = new_tibble) #> $model_metrics #> # A tibble: 2 × 2 #> metric value #> #> 1 mse 0.795 #> 2 mae 0.788 #>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"Compute trained elastic net model's performance metrics using new_data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"","code":"tof_assess_model_new_data(tof_model, new_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations used evaluate `tof_model`'s performance.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_new_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a trained elastic net model's performance metrics using new_data. — tof_assess_model_new_data","text":"list performance metrics whose components depend model type.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":null,"dir":"Reference","previous_headings":"","what":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"Access trained elastic net model's performance metrics using tuning data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"","code":"tof_assess_model_tuning(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"tof_model `tof_model` trained using tof_train_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_assess_model_tuning.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Access a trained elastic net model's performance metrics using its tuning data. — tof_assess_model_tuning","text":"list performance metrics whose components depend model type.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"function performs quantile normalization high-dimensional cytometry data tidy format using either linear rescaling quantile normalization. channel specified `channel_cols` batch corrected, `group_cols` can used break cells groups batch correction performed separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"","code":"tof_batch_correct( tof_tibble, channel_cols, group_cols, augment = TRUE, method = c(\"rescale\", \"quantile\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted. method string indicating batch correction method used. Valid options \"rescale\" linear scaling (default) \"quantile\" quantile normalization using normalize.quantiles.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"function performs quantile normalization high-dimensional cytometry data tidy format using normalize.quantiles. Optionally, groups can specified normalized separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"","code":"tof_batch_correct_quantile( tof_tibble, channel_cols, group_cols, augment = TRUE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"function performs quantile normalization high-dimensional cytometry data tidy format using normalize.quantiles.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"","code":"tof_batch_correct_quantile_tibble(tof_tibble, channel_cols, augment = TRUE)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_quantile_tibble.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Batch-correct a tibble of high-dimensional cytometry data using quantile\nnormalization. — tof_batch_correct_quantile_tibble","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"function performs quantile normalization high-dimensional cytometry data tidy format using linear rescaling. channel specified `channel_cols` rescaled maximum value 1 minimum value 0. `group_cols` specifies columns used break cells groups rescaling performed separately.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"","code":"tof_batch_correct_rescale(tof_tibble, channel_cols, group_cols, augment = TRUE)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells batch correction. Batch correction performed independently within group. Supports tidyselect helpers. augment boolean value indicating output replace `channel_cols` `tof_tibble` new, batch corrected columns (TRUE, default) return batch-corrected columns (FALSE) columns omitted.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"augment = TRUE, tibble number rows columns tof_tibble, columns specified `channel_cols` batch-corrected. augment = FALSE, tibble containing batch-corrected `channel_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_batch_correct_rescale.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform groupwise linear rescaling of high-dimensional cytometry measurements — tof_batch_correct_rescale","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"function takes `tibble` `tof_tibble` storing healthy cell measurements rows vector (`healthy_cell_labels`) representing cell subpopulation cell belongs. uses values calculate several values required perform \"developmental classification\" described paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"","code":"tof_build_classifier( healthy_tibble = NULL, healthy_cell_labels = NULL, classifier_markers = where(tof_is_numeric), verbose = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"healthy_tibble `tibble` `tof_tibble` containing cells healthy control samples (.e. disease samples). healthy_cell_labels character integer vector length `nrow(healthy_tibble)`. entry vector represent cell subpopulation label (cluster id) corresponding row `healthy_tibble`. classifier_markers Unquoted column names indicating columns `healthy_tibble` use developmental classification. Defaults numeric columns `healthy_tibble`. Supports tidyselect helpers. verbose boolean value indicating updates printed console classification. Defaults FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_build_classifier.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate centroids and covariance matrices for each cell subpopulation in\nhealthy CyTOF data. — tof_build_classifier","text":"tibble three columns: population (id healthy cell population), centroid (centroid vector cell population), covariance_matrix (covariance matrix cell population)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"Calculate relative flow rates different timepoints throughout flow mass cytometry run.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"","code":"tof_calculate_flow_rate( tof_tibble, time_col, num_timesteps = nrow(tof_tibble)/1000 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"tof_tibble `tof_tbl` `tibble`. time_col unquoted column name indicating column `tof_tibble` contains time cell collected. num_timesteps number bins `time_col` split. define \"timesteps\" data collection process. number cells analyzed cytometer counted bin separately represent relative average flow rate timestep data collection.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"tibble 3 columns num_timesteps rows. row represent single timestep (error thrown `num_timesteps` larger number rows `tof_tibble`). three columns follows: \"timestep\", numeric vector indicating timestep represented given row; \"time_window\", factor showing interval `time_col` \"timestep\" defined; \"num_cells\", number cells collected timestep.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_calculate_flow_rate.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculate the relative flow rates of different timepoints throughout a flow\nor mass cytometry run. — tof_calculate_flow_rate","text":"","code":"# simulate some data sim_data <- data.frame( cd4 = rnorm(n = 100, mean = 5, sd = 0.5), cd8 = rnorm(n = 100, mean = 0, sd = 0.1), cd33 = rnorm(n = 100, mean = 10, sd = 0.1), time = sample(1:300, size = 100) ) tof_calculate_flow_rate(tof_tibble = sim_data, time_col = time, num_timesteps = 20L) #> timestep time_window num_cells #> 1 1 (6.71,21.6] 4 #> 2 2 (21.6,36.2] 6 #> 3 3 (36.2,50.8] 7 #> 4 4 (50.8,65.4] 7 #> 5 5 (65.4,80] 5 #> 6 6 (80,94.6] 4 #> 7 7 (94.6,109] 5 #> 8 8 (109,124] 4 #> 9 9 (124,138] 1 #> 10 10 (138,153] 7 #> 11 11 (153,168] 5 #> 12 12 (168,182] 2 #> 13 13 (182,197] 7 #> 14 14 (197,211] 7 #> 15 15 (211,226] 5 #> 16 16 (226,241] 2 #> 17 17 (241,255] 4 #> 18 18 (255,270] 5 #> 19 19 (270,284] 7 #> 20 20 (284,299] 6"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":null,"dir":"Reference","previous_headings":"","what":"Check argument specifications for a glmnet model. — tof_check_model_args","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"Check argument specifications glmnet model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"","code":"tof_check_model_args( split_data, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), best_model_type = c(\"best\", \"best with sparsity\"), response_col, time_col, event_col )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. Alternatively, unsplit tbl_df can provided, though recommended. model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. best_model_type Currently unused. response_col Unquoted column name indicating column data contained `split_data` used outcome \"two-class\", \"multiclass\", \"linear\" elastic net model. Must factor \"two-class\" \"multiclass\" models must numeric \"linear\" models. Ignored `model_type` \"survival\". time_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must numeric. Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". event_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must binary column - values either 0 1 (1 indicating adverse event) FALSE TRUE (TRUE indicating adverse event). Ignored `model_type` \"two-class\", \"multiclass\", \"linear\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_check_model_args.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Check argument specifications for a glmnet model. — tof_check_model_args","text":"tibble. arguments specified correctly, tibble can used create recipe preprocessing.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":null,"dir":"Reference","previous_headings":"","what":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"function uses specified distance metric classify cell data.frame matrix (`cancer_data`) one `nrow(classifier_fit)` subpopulations based minimum distance, described paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"","code":"tof_classify_cells( classifier_fit, cancer_data, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"classifier_fit tibble produced tof_build_classifier. cancer_data matrix row corresponds cell column corresponds measured CyTOF antigen. distance_function string indicating three distance functions used calculate distances row `cancer_data` healthy developmental subpopulations corresponding row `classifier_fit`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_classify_cells.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Classify each cell (i.e. each row) in a matrix of cancer cells into its most\nsimilar healthy developmental subpopulation. — tof_classify_cells","text":"data.frame column represents distance cell input data healthy subpopulation cells classified .","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":null,"dir":"Reference","previous_headings":"","what":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"Rename glmnet's default model evaluation metrics make interpretable","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"","code":"tof_clean_metric_names(metric_tibble, model_type)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"metric_tibble tibble column represents glmnet model evaluation metric default name. model_type string indicating type glmnet model trained.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_clean_metric_names.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Rename glmnet's default model evaluation metrics to make them more interpretable — tof_clean_metric_names","text":"tibble column represents glmnet model evaluation metric \"cleaned\" name.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster high-dimensional cytometry data. — tof_cluster","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"function wrapper around tidytof's tof_cluster_* function family. performs clustering high-dimensional cytometry data using user-specified method (5 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"","code":"tof_cluster( tof_tibble, cluster_cols = where(tof_is_numeric), group_cols = NULL, ..., augment = TRUE, method )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. group_cols Optional. Unquoted column names indicating columns used group cells clustering. Clustering performed group independently. Supports tidyselect helpers. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Cluster high-dimensional cytometry data. — tof_cluster","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 500), cd38 = rnorm(n = 500), cd34 = rnorm(n = 500), cd19 = rnorm(n = 500) ) tof_cluster(tof_tibble = sim_data, method = \"kmeans\") #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 .kmeans_cluster #> #> 1 1.33 -0.447 1.50 0.436 11 #> 2 -1.20 -0.481 -0.391 -1.54 9 #> 3 -0.541 0.666 -1.68 -0.986 16 #> 4 -1.22 1.32 0.689 -0.791 10 #> 5 0.639 0.519 -1.32 -0.204 18 #> 6 -0.239 0.397 -0.780 0.372 1 #> 7 0.651 0.997 -0.665 0.805 18 #> 8 0.788 1.26 0.584 -0.953 19 #> 9 -0.344 0.388 -0.407 -0.442 13 #> 10 0.120 0.885 -2.26 0.583 17 #> # ℹ 490 more rows tof_cluster(tof_tibble = sim_data, method = \"phenograph\") #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 .phenograph_cluster #> #> 1 1.33 -0.447 1.50 0.436 2 #> 2 -1.20 -0.481 -0.391 -1.54 1 #> 3 -0.541 0.666 -1.68 -0.986 1 #> 4 -1.22 1.32 0.689 -0.791 3 #> 5 0.639 0.519 -1.32 -0.204 5 #> 6 -0.239 0.397 -0.780 0.372 5 #> 7 0.651 0.997 -0.665 0.805 4 #> 8 0.788 1.26 0.584 -0.953 8 #> 9 -0.344 0.388 -0.407 -0.442 1 #> 10 0.120 0.885 -2.26 0.583 5 #> # ℹ 490 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"function performs distance-based clustering high-dimensional cytometry data sorting cancer cells (passed function `tof_tibble`) phenotypically similar healthy cell subpopulation (passed function using `healthy_tibble`). details algorithm used perform clustering, see paper.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"","code":"tof_cluster_ddpr( tof_tibble, healthy_tibble, healthy_label_col, cluster_cols = where(tof_is_numeric), distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1L, parallel_cols, return_distances = FALSE, verbose = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"tof_tibble `tibble` `tof_tbl` containing cells classified nearest healthy subpopulation (generally cancer cells). healthy_tibble `tibble` `tof_tibble` containing cells healthy control samples (.e. disease samples). healthy_label_col unquoted column name indicating column `healthy_tibble` contains subpopulation label (cluster id) cell `healthy_tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing DDPR clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. distance_function string indicating distance function used perform classification. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). parallel_cols Optional. Unquoted column names indicating columns `tof_tibble` use breaking data order parallelize classification using `foreach` `doParallel` backend. Supports tidyselect helpers. return_distances boolean value indicating whether returned result include one column, cluster ids corresponding row `tof_tibble` (return_distances = FALSE, default), returned result include additional columns representing distance row `tof_tibble` healthy subpopulation centroids (return_distances = TRUE). verbose boolean value indicating whether progress updates printed developmental classification. Default FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"`return_distances = FALSE`, tibble one column named `.{distance_function}_cluster`, character vector length `nrow(tof_tibble)` indicating id developmental cluster cell (.e. row) `tof_tibble` assigned. `return_distances = TRUE`, tibble `nrow(tof_tibble)` rows `nrow(classifier_fit) + 1` columns. row represents cell `tof_tibble`, `nrow(classifier_fit)` columns represent distance cell healthy subpopulations' cluster centroids. final column represents cluster id healthy subpopulation minimum distance cell represented row. `return_distances = FALSE`, tibble one column named `.{distance_function}_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id developmental cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_ddpr.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform developmental clustering on high-dimensional cytometry data. — tof_cluster_ddpr","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) healthy_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) tof_cluster_ddpr( tof_tibble = sim_data, healthy_tibble = healthy_data, healthy_label_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .mahalanobis_cluster #> #> 1 b #> 2 b #> 3 b #> 4 a #> 5 b #> 6 b #> 7 b #> 8 a #> 9 a #> 10 b #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"function performs FlowSOM clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements. mostly convenient wrapper around SOM MetaClustering.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"","code":"tof_cluster_flowsom( tof_tibble = NULL, cluster_cols = where(tof_is_numeric), som_xdim = 10, som_ydim = 10, som_distance_function = c(\"euclidean\", \"manhattan\", \"chebyshev\", \"cosine\"), perform_metaclustering = TRUE, num_metaclusters = 20, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing flowSOM clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. som_xdim width grid used self-organizing map. total number clusters returned FlowSOM som_xdim * som_ydim, adjust value affect final number clusters. Defaults 10. som_ydim height grid used self-organizing map. total number clusters returned FlowSOM som_xdim * som_ydim, adjust value affect final number clusters. Defaults 10. som_distance_function distance function used self-organizing map calculations. Options \"euclidean\" (default), \"manhattan\", \"chebyshev\", \"cosine\". perform_metaclustering boolean value indicating metaclustering performed initial clustering result returned FlowSOM. Defaults TRUE. num_metaclusters integer indicating maximum number metaclusters returned metaclustering. Defaults 20. ... Optional additional parameters can passed BuildSOM function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"tibble one column named `.flowsom_cluster` `.flowsom_metacluster` depending value `perform_metaclustering`. column contain integer vector length `nrow(tof_tibble)` indicating id flowSOM cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"additional details FlowSOM algorithm, see paper.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_flowsom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform FlowSOM clustering on high-dimensional cytometry data — tof_cluster_flowsom","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) tof_cluster_flowsom(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 200 × 1 #> .flowsom_metacluster #> #> 1 10 #> 2 10 #> 3 8 #> 4 5 #> 5 10 #> 6 15 #> 7 6 #> 8 8 #> 9 15 #> 10 2 #> # ℹ 190 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"function wrapper around tidytof's tof_cluster_* function family provides low-level API clustering grouped data frames. subroutine tof_cluster called directly users.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"","code":"tof_cluster_grouped(tof_tibble, group_cols, ..., augment = TRUE, method)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"tof_tibble `tof_tbl` `tibble`. group_cols unquoted column name indicating columns used group cells clustering. Clustering performed group independently. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_grouped.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster (grouped) high-dimensional cytometry data. — tof_cluster_grouped","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"function performs k-means clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements. mostly convenient wrapper around kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"","code":"tof_cluster_kmeans( tof_tibble, cluster_cols = where(tof_is_numeric), num_clusters = 20, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"tof_tibble `tof_tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing k-means clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_clusters integer indicating maximum number clusters returned. Defaults 20. ... Optional additional arguments can passed kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"tibble one column named `.kmeans_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id k-means cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_kmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform k-means clustering on high-dimensional cytometry data. — tof_cluster_kmeans","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_cluster_kmeans(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> #> 1 14 #> 2 11 #> 3 4 #> 4 6 #> 5 20 #> 6 6 #> 7 16 #> 8 9 #> 9 3 #> 10 7 #> # ℹ 990 more rows tof_cluster_kmeans(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> #> 1 3 #> 2 9 #> 3 6 #> 4 12 #> 5 13 #> 6 15 #> 7 4 #> 8 18 #> 9 6 #> 10 18 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"function performs PhenoGraph clustering high-dimensional cytometry data using user-specified selection input variables/high-dimensional cytometry measurements.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"","code":"tof_cluster_phenograph( tof_tibble, cluster_cols = where(tof_is_numeric), num_neighbors = 30, distance_function = c(\"euclidean\", \"cosine\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"tof_tibble `tof_tbl` `tibble`. cluster_cols Unquoted column names indicating columns `tof_tibble` use computing PhenoGraph clusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_neighbors integer indicating number neighbors use constructing PhenoGraph's k-nearest-neighbor graph. Smaller values emphasize local graph structure; larger values emphasize global graph structure (add time computation). Defaults 30. distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. ... Optional additional parameters can passed tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"tibble one column named `.phenograph_cluster`. column contain integer vector length `nrow(tof_tibble)` indicating id PhenoGraph cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"additional details Phenograph algorithm, see paper.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_phenograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform PhenoGraph clustering on high-dimensional cytometry data. — tof_cluster_phenograph","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_cluster_phenograph(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> #> 1 6 #> 2 3 #> 3 4 #> 4 6 #> 5 5 #> 6 5 #> 7 1 #> 8 10 #> 9 10 #> 10 7 #> # ℹ 990 more rows tof_cluster_phenograph(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> #> 1 11 #> 2 7 #> 3 5 #> 4 5 #> 5 5 #> 6 1 #> 7 9 #> 8 6 #> 9 6 #> 10 2 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":null,"dir":"Reference","previous_headings":"","what":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"function wrapper around tidytof's tof_cluster_* function family provides low-level API clustering ungrouped data frames. subroutine tof_cluster called directly users.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"","code":"tof_cluster_tibble(tof_tibble, ..., augment = TRUE, method)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"tof_tibble `tof_tbl` `tibble`. ... Additional arguments pass `tof_cluster_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"flowsom\", \"phenograph\", \"kmeans\", \"ddpr\", \"xshift\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cluster_tibble.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Cluster (ungrouped) high-dimensional cytometry data. — tof_cluster_tibble","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"Compute Kaplan-Meier curve sample-level survival data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"","code":"tof_compute_km_curve(survival_curves)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"survival_curves tibble Kaplan-Meier curve computed. row must represent observation must two columns named \"time_to_event\" \"event\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_compute_km_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a Kaplan-Meier curve from sample-level survival data — tof_compute_km_curve","text":"tibble 3 columns: time_to_event, survival_probability, is_censored (whether event censored timepoint).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":null,"dir":"Reference","previous_headings":"","what":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"function finding cosine distance rows numeric matrix numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"","code":"tof_cosine_dist(matrix, vector)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"matrix numeric matrix. vector numeric vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"numeric vector distances length `nrow(matrix)` ith entry represents cosine distance ith row `matrix` `vector`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_cosine_dist.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"A function for finding the cosine distance between each of the rows of a numeric\nmatrix and a numeric vector. — tof_cosine_dist","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":null,"dir":"Reference","previous_headings":"","what":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"function creates regular hyperparameter search grid (form tibble) specifying search space two hyperparameters generalized linear model using glmnet package: regularization penalty term lasso/ridge regression mixture term.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"","code":"tof_create_grid( penalty_values, mixture_values, num_penalty_values = 5, num_mixture_values = 5 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"penalty_values numeric vector unique elastic net penalty values (\"lambda\") include hyperparameter grid. unspecified, regular grid `num_penalty_values` 10^(-10) 10^(0) used. mixture_values numeric vector elastic net mixture values (\"alpha\") include hyperparameter grid. unspecified, regular grid `num_mixture_values` 0 1 used. num_penalty_values Optional. `penalty_values` supplied, `num_penalty_values` (integer) can given specify many equally-spaced penalty values 10^(-10) 1 included hyperparameter grid. method used, regular grid always returned. Defaults 5. num_mixture_values Optional. `mixture_values` supplied, `num_mixture_values` (integer) can given specify many equally-spaced penalty values 0 (ridge regression) 1 (lasso) included hyperparameter grid. method used, regular grid always returned. Defaults 5.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"tibble two numeric columns: `penalty` `mixture`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_grid.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create an elastic net hyperparameter search grid of a specified size — tof_create_grid","text":"","code":"tof_create_grid() #> # A tibble: 25 × 2 #> penalty mixture #> #> 1 0.0000000001 0 #> 2 0.0000000001 0.25 #> 3 0.0000000001 0.5 #> 4 0.0000000001 0.75 #> 5 0.0000000001 1 #> 6 0.0000000316 0 #> 7 0.0000000316 0.25 #> 8 0.0000000316 0.5 #> 9 0.0000000316 0.75 #> 10 0.0000000316 1 #> # ℹ 15 more rows tof_create_grid(num_penalty_values = 10, num_mixture_values = 5) #> # A tibble: 50 × 2 #> penalty mixture #> #> 1 0.0000000001 0 #> 2 0.0000000001 0.25 #> 3 0.0000000001 0.5 #> 4 0.0000000001 0.75 #> 5 0.0000000001 1 #> 6 0.00000000129 0 #> 7 0.00000000129 0.25 #> 8 0.00000000129 0.5 #> 9 0.00000000129 0.75 #> 10 0.00000000129 1 #> # ℹ 40 more rows tof_create_grid(penalty_values = c(0.01, 0.1, 0.5)) #> # A tibble: 15 × 2 #> penalty mixture #> #> 1 0.01 0 #> 2 0.01 0.25 #> 3 0.01 0.5 #> 4 0.01 0.75 #> 5 0.01 1 #> 6 0.1 0 #> 7 0.1 0.25 #> 8 0.1 0.5 #> 9 0.1 0.75 #> 10 0.1 1 #> 11 0.5 0 #> 12 0.5 0.25 #> 13 0.5 0.5 #> 14 0.5 0.75 #> 15 0.5 1"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"Create recipe preprocessing sample-level cytometry data elastic net model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"","code":"tof_create_recipe( feature_tibble, predictor_cols, outcome_cols, standardize_predictors = TRUE, remove_zv_predictors = FALSE, impute_missing_predictors = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"feature_tibble tibble row represents sample- patient- level observation, produced tof_extract_features. predictor_cols Unquoted column names indicating columns data contained `feature_tibble` used predictors elastic net model. Supports tidyselect helpers. outcome_cols Unquoted column names indicating columns `feature_tibble` used outcome variables elastic net model. Supports tidyselect helpers. standardize_predictors logical value indicating numeric predictor columns standardized (centered scaled) model fitting. Defaults TRUE. remove_zv_predictors logical value indicating predictor columns near-zero variance removed model fitting using step_nzv. Defaults FALSE. impute_missing_predictors logical value indicating predictor columns missing values imputed using k-nearest neighbors model fitting (see step_impute_knn). Imputation performed using observation's 5 nearest-neighbors. Defaults FALSE.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_create_recipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a recipe for preprocessing sample-level cytometry data for an elastic net model — tof_create_recipe","text":"recipe object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data. — tof_downsample","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"function downsamples number cells `tof_tbl` using one three methods (randomly sampling constant number cells, randomly sampling proportion cells, performing density-dependent downsampling per algorithm Qiu et al., (2011)).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"","code":"tof_downsample( tof_tibble, group_cols = NULL, ..., method = c(\"constant\", \"prop\", \"density\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups within downsampling performed. Supports tidyselect helpers. Defaults `NULL` (grouping). ... Additional arguments pass `tof_downsample_*` function family member corresponding chosen method. method string indicating downsampling method use: \"constant\" (default), \"prop\", \"density\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"downsampled `tof_tbl` number columns input `tof_tibble`, fewer rows. number rows result depend chosen downsampling method.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data. — tof_downsample","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 200 cells from the input data tof_downsample( tof_tibble = sim_data, num_cells = 200L, method = \"constant\" ) #> # A tibble: 200 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 1.26 0.998 0.444 -0.591 h #> 2 0.341 1.50 -0.0357 1.11 a #> 3 1.39 0.0528 0.900 0.851 o #> 4 -0.542 -1.44 0.510 0.435 a #> 5 1.14 1.77 -2.72 2.04 p #> 6 -0.271 -0.450 0.541 0.325 k #> 7 -1.19 -0.680 0.459 0.557 b #> 8 -0.492 0.726 0.215 -1.06 g #> 9 -0.926 1.10 0.989 0.741 s #> 10 -1.31 0.203 1.83 -0.0195 c #> # ℹ 190 more rows # sample 10% of all cells from the input data tof_downsample( tof_tibble = sim_data, prop_cells = 0.1, method = \"prop\" ) #> # A tibble: 100 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -0.271 -0.450 0.541 0.325 k #> 2 1.49 -0.0481 1.25 0.344 q #> 3 -0.190 -1.77 -0.665 0.240 n #> 4 -1.25 -0.0155 0.439 -1.53 r #> 5 -1.05 0.616 -0.159 0.989 m #> 6 -0.236 1.32 -0.388 0.134 i #> 7 -1.22 1.36 0.209 0.293 m #> 8 -1.50 -0.976 -2.46 0.688 r #> 9 0.287 0.0766 -0.0885 0.133 l #> 10 -1.34 -0.197 -1.06 1.45 f #> # ℹ 90 more rows # sample ~10% of cells from the input data using density dependence tof_downsample( tof_tibble = sim_data, target_prop_cells = 0.1, method = \"density\" ) #> # A tibble: 93 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -0.754 -1.28 0.537 0.824 z #> 2 -1.52 0.146 0.895 -1.11 v #> 3 -0.952 0.705 -0.481 -1.59 p #> 4 0.668 0.911 -0.406 1.03 e #> 5 -1.58 0.504 1.99 -0.764 r #> 6 1.22 2.31 -0.486 0.649 a #> 7 0.417 -2.14 -1.70 0.713 y #> 8 1.36 0.199 -0.00351 1.11 b #> 9 -0.546 0.574 1.65 0.230 r #> 10 0.658 -0.0507 1.25 0.855 c #> # ℹ 83 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"function downsamples number cells `tof_tbl` randomly selecting `num_cells` cells unique combination values `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"","code":"tof_downsample_constant(tof_tibble, group_cols = NULL, num_cells)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups `num_cells` downsampled. Supports tidyselect helpers. Defaults `NULL` (grouping). num_cells integer number cells sampled group defined `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. Specifically, number rows `num_cells` multiplied number unique combinations values `group_cols`. group fewer `num_cells` number cells, cells group kept.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_constant.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a constant number of cells per group. — tof_downsample_constant","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 500 cells from the input data tof_downsample_constant( tof_tibble = sim_data, num_cells = 500L ) #> # A tibble: 500 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -1.15 -0.746 -1.73 -0.625 y #> 2 1.50 -1.49 0.641 1.54 i #> 3 -0.0911 -1.43 -1.09 0.407 n #> 4 1.28 2.04 -0.208 0.289 q #> 5 0.508 -0.314 -0.516 2.53 g #> 6 -1.28 -0.686 -1.75 -0.873 n #> 7 0.284 -1.12 -0.919 0.171 c #> 8 -0.456 1.70 -1.30 -0.718 w #> 9 0.602 -0.255 1.05 0.507 e #> 10 0.876 -0.737 1.68 2.06 g #> # ℹ 490 more rows # sample 20 cells per cluster from the input data tof_downsample_constant( tof_tibble = sim_data, group_cols = cluster_id, num_cells = 20L ) #> # A tibble: 520 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 0.545 0.0489 -0.702 1.63 x #> 2 -0.0911 -1.43 -1.09 0.407 n #> 3 -0.322 1.61 -1.02 0.315 t #> 4 0.508 -0.314 -0.516 2.53 g #> 5 -1.28 -0.686 -1.75 -0.873 n #> 6 -0.456 1.70 -1.30 -0.718 w #> 7 -0.682 -0.121 0.689 0.220 b #> 8 -0.366 0.197 -0.802 0.0356 m #> 9 0.544 0.490 -0.973 0.898 i #> 10 0.876 -0.737 1.68 2.06 g #> # ℹ 510 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"function downsamples number cells `tof_tbl` using density-dependent downsampling algorithm described Qiu et al., (2011).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"","code":"tof_downsample_density( tof_tibble, group_cols = NULL, density_cols = where(tof_is_numeric), target_num_cells, target_prop_cells, target_percentile = 0.03, outlier_percentile = 0.01, distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), density_estimation_method = c(\"mean_distance\", \"sum_distance\", \"spade\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups within downsampling performed. Supports tidyselect helpers. Defaults `NULL` (grouping). density_cols Unquoted names columns `tof_tibble` use density estimation cell. Defaults numeric columns `tof_tibble`. target_num_cells approximate constant number cells (0 1) sampled group defined `group_cols`. Slightly fewer cells may returned due density calculation performed. target_prop_cells approximate proportion cells (0 1) sampled group defined `group_cols`. Slightly fewer cells may returned due density calculation performed. Ignored `target_num_cells` specified. target_percentile local density percentile (.e. value 0 1) downsampling procedure adjust cells. short, algorithm continue remove cells input `tof_tibble` local densities remaining cells equal `target_percentile`. Lower values result cells removed. See Qiu et al., (2011) details. Defaults 0.1 (10th percentile local densities). Ignored either `target_num_cells` `target_prop_cells` specified. outlier_percentile local density percentile (.e. value 0 1) cells considered outliers (discarded). Cells local density `outlier_percentile` never selected downsampling procedure. Defaults 0.01 (cells 1st local density percentile removed). distance_function string indicating distance function use cell--cell distance calculations. Options include \"euclidean\" (default) \"cosine\" distances. density_estimation_method string indicating algorithm used calculate local density estimate cell. Options include k-nearest neighbor density estimation using mean distance cell's k-nearest neighbors (\"mean_distance\"; default), k-nearest neighbor density estimation using summed distance cell's k nearest neighbors (\"sum_distance\") counting number neighboring cells within spherical radius around cell described Qiu et al., 2011 (\"spade\"). \"spade\" often produces best results, slower knn-density estimation methods. ... Optional additional arguments pass tof_knn_density tof_spade_density.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. number rows depend chosen value `target_percentile`, fewer cells selected lower values `target_percentile`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_prop_cells = 0.5, density_estimation_method = \"spade\" ) #> # A tibble: 496 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 0.860 0.781 0.646 -0.0890 #> 2 -1.04 0.117 -0.565 0.114 #> 3 0.489 -1.27 -0.524 -0.438 #> 4 -1.01 -2.04 1.44 -0.187 #> 5 1.48 -0.0556 -0.164 -1.29 #> 6 -1.50 0.663 -0.140 -0.379 #> 7 -2.10 -0.486 0.189 -0.477 #> 8 -0.465 -1.13 0.908 -0.674 #> 9 -0.895 -0.984 -0.459 -0.00729 #> 10 0.678 2.00 0.224 -0.963 #> # ℹ 486 more rows tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_num_cells = 200L, density_estimation_method = \"spade\" ) #> # A tibble: 187 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 0.985 -2.11 -0.162 -0.342 #> 2 0.489 -1.27 -0.524 -0.438 #> 3 -0.895 -0.984 -0.459 -0.00729 #> 4 0.678 2.00 0.224 -0.963 #> 5 -0.536 -0.362 -0.705 -0.0699 #> 6 0.102 -1.93 -1.39 -1.03 #> 7 2.04 -0.528 0.177 0.406 #> 8 1.58 -0.313 0.0864 1.35 #> 9 1.62 1.23 -1.12 -0.366 #> 10 0.568 1.67 0.386 -2.56 #> # ℹ 177 more rows tof_downsample_density( tof_tibble = sim_data, density_cols = c(cd45, cd34, cd38), target_num_cells = 200L, density_estimation_method = \"mean_distance\" ) #> # A tibble: 179 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 0.262 1.34 -1.33 -0.422 #> 2 -1.50 0.663 -0.140 -0.379 #> 3 -0.654 1.56 0.730 -0.603 #> 4 -2.48 -0.611 0.623 0.906 #> 5 -0.767 -0.744 0.648 -0.214 #> 6 -1.33 0.690 0.796 -0.804 #> 7 -0.706 0.537 -0.125 -0.746 #> 8 0.795 -1.04 -1.66 -0.695 #> 9 -1.90 -0.139 -1.34 -1.92 #> 10 -1.37 -1.01 -0.892 0.426 #> # ℹ 169 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":null,"dir":"Reference","previous_headings":"","what":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"function downsamples number cells `tof_tbl` randomly selecting `prop_cells` proportion total number cells unique combination values `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"","code":"tof_downsample_prop(tof_tibble, group_cols = NULL, prop_cells)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used define groups `prop_cells` downsampled. Supports tidyselect helpers. Defaults `NULL` (grouping). prop_cells proportion cells (0 1) sampled group defined `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"`tof_tbl` number columns input `tof_tibble`, fewer rows. Specifically, number rows `prop_cells` times number rows input `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_downsample_prop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Downsample high-dimensional cytometry data by randomly selecting a proportion of the cells in each group. — tof_downsample_prop","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # sample 10% of all cells from the input data tof_downsample_prop( tof_tibble = sim_data, prop_cells = 0.1 ) #> # A tibble: 100 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 1.44 0.286 0.409 -0.554 i #> 2 -0.944 -2.00 -1.11 2.09 i #> 3 -1.30 0.893 0.643 0.217 c #> 4 0.167 -1.23 -1.91 0.895 o #> 5 -0.955 -0.00646 0.221 -0.748 r #> 6 -0.326 1.23 1.26 -0.237 w #> 7 -0.862 -0.826 -0.232 0.859 y #> 8 0.664 0.570 1.23 0.738 j #> 9 1.73 0.625 -0.695 -0.216 d #> 10 -0.613 1.07 -0.387 0.133 g #> # ℹ 90 more rows # sample 10% of all cells from each cluster in the input data tof_downsample_prop( tof_tibble = sim_data, group_cols = cluster_id, prop_cells = 0.1 ) #> # A tibble: 90 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -0.573 -2.37 1.00 0.239 a #> 2 -1.77 1.23 1.44 0.309 a #> 3 0.667 1.45 0.701 -0.234 a #> 4 -0.770 1.36 -1.04 0.806 a #> 5 0.178 0.427 1.85 -0.188 b #> 6 0.409 1.10 1.57 -0.626 b #> 7 1.02 0.316 -0.220 -0.622 b #> 8 -1.16 1.74 -0.435 0.814 c #> 9 0.478 2.03 0.692 -0.541 c #> 10 -0.418 -0.0122 0.0897 0.516 c #> # ℹ 80 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"function wrapper around tidytof's tof_*_density() function family. performs local density estimation high-dimensional cytometry data using user-specified method (3 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"","code":"tof_estimate_density( tof_tibble, distance_cols = where(tof_is_numeric), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), normalize = TRUE, ..., augment = TRUE, method = c(\"mean_distance\", \"sum_distance\", \"spade\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional arguments pass `tof_*_density()` function family member corresponding chosen `method`. augment boolean value indicating output column-bind local density estimates cell new column `tof_tibble` (TRUE; default) single-column tibble including local density estimates returned (FALSE). method string indicating local density estimation method used. Valid values include \"mean_distance\", \"sum_distance\", \"spade\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding local density estimates cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding local density estimates.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_estimate_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the local densities for all cells in a high-dimensional cytometry dataset. — tof_estimate_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # perform the density estimation tof_estimate_density(tof_tibble = sim_data, method = \"spade\") #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .spade_density #> #> 1 0.414 -1.35 1.28 -0.00732 1 #> 2 1.63 1.31 -0.726 -0.672 1 #> 3 -1.36 -0.108 -0.191 -0.884 1 #> 4 -0.975 -0.826 -0.0932 -0.586 1 #> 5 -1.03 0.359 0.379 -0.390 1 #> 6 0.649 -0.805 -0.368 0.411 1 #> 7 2.17 0.861 1.02 -0.199 1 #> 8 -1.16 1.94 -1.16 0.205 1 #> 9 1.76 -0.453 0.680 0.357 1 #> 10 -0.719 -1.65 0.947 -0.0462 1 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around # each cell tof_estimate_density( tof_tibble = sim_data, alpha_multiplier = 2, method = \"spade\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .spade_density #> #> 1 0.414 -1.35 1.28 -0.00732 0.9 #> 2 1.63 1.31 -0.726 -0.672 0.4 #> 3 -1.36 -0.108 -0.191 -0.884 1 #> 4 -0.975 -0.826 -0.0932 -0.586 1 #> 5 -1.03 0.359 0.379 -0.390 1 #> 6 0.649 -0.805 -0.368 0.411 1 #> 7 2.17 0.861 1.02 -0.199 0.1 #> 8 -1.16 1.94 -1.16 0.205 0.2 #> 9 1.76 -0.453 0.680 0.357 1 #> 10 -0.719 -1.65 0.947 -0.0462 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"feature extraction function calculates user-specified measurement central tendency (.e. median mode) cells cluster `tof_tibble` across user-specified selection CyTOF markers. calculations can done either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"","code":"tof_extract_central_tendency( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), stimulation_col = NULL, central_tendency_function = stats::median, format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"tof_tibble `tof_tibble` `tibble` row represents single cell column represents CyTOF measurement piece metadata (.e. cluster id, patient id, etc.) cell. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselection. stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). central_tendency_function function used calculate measurement central tendency cluster (used dependent variable linear model). Defaults median. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (central tendency given marker given cluster). names column containing cluster features obtained using following pattern: \"{marker_id}@{cluster_id}_ct\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_central_tendency.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the central tendencies of CyTOF markers in each cluster in a `tof_tibble`. — tof_extract_central_tendency","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_central_tendency( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 105 #> patient `cd45@a_ct` `cd38@a_ct` `cd34@a_ct` `cd19@a_ct` `cd45@b_ct` #> #> 1 kirby -0.239 -0.135 0.128 -0.489 0.0664 #> 2 mario 0.485 0.232 -0.313 -0.563 0.264 #> # ℹ 99 more variables: `cd38@b_ct` , `cd34@b_ct` , `cd19@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd34@c_ct` , `cd19@c_ct` , #> # `cd45@d_ct` , `cd38@d_ct` , `cd34@d_ct` , `cd19@d_ct` , #> # `cd45@e_ct` , `cd38@e_ct` , `cd34@e_ct` , `cd19@e_ct` , #> # `cd45@f_ct` , `cd38@f_ct` , `cd34@f_ct` , `cd19@f_ct` , #> # `cd45@g_ct` , `cd38@g_ct` , `cd34@g_ct` , `cd19@g_ct` , #> # `cd45@h_ct` , `cd38@h_ct` , `cd34@h_ct` , … # extract proportion of each cluster in each patient in long format tof_extract_central_tendency( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 208 × 4 #> patient cluster_id channel values #> #> 1 kirby a cd45 -0.239 #> 2 kirby a cd38 -0.135 #> 3 kirby a cd34 0.128 #> 4 kirby a cd19 -0.489 #> 5 kirby b cd45 0.0664 #> 6 kirby b cd38 -0.428 #> 7 kirby b cd34 0.125 #> 8 kirby b cd19 0.140 #> 9 kirby c cd45 0.0619 #> 10 kirby c cd38 -0.540 #> # ℹ 198 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"feature extraction function calculates earth-mover's distance (EMD) stimulated unstimulated (\"basal\") experimental conditions samples CyTOF experiment. calculation performed across user-specified selection CyTOF antigens can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"","code":"tof_extract_emd( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), emd_col, reference_level, format = c(\"wide\", \"long\"), num_bins = 100 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included earth-mover's distance calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. emd_col unquoted column name indicates column `tof_tibble` used group cells different distributions compared one another EMD calculation. example, want compare marker expression distributions across stimulation conditions, `emd_col` column `tof_tibble` containing information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). reference_level string indicating value `emd_col` corresponds \"reference\" value values `emd_col` compared. example, `emd_col` represents stimulation condition cell, reference_level might take value \"basal\" \"unstimulated\" want compare stimulation basal state. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row). num_bins Optional. number bins use dividing one-dimensional marker distributions discrete segments EMD calculation. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (EMD distribution given marker given cluster basal condition distribution marker given cluster stimulated condition). names column containing cluster features obtained using following pattern: \"{stimulation_id}_{marker_id}@{cluster_id}_emd\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_emd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using earth-mover's distance (EMD) — tof_extract_emd","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract emd of each cluster in each patient (using the \"basal\" stim # condition as a reference) in wide format tof_extract_emd( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, emd_col = stim, reference_level = \"basal\" ) #> # A tibble: 2 × 105 #> patient `stim_cd45@p_emd` `stim_cd38@p_emd` `stim_cd34@p_emd` #> #> 1 kirby NA NA NA #> 2 mario NA NA NA #> # ℹ 101 more variables: `stim_cd19@p_emd` , `stim_cd45@y_emd` , #> # `stim_cd38@y_emd` , `stim_cd34@y_emd` , `stim_cd19@y_emd` , #> # `stim_cd45@c_emd` , `stim_cd38@c_emd` , `stim_cd34@c_emd` , #> # `stim_cd19@c_emd` , `stim_cd45@r_emd` , `stim_cd38@r_emd` , #> # `stim_cd34@r_emd` , `stim_cd19@r_emd` , `stim_cd45@b_emd` , #> # `stim_cd38@b_emd` , `stim_cd34@b_emd` , `stim_cd19@b_emd` , #> # `stim_cd45@v_emd` , `stim_cd38@v_emd` , … # extract emd of each cluster (using the \"basal\" stim # condition as a reference) in long format tof_extract_emd( tof_tibble = sim_data, cluster_col = cluster_id, emd_col = stim, reference_level = \"basal\", format = \"long\" ) #> # A tibble: 104 × 4 #> cluster_id marker stimulation emd #> #> 1 p cd45 stim 9.59 #> 2 p cd38 stim 12.4 #> 3 p cd34 stim 10.5 #> 4 p cd19 stim 11.4 #> 5 y cd45 stim 5.34 #> 6 y cd38 stim 4.73 #> 7 y cd34 stim 4.07 #> 8 y cd19 stim 10.3 #> 9 c cd45 stim 7.95 #> 10 c cd38 stim 9.91 #> # ℹ 94 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"function wraps members `tof_extract_*` function family extract sample-level features lineage (.e. cell surface antigen) CyTOF channels assumed stable across stimulation conditions signaling CyTOF channels assumed change across stimulation conditions. Features extracted cluster within independent sample (defined `group_cols` argument).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"","code":"tof_extract_features( tof_tibble, cluster_col, group_cols = NULL, stimulation_col = NULL, lineage_cols, signaling_cols, central_tendency_function = stats::median, signaling_method = c(\"threshold\", \"emd\", \"jsd\", \"central tendency\"), basal_level = NULL, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). lineage_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) considered lineage markers feature extraction calculation. Supports tidyselect helpers. signaling_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) considered signaling markers feature extraction calculation. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster (used dependent variable linear model). Defaults median. signaling_method string indicating feature extraction method use signaling markers (identified `signaling_cols` argument). Options \"threshold\" (default), \"emd\", \"jsd\", \"central tendency\". basal_level string indicating value `stimulation_col` corresponds basal stimulation condition (.e. \"basal\" \"unstimulated\"). ... Optional additional arguments passed tof_extract_threshold, tof_extract_emd, tof_extract_jsd.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"tibble. output tibble 1 row combination grouping variables provided `group_cols` (thus, row represent considered single \"sample\" based grouping provided). one column grouping variable one column extracted feature (\"wide\" format).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"Lineage channels specified using `lineage_cols` argument, extracted features measurements central tendency (computed user-supplied `central_tendency_function`). Signaling channels specified using `signaling_cols` argument, extracted features depend user's chosen `signaling_method`. `signaling method` == \"threshold\" (default), tof_extract_threshold used calculate proportion cells cluster signaling marker expression `threshold` stimulation condition. `signaling_method` == \"emd\" `signaling_method` == \"jsd\", tof_extract_emd tof_extract_jsd used calculate earth-mover's distance (EMD) Jensen-Shannon Distance (JSD), respectively, basal condition stimulated conditions cluster sample. Finally, none options chosen, tof_extract_central_tendency used calculate measurements central tendency. addition, tof_extract_proportion used extract proportion cells cluster computed sample. calculations can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated, sample-level features from CyTOF data. — tof_extract_features","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract the following features from each cluster in each # patient/stimulation: # - proportion of each cluster # - central tendency (median) of cd45 and cd38 in each cluster # - the proportion of cells in each cluster with cd34 expression over # the default threshold (asinh(10 / 5)) tof_extract_features( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, lineage_cols = c(cd45, cd38), signaling_cols = cd34, stimulation_col = stim ) #> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.0476 0.0290 0.0373 0.0476 0.0393 0.0414 0.0331 #> 2 mario 0.0484 0.0387 0.0542 0.0309 0.0445 0.0426 0.0445 #> # ℹ 123 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` , #> # `cd45@a_ct` , `cd38@a_ct` , `cd45@b_ct` , `cd38@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd45@d_ct` , … # extract the following features from each cluster in each # patient/stimulation: # - proportion of each cluster # - central tendency (mean) of cd45 and cd38 in each cluster # - the earth mover's distance between each cluster's cd34 histogram in # the \"basal\" and \"stim\" conditions tof_extract_features( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, lineage_cols = c(cd45, cd38), signaling_cols = cd34, central_tendency_function = mean, stimulation_col = stim, signaling_method = \"emd\", basal_level = \"basal\" ) #> # A tibble: 2 × 131 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.0476 0.0290 0.0373 0.0476 0.0393 0.0414 0.0331 #> 2 mario 0.0484 0.0387 0.0542 0.0309 0.0445 0.0426 0.0445 #> # ℹ 123 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` , #> # `cd45@a_ct` , `cd38@a_ct` , `cd45@b_ct` , `cd38@b_ct` , #> # `cd45@c_ct` , `cd38@c_ct` , `cd45@d_ct` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"feature extraction function calculates Jensen-Shannon Distance (JSD) stimulated unstimulated (\"basal\") experimental conditions samples CyTOF experiment. calculation performed across user-specified selection CyTOF antigens can performed either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"","code":"tof_extract_jsd( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), jsd_col, reference_level, format = c(\"wide\", \"long\"), num_bins = 100 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. jsd_col unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). reference_level string indicating value `jsd_col` corresponds basal stimulation condition (.e. \"basal\" \"unstimulated\"). format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row). num_bins Optional. number bins use dividing one-dimensional marker distributions discrete segments JSD calculation. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (JSD distribution given marker given cluster basal condition distribution marker cluster stimulated condition). names column containing cluster features obtained using following pattern: \"{stimulation_id}_{marker_id}@{cluster_id}_jsd\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_jsd.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using the Jensen-Shannon Distance (JSD) — tof_extract_jsd","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract jsd of each cluster in each patient (using the \"basal\" stim # condition as a reference) in wide format tof_extract_jsd( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, jsd_col = stim, reference_level = \"basal\" ) #> # A tibble: 2 × 105 #> patient `stim_cd45@e_jsd` `stim_cd38@e_jsd` `stim_cd34@e_jsd` #> #> 1 mario 0.9 0.8 0.9 #> 2 kirby NA NA NA #> # ℹ 101 more variables: `stim_cd19@e_jsd` , `stim_cd45@p_jsd` , #> # `stim_cd38@p_jsd` , `stim_cd34@p_jsd` , `stim_cd19@p_jsd` , #> # `stim_cd45@s_jsd` , `stim_cd38@s_jsd` , `stim_cd34@s_jsd` , #> # `stim_cd19@s_jsd` , `stim_cd45@r_jsd` , `stim_cd38@r_jsd` , #> # `stim_cd34@r_jsd` , `stim_cd19@r_jsd` , `stim_cd45@q_jsd` , #> # `stim_cd38@q_jsd` , `stim_cd34@q_jsd` , `stim_cd19@q_jsd` , #> # `stim_cd45@c_jsd` , `stim_cd38@c_jsd` , … # extract jsd of each cluster (using the \"basal\" stim # condition as a reference) in long format tof_extract_jsd( tof_tibble = sim_data, cluster_col = cluster_id, jsd_col = stim, reference_level = \"basal\", format = \"long\" ) #> # A tibble: 104 × 4 #> cluster_id marker stimulation jsd #> #> 1 e cd45 stim 0.749 #> 2 e cd38 stim 0.785 #> 3 e cd34 stim 0.829 #> 4 e cd19 stim 0.771 #> 5 p cd45 stim 0.625 #> 6 p cd38 stim 0.823 #> 7 p cd34 stim 0.601 #> 8 p cd19 stim 0.800 #> 9 s cd45 stim 0.647 #> 10 s cd38 stim 0.858 #> # ℹ 94 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"feature extraction function allows calculate proportion cells cluster `tof_tibble` - either overall broken subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"","code":"tof_extract_proportion( tof_tibble, cluster_col, group_cols = NULL, format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). format string indicating data returned \"wide\" format (default; cluster proportion given column) \"long\" format (cluster proportion provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable well one column proportion cells cluster. names column containing cluster proportions obtained using following pattern: \"prop@{cluster_id}\". format == \"long\", tibble 1 row combination grouping variables `group_cols` cluster id (.e. level) `cluster_col`. one column grouping variable, one column cluster ids, one column (`prop`) containing cluster proportions.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_proportion.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract the proportion of cells in each cluster in a `tof_tibble`. — tof_extract_proportion","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 27 #> patient `prop@a` `prop@b` `prop@c` `prop@d` `prop@e` `prop@f` `prop@g` #> #> 1 kirby 0.036 0.028 0.042 0.022 0.05 0.044 0.046 #> 2 mario 0.046 0.046 0.028 0.04 0.028 0.06 0.02 #> # ℹ 19 more variables: `prop@h` , `prop@i` , `prop@j` , #> # `prop@k` , `prop@l` , `prop@m` , `prop@n` , #> # `prop@o` , `prop@p` , `prop@q` , `prop@r` , #> # `prop@s` , `prop@t` , `prop@u` , `prop@v` , #> # `prop@w` , `prop@x` , `prop@y` , `prop@z` # extract proportion of each cluster in each patient in long format tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 52 × 3 #> patient cluster_id prop #> #> 1 kirby a 0.036 #> 2 kirby b 0.028 #> 3 kirby c 0.042 #> 4 kirby d 0.022 #> 5 kirby e 0.05 #> 6 kirby f 0.044 #> 7 kirby g 0.046 #> 8 kirby h 0.04 #> 9 kirby i 0.026 #> 10 kirby j 0.046 #> # ℹ 42 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"feature extraction function calculates proportion cells given cluster CyTOF antigen expression user-specified threshold across user-specified selection CyTOF markers. calculations can done either overall (across cells dataset) breaking cells subgroups using `group_cols`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"","code":"tof_extract_threshold( tof_tibble, cluster_col, group_cols = NULL, marker_cols = where(tof_is_numeric), stimulation_col = NULL, threshold = asinh(10/5), format = c(\"wide\", \"long\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. group_cols Unquoted column names representing columns `tof_tibble` used break rows `tof_tibble` subgroups feature extraction calculation. Defaults NULL (.e. performing extraction without subgroups). marker_cols Unquoted column names representing columns `tof_tibble` (.e. CyTOF protein measurements) included feature extraction calculation. Defaults numeric (integer double) columns. Supports tidyselect helpers. stimulation_col Optional. unquoted column name indicates column `tof_tibble` contains information stimulation condition cell exposed data acquisition. provided, feature extraction broken subgroups stimulation condition (features stimulation condition included features wide format). threshold double integer length 1 indicating threshold used. format string indicating data returned \"wide\" format (default; cluster feature given column) \"long\" format (cluster feature provided row).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"tibble. format == \"wide\", tibble 1 row combination grouping variables provided `group_cols` one column grouping variable, one column extracted feature (proportion cells given cluster marker expression values `threshold`). names column containing cluster features obtained using following pattern: \"{marker_id}@{cluster_id}_threshold\". format == \"long\", tibble 1 row combination grouping variables `group_cols`, cluster id (.e. level) `cluster_col`, marker `marker_cols`. one column grouping variable, one column cluster ids, one column CyTOF channel names, one column (`value`) containing features.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_extract_threshold.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Extract aggregated features from CyTOF data using a binary threshold — tof_extract_threshold","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), patient = sample(c(\"kirby\", \"mario\"), size = 1000, replace = TRUE), stim = sample(c(\"basal\", \"stim\"), size = 1000, replace = TRUE) ) # extract proportion of each cluster in each patient in wide format tof_extract_threshold( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient ) #> # A tibble: 2 × 105 #> patient `cd45@a_threshold` `cd38@a_threshold` `cd34@a_threshold` #> #> 1 kirby 0.0556 0.0556 0.111 #> 2 mario 0.125 0 0 #> # ℹ 101 more variables: `cd19@a_threshold` , `cd45@b_threshold` , #> # `cd38@b_threshold` , `cd34@b_threshold` , #> # `cd19@b_threshold` , `cd45@c_threshold` , #> # `cd38@c_threshold` , `cd34@c_threshold` , #> # `cd19@c_threshold` , `cd45@d_threshold` , #> # `cd38@d_threshold` , `cd34@d_threshold` , #> # `cd19@d_threshold` , `cd45@e_threshold` , … # extract proportion of each cluster in each patient in long format tof_extract_threshold( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = patient, format = \"long\" ) #> # A tibble: 208 × 4 #> patient cluster_id channel values #> #> 1 kirby a cd45 0.0556 #> 2 kirby a cd38 0.0556 #> 3 kirby a cd34 0.111 #> 4 kirby a cd19 0.111 #> 5 kirby b cd45 0.0455 #> 6 kirby b cd38 0.0909 #> 7 kirby b cd34 0.0455 #> 8 kirby b cd19 0 #> 9 kirby c cd45 0.0526 #> 10 kirby c cd38 0.158 #> # ℹ 198 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"Find optimal hyperparameters elastic net model candidate performance metrics","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"","code":"tof_find_best(performance_metrics, model_type, optimization_metric)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"performance_metrics tibble performance metrics elastic net model (wide format) model_type string indicating type glmnet model trained. optimization_metric string indicating performance metric used select optimal model.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_best.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the optimal hyperparameters for an elastic net model from candidate performance metrics — tof_find_best","text":"tibble 3 columns: \"mixture\", \"penalty\", column containing chosen optimization metric. returned tibble 1 column, means 1 mixture/penalty combination yielded optimal result (.e. tuning procedure resulted tie).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"Calculate store predicted outcomes validation set observation model tuning","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"","code":"tof_find_cv_predictions( split_data, prepped_recipe, lambda, alpha, model_type, outcome_colnames )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"split_data `rsplit` object rsample package. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe trained recipe lambda single numeric value indicating penalty (lambda) value used make predictions alpha single numeric value indicating mixture (alpha) value used make predictions model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. outcome_colnames Quoted column names indicating columns data fit represent outcome variables (others assumed predictors).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_cv_predictions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculate and store the predicted outcomes for each validation set observation during model tuning — tof_find_cv_predictions","text":"tibble containing predicted true values outcome validation observations `split_data`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"Find earth-mover's distance two numeric vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"","code":"tof_find_emd(vec_1, vec_2, num_bins = 100)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"vec_1 numeric vector. vec_2 numeric vector. num_bins integer number bins use performing kernel density estimation two vectors. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_emd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the earth-mover's distance between two numeric vectors — tof_find_emd","text":"double (length 1) representing EMD two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"Find Jensen-Shannon Divergence (JSD) two numeric vectors","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"","code":"tof_find_jsd(vec_1, vec_2, num_bins = 100)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"vec_1 numeric vector. vec_2 numeric vector. num_bins integer number bins use binning across two vectors' combined range. Defaults 100.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_jsd.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the Jensen-Shannon Divergence (JSD) between two numeric vectors — tof_find_jsd","text":"double (length 1) representing JSD two vectors.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":null,"dir":"Reference","previous_headings":"","what":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"Find k-nearest neighbors cell high-dimensional cytometry dataset.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"","code":"tof_find_knn( .data, k = min(10, nrow(.data)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), .query, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":".data `tof_tibble` `tibble` row represents cell column represents high-dimensional cytometry measurement. k integer indicating number nearest neighbors return cell. distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. .query set cells queried .data (.e. set cells find nearest neighbors within .data). Defaults .data , .e. finding nearest neighbors cells .data. ... Optional additional arguments pass hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"list two elements: \"neighbor_ids\" \"neighbor_distances,\" n k matrices (n number cells input `.data`. [,j]-th entry \"neighbor_ids\" represents row index j-th nearest neighbor cell -th row `.data`. [,j]-th entry \"neighbor_distances\" represents distance two cells according `distance_function`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_knn.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Find the k-nearest neighbors of each cell in a high-dimensional cytometry dataset. — tof_find_knn","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # Find the 10 nearest neighbors of each cell in the dataset tof_find_knn( .data = sim_data, k = 10, distance_function = \"euclidean\" ) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 845 770 791 44 698 273 336 765 338 277 #> [2,] 873 936 304 397 677 479 667 680 230 856 #> [3,] 480 369 471 186 486 911 569 826 178 10 #> [4,] 288 481 746 93 802 252 120 849 17 291 #> [5,] 295 33 212 289 484 556 466 851 776 804 #> [6,] 999 575 125 386 36 211 695 651 20 1000 #> [7,] 510 644 30 933 275 407 990 189 483 190 #> [8,] 953 943 55 278 966 576 730 168 342 870 #> [9,] 657 493 899 38 71 404 268 401 227 307 #> [10,] 468 930 569 710 486 238 615 186 981 664 #> [11,] 456 884 872 375 752 423 218 909 738 430 #> [12,] 314 64 603 790 421 359 800 478 757 740 #> [13,] 264 889 46 394 63 131 64 314 972 88 #> [14,] 720 412 780 352 585 226 320 526 945 537 #> [15,] 475 215 83 793 521 982 217 411 900 946 #> [16,] 107 465 86 819 560 101 355 523 592 728 #> [17,] 252 481 220 675 120 672 446 19 612 302 #> [18,] 613 32 77 606 121 810 944 12 757 314 #> [19,] 446 843 836 821 189 931 269 164 988 252 #> [20,] 651 692 125 386 36 575 1000 901 6 370 #> [21,] 188 914 627 213 101 629 272 417 777 136 #> [22,] 283 906 426 256 321 992 537 945 595 501 #> [23,] 421 518 869 575 562 591 566 47 36 999 #> [24,] 492 682 787 550 912 517 463 679 522 572 #> [25,] 798 942 932 157 405 640 638 348 561 86 #> [26,] 597 704 222 191 940 40 895 90 413 735 #> [27,] 960 126 592 417 213 913 346 523 152 766 #> [28,] 588 646 169 114 512 669 750 789 768 932 #> [29,] 673 790 767 901 881 921 406 307 501 963 #> [30,] 644 933 969 7 173 483 189 510 407 275 #> [31,] 812 87 648 917 187 165 554 958 571 858 #> [32,] 18 77 613 314 944 448 606 64 12 871 #> [33,] 295 5 289 776 212 556 926 484 180 513 #> [34,] 639 109 763 498 891 702 712 827 119 519 #> [35,] 815 690 182 92 831 607 382 200 803 715 #> [36,] 386 695 651 6 165 439 20 575 999 858 #> [37,] 711 323 113 973 351 726 450 665 353 393 #> [38,] 71 53 307 657 352 9 310 827 493 956 #> [39,] 659 400 613 810 32 643 800 64 606 121 #> [40,] 357 90 702 498 735 863 222 624 75 763 #> [41,] 954 976 623 829 468 270 155 10 84 348 #> [42,] 556 289 263 225 851 337 762 466 212 5 #> [43,] 621 736 311 145 919 395 122 713 924 484 #> [44,] 601 368 777 336 654 592 126 152 188 960 #> [45,] 898 864 535 73 134 469 964 440 124 541 #> [46,] 301 264 972 13 889 413 63 256 769 998 #> [47,] 349 591 562 593 518 23 695 723 485 165 #> [48,] 599 579 146 239 936 690 461 479 803 934 #> [49,] 734 616 161 780 196 507 1000 335 370 760 #> [50,] 720 531 429 167 881 878 596 767 921 501 #> [51,] 497 96 751 227 841 310 657 478 89 659 #> [52,] 647 296 528 788 224 304 241 977 505 392 #> [53,] 956 583 903 245 38 876 320 880 424 454 #> [54,] 833 739 979 342 943 604 549 995 384 776 #> [55,] 730 953 966 8 278 342 943 99 725 384 #> [56,] 539 879 396 724 274 885 962 974 850 271 #> [57,] 680 799 241 667 839 304 2 977 397 757 #> [58,] 315 223 69 742 94 946 303 473 521 215 #> [59,] 253 701 433 914 764 860 627 485 447 473 #> [60,] 718 817 113 323 74 162 668 108 489 747 #> [61,] 694 370 939 857 616 715 142 333 692 719 #> [62,] 509 228 901 625 699 234 461 20 727 359 #> [63,] 264 394 192 889 800 64 191 13 413 46 #> [64,] 800 314 12 131 192 264 63 394 643 32 #> [65,] 701 398 957 930 829 473 238 59 886 84 #> [66,] 487 452 112 347 238 473 118 833 826 534 #> [67,] 500 807 442 408 770 608 277 136 584 929 #> [68,] 373 462 594 193 71 542 657 741 975 341 #> [69,] 315 416 58 398 764 94 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869 981 96 307 754 53 #> [479,] 936 579 856 230 397 146 2 959 48 599 #> [480,] 3 826 569 471 186 455 369 957 486 178 #> [481,] 252 120 17 4 849 214 662 93 288 220 #> [482,] 962 809 457 294 879 587 850 974 571 904 #> [483,] 30 379 203 114 7 434 557 644 514 933 #> [484,] 145 5 289 466 870 538 713 43 311 943 #> [485,] 411 211 935 614 860 813 349 897 605 187 #> [486,] 186 615 678 710 3 10 911 620 471 238 #> [487,] 452 347 66 123 739 534 833 949 312 761 #> [488,] 91 555 249 558 986 287 141 978 589 996 #> [489,] 748 793 162 747 706 809 668 982 113 630 #> [490,] 596 589 963 996 881 978 167 767 389 958 #> [491,] 195 972 170 687 525 516 301 820 461 228 #> [492,] 24 679 463 682 269 550 95 787 522 636 #> [493,] 207 758 401 355 9 869 193 259 307 533 #> [494,] 223 427 350 215 83 620 486 58 521 112 #> [495,] 374 818 279 830 837 609 333 726 216 319 #> [496,] 474 175 637 786 660 866 830 918 991 235 #> [497,] 192 96 51 454 841 971 89 201 310 754 #> [498,] 763 712 702 34 432 40 75 823 624 89 #> [499,] 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94 #> [540,] 895 617 248 147 98 940 26 330 769 704 #> [541,] 683 440 964 573 688 564 229 602 578 266 #> [542,] 341 158 71 796 887 68 373 326 519 657 #> [543,] 513 180 549 367 384 778 776 814 33 295 #> [544,] 983 143 271 43 395 122 919 437 736 311 #> [545,] 82 197 923 792 636 661 834 910 133 705 #> [546,] 507 161 366 346 356 635 533 405 259 207 #> [547,] 846 675 832 357 612 302 103 862 431 674 #> [548,] 243 218 745 683 297 602 255 246 440 414 #> [549,] 293 384 778 943 953 54 55 814 532 776 #> [550,] 522 24 572 492 707 233 787 682 463 955 #> [551,] 598 221 630 436 748 162 465 706 699 916 #> [552,] 166 574 431 365 451 802 547 746 675 280 #> [553,] 737 471 369 372 911 3 178 795 641 938 #> [554,] 571 974 917 87 812 389 31 294 958 587 #> [555,] 986 488 517 91 444 522 890 787 327 905 #> [556,] 42 289 5 851 563 466 212 263 484 295 #> [557,] 254 483 203 825 765 933 114 915 190 30 #> [558,] 249 890 287 362 488 907 948 996 389 274 #> [559,] 302 612 316 848 278 966 282 674 671 805 #> [560,] 107 84 16 436 528 138 728 954 819 566 #> [561,] 640 819 348 942 157 758 798 840 86 193 #> [562,] 518 593 723 47 23 421 591 349 632 710 #> [563,] 115 289 926 556 180 484 263 538 145 327 #> [564,] 458 686 266 99 541 573 115 134 538 578 #> [565,] 126 801 417 629 766 319 27 985 194 572 #> [566,] 401 575 959 869 208 23 436 421 138 355 #> [567,] 409 209 663 403 722 496 377 175 987 984 #> [568,] 263 159 679 852 95 538 676 99 289 492 #> [569,] 480 118 455 10 468 664 3 626 186 981 #> [570,] 981 102 186 678 935 754 740 880 47 349 #> [571,] 974 554 389 902 812 917 87 274 111 294 #> [572,] 202 550 766 782 955 319 463 734 838 194 #> [573,] 688 229 541 578 683 276 141 99 117 564 #> [574,] 431 675 93 586 832 552 365 506 547 802 #> [575,] 6 125 566 23 36 20 386 869 999 651 #> [576,] 635 245 756 583 168 8 424 604 278 840 #> [577,] 439 202 907 160 386 766 648 417 782 1000 #> [578,] 117 266 573 778 99 541 506 743 159 564 #> [579,] 48 936 803 690 146 479 149 599 677 239 #> [580,] 339 133 714 923 964 529 874 267 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368 338 336 845 495 374 654 609 126 #> [602,] 414 243 229 246 440 683 688 745 548 297 #> [603,] 478 869 227 421 880 307 790 12 23 591 #> [604,] 342 756 109 424 576 534 54 245 808 979 #> [605,] 860 331 813 411 391 764 897 914 217 946 #> [606,] 944 613 18 32 799 39 810 744 121 839 #> [607,] 299 611 182 945 520 965 715 515 815 920 #> [608,] 438 442 584 500 724 396 428 539 56 807 #> [609,] 279 722 338 830 495 855 601 261 360 818 #> [610,] 807 151 500 353 608 258 438 67 489 706 #> [611,] 299 861 607 945 920 893 927 520 877 283 #> [612,] 832 846 675 559 302 316 672 547 449 862 #> [613,] 18 32 810 757 606 77 944 12 39 121 #> [614,] 211 886 485 591 47 6 695 999 23 349 #> [615,] 678 238 486 186 10 620 473 930 570 742 #> [616,] 760 49 818 801 333 142 319 772 734 370 #> [617,] 940 597 248 237 924 26 219 73 435 413 #> [618,] 280 669 181 169 967 768 588 808 887 796 #> [619,] 633 545 923 792 177 786 286 339 82 830 #> [620,] 83 521 678 742 615 411 349 486 102 935 #> [621,] 43 736 78 924 122 781 995 980 983 686 #> [622,] 774 915 791 765 336 717 638 913 652 993 #> [623,] 179 41 842 72 976 666 829 348 954 819 #> [624,] 75 712 702 823 735 863 641 498 979 995 #> [625,] 727 509 665 422 901 62 747 816 127 759 #> [626,] 455 172 158 441 569 666 639 34 664 472 #> [627,] 914 445 629 21 764 70 447 331 213 813 #> [628,] 825 984 92 520 205 100 815 990 477 557 #> [629,] 136 627 428 21 941 331 914 764 813 565 #> [630,] 699 436 162 748 999 916 23 575 518 6 #> [631,] 948 362 907 996 692 91 249 858 385 978 #> [632,] 593 853 982 518 376 562 475 234 854 111 #> [633,] 619 792 281 786 545 496 923 830 529 637 #> [634,] 918 529 685 371 267 637 220 300 229 573 #> [635,] 576 245 840 259 168 756 546 210 260 356 #> [636,] 371 82 269 550 685 492 522 772 24 197 #> [637,] 918 529 660 634 685 786 496 300 414 402 #> [638,] 942 798 193 25 640 594 405 932 975 68 #> [639,] 34 109 472 298 702 604 534 808 119 712 #> [640,] 561 819 348 798 942 25 157 638 193 405 #> [641,] 938 372 924 624 995 75 735 736 712 437 #> 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499 220 862 #> [663,] 175 377 474 235 209 877 950 857 257 939 #> [664,] 981 827 227 468 96 210 840 758 876 754 #> [665,] 747 668 625 692 727 902 904 216 450 948 #> [666,] 441 158 976 172 626 542 348 341 796 640 #> [667,] 977 304 230 138 590 936 680 397 757 677 #> [668,] 747 162 665 113 108 625 973 699 79 6 #> [669,] 822 280 618 789 158 172 169 796 588 542 #> [670,] 662 893 783 927 861 849 697 291 104 288 #> [671,] 691 970 968 531 805 980 978 140 282 955 #> [672,] 612 688 449 316 506 832 586 117 499 17 #> [673,] 29 307 921 790 131 767 406 901 934 881 #> [674,] 863 735 357 971 90 876 53 454 559 956 #> [675,] 612 431 547 832 302 846 574 862 559 17 #> [676,] 163 834 337 568 852 762 263 225 705 133 #> [677,] 936 803 667 839 2 579 149 590 831 304 #> [678,] 615 620 486 570 935 238 742 186 102 521 #> [679,] 95 492 568 953 99 463 24 538 730 269 #> [680,] 57 667 799 304 977 241 397 230 2 839 #> [681,] 718 146 625 257 817 509 108 668 62 727 #> [682,] 787 24 912 492 517 550 194 420 463 989 #> 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631 457 902 389 948 587 #> [704,] 222 26 597 237 334 940 265 449 40 888 #> [705,] 834 163 929 408 676 337 696 148 545 912 #> [706,] 272 748 151 221 391 174 21 217 188 353 #> [707,] 978 955 996 140 719 522 196 691 282 550 #> [708,] 70 416 148 941 445 627 929 136 684 761 #> [709,] 137 128 143 958 78 290 917 781 983 87 #> [710,] 486 186 10 723 468 47 911 591 841 562 #> [711,] 973 113 37 108 450 323 985 374 353 665 #> [712,] 75 823 624 498 702 432 34 763 754 876 #> [713,] 919 395 311 303 484 43 739 742 145 833 #> [714,] 580 874 339 133 964 905 986 755 555 923 #> [715,] 965 61 939 200 250 370 857 649 607 232 #> [716,] 476 875 189 855 644 360 173 772 969 760 #> [717,] 993 250 915 381 200 392 652 224 965 649 #> [718,] 817 60 681 108 323 973 113 668 747 625 #> [719,] 707 196 978 333 142 694 631 140 319 996 #> [720,] 320 50 531 14 429 185 196 140 671 881 #> [721,] 598 551 72 797 560 465 829 107 418 84 #> [722,] 609 279 261 403 360 830 409 338 855 689 #> [723,] 591 562 421 23 47 518 710 603 954 84 #> [724,] 885 396 428 56 340 539 989 274 362 890 #> [725,] 532 364 446 55 931 794 836 778 821 953 #> [726,] 216 837 362 176 333 495 450 91 948 885 #> [727,] 625 901 694 692 509 963 20 127 759 665 #> [728,] 251 959 79 436 381 566 107 208 655 699 #> [729,] 324 204 867 148 105 317 952 761 949 684 #> [730,] 966 55 99 953 278 8 955 233 679 836 #> [731,] 330 199 499 992 262 888 334 738 218 769 #> [732,] 309 85 824 344 931 814 852 661 164 988 #> [733,] 527 788 647 382 815 52 477 677 831 35 #> [734,] 335 49 161 196 955 320 185 507 780 782 #> [735,] 863 75 624 674 995 971 40 823 265 191 #> [736,] 924 621 437 43 983 78 122 781 372 995 #> [737,] 553 795 369 3 471 826 480 156 911 178 #> [738,] 218 892 98 548 334 456 731 992 11 330 #> [739,] 833 54 756 943 604 123 536 713 534 293 #> [740,] 102 828 841 911 570 454 971 754 478 186 #> [741,] 190 462 407 275 373 378 887 990 594 933 #> [742,] 315 395 521 620 678 935 860 473 713 303 #> [743,] 117 882 506 267 380 578 844 988 586 93 #> [744,] 318 606 944 121 18 32 77 613 39 871 #> [745,] 688 229 243 683 305 548 499 602 414 968 #> [746,] 431 802 502 181 821 4 291 510 378 288 #> [747,] 665 668 809 162 625 904 113 902 489 727 #> [748,] 706 999 162 489 630 793 6 221 211 272 #> [749,] 308 482 962 511 457 358 294 872 703 491 #> [750,] 932 25 798 765 638 774 28 254 942 842 #> [751,] 270 155 51 268 947 954 810 227 977 723 #> [752,] 589 490 656 909 958 884 137 423 11 709 #> [753,] 418 174 900 65 215 151 398 69 154 797 #> [754,] 876 454 823 971 956 828 827 96 981 75 #> [755,] 905 874 144 909 246 440 129 243 683 276 #> [756,] 576 604 245 635 739 534 424 840 210 342 #> [757,] 138 613 421 12 667 590 723 810 359 230 #> [758,] 210 840 259 880 493 207 869 433 260 227 #> [759,] 399 816 257 422 509 415 727 950 127 625 #> [760,] 616 649 772 818 383 49 142 173 801 969 #> [761,] 123 80 416 536 524 204 347 452 447 867 #> [762,] 804 852 524 676 212 42 453 289 263 568 #> [763,] 498 34 89 519 357 119 712 90 702 432 #> [764,] 914 860 331 605 627 813 59 416 897 941 #> [765,] 791 774 622 700 273 254 459 557 932 750 #> [766,] 161 417 202 572 577 160 213 801 346 523 #> [767,] 881 596 963 29 901 878 501 406 167 50 #> [768,] 169 588 808 821 618 356 794 236 130 928 #> [769,] 330 256 888 63 46 704 413 906 940 237 #> [770,] 277 136 708 67 1 777 70 807 44 408 #> [771,] 835 66 33 295 776 487 867 5 212 452 #> [772,] 616 760 818 838 716 319 142 467 476 734 #> [773,] 22 283 611 321 992 893 861 426 663 906 #> [774,] 622 791 273 765 932 913 25 942 811 798 #> [775,] 894 839 48 527 677 599 936 579 2 88 #> [776,] 833 54 295 33 549 145 293 5 484 739 #> [777,] 188 101 21 654 44 592 152 27 221 126 #> [778,] 549 95 99 814 578 725 159 384 55 532 #> [779,] 967 887 103 848 794 302 275 891 642 843 #> [780,] 412 526 335 142 49 734 14 196 320 649 #> [781,] 78 122 137 621 128 958 709 143 736 290 #> [782,] 185 955 996 439 202 278 233 572 707 320 #> [783,] 262 862 670 908 662 291 802 431 574 697 #> [784,] 255 129 171 297 305 402 430 992 548 785 #> [785,] 784 868 255 297 171 129 872 153 399 516 #> [786,] 660 918 132 371 685 637 792 496 830 923 #> [787,] 682 912 24 194 517 420 550 890 233 492 #> [788,] 647 505 343 831 677 52 528 803 224 68 #> [789,] 172 669 410 158 822 441 472 169 808 796 #> [790,] 29 673 406 359 603 307 421 901 36 12 #> [791,] 774 765 622 273 913 336 698 960 173 915 #> [792,] 371 786 476 636 82 545 875 139 923 918 #> [793,] 982 111 748 217 489 632 15 809 879 475 #> [794,] 168 848 302 843 779 356 725 836 891 342 #> [795,] 156 826 737 455 859 480 3 432 553 471 #> [796,] 109 260 967 441 669 891 618 808 405 542 #> [797,] 154 84 886 723 930 954 562 560 591 900 #> [798,] 25 942 157 932 405 640 348 638 561 86 #> [799,] 680 839 57 606 184 613 944 757 667 810 #> [800,] 64 192 643 314 63 12 400 191 387 497 #> [801,] 333 319 616 985 202 417 1000 565 766 152 #> [802,] 746 4 288 431 93 574 291 552 783 502 #> [803,] 831 690 149 579 847 936 505 677 231 899 #> [804,] 762 852 524 814 212 289 453 5 293 295 #> [805,] 322 265 980 671 316 458 237 970 656 559 #> [806,] 658 926 544 223 244 851 437 563 219 983 #> [807,] 136 151 353 500 941 629 428 391 272 67 #> [808,] 472 298 768 604 109 618 796 410 639 169 #> [809,] 457 904 902 587 747 482 111 974 665 962 #> [810,] 121 613 18 757 659 39 751 270 606 12 #> [811,] 405 642 356 913 546 932 942 508 975 366 #> [812,] 31 389 858 554 87 648 571 958 439 974 #> [813,] 331 860 897 605 914 485 160 764 391 417 #> [814,] 549 778 532 364 931 804 852 95 293 309 #> [815,] 182 831 520 35 690 505 607 477 92 462 #> [816,] 950 759 422 399 257 127 625 727 509 385 #> [817,] 718 358 60 681 747 625 464 820 668 816 #> [818,] 616 142 772 760 319 333 866 991 719 61 #> [819,] 640 829 561 348 86 16 798 25 107 942 #> [820,] 170 228 358 62 195 817 461 234 509 587 #> [821,] 768 19 446 794 510 843 725 746 181 130 #> [822,] 669 280 419 618 172 789 158 181 169 542 #> [823,] 75 754 712 876 624 454 827 96 498 735 #> [824,] 309 198 85 732 743 578 266 159 852 117 #> [825,] 628 203 557 392 689 915 92 254 652 717 #> [826,] 156 795 480 455 859 3 569 471 737 178 #> [827,] 876 96 424 754 956 664 891 454 981 823 #> [828,] 971 454 903 754 740 387 876 192 191 841 #> [829,] 819 84 640 348 561 86 954 107 465 16 #> [830,] 279 495 132 609 837 866 991 786 286 722 #> [831,] 505 803 847 182 690 231 404 815 585 149 #> [832,] 846 612 316 675 547 862 449 672 586 559 #> [833,] 54 739 776 293 943 604 342 549 979 145 #> [834,] 163 705 676 929 337 408 682 912 762 24 #> [835,] 771 66 867 487 452 112 952 347 212 427 #> [836,] 467 269 463 168 843 19 953 838 794 236 #> [837,] 286 132 176 726 495 830 319 216 786 818 #> [838,] 467 463 772 572 161 546 836 766 503 734 #> [839,] 590 667 799 677 936 448 680 613 757 606 #> [840,] 210 259 758 635 260 207 245 433 576 880 #> [841,] 911 740 497 96 454 754 828 471 51 823 #> [842,] 348 819 638 640 623 829 666 72 798 750 #> [843,] 510 836 794 19 302 189 168 335 848 779 #> [844,] 849 882 743 199 380 300 104 93 662 267 #> [845,] 338 601 44 609 277 279 1 126 336 698 #> [846,] 832 612 547 862 316 675 559 302 674 888 #> [847,] 831 585 149 231 803 404 505 232 934 352 #> [848,] 335 302 794 559 583 278 779 168 320 533 #> [849,] 481 844 93 662 104 214 4 882 252 17 #> [850,] 917 271 143 879 974 87 554 571 709 294 #> [851,] 354 466 556 212 303 5 42 484 289 295 #> [852,] 159 568 263 804 198 95 762 289 676 814 #> [853,] 632 234 376 359 170 184 518 982 593 77 #> [854,] 234 994 999 111 587 904 359 695 518 790 #> [855,] 360 875 716 173 476 261 203 689 434 644 #> [856,] 916 479 397 230 699 436 304 325 2 251 #> [857,] 939 209 61 991 877 415 257 950 866 694 #> [858,] 812 439 907 648 389 948 651 963 996 249 #> [859,] 432 826 119 712 702 795 624 639 156 75 #> [860,] 897 331 813 605 764 160 914 306 485 935 #> [861,] 611 299 520 927 607 670 893 697 205 945 #> [862,] 846 832 888 262 547 675 922 612 431 662 #> [863,] 735 674 75 624 995 971 40 357 876 979 #> [864,] 73 134 266 45 564 898 541 115 180 964 #> [865,] 586 506 449 365 222 367 704 266 574 578 #> [866,] 991 818 142 857 939 474 877 139 476 719 #> [867,] 761 212 123 452 729 347 148 524 204 487 #> [868,] 582 153 785 144 235 308 950 175 129 755 #> [869,] 603 421 23 401 566 493 880 575 478 36 #> [870,] 536 943 420 538 194 8 484 311 953 466 #> [871,] 183 116 77 376 32 128 318 687 242 18 #> [872,] 11 171 375 456 218 255 884 784 752 972 #> [873,] 2 325 397 304 479 856 936 251 230 579 #> [874,] 714 755 660 637 580 144 964 600 246 918 #> [875,] 360 716 855 476 189 644 173 434 197 30 #> [876,] 754 454 956 827 424 823 971 53 96 583 #> [877,] 920 415 939 857 537 866 991 945 474 429 #> [878,] 881 415 963 767 429 50 596 501 537 490 #> [879,] 974 274 56 571 902 962 850 111 554 809 #> [880,] 210 758 478 165 603 53 869 956 840 227 #> [881,] 767 963 596 878 50 901 29 406 167 490 #> [882,] 743 844 267 380 849 117 988 85 506 93 #> [883,] 451 443 543 513 364 166 309 814 778 367 #> [884,] 11 909 752 375 653 686 548 458 456 322 #> [885,] 724 396 362 340 989 274 558 890 56 428 #> [886,] 614 84 930 591 211 485 47 107 465 433 #> [887,] 967 103 779 378 373 504 618 741 542 71 #> [888,] 262 330 862 922 769 846 832 704 731 499 #> [889,] 264 13 394 63 88 46 131 256 64 906 #> [890,] 558 444 287 787 249 488 306 555 420 274 #> [891,] 109 424 827 260 34 103 848 794 779 604 #> [892,] 98 218 738 997 548 456 992 872 731 11 #> [893,] 662 927 920 945 697 499 862 670 283 611 #> [894,] 88 106 693 839 677 343 775 590 39 97 #> [895,] 540 26 940 704 769 248 597 330 617 222 #> [896,] 222 365 40 432 26 367 451 702 119 704 #> [897,] 860 331 306 813 160 292 187 605 935 485 #> [898,] 45 964 864 535 440 134 541 73 380 683 #> [899,] 404 231 401 959 208 505 9 355 149 493 #> [900,] 632 982 215 593 154 15 793 797 361 886 #> [901,] 767 29 881 963 509 727 20 406 62 790 #> [902,] 974 571 904 948 809 274 554 362 389 457 #> [903,] 828 971 53 165 531 454 648 406 880 970 #> [904,] 587 902 809 571 703 294 854 974 554 111 #> [905,] 986 909 276 555 755 650 488 653 600 589 #> [906,] 283 22 426 256 394 537 501 430 595 889 #> [907,] 202 948 858 577 692 439 362 631 996 249 #> [908,] 922 363 321 925 862 888 783 262 313 97 #> [909,] 276 884 905 686 683 243 752 589 141 458 #> [910,] 164 197 581 344 19 875 661 189 434 82 #> [911,] 471 186 841 369 740 102 3 486 710 570 #> [912,] 517 682 787 989 24 340 225 555 890 444 #> [913,] 960 811 366 346 27 546 273 774 592 507 #> [914,] 764 627 21 860 813 605 331 59 629 897 #> [915,] 622 717 392 993 173 652 969 649 774 203 #> [916,] 436 856 630 230 699 397 757 138 304 23 #> [917,] 87 554 143 571 31 974 290 812 958 850 #> [918,] 634 529 685 637 371 786 267 660 300 220 #> [919,] 713 395 311 43 145 484 742 437 122 303 #> [920,] 945 877 537 893 226 429 412 305 283 526 #> [921,] 131 673 29 50 307 394 352 167 501 767 #> [922,] 363 908 862 888 925 547 846 262 90 832 #> [923,] 133 339 82 545 786 636 371 177 580 286 #> [924,] 736 437 641 621 938 995 43 372 145 78 #> [925,] 363 504 103 595 390 97 310 922 352 373 #> [926,] 563 437 145 33 484 180 295 5 289 556 #> [927,] 697 291 893 515 662 945 299 226 120 920 #> [928,] 236 508 332 293 949 532 808 453 524 768 #> [929,] 408 148 834 277 705 324 708 163 503 453 #> [930,] 10 468 84 886 591 154 615 238 678 614 #> [931,] 344 446 532 725 95 19 836 661 814 778 #> [932,] 25 798 942 157 811 774 405 273 638 750 #> [933,] 407 190 275 594 30 7 642 969 975 990 #> [934,] 232 239 146 847 231 149 208 673 537 29 #> [935,] 253 485 160 187 860 897 678 570 742 395 #> [936,] 677 579 803 149 479 667 2 48 839 590 #> [937,] 177 600 328 286 393 340 584 132 176 650 #> [938,] 641 372 924 437 995 624 736 75 145 919 #> [939,] 857 61 209 991 877 415 257 715 866 694 #> [940,] 597 248 237 617 413 26 704 265 456 191 #> [941,] 428 629 764 331 94 914 136 306 627 605 #> [942,] 798 25 405 157 932 640 638 561 811 366 #> [943,] 8 953 870 54 55 739 342 979 549 536 #> [944,] 606 32 613 18 39 799 448 744 77 184 #> [945,] 920 537 226 283 14 595 426 893 927 515 #> [946,] 94 605 521 411 217 215 742 391 292 860 #> [947,] 285 268 751 326 270 51 657 977 343 106 #> [948,] 362 631 907 902 858 692 389 558 249 216 #> [949,] 332 312 508 928 347 204 472 157 487 808 #> [950,] 816 235 257 759 857 399 377 175 939 127 #> [951,] 308 516 464 195 817 358 820 749 377 816 #> [952,] 949 729 317 204 347 312 332 410 487 867 #> [953,] 8 55 730 943 966 679 278 463 99 836 #> [954,] 84 268 468 591 723 10 41 561 227 930 #> [955,] 782 278 707 185 966 734 282 320 572 140 #> [956,] 53 424 245 583 876 754 827 210 454 260 #> [957,] 112 238 118 480 569 615 10 65 468 473 #> [958,] 812 290 137 284 389 31 128 554 917 87 #> [959,] 208 401 231 566 899 381 232 355 404 728 #> [960,] 27 592 913 126 346 152 366 161 546 766 #> [961,] 997 321 88 147 98 330 773 256 908 769 #> [962,] 457 482 879 809 56 974 902 571 703 274 #> [963,] 881 767 596 878 490 901 858 631 406 996 #> [964,] 541 440 683 134 714 380 602 898 580 573 #> [965,] 715 232 231 250 182 649 585 381 780 717 #> [966,] 730 278 55 955 8 953 282 559 99 233 #> [967,] 887 779 594 642 975 618 190 933 796 407 #> [968,] 305 429 671 691 140 141 978 50 656 745 #> [969,] 173 649 30 642 644 933 975 760 716 526 #> [970,] 671 531 980 167 996 782 185 490 805 903 #> [971,] 828 454 754 903 876 387 192 674 735 191 #> [972,] 46 301 13 264 687 889 423 171 256 998 #> [973,] 108 711 113 374 323 37 665 351 718 668 #> [974,] 571 554 274 902 389 87 917 879 812 111 #> [975,] 642 594 193 967 533 405 207 933 969 507 #> [976,] 666 41 626 441 158 268 172 947 751 569 #> [977,] 667 304 138 241 680 230 528 397 268 270 #> [978,] 996 707 691 141 490 719 671 589 488 249 #> [979,] 995 342 583 54 424 75 245 943 876 863 #> [980,] 805 970 671 995 265 322 278 979 966 583 #> [981,] 664 570 227 754 96 827 478 876 210 468 #> [982,] 793 632 111 475 748 593 853 489 15 854 #> [983,] 736 544 122 78 437 621 709 781 43 924 #> [984,] 100 92 403 409 628 205 689 299 520 261 #> [985,] 450 801 216 152 417 333 692 188 665 565 #> [986,] 555 905 488 91 444 276 327 287 517 890 #> [987,] 722 409 567 261 403 609 279 689 338 360 #> [988,] 252 446 19 267 17 85 117 931 743 481 #> [989,] 340 912 885 517 724 428 682 787 396 890 #> [990,] 275 407 515 7 510 933 190 741 502 644 #> [991,] 866 857 939 818 142 209 877 474 476 61 #> [992,] 499 218 256 731 784 22 906 430 738 330 #> [993,] 717 652 381 250 200 392 224 915 355 182 #> [994,] 284 290 128 406 854 958 234 587 294 790 #> [995,] 979 980 735 863 641 924 75 621 624 265 #> [996,] 978 249 707 782 185 490 907 970 631 691 #> [997,] 98 256 892 972 46 992 769 218 330 738 #> [998,] 387 167 394 423 131 430 921 656 284 413 #> [999,] 6 695 854 211 36 111 575 748 23 386 #> [1000,] 125 386 651 523 49 20 801 417 692 577 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 1.21046951 1.2814328 1.3088671 1.3903896 1.4093499 1.5123118 1.5177298 #> [2,] 0.55047599 0.7540306 0.7634534 0.7722017 0.8163211 0.8700257 0.9226647 #> [3,] 0.35784179 0.4869218 0.5242352 0.5781613 0.6461551 0.6900840 0.7158303 #> [4,] 0.53401809 0.5887559 0.7787472 0.7808913 0.7860686 0.8376048 0.9398310 #> [5,] 0.34914673 0.4117812 0.4147237 0.5550794 0.5681887 0.5832709 0.6670932 #> [6,] 0.34558386 0.3718587 0.3866957 0.4058981 0.4168423 0.4366775 0.4563907 #> [7,] 0.46565599 0.4989607 0.5344266 0.5990700 0.6116902 0.6150030 0.6260920 #> [8,] 0.24524756 0.3766672 0.4190034 0.4619930 0.4910142 0.4985297 0.5080894 #> [9,] 0.26592251 0.5272305 0.5303869 0.5855830 0.5917343 0.5936955 0.6659433 #> [10,] 0.20624216 0.4890596 0.6274324 0.6552598 0.6766045 0.6837926 0.6886020 #> [11,] 0.57189888 0.5912276 0.6606174 0.6676028 0.7789707 0.9019471 0.9936128 #> [12,] 0.59154586 0.5943194 0.6043368 0.6477592 0.6792485 0.6849131 0.6892158 #> [13,] 0.19424836 0.2125101 0.6348915 0.6565775 0.7102408 0.7548653 0.7556356 #> [14,] 0.50366019 0.5213843 0.5306987 0.5441858 0.5766456 0.6146114 0.6332020 #> [15,] 0.66621597 0.7634713 0.9313510 0.9380089 0.9422618 0.9714016 1.0029979 #> [16,] 0.42796979 0.5702875 0.6811840 0.7296882 0.7650716 0.8027896 0.8264574 #> [17,] 0.43980417 0.5712633 0.6833969 0.7022311 0.7234893 0.7255583 0.7870018 #> [18,] 0.32998788 0.3715182 0.4313687 0.7057181 0.7156661 0.7459431 0.7606691 #> [19,] 0.53175341 0.5804025 0.6025482 0.6220356 0.6549309 0.6895867 0.7023902 #> [20,] 0.25537990 0.4888065 0.5012188 0.5132578 0.5201907 0.5244126 0.5297184 #> [21,] 0.44869504 0.5176139 0.5201538 0.5333403 0.5528347 0.5637833 0.6056317 #> [22,] 0.24270294 0.4181342 0.4994289 0.7301984 0.7687906 0.8701522 0.8722797 #> [23,] 0.27916247 0.4182686 0.4627760 0.5187923 0.5625505 0.5745288 0.5912516 #> [24,] 0.27801534 0.2808169 0.4150215 0.4842020 0.5161527 0.5207735 0.5940268 #> [25,] 0.07900148 0.2370228 0.3602234 0.3902051 0.5426235 0.6006307 0.6474137 #> [26,] 0.61071508 0.6573590 0.7340550 0.7405824 0.7735499 0.7830390 0.8050931 #> [27,] 0.34159301 0.3748690 0.3932452 0.5942108 0.6043088 0.6143347 0.6237749 #> [28,] 0.62085681 0.6916244 0.7386633 0.9016987 1.0270393 1.0317743 1.1095360 #> [29,] 0.22512765 0.3803994 0.3970135 0.4499811 0.4840932 0.4850417 0.4858446 #> [30,] 0.39126729 0.4931540 0.5295156 0.5344266 0.5987257 0.6567391 0.6665549 #> [31,] 0.30912486 0.3716844 0.4069067 0.4383080 0.4538033 0.4771445 0.4805272 #> [32,] 0.37151820 0.4253336 0.4780872 0.6146927 0.6674207 0.7188448 0.7326479 #> [33,] 0.09113993 0.4117812 0.6715584 0.7239671 0.7447867 0.7682577 0.8076352 #> [34,] 0.37734773 0.4000469 0.4212806 0.5560209 0.5659865 0.5809123 0.6173314 #> [35,] 0.69441613 0.7249473 0.7624164 0.8119654 0.9839406 1.0092612 1.0173604 #> [36,] 0.24808700 0.2723521 0.4092183 0.4168423 0.4383710 0.5023097 0.5201907 #> [37,] 0.52469938 0.6230936 0.7173692 0.7371090 0.7860783 0.9153443 0.9223275 #> [38,] 0.45160491 0.5060944 0.5127088 0.5314992 0.5812371 0.5855830 0.6128921 #> [39,] 0.33992786 0.6105890 0.7697521 0.8120088 0.8502975 0.8543398 0.8718331 #> [40,] 0.45826699 0.4952567 0.5293771 0.5759559 0.5790945 0.5833047 0.5974994 #> [41,] 0.78237982 0.8547462 0.9179658 0.9656688 1.0256551 1.0375951 1.0403373 #> [42,] 0.34658311 0.5914036 0.6843618 0.7177654 0.8091027 0.8208785 0.8380186 #> [43,] 0.36424210 0.4869968 0.5056829 0.5323747 0.5782066 0.5927128 0.6488748 #> [44,] 0.45940455 0.6715617 0.6978360 0.7154144 0.7471025 0.7953747 0.8342867 #> [45,] 0.66295655 1.0285802 1.1348424 1.1780126 1.5407360 1.5456936 1.5925654 #> [46,] 0.59517291 0.6055320 0.6175850 0.6348915 0.6934208 0.7472936 0.7604855 #> [47,] 0.26748597 0.4514422 0.5305996 0.5355726 0.5653137 0.5940433 0.6347029 #> [48,] 0.44182012 0.4790345 0.7311678 0.7590097 0.7912512 0.8369607 0.8826947 #> [49,] 0.39492438 0.4265521 0.4639095 0.4944305 0.4999843 0.5031262 0.5226127 #> [50,] 0.36671734 0.3998036 0.4010056 0.4295132 0.4577139 0.4684129 0.4841514 #> [51,] 0.48536186 0.5327137 0.6370667 0.6729774 0.6874784 0.6910342 0.6948833 #> [52,] 0.58197751 0.8388852 0.9412836 1.0082361 1.0723705 1.1975280 1.2467210 #> [53,] 0.28197372 0.4617600 0.4898238 0.5057847 0.5060944 0.5117953 0.5443672 #> [54,] 0.31425204 0.4628165 0.4717548 0.4836014 0.4837092 0.5745749 0.6417850 #> [55,] 0.33077154 0.3722042 0.3859907 0.4190034 0.4615879 0.5161005 0.5482435 #> [56,] 0.35792720 0.5561728 0.6250605 0.6409314 0.7018025 0.7086441 0.7429670 #> [57,] 0.47956545 0.7789857 0.9671439 1.1418139 1.1795548 1.2010407 1.2126780 #> [58,] 0.63049552 0.9036267 0.9673234 0.9727745 1.0094522 1.0096174 1.0156655 #> [59,] 0.46314350 0.5494480 0.5521143 0.5776104 0.6092869 0.6229224 0.7165581 #> [60,] 0.66813811 0.7942737 0.8143436 0.9562376 0.9588842 0.9783526 1.0074788 #> [61,] 0.30665876 0.3083442 0.5477442 0.5914676 0.6283700 0.6488153 0.6576743 #> [62,] 0.48137672 0.5174087 0.5922966 0.6447556 0.6930791 0.6942257 0.7407218 #> [63,] 0.53136017 0.5715258 0.6273539 0.6438647 0.6653796 0.6855298 0.6951885 #> [64,] 0.29071253 0.3427768 0.5943194 0.6082906 0.6168765 0.6543068 0.6855298 #> [65,] 0.90797943 1.0011695 1.0073150 1.0568285 1.1074157 1.1380730 1.1655519 #> [66,] 0.51356621 0.6649191 0.7882149 0.8889239 0.9799349 1.0152143 1.0274019 #> [67,] 0.91009161 1.1452958 1.1939267 1.2397482 1.2778259 1.3142367 1.3691782 #> [68,] 0.43896058 0.6626579 0.7063940 0.7192270 0.7217734 0.7289212 0.7324372 #> [69,] 0.89069032 0.9313217 0.9673234 0.9976411 0.9996854 1.0331601 1.0587833 #> [70,] 0.33989198 0.4327614 0.5350378 0.6258618 0.6469255 0.7164036 0.7242920 #> [71,] 0.38397973 0.3862299 0.4516049 0.4913964 0.4920131 0.5414338 0.5917343 #> [72,] 1.00848047 1.0768269 1.0943501 1.1057459 1.1238643 1.1548307 1.2246481 #> [73,] 0.49674503 1.1517222 1.1530540 1.1780126 1.2000227 1.2599610 1.2891031 #> [74,] 0.95888418 1.4328568 1.4894015 1.6543722 1.6595918 1.6635901 1.6692800 #> [75,] 0.20286252 0.2775229 0.3058515 0.4774863 0.4874333 0.5379454 0.5445072 #> [76,] 0.56327896 0.6449463 0.8223549 0.8228872 0.9294344 0.9357540 1.0041295 #> [77,] 0.42533361 0.4313687 0.5218097 0.6681459 0.6745087 0.7003972 0.7146743 #> [78,] 0.31406018 0.4260126 0.4445334 0.5684864 0.5926834 0.6159768 0.6696777 #> [79,] 0.58282320 0.5919036 0.6241585 0.6445785 0.6560100 0.7319469 0.7938257 #> [80,] 0.19979753 0.5161356 0.5565107 0.5836934 0.5945889 0.6472249 0.6842989 #> [81,] 1.81951854 1.9922656 2.0205986 2.0790480 2.0946907 2.1277494 2.1302952 #> [82,] 0.55400715 0.5834456 0.7181371 0.7294277 0.7365553 0.7525134 0.7698621 #> [83,] 0.38538472 0.4618722 0.6916650 0.7051790 0.7148987 0.7293677 0.7540948 #> [84,] 0.47027993 0.5917161 0.6165220 0.6246316 0.6297133 0.7374528 0.7390052 #> [85,] 0.72936937 0.8543112 0.9746944 1.0084146 1.1090694 1.1174333 1.1207087 #> [86,] 0.59078288 0.6462234 0.6689212 0.6699648 0.6811840 0.6858087 0.6860345 #> [87,] 0.27254531 0.3651141 0.3716844 0.3921957 0.4445022 0.4630358 0.4686448 #> [88,] 0.66562908 0.8177474 0.8235249 0.9084179 0.9557719 1.0141622 1.0408833 #> [89,] 0.31747977 0.4114420 0.4268198 0.6100400 0.6136000 0.6282401 0.6387592 #> [90,] 0.43156060 0.4952567 0.5908792 0.6033091 0.6173670 0.6258699 0.6387592 #> [91,] 0.31421513 0.5338823 0.5412133 0.5900256 0.6272943 0.6422183 0.6592550 #> [92,] 0.81196536 0.9115486 0.9346957 0.9929918 1.0456172 1.0754124 1.0787575 #> [93,] 0.64736887 0.7271914 0.7541340 0.7808913 0.7979371 0.8244759 0.8497188 #> [94,] 0.49533234 0.6598227 0.7076012 0.7186929 0.7863789 0.8153246 0.8174131 #> [95,] 0.34692452 0.5627264 0.6081151 0.6135268 0.6495568 0.6515160 0.6583607 #> [96,] 0.39060104 0.4790116 0.4927501 0.4989591 0.5117631 0.5192327 0.5327137 #> [97,] 0.55207656 0.5697016 0.6301777 0.6495728 0.6696607 0.7135412 0.8605164 #> [98,] 0.63783218 0.7148571 0.7776584 0.8507334 0.9494797 1.0558888 1.0583920 #> [99,] 0.39020552 0.4962837 0.5675716 0.5861157 0.5976980 0.6081151 0.6092434 #> [100,] 0.89978391 0.9308147 0.9408754 0.9696101 0.9858472 0.9923786 1.0627488 #> [101,] 0.55283471 0.5907829 0.5922631 0.6141458 0.6261074 0.6392327 0.6421244 #> [102,] 0.35502072 0.4501754 0.6099109 0.6260703 0.6280210 0.6670661 0.6866346 #> [103,] 0.37801086 0.4721603 0.4913964 0.5124604 0.5450710 0.6028122 0.6238292 #> [104,] 0.34134019 0.5929541 0.6372357 0.7820931 0.7897595 0.8339584 0.8447999 #> [105,] 0.54679682 0.9584708 0.9619867 0.9668903 1.1132133 1.1147389 1.1797309 #> [106,] 0.42115831 0.5175675 0.5697016 0.7871844 1.0125592 1.0360698 1.0951413 #> [107,] 0.37049110 0.4279698 0.7430611 0.7789587 0.7795453 0.7839516 0.7865491 #> [108,] 0.37950826 0.5067155 0.6800699 0.6945929 0.7168134 0.7420902 0.7524010 #> [109,] 0.31009228 0.4000469 0.4590026 0.5164836 0.5549018 0.6026765 0.6227651 #> [110,] 1.01791160 1.0815005 1.0840109 1.2249167 1.2279099 1.2384120 1.2896179 #> [111,] 0.51360556 0.5314622 0.5624893 0.5767651 0.5827102 0.6198561 0.6221631 #> [112,] 0.70836509 0.7513724 0.7882149 0.8676796 0.8703300 0.9034353 0.9156993 #> [113,] 0.49869553 0.5067155 0.5460506 0.5801585 0.6696494 0.7173692 0.7608483 #> [114,] 0.40062803 0.7368107 0.8528844 0.8633145 0.8855950 0.9016987 0.9022142 #> [115,] 0.39939033 0.5879220 0.6793512 0.6805757 0.7058957 0.7344912 0.7391128 #> [116,] 0.31704666 0.3983268 0.5037551 0.5216523 0.5296416 0.6908122 0.7452978 #> [117,] 0.37326479 0.5340094 0.5787218 0.5794222 0.7135245 0.7212985 0.7981541 #> [118,] 0.58711013 0.6636078 0.6993385 0.7835613 0.7958064 0.8146638 0.8318024 #> [119,] 0.38781056 0.6292556 0.6395271 0.6717917 0.6905921 0.7365392 0.7809893 #> [120,] 0.51972264 0.5565621 0.5588898 0.6499334 0.6994073 0.7234893 0.7733899 #> [121,] 0.52007377 0.7156661 0.7769445 0.8729608 0.9187279 0.9307765 0.9801876 #> [122,] 0.37462522 0.5608964 0.5684864 0.5867999 0.6280210 0.6488748 0.6532210 #> [123,] 0.28637481 0.5232306 0.5870548 0.5945889 0.6033274 0.6372778 0.6648775 #> [124,] 1.46107996 1.6845896 1.6912515 1.6925196 1.7416590 1.7471112 1.8158605 #> [125,] 0.34397845 0.3866957 0.3924534 0.4412515 0.5012188 0.5058887 0.5131453 #> [126,] 0.37486905 0.4751257 0.5087351 0.5887325 0.7051331 0.7298008 0.7491495 #> [127,] 0.29550828 0.3681418 0.5015297 0.6060122 0.6288299 0.6308819 0.6992010 #> [128,] 0.21279658 0.3983268 0.4456823 0.4821855 0.5067848 0.5608770 0.5763736 #> [129,] 0.33082271 0.3812432 0.4656621 0.5812384 0.6536513 0.7041940 0.7333086 #> [130,] 0.59124589 0.7267236 0.7863414 0.8405639 0.8473076 0.8762039 0.8769079 #> [131,] 0.29341112 0.3539795 0.5754289 0.5817286 0.6082906 0.6362094 0.6390304 #> [132,] 0.38097849 0.4882768 0.5360774 0.7430517 0.7626059 0.8503862 0.8728612 #> [133,] 0.34767639 0.5731660 0.6695476 0.8528346 0.8894494 0.8955452 0.9042629 #> [134,] 0.67153583 0.7344912 0.7583382 0.7931788 0.8128573 0.8623701 0.9081630 #> [135,] 1.14665624 1.2219094 1.2304212 1.3831278 1.4038045 1.4243506 1.5006148 #> [136,] 0.44825377 0.6461841 0.7169382 0.7503173 0.7732450 0.7867631 0.8033412 #> [137,] 0.26411020 0.4260126 0.4456823 0.4673949 0.4828882 0.5533188 0.6529094 #> [138,] 0.52191310 0.5914450 0.5995905 0.6128027 0.6217932 0.6322603 0.6677923 #> [139,] 0.58668618 0.6357454 0.6994073 0.7920784 0.8237250 0.8393177 0.8492497 #> [140,] 0.29261567 0.4439449 0.5403259 0.5471188 0.5475349 0.5604020 0.5725399 #> [141,] 0.25265749 0.4204692 0.4459950 0.4995554 0.5339018 0.5646986 0.5659258 #> [142,] 0.44731276 0.4693085 0.4891228 0.4957363 0.5366771 0.5708846 0.5962150 #> [143,] 0.43630431 0.5608964 0.5913644 0.6018143 0.6031855 0.6383166 0.6529094 #> [144,] 0.55029717 0.6149651 0.6536513 0.6960493 0.7244467 0.7711151 0.8378862 #> [145,] 0.46758775 0.5323747 0.6548214 0.6687391 0.6954821 0.6959315 0.6987302 #> [146,] 0.56358593 0.6714041 0.7109351 0.7311678 0.7458959 0.7665936 0.7959432 #> [147,] 1.27924469 1.3343597 1.4185485 1.4773048 1.7597464 1.7606057 1.7904207 #> [148,] 0.69682063 0.8924923 0.9030081 0.9054818 1.0016268 1.0311475 1.0401675 #> [149,] 0.47105967 0.5015533 0.5732198 0.5992964 0.6070402 0.6333816 0.6404007 #> [150,] 1.08881693 1.1088618 1.1437963 1.2449947 1.2620592 1.2672230 1.3379752 #> [151,] 0.60289777 0.6267536 0.6512268 0.8387839 0.8905984 0.9767534 1.0555368 #> [152,] 0.38434510 0.5474227 0.5580321 0.6047128 0.6159771 0.6368901 0.6379932 #> [153,] 0.76091799 1.0115355 1.0132570 1.0881376 1.0951688 1.1263791 1.1440797 #> [154,] 0.42601353 0.7362994 0.7374528 0.9115949 0.9399216 0.9703215 0.9797734 #> [155,] 0.55380071 0.5868136 0.8971739 0.9859653 0.9871506 1.0210517 1.0258789 #> [156,] 0.38074638 0.5154705 0.8954715 0.9021617 0.9227504 1.0985167 1.1493428 #> [157,] 0.33667180 0.3653168 0.3902051 0.4978214 0.5171986 0.6065168 0.6329947 #> [158,] 0.35433111 0.4546433 0.5162549 0.5985664 0.6256440 0.6345276 0.7123310 #> [159,] 0.46309918 0.4648272 0.4978386 0.5025315 0.6583607 0.6659381 0.7088686 #> [160,] 0.43700057 0.4744331 0.5245809 0.5412125 0.5517345 0.5612849 0.5615328 #> [161,] 0.26497463 0.3009462 0.3889990 0.4135342 0.4214572 0.4266499 0.4639095 #> [162,] 0.42839864 0.5531608 0.5698067 0.6107278 0.6536423 0.7160076 0.7929313 #> [163,] 0.35057975 0.4309376 0.5965018 0.7168258 0.7637036 0.7787982 0.7869576 #> [164,] 0.37272716 0.6543357 0.6840445 0.7475963 0.8614908 0.9399279 0.9457978 #> [165,] 0.28816770 0.3682785 0.3965755 0.4383710 0.4658402 0.4771445 0.5262443 #> [166,] 0.49137345 0.6205306 0.6278612 0.7186285 0.7435339 0.7440504 0.7887943 #> [167,] 0.32875221 0.4161305 0.4295132 0.4324372 0.4674339 0.4922360 0.5073235 #> [168,] 0.34534255 0.4533731 0.4648813 0.4786173 0.5276847 0.5344793 0.5385578 #> [169,] 0.23154039 0.4996064 0.5751151 0.6726457 0.6773536 0.7041913 0.7237772 #> [170,] 0.52243421 0.7852364 0.8055764 0.8948322 0.9694110 1.0355170 1.0355223 #> [171,] 0.57831635 0.6068026 0.7323271 0.8061708 0.9309761 0.9451110 0.9462643 #> [172,] 0.35433111 0.4766277 0.5867573 0.5916953 0.6389778 0.6756414 0.7301892 #> [173,] 0.39210704 0.5408687 0.5812758 0.5987257 0.6104140 0.6381541 0.7037034 #> [174,] 0.60447661 0.6267536 0.7537042 0.7770416 0.7910509 0.7976067 0.8122335 #> [175,] 0.37131688 0.4732668 0.6357584 0.7240616 0.7558192 0.8210686 0.8438541 #> [176,] 0.38097849 0.6842423 0.6960493 0.7390536 0.7940312 0.8275767 0.8500671 #> [177,] 0.61987271 0.9503676 0.9604526 1.1101986 1.1372493 1.1962238 1.1983213 #> [178,] 0.86356366 0.8696071 0.8979664 0.9135622 0.9136056 0.9146832 0.9156993 #> [179,] 0.90251624 1.2391223 1.2551132 1.3247592 1.3892696 1.4173955 1.5960577 #> [180,] 0.65482141 0.6582334 0.6583989 0.6872535 0.7058957 0.7233368 0.7298838 #> [181,] 0.47581826 0.5598178 0.6172547 0.6436547 0.7041913 0.7288602 0.7499769 #> [182,] 0.46401044 0.5627313 0.5956964 0.6124989 0.6997715 0.7332486 0.7392277 #> [183,] 0.31704666 0.3587281 0.6026883 0.6088547 0.6555160 0.7286017 0.7831765 #> [184,] 0.78523641 0.9052024 0.9599744 1.0731588 1.0797704 1.1046482 1.1092652 #> [185,] 0.26858368 0.3132308 0.3914943 0.4122333 0.4825606 0.4826539 0.4865361 #> [186,] 0.42992809 0.4314484 0.4412957 0.5306193 0.5781613 0.6099109 0.6317479 #> [187,] 0.32558630 0.4538033 0.4867114 0.5004484 0.5212093 0.5499718 0.5767945 #> [188,] 0.27986585 0.4486950 0.6261074 0.6478857 0.6791720 0.6830734 0.7098652 #> [189,] 0.35281360 0.4997944 0.5622268 0.6197868 0.6285987 0.6537978 0.6549309 #> [190,] 0.27383529 0.3569462 0.3955108 0.4536409 0.4845361 0.5086319 0.6688656 #> [191,] 0.43967830 0.4634355 0.5661557 0.6033091 0.6095929 0.6202265 0.6597499 #> [192,] 0.44065919 0.4634355 0.5367478 0.5504818 0.5631576 0.5679693 0.6168765 #> [193,] 0.35144341 0.4494418 0.4993466 0.5087696 0.5155247 0.5654223 0.5736881 #> [194,] 0.22112194 0.3996389 0.5086507 0.5654880 0.5935245 0.5983421 0.6027282 #> [195,] 0.80557643 0.9636475 0.9959560 1.0036229 1.2343316 1.2907756 1.2967553 #> [196,] 0.39149426 0.4101772 0.4281875 0.4773816 0.4779779 0.4999843 0.5505670 #> [197,] 0.55400715 0.7314181 0.7574815 0.7799084 0.7873526 0.8264940 0.8332343 #> [198,] 0.50253147 0.6811196 0.6893140 0.8128573 0.8158326 0.8572884 0.9836412 #> [199,] 0.72804450 0.8080975 0.8230186 0.8388021 0.9040104 0.9090553 0.9122911 #> [200,] 0.36816787 0.5370509 0.5996214 0.6130515 0.7065292 0.7703608 0.7938257 #> [201,] 0.63311158 0.7397344 0.7983179 0.8409059 0.8487797 0.8629053 0.8852884 #> [202,] 0.35553709 0.3656315 0.3725824 0.4895812 0.4997822 0.5088901 0.5389288 #> [203,] 0.65886490 0.7329002 0.7347903 0.7411261 0.7580537 0.8624063 0.8720974 #> [204,] 0.55651070 0.5587885 0.5830566 0.5955075 0.6618140 0.7156208 0.7315282 #> [205,] 1.12746773 1.1447031 1.1611430 1.2061711 1.2973666 1.2998117 1.4372318 #> [206,] 1.04008113 1.0741286 1.0838163 1.0959222 1.1050789 1.1226859 1.1568236 #> [207,] 0.21900050 0.2742296 0.3514434 0.4414084 0.4509631 0.4979605 0.4985219 #> [208,] 0.21563769 0.3590978 0.3669434 0.3987107 0.4993616 0.5127696 0.5669487 #> [209,] 0.55611113 0.5794918 0.7240616 0.7361423 0.8079934 0.8221198 0.8290688 #> [210,] 0.20603309 0.2903457 0.3807584 0.3999190 0.4606817 0.4924360 0.5132561 #> [211,] 0.35146422 0.4366775 0.4686757 0.4812793 0.5317383 0.5949025 0.6197411 #> [212,] 0.41472370 0.5742801 0.6313599 0.6807774 0.6951039 0.7447867 0.7760244 #> [213,] 0.24306608 0.2691089 0.4780553 0.5333403 0.5817011 0.6043088 0.6178096 #> [214,] 0.34134019 0.5565621 0.5584969 0.6664560 0.6867161 0.8019273 0.8237250 #> [215,] 0.46304774 0.7293677 0.7351532 0.7634713 0.8081023 0.8122335 0.8499778 #> [216,] 0.40235927 0.4250036 0.5136656 0.5626172 0.5915929 0.5997113 0.6013005 #> [217,] 0.36827211 0.4581167 0.5627529 0.6870920 0.6881646 0.7117143 0.7378429 #> [218,] 0.33982358 0.5976765 0.7207886 0.7449855 0.8507334 0.9094096 0.9321405 #> [219,] 0.73585058 0.9232594 0.9565458 1.0190818 1.0440702 1.0562080 1.0697942 #> [220,] 0.55849685 0.5866862 0.6150630 0.6372357 0.6499334 0.6833969 0.6999140 #> [221,] 0.63004997 0.6805731 0.7160076 0.7317062 0.7545404 0.7969842 0.7992506 #> [222,] 0.44727767 0.5974994 0.7193678 0.7340550 0.7608659 0.7996507 0.7997487 #> [223,] 0.90362672 0.9146832 0.9180192 0.9874210 0.9927248 1.0419432 1.1020623 #> [224,] 0.46826716 0.6439832 0.7046757 0.7119260 0.7290976 0.7391057 0.7963469 #> [225,] 0.49106116 0.5244854 0.6788071 0.7176650 0.7177654 0.7749013 0.7870105 #> [226,] 0.32322310 0.3357362 0.4338062 0.6146114 0.6173491 0.6359929 0.6747795 #> [227,] 0.28556909 0.4419782 0.4688051 0.4989591 0.5661041 0.5827281 0.6393244 #> [228,] 0.43303596 0.5174087 0.5792943 0.5882164 0.6120067 0.6697537 0.6772856 #> [229,] 0.24611185 0.3842782 0.3983348 0.4293245 0.4612341 0.4862838 0.5753730 #> [230,] 0.39388994 0.4755652 0.5219131 0.5983490 0.6630029 0.6829525 0.6841384 #> [231,] 0.30036760 0.3198414 0.3590978 0.4168866 0.4785825 0.4997144 0.5040527 #> [232,] 0.30036760 0.3669434 0.3815305 0.5653624 0.5656248 0.5965183 0.5996629 #> [233,] 0.50544474 0.5182948 0.5418878 0.5623380 0.5669282 0.5773719 0.5893375 #> [234,] 0.31343717 0.4221985 0.5792943 0.6308612 0.6487788 0.6565327 0.6942257 #> [235,] 0.32559725 0.4732668 0.6090162 0.6755251 0.7244467 0.7776300 0.8144492 #> [236,] 0.42319471 0.4568666 0.5344793 0.5792409 0.5955864 0.6276450 0.6599847 #> [237,] 0.46543336 0.4792014 0.5336176 0.5733939 0.5851822 0.6046285 0.6290967 #> [238,] 0.42144588 0.5031239 0.6132328 0.6837926 0.7049319 0.7312486 0.7506026 #> [239,] 0.46484263 0.5704845 0.7109351 0.7118330 0.7417987 0.7526987 0.7569339 #> [240,] 1.09317617 1.1040206 1.1193103 1.2529562 1.2863927 1.2939069 1.3269674 #> [241,] 0.69303540 0.8205277 0.9671439 0.9847831 1.0134838 1.0473081 1.1663187 #> [242,] 0.78317649 0.8697816 0.8849601 0.9668492 1.0168150 1.0595519 1.1677669 #> [243,] 0.35963054 0.3806475 0.3983348 0.4068962 0.4617965 0.4657141 0.5526758 #> [244,] 1.26864772 1.6713056 1.7214903 1.7557255 1.7616370 1.7898021 1.8336938 #> [245,] 0.23444596 0.2765470 0.2852333 0.2970063 0.4048737 0.4606817 0.4664689 #> [246,] 0.47238131 0.4935583 0.5652610 0.6414176 0.6576266 0.6836239 0.8132373 #> [247,] 0.78307298 0.8013535 0.8650611 0.8930465 0.9029668 0.9399767 0.9486970 #> [248,] 0.51792634 0.6692384 0.7227889 0.7777086 0.7952843 0.8018866 0.9766410 #> [249,] 0.31072384 0.3755687 0.4014202 0.4074870 0.4573402 0.5314506 0.5385851 #> [250,] 0.36816787 0.4060399 0.5202421 0.5714498 0.5858707 0.5880933 0.5919036 #> [251,] 0.60180716 0.6018285 0.9662172 0.9774597 0.9816729 1.0145107 1.0180882 #> [252,] 0.43980417 0.4874431 0.5197226 0.6150630 0.6634790 0.6664560 0.7864254 #> [253,] 0.44190042 0.4631435 0.5505767 0.5886008 0.6377367 0.6873164 0.7012542 #> [254,] 0.39100186 0.7409369 0.7806299 0.8388192 0.9217313 0.9222943 0.9345085 #> [255,] 0.34632790 0.3812432 0.5636713 0.6431622 0.6769138 0.6824575 0.6911725 #> [256,] 0.54436133 0.5948165 0.7289802 0.7301984 0.7438620 0.7875841 0.7967487 #> [257,] 0.57458790 0.5926552 0.6283424 0.6438393 0.6785323 0.6825467 0.7500568 #> [258,] 1.27783404 1.2900982 1.2950464 1.4985037 1.5286311 1.5460156 1.5713194 #> [259,] 0.21900050 0.3075903 0.3096341 0.3999190 0.4035350 0.4148944 0.4527564 #> [260,] 0.40353499 0.4664689 0.4852165 0.4871731 0.4873610 0.4924360 0.5023658 #> [261,] 0.41794591 0.5664209 0.5781964 0.5864097 0.6685038 0.6909388 0.8368906 #> [262,] 0.48463135 0.6728008 0.8050962 0.8370144 0.8376331 0.8393648 0.8598953 #> [263,] 0.32319175 0.4870720 0.4978386 0.5671789 0.6692245 0.6793512 0.6843618 #> [264,] 0.19424836 0.2040277 0.4992867 0.5313602 0.6055320 0.6362094 0.6543068 #> [265,] 0.36833863 0.4654334 0.5017085 0.5656454 0.6003747 0.6170498 0.6251389 #> [266,] 0.58386724 0.6659381 0.6715358 0.6811196 0.6829526 0.7913083 0.8226197 #> [267,] 0.53151133 0.5787218 0.6020524 0.6985332 0.7286656 0.7835030 0.7867163 #> [268,] 0.64847362 0.6573676 0.6659433 0.7604574 0.7968490 0.8643554 0.9069869 #> [269,] 0.49223996 0.5229711 0.5686139 0.6100679 0.6674642 0.6830877 0.6999881 #> [270,] 0.47282196 0.5868136 0.8988946 0.9045820 0.9142488 0.9619491 0.9636785 #> [271,] 0.56905828 0.7277196 0.7705092 0.8684019 0.8710113 0.8973821 0.8976672 #> [272,] 0.32987731 0.5085852 0.6028978 0.6044766 0.6056317 0.7368727 0.7378429 #> [273,] 0.57827386 0.6399215 0.6848409 0.7001828 0.7300695 0.7999589 0.8096686 #> [274,] 0.38353125 0.4820388 0.5002889 0.5103997 0.5439551 0.5473826 0.5618381 #> [275,] 0.31634825 0.3453730 0.4488479 0.4783741 0.5086319 0.5134158 0.6014621 #> [276,] 0.42046922 0.4730321 0.6023254 0.6381665 0.6473181 0.6619429 0.6708163 #> [277,] 0.75559906 0.8434938 0.9864432 1.0654337 1.0883128 1.1004546 1.1442323 #> [278,] 0.23177341 0.3876206 0.4300517 0.4489872 0.4615879 0.4619930 0.5083644 #> [279,] 0.17043615 0.6115139 0.6144349 0.7220758 0.7441820 0.9432558 0.9551188 #> [280,] 0.31136211 0.4758183 0.5105971 0.5345327 0.6773536 0.7440504 0.7614294 #> [281,] 1.28735206 1.4495158 1.5862658 1.5876154 1.6128527 1.6299672 1.6443932 #> [282,] 0.29261567 0.4436452 0.5262098 0.5505027 0.5601840 0.5625149 0.5846818 #> [283,] 0.24270294 0.3663440 0.3688916 0.6605804 0.6611236 0.8047881 0.8244194 #> [284,] 0.34324427 0.3852723 0.4807027 0.5292069 0.5543009 0.5608770 0.5657912 #> [285,] 0.62918180 1.0125592 1.1663187 1.2016624 1.2094716 1.2117365 1.2418795 #> [286,] 0.29686347 0.5360774 0.7940312 0.8762371 0.9201335 0.9503676 0.9619168 #> [287,] 0.40142020 0.4634532 0.4824160 0.5110722 0.5282545 0.6008158 0.6378858 #> [288,] 0.53401809 0.6609695 0.7889064 0.7917791 0.8890348 0.8939098 0.9408754 #> [289,] 0.38448622 0.4870720 0.5550794 0.5776140 0.5852819 0.5914036 0.6358499 #> [290,] 0.21279658 0.3871573 0.4590166 0.5166758 0.5296416 0.5533188 0.5543009 #> [291,] 0.35604642 0.5907578 0.6481719 0.6609695 0.7840776 0.8362565 0.8475800 #> [292,] 0.32558630 0.4674089 0.5277063 0.5832308 0.6203573 0.6430868 0.6531550 #> [293,] 0.58301167 0.6375903 0.6964726 0.7071698 0.7245649 0.7315685 0.7625511 #> [294,] 0.27397800 0.4990195 0.5213306 0.5736358 0.5985232 0.6072090 0.6459071 #> [295,] 0.09113993 0.3491467 0.6358499 0.6807774 0.6979795 0.7459699 0.7863417 #> [296,] 0.64120027 0.8388852 0.8525389 0.9818075 1.0069490 1.0293006 1.0614266 #> [297,] 0.46566207 0.5201454 0.5636713 0.5652610 0.6131437 0.6701657 0.6707414 #> [298,] 0.57944873 0.5921952 0.6205306 0.6440024 0.6743616 0.6780611 0.7077063 #> [299,] 0.35056102 0.4323315 0.5741677 0.6964146 0.7629855 0.7754890 0.8597179 #> [300,] 0.59295405 0.6744941 0.6985332 0.6999140 0.7287384 0.7367904 0.7500358 #> [301,] 0.58706661 0.5951729 0.6874442 0.7326062 0.7522365 0.8230275 0.8272819 #> [302,] 0.36175024 0.4209899 0.4813064 0.5223277 0.5475791 0.6160059 0.6233547 #> [303,] 0.63812120 0.6595335 0.6641357 0.7310347 0.7366293 0.7377899 0.7552492 #> [304,] 0.28967890 0.4755652 0.5373530 0.6050027 0.7367644 0.7569945 0.7634534 #> [305,] 0.39860107 0.5109188 0.5967728 0.6581444 0.6616765 0.6637005 0.6784733 #> [306,] 0.35967592 0.3691845 0.3996389 0.4213417 0.5454917 0.5615328 0.5832308 #> [307,] 0.39541408 0.5084299 0.5127088 0.5249234 0.5292019 0.5643657 0.5927760 #> [308,] 0.72699233 1.0108327 1.0447182 1.0627107 1.1576053 1.1857721 1.2353420 #> [309,] 0.72936937 0.7679370 0.8357712 0.9106449 0.9236965 1.0219507 1.0300689 #> [310,] 0.38622987 0.4114420 0.4536031 0.5582578 0.6083764 0.6128921 0.6449446 #> [311,] 0.22968159 0.3896564 0.4692602 0.5056829 0.6336309 0.6535046 0.6674393 #> [312,] 0.49782138 0.6603331 0.6654162 0.6993385 0.7263683 0.7397532 0.7719971 #> [313,] 0.94691536 1.0304489 1.2154122 1.2366640 1.2861856 1.3044798 1.3060265 #> [314,] 0.34277683 0.5761198 0.5915459 0.5993767 0.6146927 0.6753620 0.7003972 #> [315,] 0.50998616 0.5479445 0.6304955 0.7377899 0.7726233 0.8401201 0.8418575 #> [316,] 0.35402695 0.4413732 0.4589638 0.4667636 0.5178635 0.5726849 0.6361679 #> [317,] 0.54679682 1.1183533 1.1625312 1.2716053 1.2726941 1.2864367 1.3436808 #> [318,] 0.78307298 0.8124752 0.8480663 0.8515825 0.8729608 0.9198239 1.0690410 #> [319,] 0.30066148 0.4654537 0.4985146 0.5389288 0.5498350 0.5507061 0.6302704 #> [320,] 0.31021064 0.3132308 0.4101772 0.4694301 0.4810949 0.5101946 0.5164189 #> [321,] 0.75567952 0.7687906 0.8486365 0.8781762 0.8926911 0.9084179 0.9230808 #> [322,] 0.34928645 0.3637145 0.3923042 0.4792014 0.5017085 0.5937359 0.6005207 #> [323,] 0.58015850 0.6230936 0.6470833 0.7195649 0.7420902 0.7616721 0.7977826 #> [324,] 0.79251236 0.9054818 0.9668903 1.0041571 1.0512183 1.1058004 1.1671591 #> [325,] 0.60182850 0.9306545 0.9786673 1.0296871 1.0457754 1.1541700 1.2631363 #> [326,] 0.64957283 0.7590596 0.7749350 0.7850495 0.7871844 0.7927290 0.8156598 #> [327,] 0.42672090 0.5879220 0.6760600 0.6836464 0.6876713 0.7225715 0.7573887 #> [328,] 0.91433588 0.9158306 0.9784031 1.2583021 1.2764300 1.3413194 1.5356199 #> [329,] 1.01843729 1.2450534 1.4353399 1.4878778 1.4932276 1.5408669 1.5720413 #> [330,] 0.53075473 0.5583400 0.6635461 0.8393648 0.8629734 0.9292874 0.9293687 #> [331,] 0.26707323 0.2953271 0.3320452 0.4213417 0.4737726 0.4925101 0.5484479 #> [332,] 0.28263796 0.4708422 0.5357388 0.5955864 0.6937404 0.7394286 0.7719971 #> [333,] 0.30066148 0.4250036 0.4539085 0.4947684 0.4997310 0.5221296 0.5411184 #> [334,] 0.60462854 0.6625574 0.6892968 0.7561637 0.7664300 0.7800571 0.7937415 #> [335,] 0.35753455 0.3898205 0.4122781 0.4397189 0.4454174 0.4694301 0.5457032 #> [336,] 0.71541443 0.7496531 0.7674448 0.7797565 0.8114114 0.8249625 0.8688823 #> [337,] 0.44798338 0.4910612 0.5965018 0.6305607 0.8125750 0.8208785 0.8604156 #> [338,] 0.59733251 0.6119211 0.7441820 0.7640671 0.8495275 0.9484934 0.9795572 #> [339,] 0.34767639 0.4231106 0.7018844 0.7598041 1.1616720 1.1800716 1.1930159 #> [340,] 0.25283902 0.5982736 0.6251264 0.6512282 0.6701734 0.6992505 0.8306882 #> [341,] 0.62170404 0.7770250 0.8326278 0.8458111 0.9135953 1.0200667 1.0227937 #> [342,] 0.33306395 0.4476540 0.4836014 0.5161005 0.5396148 0.5397691 0.5607260 #> [343,] 0.51756753 0.5550128 0.6301777 0.7850495 0.7903039 0.8444186 0.8738890 #> [344,] 0.46604194 0.6543357 0.6882759 0.7544034 0.8082659 0.8203685 0.8405639 #> [345,] 2.08627568 2.2164901 2.3605552 2.4335841 2.4893736 2.7627295 2.8010679 #> [346,] 0.13034995 0.4238135 0.4250519 0.4266499 0.5022303 0.5382494 0.5479820 #> [347,] 0.34099743 0.4887354 0.5830566 0.6372778 0.6603331 0.6619228 0.6842989 #> [348,] 0.51517039 0.5962294 0.6121996 0.6451439 0.6529798 0.6540016 0.6602214 #> [349,] 0.26748597 0.4683940 0.5793739 0.6053595 0.6260703 0.6309714 0.6394856 #> [350,] 0.76858466 1.1761635 1.2582593 1.2940643 1.5105286 1.6101623 1.6421377 #> [351,] 0.76916011 0.7860783 0.7977826 0.8974579 0.9256936 0.9290629 0.9301239 #> [352,] 0.52825894 0.5441858 0.5634698 0.5812371 0.6239770 0.6274126 0.6737094 #> [353,] 0.58578655 0.7566644 0.7721850 0.7867631 0.7960317 0.8069427 0.8407096 #> [354,] 0.58458911 0.6064475 0.7186929 0.7366293 0.8120694 0.8515900 0.9056615 #> [355,] 0.52335784 0.5589289 0.5656257 0.5669487 0.5721419 0.5722827 0.6008707 #> [356,] 0.34534255 0.4616561 0.5112414 0.5470238 0.5596469 0.5750012 0.5792409 #> [357,] 0.43156060 0.4582670 0.5476464 0.5485833 0.5877672 0.6220212 0.6434446 #> [358,] 0.73220521 0.8099764 0.8445454 0.9186037 0.9566207 0.9656704 1.0108327 #> [359,] 0.31343717 0.5510211 0.6036509 0.6120067 0.6209275 0.6326844 0.6540326 #> [360,] 0.36655786 0.3690922 0.5734929 0.5834994 0.5864097 0.6540622 0.7491659 #> [361,] 0.76858466 1.0874815 1.1149484 1.1430548 1.2046681 1.2715879 1.2920916 #> [362,] 0.19062462 0.3839844 0.4583266 0.4943451 0.5136656 0.5699897 0.5736296 #> [363,] 0.28951401 0.4910873 0.6248821 0.6611893 0.6696607 0.7183489 0.7233776 #> [364,] 0.42181553 0.4877192 0.5912459 0.6743616 0.7056267 0.7461754 0.8017895 #> [365,] 0.76086589 0.7760302 0.8728537 0.9342568 0.9361687 0.9381583 0.9653260 #> [366,] 0.13034995 0.3167348 0.3422170 0.3889990 0.4509631 0.4680016 0.4929429 #> [367,] 0.57134342 0.7193678 0.8413470 0.8535926 0.8536220 0.8554087 0.8668236 #> [368,] 0.63441274 0.6715617 0.8162489 1.0910088 1.1201771 1.1260417 1.1408357 #> [369,] 0.48692180 0.5494740 0.5578253 0.7312511 0.8092917 0.8365102 0.8545020 #> [370,] 0.30834417 0.3717870 0.5505670 0.5663475 0.5913376 0.6025436 0.6040373 #> [371,] 0.37423009 0.4064087 0.5369217 0.5752935 0.6668281 0.7664228 0.7809228 #> [372,] 0.54015491 0.5936938 0.6481829 0.6765533 0.7104502 0.8064179 0.8720588 #> [373,] 0.43896058 0.4437918 0.4920131 0.5450710 0.5919320 0.6239316 0.6245348 #> [374,] 0.44397251 0.5654979 0.6834941 0.6972669 0.7510266 0.7524010 0.7836004 #> [375,] 0.66760280 0.7469794 0.9887768 1.0101594 1.1013004 1.1914199 1.2538680 #> [376,] 0.59437982 0.6026883 0.6497441 0.6697146 0.6745087 0.7748304 0.7891744 #> [377,] 0.66407082 0.7500568 0.7535016 0.8290688 0.8438541 0.9890276 1.0188957 #> [378,] 0.41627299 0.5488501 0.6116729 0.6436547 0.6610865 0.6855123 0.7180888 #> [379,] 0.69345760 0.7210034 0.8104446 0.8915143 0.9844797 1.0110316 1.0172064 #> [380,] 0.76087780 0.8230186 0.9407532 0.9450609 0.9547859 0.9661827 0.9709767 #> [381,] 0.40603994 0.5180424 0.5222549 0.5370509 0.5608112 0.5768237 0.5923872 #> [382,] 0.73832735 1.0173604 1.1611430 1.1846141 1.1984804 1.2295837 1.2735942 #> [383,] 0.53100533 0.5449393 0.5474227 0.5828232 0.5858707 0.6840361 0.6868059 #> [384,] 0.57134342 0.5851683 0.6375299 0.6651148 0.6688456 0.6987302 0.6993011 #> [385,] 0.27373125 0.2955083 0.5659957 0.6637059 0.6880188 0.6971506 0.7213557 #> [386,] 0.24808700 0.3510083 0.3794034 0.3877919 0.4058981 0.4412515 0.4755460 #> [387,] 0.43967830 0.4726402 0.4932683 0.5055582 0.5679693 0.5897617 0.6561252 #> [388,] 0.84819084 1.1595079 1.1723107 1.1948092 1.2608081 1.2782794 1.2918580 #> [389,] 0.32217673 0.3829084 0.4074870 0.4187954 0.4443748 0.4634532 0.4686448 #> [390,] 0.52825894 0.5631576 0.6271125 0.6557014 0.6611893 0.6657764 0.6932175 #> [391,] 0.45811669 0.5054670 0.5085852 0.6096429 0.6646026 0.7051882 0.7080762 #> [392,] 0.46826716 0.6150778 0.6268764 0.6573377 0.7076093 0.8563626 0.9239508 #> [393,] 0.95746914 1.0086629 1.0406724 1.0532450 1.1052959 1.1057283 1.1518480 #> [394,] 0.29341112 0.4992867 0.5268116 0.5280320 0.5652156 0.5715258 0.6271125 #> [395,] 0.22968159 0.3101792 0.3454661 0.5909986 0.5927128 0.6689339 0.6976758 #> [396,] 0.36120649 0.4103122 0.6250605 0.6992505 0.7602595 0.8301865 0.8850193 #> [397,] 0.28967890 0.3938899 0.7029221 0.7187057 0.7722017 0.8253453 0.8307571 #> [398,] 0.75147404 0.7849116 0.9170330 0.9208913 0.9440306 0.9802764 0.9832832 #> [399,] 0.24282195 0.5745879 0.6237231 0.7167928 0.7501435 0.7539793 0.7585124 #> [400,] 0.44085544 0.6105890 0.6449463 0.6787343 0.7155752 0.7958527 0.8565443 #> [401,] 0.33143558 0.3987107 0.4398471 0.4584524 0.4812428 0.5219024 0.5589289 #> [402,] 0.33082271 0.5502972 0.6431622 0.6637005 0.6701657 0.6755251 0.7168742 #> [403,] 0.56642090 0.6540622 0.8223332 0.8576855 0.8731733 0.8882479 0.9480153 #> [404,] 0.26714363 0.3198414 0.4024143 0.4422043 0.5714739 0.5722827 0.5828092 #> [405,] 0.30823539 0.4929429 0.5151064 0.5155247 0.5171986 0.5267013 0.5308653 #> [406,] 0.38527232 0.4324372 0.4494279 0.4531303 0.4787794 0.4858446 0.4919623 #> [407,] 0.27383529 0.2797752 0.3163482 0.4943825 0.5064923 0.5986820 0.6116729 #> [408,] 0.38240555 0.8434938 0.8584295 0.8924923 0.9832103 1.1613203 1.2121301 #> [409,] 0.57819639 0.6505936 0.8731733 0.9346957 0.9573444 1.0097077 1.0714018 #> [410,] 0.52591639 0.6012587 0.7080196 0.7624489 0.7958698 0.8552217 0.8827765 #> [411,] 0.36827211 0.4711137 0.4719894 0.4929756 0.6203573 0.6707708 0.6902118 #> [412,] 0.23216678 0.3232231 0.3311819 0.5213843 0.5366771 0.5760775 0.5889128 #> [413,] 0.58976169 0.6202265 0.6450097 0.6692384 0.6843983 0.7223482 0.7326062 #> [414,] 0.24263568 0.4612341 0.4657141 0.4723813 0.5201454 0.6181234 0.6518934 #> [415,] 0.36476374 0.5452436 0.5489041 0.6107487 0.6231284 0.6486865 0.6606610 #> [416,] 0.60728448 0.6157924 0.6258618 0.7511654 0.7687882 0.7726233 0.7889009 #> [417,] 0.26910893 0.3252515 0.4800824 0.5574661 0.5587419 0.5779394 0.5942108 #> [418,] 1.12512471 1.3314374 1.4135245 1.4562553 1.5906817 1.6334235 1.6924701 #> [419,] 0.62786122 0.6749549 0.6909721 0.7614294 0.7672396 0.8301462 0.8979768 #> [420,] 0.22112194 0.3691845 0.5145285 0.5252724 0.5844169 0.6151094 0.6728585 #> [421,] 0.27916247 0.4592070 0.4632790 0.4938952 0.5738974 0.5998774 0.6008497 #> [422,] 0.50152971 0.5310501 0.5585375 0.6059301 0.6115119 0.6137666 0.6637059 #> [423,] 0.53080944 0.5963091 0.6491302 0.6666116 0.6843983 0.6985799 0.7032316 #> [424,] 0.27654704 0.2951427 0.3070175 0.3842054 0.4358474 0.4817677 0.4873610 #> [425,] 0.97840314 1.7536070 1.8131297 2.0017496 2.1434535 2.1819352 2.2393017 #> [426,] 0.36634397 0.4353989 0.4994289 0.5350266 0.7159151 0.7472615 0.7556795 #> [427,] 0.70836509 1.0437403 1.1130162 1.2179101 1.2229333 1.3177732 1.3912272 #> [428,] 0.55813831 0.5829462 0.5906029 0.6822739 0.7171960 0.8027568 0.8069427 #> [429,] 0.40100564 0.4380599 0.4580945 0.4843531 0.5109188 0.5388405 0.5403259 #> [430,] 0.57831635 0.5799145 0.6269897 0.6892983 0.6985799 0.7360508 0.7376631 #> [431,] 0.44682494 0.4837806 0.5786943 0.6870117 0.7904464 0.8035448 0.8086243 #> [432,] 0.38781056 0.4518521 0.5390001 0.5638302 0.6139850 0.6920587 0.7199420 #> [433,] 0.47805534 0.5401356 0.5505767 0.5521143 0.5617291 0.6032702 0.6186321 #> [434,] 0.64271917 0.7210034 0.7781986 0.8294768 0.8347201 0.9185371 0.9272091 #> [435,] 0.78213116 0.8018866 0.8228872 0.8803566 1.0575084 1.0734745 1.0943855 #> [436,] 0.42797926 0.5164301 0.5924125 0.6018320 0.6691727 0.6717369 0.6841384 #> [437,] 0.46247887 0.5067058 0.6335023 0.6481829 0.6948617 0.7102101 0.7358506 #> [438,] 0.21124475 0.8079009 0.8139015 0.8850193 0.9155204 0.9589698 1.0491241 #> [439,] 0.25940823 0.3564569 0.3725663 0.3965755 0.4547488 0.4755460 0.4823437 #> [440,] 0.40146034 0.5058961 0.5291942 0.5949664 0.6414176 0.6517776 0.6678141 #> [441,] 0.43381139 0.5162549 0.5867573 0.6451439 0.6698491 0.6805422 0.7958064 #> [442,] 0.27226466 0.7965124 0.8139015 0.9680754 1.0481851 1.0729468 1.1314613 #> [443,] 0.67495487 0.6964098 0.7077063 0.7435339 0.8941679 0.9820487 0.9932132 #> [444,] 0.42672090 0.4742585 0.6577665 0.6621273 0.7176650 0.7227302 0.7306703 #> [445,] 0.27374113 0.3398920 0.4336246 0.4878851 0.5836934 0.7156208 0.7161151 #> [446,] 0.49775036 0.5057079 0.5317534 0.6670638 0.6812482 0.7011247 0.7810805 #> [447,] 0.19979753 0.4336246 0.5156453 0.5955075 0.6469255 0.6540243 0.6858037 #> [448,] 0.57611984 0.6540326 0.6697537 0.7188448 0.7964828 0.8270086 0.8270891 #> [449,] 0.35402695 0.5317214 0.5661899 0.6237384 0.6483689 0.6932376 0.7014995 #> [450,] 0.37853720 0.5857865 0.6541418 0.6932700 0.7015655 0.7664324 0.7808781 #> [451,] 0.49137345 0.6440024 0.6964098 0.7731717 0.8979768 0.9132668 0.9222713 #> [452,] 0.34099743 0.3787914 0.6033274 0.6649191 0.6806684 0.7186290 0.8443421 #> [453,] 0.32777468 0.4084296 0.5964598 0.6985484 0.7299506 0.7334949 0.7418415 #> [454,] 0.24444572 0.2539434 0.3194772 0.3515583 0.4927501 0.5041885 0.5241539 #> [455,] 0.55947715 0.6226706 0.7406254 0.7418054 0.8031751 0.8122835 0.8346231 #> [456,] 0.57189888 0.7630114 0.7777086 0.8392729 0.8726374 0.9481810 0.9678224 #> [457,] 0.41927666 0.4611625 0.6163551 0.6427449 0.6779266 0.6880188 0.7513486 #> [458,] 0.39230424 0.5280235 0.5472313 0.5493735 0.6251389 0.6625574 0.6653248 #> [459,] 0.77560656 0.9673221 1.3283529 1.4040438 1.4118502 1.4425507 1.4774432 #> [460,] 0.61725469 0.7662297 0.7672396 0.7733261 0.7887943 0.8812575 0.9060264 #> [461,] 0.43303596 0.5888637 0.7407218 0.8279811 0.8655723 0.8826947 0.9146768 #> [462,] 0.42912632 0.4845361 0.6626579 0.6728326 0.6761037 0.6790748 0.7104630 #> [463,] 0.41043967 0.4491331 0.4698574 0.5527091 0.5605946 0.5686139 0.5844173 #> [464,] 0.72699233 0.8099764 0.9777157 1.2406322 1.2518960 1.3226513 1.3457507 #> [465,] 0.57028752 0.5922631 0.7839516 0.7948862 0.7992506 0.8687603 0.9287249 #> [466,] 0.52735350 0.5742801 0.5845891 0.6067881 0.6068605 0.6641357 0.6670932 #> [467,] 0.28297282 0.4491331 0.4689097 0.6411230 0.6674642 0.6797679 0.6916542 #> [468,] 0.20624216 0.5091761 0.5980774 0.6159974 0.6453148 0.6593648 0.7085769 #> [469,] 1.19141986 1.5441863 1.5456936 1.7348901 1.8111149 1.8559001 1.9191438 #> [470,] 1.41774477 1.4487873 1.4903795 1.6992258 1.7577959 1.7602376 1.8522810 #> [471,] 0.29797572 0.4299281 0.5242352 0.5578253 0.6776225 0.7056283 0.7425035 #> [472,] 0.38302962 0.5127490 0.5259164 0.5390705 0.6286051 0.6704237 0.7531162 #> [473,] 0.50312387 0.5099862 0.6608039 0.7022452 0.7096227 0.7186290 0.7293509 #> [474,] 0.32559725 0.3713169 0.7130241 0.7659304 0.7700191 0.7789670 0.7808682 #> [475,] 0.66621597 0.7830272 0.7831751 0.8182106 0.8966966 0.9351781 0.9509069 #> [476,] 0.31706208 0.4752431 0.5834994 0.6357454 0.6537978 0.6696843 0.7443812 #> [477,] 0.63307436 0.8746795 0.9048494 1.0334246 1.0734693 1.0859972 1.1214003 #> [478,] 0.24835875 0.2855691 0.4485579 0.5506574 0.5675909 0.5897147 0.5921158 #> [479,] 0.67299483 0.6867017 0.6929659 0.7637374 0.8307571 0.8561672 0.8700257 #> [480,] 0.35784179 0.5513518 0.5670087 0.7056283 0.7706672 0.8031751 0.8092917 #> [481,] 0.48744310 0.5588898 0.5712633 0.5887559 0.6020757 0.6867161 0.6954605 #> [482,] 0.64034442 0.7109808 0.7513486 0.7552150 0.7938263 0.8022374 0.9097428 #> [483,] 0.65673910 0.6934576 0.7347903 0.7368107 0.7609579 0.8347201 0.8816674 #> [484,] 0.46758775 0.5681887 0.5852819 0.6067881 0.6260452 0.6605836 0.6614731 #> [485,] 0.47111371 0.4812793 0.4850204 0.5201222 0.5477646 0.5577346 0.5793739 #> [486,] 0.43144845 0.4391736 0.5432727 0.5923682 0.6461551 0.6766045 0.7690241 #> [487,] 0.37879138 0.4887354 0.5135662 0.7270004 0.7552587 0.7578103 0.8245740 #> [488,] 0.31421513 0.4441723 0.4573402 0.4961898 0.5060788 0.5282545 0.5339018 #> [489,] 0.65656076 0.7463260 0.7929313 0.8194621 0.9020570 0.9177525 0.9422374 #> [490,] 0.30104083 0.3744047 0.4592138 0.5041361 0.5161984 0.5169359 0.5456778 #> [491,] 0.96364754 1.1528099 1.1904007 1.2277327 1.2608628 1.3413665 1.3659466 #> [492,] 0.27801534 0.4372083 0.4698574 0.5177640 0.5229711 0.5237595 0.6135268 #> [493,] 0.49796048 0.5036863 0.5219024 0.5233578 0.5272305 0.5284553 0.5654223 #> [494,] 0.98742099 1.0437403 1.2940643 1.3715098 1.3899578 1.3997916 1.4121160 #> [495,] 0.44397251 0.7141122 0.7220758 0.7276547 0.7367931 0.7933537 0.7945902 #> [496,] 0.78865536 0.8210686 0.8651220 0.8884697 0.9370270 1.0128090 1.0499836 #> [497,] 0.44065919 0.4790116 0.4853619 0.5303881 0.5448443 0.6220982 0.6282401 #> [498,] 0.34765312 0.4310536 0.5417388 0.5560209 0.5638302 0.5759559 0.5821031 #> [499,] 0.62476464 0.6853526 0.7231728 0.7364530 0.7754434 0.7937415 0.8122482 #> [500,] 0.86382960 0.9100916 0.9574691 0.9589698 0.9872226 1.0080292 1.0085337 #> [501,] 0.44876414 0.5102163 0.5347131 0.5379716 0.5645399 0.5704845 0.6053305 #> [502,] 0.41627299 0.6481719 0.6935705 0.6963693 0.6982418 0.7340551 0.7619814 #> [503,] 0.32777468 0.4800625 0.6171153 0.6411230 0.6927322 0.7000504 0.7321665 #> [504,] 0.35071989 0.3780109 0.4437918 0.5414338 0.6048693 0.6083764 0.6248821 #> [505,] 0.33721916 0.4024143 0.4997144 0.5299611 0.5741004 0.6124989 0.6187411 #> [506,] 0.29866995 0.5794222 0.6637734 0.6932376 0.7236006 0.7750488 0.7979371 #> [507,] 0.21629621 0.2649746 0.3422170 0.3558626 0.4238135 0.4414084 0.4454174 #> [508,] 0.28263796 0.4568666 0.4619486 0.7048069 0.7134353 0.7177440 0.7321665 #> [509,] 0.48137672 0.4824022 0.5419171 0.5632433 0.5882164 0.5888637 0.6137666 #> [510,] 0.46565599 0.4783741 0.5334498 0.5335707 0.6197868 0.6302965 0.6626189 #> [511,] 1.08972495 1.4503325 1.4872049 1.6333087 1.6377813 1.6615663 1.6656201 #> [512,] 0.40062803 0.7811736 0.9422343 0.9619867 0.9771794 0.9813070 1.0157656 #> [513,] 0.24363560 0.7233368 0.9136292 0.9197246 0.9389733 0.9935159 1.0471151 #> [514,] 0.57508362 0.8877721 0.9844797 1.0681754 1.0911114 1.1350714 1.1527929 #> [515,] 0.33573616 0.5134158 0.5348730 0.5760775 0.6140883 0.6615503 0.6626189 #> [516,] 1.34136645 1.3838227 1.4342775 1.4943989 1.5133858 1.5837403 1.6842036 #> [517,] 0.31680663 0.5132193 0.5207735 0.5239781 0.5288084 0.6530337 0.6701734 #> [518,] 0.33083441 0.3806689 0.4182686 0.4632790 0.5653137 0.6036509 0.6394856 #> [519,] 0.31747977 0.4444787 0.4536031 0.6238292 0.6440949 0.6693203 0.6872239 #> [520,] 0.63307436 0.6750776 0.6964146 0.7806849 0.7888541 0.9482153 0.9678648 #> [521,] 0.43546948 0.4618722 0.4630477 0.4719894 0.6224500 0.6884615 0.7096870 #> [522,] 0.27135405 0.4652345 0.4995554 0.5058957 0.5669282 0.6064614 0.6081116 #> [523,] 0.24306608 0.3252515 0.4779854 0.5131453 0.5564435 0.5580321 0.5617291 #> [524,] 0.40842964 0.4800625 0.5511625 0.5870548 0.6589856 0.6864307 0.7071698 #> [525,] 0.86978163 1.0184373 1.2608628 1.2638072 1.3526222 1.4484502 1.4599987 #> [526,] 0.23216678 0.3463831 0.4338062 0.4473128 0.6140883 0.6430809 0.6863041 #> [527,] 0.73832735 1.0898115 1.1625550 1.2302076 1.2953288 1.3461254 1.3687894 #> [528,] 0.64120027 0.7090600 0.7290976 0.8012898 0.8137912 0.8145357 0.8163358 #> [529,] 0.31341527 0.3253701 0.5315113 0.5945311 0.6297303 0.6668281 0.7500358 #> [530,] 1.14549023 1.2158055 1.3127295 1.4186153 1.4811360 1.5310853 1.5420888 #> [531,] 0.39980361 0.4032872 0.4148328 0.4161305 0.4579549 0.5164189 0.5330906 #> [532,] 0.37766750 0.4218155 0.5093474 0.6618992 0.6812482 0.6834770 0.6870574 #> [533,] 0.27422963 0.3075903 0.3558626 0.3898205 0.4871731 0.5087696 0.5242883 #> [534,] 0.38302962 0.5250651 0.5477875 0.6636078 0.7090112 0.7146490 0.7397532 #> [535,] 1.12806137 1.1348424 1.1938812 1.4175963 1.5498325 1.5511832 1.6104801 #> [536,] 0.41300116 0.5232306 0.5273535 0.5511625 0.5844169 0.6403226 0.6632456 #> [537,] 0.48435314 0.5178139 0.5347131 0.5489041 0.5685398 0.5879447 0.6548786 #> [538,] 0.49628373 0.5831077 0.6269285 0.6550708 0.6605836 0.6656679 0.6692245 #> [539,] 0.35792720 0.6560213 0.7569771 0.7602595 0.7759812 0.7785397 0.8970102 #> [540,] 0.76904320 1.4661106 1.4702632 1.4773048 1.5262415 1.5297211 1.5432717 #> [541,] 0.44133972 0.5291942 0.6068363 0.6208701 0.7134594 0.7263323 0.7303278 #> [542,] 0.62170404 0.6256440 0.6446191 0.7070516 0.7112517 0.7289212 0.7560268 #> [543,] 0.24363560 0.6583989 0.8760443 0.8771099 0.8894776 0.8955944 0.9222511 #> [544,] 0.65932573 0.8223836 0.8684019 0.8693571 0.8740993 0.8921200 0.9172862 #> [545,] 0.71813707 0.8264940 0.9067110 1.0099780 1.1022822 1.2036360 1.2095566 #> [546,] 0.21629621 0.3009462 0.3167348 0.4250519 0.4616561 0.5050785 0.5242883 #> [547,] 0.45699431 0.4590581 0.5355108 0.5476464 0.5826571 0.6701486 0.6719392 #> [548,] 0.35963054 0.5976765 0.6206061 0.6336101 0.6707414 0.6790821 0.6824575 #> [549,] 0.58301167 0.5851683 0.6196863 0.6228291 0.6402223 0.6417850 0.6535752 #> [550,] 0.27135405 0.4842020 0.4882325 0.5237595 0.5327186 0.5418878 0.5643148 #> [551,] 0.80281248 0.8087226 0.9071681 0.9365881 0.9549767 0.9721787 1.0265847 #> [552,] 0.71862846 0.7597058 0.8234266 0.8728537 0.9222713 0.9789055 1.0440381 #> [553,] 0.45094915 0.8421501 0.8582952 0.8886290 0.9141369 0.9763113 0.9821884 #> [554,] 0.13934368 0.2428452 0.3342411 0.3651141 0.3679166 0.3829084 0.4805272 #> [555,] 0.27237342 0.4441723 0.5132193 0.5338823 0.6621273 0.6778590 0.6797562 #> [556,] 0.34658311 0.3844862 0.5832709 0.6088823 0.6749278 0.6793633 0.6951039 #> [557,] 0.39100186 0.8816674 0.9568694 0.9716891 1.0118846 1.0173781 1.0519565 #> [558,] 0.31072384 0.4064494 0.4824160 0.4943451 0.4961898 0.5423066 0.5552995 #> [559,] 0.36175024 0.4205963 0.4667636 0.5322349 0.5562671 0.5765295 0.5846818 #> [560,] 0.37049110 0.7604475 0.7650716 0.8009941 0.8012898 0.8748209 0.8785264 #> [561,] 0.21399890 0.5146832 0.5962294 0.6301110 0.6329947 0.6412104 0.6431744 #> [562,] 0.33083441 0.4327513 0.4589491 0.5305996 0.5625505 0.6008497 0.6177383 #> [563,] 0.39939033 0.5776140 0.6556661 0.6749278 0.6872535 0.7165905 0.7329333 #> [564,] 0.54723126 0.5513452 0.6829526 0.7028998 0.7263323 0.7384764 0.7391128 #> [565,] 0.50873514 0.5717532 0.6554691 0.6807494 0.6955825 0.7046757 0.7062653 #> [566,] 0.33143558 0.4451621 0.4826764 0.4883416 0.5127696 0.5912516 0.5924125 #> [567,] 1.41926620 1.4453761 1.5806618 1.6063033 1.6172418 1.6276178 1.6809096 #> [568,] 0.32319175 0.4648272 0.5217926 0.5503828 0.5627264 0.6550708 0.7109863 #> [569,] 0.56700865 0.5871101 0.6226706 0.6274324 0.6453148 0.7142274 0.7158303 #> [570,] 0.43535072 0.4501754 0.5306193 0.5830972 0.5962874 0.6341743 0.6489536 #> [571,] 0.13267935 0.1393437 0.4187954 0.4336526 0.4364860 0.4371461 0.4445022 #> [572,] 0.36563150 0.4882325 0.5004472 0.5279354 0.5432098 0.5498350 0.5527091 #> [573,] 0.50642499 0.5753730 0.6208701 0.6528180 0.6671471 0.6804542 0.6851323 #> [574,] 0.48378059 0.6244787 0.6473689 0.6603144 0.7580379 0.7597058 0.7760302 #> [575,] 0.37185869 0.3924534 0.4451621 0.5187923 0.5219579 0.5244126 0.5347960 #> [576,] 0.23445424 0.2852333 0.4086348 0.4174691 0.4786173 0.4985297 0.5030537 #> [577,] 0.37256625 0.3725824 0.4192504 0.4744331 0.5042462 0.5308093 0.5538165 #> [578,] 0.37326479 0.5838672 0.6528180 0.6782440 0.7643309 0.7686223 0.7750488 #> [579,] 0.47903449 0.5110472 0.5121367 0.5338226 0.6714041 0.6867017 0.7523310 #> [580,] 0.42311058 0.6695476 0.7311306 0.9776841 1.0525541 1.0632890 1.0634033 #> [581,] 0.57508362 0.6840445 0.8620530 0.9108403 0.9978395 1.0064777 1.0194699 #> [582,] 0.76091799 0.7711151 0.8032878 0.8050413 0.8144492 0.8472938 0.9161961 #> [583,] 0.23444596 0.3015421 0.3070175 0.4174691 0.4489872 0.4617600 0.4706032 #> [584,] 0.27226466 0.8079009 0.8499023 1.0784853 1.0893290 1.1458741 1.1687427 #> [585,] 0.44220434 0.4669321 0.5040527 0.5766456 0.6187411 0.6245348 0.6274126 #> [586,] 0.29866995 0.5317214 0.6603144 0.7035090 0.7042937 0.7435667 0.8003562 #> [587,] 0.27397800 0.3872981 0.5268575 0.5447722 0.5489092 0.6181197 0.6198561 #> [588,] 0.23154039 0.5680358 0.6208568 0.6608245 0.7288602 0.7413602 0.7439166 #> [589,] 0.37440467 0.5032150 0.5277032 0.5510784 0.5646986 0.5658666 0.5828211 #> [590,] 0.64620569 0.6604637 0.7526997 0.7527220 0.8270891 0.8307374 0.8406601 #> [591,] 0.40406787 0.4514422 0.5745288 0.5820708 0.6177383 0.6246316 0.6402169 #> [592,] 0.38434510 0.3900757 0.3932452 0.5887325 0.6267533 0.6392327 0.6516217 #> [593,] 0.38066893 0.4327513 0.4683940 0.4974019 0.5355726 0.6497441 0.7012273 #> [594,] 0.38085583 0.4536409 0.4904569 0.5187908 0.5235230 0.5986820 0.6696586 #> [595,] 0.53502656 0.6239770 0.6597689 0.6757738 0.7233776 0.7877170 0.8047881 #> [596,] 0.27831665 0.3010408 0.3120374 0.3287522 0.3432095 0.4494279 0.4723983 #> [597,] 0.37616048 0.6107151 0.6290967 0.6931054 0.6972586 0.7213988 0.7268775 #> [598,] 0.76376970 0.8028125 1.4043754 1.4072762 1.4190972 1.4209938 1.4947212 #> [599,] 0.44182012 0.7566974 0.8450309 0.8655723 0.9357569 1.0665545 1.0948709 #> [600,] 0.55397237 0.7960216 0.8821900 0.9143359 0.9184174 0.9304678 0.9681963 #> [601,] 0.45940455 0.6344127 0.7640671 0.8751113 0.9069941 0.9339558 0.9425497 #> [602,] 0.24263568 0.4617965 0.4862838 0.4935583 0.5058961 0.5164612 0.6160175 #> [603,] 0.24835875 0.3899307 0.4419782 0.4938952 0.5140316 0.5292019 0.6025951 #> [604,] 0.33306395 0.4201660 0.5164836 0.5178701 0.5361367 0.5477875 0.5745749 #> [605,] 0.40116324 0.4737726 0.4747191 0.4929756 0.5054670 0.5098325 0.5183205 #> [606,] 0.26497430 0.6516321 0.7057181 0.7326479 0.8690916 0.9142811 0.9759745 #> [607,] 0.43233153 0.6705407 0.7719599 0.7760562 0.7888541 0.8285201 0.8285825 #> [608,] 0.21124475 0.7965124 0.8499023 0.8638296 0.9862154 1.0242589 1.0566198 #> [609,] 0.17043615 0.5791031 0.6119211 0.7568780 0.7933537 0.9401932 0.9784879 #> [610,] 1.27251818 1.4007409 1.5872242 1.6502714 1.6759689 1.7931794 1.7962503 #> [611,] 0.35056102 0.5386376 0.6705407 0.8652738 0.8902867 0.9165879 0.9442007 #> [612,] 0.36081713 0.4053927 0.4144134 0.4205963 0.4209899 0.4413732 0.5342375 #> [613,] 0.32998788 0.4780872 0.5829748 0.6451118 0.6516321 0.6681459 0.7481556 #> [614,] 0.35146422 0.3814274 0.5201222 0.7057621 0.7107186 0.7137567 0.7610594 #> [615,] 0.36291605 0.4214459 0.4391736 0.6600433 0.6886020 0.7072703 0.7096227 #> [616,] 0.34646260 0.4265521 0.4487823 0.4702363 0.4947684 0.4957363 0.4985146 #> [617,] 0.62805720 0.6972586 0.7227889 1.0564521 1.0855136 1.0942662 1.1260716 #> [618,] 0.31136211 0.5369502 0.5598178 0.5751151 0.6118213 0.6281894 0.6608245 #> [619,] 1.11726240 1.3848146 1.6063874 1.6113179 1.6482571 1.8290001 1.8811300 #> [620,] 0.38538472 0.4354695 0.4614661 0.6278003 0.7072703 0.7150355 0.7591022 #> [621,] 0.36424210 0.4117843 0.4445334 0.5619969 0.5867999 0.5987444 0.6254579 #> [622,] 0.42107740 0.5226775 0.6905425 0.8072431 0.8114114 0.8218081 0.8353734 #> [623,] 0.90251624 0.9179658 1.0056713 1.0084805 1.1554840 1.3880295 1.4135907 #> [624,] 0.20286252 0.3610021 0.4372544 0.4740533 0.4997761 0.5182454 0.5524235 #> [625,] 0.25229778 0.4824022 0.5822296 0.6059301 0.6334506 0.6447556 0.6600611 #> [626,] 0.55947715 0.5916953 0.6345276 0.6698491 0.7170007 0.7471335 0.7557454 #> [627,] 0.46573585 0.4878851 0.5195055 0.5201538 0.5315147 0.5350378 0.6540243 #> [628,] 0.93623884 1.0977995 1.1947872 1.2876165 1.2998117 1.4436202 1.4755800 #> [629,] 0.44825377 0.5195055 0.5581383 0.5637833 0.5975474 0.5977062 0.6021255 #> [630,] 0.37536730 0.5164301 0.5531608 0.6666422 0.7220469 0.7574397 0.7691004 #> [631,] 0.32903489 0.3839844 0.4796330 0.5315577 0.5368963 0.5412133 0.5453723 #> [632,] 0.49740194 0.5447744 0.6280775 0.6527705 0.6697146 0.7372376 0.7830272 #> [633,] 1.11726240 1.2423258 1.5862658 1.6544705 1.6930723 1.7550223 1.8218150 #> [634,] 0.27820372 0.3253701 0.3660151 0.5369217 0.6020524 0.7003384 0.7089577 #> [635,] 0.23445424 0.4048737 0.4322466 0.4608005 0.4648813 0.4977342 0.5050785 #> [636,] 0.40640874 0.5834456 0.6100679 0.6851942 0.7146722 0.7241467 0.7301356 #> [637,] 0.46252459 0.6297303 0.6331314 0.7003384 0.8112534 0.8124345 0.8651220 #> [638,] 0.60088987 0.6339919 0.6388741 0.6474137 0.6474838 0.6696586 0.6988707 #> [639,] 0.37734773 0.4590026 0.5390705 0.5921952 0.6671026 0.6974902 0.7090112 #> [640,] 0.21399890 0.4031256 0.5151704 0.5494136 0.5664058 0.6006307 0.6424917 #> [641,] 0.29207978 0.5401549 0.5474909 0.5524235 0.5796832 0.6059761 0.6585259 #> [642,] 0.29137886 0.5112414 0.5187908 0.5307719 0.5494953 0.5495123 0.5756728 #> [643,] 0.44085544 0.5514176 0.5632790 0.7421846 0.7821312 0.7895657 0.7912623 #> [644,] 0.35281360 0.3912673 0.4989607 0.5335707 0.5414298 0.6156885 0.6527913 #> [645,] 0.78117363 0.8633145 1.0734705 1.0875151 1.0911114 1.1794586 1.2188109 #> [646,] 0.69162439 0.7439166 0.9022142 0.9147525 0.9972726 1.0179116 1.0717781 #> [647,] 0.58197751 0.7391057 0.8008340 0.9239508 0.9941564 1.0069490 1.0591957 #> [648,] 0.25940823 0.2881677 0.4069067 0.4167995 0.4490281 0.5004484 0.5369063 #> [649,] 0.43361530 0.4576543 0.5310053 0.5408687 0.5907444 0.6274188 0.6276467 #> [650,] 0.55397237 0.7664153 0.9252118 0.9803168 1.0258493 1.0333822 1.0508506 #> [651,] 0.25537990 0.3510083 0.3896725 0.4092183 0.4114427 0.5009519 0.5058887 #> [652,] 0.50856221 0.6573377 0.6668035 0.7638306 0.8564216 0.8851545 0.9487532 #> [653,] 0.72771956 0.7701128 0.8085942 0.8565908 0.8612755 0.8616950 0.9033424 #> [654,] 0.63004997 0.6790883 0.6930265 0.7471025 0.7796087 0.8001579 0.8162489 #> [655,] 0.62415853 0.6790883 0.8149223 0.8164451 0.8245908 0.8249625 0.8542619 #> [656,] 0.36371447 0.5336176 0.5416509 0.5799145 0.5927017 0.5963091 0.5968608 #> [657,] 0.26592251 0.3839797 0.5314992 0.5582578 0.6573676 0.6859789 0.6869186 #> [658,] 0.96860515 1.5596684 1.7214903 1.8390047 1.8732701 2.0095196 2.0340435 #> [659,] 0.33992786 0.6787343 0.7953610 0.7995270 0.8406601 0.8451771 0.8946463 #> [660,] 0.57850160 0.6149651 0.6331314 0.7958120 0.8032878 0.8500671 0.8728612 #> [661,] 0.59645976 0.6882759 0.7294277 0.7314181 0.7398650 0.7451284 0.7905992 #> [662,] 0.51562884 0.6948035 0.6954605 0.7612766 0.7820931 0.7821108 0.8110738 #> [663,] 0.63575836 0.6640708 0.7659304 0.7776300 0.8079934 0.8763540 0.9216423 #> [664,] 0.32642149 0.5204109 0.5661041 0.5980774 0.6062228 0.6408244 0.6936385 #> [665,] 0.34583666 0.5094248 0.5822296 0.6019879 0.6508259 0.6516076 0.6744814 #> [666,] 0.43381139 0.4546433 0.5788702 0.6756414 0.7471335 0.8011819 0.8455020 #> [667,] 0.53467357 0.5373530 0.5983490 0.5995905 0.6462057 0.7008084 0.7061636 #> [668,] 0.37654869 0.4283986 0.5094248 0.6696494 0.7168134 0.7303654 0.7825482 #> [669,] 0.45033714 0.5345327 0.5369502 0.5948893 0.5985664 0.6389778 0.6726457 #> [670,] 0.83931843 0.8628560 0.9748641 0.9761721 1.0343402 1.0928670 1.0943788 #> [671,] 0.36481889 0.3875701 0.4533006 0.4579549 0.5029217 0.5244214 0.5263868 #> [672,] 0.53423748 0.6205033 0.6237384 0.6597708 0.6637734 0.6859404 0.7042937 #> [673,] 0.22512765 0.3954141 0.4057245 0.4066714 0.5754289 0.5981497 0.6459042 #> [674,] 0.41796157 0.5450184 0.5485833 0.5590485 0.5908792 0.5971899 0.6076136 #> [675,] 0.41441336 0.4468249 0.4590581 0.4647662 0.5223277 0.5907237 0.6244787 #> [676,] 0.35057975 0.6053708 0.6305607 0.7109863 0.7912119 0.8047230 0.8227136 #> [677,] 0.47357398 0.6408472 0.7471467 0.7822333 0.8163211 0.8184313 0.8571742 #> [678,] 0.36291605 0.4614661 0.5432727 0.5830972 0.5858808 0.6132328 0.6351667 #> [679,] 0.34692452 0.4372083 0.5217926 0.5248431 0.5675716 0.5873715 0.6131153 #> [680,] 0.47956545 0.7061636 0.7550567 0.7569945 0.8113778 0.8205277 0.8486483 #> [681,] 0.69219525 0.7959432 0.7989554 0.8713739 0.8858275 0.8900467 0.9009966 #> [682,] 0.23817426 0.2808169 0.3592144 0.5177640 0.5288084 0.5930796 0.5983421 #> [683,] 0.38064751 0.4014603 0.4293245 0.4413397 0.5164612 0.5171589 0.5566918 #> [684,] 0.27374113 0.4327614 0.5156453 0.5587885 0.6472249 0.7160519 0.7567857 #> [685,] 0.36601515 0.3742301 0.4121055 0.5945311 0.7146722 0.7312741 0.7746565 #> [686,] 0.52802346 0.5513452 0.6314889 0.6345006 0.7183474 0.7496084 0.7939503 #> [687,] 0.50375515 0.5870666 0.6666116 0.7169169 0.7286017 0.8465621 0.8663543 #> [688,] 0.24611185 0.3370508 0.5064250 0.5171589 0.5526758 0.6160175 0.6205033 #> [689,] 0.41794591 0.6505936 0.6588649 0.7485899 0.7491659 0.7548487 0.8576855 #> [690,] 0.48897230 0.5338226 0.5631145 0.6876064 0.7249473 0.7610390 0.7665936 #> [691,] 0.25265749 0.3648189 0.4231851 0.4983350 0.5054447 0.5058957 0.5379967 #> [692,] 0.37194528 0.3896725 0.4518190 0.4888065 0.5286499 0.5368963 0.5411184 #> [693,] 0.42115831 0.5520766 0.5550128 0.9107576 0.9213139 0.9311899 1.0733480 #> [694,] 0.30665876 0.3717870 0.3719453 0.4997310 0.5480230 0.6036892 0.6316852 #> [695,] 0.27235215 0.3682785 0.3877919 0.4563907 0.4686757 0.4790775 0.5102164 #> [696,] 0.44798338 0.5244854 0.8689665 0.9423840 0.9442252 0.9472707 0.9680754 #> [697,] 0.27562530 0.3560464 0.6615503 0.7733899 0.7821108 0.7931469 0.8066735 #> [698,] 0.64271917 0.8104446 0.8518589 0.9819177 1.0527555 1.0704130 1.1284419 #> [699,] 0.37536730 0.4279793 0.5698067 0.6805403 0.6930791 0.7037863 0.7775509 #> [700,] 0.77560656 0.8556852 1.1021752 1.1159335 1.1821154 1.2422865 1.2640127 #> [701,] 0.54944796 0.6858087 0.7160519 0.7161151 0.7514740 0.8011942 0.8040033 #> [702,] 0.43725443 0.4518521 0.5293771 0.5417388 0.5617081 0.5693952 0.5809123 #> [703,] 0.27373125 0.3681418 0.5585375 0.5629239 0.6276305 0.6427449 0.6776797 #> [704,] 0.44727767 0.6573590 0.7268775 0.7276097 0.7664300 0.7956606 0.8151043 #> [705,] 0.48215564 0.7168258 0.7797592 0.8584295 0.8881306 0.9258788 1.1747701 #> [706,] 0.32987731 0.5539253 0.6512268 0.6805731 0.7051882 0.7770416 0.8028310 #> [707,] 0.34290313 0.3916555 0.4384343 0.4439449 0.4451151 0.4652345 0.4779779 #> [708,] 0.72429204 0.8485679 0.9030081 1.0299906 1.0379055 1.0509786 1.0511232 #> [709,] 0.26411020 0.5763736 0.5913644 0.6009726 0.6159768 0.6332756 0.6387827 #> [710,] 0.59236817 0.6317479 0.6552598 0.6905037 0.7620866 0.7751092 0.7835180 #> [711,] 0.49777424 0.4986955 0.5246994 0.6800699 0.6932700 0.7195649 0.7310794 #> [712,] 0.30585146 0.3569595 0.3610021 0.4310536 0.5617081 0.6139850 0.6173314 #> [713,] 0.22730905 0.3101792 0.3896564 0.6595335 0.6614731 0.6616534 0.7026276 #> [714,] 0.73113062 0.7404447 0.7598041 0.8894494 0.9218340 0.9772646 1.0149214 #> [715,] 0.53546582 0.6488153 0.6827288 0.7065292 0.7176423 0.7196598 0.7858885 #> [716,] 0.31706208 0.3824672 0.4997944 0.5211668 0.5414298 0.5734929 0.6104140 #> [717,] 0.29573626 0.5202421 0.5274417 0.5923872 0.6130515 0.6268764 0.6668035 #> [718,] 0.51787090 0.6681381 0.6921952 0.6945929 0.7616721 0.7728200 0.7767504 #> [719,] 0.44511515 0.4773816 0.5208903 0.5221296 0.5708846 0.6036892 0.6071663 #> [720,] 0.31021064 0.3667173 0.4148328 0.5036602 0.5388405 0.5443826 0.5743608 #> [721,] 0.76376970 1.4945173 1.5618344 1.6583313 1.7226438 1.7337409 1.8270387 #> [722,] 0.57910311 0.6115139 0.6685038 0.8223332 0.8940572 0.9323082 0.9573444 #> [723,] 0.40406787 0.4589491 0.5738974 0.6282063 0.6353663 0.6645386 0.6905037 #> [724,] 0.26361352 0.3612065 0.5829462 0.6409314 0.6512282 0.6560213 0.6666938 #> [725,] 0.37766750 0.4877192 0.4977504 0.6141899 0.6374305 0.6463524 0.7035879 #> [726,] 0.40235927 0.7095820 0.7211537 0.7390536 0.7565762 0.8014326 0.8093141 #> [727,] 0.25229778 0.5443461 0.5480230 0.5517974 0.5632433 0.6230362 0.6285004 #> [728,] 0.60180716 0.6345307 0.7319469 0.7498497 0.7510102 0.7522254 0.7795453 #> [729,] 0.79251236 0.9169129 0.9624720 1.0016268 1.1132133 1.1183533 1.1703586 #> [730,] 0.21651187 0.3307715 0.3902055 0.4048998 0.4300517 0.5080894 0.5970754 #> [731,] 0.66354613 0.8080975 0.8122482 0.8448919 0.8598953 0.8703998 0.8736572 #> [732,] 0.76793703 0.9746944 1.0603844 1.1809858 1.3134783 1.3312986 1.4184369 #> [733,] 1.41806022 1.4979673 1.5334406 1.6314486 1.6429539 1.7030105 1.7172150 #> [734,] 0.35753455 0.3949244 0.4214572 0.4281875 0.4408832 0.4810949 0.4826539 #> [735,] 0.16898732 0.4774863 0.4997761 0.5450184 0.5577090 0.5654247 0.5790945 #> [736,] 0.27779408 0.4117843 0.4624789 0.4869968 0.5152503 0.5926834 0.6532210 #> [737,] 0.45094915 0.7870609 0.8365102 0.9197372 0.9669580 0.9771254 1.0148486 #> [738,] 0.33982358 0.7319139 0.7776584 0.7905012 0.8014663 0.8392729 0.9160105 #> [739,] 0.39752836 0.4628165 0.5139286 0.5557389 0.6299496 0.6648775 0.6892268 #> [740,] 0.35502072 0.5015861 0.5160277 0.6083196 0.6489536 0.6874109 0.6959152 #> [741,] 0.35694620 0.4291263 0.5064923 0.6014621 0.6239316 0.6610865 0.6857948 #> [742,] 0.54794447 0.5909986 0.6224500 0.6278003 0.6351667 0.6394549 0.6607352 #> [743,] 0.53400936 0.5574238 0.7236006 0.7286656 0.7608778 0.7813525 0.8909473 #> [744,] 0.81247524 1.0111774 1.0421754 1.0457865 1.1062559 1.1693766 1.2517755 #> [745,] 0.33705076 0.3842782 0.4068962 0.5566918 0.5967728 0.6206061 0.6247646 #> [746,] 0.57869430 0.6643098 0.6963693 0.7499769 0.7532504 0.7787472 0.7840776 #> [747,] 0.34583666 0.3765487 0.6485353 0.6536423 0.6600611 0.6950010 0.7608483 #> [748,] 0.55392527 0.6011789 0.6107278 0.6565608 0.6666422 0.7067511 0.7295667 #> [749,] 1.28096940 1.3035623 1.4027108 1.4503325 1.5398309 1.6228690 1.6251855 #> [750,] 0.75682393 0.9759578 1.0086455 1.0171610 1.0781083 1.0961189 1.1095360 #> [751,] 0.47282196 0.5538007 0.6370667 0.7604574 0.8299728 0.8469233 0.8905465 #> [752,] 0.50321505 0.6012756 0.6440738 0.6763688 0.6811249 0.6878932 0.7024877 #> [753,] 1.12512471 1.5002459 1.6186691 1.6746142 1.6971242 1.7244525 1.7529364 #> [754,] 0.19126137 0.2539434 0.3530391 0.4211737 0.4610017 0.4735912 0.4812976 #> [755,] 0.74738137 0.8490869 0.8598790 0.8819419 0.8834692 0.8956083 0.9020044 #> [756,] 0.40863482 0.4201660 0.4875000 0.4977342 0.5139286 0.5250651 0.5548998 #> [757,] 0.59144499 0.6451118 0.6556036 0.6960641 0.7246572 0.7526997 0.7562335 #> [758,] 0.29034567 0.3153831 0.4148944 0.4420203 0.5036863 0.5100027 0.5967067 #> [759,] 0.24282195 0.5158832 0.5926552 0.6115119 0.6196056 0.6231284 0.6441286 #> [760,] 0.34646260 0.4576543 0.5116226 0.5403305 0.5449393 0.5790349 0.5962150 #> [761,] 0.28637481 0.5161356 0.6072845 0.6403226 0.6589856 0.6618140 0.6619228 #> [762,] 0.22663217 0.7094030 0.7527483 0.8047230 0.8173328 0.8380186 0.8391967 #> [763,] 0.34765312 0.4212806 0.4268198 0.4444787 0.6220212 0.6292556 0.6796196 #> [764,] 0.25529604 0.4244684 0.4925101 0.5098325 0.5315147 0.5866931 0.6092869 #> [765,] 0.63149423 0.6344471 0.8072431 0.8556852 0.9310300 0.9539625 0.9673221 #> [766,] 0.41353422 0.4800824 0.4997822 0.5004472 0.5308093 0.5517345 0.5817011 #> [767,] 0.17192229 0.3120374 0.3274886 0.3970135 0.4186993 0.4204261 0.4487641 #> [768,] 0.49960642 0.5680358 0.5989883 0.6187675 0.6281894 0.6901499 0.7043782 #> [769,] 0.53075473 0.5948165 0.7371089 0.7845360 0.8615314 0.8746930 0.9299736 #> [770,] 1.08831280 1.1290978 1.2708480 1.2778259 1.2814328 1.3589799 1.4092152 #> [771,] 0.84050232 1.2081018 1.3537660 1.3676314 1.3998444 1.4217575 1.4250399 #> [772,] 0.49874972 0.5116226 0.5348619 0.5606675 0.6284367 0.6302704 0.6997016 #> [773,] 1.01823009 1.1625109 1.1724161 1.2807257 1.3346802 1.3372022 1.3953495 #> [774,] 0.42107740 0.5228137 0.5782739 0.6344471 0.6408634 0.6552209 0.7278126 #> [775,] 1.44425777 1.5024005 1.5148908 1.5275841 1.5392507 1.5788080 1.6184525 #> [776,] 0.51100732 0.6797352 0.6979795 0.7239671 0.7271320 0.7607239 0.7625511 #> [777,] 0.27986585 0.6141458 0.6186548 0.6930265 0.6978360 0.7064858 0.7565171 #> [778,] 0.61968631 0.6495568 0.6692727 0.6764916 0.6782440 0.7079180 0.7088686 #> [779,] 0.45979711 0.4943986 0.5124604 0.5573640 0.5791735 0.6387119 0.6584954 #> [780,] 0.33118191 0.3463831 0.4397189 0.4891228 0.4944305 0.5297014 0.5306987 #> [781,] 0.31406018 0.3746252 0.4828882 0.5987444 0.6003784 0.6580683 0.6789879 #> [782,] 0.26858368 0.3672683 0.4606300 0.4823437 0.4895812 0.5083644 0.5182948 #> [783,] 0.80509624 0.9642723 0.9748641 0.9932183 1.0175262 1.0440972 1.0506605 #> [784,] 0.34632790 0.5812384 0.6068026 0.6817921 0.7782817 0.7818776 0.8135992 #> [785,] 0.95031389 1.1390276 1.1488276 1.1717946 1.1885282 1.2093484 1.2547582 #> [786,] 0.57850160 0.7428819 0.7626059 0.7664228 0.7746565 0.8124345 0.8786955 #> [787,] 0.23817426 0.3740172 0.4150215 0.5086507 0.5239781 0.5252724 0.5643148 #> [788,] 0.80083402 0.8688661 0.8952005 0.9025712 1.0016799 1.0082361 1.0213946 #> [789,] 0.47662769 0.5948893 0.6012587 0.7134555 0.7582208 0.8186225 0.8845991 #> [790,] 0.38039938 0.4066714 0.4787794 0.5510211 0.6025951 0.6032960 0.6046658 #> [791,] 0.52281365 0.6314942 0.6905425 0.7001828 0.7370280 0.7496531 0.8518589 #> [792,] 0.87344003 0.8786955 0.9130445 0.9356086 1.0061377 1.0099780 1.0406190 #> [793,] 0.39295645 0.6883166 0.7067511 0.7409552 0.7463260 0.9268664 0.9380089 #> [794,] 0.45337305 0.5127075 0.5475791 0.5705612 0.5791735 0.5867750 0.6463524 #> [795,] 0.38074638 0.5428344 0.7870609 0.8122835 0.8285331 0.9000570 1.0503715 #> [796,] 0.65741762 0.6595386 0.6786018 0.6805422 0.6911190 0.6945954 0.6964581 #> [797,] 0.42601353 0.6297133 0.8849188 0.9089493 0.9556250 0.9724911 1.0142959 #> [798,] 0.07900148 0.2134423 0.3366718 0.3990870 0.5151064 0.5494136 0.6121996 #> [799,] 0.75505674 0.7705614 0.7789857 0.8690916 0.9599744 0.9880098 1.0048817 #> [800,] 0.29071253 0.5367478 0.5514176 0.5993767 0.6653796 0.6892158 0.7155752 #> [801,] 0.45390851 0.4654537 0.4702363 0.4998753 0.5088901 0.5587419 0.5677993 #> [802,] 0.66430980 0.7860686 0.7917791 0.8035448 0.8512671 0.8943658 0.9688910 #> [803,] 0.43339983 0.4889723 0.5015533 0.5121367 0.5496062 0.5623404 0.6369267 #> [804,] 0.22663217 0.6737981 0.6864307 0.7741433 0.7760244 0.7914695 0.8054832 #> [805,] 0.34928645 0.3683386 0.3883146 0.5029217 0.5178635 0.5493735 0.5851822 #> [806,] 0.96860515 1.5474225 1.6916781 1.7508751 1.8336938 1.8432269 1.8601614 #> [807,] 0.81823719 0.8905984 0.9877874 1.0080292 1.0574307 1.0694985 1.0751693 #> [808,] 0.51274896 0.5794487 0.5989883 0.6016442 0.6227651 0.6675875 0.7007486 #> [809,] 0.41927666 0.4758018 0.5229936 0.6353158 0.6485353 0.7109808 0.7184761 #> [810,] 0.52007377 0.5829748 0.7459431 0.7847037 0.7953610 0.8120088 0.8905465 #> [811,] 0.52670130 0.5495123 0.5750012 0.5960382 0.6278278 0.6401071 0.6482104 #> [812,] 0.30912486 0.3221767 0.3445838 0.3679166 0.3921957 0.4167995 0.4364860 #> [813,] 0.26707323 0.3675232 0.3676174 0.4747191 0.5430170 0.5577346 0.5612849 #> [814,] 0.66246718 0.6764916 0.6870574 0.7056267 0.7653108 0.7741433 0.7975241 #> [815,] 0.46401044 0.6718409 0.6750776 0.6944161 0.8141279 0.8415446 0.8674535 #> [816,] 0.40585333 0.5158832 0.5310501 0.6237231 0.6283424 0.6308819 0.6735509 #> [817,] 0.51787090 0.7322052 0.7942737 0.8858275 0.8992017 0.9529758 0.9777157 #> [818,] 0.44878229 0.4693085 0.5348619 0.5403305 0.5507061 0.5780262 0.5890797 #> [819,] 0.40312556 0.4724889 0.5146832 0.6540016 0.6699648 0.7296882 0.7548694 #> [820,] 0.52243421 0.9528990 0.9566207 0.9690615 0.9959560 1.0041419 1.0048973 #> [821,] 0.61876751 0.6220356 0.6670638 0.7302815 0.7320587 0.7336890 0.7419142 #> [822,] 0.45033714 0.5105971 0.6909721 0.7200852 0.7301892 0.7582208 0.7617684 #> [823,] 0.27752292 0.3530391 0.3569595 0.4105468 0.4740533 0.5041885 0.5822294 #> [824,] 0.92369646 0.9935013 1.0084146 1.0603844 1.1688281 1.2238993 1.2536617 #> [825,] 0.93623884 0.9670659 0.9716891 1.0213059 1.0791829 1.0908452 1.2003763 #> [826,] 0.51547045 0.5428344 0.5513518 0.7418054 0.7596351 0.8207332 0.8464236 #> [827,] 0.36357445 0.3906010 0.4358474 0.4812976 0.4851370 0.5204109 0.5300283 #> [828,] 0.21918209 0.3515583 0.4460121 0.4735912 0.5015861 0.5055582 0.5928341 #> [829,] 0.47248888 0.7390052 0.7862303 0.7934345 0.8240780 0.8774079 0.8816378 #> [830,] 0.61443490 0.7276547 0.7430517 0.7568780 0.8004466 0.8797075 0.8800598 #> [831,] 0.33721916 0.4333998 0.4494589 0.5627313 0.5631145 0.6392648 0.6425977 #> [832,] 0.35584388 0.3608171 0.4589638 0.4647662 0.5355108 0.5409202 0.5661899 #> [833,] 0.31425204 0.3975284 0.5110073 0.6375903 0.6674449 0.7254397 0.7415100 #> [834,] 0.43093765 0.4821556 0.6053708 0.7107931 0.8125750 0.9832103 1.0043076 #> [835,] 0.84050232 1.4143479 1.5009154 1.6691936 1.6755054 1.6854697 1.8534532 #> [836,] 0.46890975 0.4922400 0.5605946 0.5643164 0.5666706 0.6025482 0.6248032 #> [837,] 0.29686347 0.4882768 0.6842423 0.7095820 0.7367931 0.8004466 0.8982905 #> [838,] 0.28297282 0.4104397 0.5606675 0.5922417 0.5930066 0.6435596 0.6506995 #> [839,] 0.66046375 0.7587216 0.7705614 0.7822333 0.8118788 0.9794113 0.9822419 #> [840,] 0.20603309 0.3096341 0.3153831 0.4322466 0.4852165 0.4985219 0.5298338 #> [841,] 0.51426500 0.5160277 0.5448443 0.6115975 0.6209217 0.6602422 0.6673354 #> [842,] 0.87682996 0.8871343 0.9657971 0.9972174 1.0056713 1.0089594 1.0226326 #> [843,] 0.53344981 0.5666706 0.5705612 0.5804025 0.6160059 0.6285987 0.6888110 #> [844,] 0.63329928 0.7035094 0.8909473 0.9090553 0.9450609 1.0034860 1.0322017 #> [845,] 0.59733251 0.9069941 1.0315972 1.0651302 1.1004546 1.1819033 1.2104695 #> [846,] 0.35584388 0.4053927 0.4569943 0.4801926 0.5726849 0.5907237 0.6562850 #> [847,] 0.44945895 0.4669321 0.4710597 0.5335359 0.5496062 0.5714739 0.5741004 #> [848,] 0.41227811 0.4813064 0.5127075 0.5322349 0.5396463 0.5524752 0.5573640 #> [849,] 0.60207570 0.6332993 0.7541340 0.7612766 0.8339584 0.8695335 0.9403258 #> [850,] 0.56667337 0.5690583 0.6806436 0.6841255 0.7057482 0.7196784 0.7266933 #> [851,] 0.60644755 0.6068605 0.6088823 0.6313599 0.6381212 0.7043648 0.8091027 #> [852,] 0.46309918 0.5503828 0.5671789 0.6737981 0.6893140 0.6897462 0.7094030 #> [853,] 0.54477441 0.7328934 0.7748304 0.7982394 0.8948322 0.9052024 0.9099494 #> [854,] 0.42219845 0.5288742 0.5313286 0.5314622 0.5489092 0.6208746 0.6326844 #> [855,] 0.36655786 0.4248826 0.5211668 0.5812758 0.6696843 0.6909388 0.7411261 #> [856,] 0.66906906 0.6929659 0.7029221 0.7363367 0.8576981 0.9298216 0.9509733 #> [857,] 0.10961117 0.5561111 0.5914676 0.6253939 0.6720140 0.6791187 0.6825467 #> [858,] 0.34458382 0.3564569 0.4068168 0.4490281 0.4817999 0.5008421 0.5009519 #> [859,] 0.53900013 0.7596351 0.7809893 0.7977962 0.8110831 0.8285331 0.8717266 #> [860,] 0.26056421 0.3320452 0.3675232 0.4011632 0.4244684 0.5245809 0.5277923 #> [861,] 0.53863757 0.5741677 0.7806849 0.9257462 0.9832158 1.0343402 1.0652415 #> [862,] 0.48019263 0.5409202 0.6188566 0.6728008 0.6845853 0.6976790 0.6983050 #> [863,] 0.16898732 0.4179616 0.4874333 0.5182454 0.5581737 0.5727718 0.5833047 #> [864,] 0.49674503 0.8623701 0.9361598 1.0285802 1.0919945 1.1454716 1.1467348 #> [865,] 0.74356670 0.8506358 0.8988180 0.9653260 0.9658914 1.0456813 1.1495489 #> [866,] 0.19274415 0.5890797 0.6656190 0.6929399 0.7092455 0.7130241 0.7300549 #> [867,] 0.82780332 0.8682733 0.8819994 0.9382707 0.9624720 1.0012975 1.0401675 #> [868,] 0.80504130 1.0132570 1.1390276 1.2647887 1.2893205 1.3519468 1.4066961 #> [869,] 0.38993068 0.4592070 0.4627760 0.4812428 0.4883416 0.5284553 0.5554485 #> [870,] 0.41300116 0.4726289 0.5145285 0.5831077 0.6027282 0.6196082 0.6260452 #> [871,] 0.35872808 0.5216523 0.5218097 0.5943798 0.8396827 0.8405865 0.8480663 #> [872,] 0.66061744 0.9751220 0.9887768 1.0250606 1.1462059 1.1488878 1.1533324 #> [873,] 0.55047599 0.9306545 0.9634239 1.0486355 1.0540035 1.1314857 1.1924546 #> [874,] 0.74044467 0.8490869 0.8839707 1.0353342 1.0634033 1.0914634 1.1081549 #> [875,] 0.36909222 0.3824672 0.4248826 0.4752431 0.5622268 0.6527913 0.7932064 #> [876,] 0.19126137 0.3194772 0.3331275 0.3635745 0.3842054 0.4105468 0.4783768 #> [877,] 0.52330958 0.5452436 0.6420042 0.6720140 0.7052971 0.7300549 0.7423220 #> [878,] 0.35754567 0.3647637 0.3856706 0.4204261 0.4580945 0.4684129 0.4723983 #> [879,] 0.48698634 0.5103997 0.5561728 0.5943264 0.6441364 0.6522988 0.6841255 #> [880,] 0.38075836 0.4420203 0.4485579 0.4658402 0.5140316 0.5446332 0.5554485 #> [881,] 0.17192229 0.2007145 0.2783166 0.3575457 0.4577139 0.4804332 0.4840932 #> [882,] 0.55742381 0.7035094 0.8815920 0.9407532 0.9665835 1.0070158 1.0256505 #> [883,] 1.06671323 1.1098776 1.1939526 1.2708891 1.3019662 1.3272026 1.3346704 #> [884,] 0.59122759 0.5924289 0.6878932 0.7469794 0.7701128 0.7939503 0.9124692 #> [885,] 0.26361352 0.4103122 0.5966234 0.6251264 0.6412155 0.6638039 0.6959499 #> [886,] 0.38142744 0.4702799 0.6183745 0.6692462 0.7132491 0.7992174 0.8603296 #> [887,] 0.41103442 0.4721603 0.4943986 0.5488501 0.5919320 0.6048693 0.6696642 #> [888,] 0.48463135 0.5583400 0.6188566 0.7207993 0.7371089 0.7768013 0.7927239 #> [889,] 0.20402766 0.2125101 0.5652156 0.6438647 0.6656291 0.6934208 0.7234980 #> [890,] 0.40644938 0.4742585 0.5110722 0.5993060 0.6140650 0.6602692 0.6758489 #> [891,] 0.31009228 0.5020218 0.5300283 0.5325700 0.5659865 0.6462933 0.6560148 #> [892,] 0.63783218 0.7207886 0.7319139 1.1488815 1.2363910 1.2394352 1.2466568 #> [893,] 0.51562884 0.6047001 0.6736678 0.7180052 0.8424851 0.8446939 0.8627030 #> [894,] 1.16058388 1.2652724 1.3307926 1.3720424 1.4087473 1.4230225 1.4442578 #> [895,] 0.76904320 0.8050931 1.0306609 1.0424817 1.0576121 1.1172876 1.1341513 #> [896,] 1.03405675 1.0807985 1.1629530 1.1726818 1.2371444 1.2431209 1.2989847 #> [897,] 0.26056421 0.2953271 0.3596759 0.3676174 0.4370006 0.4674089 0.4867114 #> [898,] 0.66295655 1.0524327 1.1454716 1.1938812 1.2348503 1.2659794 1.3541057 #> [899,] 0.26714363 0.4168866 0.4398471 0.4827427 0.4993616 0.5299611 0.5303869 #> [900,] 0.99383792 1.0207571 1.0337819 1.0365731 1.0417322 1.0553590 1.0669284 #> [901,] 0.41869934 0.4499811 0.4804332 0.4892405 0.5419171 0.5443461 0.5630225 #> [902,] 0.41001145 0.4336526 0.4356013 0.4651105 0.5229936 0.5439551 0.5553379 #> [903,] 0.44601213 0.4739712 0.4898238 0.5262443 0.5330906 0.5404534 0.5571109 #> [904,] 0.38729811 0.4356013 0.4758018 0.5605504 0.5629239 0.6072090 0.6208746 #> [905,] 0.43376457 0.6284165 0.6473181 0.6926234 0.7473814 0.7664153 0.8196974 #> [906,] 0.36889156 0.4181342 0.4353989 0.5443613 0.6925852 0.7820596 0.8175952 #> [907,] 0.35553709 0.3639759 0.4068168 0.4192504 0.4518190 0.4547488 0.4583266 #> [908,] 0.62373689 0.7183489 0.8926911 0.9374606 0.9533518 0.9709443 0.9932183 #> [909,] 0.47303215 0.5924289 0.6284165 0.6345006 0.6550931 0.6738575 0.6763688 #> [910,] 0.37272716 0.7574815 0.8620530 0.9235493 1.0293627 1.0346818 1.0506003 #> [911,] 0.29797572 0.4412957 0.5142650 0.5494740 0.6083196 0.6670661 0.6900840 #> [912,] 0.31680663 0.3592144 0.3740172 0.4725692 0.5161527 0.5982736 0.6788071 #> [913,] 0.40154240 0.5960382 0.6015735 0.6039505 0.6143347 0.6173411 0.6399215 #> [914,] 0.25529604 0.4657359 0.5176139 0.5277923 0.5430170 0.5472789 0.5484479 #> [915,] 0.52267751 0.5274417 0.7076093 0.7294261 0.7614309 0.7638306 0.7763127 #> [916,] 0.60183197 0.6690691 0.7574397 0.7667885 0.7775509 0.8601285 0.9643448 #> [917,] 0.27254531 0.3342411 0.4363043 0.4371461 0.4383080 0.4857113 0.5166758 #> [918,] 0.27820372 0.3134153 0.4121055 0.4625246 0.5752935 0.7428819 0.7835030 #> [919,] 0.22730905 0.3454661 0.4692602 0.5782066 0.7374615 0.7477163 0.7979180 #> [920,] 0.38219942 0.5233096 0.6548786 0.6736678 0.6747795 0.7224611 0.7872064 #> [921,] 0.35397953 0.4057245 0.4850417 0.5075179 0.5084299 0.5268116 0.5634698 #> [922,] 0.49108733 0.6237369 0.6983050 0.7207993 0.7317220 0.7329495 0.8043957 #> [923,] 0.57316599 0.7018844 0.7698621 0.9067110 0.9072002 0.9167711 0.9358035 #> [924,] 0.27779408 0.5067058 0.5474909 0.5619969 0.5990116 0.6007823 0.6651803 #> [925,] 0.28951401 0.3507199 0.6028122 0.6597689 0.6932175 0.7135412 0.7213954 #> [926,] 0.65566614 0.7996484 0.8013142 0.8076352 0.8094535 0.8484571 0.8515109 #> [927,] 0.27562530 0.5907578 0.6047001 0.6923461 0.6948035 0.7315810 0.7754890 #> [928,] 0.42319471 0.4619486 0.5357388 0.6964726 0.7374605 0.7408393 0.7504980 #> [929,] 0.38240555 0.6968206 0.7107931 0.7555991 0.7797592 1.0041571 1.0511232 #> [930,] 0.48905962 0.5091761 0.6165220 0.6183745 0.6802482 0.7362994 0.7480086 #> [931,] 0.46604194 0.5057079 0.5093474 0.6374305 0.6515160 0.6895867 0.7152979 #> [932,] 0.36022339 0.3990870 0.4527244 0.6065168 0.6401071 0.6408634 0.6579255 #> [933,] 0.27977522 0.3955108 0.4488479 0.4904569 0.4931540 0.5990700 0.6173134 #> [934,] 0.38153048 0.4648426 0.5635859 0.6371110 0.6375488 0.6404007 0.6428293 #> [935,] 0.44190042 0.4850204 0.5412125 0.5499718 0.5658402 0.5743258 0.5858808 #> [936,] 0.47357398 0.5110472 0.5623404 0.6583454 0.6729948 0.7008084 0.7540306 #> [937,] 0.61987271 0.7960216 0.9158306 1.0644285 1.1518480 1.1549978 1.1687427 #> [938,] 0.29207978 0.5936938 0.5990116 0.6335023 0.6814437 0.6869994 0.7562474 #> [939,] 0.10961117 0.5477442 0.5794918 0.6271646 0.6420042 0.6697815 0.6785323 #> [940,] 0.37616048 0.5179263 0.5733939 0.6280572 0.6450097 0.7735499 0.7956606 #> [941,] 0.59060294 0.5975474 0.6558207 0.6976132 0.7076012 0.7717883 0.8033412 #> [942,] 0.21344226 0.2370228 0.3082354 0.3653168 0.4527244 0.5664058 0.6008899 #> [943,] 0.37666715 0.4403305 0.4726289 0.4837092 0.5482435 0.5557389 0.5831863 #> [944,] 0.26497430 0.6674207 0.7481556 0.7606691 0.9812453 1.0048817 1.0092092 #> [945,] 0.38219942 0.5879447 0.6173491 0.6611236 0.6726969 0.6757738 0.7159151 #> [946,] 0.49533234 0.6346750 0.6884615 0.7041224 0.7872879 0.8081023 0.8233290 #> [947,] 0.62918180 0.7968490 0.8299728 0.8488415 0.9636785 0.9671748 0.9727649 #> [948,] 0.19062462 0.3290349 0.3639759 0.4651105 0.5008421 0.5286499 0.5410208 #> [949,] 0.47084221 0.6654162 0.7048069 0.7374605 0.7551687 0.8144466 0.8210441 #> [950,] 0.40585333 0.6090162 0.6438393 0.6645667 0.6893189 0.7167928 0.7535016 #> [951,] 1.71535420 1.7710182 1.8012323 1.9644858 2.0306185 2.1379828 2.1948562 #> [952,] 0.94719990 1.1703586 1.2716053 1.2792180 1.2884615 1.3356232 1.3512644 #> [953,] 0.24524756 0.3722042 0.4048998 0.4403305 0.4920482 0.5248431 0.5715971 #> [954,] 0.59171612 0.6484736 0.6593648 0.7002319 0.7342629 0.7738593 0.7823798 #> [955,] 0.36726831 0.3876206 0.3916555 0.4122333 0.4146105 0.4408832 0.4436452 #> [956,] 0.28197372 0.2951427 0.2970063 0.3015421 0.3331275 0.4610017 0.4851370 #> [957,] 0.75137243 0.7668919 0.8383914 0.8551747 0.8874871 0.8942799 0.9154840 #> [958,] 0.43970503 0.4590166 0.4673949 0.4807027 0.4878093 0.4942333 0.5067848 #> [959,] 0.21563769 0.4584524 0.4785825 0.4826764 0.4827427 0.5608112 0.5656248 #> [960,] 0.34159301 0.3900757 0.4015424 0.4751257 0.5479820 0.6047128 0.6139697 #> [961,] 1.64730746 1.7606569 1.8298994 1.8558945 1.8647541 1.9069203 2.0772323 #> [962,] 0.46116246 0.6403444 0.6522988 0.7392183 0.7429670 0.7526534 0.8085424 #> [963,] 0.20071453 0.3274886 0.3432095 0.3856706 0.4592138 0.4892405 0.5513524 #> [964,] 0.60683629 0.6517776 0.8600679 0.9081630 0.9218340 0.9547859 0.9884917 #> [965,] 0.53546582 0.5653624 0.5850334 0.5880933 0.5956964 0.6276467 0.6442174 #> [966,] 0.21651187 0.2317734 0.3859907 0.4146105 0.4910142 0.4920482 0.5625149 #> [967,] 0.41103442 0.4597971 0.5235230 0.5307719 0.5510616 0.6118213 0.6688656 #> [968,] 0.39860107 0.4380599 0.4533006 0.5529875 0.5604020 0.5783303 0.5932555 #> [969,] 0.39210704 0.4336153 0.5295156 0.6056370 0.6156885 0.6515893 0.6693918 #> [970,] 0.38757011 0.4032872 0.4681296 0.5233900 0.5238755 0.5789343 0.5793320 #> [971,] 0.21918209 0.2444457 0.4211737 0.4739712 0.4783768 0.4932683 0.5504818 #> [972,] 0.61758497 0.6874442 0.8036485 0.8796937 0.8933293 0.9167823 0.9520005 #> [973,] 0.37950826 0.4977742 0.5460506 0.5654979 0.6470833 0.7371090 0.7557208 #> [974,] 0.13267935 0.2428452 0.3835312 0.4100114 0.4443748 0.4630358 0.4857113 #> [975,] 0.29137886 0.3808558 0.4494418 0.5510616 0.5705460 0.5788722 0.5844677 #> [976,] 0.57887022 0.8547462 0.8845617 0.8948754 0.9142141 0.9572253 1.0449902 #> [977,] 0.53467357 0.6050027 0.6217932 0.6930354 0.8113778 0.8167004 0.8336929 #> [978,] 0.24855464 0.3429031 0.4231851 0.4459950 0.5169359 0.5208903 0.5263868 #> [979,] 0.38428962 0.4476540 0.4706032 0.4717548 0.4817677 0.5750238 0.5753789 #> [980,] 0.38831457 0.4681296 0.5244214 0.5359329 0.5656454 0.6005207 0.6054502 #> [981,] 0.32642149 0.4353507 0.4688051 0.5189561 0.5746850 0.5750395 0.5897147 #> [982,] 0.39295645 0.6280775 0.7546048 0.8182106 0.8942453 0.9120783 0.9266390 #> [983,] 0.51525033 0.6593257 0.6665253 0.6819764 0.6948617 0.7044144 0.7242251 #> [984,] 0.93081475 0.9929918 1.0686850 1.0714018 1.0977995 1.1447031 1.1805190 #> [985,] 0.37853720 0.4998753 0.6013005 0.6443655 0.6587918 0.6663823 0.6978056 #> [986,] 0.27237342 0.4337646 0.5060788 0.6272943 0.6577665 0.6708163 0.6836464 #> [987,] 1.38856912 1.6361155 1.7030627 1.7118549 1.7578300 1.7924477 1.8974918 #> [988,] 0.66347900 0.7011247 0.7657464 0.7867163 0.8439553 0.8543112 0.8612993 #> [989,] 0.25283902 0.4725692 0.6412155 0.6530337 0.6666938 0.6822739 0.6852734 #> [990,] 0.34537301 0.4943825 0.5348730 0.6260920 0.6302965 0.6686640 0.6707097 #> [991,] 0.19274415 0.6253939 0.6271646 0.6384520 0.7050826 0.7361423 0.7423220 #> [992,] 0.68535263 0.7449855 0.8332449 0.8448919 0.8546296 0.8701522 0.9151551 #> [993,] 0.29573626 0.5085622 0.5222549 0.5714498 0.5996214 0.6150778 0.6439832 #> [994,] 0.34324427 0.3871573 0.4821855 0.4984314 0.5288742 0.5620869 0.6308612 #> [995,] 0.38428962 0.5359329 0.5577090 0.5581737 0.5796832 0.6007823 0.6205757 #> [996,] 0.24855464 0.3755687 0.4384343 0.4606300 0.4865361 0.5041361 0.5170806 #> [997,] 0.71485709 1.0349787 1.1488815 1.1793684 1.1988625 1.2671123 1.2780407 #> [998,] 0.47264023 0.5073235 0.5280320 0.5308094 0.5817286 0.6269897 0.6459467 #> [999,] 0.34558386 0.4790775 0.5313286 0.5317383 0.5337046 0.5827102 0.5840487 #> [1000,] 0.34397845 0.3794034 0.4114427 0.4779854 0.5226127 0.5297184 0.5677993 #> [,8] [,9] [,10] #> [1,] 1.5243659 1.5808169 1.6309819 #> [2,] 0.9611154 0.9978109 1.0056389 #> [3,] 0.8207332 0.8635637 0.8753390 #> [4,] 0.9403258 0.9432942 0.9694713 #> [5,] 0.7043648 0.7952674 0.8274985 #> [6,] 0.5439281 0.5770422 0.5982568 #> [7,] 0.7036611 0.7609579 0.8734171 #> [8,] 0.5276847 0.5397691 0.6196082 #> [9,] 0.6723708 0.7324915 0.7489528 #> [10,] 0.6932124 0.7214650 0.7540280 #> [11,] 1.0114782 1.0153096 1.0194461 #> [12,] 0.6912918 0.6960641 0.7493430 #> [13,] 0.7598048 0.8036485 0.8177474 #> [14,] 0.6430809 0.6726969 0.6822545 #> [15,] 1.0414107 1.0553590 1.0591613 #> [16,] 0.8700759 0.8739138 0.8938110 #> [17,] 0.7917640 0.7923931 0.8010192 #> [18,] 0.8303925 0.8822084 0.8877890 #> [19,] 0.7475963 0.7657464 0.7864254 #> [20,] 0.5630225 0.5770422 0.6040373 #> [21,] 0.6078729 0.6186548 0.6461841 #> [22,] 0.8864625 0.9761193 0.9881459 #> [23,] 0.5940433 0.5999559 0.6055215 #> [24,] 0.6131153 0.6777096 0.7524123 #> [25,] 0.6602214 0.6994899 0.7164263 #> [26,] 0.8288068 0.9339935 0.9397074 #> [27,] 0.6342003 0.6379932 0.6517197 #> [28,] 1.1203890 1.1337488 1.2100997 #> [29,] 0.5249234 0.6053305 0.6375590 #> [30,] 0.7140120 0.7154912 0.7488831 #> [31,] 0.4942333 0.5761194 0.5855809 #> [32,] 0.7520886 0.8040873 0.8396827 #> [33,] 0.8323721 0.8906242 0.9136292 #> [34,] 0.6641804 0.6717917 0.6872239 #> [35,] 1.0939944 1.1142197 1.1249202 #> [36,] 0.5219579 0.5337046 0.5643711 #> [37,] 0.9828091 1.0030337 1.0086629 #> [38,] 0.6305399 0.6523084 0.6545695 #> [39,] 0.8966548 0.9142811 0.9187279 #> [40,] 0.6468075 0.7282995 0.7573701 #> [41,] 1.0466836 1.0745694 1.0879764 #> [42,] 0.8402753 0.8565728 0.8618696 #> [43,] 0.6616534 0.6651803 0.6820772 #> [44,] 0.8484498 0.8826854 0.9821245 #> [45,] 1.6800755 1.6845896 1.7288681 #> [46,] 0.8054544 0.8615314 0.8707869 #> [47,] 0.6353663 0.6555521 0.6722421 #> [48,] 0.9178182 0.9200243 0.9927841 #> [49,] 0.5457032 0.5663475 0.5790349 #> [50,] 0.4860429 0.5075179 0.5379716 #> [51,] 0.7611719 0.7637640 0.7995270 #> [52,] 1.2980012 1.3192358 1.3233202 #> [53,] 0.5446332 0.5478312 0.5535708 #> [54,] 0.6638055 0.6651148 0.6797352 #> [55,] 0.5976980 0.6141899 0.6375299 #> [56,] 0.8755020 0.8962753 0.9500238 #> [57,] 1.2146188 1.2815139 1.4096943 #> [58,] 1.0638888 1.1074634 1.1145257 #> [59,] 0.7242678 0.7270035 0.7293509 #> [60,] 1.0101676 1.0546030 1.0602406 #> [61,] 0.6618560 0.6654939 0.6703510 #> [62,] 0.7501233 0.7567534 0.8105553 #> [63,] 0.7102408 0.7406132 0.7604855 #> [64,] 0.6923005 0.7421846 0.7520886 #> [65,] 1.2056942 1.2894449 1.2991575 #> [66,] 1.0340444 1.0554123 1.0610237 #> [67,] 1.3881422 1.3955733 1.4387810 #> [68,] 0.7332208 0.7571446 0.7770250 #> [69,] 1.0857663 1.1366361 1.1438864 #> [70,] 0.7315282 0.7459833 0.7725486 #> [71,] 0.6440949 0.6446191 0.6983985 #> [72,] 1.2722014 1.2976706 1.3110711 #> [73,] 1.3203683 1.3419641 1.3451671 #> [74,] 1.6801885 1.6853579 1.7120924 #> [75,] 0.5693952 0.5750238 0.5821031 #> [76,] 1.0838494 1.1065840 1.1169843 #> [77,] 0.8086745 0.8116027 0.8389967 #> [78,] 0.6819764 0.7163205 0.7215572 #> [79,] 0.7950445 0.8059375 0.8063845 #> [80,] 0.7012542 0.7164036 0.7310572 #> [81,] 2.1642842 2.2000876 2.2331132 #> [82,] 0.8300079 0.8348768 0.8955452 #> [83,] 0.7831751 0.8878947 0.8923720 #> [84,] 0.7421833 0.7430611 0.7604475 #> [85,] 1.1437027 1.2023561 1.3051234 #> [86,] 0.7069953 0.7164263 0.7207612 #> [87,] 0.5002889 0.5212093 0.5277063 #> [88,] 1.0414267 1.0564780 1.1446718 #> [89,] 0.7087140 0.7210939 0.7300471 #> [90,] 0.6453419 0.6832413 0.7144920 #> [91,] 0.6620797 0.6756653 0.7069087 #> [92,] 1.1947872 1.2003763 1.2043890 #> [93,] 0.8512671 0.8513194 0.9902324 #> [94,] 0.8398371 0.8401201 0.8559189 #> [95,] 0.6780016 0.6897462 0.7036488 #> [96,] 0.5746850 0.5875314 0.5921158 #> [97,] 0.8658725 0.9338371 0.9690399 #> [98,] 1.0853816 1.1052694 1.1294021 #> [99,] 0.6692727 0.6825616 0.7028998 #> [100,] 1.1663184 1.1840966 1.1903360 #> [101,] 0.6834033 0.7182481 0.7783143 #> [102,] 0.7084309 0.7091938 0.7659119 #> [103,] 0.6449446 0.6462933 0.6719392 #> [104,] 0.9233463 0.9835241 0.9947733 #> [105,] 1.1891001 1.2249966 1.2384766 #> [106,] 1.1099235 1.1355901 1.1725647 #> [107,] 0.8123415 0.8325188 0.8671570 #> [108,] 0.8365915 0.8835575 0.9009966 #> [109,] 0.6326446 0.6574176 0.6704237 #> [110,] 1.3458911 1.3519802 1.3581095 #> [111,] 0.6545029 0.6869348 0.6881646 #> [112,] 1.0572347 1.0772545 1.0896860 #> [113,] 0.7767504 0.8143436 0.8462347 #> [114,] 0.9217313 0.9487040 0.9488802 #> [115,] 0.7756343 0.8557102 0.8710254 #> [116,] 0.7891744 0.8032448 0.8230275 #> [117,] 0.8484029 0.8488996 0.8612993 #> [118,] 0.8383914 0.8446840 0.8528961 #> [119,] 0.8192676 0.9261510 0.9664756 #> [120,] 0.7897595 0.8170575 0.8347737 #> [121,] 0.9853501 1.0255424 1.0457865 #> [122,] 0.6665253 0.7115648 0.7229703 #> [123,] 0.7270004 0.7878174 0.7915487 #> [124,] 1.8305911 1.9078020 1.9944160 #> [125,] 0.5928401 0.6368901 0.6445785 #> [126,] 0.7807276 0.7853142 0.8001476 #> [127,] 0.7061602 0.7241037 0.7882429 #> [128,] 0.6003784 0.6088547 0.6383166 #> [129,] 0.7535734 0.7603356 0.7929358 #> [130,] 0.9012099 0.9060264 0.9358829 #> [131,] 0.6657764 0.6753620 0.6839478 #> [132,] 0.9469076 0.9695313 1.0316372 #> [133,] 0.9726798 1.0643520 1.0705907 #> [134,] 0.9120428 0.9666811 1.0409997 #> [135,] 1.5699467 1.5947757 1.6392738 #> [136,] 0.8182372 0.8191019 0.8344704 #> [137,] 0.6936492 0.7024877 0.7115648 #> [138,] 0.6849311 0.7284462 0.7367644 #> [139,] 0.8675932 0.8743178 0.8836714 #> [140,] 0.5796756 0.5953103 0.6121718 #> [141,] 0.5783303 0.5966800 0.6036024 #> [142,] 0.6007914 0.6438789 0.6502767 #> [143,] 0.6620525 0.6806436 0.6867477 #> [144,] 0.8598790 0.8724186 0.9304678 #> [145,] 0.7014700 0.7119572 0.7208976 #> [146,] 0.8137668 0.8385605 0.8450309 #> [147,] 1.8041595 1.8161606 1.8558945 #> [148,] 1.1210947 1.1285060 1.1598155 #> [149,] 0.6452655 0.6583454 0.6593995 #> [150,] 1.3792715 1.4307066 1.5478979 #> [151,] 1.0860630 1.1100208 1.1332205 #> [152,] 0.6403226 0.6421274 0.6443655 #> [153,] 1.1906040 1.2140674 1.2141235 #> [154,] 1.0057026 1.0182671 1.0256053 #> [155,] 1.0403373 1.0696501 1.0827458 #> [156,] 1.1766876 1.2167468 1.2247420 #> [157,] 0.6424917 0.6529798 0.6937404 #> [158,] 0.7134555 0.7617684 0.8326278 #> [159,] 0.7298838 0.7843733 0.8009879 #> [160,] 0.5654880 0.5663867 0.5886008 #> [161,] 0.5649241 0.5693824 0.5812760 #> [162,] 0.8142111 0.8256681 0.8272541 #> [163,] 0.8281454 0.8328057 0.8483943 #> [164,] 0.9857413 1.0088831 1.0380476 #> [165,] 0.5506159 0.5767945 0.5830846 #> [166,] 0.8656360 0.8914866 0.9326425 #> [167,] 0.5233900 0.5292069 0.5416509 #> [168,] 0.5578011 0.5643164 0.5944404 #> [169,] 0.7386633 0.7662297 0.9012099 #> [170,] 1.1387468 1.1658656 1.1679701 #> [171,] 0.9562380 0.9585808 0.9690550 #> [172,] 0.7958698 0.8491134 0.8656282 #> [173,] 0.7329002 0.7548487 0.7614309 #> [174,] 0.8888326 0.9138311 1.0139692 #> [175,] 0.8472938 0.8731073 0.9566480 #> [176,] 0.9184091 0.9184174 0.9258757 #> [177,] 1.2139058 1.2432262 1.2583021 #> [178,] 0.9205091 0.9403023 0.9821884 #> [179,] 1.6050450 1.6194342 1.6253970 #> [180,] 0.7539741 0.7678168 0.7904194 #> [181,] 0.7747172 0.8280436 0.8445258 #> [182,] 0.7610390 0.7624164 0.7719599 #> [183,] 0.8098735 0.8116027 0.9110454 #> [184,] 1.1174933 1.1827852 1.2005486 #> [185,] 0.5391978 0.5443826 0.5470348 #> [186,] 0.6459826 0.6600433 0.6793389 #> [187,] 0.5965657 0.6096712 0.6342035 #> [188,] 0.7144528 0.7317062 0.7411022 #> [189,] 0.6665549 0.7036611 0.8455571 #> [190,] 0.6707097 0.6855123 0.7104163 #> [191,] 0.6880536 0.6951885 0.7146643 #> [192,] 0.6223099 0.6273539 0.6390304 #> [193,] 0.6342512 0.6388741 0.6752632 #> [194,] 0.6327771 0.6469318 0.6602149 #> [195,] 1.3646138 1.4342775 1.4604686 #> [196,] 0.5524353 0.5610534 0.5743608 #> [197,] 0.8344419 0.8614908 0.8924273 #> [198,] 0.9935013 1.0102038 1.0604318 #> [199,] 0.9424759 0.9766749 0.9793582 #> [200,] 0.8082735 0.8481908 0.8727487 #> [201,] 0.9090066 0.9098650 0.9413194 #> [202,] 0.5442527 0.5662443 0.5758904 #> [203,] 0.8915143 0.9333763 0.9400642 #> [204,] 0.8024589 0.8144466 0.8153928 #> [205,] 1.4519661 1.6070510 1.6374261 #> [206,] 1.1985378 1.2227509 1.2647286 #> [207,] 0.5022303 0.5100027 0.5204578 #> [208,] 0.5768237 0.5828092 0.6070402 #> [209,] 0.8405143 0.8704515 0.8730958 #> [210,] 0.5245977 0.5732797 0.5897997 #> [211,] 0.6331277 0.6618434 0.6851041 #> [212,] 0.8040936 0.8173328 0.8276968 #> [213,] 0.6421244 0.6618434 0.6669356 #> [214,] 0.8695335 0.8838543 0.9361518 #> [215,] 0.8780073 0.9322287 0.9854342 #> [216,] 0.6316852 0.6329600 0.6541418 #> [217,] 0.7409552 0.7749358 0.7765259 #> [218,] 0.9633232 0.9700862 0.9714272 #> [219,] 1.0734137 1.1260716 1.2022306 #> [220,] 0.7089577 0.7958406 0.8049686 #> [221,] 0.8087226 0.8820261 0.8979552 #> [222,] 0.8423325 0.8752929 0.8792610 #> [223,] 1.1291117 1.1304449 1.1406933 #> [224,] 0.8237598 0.8477494 0.8525389 #> [225,] 0.8003785 0.8120694 0.8328057 #> [226,] 0.7349225 0.7711570 0.7720764 #> [227,] 0.6475480 0.6542009 0.6584288 #> [228,] 0.7708681 0.7972449 0.8793620 #> [229,] 0.6023254 0.6616765 0.7303278 #> [230,] 0.7363367 0.7637374 0.7667885 #> [231,] 0.5180424 0.5335359 0.5597946 #> [232,] 0.6078622 0.6278613 0.6333816 #> [233,] 0.5992267 0.6017974 0.6051926 #> [234,] 0.7275223 0.7328934 0.7858247 #> [235,] 0.8318334 0.8319812 0.8925846 #> [236,] 0.6797679 0.6834770 0.6946250 #> [237,] 0.6653248 0.7276097 0.7601208 #> [238,] 0.7668919 0.7835613 0.7921361 #> [239,] 0.7590097 0.7645542 0.7747119 #> [240,] 1.4700593 1.4715203 1.4952204 #> [241,] 1.2063140 1.2099781 1.2246481 #> [242,] 1.1757867 1.2450534 1.2567412 #> [243,] 0.5949664 0.6131437 0.6576266 #> [244,] 1.8735486 1.8739523 1.9042581 #> [245,] 0.4875000 0.5057847 0.5298338 #> [246,] 0.8673738 0.8834692 0.9245452 #> [247,] 0.9667485 0.9776315 0.9832663 #> [248,] 0.9853571 1.0121351 1.0502786 #> [249,] 0.5453723 0.5486410 0.5569566 #> [250,] 0.6523133 0.7128838 0.7176423 #> [251,] 1.0271251 1.0428790 1.0444198 #> [252,] 0.8376048 0.8393177 0.9233463 #> [253,] 0.7022452 0.7049319 0.7232298 #> [254,] 0.9378070 0.9465619 0.9539625 #> [255,] 0.7323271 0.8051645 0.8157303 #> [256,] 0.8054544 0.8320862 0.8332449 #> [257,] 0.8221198 0.8385605 0.8713739 #> [258,] 1.5881133 1.6214879 1.6598806 #> [259,] 0.4608005 0.4680016 0.4993466 #> [260,] 0.5204578 0.5257055 0.5325700 #> [261,] 0.8619552 0.8927870 0.9265075 #> [262,] 0.9361687 0.9668368 0.9751466 #> [263,] 0.6995904 0.7329333 0.7573726 #> [264,] 0.7285425 0.7669078 0.7967487 #> [265,] 0.6361679 0.6626872 0.6931054 #> [266,] 0.8464808 0.9042451 0.9361598 #> [267,] 0.7958406 0.8504945 0.8575893 #> [268,] 0.9130299 0.9142488 0.9184519 #> [269,] 0.7023902 0.7036488 0.7190904 #> [270,] 1.0134838 1.0375951 1.0953411 #> [271,] 0.9033689 0.9500238 0.9779554 #> [272,] 0.7569841 0.7713455 0.7743237 #> [273,] 0.8281020 0.8404081 0.8997864 #> [274,] 0.6021311 0.6329169 0.6451058 #> [275,] 0.6116902 0.6584954 0.6982418 #> [276,] 0.6766993 0.6804542 0.6970547 #> [277,] 1.1671591 1.1940877 1.2013685 #> [278,] 0.5385578 0.5524752 0.5540552 #> [279,] 0.9806397 0.9903009 1.0124594 #> [280,] 0.7733261 0.7757703 0.7814405 #> [281,] 1.6651384 1.7565781 1.7573194 #> [282,] 0.6233547 0.6381087 0.6790567 #> [283,] 0.8320862 0.8486365 0.8659943 #> [284,] 0.6353006 0.6440505 0.6491302 #> [285,] 1.2464875 1.2563855 1.3767573 #> [286,] 0.9741064 0.9807108 1.0087750 #> [287,] 0.6533300 0.7024012 0.7046043 #> [288,] 0.9766857 1.0721776 1.0851837 #> [289,] 0.6582334 0.6715584 0.7489660 #> [290,] 0.5736358 0.6016753 0.6031855 #> [291,] 0.9047764 0.9416227 0.9437803 #> [292,] 0.6534406 0.6535046 0.6689339 #> [293,] 0.7839534 0.7906479 0.7915487 #> [294,] 0.6527325 0.7207140 0.7265732 #> [295,] 0.8515109 0.8821759 0.8829943 #> [296,] 1.0891309 1.1238643 1.1280101 #> [297,] 0.6817921 0.7024531 0.7885705 #> [298,] 0.7650493 0.7830500 0.7850708 #> [299,] 0.9253394 0.9618571 0.9675878 #> [300,] 0.8019273 0.8136202 0.8417530 #> [301,] 0.8982530 0.9216886 0.9237611 #> [302,] 0.6387119 0.6701486 0.6924840 #> [303,] 0.8194054 0.8245683 0.8276968 #> [304,] 0.8163358 0.8968895 0.9190604 #> [305,] 0.6911725 0.7333086 0.7364530 #> [306,] 0.6273922 0.6494594 0.6758489 #> [307,] 0.6020250 0.6032960 0.6141436 #> [308,] 1.2462559 1.2627624 1.2734046 #> [309,] 1.0961978 1.1436299 1.1709643 #> [310,] 0.6557014 0.6608235 0.6910342 #> [311,] 0.6901804 0.7042251 0.7121510 #> [312,] 0.7725283 0.8024589 0.8167136 #> [313,] 1.3093299 1.3269674 1.3276929 #> [314,] 0.7285425 0.7598048 0.7688160 #> [315,] 0.8906903 0.9014016 0.9082654 #> [316,] 0.6597708 0.6716182 0.7138331 #> [317,] 1.3444230 1.3969292 1.4297278 #> [318,] 1.0851945 1.1559070 1.1757867 #> [319,] 0.6329600 0.6380099 0.6387574 #> [320,] 0.5443672 0.5488831 0.5624516 #> [321,] 1.0981134 1.1148162 1.1424652 #> [322,] 0.6027873 0.6314889 0.6915544 #> [323,] 0.9562376 1.0873696 1.1439529 #> [324,] 1.1809562 1.1949260 1.2121301 #> [325,] 1.2758465 1.2764101 1.3314925 #> [326,] 0.8488415 0.8858925 0.9049453 #> [327,] 0.7816092 0.8003785 0.8061216 #> [328,] 1.5466154 1.6159929 1.6380888 #> [329,] 1.5863012 1.5911419 1.7342734 #> [330,] 0.9494797 0.9970640 1.0387814 #> [331,] 0.5663867 0.5977062 0.6327771 #> [332,] 0.7786057 0.7926984 0.8472351 #> [333,] 0.5780262 0.5830670 0.6025930 #> [334,] 0.8014663 0.8420080 0.8437626 #> [335,] 0.5649241 0.5889128 0.6072000 #> [336,] 0.8751113 0.8806370 0.9049909 #> [337,] 0.9169476 0.9258788 0.9786166 #> [338,] 1.0001497 1.1000918 1.1152972 #> [339,] 1.1983213 1.2084839 1.2476221 #> [340,] 0.8387845 0.8578511 0.8625806 #> [341,] 1.0503849 1.0581414 1.0981878 #> [342,] 0.5831863 0.5897049 0.6166476 #> [343,] 0.8909072 0.8952005 0.9274179 #> [344,] 0.9235493 0.9288125 0.9731136 #> [345,] 2.8094835 2.8886078 2.9410917 #> [346,] 0.5564435 0.5892740 0.6000514 #> [347,] 0.7551687 0.8334741 0.8581683 #> [348,] 0.7263683 0.7687818 0.7934345 #> [349,] 0.6638946 0.7025544 0.7051790 #> [350,] 1.6618546 1.6642624 1.7089094 #> [351,] 1.0794372 1.1290306 1.1451370 #> [352,] 0.6767724 0.7167063 0.7404281 #> [353,] 0.8472273 0.8487451 0.8598339 #> [354,] 0.9188042 0.9250145 0.9784457 #> [355,] 0.6427334 0.6523133 0.6591489 #> [356,] 0.5867750 0.6096996 0.6609022 #> [357,] 0.6504095 0.6693203 0.6915823 #> [358,] 1.0662185 1.0938059 1.1255470 #> [359,] 0.6849131 0.7253001 0.7567343 #> [360,] 0.8253925 0.8469022 0.8507445 #> [361,] 1.2972732 1.3129135 1.3208724 #> [362,] 0.5900256 0.5966234 0.6255330 #> [363,] 0.8098279 0.8405189 0.8419364 #> [364,] 0.8382100 0.8417069 0.8511844 #> [365,] 0.9920899 0.9990774 1.0466645 #> [366,] 0.5600479 0.6000714 0.6015735 #> [367,] 0.8771099 0.8793941 0.8808018 #> [368,] 1.1420320 1.2435833 1.2576645 #> [369,] 0.8582952 0.8599602 0.8648614 #> [370,] 0.6438789 0.6464029 0.6594544 #> [371,] 0.8132519 0.8348768 0.8718345 #> [372,] 0.8886290 0.8980959 0.9182436 #> [373,] 0.6728326 0.6859789 0.7359366 #> [374,] 0.7909004 0.8103030 0.8672868 #> [375,] 1.2857568 1.3381113 1.3387273 #> [376,] 0.7944559 0.8013535 0.8304747 #> [377,] 1.0194842 1.0607650 1.1032316 #> [378,] 0.7188675 0.8235059 0.8276617 #> [379,] 1.0211121 1.1530092 1.1705561 #> [380,] 0.9923883 1.0221610 1.0783806 #> [381,] 0.6207979 0.6427334 0.6639098 #> [382,] 1.2759019 1.2801700 1.3483353 #> [383,] 0.7151393 0.7507690 0.7514525 #> [384,] 0.7303102 0.7447334 0.7563054 #> [385,] 0.7729818 0.7752801 0.8192156 #> [386,] 0.5042462 0.5132578 0.5347960 #> [387,] 0.6685003 0.7032316 0.7095077 #> [388,] 1.2940043 1.3116532 1.3305883 #> [389,] 0.4817999 0.4878093 0.5410208 #> [390,] 0.7331032 0.7444346 0.7671677 #> [391,] 0.7537042 0.7746594 0.7845776 #> [392,] 0.9354164 0.9916784 1.0038588 #> [393,] 1.1592034 1.1789048 1.1881525 #> [394,] 0.6467058 0.6565775 0.6923005 #> [395,] 0.7310763 0.7552492 0.7594214 #> [396,] 0.8986675 0.9143464 0.9276981 #> [397,] 0.8486483 0.8508795 0.8601285 #> [398,] 0.9976411 1.0011695 1.0739424 #> [399,] 0.8745644 0.8782130 0.9043587 #> [400,] 0.9098650 1.0092030 1.0362576 #> [401,] 0.5597946 0.5927760 0.6031826 #> [402,] 0.7700191 0.7817334 0.7818776 #> [403,] 0.9737559 1.0240651 1.0344264 #> [404,] 0.5936955 0.5965183 0.6031826 #> [405,] 0.5359840 0.5426235 0.5494953 #> [406,] 0.4984314 0.5700570 0.5701457 #> [407,] 0.6150030 0.6689186 0.6761037 #> [408,] 1.2397482 1.3744765 1.4820356 #> [409,] 1.1517586 1.1595079 1.1607152 #> [410,] 0.8970618 0.9174193 0.9612071 #> [411,] 0.6916650 0.6981241 0.7041224 #> [412,] 0.6471527 0.6816065 0.6878781 #> [413,] 0.7327581 0.7406132 0.7452370 #> [414,] 0.6648266 0.6678141 0.6744941 #> [415,] 0.6697815 0.6791187 0.6953316 #> [416,] 0.8168176 0.8194116 0.8211489 #> [417,] 0.6078729 0.6152754 0.6421274 #> [418,] 1.7155196 1.7207718 1.7415727 #> [419,] 0.9481335 0.9612071 0.9820950 #> [420,] 0.6800200 0.6808957 0.6992552 #> [421,] 0.6046658 0.6209275 0.6402169 #> [422,] 0.6835824 0.7585124 0.7881878 #> [423,] 0.7491987 0.7522365 0.7601292 #> [424,] 0.5020218 0.5030537 0.5178701 #> [425,] 2.2932696 2.3723662 2.4223384 #> [426,] 0.7645542 0.8054434 0.8494835 #> [427,] 1.4016451 1.4382258 1.4433517 #> [428,] 0.8341936 0.8344704 0.8387845 #> [429,] 0.6107487 0.6735196 0.6762099 #> [430,] 0.7438620 0.7674429 0.7714117 #> [431,] 0.8219423 0.8234266 0.8463815 #> [432,] 0.7341805 0.7534588 0.7727361 #> [433,] 0.6377760 0.6462234 0.6638736 #> [434,] 0.9333763 0.9422343 0.9488802 #> [435,] 1.1427459 1.1572862 1.1624329 #> [436,] 0.7498497 0.7768013 0.7789587 #> [437,] 0.7549707 0.7669871 0.7987205 #> [438,] 1.0791655 1.0973817 1.1052959 #> [439,] 0.4825606 0.5023097 0.5102164 #> [440,] 0.7093803 0.7365695 0.8450230 #> [441,] 0.8186225 0.8575886 0.8617306 #> [442,] 1.1939267 1.2530121 1.2676821 #> [443,] 1.0690217 1.0938534 1.1098776 #> [444,] 0.7602211 0.7900737 0.8484472 #> [445,] 0.7305650 0.7666968 0.7783143 #> [446,] 0.7870018 0.8203685 0.8417069 #> [447,] 0.7270035 0.7565922 0.7755959 #> [448,] 0.8279811 0.8490338 0.8573882 #> [449,] 0.7283879 0.7563002 0.7800571 #> [450,] 0.7829672 0.7968122 0.8093141 #> [451,] 0.9289359 0.9913643 1.0041851 #> [452,] 0.8580262 0.8603759 0.8625607 #> [453,] 0.7599411 0.8054832 0.8322838 #> [454,] 0.5303881 0.5404534 0.5506574 #> [455,] 0.8750339 0.8754412 0.8896112 #> [456,] 0.9700862 0.9895414 1.0009251 #> [457,] 0.7884800 0.7985785 0.8309672 #> [458,] 0.7219261 0.7259888 0.7685295 #> [459,] 1.5169999 1.5208390 1.5669053 #> [460,] 0.9304993 1.0034544 1.0204791 #> [461,] 0.9252409 0.9413701 1.0006566 #> [462,] 0.7252742 0.7520877 0.7681750 #> [463,] 0.5873715 0.5940268 0.6009576 #> [464,] 1.4102500 1.4254980 1.4289465 #> [465,] 0.9724693 0.9892153 1.0053203 #> [466,] 0.6776049 0.6793633 0.7444877 #> [467,] 0.7234270 0.7373352 0.7395970 #> [468,] 0.7312486 0.7620866 0.7834036 #> [469,] 1.9449903 1.9997245 2.0342805 #> [470,] 1.8673973 1.9165614 1.9322301 #> [471,] 0.7849593 0.7867677 0.8164522 #> [472,] 0.8077282 0.8113149 0.8162749 #> [473,] 0.8092988 0.8227479 0.8581683 #> [474,] 0.7886554 0.8040224 0.8216809 #> [475,] 0.9568295 0.9668492 0.9772124 #> [476,] 0.7496162 0.7635732 0.7935786 #> [477,] 1.1293331 1.1469452 1.1471375 #> [478,] 0.6020250 0.6364563 0.6564804 #> [479,] 0.8828625 0.9178182 0.9357569 #> [480,] 0.8551747 0.8561162 0.8696071 #> [481,] 0.7271914 0.7889064 0.8276440 #> [482,] 0.9167345 0.9432508 0.9734263 #> [483,] 0.8877356 0.8877721 0.9464073 #> [484,] 0.6820772 0.6901804 0.7073480 #> [485,] 0.6032926 0.6355482 0.6519567 #> [486,] 0.7694448 0.7867677 0.7921361 #> [487,] 0.8669934 0.8987052 0.9039787 #> [488,] 0.5386993 0.5510784 0.5615143 #> [489,] 0.9526243 0.9738814 1.0211781 #> [490,] 0.5797474 0.5819609 0.5829153 #> [491,] 1.3827639 1.4192413 1.4736857 #> [492,] 0.6208781 0.6852571 0.7241467 #> [493,] 0.5825832 0.6224511 0.6276391 #> [494,] 1.4299967 1.4388615 1.4452256 #> [495,] 0.8014326 0.8063517 0.8796086 #> [496,] 1.0511263 1.0638448 1.0654817 #> [497,] 0.6331116 0.6969062 0.6987263 #> [498,] 0.5942840 0.6013444 0.6100400 #> [499,] 0.8186754 0.8388021 0.8446939 #> [500,] 1.0481851 1.1458741 1.1547490 #> [501,] 0.6127121 0.6392152 0.6698288 #> [502,] 0.7746160 0.8066735 0.8549282 #> [503,] 0.7550132 0.8247882 0.8276536 #> [504,] 0.7007891 0.7760447 0.7927290 #> [505,] 0.6207979 0.6369267 0.7119260 #> [506,] 0.8450073 0.8506358 0.8947993 #> [507,] 0.4527564 0.4929968 0.5031262 #> [508,] 0.8210360 0.8356232 0.8370369 #> [509,] 0.6196056 0.7501435 0.8137668 #> [510,] 0.6689186 0.6801213 0.7140120 #> [511,] 1.6786398 1.6801571 1.6809669 #> [512,] 1.0270393 1.0378446 1.0830380 #> [513,] 1.0686446 1.0745639 1.0884353 #> [514,] 1.2383352 1.3142746 1.3165361 #> [515,] 0.6923461 0.7222090 0.7349476 #> [516,] 1.6999924 1.7366141 1.7710182 #> [517,] 0.7227302 0.7333383 0.7438428 #> [518,] 0.6412565 0.6487788 0.6527705 #> [519,] 0.7007891 0.7166611 0.7211109 #> [520,] 1.0286506 1.1018465 1.1064026 #> [521,] 0.7539787 0.7646527 0.7749358 #> [522,] 0.6460914 0.6592550 0.6777096 #> [523,] 0.6011296 0.6099995 0.6267533 #> [524,] 0.7527483 0.7889473 0.7951141 #> [525,] 1.4603744 1.6534632 1.6554812 #> [526,] 0.6993946 0.7225583 0.7272496 #> [527,] 1.4180602 1.4306645 1.4584196 #> [528,] 0.8336929 0.8786696 0.8990421 #> [529,] 0.8049686 0.9500680 0.9775793 #> [530,] 1.5558516 1.5605163 1.6140382 #> [531,] 0.5470348 0.5673608 0.5755022 #> [532,] 0.7040888 0.7044367 0.7181985 #> [533,] 0.5600479 0.5693824 0.5705460 #> [534,] 0.7481863 0.7556583 0.7578103 #> [535,] 1.6764237 1.6891714 1.6925196 #> [536,] 0.6892268 0.7104893 0.7386475 #> [537,] 0.6605804 0.6822545 0.6859765 #> [538,] 0.6724188 0.6760600 0.6805757 #> [539,] 0.9236578 0.9779554 0.9825563 #> [540,] 1.5450570 1.5982529 1.6469659 #> [541,] 0.7671446 0.7686223 0.7913083 #> [542,] 0.7749350 0.7760432 0.7970975 #> [543,] 0.9972164 1.0454226 1.0554706 #> [544,] 0.9734595 0.9762631 0.9798293 #> [545,] 1.2253034 1.2347755 1.2356855 #> [546,] 0.5308653 0.5433494 0.5648950 #> [547,] 0.6845853 0.6870117 0.7195420 #> [548,] 0.6836239 0.7093803 0.7112369 #> [549,] 0.6624672 0.7044367 0.7271320 #> [550,] 0.5930796 0.6009576 0.6332259 #> [551,] 1.0512387 1.0828803 1.0834900 #> [552,] 1.0671486 1.0812531 1.0817206 #> [553,] 1.0822813 1.0942597 1.1059081 #> [554,] 0.4990195 0.5265139 0.5447722 #> [555,] 0.6851350 0.6876713 0.6926234 #> [556,] 0.6995904 0.7367393 0.7459699 #> [557,] 1.0643377 1.0757091 1.0772153 #> [558,] 0.5876273 0.5937526 0.6021311 #> [559,] 0.6316512 0.6322469 0.6470051 #> [560,] 0.8883228 0.9206066 0.9323522 #> [561,] 0.6731440 0.6860345 0.6885872 #> [562,] 0.6638946 0.7372376 0.8081303 #> [563,] 0.8289467 0.8349365 0.8653335 #> [564,] 0.7583382 0.7673915 0.8239604 #> [565,] 0.7240913 0.7753555 0.7841834 #> [566,] 0.5998774 0.6128027 0.6591489 #> [567,] 1.6908154 1.7030627 1.7225823 #> [568,] 0.7425193 0.7489660 0.7500161 #> [569,] 0.7170007 0.8089017 0.8122410 #> [570,] 0.6774092 0.6799657 0.7025544 #> [571,] 0.4820388 0.5136056 0.5213306 #> [572,] 0.5566345 0.5922417 0.5935245 #> [573,] 0.7110430 0.7135245 0.7384764 #> [574,] 0.8450073 0.8713145 0.8943658 #> [575,] 0.5573569 0.5840487 0.6198596 #> [576,] 0.5361367 0.5540552 0.5557784 #> [577,] 0.5574661 0.5752938 0.5902023 #> [578,] 0.7813525 0.8041972 0.8239604 #> [579,] 0.7566974 0.8184313 0.8656965 #> [580,] 1.1771946 1.1826661 1.1970214 #> [581,] 1.0231257 1.1010580 1.1155543 #> [582,] 0.9441800 1.0828442 1.1472337 #> [583,] 0.5396463 0.5420004 0.5607260 #> [584,] 1.2029668 1.2605534 1.3054052 #> [585,] 0.6278613 0.6442174 0.6732055 #> [586,] 0.8234069 0.8244759 0.8432501 #> [587,] 0.6341530 0.6353158 0.6772174 #> [588,] 0.7814405 0.8528844 0.8812575 #> [589,] 0.6008158 0.6372487 0.6381665 #> [590,] 0.8535271 0.8584035 0.8604812 #> [591,] 0.6475480 0.6583511 0.6692462 #> [592,] 0.6862504 0.7064858 0.7096041 #> [593,] 0.7141790 0.7540948 0.7743470 #> [594,] 0.6790748 0.7063940 0.7134847 #> [595,] 0.8243222 0.8426470 0.8441713 #> [596,] 0.4841514 0.5657912 0.5755022 #> [597,] 0.7452370 0.7575772 0.7952843 #> [598,] 1.4963948 1.5203882 1.6024957 #> [599,] 1.1383098 1.1702267 1.2238629 #> [600,] 1.0364890 1.0783860 1.0881871 #> [601,] 0.9747691 0.9784879 0.9891670 #> [602,] 0.6325404 0.6790821 0.7024531 #> [603,] 0.6043368 0.6424482 0.6583511 #> [604,] 0.5864847 0.6016442 0.6022500 #> [605,] 0.5472789 0.5627529 0.6346750 #> [606,] 1.0111774 1.0255424 1.0621965 #> [607,] 0.8565637 0.8674535 0.8724976 #> [608,] 1.0922215 1.1643951 1.1807064 #> [609,] 0.9794582 1.0258198 1.0537422 #> [610,] 1.8744437 1.8869722 1.9029999 #> [611,] 0.9482153 1.0703472 1.0738687 #> [612,] 0.5826571 0.6483689 0.7058580 #> [613,] 0.7554312 0.7697521 0.7769445 #> [614,] 0.7722574 0.7785427 0.7852635 #> [615,] 0.7480086 0.8008770 0.8052328 #> [616,] 0.4987497 0.5915236 0.6025436 #> [617,] 1.1530540 1.1624329 1.1696436 #> [618,] 0.6675875 0.6696642 0.6964581 #> [619,] 1.9258524 1.9589473 1.9616130 #> [620,] 0.7694448 0.7945205 0.8112945 #> [621,] 0.6608919 0.7044144 0.7183474 #> [622,] 0.8357592 0.8851545 0.9266689 #> [623,] 1.4453892 1.5597602 1.5799055 #> [624,] 0.6013444 0.6160422 0.6277272 #> [625,] 0.6735509 0.7061602 0.7103009 #> [626,] 0.7594411 0.7819699 0.8162749 #> [627,] 0.6639936 0.6704440 0.7070445 #> [628,] 1.4792002 1.4858616 1.5553256 #> [629,] 0.6801713 0.6803374 0.6807494 #> [630,] 0.7838042 0.8330158 0.8551235 #> [631,] 0.5658423 0.5659957 0.5706138 #> [632,] 0.8275516 0.8398715 0.8894340 #> [633,] 1.8945030 1.9278525 1.9424763 #> [634,] 0.7367904 0.7715897 0.7772676 #> [635,] 0.5245977 0.5419030 0.5470238 #> [636,] 0.7606916 0.7766755 0.7873526 #> [637,] 0.8667406 0.9232460 0.9668317 #> [638,] 0.7093980 0.7179996 0.8043226 #> [639,] 0.7107465 0.7365392 0.7509218 #> [640,] 0.6474838 0.6752632 0.7081496 #> [641,] 0.7407793 0.7601183 0.7669871 #> [642,] 0.5757339 0.5988755 0.6056370 #> [643,] 0.8543398 0.9490901 0.9616271 #> [644,] 0.7037034 0.7089464 0.7225416 #> [645,] 1.2614159 1.3394622 1.4055323 #> [646,] 1.0875151 1.1115924 1.1613842 #> [647,] 1.2023717 1.2228821 1.2469979 #> [648,] 0.5538165 0.5571109 0.5848674 #> [649,] 0.6731897 0.6863041 0.7436029 #> [650,] 1.0723583 1.0829244 1.0888367 #> [651,] 0.5360911 0.5439281 0.5514637 #> [652,] 0.9823087 1.0061961 1.0161166 #> [653,] 0.9615576 0.9772413 1.0220572 #> [654,] 0.8567612 0.8835575 0.9073879 #> [655,] 0.8739169 0.9189904 0.9469421 #> [656,] 0.6440738 0.6553880 0.6621010 #> [657,] 0.6948833 0.7239879 0.7303132 #> [658,] 2.0539740 2.0902739 2.1038077 #> [659,] 0.9031826 0.9270452 0.9469395 #> [660,] 0.8741287 0.8839707 0.9087493 #> [661,] 0.8132943 0.8224501 0.8247882 #> [662,] 0.8186754 0.8330141 0.8343830 #> [663,] 0.9810485 0.9927890 1.0106749 #> [664,] 0.7029838 0.7151743 0.7163519 #> [665,] 0.6817316 0.7015655 0.7115043 #> [666,] 0.8458111 0.9004967 0.9803732 #> [667,] 0.7187057 0.7246572 0.7471467 #> [668,] 0.7927652 0.7950445 0.8107627 #> [669,] 0.6911190 0.7413602 0.8334453 #> [670,] 1.1509168 1.1776238 1.1924359 #> [671,] 0.5475349 0.5505027 0.5639221 #> [672,] 0.7212985 0.7231728 0.7255583 #> [673,] 0.6546189 0.6678590 0.6745093 #> [674,] 0.6236229 0.6316512 0.6448085 #> [675,] 0.6976790 0.6988070 0.7022311 #> [676,] 0.8647881 0.8881306 0.9042629 #> [677,] 0.8584035 0.9732872 0.9841871 #> [678,] 0.6459826 0.7084309 0.7096870 #> [679,] 0.6269285 0.6510310 0.7190904 #> [680,] 0.9532322 0.9611154 0.9822419 #> [681,] 0.9136462 0.9197724 0.9385553 #> [682,] 0.6151094 0.6664986 0.6852734 #> [683,] 0.6181234 0.6336101 0.6550931 #> [684,] 0.8281020 0.8915035 0.9109314 #> [685,] 0.7855720 0.8112534 0.8486313 #> [686,] 0.7968281 0.7993908 0.8096249 #> [687,] 0.8684122 0.8933293 0.9039735 #> [688,] 0.6518934 0.6784733 0.7134594 #> [689,] 0.9237826 0.9400302 0.9471953 #> [690,] 0.8141279 0.8358715 0.8369607 #> [691,] 0.5529875 0.5601840 0.5644245 #> [692,] 0.5517974 0.5693161 0.5873525 #> [693,] 1.0859972 1.1167549 1.1855703 #> [694,] 0.6327384 0.6403465 0.6522010 #> [695,] 0.5369063 0.5844722 0.6025021 #> [696,] 0.9683329 0.9684100 1.0204332 #> [697,] 0.8111384 0.8424851 0.8542503 #> [698,] 1.1814480 1.2263634 1.2384766 #> [699,] 0.7927652 0.8077602 0.8295842 #> [700,] 1.2702424 1.2908346 1.3338531 #> [701,] 0.8291931 0.8403914 0.8738022 #> [702,] 0.6434446 0.6671026 0.6860087 #> [703,] 0.7074719 0.7149534 0.7375093 #> [704,] 0.8169586 0.8272137 0.8283524 #> [705,] 1.2313260 1.2356855 1.2885794 #> [706,] 0.8154469 0.8226472 0.8487451 #> [707,] 0.4983350 0.5262098 0.5327186 #> [708,] 1.0624992 1.0715707 1.1079968 #> [709,] 0.6789879 0.7242251 0.7460334 #> [710,] 0.7968163 0.8044401 0.8081303 #> [711,] 0.8103030 0.8407096 0.8612372 #> [712,] 0.6796196 0.7025482 0.7212138 #> [713,] 0.7080254 0.7608227 0.8142425 #> [714,] 1.0440521 1.0512421 1.0767011 #> [715,] 0.8151947 0.8285825 0.8582232 #> [716,] 0.6284367 0.7223476 0.7408906 #> [717,] 0.7046757 0.7136841 0.7604333 #> [718,] 0.8838055 0.9219829 0.9919327 #> [719,] 0.6121718 0.6380099 0.6388043 #> [720,] 0.5953103 0.6349862 0.6359090 #> [721,] 1.8820315 1.9001787 1.9088798 #> [722,] 0.9795572 0.9823719 1.0485794 #> [723,] 0.7339828 0.7342629 0.7421833 #> [724,] 0.7866264 0.8289504 0.8772428 #> [725,] 0.7079180 0.7419142 0.7869214 #> [726,] 0.8201243 0.8342268 0.8486467 #> [727,] 0.6288299 0.6441286 0.6508259 #> [728,] 0.7837983 0.8149223 0.8295842 #> [729,] 1.1908658 1.2187422 1.2412926 #> [730,] 0.6217570 0.6510310 0.6630560 #> [731,] 0.9160105 0.9928598 1.1118426 #> [732,] 1.4420573 1.4974156 1.5126267 #> [733,] 1.7203979 1.7855192 1.8151633 #> [734,] 0.4929968 0.5297014 0.5530272 #> [735,] 0.5965755 0.6003747 0.6095929 #> [736,] 0.6981032 0.7104502 0.7132787 #> [737,] 1.0985167 1.1041007 1.1451672 #> [738,] 0.9223074 1.0153096 1.0387814 #> [739,] 0.7026276 0.7146490 0.7245649 #> [740,] 0.7207847 0.7329248 0.7348654 #> [741,] 0.6898966 0.7134847 0.7154010 #> [742,] 0.6608039 0.7080254 0.7310347 #> [743,] 0.9439670 1.0013745 1.1012426 #> [744,] 1.3015360 1.3296636 1.3845750 #> [745,] 0.6325404 0.6648266 0.6837852 #> [746,] 0.8505685 0.8759186 0.8890348 #> [747,] 0.7840299 0.8194621 0.8238080 #> [748,] 0.7545404 0.7563807 0.8210554 #> [749,] 1.6518706 1.6901572 1.7073382 #> [750,] 1.1184452 1.1456799 1.1546014 #> [751,] 0.9202416 0.9760870 0.9904668 #> [752,] 0.7601292 0.7789707 0.7888291 #> [753,] 1.8041411 1.8399981 1.9228155 #> [754,] 0.5117631 0.5189561 0.5379454 #> [755,] 0.9188856 0.9264988 0.9320816 #> [756,] 0.6017029 0.6238293 0.6410903 #> [757,] 0.7847037 0.7996425 0.8101178 #> [758,] 0.6032702 0.6093081 0.6393244 #> [759,] 0.6645667 0.6992010 0.7103009 #> [760,] 0.6381541 0.7110158 0.7136425 #> [761,] 0.6806684 0.6858037 0.8278033 #> [762,] 0.8708349 0.9128123 0.9241553 #> [763,] 0.6832413 0.6860087 0.7341805 #> [764,] 0.6157924 0.6343072 0.6558207 #> [765,] 1.0118846 1.0134499 1.0171610 #> [766,] 0.5975777 0.6000514 0.6011296 #> [767,] 0.4531303 0.4674339 0.4860429 #> [768,] 0.7399173 0.7863414 0.7904919 #> [769,] 0.9369696 0.9517156 0.9656858 #> [770,] 1.4471992 1.4562042 1.4820356 #> [771,] 1.4930419 1.5148422 1.5548330 #> [772,] 0.7373352 0.7443812 0.7529223 #> [773,] 1.3959402 1.4042072 1.4333654 #> [774,] 0.7428682 0.7751488 0.7923230 #> [775,] 1.6821274 1.7425683 1.8219406 #> [776,] 0.7952674 0.8616408 0.8749683 #> [777,] 0.7886363 0.7969842 0.8028407 #> [778,] 0.7563054 0.7619809 0.7651778 #> [779,] 0.6701998 0.6882292 0.7097852 #> [780,] 0.5524353 0.5626330 0.6274188 #> [781,] 0.6867477 0.6981032 0.7169447 #> [782,] 0.5279354 0.5476963 0.5488831 #> [783,] 1.0564007 1.0920713 1.1147534 #> [784,] 0.8546296 0.9293673 0.9503139 #> [785,] 1.2862190 1.3487894 1.3838227 #> [786,] 0.8884697 0.8903167 0.9072002 #> [787,] 0.5993060 0.6125669 0.6208781 #> [788,] 1.0283774 1.0756508 1.0760060 #> [789,] 0.9727410 0.9951119 0.9999906 #> [790,] 0.6117980 0.6456994 0.6477592 #> [791,] 0.9312645 0.9560108 0.9569102 #> [792,] 1.0465306 1.0909766 1.0914729 #> [793,] 0.9404725 0.9429739 0.9568295 #> [794,] 0.6590509 0.6658791 0.6763955 #> [795,] 1.0802310 1.0822813 1.1103753 #> [796,] 0.7007486 0.7014731 0.7070516 #> [797,] 1.0239352 1.0372344 1.0721517 #> [798,] 0.6339919 0.6431744 0.7286949 #> [799,] 1.0520687 1.0530698 1.1656796 #> [800,] 0.7208518 0.7217458 0.7246355 #> [801,] 0.5717532 0.5975777 0.6159771 #> [802,] 0.9789055 1.0506605 1.1064540 #> [803,] 0.6408472 0.7736341 0.8250772 #> [804,] 0.8274985 0.8491893 0.8821759 #> [805,] 0.5870801 0.5927017 0.6470051 #> [806,] 1.9180341 1.9459520 2.0220995 #> [807,] 1.1019302 1.1048826 1.1452958 #> [808,] 0.7080196 0.7107465 0.7237772 #> [809,] 0.7306420 0.7346647 0.7392183 #> [810,] 0.9619491 0.9759745 0.9819215 #> [811,] 0.7134353 0.7410821 0.7671390 #> [812,] 0.4397050 0.5120447 0.5158111 #> [813,] 0.5866931 0.6096429 0.6152754 #> [814,] 0.7999641 0.8117286 0.8357712 #> [815,] 0.8746795 0.9115486 0.9335750 #> [816,] 0.7146225 0.8258685 0.8431029 #> [817,] 1.0041419 1.0242532 1.0347041 #> [818,] 0.6384520 0.6638735 0.7109461 #> [819,] 0.7700035 0.7865491 0.8122098 #> [820,] 1.0418785 1.0676253 1.1292646 #> [821,] 0.7532504 0.7747172 0.8473076 #> [822,] 0.9227195 0.9487566 0.9581983 #> [823,] 0.5875314 0.5942840 0.5965755 #> [824,] 1.2553628 1.2694406 1.3343760 #> [825,] 1.2048136 1.3095050 1.3428537 #> [826,] 0.9132950 0.9771254 1.0106245 #> [827,] 0.5661952 0.5750395 0.5822294 #> [828,] 0.6576110 0.6597499 0.6673354 #> [829,] 0.9136389 0.9287249 0.9310701 #> [830,] 0.8903167 0.9201335 0.9323082 #> [831,] 0.6718409 0.6869043 0.6991622 #> [832,] 0.6859404 0.7035090 0.7048176 #> [833,] 0.7420225 0.7588907 0.7750704 #> [834,] 1.0100578 1.0138136 1.0163078 #> [835,] 1.8535687 1.9005632 1.9730804 #> [836,] 0.6506995 0.6590509 0.6599847 #> [837,] 0.9098862 0.9364781 0.9552696 #> [838,] 0.6858191 0.6927322 0.7062499 #> [839,] 1.0220518 1.0238183 1.0621965 #> [840,] 0.5401356 0.5557784 0.5687479 #> [841,] 0.6776225 0.6874784 0.6990156 #> [842,] 1.0768269 1.1514249 1.1546014 #> [843,] 0.6942737 0.7040666 0.7097852 #> [844,] 1.0846216 1.1308978 1.1327691 #> [845,] 1.2357066 1.2465444 1.2791774 #> [846,] 0.6924840 0.6987541 0.7768013 #> [847,] 0.5996629 0.6371110 0.6737094 #> [848,] 0.5578011 0.5624516 0.5842370 #> [849,] 0.9665835 0.9976055 1.0352665 #> [850,] 0.7457151 0.7990490 0.8223862 #> [851,] 0.8717785 0.9060917 0.9801779 #> [852,] 0.7719812 0.7912119 0.7975241 #> [853,] 0.9266390 0.9300174 0.9645604 #> [854,] 0.6398836 0.6412565 0.6775025 #> [855,] 0.7485899 0.7781986 0.8268827 #> [856,] 0.9786673 1.0056389 1.0444198 #> [857,] 0.6893189 0.6929399 0.7584337 #> [858,] 0.5513524 0.5553564 0.5569566 #> [859,] 0.8832566 0.9021617 0.9851522 #> [860,] 0.5454917 0.5477646 0.5658402 #> [861,] 1.1027024 1.1274677 1.1693164 #> [862,] 0.7058580 0.8086243 0.8343830 #> [863,] 0.5877672 0.5956524 0.5991496 #> [864,] 1.2654594 1.3005064 1.3382089 #> [865,] 1.1711535 1.1903278 1.2141117 #> [866,] 0.7920784 0.7935786 0.8320460 #> [867,] 1.0431132 1.0607671 1.0757144 #> [868,] 1.4160604 1.4465604 1.4920582 #> [869,] 0.5573569 0.5675909 0.5761090 #> [870,] 0.6336309 0.6358192 0.6776049 #> [871,] 0.8684122 0.8849601 0.8927541 #> [872,] 1.1902981 1.2012097 1.2430482 #> [873,] 1.2633157 1.2769944 1.2891117 #> [874,] 1.1157276 1.1936167 1.1995324 #> [875,] 0.8294768 0.8344419 0.8362827 #> [876,] 0.5117953 0.5192327 0.5420004 #> [877,] 0.8030448 0.8216809 0.8369158 #> [878,] 0.5102163 0.5178139 0.6454089 #> [879,] 0.6869348 0.6993587 0.7524557 #> [880,] 0.5584951 0.5687479 0.5827281 #> [881,] 0.4919623 0.4922360 0.5161984 #> [882,] 1.1207087 1.1468858 1.1637005 #> [883,] 1.4398054 1.4933724 1.5107034 #> [884,] 0.9321315 0.9481810 0.9830945 #> [885,] 0.7053919 0.7086441 0.7171960 #> [886,] 0.8671570 0.8687603 0.8753464 #> [887,] 0.6857948 0.7112517 0.7573017 #> [888,] 0.8283524 0.8703998 0.9551885 #> [889,] 0.7289802 0.8437187 0.8561901 #> [890,] 0.6797562 0.6992552 0.6998319 #> [891,] 0.6658791 0.6701998 0.6801707 #> [892,] 1.3072786 1.3114265 1.3533247 #> [893,] 0.8628560 0.9132747 0.9165879 #> [894,] 1.5536419 1.5676981 1.5715458 #> [895,] 1.1455775 1.1833642 1.2430949 #> [896,] 1.3007429 1.3456632 1.3779056 #> [897,] 0.5183205 0.5743258 0.6032926 #> [898,] 1.3950150 1.4845992 1.5350760 #> [899,] 0.5721419 0.5732198 0.6285374 #> [900,] 1.0721517 1.1149484 1.1474075 #> [901,] 0.5700570 0.5922966 0.6117980 #> [902,] 0.5699897 0.5910700 0.6163551 #> [903,] 0.5701457 0.5879070 0.5888147 #> [904,] 0.6237141 0.6314061 0.6545029 #> [905,] 0.8616950 0.8821900 0.8904956 #> [906,] 0.8366904 0.8441713 0.8561901 #> [907,] 0.4796330 0.5170806 0.5385851 #> [908,] 0.9966891 1.0304489 1.0453836 #> [909,] 0.6767015 0.7246652 0.7910346 #> [910,] 1.0512462 1.0634887 1.1017909 #> [911,] 0.7690241 0.7835180 0.7842613 #> [912,] 0.6977815 0.7157679 0.7306703 #> [913,] 0.6552209 0.6862504 0.7353109 #> [914,] 0.5776104 0.6021255 0.7304606 #> [915,] 0.7863488 0.8475656 0.8720974 #> [916,] 1.0116410 1.0413067 1.0591309 #> [917,] 0.5229253 0.5461736 0.5666734 #> [918,] 0.7958120 0.8136202 0.8537410 #> [919,] 0.7987205 0.8064646 0.8194054 #> [920,] 0.8142671 0.8244194 0.8358189 #> [921,] 0.6044993 0.6127121 0.6399026 #> [922,] 0.8370144 0.8698289 0.9121935 #> [923,] 0.9604526 0.9776841 1.0087750 #> [924,] 0.6765533 0.7119572 0.7163205 #> [925,] 0.7317220 0.7530499 0.7623217 #> [926,] 0.8837417 0.8855182 0.9463822 #> [927,] 0.7803086 0.8170575 0.8442191 #> [928,] 0.7599411 0.7889473 0.7904919 #> [929,] 1.1081530 1.2327271 1.2911965 #> [930,] 0.7506026 0.8026476 0.8214476 #> [931,] 0.7451284 0.7653108 0.7774373 #> [932,] 0.6848409 0.7093980 0.7568239 #> [933,] 0.6515893 0.6556246 0.6686640 #> [934,] 0.6678590 0.6910904 0.7055392 #> [935,] 0.5962874 0.6394549 0.6976758 #> [936,] 0.7912512 0.8118788 0.8307374 #> [937,] 1.1736185 1.2208004 1.2446415 #> [938,] 0.7667661 0.7940193 0.8452377 #> [939,] 0.6827288 0.7092455 0.7464431 #> [940,] 0.8604242 0.8726374 0.8772334 #> [941,] 0.8093763 0.8289961 0.8344334 #> [942,] 0.6301110 0.6482104 0.6788163 #> [943,] 0.5850961 0.6228291 0.6632456 #> [944,] 1.0421754 1.0446976 1.0731588 #> [945,] 0.7180052 0.7315810 0.7349476 #> [946,] 0.8506424 0.8683091 0.9293002 #> [947,] 1.0121738 1.0359470 1.0360698 #> [948,] 0.5552995 0.5578163 0.5626172 #> [949,] 0.8668694 0.8669934 0.8724862 #> [950,] 0.7558192 0.7727489 0.7882429 #> [951,] 2.2518509 2.2876735 2.3032924 #> [952,] 1.3832696 1.4011508 1.4280204 #> [953,] 0.5844173 0.6092434 0.6248032 #> [954,] 0.8162019 0.8250067 0.8313569 #> [955,] 0.5101946 0.5432098 0.5471188 #> [956,] 0.5132561 0.5241539 0.5257055 #> [957,] 1.0073150 1.0311136 1.0359089 #> [958,] 0.5265139 0.5461736 0.5519625 #> [959,] 0.6008707 0.6280089 0.6345307 #> [960,] 0.6509555 0.7058413 0.7246035 #> [961,] 2.1073527 2.1123186 2.1230725 #> [962,] 0.8408857 0.8688455 0.8930393 #> [963,] 0.5797989 0.5860902 0.5995586 #> [964,] 1.0524327 1.0525541 1.0626364 #> [965,] 0.6757378 0.6832628 0.7136841 #> [966,] 0.5765295 0.5861157 0.5992267 #> [967,] 0.6782984 0.6786018 0.6802397 #> [968,] 0.6391293 0.6621010 0.6837852 #> [969,] 0.7136425 0.7223476 0.7272496 #> [970,] 0.5855896 0.5870801 0.5888147 #> [971,] 0.5590485 0.5654247 0.5661557 #> [972,] 0.9690550 0.9994436 1.0083670 #> [973,] 0.7691601 0.7728200 0.7825482 #> [974,] 0.4869863 0.5158111 0.5624893 #> [975,] 0.6556246 0.6693918 0.6756131 #> [976,] 1.0606498 1.0695690 1.0897265 #> [977,] 0.8508795 0.8643554 0.9045820 #> [978,] 0.5277032 0.5386993 0.5486410 #> [979,] 0.5850961 0.5925284 0.5991496 #> [980,] 0.6278819 0.6315640 0.6414373 #> [981,] 0.5928978 0.6146377 0.6159974 #> [982,] 0.9526243 0.9714016 1.0005670 #> [983,] 0.7373603 0.7435158 0.7604729 #> [984,] 1.1850158 1.2176165 1.2439219 #> [985,] 0.7098652 0.7231843 0.7240913 #> [986,] 0.7024012 0.7333383 0.7539410 #> [987,] 1.8988976 1.9063222 1.9955230 #> [988,] 0.8699772 0.9439670 0.9859146 #> [989,] 0.7045640 0.8301865 0.8381168 #> [990,] 0.6898966 0.6935705 0.7925446 #> [991,] 0.7808682 0.8238527 0.8348792 #> [992,] 0.9184891 0.9223074 0.9293687 #> [993,] 0.7294261 0.8360314 0.8809900 #> [994,] 0.6341530 0.6459071 0.6519416 #> [995,] 0.6254579 0.6277272 0.6626872 #> [996,] 0.5238755 0.5315577 0.5379967 #> [997,] 1.3163073 1.3237790 1.3668184 #> [998,] 0.6553880 0.6724235 0.7223482 #> [999,] 0.6011789 0.6055215 0.6515013 #> [1000,] 0.5779394 0.5873525 0.5902023 #> # Find the 10 approximate nearest neighbors tof_find_knn( .data = sim_data, k = 10, distance_function = \"euclidean\", ) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] #> [1,] 845 770 791 44 698 273 336 765 338 277 #> [2,] 873 936 304 397 677 479 667 680 230 856 #> [3,] 480 369 471 186 486 911 569 826 178 10 #> [4,] 288 481 746 93 802 252 120 849 17 291 #> [5,] 295 33 212 289 484 556 466 851 776 804 #> [6,] 999 575 125 386 36 211 695 651 20 1000 #> [7,] 510 644 30 933 275 407 990 189 483 190 #> [8,] 953 943 55 278 966 576 730 168 342 870 #> [9,] 657 493 899 38 71 404 268 401 227 307 #> [10,] 468 930 569 710 486 238 615 186 981 664 #> [11,] 456 884 872 375 752 423 218 909 738 430 #> [12,] 314 64 603 790 421 359 800 478 757 740 #> [13,] 264 889 46 394 63 131 64 314 972 88 #> [14,] 720 412 780 352 585 226 320 526 945 537 #> [15,] 475 215 83 793 521 982 217 411 900 946 #> [16,] 107 465 86 819 560 101 355 523 592 728 #> [17,] 252 481 220 675 120 672 446 19 612 302 #> [18,] 613 32 77 606 121 810 944 12 757 314 #> [19,] 446 843 836 821 189 931 269 164 988 252 #> [20,] 651 692 125 386 36 575 1000 901 6 370 #> [21,] 188 914 627 213 101 629 272 417 777 136 #> [22,] 283 906 426 256 321 992 537 945 595 501 #> [23,] 421 518 869 575 562 591 566 47 36 999 #> [24,] 492 682 787 550 912 517 463 679 522 572 #> [25,] 798 942 932 157 405 640 638 348 561 86 #> [26,] 597 704 222 191 940 40 895 90 413 735 #> [27,] 960 126 592 417 213 913 346 523 152 766 #> [28,] 588 646 169 114 512 669 750 789 768 932 #> [29,] 673 790 767 901 881 921 406 307 501 963 #> [30,] 644 933 969 7 173 483 189 510 407 275 #> [31,] 812 87 648 917 187 165 554 958 571 858 #> [32,] 18 77 613 314 944 448 606 64 12 871 #> [33,] 295 5 289 776 212 556 926 484 180 513 #> [34,] 639 109 763 498 891 702 712 827 119 519 #> [35,] 815 690 182 92 831 607 382 200 803 715 #> [36,] 386 695 651 6 165 439 20 575 999 858 #> [37,] 711 323 113 973 351 726 450 665 353 393 #> [38,] 71 53 307 657 352 9 310 827 493 956 #> [39,] 659 400 613 810 32 643 800 64 606 121 #> [40,] 357 90 702 498 735 863 222 624 75 763 #> [41,] 954 976 623 829 468 270 155 10 84 348 #> [42,] 556 289 263 225 851 337 762 466 212 5 #> [43,] 621 736 311 145 919 395 122 713 924 484 #> [44,] 601 368 777 336 654 592 126 152 188 960 #> [45,] 898 864 535 73 134 469 964 440 124 541 #> [46,] 301 264 972 13 889 413 63 256 769 998 #> [47,] 349 591 562 593 518 23 695 723 485 165 #> [48,] 599 579 146 239 936 690 461 479 803 934 #> [49,] 734 616 161 780 196 507 1000 335 370 760 #> [50,] 720 531 429 167 881 878 596 767 921 501 #> [51,] 497 96 751 227 841 310 657 478 89 659 #> [52,] 647 296 528 788 224 304 241 977 505 392 #> [53,] 956 583 903 245 38 876 320 880 424 454 #> [54,] 833 739 979 342 943 604 549 995 384 776 #> [55,] 730 953 966 8 278 342 943 99 725 384 #> [56,] 539 879 396 724 274 885 962 974 850 271 #> [57,] 680 799 241 667 839 304 2 977 397 757 #> [58,] 315 223 69 742 94 946 303 473 521 215 #> [59,] 253 701 433 914 764 860 627 485 447 473 #> [60,] 718 817 113 323 74 162 668 108 489 747 #> [61,] 694 370 939 857 616 715 142 333 692 719 #> [62,] 509 228 901 625 699 234 461 20 727 359 #> [63,] 264 394 192 889 800 64 191 13 413 46 #> [64,] 800 314 12 131 192 264 63 394 643 32 #> [65,] 701 398 957 930 829 473 238 59 886 84 #> [66,] 487 452 112 347 238 473 118 833 826 534 #> [67,] 500 807 442 408 770 608 277 136 584 929 #> [68,] 373 462 594 193 71 542 657 741 975 341 #> [69,] 315 416 58 398 764 94 914 174 708 941 #> [70,] 445 684 627 416 447 80 708 204 914 764 #> [71,] 657 310 38 103 373 504 9 519 542 827 #> [72,] 623 842 41 560 296 829 241 954 819 528 #> [73,] 864 134 617 45 564 219 266 458 884 115 #> [74,] 60 817 718 358 530 820 258 325 489 162 #> [75,] 624 823 712 735 863 754 876 702 979 498 #> [76,] 643 400 63 435 413 46 800 26 264 895 #> [77,] 32 18 871 613 376 314 12 64 183 800 #> [78,] 781 137 621 122 736 709 958 983 924 322 #> [79,] 383 250 655 125 152 728 200 668 575 355 #> [80,] 447 761 204 445 123 684 347 253 70 59 #> [81,] 844 240 535 849 93 865 882 380 199 743 #> [82,] 197 636 545 661 492 269 923 24 371 133 #> [83,] 620 521 411 349 678 215 593 475 486 47 #> [84,] 886 954 930 591 797 154 829 723 107 560 #> [85,] 309 988 732 824 743 446 882 931 344 117 #> [86,] 101 433 346 819 16 701 561 942 25 640 #> [87,] 917 554 31 812 571 974 389 274 187 292 #> [88,] 889 13 264 321 63 256 394 426 906 22 #> [89,] 519 310 763 498 96 497 90 71 827 34 #> [90,] 357 40 674 191 498 863 89 735 763 192 #> [91,] 488 555 631 362 986 978 522 558 249 948 #> [92,] 35 815 409 984 182 520 607 628 825 689 #> [93,] 574 481 849 4 506 586 17 802 431 746 #> [94,] 946 605 941 354 764 860 742 331 315 306 #> [95,] 679 568 99 492 778 931 159 953 852 269 #> [96,] 827 497 454 227 754 876 51 981 823 478 #> [97,] 693 106 343 326 363 925 310 504 519 390 #> [98,] 892 997 738 218 330 769 992 256 456 731 #> [99,] 730 538 679 966 55 95 953 778 233 564 #> [100,] 120 984 288 990 291 7 697 4 927 510 #> [101,] 21 86 465 777 188 592 213 27 523 445 #> [102,] 740 570 186 349 122 911 828 678 47 841 #> [103,] 504 887 71 779 373 925 519 310 891 547 #> [104,] 214 300 220 662 120 849 481 252 893 267 #> [105,] 317 273 512 324 729 114 932 508 332 698 #> [106,] 693 343 97 326 285 947 788 925 363 310 #> [107,] 560 16 84 436 728 465 819 566 355 886 #> [108,] 973 113 711 718 668 323 374 79 654 681 #> [109,] 891 34 639 604 424 827 808 342 796 472 #> [110,] 646 378 502 746 7 181 407 288 990 514 #> [111,] 571 854 974 554 999 587 902 904 879 217 #> [112,] 427 957 66 238 473 615 178 480 315 452 #> [113,] 711 108 973 323 668 37 747 718 60 162 #> [114,] 512 483 588 645 811 28 646 254 30 434 #> [115,] 563 327 263 538 180 134 564 289 159 568 #> [116,] 183 128 687 871 290 994 137 376 284 301 #> [117,] 578 743 267 506 573 672 688 586 541 988 #> [118,] 569 534 312 238 441 468 626 957 664 472 #> [119,] 432 763 498 34 702 639 859 712 455 40 #> [120,] 252 214 481 220 139 17 697 104 927 662 #> [121,] 810 18 613 318 39 77 32 659 606 744 #> [122,] 781 143 78 621 102 43 736 983 137 31 #> [123,] 761 536 524 80 452 347 739 487 447 293 #> [124,] 864 45 513 535 543 73 266 865 198 883 #> [125,] 1000 6 575 386 20 651 523 36 152 79 #> [126,] 27 960 565 592 801 152 417 188 913 766 #> [127,] 385 703 422 631 727 816 759 625 963 950 #> [128,] 290 116 137 994 958 284 709 781 183 143 #> [129,] 402 255 297 784 144 243 305 276 414 229 #> [130,] 364 532 768 344 821 725 928 169 460 931 #> [131,] 394 921 673 998 64 264 192 390 314 29 #> [132,] 176 837 286 830 786 726 660 495 144 474 #> [133,] 339 923 580 163 714 82 676 517 568 24 #> [134,] 266 115 564 541 198 864 964 159 327 263 #> [135,] 419 119 443 432 451 859 166 822 896 410 #> [136,] 629 21 627 914 188 353 941 807 777 428 #> [137,] 709 78 128 958 781 290 143 917 752 122 #> [138,] 230 757 667 566 977 401 421 899 869 304 #> [139,] 220 476 120 866 214 252 716 189 991 371 #> [140,] 282 707 429 955 671 968 978 691 720 719 #> [141,] 691 276 978 522 488 589 671 968 707 996 #> [142,] 526 818 780 616 412 719 760 196 370 49 #> [143,] 917 122 709 87 290 128 137 31 850 781 #> [144,] 402 660 129 176 235 582 474 755 276 600 #> [145,] 484 43 180 943 54 538 384 995 924 736 #> [146,] 934 579 239 48 232 690 681 509 257 599 #> [147,] 895 313 76 540 769 26 330 400 88 961 #> [148,] 929 408 708 324 729 834 867 761 524 762 #> [149,] 847 803 899 231 208 232 934 959 936 401 #> [150,] 851 42 354 696 556 337 225 212 466 148 #> [151,] 272 174 706 391 807 353 217 136 748 21 #> [152,] 592 383 523 960 801 125 27 1000 417 985 #> [153,] 582 660 868 637 144 297 235 175 474 129 #> [154,] 797 930 84 886 723 10 710 562 954 591 #> [155,] 751 270 710 954 810 369 10 41 51 468 #> [156,] 795 826 455 859 480 737 569 3 626 119 #> [157,] 798 942 25 312 405 932 561 640 348 332 #> [158,] 172 666 441 669 542 626 796 789 822 341 #> [159,] 852 568 263 198 95 266 778 180 99 289 #> [160,] 897 577 860 935 766 813 306 194 331 253 #> [161,] 507 546 366 766 734 346 49 335 533 635 #> [162,] 668 630 699 748 747 221 489 999 6 575 #> [163,] 676 834 337 705 912 517 24 682 225 568 #> [164,] 910 344 581 19 197 931 189 661 988 821 #> [165,] 648 695 439 36 880 31 903 386 187 812 #> [166,] 451 298 419 552 443 280 460 181 618 364 #> [167,] 596 531 50 406 767 881 998 970 284 656 #> [168,] 356 794 635 576 8 236 278 848 836 546 #> [169,] 588 768 618 669 280 181 808 28 460 130 #> [170,] 820 184 195 853 228 461 234 359 62 448 #> [171,] 430 784 255 501 399 752 759 878 596 972 #> [172,] 158 789 441 626 669 666 822 410 472 796 #> [173,] 969 649 855 30 716 760 644 203 689 915 #> [174,] 272 151 391 706 605 217 215 914 411 946 #> [175,] 474 235 663 209 950 496 377 582 857 939 #> [176,] 132 837 144 726 286 91 660 235 600 474 #> [177,] 937 286 923 600 132 837 339 786 714 328 #> [178,] 3 480 615 186 486 223 112 471 919 553 #> [179,] 623 41 842 348 829 65 976 72 441 666 #> [180,] 145 289 543 563 115 513 159 538 549 484 #> [181,] 280 618 460 378 169 588 746 821 768 887 #> [182,] 815 831 965 505 381 231 585 690 35 607 #> [183,] 116 871 376 128 290 687 242 994 77 143 #> [184,] 170 853 799 944 606 448 613 18 359 32 #> [185,] 782 320 196 955 439 734 996 707 720 531 #> [186,] 471 486 911 570 3 102 710 678 615 981 #> [187,] 292 31 897 648 87 935 165 812 160 439 #> [188,] 777 21 101 417 152 213 985 592 221 27 #> [189,] 644 716 875 510 843 476 19 30 7 969 #> [190,] 407 741 933 594 462 275 967 990 378 975 #> [191,] 387 192 971 90 735 413 828 863 63 674 #> [192,] 497 191 800 971 390 387 64 454 63 131 #> [193,] 207 975 259 533 405 493 260 642 638 640 #> [194,] 420 306 787 160 572 682 870 331 897 558 #> [195,] 170 491 820 461 228 184 599 448 516 509 #> [196,] 185 320 734 719 707 49 370 780 955 720 #> [197,] 82 661 910 467 636 545 269 875 164 838 #> [198,] 159 266 852 134 263 568 578 824 180 115 #> [199,] 602 731 380 499 414 844 745 300 440 243 #> [200,] 250 381 993 717 715 965 79 383 388 182 #> [201,] 497 89 498 90 51 192 841 763 400 191 #> [202,] 907 572 577 782 766 801 319 439 185 196 #> [203,] 689 173 483 855 30 360 915 379 434 261 #> [204,] 80 684 347 447 761 445 70 312 949 123 #> [205,] 861 984 382 520 92 628 299 611 815 477 #> [206,] 327 225 444 115 696 134 337 563 263 42 #> [207,] 259 533 193 507 366 493 840 346 758 260 #> [208,] 959 231 232 401 899 566 355 381 404 149 #> [209,] 857 939 175 991 663 257 377 474 866 950 #> [210,] 840 758 880 259 245 260 956 635 576 207 #> [211,] 614 6 695 485 999 386 36 813 213 523 #> [212,] 5 466 851 295 556 33 804 289 762 303 #> [213,] 523 417 433 21 766 27 813 101 211 346 #> [214,] 104 120 220 252 481 300 139 849 662 17 #> [215,] 521 83 411 15 946 174 620 217 605 742 #> [216,] 726 333 362 948 692 631 985 694 319 450 #> [217,] 411 391 605 485 111 813 272 793 521 211 #> [218,] 738 548 892 992 98 243 334 255 456 745 #> [219,] 437 924 938 926 736 367 372 641 617 145 #> [220,] 214 139 252 104 120 17 300 634 267 529 #> [221,] 654 706 162 188 748 777 465 551 125 272 #> [222,] 704 40 367 26 365 597 449 265 735 316 #> [223,] 58 178 620 494 742 521 315 919 615 544 #> [224,] 392 993 717 505 528 647 462 68 404 296 #> [225,] 337 696 912 444 42 263 517 327 354 163 #> [226,] 412 515 526 14 945 780 920 302 510 275 #> [227,] 478 603 981 96 664 880 758 591 869 827 #> [228,] 461 62 234 509 359 448 901 790 29 673 #> [229,] 688 745 243 683 414 602 573 276 305 541 #> [230,] 397 304 138 667 959 566 436 856 479 916 #> [231,] 232 404 208 899 959 505 585 381 847 401 #> [232,] 231 208 934 965 959 404 847 401 585 149 #> [233,] 691 782 550 996 522 955 249 966 572 558 #> [234,] 359 854 228 994 518 790 62 587 853 421 #> [235,] 474 175 950 402 144 663 582 857 129 877 #> [236,] 928 508 168 356 332 8 836 467 532 953 #> [237,] 265 322 656 940 805 334 597 458 704 387 #> [238,] 615 473 678 10 253 468 930 957 118 486 #> [239,] 934 501 146 537 673 149 921 48 426 131 #> [240,] 552 365 783 262 574 802 313 922 908 431 #> [241,] 977 680 57 304 270 667 285 397 947 72 #> [242,] 183 525 871 475 376 116 853 318 329 632 #> [243,] 548 683 229 745 602 414 688 440 297 246 #> [244,] 206 271 658 563 653 115 806 444 544 134 #> [245,] 583 424 576 956 635 210 260 756 53 840 #> [246,] 414 602 297 440 243 548 683 229 755 129 #> [247,] 318 376 710 911 486 740 102 369 83 871 #> [248,] 940 413 617 456 597 435 423 301 237 46 #> [249,] 558 996 287 389 488 812 907 631 978 858 #> [250,] 200 381 717 993 383 965 79 355 231 715 #> [251,] 728 325 655 699 79 681 479 959 397 856 #> [252,] 17 481 120 220 988 214 19 4 139 104 #> [253,] 935 59 433 160 860 756 80 473 238 485 #> [254,] 557 594 933 190 114 407 915 30 483 765 #> [255,] 784 129 297 402 243 548 305 171 745 430 #> [256,] 906 769 889 22 430 394 264 46 283 992 #> [257,] 399 759 816 950 939 857 377 209 146 681 #> [258,] 323 60 113 368 711 654 37 108 489 530 #> [259,] 207 533 840 210 260 758 507 635 366 193 #> [260,] 259 245 840 533 424 210 827 207 956 891 #> [261,] 689 403 409 360 722 855 476 716 875 173 #> [262,] 888 862 783 922 832 330 731 365 574 846 #> [263,] 568 289 159 852 538 115 42 556 563 679 #> [264,] 13 889 394 63 46 131 64 314 998 256 #> [265,] 805 237 322 980 735 863 458 316 995 597 #> [266,] 578 159 134 198 564 541 778 180 117 864 #> [267,] 529 117 634 300 743 918 988 220 573 578 #> [268,] 954 657 9 751 947 977 528 51 270 640 #> [269,] 836 492 463 636 467 550 730 19 95 679 #> [270,] 751 155 954 977 268 810 947 241 41 51 #> [271,] 850 653 709 544 917 287 87 143 56 539 #> [272,] 706 391 151 174 21 914 217 813 188 605 #> [273,] 774 913 932 791 25 798 942 684 811 86 #> [274,] 974 571 87 879 902 554 389 558 812 948 #> [275,] 407 990 933 510 190 515 741 7 779 502 #> [276,] 141 909 229 589 905 691 986 488 573 683 #> [277,] 929 408 126 565 770 845 136 324 708 629 #> [278,] 966 955 730 583 55 8 782 168 848 576 #> [279,] 609 722 830 495 338 818 855 476 261 360 #> [280,] 618 181 822 669 169 166 419 460 887 588 #> [281,] 910 164 633 85 988 581 792 732 514 545 #> [282,] 140 955 707 671 691 966 559 302 278 522 #> [283,] 22 426 906 537 945 595 920 256 321 501 #> [284,] 994 406 958 167 290 128 596 767 490 423 #> [285,] 947 106 241 270 326 751 343 788 977 268 #> [286,] 837 132 176 726 830 177 495 340 786 923 #> [287,] 249 389 558 890 488 589 87 274 986 812 #> [288,] 4 291 481 802 746 697 100 120 849 93 #> [289,] 556 263 5 563 484 42 295 180 33 568 #> [290,] 128 994 958 917 116 137 284 294 554 143 #> [291,] 697 927 502 288 746 990 431 515 120 481 #> [292,] 187 897 87 306 411 31 860 274 311 395 #> [293,] 549 833 928 524 739 54 776 236 943 123 #> [294,] 587 554 571 290 974 904 994 917 958 854 #> [295,] 33 5 289 212 776 556 484 926 804 180 #> [296,] 528 52 224 560 647 652 993 107 72 392 #> [297,] 129 414 255 246 243 402 548 784 602 229 #> [298,] 808 639 166 451 364 109 443 342 34 604 #> [299,] 611 607 861 520 945 927 920 515 893 697 #> [300,] 104 414 267 220 602 634 529 214 918 229 #> [301,] 687 46 972 413 423 116 314 64 998 871 #> [302,] 559 612 848 675 794 843 282 779 547 846 #> [303,] 851 713 466 742 354 315 395 919 311 212 #> [304,] 397 230 667 977 138 680 2 528 936 959 #> [305,] 968 429 745 140 229 402 688 255 129 499 #> [306,] 897 420 194 331 860 160 292 813 187 890 #> [307,] 673 921 38 29 603 53 401 478 790 869 #> [308,] 464 358 422 816 817 457 385 950 127 703 #> [309,] 85 732 814 931 824 344 778 446 364 852 #> [310,] 71 89 519 657 504 38 103 390 96 51 #> [311,] 395 713 919 43 870 292 187 484 935 306 #> [312,] 157 347 949 118 348 534 332 798 204 25 #> [313,] 922 908 97 363 888 26 262 365 240 90 #> [314,] 64 448 12 800 32 131 77 264 13 359 #> [315,] 473 742 58 303 416 94 764 69 860 521 #> [316,] 449 612 832 559 805 846 265 672 674 675 #> [317,] 105 729 273 952 750 324 932 204 684 512 #> [318,] 247 744 871 77 121 18 32 376 183 242 #> [319,] 333 801 616 202 572 818 772 216 719 550 #> [320,] 720 185 196 335 734 955 531 53 782 848 #> [321,] 426 22 283 906 908 88 595 888 256 363 #> [322,] 805 656 458 237 265 671 980 970 686 968 #> [323,] 113 37 973 711 108 718 351 60 817 747 #> [324,] 729 148 105 929 503 453 277 204 508 408 #> [325,] 251 873 856 397 728 479 2 304 230 699 #> [326,] 97 310 542 343 106 504 519 947 71 89 #> [327,] 444 115 538 986 555 890 517 263 225 568 #> [328,] 600 937 425 177 650 874 714 755 582 660 #> [329,] 525 242 871 318 183 687 744 301 116 491 #> [330,] 769 888 731 262 256 704 992 98 334 738 #> [331,] 813 897 860 306 605 764 914 160 629 194 #> [332,] 508 949 928 236 157 808 312 811 356 768 #> [333,] 319 216 801 616 694 719 692 818 202 907 #> [334,] 237 458 745 322 704 449 499 738 265 805 #> [335,] 734 533 848 780 507 320 49 161 412 546 #> [336,] 44 791 383 592 622 655 960 601 152 915 #> [337,] 696 225 163 676 834 42 912 517 705 263 #> [338,] 845 609 279 601 855 336 722 44 360 173 #> [339,] 133 580 923 714 163 82 676 177 517 874 #> [340,] 989 912 885 724 517 396 555 428 787 682 #> [341,] 542 68 158 666 326 373 638 669 887 594 #> [342,] 604 979 54 55 424 8 583 943 576 245 #> [343,] 106 693 97 326 925 504 310 363 788 390 #> [344,] 931 164 661 532 19 446 130 910 836 725 #> [345,] 773 785 516 377 663 951 868 153 997 175 #> [346,] 366 507 546 161 207 259 960 523 405 766 #> [347,] 452 487 204 123 312 761 80 949 447 473 #> [348,] 640 561 798 441 157 819 25 312 942 829 #> [349,] 47 593 485 695 102 165 518 562 570 83 #> [350,] 361 247 318 494 242 83 121 475 376 486 #> [351,] 973 37 323 374 495 726 711 108 132 113 #> [352,] 390 14 921 38 595 585 847 307 131 720 #> [353,] 450 188 629 136 272 428 711 391 706 985 #> [354,] 466 851 94 303 225 941 420 306 556 212 #> [355,] 493 401 231 208 899 404 959 381 250 566 #> [356,] 168 546 642 635 507 811 236 794 405 533 #> [357,] 90 40 547 674 863 763 702 498 519 735 #> [358,] 817 464 809 457 820 747 308 489 482 60 #> [359,] 234 790 518 228 421 854 448 12 994 23 #> [360,] 855 875 716 476 261 403 689 644 173 189 #> [361,] 350 247 900 632 83 475 376 593 562 15 #> [362,] 948 631 907 558 216 902 249 91 885 389 #> [363,] 925 922 504 390 97 908 595 103 547 310 #> [364,] 532 725 130 298 814 549 931 293 446 778 #> [365,] 222 574 552 586 262 704 865 40 832 451 #> [366,] 346 546 507 161 207 259 405 533 840 913 #> [367,] 384 222 265 624 641 924 702 543 938 40 #> [368,] 601 44 654 711 336 655 108 777 113 973 #> [369,] 3 911 471 186 480 737 486 553 710 841 #> [370,] 61 694 196 49 692 616 20 142 651 780 #> [371,] 685 636 634 918 529 786 522 269 82 550 #> [372,] 641 938 437 924 736 122 983 553 781 911 #> [373,] 68 504 71 103 887 741 585 462 657 967 #> [374,] 495 973 61 694 216 108 333 985 711 818 #> [375,] 11 884 872 456 653 469 752 909 738 548 #> [376,] 871 183 593 632 77 853 116 518 247 349 #> [377,] 663 257 950 209 175 816 399 235 857 939 #> [378,] 502 887 407 181 741 190 967 275 779 933 #> [379,] 483 434 698 203 514 855 360 875 30 910 #> [380,] 743 199 882 844 964 602 440 541 117 267 #> [381,] 250 231 993 200 959 208 717 505 355 232 #> [382,] 527 35 205 520 815 861 611 92 690 299 #> [383,] 649 760 152 79 250 49 616 370 592 61 #> [384,] 367 549 55 54 995 145 979 342 943 778 #> [385,] 703 127 631 422 457 948 362 904 902 389 #> [386,] 36 651 1000 695 6 125 439 577 20 575 #> [387,] 191 998 971 828 192 413 903 167 423 656 #> [388,] 200 409 715 250 35 681 655 108 993 251 #> [389,] 812 554 249 571 974 287 87 858 958 948 #> [390,] 352 192 394 310 363 131 925 921 38 497 #> [391,] 217 605 272 813 331 706 411 174 914 629 #> [392,] 224 993 717 652 915 462 647 182 381 505 #> [393,] 500 37 286 726 438 396 937 351 837 724 #> [394,] 131 264 921 998 889 63 390 192 13 64 #> [395,] 311 713 919 742 43 292 935 187 303 122 #> [396,] 724 885 56 340 539 989 438 962 362 274 #> [397,] 304 230 856 667 2 138 479 680 977 916 #> [398,] 701 70 416 684 445 59 914 69 65 764 #> [399,] 759 257 816 950 509 415 422 727 501 127 #> [400,] 643 39 76 659 800 63 64 201 192 264 #> [401,] 566 208 899 959 869 493 355 231 307 404 #> [402,] 129 144 255 305 297 235 276 474 229 784 #> [403,] 261 360 722 689 409 476 875 855 139 716 #> [404,] 899 231 505 585 847 355 208 9 232 401 #> [405,] 942 366 798 193 157 811 546 207 25 642 #> [406,] 284 167 596 767 790 29 881 994 901 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141 978 589 996 #> [489,] 748 793 162 747 706 809 668 982 113 630 #> [490,] 596 589 963 996 881 978 167 767 389 958 #> [491,] 195 972 170 687 525 516 301 820 461 228 #> [492,] 24 679 463 682 269 550 95 787 522 636 #> [493,] 207 758 401 355 9 869 193 259 307 533 #> [494,] 223 427 350 215 83 620 486 58 521 112 #> [495,] 374 818 279 830 837 609 333 726 216 319 #> [496,] 474 175 637 786 660 866 830 918 991 235 #> [497,] 192 96 51 454 841 971 89 201 310 754 #> [498,] 763 712 702 34 432 40 75 823 624 89 #> [499,] 745 992 672 305 688 334 731 662 199 893 #> [500,] 608 67 393 438 428 807 353 442 584 136 #> [501,] 767 878 537 50 881 239 29 921 596 167 #> [502,] 378 291 990 746 275 407 741 887 697 510 #> [503,] 453 524 463 467 838 236 508 536 661 928 #> [504,] 925 103 373 71 887 310 363 519 352 326 #> [505,] 831 404 231 899 847 182 585 381 803 224 #> [506,] 586 117 672 449 743 578 93 574 865 17 #> [507,] 546 161 366 533 346 207 335 259 734 49 #> [508,] 332 236 928 949 811 356 503 467 80 768 #> 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532 776 #> [550,] 522 24 572 492 707 233 787 682 463 955 #> [551,] 598 221 630 436 748 162 465 706 699 916 #> [552,] 166 574 431 365 451 802 547 746 675 280 #> [553,] 737 471 369 372 911 3 178 795 641 938 #> [554,] 571 974 917 87 812 389 31 294 958 587 #> [555,] 986 488 517 91 444 522 890 787 327 905 #> [556,] 42 289 5 851 563 466 212 263 484 295 #> [557,] 254 483 203 825 765 933 114 915 190 30 #> [558,] 249 890 287 362 488 907 948 996 389 274 #> [559,] 302 612 316 848 278 966 282 674 671 805 #> [560,] 107 84 16 436 528 138 728 954 819 566 #> [561,] 640 819 348 942 157 758 798 840 86 193 #> [562,] 518 593 723 47 23 421 591 349 632 710 #> [563,] 115 289 926 556 180 484 263 538 145 327 #> [564,] 458 686 266 99 541 573 115 134 538 578 #> [565,] 126 801 417 629 766 319 27 985 194 572 #> [566,] 401 575 959 869 208 23 436 421 138 355 #> [567,] 409 209 663 403 722 496 377 175 987 984 #> [568,] 263 159 679 852 95 538 676 99 289 492 #> [569,] 480 118 455 10 468 664 3 626 186 981 #> [570,] 981 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831 35 #> [734,] 335 49 161 196 955 320 185 507 780 782 #> [735,] 863 75 624 674 995 971 40 823 265 191 #> [736,] 924 621 437 43 983 78 122 781 372 995 #> [737,] 553 795 369 3 471 826 480 156 911 178 #> [738,] 218 892 98 548 334 456 731 992 11 330 #> [739,] 833 54 756 943 604 123 536 713 534 293 #> [740,] 102 828 841 911 570 454 971 754 478 186 #> [741,] 190 462 407 275 373 378 887 990 594 933 #> [742,] 315 395 521 620 678 935 860 473 713 303 #> [743,] 117 882 506 267 380 578 844 988 586 93 #> [744,] 318 606 944 121 18 32 77 613 39 871 #> [745,] 688 229 243 683 305 548 499 602 414 968 #> [746,] 431 802 502 181 821 4 291 510 378 288 #> [747,] 665 668 809 162 625 904 113 902 489 727 #> [748,] 706 999 162 489 630 793 6 221 211 272 #> [749,] 308 482 962 511 457 358 294 872 703 491 #> [750,] 932 25 798 765 638 774 28 254 942 842 #> [751,] 270 155 51 268 947 954 810 227 977 723 #> [752,] 589 490 656 909 958 884 137 423 11 709 #> [753,] 418 174 900 65 215 151 398 69 154 797 #> [754,] 876 454 823 971 956 828 827 96 981 75 #> [755,] 905 874 144 909 246 440 129 243 683 276 #> [756,] 576 604 245 635 739 534 424 840 210 342 #> [757,] 138 613 421 12 667 590 723 810 359 230 #> [758,] 210 840 259 880 493 207 869 433 260 227 #> [759,] 399 816 257 422 509 415 727 950 127 625 #> [760,] 616 649 772 818 383 49 142 173 801 969 #> [761,] 123 80 416 536 524 204 347 452 447 867 #> [762,] 804 852 524 676 212 42 453 289 263 568 #> [763,] 498 34 89 519 357 119 712 90 702 432 #> [764,] 914 860 331 605 627 813 59 416 897 941 #> [765,] 791 774 622 700 273 254 459 557 932 750 #> [766,] 161 417 202 572 577 160 213 801 346 523 #> [767,] 881 596 963 29 901 878 501 406 167 50 #> [768,] 169 588 808 821 618 356 794 236 130 928 #> [769,] 330 256 888 63 46 704 413 906 940 237 #> [770,] 277 136 708 67 1 777 70 807 44 408 #> [771,] 835 66 33 295 776 487 867 5 212 452 #> [772,] 616 760 818 838 716 319 142 467 476 734 #> [773,] 22 283 611 321 992 893 861 426 663 906 #> [774,] 622 791 273 765 932 913 25 942 811 798 #> [775,] 894 839 48 527 677 599 936 579 2 88 #> [776,] 833 54 295 33 549 145 293 5 484 739 #> [777,] 188 101 21 654 44 592 152 27 221 126 #> [778,] 549 95 99 814 578 725 159 384 55 532 #> [779,] 967 887 103 848 794 302 275 891 642 843 #> [780,] 412 526 335 142 49 734 14 196 320 649 #> [781,] 78 122 137 621 128 958 709 143 736 290 #> [782,] 185 955 996 439 202 278 233 572 707 320 #> [783,] 262 862 670 908 662 291 802 431 574 697 #> [784,] 255 129 171 297 305 402 430 992 548 785 #> [785,] 784 868 255 297 171 129 872 153 399 516 #> [786,] 660 918 132 371 685 637 792 496 830 923 #> [787,] 682 912 24 194 517 420 550 890 233 492 #> [788,] 647 505 343 831 677 52 528 803 224 68 #> [789,] 172 669 410 158 822 441 472 169 808 796 #> [790,] 29 673 406 359 603 307 421 901 36 12 #> [791,] 774 765 622 273 913 336 698 960 173 915 #> [792,] 371 786 476 636 82 545 875 139 923 918 #> [793,] 982 111 748 217 489 632 15 809 879 475 #> [794,] 168 848 302 843 779 356 725 836 891 342 #> [795,] 156 826 737 455 859 480 3 432 553 471 #> [796,] 109 260 967 441 669 891 618 808 405 542 #> [797,] 154 84 886 723 930 954 562 560 591 900 #> [798,] 25 942 157 932 405 640 348 638 561 86 #> [799,] 680 839 57 606 184 613 944 757 667 810 #> [800,] 64 192 643 314 63 12 400 191 387 497 #> [801,] 333 319 616 985 202 417 1000 565 766 152 #> [802,] 746 4 288 431 93 574 291 552 783 502 #> [803,] 831 690 149 579 847 936 505 677 231 899 #> [804,] 762 852 524 814 212 289 453 5 293 295 #> [805,] 322 265 980 671 316 458 237 970 656 559 #> [806,] 658 926 544 223 244 851 437 563 219 983 #> [807,] 136 151 353 500 941 629 428 391 272 67 #> [808,] 472 298 768 604 109 618 796 410 639 169 #> [809,] 457 904 902 587 747 482 111 974 665 962 #> [810,] 121 613 18 757 659 39 751 270 606 12 #> [811,] 405 642 356 913 546 932 942 508 975 366 #> [812,] 31 389 858 554 87 648 571 958 439 974 #> [813,] 331 860 897 605 914 485 160 764 391 417 #> [814,] 549 778 532 364 931 804 852 95 293 309 #> [815,] 182 831 520 35 690 505 607 477 92 462 #> [816,] 950 759 422 399 257 127 625 727 509 385 #> [817,] 718 358 60 681 747 625 464 820 668 816 #> [818,] 616 142 772 760 319 333 866 991 719 61 #> [819,] 640 829 561 348 86 16 798 25 107 942 #> [820,] 170 228 358 62 195 817 461 234 509 587 #> [821,] 768 19 446 794 510 843 725 746 181 130 #> [822,] 669 280 419 618 172 789 158 181 169 542 #> [823,] 75 754 712 876 624 454 827 96 498 735 #> [824,] 309 198 85 732 743 578 266 159 852 117 #> [825,] 628 203 557 392 689 915 92 254 652 717 #> [826,] 156 795 480 455 859 3 569 471 737 178 #> [827,] 876 96 424 754 956 664 891 454 981 823 #> [828,] 971 454 903 754 740 387 876 192 191 841 #> [829,] 819 84 640 348 561 86 954 107 465 16 #> [830,] 279 495 132 609 837 866 991 786 286 722 #> [831,] 505 803 847 182 690 231 404 815 585 149 #> [832,] 846 612 316 675 547 862 449 672 586 559 #> [833,] 54 739 776 293 943 604 342 549 979 145 #> [834,] 163 705 676 929 337 408 682 912 762 24 #> [835,] 771 66 867 487 452 112 952 347 212 427 #> [836,] 467 269 463 168 843 19 953 838 794 236 #> [837,] 286 132 176 726 495 830 319 216 786 818 #> [838,] 467 463 772 572 161 546 836 766 503 734 #> [839,] 590 667 799 677 936 448 680 613 757 606 #> [840,] 210 259 758 635 260 207 245 433 576 880 #> [841,] 911 740 497 96 454 754 828 471 51 823 #> [842,] 348 819 638 640 623 829 666 72 798 750 #> [843,] 510 836 794 19 302 189 168 335 848 779 #> [844,] 849 882 743 199 380 300 104 93 662 267 #> [845,] 338 601 44 609 277 279 1 126 336 698 #> [846,] 832 612 547 862 316 675 559 302 674 888 #> [847,] 831 585 149 231 803 404 505 232 934 352 #> [848,] 335 302 794 559 583 278 779 168 320 533 #> [849,] 481 844 93 662 104 214 4 882 252 17 #> [850,] 917 271 143 879 974 87 554 571 709 294 #> [851,] 354 466 556 212 303 5 42 484 289 295 #> [852,] 159 568 263 804 198 95 762 289 676 814 #> [853,] 632 234 376 359 170 184 518 982 593 77 #> [854,] 234 994 999 111 587 904 359 695 518 790 #> [855,] 360 875 716 173 476 261 203 689 434 644 #> [856,] 916 479 397 230 699 436 304 325 2 251 #> [857,] 939 209 61 991 877 415 257 950 866 694 #> [858,] 812 439 907 648 389 948 651 963 996 249 #> [859,] 432 826 119 712 702 795 624 639 156 75 #> [860,] 897 331 813 605 764 160 914 306 485 935 #> [861,] 611 299 520 927 607 670 893 697 205 945 #> [862,] 846 832 888 262 547 675 922 612 431 662 #> [863,] 735 674 75 624 995 971 40 357 876 979 #> [864,] 73 134 266 45 564 898 541 115 180 964 #> [865,] 586 506 449 365 222 367 704 266 574 578 #> [866,] 991 818 142 857 939 474 877 139 476 719 #> [867,] 761 212 123 452 729 347 148 524 204 487 #> [868,] 582 153 785 144 235 308 950 175 129 755 #> [869,] 603 421 23 401 566 493 880 575 478 36 #> [870,] 536 943 420 538 194 8 484 311 953 466 #> [871,] 183 116 77 376 32 128 318 687 242 18 #> [872,] 11 171 375 456 218 255 884 784 752 972 #> [873,] 2 325 397 304 479 856 936 251 230 579 #> [874,] 714 755 660 637 580 144 964 600 246 918 #> [875,] 360 716 855 476 189 644 173 434 197 30 #> [876,] 754 454 956 827 424 823 971 53 96 583 #> [877,] 920 415 939 857 537 866 991 945 474 429 #> [878,] 881 415 963 767 429 50 596 501 537 490 #> [879,] 974 274 56 571 902 962 850 111 554 809 #> [880,] 210 758 478 165 603 53 869 956 840 227 #> [881,] 767 963 596 878 50 901 29 406 167 490 #> [882,] 743 844 267 380 849 117 988 85 506 93 #> [883,] 451 443 543 513 364 166 309 814 778 367 #> [884,] 11 909 752 375 653 686 548 458 456 322 #> [885,] 724 396 362 340 989 274 558 890 56 428 #> [886,] 614 84 930 591 211 485 47 107 465 433 #> [887,] 967 103 779 378 373 504 618 741 542 71 #> [888,] 262 330 862 922 769 846 832 704 731 499 #> [889,] 264 13 394 63 88 46 131 256 64 906 #> [890,] 558 444 287 787 249 488 306 555 420 274 #> [891,] 109 424 827 260 34 103 848 794 779 604 #> [892,] 98 218 738 997 548 456 992 872 731 11 #> [893,] 662 927 920 945 697 499 862 670 283 611 #> [894,] 88 106 693 839 677 343 775 590 39 97 #> [895,] 540 26 940 704 769 248 597 330 617 222 #> [896,] 222 365 40 432 26 367 451 702 119 704 #> [897,] 860 331 306 813 160 292 187 605 935 485 #> [898,] 45 964 864 535 440 134 541 73 380 683 #> [899,] 404 231 401 959 208 505 9 355 149 493 #> [900,] 632 982 215 593 154 15 793 797 361 886 #> [901,] 767 29 881 963 509 727 20 406 62 790 #> [902,] 974 571 904 948 809 274 554 362 389 457 #> [903,] 828 971 53 165 531 454 648 406 880 970 #> [904,] 587 902 809 571 703 294 854 974 554 111 #> [905,] 986 909 276 555 755 650 488 653 600 589 #> [906,] 283 22 426 256 394 537 501 430 595 889 #> [907,] 202 948 858 577 692 439 362 631 996 249 #> [908,] 922 363 321 925 862 888 783 262 313 97 #> [909,] 276 884 905 686 683 243 752 589 141 458 #> [910,] 164 197 581 344 19 875 661 189 434 82 #> [911,] 471 186 841 369 740 102 3 486 710 570 #> [912,] 517 682 787 989 24 340 225 555 890 444 #> [913,] 960 811 366 346 27 546 273 774 592 507 #> [914,] 764 627 21 860 813 605 331 59 629 897 #> [915,] 622 717 392 993 173 652 969 649 774 203 #> [916,] 436 856 630 230 699 397 757 138 304 23 #> [917,] 87 554 143 571 31 974 290 812 958 850 #> [918,] 634 529 685 637 371 786 267 660 300 220 #> [919,] 713 395 311 43 145 484 742 437 122 303 #> [920,] 945 877 537 893 226 429 412 305 283 526 #> [921,] 131 673 29 50 307 394 352 167 501 767 #> [922,] 363 908 862 888 925 547 846 262 90 832 #> [923,] 133 339 82 545 786 636 371 177 580 286 #> [924,] 736 437 641 621 938 995 43 372 145 78 #> [925,] 363 504 103 595 390 97 310 922 352 373 #> [926,] 563 437 145 33 484 180 295 5 289 556 #> [927,] 697 291 893 515 662 945 299 226 120 920 #> [928,] 236 508 332 293 949 532 808 453 524 768 #> [929,] 408 148 834 277 705 324 708 163 503 453 #> [930,] 10 468 84 886 591 154 615 238 678 614 #> [931,] 344 446 532 725 95 19 836 661 814 778 #> [932,] 25 798 942 157 811 774 405 273 638 750 #> [933,] 407 190 275 594 30 7 642 969 975 990 #> [934,] 232 239 146 847 231 149 208 673 537 29 #> [935,] 253 485 160 187 860 897 678 570 742 395 #> [936,] 677 579 803 149 479 667 2 48 839 590 #> [937,] 177 600 328 286 393 340 584 132 176 650 #> [938,] 641 372 924 437 995 624 736 75 145 919 #> [939,] 857 61 209 991 877 415 257 715 866 694 #> [940,] 597 248 237 617 413 26 704 265 456 191 #> [941,] 428 629 764 331 94 914 136 306 627 605 #> [942,] 798 25 405 157 932 640 638 561 811 366 #> [943,] 8 953 870 54 55 739 342 979 549 536 #> [944,] 606 32 613 18 39 799 448 744 77 184 #> [945,] 920 537 226 283 14 595 426 893 927 515 #> [946,] 94 605 521 411 217 215 742 391 292 860 #> [947,] 285 268 751 326 270 51 657 977 343 106 #> [948,] 362 631 907 902 858 692 389 558 249 216 #> [949,] 332 312 508 928 347 204 472 157 487 808 #> [950,] 816 235 257 759 857 399 377 175 939 127 #> [951,] 308 516 464 195 817 358 820 749 377 816 #> [952,] 949 729 317 204 347 312 332 410 487 867 #> [953,] 8 55 730 943 966 679 278 463 99 836 #> [954,] 84 268 468 591 723 10 41 561 227 930 #> [955,] 782 278 707 185 966 734 282 320 572 140 #> [956,] 53 424 245 583 876 754 827 210 454 260 #> [957,] 112 238 118 480 569 615 10 65 468 473 #> [958,] 812 290 137 284 389 31 128 554 917 87 #> [959,] 208 401 231 566 899 381 232 355 404 728 #> [960,] 27 592 913 126 346 152 366 161 546 766 #> [961,] 997 321 88 147 98 330 773 256 908 769 #> [962,] 457 482 879 809 56 974 902 571 703 274 #> [963,] 881 767 596 878 490 901 858 631 406 996 #> [964,] 541 440 683 134 714 380 602 898 580 573 #> [965,] 715 232 231 250 182 649 585 381 780 717 #> [966,] 730 278 55 955 8 953 282 559 99 233 #> [967,] 887 779 594 642 975 618 190 933 796 407 #> [968,] 305 429 671 691 140 141 978 50 656 745 #> [969,] 173 649 30 642 644 933 975 760 716 526 #> [970,] 671 531 980 167 996 782 185 490 805 903 #> [971,] 828 454 754 903 876 387 192 674 735 191 #> [972,] 46 301 13 264 687 889 423 171 256 998 #> [973,] 108 711 113 374 323 37 665 351 718 668 #> [974,] 571 554 274 902 389 87 917 879 812 111 #> [975,] 642 594 193 967 533 405 207 933 969 507 #> [976,] 666 41 626 441 158 268 172 947 751 569 #> [977,] 667 304 138 241 680 230 528 397 268 270 #> [978,] 996 707 691 141 490 719 671 589 488 249 #> [979,] 995 342 583 54 424 75 245 943 876 863 #> [980,] 805 970 671 995 265 322 278 979 966 583 #> [981,] 664 570 227 754 96 827 478 876 210 468 #> [982,] 793 632 111 475 748 593 853 489 15 854 #> [983,] 736 544 122 78 437 621 709 781 43 924 #> [984,] 100 92 403 409 628 205 689 299 520 261 #> [985,] 450 801 216 152 417 333 692 188 665 565 #> [986,] 555 905 488 91 444 276 327 287 517 890 #> [987,] 722 409 567 261 403 609 279 689 338 360 #> [988,] 252 446 19 267 17 85 117 931 743 481 #> [989,] 340 912 885 517 724 428 682 787 396 890 #> [990,] 275 407 515 7 510 933 190 741 502 644 #> [991,] 866 857 939 818 142 209 877 474 476 61 #> [992,] 499 218 256 731 784 22 906 430 738 330 #> [993,] 717 652 381 250 200 392 224 915 355 182 #> [994,] 284 290 128 406 854 958 234 587 294 790 #> [995,] 979 980 735 863 641 924 75 621 624 265 #> [996,] 978 249 707 782 185 490 907 970 631 691 #> [997,] 98 256 892 972 46 992 769 218 330 738 #> [998,] 387 167 394 423 131 430 921 656 284 413 #> [999,] 6 695 854 211 36 111 575 748 23 386 #> [1000,] 125 386 651 523 49 20 801 417 692 577 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 1.21046951 1.2814328 1.3088671 1.3903896 1.4093499 1.5123118 1.5177298 #> [2,] 0.55047599 0.7540306 0.7634534 0.7722017 0.8163211 0.8700257 0.9226647 #> [3,] 0.35784179 0.4869218 0.5242352 0.5781613 0.6461551 0.6900840 0.7158303 #> [4,] 0.53401809 0.5887559 0.7787472 0.7808913 0.7860686 0.8376048 0.9398310 #> [5,] 0.34914673 0.4117812 0.4147237 0.5550794 0.5681887 0.5832709 0.6670932 #> [6,] 0.34558386 0.3718587 0.3866957 0.4058981 0.4168423 0.4366775 0.4563907 #> [7,] 0.46565599 0.4989607 0.5344266 0.5990700 0.6116902 0.6150030 0.6260920 #> [8,] 0.24524756 0.3766672 0.4190034 0.4619930 0.4910142 0.4985297 0.5080894 #> [9,] 0.26592251 0.5272305 0.5303869 0.5855830 0.5917343 0.5936955 0.6659433 #> [10,] 0.20624216 0.4890596 0.6274324 0.6552598 0.6766045 0.6837926 0.6886020 #> [11,] 0.57189888 0.5912276 0.6606174 0.6676028 0.7789707 0.9019471 0.9936128 #> [12,] 0.59154586 0.5943194 0.6043368 0.6477592 0.6792485 0.6849131 0.6892158 #> [13,] 0.19424836 0.2125101 0.6348915 0.6565775 0.7102408 0.7548653 0.7556356 #> [14,] 0.50366019 0.5213843 0.5306987 0.5441858 0.5766456 0.6146114 0.6332020 #> [15,] 0.66621597 0.7634713 0.9313510 0.9380089 0.9422618 0.9714016 1.0029979 #> [16,] 0.42796979 0.5702875 0.6811840 0.7296882 0.7650716 0.8027896 0.8264574 #> [17,] 0.43980417 0.5712633 0.6833969 0.7022311 0.7234893 0.7255583 0.7870018 #> [18,] 0.32998788 0.3715182 0.4313687 0.7057181 0.7156661 0.7459431 0.7606691 #> [19,] 0.53175341 0.5804025 0.6025482 0.6220356 0.6549309 0.6895867 0.7023902 #> [20,] 0.25537990 0.4888065 0.5012188 0.5132578 0.5201907 0.5244126 0.5297184 #> [21,] 0.44869504 0.5176139 0.5201538 0.5333403 0.5528347 0.5637833 0.6056317 #> [22,] 0.24270294 0.4181342 0.4994289 0.7301984 0.7687906 0.8701522 0.8722797 #> [23,] 0.27916247 0.4182686 0.4627760 0.5187923 0.5625505 0.5745288 0.5912516 #> [24,] 0.27801534 0.2808169 0.4150215 0.4842020 0.5161527 0.5207735 0.5940268 #> [25,] 0.07900148 0.2370228 0.3602234 0.3902051 0.5426235 0.6006307 0.6474137 #> [26,] 0.61071508 0.6573590 0.7340550 0.7405824 0.7735499 0.7830390 0.8050931 #> [27,] 0.34159301 0.3748690 0.3932452 0.5942108 0.6043088 0.6143347 0.6237749 #> [28,] 0.62085681 0.6916244 0.7386633 0.9016987 1.0270393 1.0317743 1.1095360 #> [29,] 0.22512765 0.3803994 0.3970135 0.4499811 0.4840932 0.4850417 0.4858446 #> [30,] 0.39126729 0.4931540 0.5295156 0.5344266 0.5987257 0.6567391 0.6665549 #> [31,] 0.30912486 0.3716844 0.4069067 0.4383080 0.4538033 0.4771445 0.4805272 #> [32,] 0.37151820 0.4253336 0.4780872 0.6146927 0.6674207 0.7188448 0.7326479 #> [33,] 0.09113993 0.4117812 0.6715584 0.7239671 0.7447867 0.7682577 0.8076352 #> [34,] 0.37734773 0.4000469 0.4212806 0.5560209 0.5659865 0.5809123 0.6173314 #> [35,] 0.69441613 0.7249473 0.7624164 0.8119654 0.9839406 1.0092612 1.0173604 #> [36,] 0.24808700 0.2723521 0.4092183 0.4168423 0.4383710 0.5023097 0.5201907 #> [37,] 0.52469938 0.6230936 0.7173692 0.7371090 0.7860783 0.9153443 0.9223275 #> [38,] 0.45160491 0.5060944 0.5127088 0.5314992 0.5812371 0.5855830 0.6128921 #> [39,] 0.33992786 0.6105890 0.7697521 0.8120088 0.8502975 0.8543398 0.8718331 #> [40,] 0.45826699 0.4952567 0.5293771 0.5759559 0.5790945 0.5833047 0.5974994 #> [41,] 0.78237982 0.8547462 0.9179658 0.9656688 1.0256551 1.0375951 1.0403373 #> [42,] 0.34658311 0.5914036 0.6843618 0.7177654 0.8091027 0.8208785 0.8380186 #> [43,] 0.36424210 0.4869968 0.5056829 0.5323747 0.5782066 0.5927128 0.6488748 #> [44,] 0.45940455 0.6715617 0.6978360 0.7154144 0.7471025 0.7953747 0.8342867 #> [45,] 0.66295655 1.0285802 1.1348424 1.1780126 1.5407360 1.5456936 1.5925654 #> [46,] 0.59517291 0.6055320 0.6175850 0.6348915 0.6934208 0.7472936 0.7604855 #> [47,] 0.26748597 0.4514422 0.5305996 0.5355726 0.5653137 0.5940433 0.6347029 #> [48,] 0.44182012 0.4790345 0.7311678 0.7590097 0.7912512 0.8369607 0.8826947 #> [49,] 0.39492438 0.4265521 0.4639095 0.4944305 0.4999843 0.5031262 0.5226127 #> [50,] 0.36671734 0.3998036 0.4010056 0.4295132 0.4577139 0.4684129 0.4841514 #> [51,] 0.48536186 0.5327137 0.6370667 0.6729774 0.6874784 0.6910342 0.6948833 #> [52,] 0.58197751 0.8388852 0.9412836 1.0082361 1.0723705 1.1975280 1.2467210 #> [53,] 0.28197372 0.4617600 0.4898238 0.5057847 0.5060944 0.5117953 0.5443672 #> [54,] 0.31425204 0.4628165 0.4717548 0.4836014 0.4837092 0.5745749 0.6417850 #> [55,] 0.33077154 0.3722042 0.3859907 0.4190034 0.4615879 0.5161005 0.5482435 #> [56,] 0.35792720 0.5561728 0.6250605 0.6409314 0.7018025 0.7086441 0.7429670 #> [57,] 0.47956545 0.7789857 0.9671439 1.1418139 1.1795548 1.2010407 1.2126780 #> [58,] 0.63049552 0.9036267 0.9673234 0.9727745 1.0094522 1.0096174 1.0156655 #> [59,] 0.46314350 0.5494480 0.5521143 0.5776104 0.6092869 0.6229224 0.7165581 #> [60,] 0.66813811 0.7942737 0.8143436 0.9562376 0.9588842 0.9783526 1.0074788 #> [61,] 0.30665876 0.3083442 0.5477442 0.5914676 0.6283700 0.6488153 0.6576743 #> [62,] 0.48137672 0.5174087 0.5922966 0.6447556 0.6930791 0.6942257 0.7407218 #> [63,] 0.53136017 0.5715258 0.6273539 0.6438647 0.6653796 0.6855298 0.6951885 #> [64,] 0.29071253 0.3427768 0.5943194 0.6082906 0.6168765 0.6543068 0.6855298 #> [65,] 0.90797943 1.0011695 1.0073150 1.0568285 1.1074157 1.1380730 1.1655519 #> [66,] 0.51356621 0.6649191 0.7882149 0.8889239 0.9799349 1.0152143 1.0274019 #> [67,] 0.91009161 1.1452958 1.1939267 1.2397482 1.2778259 1.3142367 1.3691782 #> [68,] 0.43896058 0.6626579 0.7063940 0.7192270 0.7217734 0.7289212 0.7324372 #> [69,] 0.89069032 0.9313217 0.9673234 0.9976411 0.9996854 1.0331601 1.0587833 #> [70,] 0.33989198 0.4327614 0.5350378 0.6258618 0.6469255 0.7164036 0.7242920 #> [71,] 0.38397973 0.3862299 0.4516049 0.4913964 0.4920131 0.5414338 0.5917343 #> [72,] 1.00848047 1.0768269 1.0943501 1.1057459 1.1238643 1.1548307 1.2246481 #> [73,] 0.49674503 1.1517222 1.1530540 1.1780126 1.2000227 1.2599610 1.2891031 #> [74,] 0.95888418 1.4328568 1.4894015 1.6543722 1.6595918 1.6635901 1.6692800 #> [75,] 0.20286252 0.2775229 0.3058515 0.4774863 0.4874333 0.5379454 0.5445072 #> [76,] 0.56327896 0.6449463 0.8223549 0.8228872 0.9294344 0.9357540 1.0041295 #> [77,] 0.42533361 0.4313687 0.5218097 0.6681459 0.6745087 0.7003972 0.7146743 #> [78,] 0.31406018 0.4260126 0.4445334 0.5684864 0.5926834 0.6159768 0.6696777 #> [79,] 0.58282320 0.5919036 0.6241585 0.6445785 0.6560100 0.7319469 0.7938257 #> [80,] 0.19979753 0.5161356 0.5565107 0.5836934 0.5945889 0.6472249 0.6842989 #> [81,] 1.81951854 1.9922656 2.0205986 2.0790480 2.0946907 2.1277494 2.1302952 #> [82,] 0.55400715 0.5834456 0.7181371 0.7294277 0.7365553 0.7525134 0.7698621 #> [83,] 0.38538472 0.4618722 0.6916650 0.7051790 0.7148987 0.7293677 0.7540948 #> [84,] 0.47027993 0.5917161 0.6165220 0.6246316 0.6297133 0.7374528 0.7390052 #> [85,] 0.72936937 0.8543112 0.9746944 1.0084146 1.1090694 1.1174333 1.1207087 #> [86,] 0.59078288 0.6462234 0.6689212 0.6699648 0.6811840 0.6858087 0.6860345 #> [87,] 0.27254531 0.3651141 0.3716844 0.3921957 0.4445022 0.4630358 0.4686448 #> [88,] 0.66562908 0.8177474 0.8235249 0.9084179 0.9557719 1.0141622 1.0408833 #> [89,] 0.31747977 0.4114420 0.4268198 0.6100400 0.6136000 0.6282401 0.6387592 #> [90,] 0.43156060 0.4952567 0.5908792 0.6033091 0.6173670 0.6258699 0.6387592 #> [91,] 0.31421513 0.5338823 0.5412133 0.5900256 0.6272943 0.6422183 0.6592550 #> [92,] 0.81196536 0.9115486 0.9346957 0.9929918 1.0456172 1.0754124 1.0787575 #> [93,] 0.64736887 0.7271914 0.7541340 0.7808913 0.7979371 0.8244759 0.8497188 #> [94,] 0.49533234 0.6598227 0.7076012 0.7186929 0.7863789 0.8153246 0.8174131 #> [95,] 0.34692452 0.5627264 0.6081151 0.6135268 0.6495568 0.6515160 0.6583607 #> [96,] 0.39060104 0.4790116 0.4927501 0.4989591 0.5117631 0.5192327 0.5327137 #> [97,] 0.55207656 0.5697016 0.6301777 0.6495728 0.6696607 0.7135412 0.8605164 #> [98,] 0.63783218 0.7148571 0.7776584 0.8507334 0.9494797 1.0558888 1.0583920 #> [99,] 0.39020552 0.4962837 0.5675716 0.5861157 0.5976980 0.6081151 0.6092434 #> [100,] 0.89978391 0.9308147 0.9408754 0.9696101 0.9858472 0.9923786 1.0627488 #> [101,] 0.55283471 0.5907829 0.5922631 0.6141458 0.6261074 0.6392327 0.6421244 #> [102,] 0.35502072 0.4501754 0.6099109 0.6260703 0.6280210 0.6670661 0.6866346 #> [103,] 0.37801086 0.4721603 0.4913964 0.5124604 0.5450710 0.6028122 0.6238292 #> [104,] 0.34134019 0.5929541 0.6372357 0.7820931 0.7897595 0.8339584 0.8447999 #> [105,] 0.54679682 0.9584708 0.9619867 0.9668903 1.1132133 1.1147389 1.1797309 #> [106,] 0.42115831 0.5175675 0.5697016 0.7871844 1.0125592 1.0360698 1.0951413 #> [107,] 0.37049110 0.4279698 0.7430611 0.7789587 0.7795453 0.7839516 0.7865491 #> [108,] 0.37950826 0.5067155 0.6800699 0.6945929 0.7168134 0.7420902 0.7524010 #> [109,] 0.31009228 0.4000469 0.4590026 0.5164836 0.5549018 0.6026765 0.6227651 #> [110,] 1.01791160 1.0815005 1.0840109 1.2249167 1.2279099 1.2384120 1.2896179 #> [111,] 0.51360556 0.5314622 0.5624893 0.5767651 0.5827102 0.6198561 0.6221631 #> [112,] 0.70836509 0.7513724 0.7882149 0.8676796 0.8703300 0.9034353 0.9156993 #> [113,] 0.49869553 0.5067155 0.5460506 0.5801585 0.6696494 0.7173692 0.7608483 #> [114,] 0.40062803 0.7368107 0.8528844 0.8633145 0.8855950 0.9016987 0.9022142 #> [115,] 0.39939033 0.5879220 0.6793512 0.6805757 0.7058957 0.7344912 0.7391128 #> [116,] 0.31704666 0.3983268 0.5037551 0.5216523 0.5296416 0.6908122 0.7452978 #> [117,] 0.37326479 0.5340094 0.5787218 0.5794222 0.7135245 0.7212985 0.7981541 #> [118,] 0.58711013 0.6636078 0.6993385 0.7835613 0.7958064 0.8146638 0.8318024 #> [119,] 0.38781056 0.6292556 0.6395271 0.6717917 0.6905921 0.7365392 0.7809893 #> [120,] 0.51972264 0.5565621 0.5588898 0.6499334 0.6994073 0.7234893 0.7733899 #> [121,] 0.52007377 0.7156661 0.7769445 0.8729608 0.9187279 0.9307765 0.9801876 #> [122,] 0.37462522 0.5608964 0.5684864 0.5867999 0.6280210 0.6488748 0.6532210 #> [123,] 0.28637481 0.5232306 0.5870548 0.5945889 0.6033274 0.6372778 0.6648775 #> [124,] 1.46107996 1.6845896 1.6912515 1.6925196 1.7416590 1.7471112 1.8158605 #> [125,] 0.34397845 0.3866957 0.3924534 0.4412515 0.5012188 0.5058887 0.5131453 #> [126,] 0.37486905 0.4751257 0.5087351 0.5887325 0.7051331 0.7298008 0.7491495 #> [127,] 0.29550828 0.3681418 0.5015297 0.6060122 0.6288299 0.6308819 0.6992010 #> [128,] 0.21279658 0.3983268 0.4456823 0.4821855 0.5067848 0.5608770 0.5763736 #> [129,] 0.33082271 0.3812432 0.4656621 0.5812384 0.6536513 0.7041940 0.7333086 #> [130,] 0.59124589 0.7267236 0.7863414 0.8405639 0.8473076 0.8762039 0.8769079 #> [131,] 0.29341112 0.3539795 0.5754289 0.5817286 0.6082906 0.6362094 0.6390304 #> [132,] 0.38097849 0.4882768 0.5360774 0.7430517 0.7626059 0.8503862 0.8728612 #> [133,] 0.34767639 0.5731660 0.6695476 0.8528346 0.8894494 0.8955452 0.9042629 #> [134,] 0.67153583 0.7344912 0.7583382 0.7931788 0.8128573 0.8623701 0.9081630 #> [135,] 1.14665624 1.2219094 1.2304212 1.3831278 1.4038045 1.4243506 1.5006148 #> [136,] 0.44825377 0.6461841 0.7169382 0.7503173 0.7732450 0.7867631 0.8033412 #> [137,] 0.26411020 0.4260126 0.4456823 0.4673949 0.4828882 0.5533188 0.6529094 #> [138,] 0.52191310 0.5914450 0.5995905 0.6128027 0.6217932 0.6322603 0.6677923 #> [139,] 0.58668618 0.6357454 0.6994073 0.7920784 0.8237250 0.8393177 0.8492497 #> [140,] 0.29261567 0.4439449 0.5403259 0.5471188 0.5475349 0.5604020 0.5725399 #> [141,] 0.25265749 0.4204692 0.4459950 0.4995554 0.5339018 0.5646986 0.5659258 #> [142,] 0.44731276 0.4693085 0.4891228 0.4957363 0.5366771 0.5708846 0.5962150 #> [143,] 0.43630431 0.5608964 0.5913644 0.6018143 0.6031855 0.6383166 0.6529094 #> [144,] 0.55029717 0.6149651 0.6536513 0.6960493 0.7244467 0.7711151 0.8378862 #> [145,] 0.46758775 0.5323747 0.6548214 0.6687391 0.6954821 0.6959315 0.6987302 #> [146,] 0.56358593 0.6714041 0.7109351 0.7311678 0.7458959 0.7665936 0.7959432 #> [147,] 1.27924469 1.3343597 1.4185485 1.4773048 1.7597464 1.7606057 1.7904207 #> [148,] 0.69682063 0.8924923 0.9030081 0.9054818 1.0016268 1.0311475 1.0401675 #> [149,] 0.47105967 0.5015533 0.5732198 0.5992964 0.6070402 0.6333816 0.6404007 #> [150,] 1.08881693 1.1088618 1.1437963 1.2449947 1.2620592 1.2672230 1.3379752 #> [151,] 0.60289777 0.6267536 0.6512268 0.8387839 0.8905984 0.9767534 1.0555368 #> [152,] 0.38434510 0.5474227 0.5580321 0.6047128 0.6159771 0.6368901 0.6379932 #> [153,] 0.76091799 1.0115355 1.0132570 1.0881376 1.0951688 1.1263791 1.1440797 #> [154,] 0.42601353 0.7362994 0.7374528 0.9115949 0.9399216 0.9703215 0.9797734 #> [155,] 0.55380071 0.5868136 0.8971739 0.9859653 0.9871506 1.0210517 1.0258789 #> [156,] 0.38074638 0.5154705 0.8954715 0.9021617 0.9227504 1.0985167 1.1493428 #> [157,] 0.33667180 0.3653168 0.3902051 0.4978214 0.5171986 0.6065168 0.6329947 #> [158,] 0.35433111 0.4546433 0.5162549 0.5985664 0.6256440 0.6345276 0.7123310 #> [159,] 0.46309918 0.4648272 0.4978386 0.5025315 0.6583607 0.6659381 0.7088686 #> [160,] 0.43700057 0.4744331 0.5245809 0.5412125 0.5517345 0.5612849 0.5615328 #> [161,] 0.26497463 0.3009462 0.3889990 0.4135342 0.4214572 0.4266499 0.4639095 #> [162,] 0.42839864 0.5531608 0.5698067 0.6107278 0.6536423 0.7160076 0.7929313 #> [163,] 0.35057975 0.4309376 0.5965018 0.7168258 0.7637036 0.7787982 0.7869576 #> [164,] 0.37272716 0.6543357 0.6840445 0.7475963 0.8614908 0.9399279 0.9457978 #> [165,] 0.28816770 0.3682785 0.3965755 0.4383710 0.4658402 0.4771445 0.5262443 #> [166,] 0.49137345 0.6205306 0.6278612 0.7186285 0.7435339 0.7440504 0.7887943 #> [167,] 0.32875221 0.4161305 0.4295132 0.4324372 0.4674339 0.4922360 0.5073235 #> [168,] 0.34534255 0.4533731 0.4648813 0.4786173 0.5276847 0.5344793 0.5385578 #> [169,] 0.23154039 0.4996064 0.5751151 0.6726457 0.6773536 0.7041913 0.7237772 #> [170,] 0.52243421 0.7852364 0.8055764 0.8948322 0.9694110 1.0355170 1.0355223 #> [171,] 0.57831635 0.6068026 0.7323271 0.8061708 0.9309761 0.9451110 0.9462643 #> [172,] 0.35433111 0.4766277 0.5867573 0.5916953 0.6389778 0.6756414 0.7301892 #> [173,] 0.39210704 0.5408687 0.5812758 0.5987257 0.6104140 0.6381541 0.7037034 #> [174,] 0.60447661 0.6267536 0.7537042 0.7770416 0.7910509 0.7976067 0.8122335 #> [175,] 0.37131688 0.4732668 0.6357584 0.7240616 0.7558192 0.8210686 0.8438541 #> [176,] 0.38097849 0.6842423 0.6960493 0.7390536 0.7940312 0.8275767 0.8500671 #> [177,] 0.61987271 0.9503676 0.9604526 1.1101986 1.1372493 1.1962238 1.1983213 #> [178,] 0.86356366 0.8696071 0.8979664 0.9135622 0.9136056 0.9146832 0.9156993 #> [179,] 0.90251624 1.2391223 1.2551132 1.3247592 1.3892696 1.4173955 1.5960577 #> [180,] 0.65482141 0.6582334 0.6583989 0.6872535 0.7058957 0.7233368 0.7298838 #> [181,] 0.47581826 0.5598178 0.6172547 0.6436547 0.7041913 0.7288602 0.7499769 #> [182,] 0.46401044 0.5627313 0.5956964 0.6124989 0.6997715 0.7332486 0.7392277 #> [183,] 0.31704666 0.3587281 0.6026883 0.6088547 0.6555160 0.7286017 0.7831765 #> [184,] 0.78523641 0.9052024 0.9599744 1.0731588 1.0797704 1.1046482 1.1092652 #> [185,] 0.26858368 0.3132308 0.3914943 0.4122333 0.4825606 0.4826539 0.4865361 #> [186,] 0.42992809 0.4314484 0.4412957 0.5306193 0.5781613 0.6099109 0.6317479 #> [187,] 0.32558630 0.4538033 0.4867114 0.5004484 0.5212093 0.5499718 0.5767945 #> [188,] 0.27986585 0.4486950 0.6261074 0.6478857 0.6791720 0.6830734 0.7098652 #> [189,] 0.35281360 0.4997944 0.5622268 0.6197868 0.6285987 0.6537978 0.6549309 #> [190,] 0.27383529 0.3569462 0.3955108 0.4536409 0.4845361 0.5086319 0.6688656 #> [191,] 0.43967830 0.4634355 0.5661557 0.6033091 0.6095929 0.6202265 0.6597499 #> [192,] 0.44065919 0.4634355 0.5367478 0.5504818 0.5631576 0.5679693 0.6168765 #> [193,] 0.35144341 0.4494418 0.4993466 0.5087696 0.5155247 0.5654223 0.5736881 #> [194,] 0.22112194 0.3996389 0.5086507 0.5654880 0.5935245 0.5983421 0.6027282 #> [195,] 0.80557643 0.9636475 0.9959560 1.0036229 1.2343316 1.2907756 1.2967553 #> [196,] 0.39149426 0.4101772 0.4281875 0.4773816 0.4779779 0.4999843 0.5505670 #> [197,] 0.55400715 0.7314181 0.7574815 0.7799084 0.7873526 0.8264940 0.8332343 #> [198,] 0.50253147 0.6811196 0.6893140 0.8128573 0.8158326 0.8572884 0.9836412 #> [199,] 0.72804450 0.8080975 0.8230186 0.8388021 0.9040104 0.9090553 0.9122911 #> [200,] 0.36816787 0.5370509 0.5996214 0.6130515 0.7065292 0.7703608 0.7938257 #> [201,] 0.63311158 0.7397344 0.7983179 0.8409059 0.8487797 0.8629053 0.8852884 #> [202,] 0.35553709 0.3656315 0.3725824 0.4895812 0.4997822 0.5088901 0.5389288 #> [203,] 0.65886490 0.7329002 0.7347903 0.7411261 0.7580537 0.8624063 0.8720974 #> [204,] 0.55651070 0.5587885 0.5830566 0.5955075 0.6618140 0.7156208 0.7315282 #> [205,] 1.12746773 1.1447031 1.1611430 1.2061711 1.2973666 1.2998117 1.4372318 #> [206,] 1.04008113 1.0741286 1.0838163 1.0959222 1.1050789 1.1226859 1.1568236 #> [207,] 0.21900050 0.2742296 0.3514434 0.4414084 0.4509631 0.4979605 0.4985219 #> [208,] 0.21563769 0.3590978 0.3669434 0.3987107 0.4993616 0.5127696 0.5669487 #> [209,] 0.55611113 0.5794918 0.7240616 0.7361423 0.8079934 0.8221198 0.8290688 #> [210,] 0.20603309 0.2903457 0.3807584 0.3999190 0.4606817 0.4924360 0.5132561 #> [211,] 0.35146422 0.4366775 0.4686757 0.4812793 0.5317383 0.5949025 0.6197411 #> [212,] 0.41472370 0.5742801 0.6313599 0.6807774 0.6951039 0.7447867 0.7760244 #> [213,] 0.24306608 0.2691089 0.4780553 0.5333403 0.5817011 0.6043088 0.6178096 #> [214,] 0.34134019 0.5565621 0.5584969 0.6664560 0.6867161 0.8019273 0.8237250 #> [215,] 0.46304774 0.7293677 0.7351532 0.7634713 0.8081023 0.8122335 0.8499778 #> [216,] 0.40235927 0.4250036 0.5136656 0.5626172 0.5915929 0.5997113 0.6013005 #> [217,] 0.36827211 0.4581167 0.5627529 0.6870920 0.6881646 0.7117143 0.7378429 #> [218,] 0.33982358 0.5976765 0.7207886 0.7449855 0.8507334 0.9094096 0.9321405 #> [219,] 0.73585058 0.9232594 0.9565458 1.0190818 1.0440702 1.0562080 1.0697942 #> [220,] 0.55849685 0.5866862 0.6150630 0.6372357 0.6499334 0.6833969 0.6999140 #> [221,] 0.63004997 0.6805731 0.7160076 0.7317062 0.7545404 0.7969842 0.7992506 #> [222,] 0.44727767 0.5974994 0.7193678 0.7340550 0.7608659 0.7996507 0.7997487 #> [223,] 0.90362672 0.9146832 0.9180192 0.9874210 0.9927248 1.0419432 1.1020623 #> [224,] 0.46826716 0.6439832 0.7046757 0.7119260 0.7290976 0.7391057 0.7963469 #> [225,] 0.49106116 0.5244854 0.6788071 0.7176650 0.7177654 0.7749013 0.7870105 #> [226,] 0.32322310 0.3357362 0.4338062 0.6146114 0.6173491 0.6359929 0.6747795 #> [227,] 0.28556909 0.4419782 0.4688051 0.4989591 0.5661041 0.5827281 0.6393244 #> [228,] 0.43303596 0.5174087 0.5792943 0.5882164 0.6120067 0.6697537 0.6772856 #> [229,] 0.24611185 0.3842782 0.3983348 0.4293245 0.4612341 0.4862838 0.5753730 #> [230,] 0.39388994 0.4755652 0.5219131 0.5983490 0.6630029 0.6829525 0.6841384 #> [231,] 0.30036760 0.3198414 0.3590978 0.4168866 0.4785825 0.4997144 0.5040527 #> [232,] 0.30036760 0.3669434 0.3815305 0.5653624 0.5656248 0.5965183 0.5996629 #> [233,] 0.50544474 0.5182948 0.5418878 0.5623380 0.5669282 0.5773719 0.5893375 #> [234,] 0.31343717 0.4221985 0.5792943 0.6308612 0.6487788 0.6565327 0.6942257 #> [235,] 0.32559725 0.4732668 0.6090162 0.6755251 0.7244467 0.7776300 0.8144492 #> [236,] 0.42319471 0.4568666 0.5344793 0.5792409 0.5955864 0.6276450 0.6599847 #> [237,] 0.46543336 0.4792014 0.5336176 0.5733939 0.5851822 0.6046285 0.6290967 #> [238,] 0.42144588 0.5031239 0.6132328 0.6837926 0.7049319 0.7312486 0.7506026 #> [239,] 0.46484263 0.5704845 0.7109351 0.7118330 0.7417987 0.7526987 0.7569339 #> [240,] 1.09317617 1.1040206 1.1193103 1.2529562 1.2863927 1.2939069 1.3269674 #> [241,] 0.69303540 0.8205277 0.9671439 0.9847831 1.0134838 1.0473081 1.1663187 #> [242,] 0.78317649 0.8697816 0.8849601 0.9668492 1.0168150 1.0595519 1.1677669 #> [243,] 0.35963054 0.3806475 0.3983348 0.4068962 0.4617965 0.4657141 0.5526758 #> [244,] 1.26864772 1.6713056 1.7214903 1.7557255 1.7616370 1.7898021 1.8336938 #> [245,] 0.23444596 0.2765470 0.2852333 0.2970063 0.4048737 0.4606817 0.4664689 #> [246,] 0.47238131 0.4935583 0.5652610 0.6414176 0.6576266 0.6836239 0.8132373 #> [247,] 0.78307298 0.8013535 0.8650611 0.8930465 0.9029668 0.9399767 0.9486970 #> [248,] 0.51792634 0.6692384 0.7227889 0.7777086 0.7952843 0.8018866 0.9766410 #> [249,] 0.31072384 0.3755687 0.4014202 0.4074870 0.4573402 0.5314506 0.5385851 #> [250,] 0.36816787 0.4060399 0.5202421 0.5714498 0.5858707 0.5880933 0.5919036 #> [251,] 0.60180716 0.6018285 0.9662172 0.9774597 0.9816729 1.0145107 1.0180882 #> [252,] 0.43980417 0.4874431 0.5197226 0.6150630 0.6634790 0.6664560 0.7864254 #> [253,] 0.44190042 0.4631435 0.5505767 0.5886008 0.6377367 0.6873164 0.7012542 #> [254,] 0.39100186 0.7409369 0.7806299 0.8388192 0.9217313 0.9222943 0.9345085 #> [255,] 0.34632790 0.3812432 0.5636713 0.6431622 0.6769138 0.6824575 0.6911725 #> [256,] 0.54436133 0.5948165 0.7289802 0.7301984 0.7438620 0.7875841 0.7967487 #> [257,] 0.57458790 0.5926552 0.6283424 0.6438393 0.6785323 0.6825467 0.7500568 #> [258,] 1.27783404 1.2900982 1.2950464 1.4985037 1.5286311 1.5460156 1.5713194 #> [259,] 0.21900050 0.3075903 0.3096341 0.3999190 0.4035350 0.4148944 0.4527564 #> [260,] 0.40353499 0.4664689 0.4852165 0.4871731 0.4873610 0.4924360 0.5023658 #> [261,] 0.41794591 0.5664209 0.5781964 0.5864097 0.6685038 0.6909388 0.8368906 #> [262,] 0.48463135 0.6728008 0.8050962 0.8370144 0.8376331 0.8393648 0.8598953 #> [263,] 0.32319175 0.4870720 0.4978386 0.5671789 0.6692245 0.6793512 0.6843618 #> [264,] 0.19424836 0.2040277 0.4992867 0.5313602 0.6055320 0.6362094 0.6543068 #> [265,] 0.36833863 0.4654334 0.5017085 0.5656454 0.6003747 0.6170498 0.6251389 #> [266,] 0.58386724 0.6659381 0.6715358 0.6811196 0.6829526 0.7913083 0.8226197 #> [267,] 0.53151133 0.5787218 0.6020524 0.6985332 0.7286656 0.7835030 0.7867163 #> [268,] 0.64847362 0.6573676 0.6659433 0.7604574 0.7968490 0.8643554 0.9069869 #> [269,] 0.49223996 0.5229711 0.5686139 0.6100679 0.6674642 0.6830877 0.6999881 #> [270,] 0.47282196 0.5868136 0.8988946 0.9045820 0.9142488 0.9619491 0.9636785 #> [271,] 0.56905828 0.7277196 0.7705092 0.8684019 0.8710113 0.8973821 0.8976672 #> [272,] 0.32987731 0.5085852 0.6028978 0.6044766 0.6056317 0.7368727 0.7378429 #> [273,] 0.57827386 0.6399215 0.6848409 0.7001828 0.7300695 0.7999589 0.8096686 #> [274,] 0.38353125 0.4820388 0.5002889 0.5103997 0.5439551 0.5473826 0.5618381 #> [275,] 0.31634825 0.3453730 0.4488479 0.4783741 0.5086319 0.5134158 0.6014621 #> [276,] 0.42046922 0.4730321 0.6023254 0.6381665 0.6473181 0.6619429 0.6708163 #> [277,] 0.75559906 0.8434938 0.9864432 1.0654337 1.0883128 1.1004546 1.1442323 #> [278,] 0.23177341 0.3876206 0.4300517 0.4489872 0.4615879 0.4619930 0.5083644 #> [279,] 0.17043615 0.6115139 0.6144349 0.7220758 0.7441820 0.9432558 0.9551188 #> [280,] 0.31136211 0.4758183 0.5105971 0.5345327 0.6773536 0.7440504 0.7614294 #> [281,] 1.28735206 1.4495158 1.5862658 1.5876154 1.6128527 1.6299672 1.6443932 #> [282,] 0.29261567 0.4436452 0.5262098 0.5505027 0.5601840 0.5625149 0.5846818 #> [283,] 0.24270294 0.3663440 0.3688916 0.6605804 0.6611236 0.8047881 0.8244194 #> [284,] 0.34324427 0.3852723 0.4807027 0.5292069 0.5543009 0.5608770 0.5657912 #> [285,] 0.62918180 1.0125592 1.1663187 1.2016624 1.2094716 1.2117365 1.2418795 #> [286,] 0.29686347 0.5360774 0.7940312 0.8762371 0.9201335 0.9503676 0.9619168 #> [287,] 0.40142020 0.4634532 0.4824160 0.5110722 0.5282545 0.6008158 0.6378858 #> [288,] 0.53401809 0.6609695 0.7889064 0.7917791 0.8890348 0.8939098 0.9408754 #> [289,] 0.38448622 0.4870720 0.5550794 0.5776140 0.5852819 0.5914036 0.6358499 #> [290,] 0.21279658 0.3871573 0.4590166 0.5166758 0.5296416 0.5533188 0.5543009 #> [291,] 0.35604642 0.5907578 0.6481719 0.6609695 0.7840776 0.8362565 0.8475800 #> [292,] 0.32558630 0.4674089 0.5277063 0.5832308 0.6203573 0.6430868 0.6531550 #> [293,] 0.58301167 0.6375903 0.6964726 0.7071698 0.7245649 0.7315685 0.7625511 #> [294,] 0.27397800 0.4990195 0.5213306 0.5736358 0.5985232 0.6072090 0.6459071 #> [295,] 0.09113993 0.3491467 0.6358499 0.6807774 0.6979795 0.7459699 0.7863417 #> [296,] 0.64120027 0.8388852 0.8525389 0.9818075 1.0069490 1.0293006 1.0614266 #> [297,] 0.46566207 0.5201454 0.5636713 0.5652610 0.6131437 0.6701657 0.6707414 #> [298,] 0.57944873 0.5921952 0.6205306 0.6440024 0.6743616 0.6780611 0.7077063 #> [299,] 0.35056102 0.4323315 0.5741677 0.6964146 0.7629855 0.7754890 0.8597179 #> [300,] 0.59295405 0.6744941 0.6985332 0.6999140 0.7287384 0.7367904 0.7500358 #> [301,] 0.58706661 0.5951729 0.6874442 0.7326062 0.7522365 0.8230275 0.8272819 #> [302,] 0.36175024 0.4209899 0.4813064 0.5223277 0.5475791 0.6160059 0.6233547 #> [303,] 0.63812120 0.6595335 0.6641357 0.7310347 0.7366293 0.7377899 0.7552492 #> [304,] 0.28967890 0.4755652 0.5373530 0.6050027 0.7367644 0.7569945 0.7634534 #> [305,] 0.39860107 0.5109188 0.5967728 0.6581444 0.6616765 0.6637005 0.6784733 #> [306,] 0.35967592 0.3691845 0.3996389 0.4213417 0.5454917 0.5615328 0.5832308 #> [307,] 0.39541408 0.5084299 0.5127088 0.5249234 0.5292019 0.5643657 0.5927760 #> [308,] 0.72699233 1.0108327 1.0447182 1.0627107 1.1576053 1.1857721 1.2353420 #> [309,] 0.72936937 0.7679370 0.8357712 0.9106449 0.9236965 1.0219507 1.0300689 #> [310,] 0.38622987 0.4114420 0.4536031 0.5582578 0.6083764 0.6128921 0.6449446 #> [311,] 0.22968159 0.3896564 0.4692602 0.5056829 0.6336309 0.6535046 0.6674393 #> [312,] 0.49782138 0.6603331 0.6654162 0.6993385 0.7263683 0.7397532 0.7719971 #> [313,] 0.94691536 1.0304489 1.2154122 1.2366640 1.2861856 1.3044798 1.3060265 #> [314,] 0.34277683 0.5761198 0.5915459 0.5993767 0.6146927 0.6753620 0.7003972 #> [315,] 0.50998616 0.5479445 0.6304955 0.7377899 0.7726233 0.8401201 0.8418575 #> [316,] 0.35402695 0.4413732 0.4589638 0.4667636 0.5178635 0.5726849 0.6361679 #> [317,] 0.54679682 1.1183533 1.1625312 1.2716053 1.2726941 1.2864367 1.3436808 #> [318,] 0.78307298 0.8124752 0.8480663 0.8515825 0.8729608 0.9198239 1.0690410 #> [319,] 0.30066148 0.4654537 0.4985146 0.5389288 0.5498350 0.5507061 0.6302704 #> [320,] 0.31021064 0.3132308 0.4101772 0.4694301 0.4810949 0.5101946 0.5164189 #> [321,] 0.75567952 0.7687906 0.8486365 0.8781762 0.8926911 0.9084179 0.9230808 #> [322,] 0.34928645 0.3637145 0.3923042 0.4792014 0.5017085 0.5937359 0.6005207 #> [323,] 0.58015850 0.6230936 0.6470833 0.7195649 0.7420902 0.7616721 0.7977826 #> [324,] 0.79251236 0.9054818 0.9668903 1.0041571 1.0512183 1.1058004 1.1671591 #> [325,] 0.60182850 0.9306545 0.9786673 1.0296871 1.0457754 1.1541700 1.2631363 #> [326,] 0.64957283 0.7590596 0.7749350 0.7850495 0.7871844 0.7927290 0.8156598 #> [327,] 0.42672090 0.5879220 0.6760600 0.6836464 0.6876713 0.7225715 0.7573887 #> [328,] 0.91433588 0.9158306 0.9784031 1.2583021 1.2764300 1.3413194 1.5356199 #> [329,] 1.01843729 1.2450534 1.4353399 1.4878778 1.4932276 1.5408669 1.5720413 #> [330,] 0.53075473 0.5583400 0.6635461 0.8393648 0.8629734 0.9292874 0.9293687 #> [331,] 0.26707323 0.2953271 0.3320452 0.4213417 0.4737726 0.4925101 0.5484479 #> [332,] 0.28263796 0.4708422 0.5357388 0.5955864 0.6937404 0.7394286 0.7719971 #> [333,] 0.30066148 0.4250036 0.4539085 0.4947684 0.4997310 0.5221296 0.5411184 #> [334,] 0.60462854 0.6625574 0.6892968 0.7561637 0.7664300 0.7800571 0.7937415 #> [335,] 0.35753455 0.3898205 0.4122781 0.4397189 0.4454174 0.4694301 0.5457032 #> [336,] 0.71541443 0.7496531 0.7674448 0.7797565 0.8114114 0.8249625 0.8688823 #> [337,] 0.44798338 0.4910612 0.5965018 0.6305607 0.8125750 0.8208785 0.8604156 #> [338,] 0.59733251 0.6119211 0.7441820 0.7640671 0.8495275 0.9484934 0.9795572 #> [339,] 0.34767639 0.4231106 0.7018844 0.7598041 1.1616720 1.1800716 1.1930159 #> [340,] 0.25283902 0.5982736 0.6251264 0.6512282 0.6701734 0.6992505 0.8306882 #> [341,] 0.62170404 0.7770250 0.8326278 0.8458111 0.9135953 1.0200667 1.0227937 #> [342,] 0.33306395 0.4476540 0.4836014 0.5161005 0.5396148 0.5397691 0.5607260 #> [343,] 0.51756753 0.5550128 0.6301777 0.7850495 0.7903039 0.8444186 0.8738890 #> [344,] 0.46604194 0.6543357 0.6882759 0.7544034 0.8082659 0.8203685 0.8405639 #> [345,] 2.08627568 2.2164901 2.3605552 2.4335841 2.4893736 2.7627295 2.8010679 #> [346,] 0.13034995 0.4238135 0.4250519 0.4266499 0.5022303 0.5382494 0.5479820 #> [347,] 0.34099743 0.4887354 0.5830566 0.6372778 0.6603331 0.6619228 0.6842989 #> [348,] 0.51517039 0.5962294 0.6121996 0.6451439 0.6529798 0.6540016 0.6602214 #> [349,] 0.26748597 0.4683940 0.5793739 0.6053595 0.6260703 0.6309714 0.6394856 #> [350,] 0.76858466 1.1761635 1.2582593 1.2940643 1.5105286 1.6101623 1.6421377 #> [351,] 0.76916011 0.7860783 0.7977826 0.8974579 0.9256936 0.9290629 0.9301239 #> [352,] 0.52825894 0.5441858 0.5634698 0.5812371 0.6239770 0.6274126 0.6737094 #> [353,] 0.58578655 0.7566644 0.7721850 0.7867631 0.7960317 0.8069427 0.8407096 #> [354,] 0.58458911 0.6064475 0.7186929 0.7366293 0.8120694 0.8515900 0.9056615 #> [355,] 0.52335784 0.5589289 0.5656257 0.5669487 0.5721419 0.5722827 0.6008707 #> [356,] 0.34534255 0.4616561 0.5112414 0.5470238 0.5596469 0.5750012 0.5792409 #> [357,] 0.43156060 0.4582670 0.5476464 0.5485833 0.5877672 0.6220212 0.6434446 #> [358,] 0.73220521 0.8099764 0.8445454 0.9186037 0.9566207 0.9656704 1.0108327 #> [359,] 0.31343717 0.5510211 0.6036509 0.6120067 0.6209275 0.6326844 0.6540326 #> [360,] 0.36655786 0.3690922 0.5734929 0.5834994 0.5864097 0.6540622 0.7491659 #> [361,] 0.76858466 1.0874815 1.1149484 1.1430548 1.2046681 1.2715879 1.2920916 #> [362,] 0.19062462 0.3839844 0.4583266 0.4943451 0.5136656 0.5699897 0.5736296 #> [363,] 0.28951401 0.4910873 0.6248821 0.6611893 0.6696607 0.7183489 0.7233776 #> [364,] 0.42181553 0.4877192 0.5912459 0.6743616 0.7056267 0.7461754 0.8017895 #> [365,] 0.76086589 0.7760302 0.8728537 0.9342568 0.9361687 0.9381583 0.9653260 #> [366,] 0.13034995 0.3167348 0.3422170 0.3889990 0.4509631 0.4680016 0.4929429 #> [367,] 0.57134342 0.7193678 0.8413470 0.8535926 0.8536220 0.8554087 0.8668236 #> [368,] 0.63441274 0.6715617 0.8162489 1.0910088 1.1201771 1.1260417 1.1408357 #> [369,] 0.48692180 0.5494740 0.5578253 0.7312511 0.8092917 0.8365102 0.8545020 #> [370,] 0.30834417 0.3717870 0.5505670 0.5663475 0.5913376 0.6025436 0.6040373 #> [371,] 0.37423009 0.4064087 0.5369217 0.5752935 0.6668281 0.7664228 0.7809228 #> [372,] 0.54015491 0.5936938 0.6481829 0.6765533 0.7104502 0.8064179 0.8720588 #> [373,] 0.43896058 0.4437918 0.4920131 0.5450710 0.5919320 0.6239316 0.6245348 #> [374,] 0.44397251 0.5654979 0.6834941 0.6972669 0.7510266 0.7524010 0.7836004 #> [375,] 0.66760280 0.7469794 0.9887768 1.0101594 1.1013004 1.1914199 1.2538680 #> [376,] 0.59437982 0.6026883 0.6497441 0.6697146 0.6745087 0.7748304 0.7891744 #> [377,] 0.66407082 0.7500568 0.7535016 0.8290688 0.8438541 0.9890276 1.0188957 #> [378,] 0.41627299 0.5488501 0.6116729 0.6436547 0.6610865 0.6855123 0.7180888 #> [379,] 0.69345760 0.7210034 0.8104446 0.8915143 0.9844797 1.0110316 1.0172064 #> [380,] 0.76087780 0.8230186 0.9407532 0.9450609 0.9547859 0.9661827 0.9709767 #> [381,] 0.40603994 0.5180424 0.5222549 0.5370509 0.5608112 0.5768237 0.5923872 #> [382,] 0.73832735 1.0173604 1.1611430 1.1846141 1.1984804 1.2295837 1.2735942 #> [383,] 0.53100533 0.5449393 0.5474227 0.5828232 0.5858707 0.6840361 0.6868059 #> [384,] 0.57134342 0.5851683 0.6375299 0.6651148 0.6688456 0.6987302 0.6993011 #> [385,] 0.27373125 0.2955083 0.5659957 0.6637059 0.6880188 0.6971506 0.7213557 #> [386,] 0.24808700 0.3510083 0.3794034 0.3877919 0.4058981 0.4412515 0.4755460 #> [387,] 0.43967830 0.4726402 0.4932683 0.5055582 0.5679693 0.5897617 0.6561252 #> [388,] 0.84819084 1.1595079 1.1723107 1.1948092 1.2608081 1.2782794 1.2918580 #> [389,] 0.32217673 0.3829084 0.4074870 0.4187954 0.4443748 0.4634532 0.4686448 #> [390,] 0.52825894 0.5631576 0.6271125 0.6557014 0.6611893 0.6657764 0.6932175 #> [391,] 0.45811669 0.5054670 0.5085852 0.6096429 0.6646026 0.7051882 0.7080762 #> [392,] 0.46826716 0.6150778 0.6268764 0.6573377 0.7076093 0.8563626 0.9239508 #> [393,] 0.95746914 1.0086629 1.0406724 1.0532450 1.1052959 1.1057283 1.1518480 #> [394,] 0.29341112 0.4992867 0.5268116 0.5280320 0.5652156 0.5715258 0.6271125 #> [395,] 0.22968159 0.3101792 0.3454661 0.5909986 0.5927128 0.6689339 0.6976758 #> [396,] 0.36120649 0.4103122 0.6250605 0.6992505 0.7602595 0.8301865 0.8850193 #> [397,] 0.28967890 0.3938899 0.7029221 0.7187057 0.7722017 0.8253453 0.8307571 #> [398,] 0.75147404 0.7849116 0.9170330 0.9208913 0.9440306 0.9802764 0.9832832 #> [399,] 0.24282195 0.5745879 0.6237231 0.7167928 0.7501435 0.7539793 0.7585124 #> [400,] 0.44085544 0.6105890 0.6449463 0.6787343 0.7155752 0.7958527 0.8565443 #> [401,] 0.33143558 0.3987107 0.4398471 0.4584524 0.4812428 0.5219024 0.5589289 #> [402,] 0.33082271 0.5502972 0.6431622 0.6637005 0.6701657 0.6755251 0.7168742 #> [403,] 0.56642090 0.6540622 0.8223332 0.8576855 0.8731733 0.8882479 0.9480153 #> [404,] 0.26714363 0.3198414 0.4024143 0.4422043 0.5714739 0.5722827 0.5828092 #> [405,] 0.30823539 0.4929429 0.5151064 0.5155247 0.5171986 0.5267013 0.5308653 #> [406,] 0.38527232 0.4324372 0.4494279 0.4531303 0.4787794 0.4858446 0.4919623 #> [407,] 0.27383529 0.2797752 0.3163482 0.4943825 0.5064923 0.5986820 0.6116729 #> [408,] 0.38240555 0.8434938 0.8584295 0.8924923 0.9832103 1.1613203 1.2121301 #> [409,] 0.57819639 0.6505936 0.8731733 0.9346957 0.9573444 1.0097077 1.0714018 #> [410,] 0.52591639 0.6012587 0.7080196 0.7624489 0.7958698 0.8552217 0.8827765 #> [411,] 0.36827211 0.4711137 0.4719894 0.4929756 0.6203573 0.6707708 0.6902118 #> [412,] 0.23216678 0.3232231 0.3311819 0.5213843 0.5366771 0.5760775 0.5889128 #> [413,] 0.58976169 0.6202265 0.6450097 0.6692384 0.6843983 0.7223482 0.7326062 #> [414,] 0.24263568 0.4612341 0.4657141 0.4723813 0.5201454 0.6181234 0.6518934 #> [415,] 0.36476374 0.5452436 0.5489041 0.6107487 0.6231284 0.6486865 0.6606610 #> [416,] 0.60728448 0.6157924 0.6258618 0.7511654 0.7687882 0.7726233 0.7889009 #> [417,] 0.26910893 0.3252515 0.4800824 0.5574661 0.5587419 0.5779394 0.5942108 #> [418,] 1.12512471 1.3314374 1.4135245 1.4562553 1.5906817 1.6334235 1.6924701 #> [419,] 0.62786122 0.6749549 0.6909721 0.7614294 0.7672396 0.8301462 0.8979768 #> [420,] 0.22112194 0.3691845 0.5145285 0.5252724 0.5844169 0.6151094 0.6728585 #> [421,] 0.27916247 0.4592070 0.4632790 0.4938952 0.5738974 0.5998774 0.6008497 #> [422,] 0.50152971 0.5310501 0.5585375 0.6059301 0.6115119 0.6137666 0.6637059 #> [423,] 0.53080944 0.5963091 0.6491302 0.6666116 0.6843983 0.6985799 0.7032316 #> [424,] 0.27654704 0.2951427 0.3070175 0.3842054 0.4358474 0.4817677 0.4873610 #> [425,] 0.97840314 1.7536070 1.8131297 2.0017496 2.1434535 2.1819352 2.2393017 #> [426,] 0.36634397 0.4353989 0.4994289 0.5350266 0.7159151 0.7472615 0.7556795 #> [427,] 0.70836509 1.0437403 1.1130162 1.2179101 1.2229333 1.3177732 1.3912272 #> [428,] 0.55813831 0.5829462 0.5906029 0.6822739 0.7171960 0.8027568 0.8069427 #> [429,] 0.40100564 0.4380599 0.4580945 0.4843531 0.5109188 0.5388405 0.5403259 #> [430,] 0.57831635 0.5799145 0.6269897 0.6892983 0.6985799 0.7360508 0.7376631 #> [431,] 0.44682494 0.4837806 0.5786943 0.6870117 0.7904464 0.8035448 0.8086243 #> [432,] 0.38781056 0.4518521 0.5390001 0.5638302 0.6139850 0.6920587 0.7199420 #> [433,] 0.47805534 0.5401356 0.5505767 0.5521143 0.5617291 0.6032702 0.6186321 #> [434,] 0.64271917 0.7210034 0.7781986 0.8294768 0.8347201 0.9185371 0.9272091 #> [435,] 0.78213116 0.8018866 0.8228872 0.8803566 1.0575084 1.0734745 1.0943855 #> [436,] 0.42797926 0.5164301 0.5924125 0.6018320 0.6691727 0.6717369 0.6841384 #> [437,] 0.46247887 0.5067058 0.6335023 0.6481829 0.6948617 0.7102101 0.7358506 #> [438,] 0.21124475 0.8079009 0.8139015 0.8850193 0.9155204 0.9589698 1.0491241 #> [439,] 0.25940823 0.3564569 0.3725663 0.3965755 0.4547488 0.4755460 0.4823437 #> [440,] 0.40146034 0.5058961 0.5291942 0.5949664 0.6414176 0.6517776 0.6678141 #> [441,] 0.43381139 0.5162549 0.5867573 0.6451439 0.6698491 0.6805422 0.7958064 #> [442,] 0.27226466 0.7965124 0.8139015 0.9680754 1.0481851 1.0729468 1.1314613 #> [443,] 0.67495487 0.6964098 0.7077063 0.7435339 0.8941679 0.9820487 0.9932132 #> [444,] 0.42672090 0.4742585 0.6577665 0.6621273 0.7176650 0.7227302 0.7306703 #> [445,] 0.27374113 0.3398920 0.4336246 0.4878851 0.5836934 0.7156208 0.7161151 #> [446,] 0.49775036 0.5057079 0.5317534 0.6670638 0.6812482 0.7011247 0.7810805 #> [447,] 0.19979753 0.4336246 0.5156453 0.5955075 0.6469255 0.6540243 0.6858037 #> [448,] 0.57611984 0.6540326 0.6697537 0.7188448 0.7964828 0.8270086 0.8270891 #> [449,] 0.35402695 0.5317214 0.5661899 0.6237384 0.6483689 0.6932376 0.7014995 #> [450,] 0.37853720 0.5857865 0.6541418 0.6932700 0.7015655 0.7664324 0.7808781 #> [451,] 0.49137345 0.6440024 0.6964098 0.7731717 0.8979768 0.9132668 0.9222713 #> [452,] 0.34099743 0.3787914 0.6033274 0.6649191 0.6806684 0.7186290 0.8443421 #> [453,] 0.32777468 0.4084296 0.5964598 0.6985484 0.7299506 0.7334949 0.7418415 #> [454,] 0.24444572 0.2539434 0.3194772 0.3515583 0.4927501 0.5041885 0.5241539 #> [455,] 0.55947715 0.6226706 0.7406254 0.7418054 0.8031751 0.8122835 0.8346231 #> [456,] 0.57189888 0.7630114 0.7777086 0.8392729 0.8726374 0.9481810 0.9678224 #> [457,] 0.41927666 0.4611625 0.6163551 0.6427449 0.6779266 0.6880188 0.7513486 #> [458,] 0.39230424 0.5280235 0.5472313 0.5493735 0.6251389 0.6625574 0.6653248 #> [459,] 0.77560656 0.9673221 1.3283529 1.4040438 1.4118502 1.4425507 1.4774432 #> [460,] 0.61725469 0.7662297 0.7672396 0.7733261 0.7887943 0.8812575 0.9060264 #> [461,] 0.43303596 0.5888637 0.7407218 0.8279811 0.8655723 0.8826947 0.9146768 #> [462,] 0.42912632 0.4845361 0.6626579 0.6728326 0.6761037 0.6790748 0.7104630 #> [463,] 0.41043967 0.4491331 0.4698574 0.5527091 0.5605946 0.5686139 0.5844173 #> [464,] 0.72699233 0.8099764 0.9777157 1.2406322 1.2518960 1.3226513 1.3457507 #> [465,] 0.57028752 0.5922631 0.7839516 0.7948862 0.7992506 0.8687603 0.9287249 #> [466,] 0.52735350 0.5742801 0.5845891 0.6067881 0.6068605 0.6641357 0.6670932 #> [467,] 0.28297282 0.4491331 0.4689097 0.6411230 0.6674642 0.6797679 0.6916542 #> [468,] 0.20624216 0.5091761 0.5980774 0.6159974 0.6453148 0.6593648 0.7085769 #> [469,] 1.19141986 1.5441863 1.5456936 1.7348901 1.8111149 1.8559001 1.9191438 #> [470,] 1.41774477 1.4487873 1.4903795 1.6992258 1.7577959 1.7602376 1.8522810 #> [471,] 0.29797572 0.4299281 0.5242352 0.5578253 0.6776225 0.7056283 0.7425035 #> [472,] 0.38302962 0.5127490 0.5259164 0.5390705 0.6286051 0.6704237 0.7531162 #> [473,] 0.50312387 0.5099862 0.6608039 0.7022452 0.7096227 0.7186290 0.7293509 #> [474,] 0.32559725 0.3713169 0.7130241 0.7659304 0.7700191 0.7789670 0.7808682 #> [475,] 0.66621597 0.7830272 0.7831751 0.8182106 0.8966966 0.9351781 0.9509069 #> [476,] 0.31706208 0.4752431 0.5834994 0.6357454 0.6537978 0.6696843 0.7443812 #> [477,] 0.63307436 0.8746795 0.9048494 1.0334246 1.0734693 1.0859972 1.1214003 #> [478,] 0.24835875 0.2855691 0.4485579 0.5506574 0.5675909 0.5897147 0.5921158 #> [479,] 0.67299483 0.6867017 0.6929659 0.7637374 0.8307571 0.8561672 0.8700257 #> [480,] 0.35784179 0.5513518 0.5670087 0.7056283 0.7706672 0.8031751 0.8092917 #> [481,] 0.48744310 0.5588898 0.5712633 0.5887559 0.6020757 0.6867161 0.6954605 #> [482,] 0.64034442 0.7109808 0.7513486 0.7552150 0.7938263 0.8022374 0.9097428 #> [483,] 0.65673910 0.6934576 0.7347903 0.7368107 0.7609579 0.8347201 0.8816674 #> [484,] 0.46758775 0.5681887 0.5852819 0.6067881 0.6260452 0.6605836 0.6614731 #> [485,] 0.47111371 0.4812793 0.4850204 0.5201222 0.5477646 0.5577346 0.5793739 #> [486,] 0.43144845 0.4391736 0.5432727 0.5923682 0.6461551 0.6766045 0.7690241 #> [487,] 0.37879138 0.4887354 0.5135662 0.7270004 0.7552587 0.7578103 0.8245740 #> [488,] 0.31421513 0.4441723 0.4573402 0.4961898 0.5060788 0.5282545 0.5339018 #> [489,] 0.65656076 0.7463260 0.7929313 0.8194621 0.9020570 0.9177525 0.9422374 #> [490,] 0.30104083 0.3744047 0.4592138 0.5041361 0.5161984 0.5169359 0.5456778 #> [491,] 0.96364754 1.1528099 1.1904007 1.2277327 1.2608628 1.3413665 1.3659466 #> [492,] 0.27801534 0.4372083 0.4698574 0.5177640 0.5229711 0.5237595 0.6135268 #> [493,] 0.49796048 0.5036863 0.5219024 0.5233578 0.5272305 0.5284553 0.5654223 #> [494,] 0.98742099 1.0437403 1.2940643 1.3715098 1.3899578 1.3997916 1.4121160 #> [495,] 0.44397251 0.7141122 0.7220758 0.7276547 0.7367931 0.7933537 0.7945902 #> [496,] 0.78865536 0.8210686 0.8651220 0.8884697 0.9370270 1.0128090 1.0499836 #> [497,] 0.44065919 0.4790116 0.4853619 0.5303881 0.5448443 0.6220982 0.6282401 #> [498,] 0.34765312 0.4310536 0.5417388 0.5560209 0.5638302 0.5759559 0.5821031 #> [499,] 0.62476464 0.6853526 0.7231728 0.7364530 0.7754434 0.7937415 0.8122482 #> [500,] 0.86382960 0.9100916 0.9574691 0.9589698 0.9872226 1.0080292 1.0085337 #> [501,] 0.44876414 0.5102163 0.5347131 0.5379716 0.5645399 0.5704845 0.6053305 #> [502,] 0.41627299 0.6481719 0.6935705 0.6963693 0.6982418 0.7340551 0.7619814 #> [503,] 0.32777468 0.4800625 0.6171153 0.6411230 0.6927322 0.7000504 0.7321665 #> [504,] 0.35071989 0.3780109 0.4437918 0.5414338 0.6048693 0.6083764 0.6248821 #> [505,] 0.33721916 0.4024143 0.4997144 0.5299611 0.5741004 0.6124989 0.6187411 #> [506,] 0.29866995 0.5794222 0.6637734 0.6932376 0.7236006 0.7750488 0.7979371 #> [507,] 0.21629621 0.2649746 0.3422170 0.3558626 0.4238135 0.4414084 0.4454174 #> [508,] 0.28263796 0.4568666 0.4619486 0.7048069 0.7134353 0.7177440 0.7321665 #> [509,] 0.48137672 0.4824022 0.5419171 0.5632433 0.5882164 0.5888637 0.6137666 #> [510,] 0.46565599 0.4783741 0.5334498 0.5335707 0.6197868 0.6302965 0.6626189 #> [511,] 1.08972495 1.4503325 1.4872049 1.6333087 1.6377813 1.6615663 1.6656201 #> [512,] 0.40062803 0.7811736 0.9422343 0.9619867 0.9771794 0.9813070 1.0157656 #> [513,] 0.24363560 0.7233368 0.9136292 0.9197246 0.9389733 0.9935159 1.0471151 #> [514,] 0.57508362 0.8877721 0.9844797 1.0681754 1.0911114 1.1350714 1.1527929 #> [515,] 0.33573616 0.5134158 0.5348730 0.5760775 0.6140883 0.6615503 0.6626189 #> [516,] 1.34136645 1.3838227 1.4342775 1.4943989 1.5133858 1.5837403 1.6842036 #> [517,] 0.31680663 0.5132193 0.5207735 0.5239781 0.5288084 0.6530337 0.6701734 #> [518,] 0.33083441 0.3806689 0.4182686 0.4632790 0.5653137 0.6036509 0.6394856 #> [519,] 0.31747977 0.4444787 0.4536031 0.6238292 0.6440949 0.6693203 0.6872239 #> [520,] 0.63307436 0.6750776 0.6964146 0.7806849 0.7888541 0.9482153 0.9678648 #> [521,] 0.43546948 0.4618722 0.4630477 0.4719894 0.6224500 0.6884615 0.7096870 #> [522,] 0.27135405 0.4652345 0.4995554 0.5058957 0.5669282 0.6064614 0.6081116 #> [523,] 0.24306608 0.3252515 0.4779854 0.5131453 0.5564435 0.5580321 0.5617291 #> [524,] 0.40842964 0.4800625 0.5511625 0.5870548 0.6589856 0.6864307 0.7071698 #> [525,] 0.86978163 1.0184373 1.2608628 1.2638072 1.3526222 1.4484502 1.4599987 #> [526,] 0.23216678 0.3463831 0.4338062 0.4473128 0.6140883 0.6430809 0.6863041 #> [527,] 0.73832735 1.0898115 1.1625550 1.2302076 1.2953288 1.3461254 1.3687894 #> [528,] 0.64120027 0.7090600 0.7290976 0.8012898 0.8137912 0.8145357 0.8163358 #> [529,] 0.31341527 0.3253701 0.5315113 0.5945311 0.6297303 0.6668281 0.7500358 #> [530,] 1.14549023 1.2158055 1.3127295 1.4186153 1.4811360 1.5310853 1.5420888 #> [531,] 0.39980361 0.4032872 0.4148328 0.4161305 0.4579549 0.5164189 0.5330906 #> [532,] 0.37766750 0.4218155 0.5093474 0.6618992 0.6812482 0.6834770 0.6870574 #> [533,] 0.27422963 0.3075903 0.3558626 0.3898205 0.4871731 0.5087696 0.5242883 #> [534,] 0.38302962 0.5250651 0.5477875 0.6636078 0.7090112 0.7146490 0.7397532 #> [535,] 1.12806137 1.1348424 1.1938812 1.4175963 1.5498325 1.5511832 1.6104801 #> [536,] 0.41300116 0.5232306 0.5273535 0.5511625 0.5844169 0.6403226 0.6632456 #> [537,] 0.48435314 0.5178139 0.5347131 0.5489041 0.5685398 0.5879447 0.6548786 #> [538,] 0.49628373 0.5831077 0.6269285 0.6550708 0.6605836 0.6656679 0.6692245 #> [539,] 0.35792720 0.6560213 0.7569771 0.7602595 0.7759812 0.7785397 0.8970102 #> [540,] 0.76904320 1.4661106 1.4702632 1.4773048 1.5262415 1.5297211 1.5432717 #> [541,] 0.44133972 0.5291942 0.6068363 0.6208701 0.7134594 0.7263323 0.7303278 #> [542,] 0.62170404 0.6256440 0.6446191 0.7070516 0.7112517 0.7289212 0.7560268 #> [543,] 0.24363560 0.6583989 0.8760443 0.8771099 0.8894776 0.8955944 0.9222511 #> [544,] 0.65932573 0.8223836 0.8684019 0.8693571 0.8740993 0.8921200 0.9172862 #> [545,] 0.71813707 0.8264940 0.9067110 1.0099780 1.1022822 1.2036360 1.2095566 #> [546,] 0.21629621 0.3009462 0.3167348 0.4250519 0.4616561 0.5050785 0.5242883 #> [547,] 0.45699431 0.4590581 0.5355108 0.5476464 0.5826571 0.6701486 0.6719392 #> [548,] 0.35963054 0.5976765 0.6206061 0.6336101 0.6707414 0.6790821 0.6824575 #> [549,] 0.58301167 0.5851683 0.6196863 0.6228291 0.6402223 0.6417850 0.6535752 #> [550,] 0.27135405 0.4842020 0.4882325 0.5237595 0.5327186 0.5418878 0.5643148 #> [551,] 0.80281248 0.8087226 0.9071681 0.9365881 0.9549767 0.9721787 1.0265847 #> [552,] 0.71862846 0.7597058 0.8234266 0.8728537 0.9222713 0.9789055 1.0440381 #> [553,] 0.45094915 0.8421501 0.8582952 0.8886290 0.9141369 0.9763113 0.9821884 #> [554,] 0.13934368 0.2428452 0.3342411 0.3651141 0.3679166 0.3829084 0.4805272 #> [555,] 0.27237342 0.4441723 0.5132193 0.5338823 0.6621273 0.6778590 0.6797562 #> [556,] 0.34658311 0.3844862 0.5832709 0.6088823 0.6749278 0.6793633 0.6951039 #> [557,] 0.39100186 0.8816674 0.9568694 0.9716891 1.0118846 1.0173781 1.0519565 #> [558,] 0.31072384 0.4064494 0.4824160 0.4943451 0.4961898 0.5423066 0.5552995 #> [559,] 0.36175024 0.4205963 0.4667636 0.5322349 0.5562671 0.5765295 0.5846818 #> [560,] 0.37049110 0.7604475 0.7650716 0.8009941 0.8012898 0.8748209 0.8785264 #> [561,] 0.21399890 0.5146832 0.5962294 0.6301110 0.6329947 0.6412104 0.6431744 #> [562,] 0.33083441 0.4327513 0.4589491 0.5305996 0.5625505 0.6008497 0.6177383 #> [563,] 0.39939033 0.5776140 0.6556661 0.6749278 0.6872535 0.7165905 0.7329333 #> [564,] 0.54723126 0.5513452 0.6829526 0.7028998 0.7263323 0.7384764 0.7391128 #> [565,] 0.50873514 0.5717532 0.6554691 0.6807494 0.6955825 0.7046757 0.7062653 #> [566,] 0.33143558 0.4451621 0.4826764 0.4883416 0.5127696 0.5912516 0.5924125 #> [567,] 1.41926620 1.4453761 1.5806618 1.6063033 1.6172418 1.6276178 1.6809096 #> [568,] 0.32319175 0.4648272 0.5217926 0.5503828 0.5627264 0.6550708 0.7109863 #> [569,] 0.56700865 0.5871101 0.6226706 0.6274324 0.6453148 0.7142274 0.7158303 #> [570,] 0.43535072 0.4501754 0.5306193 0.5830972 0.5962874 0.6341743 0.6489536 #> [571,] 0.13267935 0.1393437 0.4187954 0.4336526 0.4364860 0.4371461 0.4445022 #> [572,] 0.36563150 0.4882325 0.5004472 0.5279354 0.5432098 0.5498350 0.5527091 #> [573,] 0.50642499 0.5753730 0.6208701 0.6528180 0.6671471 0.6804542 0.6851323 #> [574,] 0.48378059 0.6244787 0.6473689 0.6603144 0.7580379 0.7597058 0.7760302 #> [575,] 0.37185869 0.3924534 0.4451621 0.5187923 0.5219579 0.5244126 0.5347960 #> [576,] 0.23445424 0.2852333 0.4086348 0.4174691 0.4786173 0.4985297 0.5030537 #> [577,] 0.37256625 0.3725824 0.4192504 0.4744331 0.5042462 0.5308093 0.5538165 #> [578,] 0.37326479 0.5838672 0.6528180 0.6782440 0.7643309 0.7686223 0.7750488 #> [579,] 0.47903449 0.5110472 0.5121367 0.5338226 0.6714041 0.6867017 0.7523310 #> [580,] 0.42311058 0.6695476 0.7311306 0.9776841 1.0525541 1.0632890 1.0634033 #> [581,] 0.57508362 0.6840445 0.8620530 0.9108403 0.9978395 1.0064777 1.0194699 #> [582,] 0.76091799 0.7711151 0.8032878 0.8050413 0.8144492 0.8472938 0.9161961 #> [583,] 0.23444596 0.3015421 0.3070175 0.4174691 0.4489872 0.4617600 0.4706032 #> [584,] 0.27226466 0.8079009 0.8499023 1.0784853 1.0893290 1.1458741 1.1687427 #> [585,] 0.44220434 0.4669321 0.5040527 0.5766456 0.6187411 0.6245348 0.6274126 #> [586,] 0.29866995 0.5317214 0.6603144 0.7035090 0.7042937 0.7435667 0.8003562 #> [587,] 0.27397800 0.3872981 0.5268575 0.5447722 0.5489092 0.6181197 0.6198561 #> [588,] 0.23154039 0.5680358 0.6208568 0.6608245 0.7288602 0.7413602 0.7439166 #> [589,] 0.37440467 0.5032150 0.5277032 0.5510784 0.5646986 0.5658666 0.5828211 #> [590,] 0.64620569 0.6604637 0.7526997 0.7527220 0.8270891 0.8307374 0.8406601 #> [591,] 0.40406787 0.4514422 0.5745288 0.5820708 0.6177383 0.6246316 0.6402169 #> [592,] 0.38434510 0.3900757 0.3932452 0.5887325 0.6267533 0.6392327 0.6516217 #> [593,] 0.38066893 0.4327513 0.4683940 0.4974019 0.5355726 0.6497441 0.7012273 #> [594,] 0.38085583 0.4536409 0.4904569 0.5187908 0.5235230 0.5986820 0.6696586 #> [595,] 0.53502656 0.6239770 0.6597689 0.6757738 0.7233776 0.7877170 0.8047881 #> [596,] 0.27831665 0.3010408 0.3120374 0.3287522 0.3432095 0.4494279 0.4723983 #> [597,] 0.37616048 0.6107151 0.6290967 0.6931054 0.6972586 0.7213988 0.7268775 #> [598,] 0.76376970 0.8028125 1.4043754 1.4072762 1.4190972 1.4209938 1.4947212 #> [599,] 0.44182012 0.7566974 0.8450309 0.8655723 0.9357569 1.0665545 1.0948709 #> [600,] 0.55397237 0.7960216 0.8821900 0.9143359 0.9184174 0.9304678 0.9681963 #> [601,] 0.45940455 0.6344127 0.7640671 0.8751113 0.9069941 0.9339558 0.9425497 #> [602,] 0.24263568 0.4617965 0.4862838 0.4935583 0.5058961 0.5164612 0.6160175 #> [603,] 0.24835875 0.3899307 0.4419782 0.4938952 0.5140316 0.5292019 0.6025951 #> [604,] 0.33306395 0.4201660 0.5164836 0.5178701 0.5361367 0.5477875 0.5745749 #> [605,] 0.40116324 0.4737726 0.4747191 0.4929756 0.5054670 0.5098325 0.5183205 #> [606,] 0.26497430 0.6516321 0.7057181 0.7326479 0.8690916 0.9142811 0.9759745 #> [607,] 0.43233153 0.6705407 0.7719599 0.7760562 0.7888541 0.8285201 0.8285825 #> [608,] 0.21124475 0.7965124 0.8499023 0.8638296 0.9862154 1.0242589 1.0566198 #> [609,] 0.17043615 0.5791031 0.6119211 0.7568780 0.7933537 0.9401932 0.9784879 #> [610,] 1.27251818 1.4007409 1.5872242 1.6502714 1.6759689 1.7931794 1.7962503 #> [611,] 0.35056102 0.5386376 0.6705407 0.8652738 0.8902867 0.9165879 0.9442007 #> [612,] 0.36081713 0.4053927 0.4144134 0.4205963 0.4209899 0.4413732 0.5342375 #> [613,] 0.32998788 0.4780872 0.5829748 0.6451118 0.6516321 0.6681459 0.7481556 #> [614,] 0.35146422 0.3814274 0.5201222 0.7057621 0.7107186 0.7137567 0.7610594 #> [615,] 0.36291605 0.4214459 0.4391736 0.6600433 0.6886020 0.7072703 0.7096227 #> [616,] 0.34646260 0.4265521 0.4487823 0.4702363 0.4947684 0.4957363 0.4985146 #> [617,] 0.62805720 0.6972586 0.7227889 1.0564521 1.0855136 1.0942662 1.1260716 #> [618,] 0.31136211 0.5369502 0.5598178 0.5751151 0.6118213 0.6281894 0.6608245 #> [619,] 1.11726240 1.3848146 1.6063874 1.6113179 1.6482571 1.8290001 1.8811300 #> [620,] 0.38538472 0.4354695 0.4614661 0.6278003 0.7072703 0.7150355 0.7591022 #> [621,] 0.36424210 0.4117843 0.4445334 0.5619969 0.5867999 0.5987444 0.6254579 #> [622,] 0.42107740 0.5226775 0.6905425 0.8072431 0.8114114 0.8218081 0.8353734 #> [623,] 0.90251624 0.9179658 1.0056713 1.0084805 1.1554840 1.3880295 1.4135907 #> [624,] 0.20286252 0.3610021 0.4372544 0.4740533 0.4997761 0.5182454 0.5524235 #> [625,] 0.25229778 0.4824022 0.5822296 0.6059301 0.6334506 0.6447556 0.6600611 #> [626,] 0.55947715 0.5916953 0.6345276 0.6698491 0.7170007 0.7471335 0.7557454 #> [627,] 0.46573585 0.4878851 0.5195055 0.5201538 0.5315147 0.5350378 0.6540243 #> [628,] 0.93623884 1.0977995 1.1947872 1.2876165 1.2998117 1.4436202 1.4755800 #> [629,] 0.44825377 0.5195055 0.5581383 0.5637833 0.5975474 0.5977062 0.6021255 #> [630,] 0.37536730 0.5164301 0.5531608 0.6666422 0.7220469 0.7574397 0.7691004 #> [631,] 0.32903489 0.3839844 0.4796330 0.5315577 0.5368963 0.5412133 0.5453723 #> [632,] 0.49740194 0.5447744 0.6280775 0.6527705 0.6697146 0.7372376 0.7830272 #> [633,] 1.11726240 1.2423258 1.5862658 1.6544705 1.6930723 1.7550223 1.8218150 #> [634,] 0.27820372 0.3253701 0.3660151 0.5369217 0.6020524 0.7003384 0.7089577 #> [635,] 0.23445424 0.4048737 0.4322466 0.4608005 0.4648813 0.4977342 0.5050785 #> [636,] 0.40640874 0.5834456 0.6100679 0.6851942 0.7146722 0.7241467 0.7301356 #> [637,] 0.46252459 0.6297303 0.6331314 0.7003384 0.8112534 0.8124345 0.8651220 #> [638,] 0.60088987 0.6339919 0.6388741 0.6474137 0.6474838 0.6696586 0.6988707 #> [639,] 0.37734773 0.4590026 0.5390705 0.5921952 0.6671026 0.6974902 0.7090112 #> [640,] 0.21399890 0.4031256 0.5151704 0.5494136 0.5664058 0.6006307 0.6424917 #> [641,] 0.29207978 0.5401549 0.5474909 0.5524235 0.5796832 0.6059761 0.6585259 #> [642,] 0.29137886 0.5112414 0.5187908 0.5307719 0.5494953 0.5495123 0.5756728 #> [643,] 0.44085544 0.5514176 0.5632790 0.7421846 0.7821312 0.7895657 0.7912623 #> [644,] 0.35281360 0.3912673 0.4989607 0.5335707 0.5414298 0.6156885 0.6527913 #> [645,] 0.78117363 0.8633145 1.0734705 1.0875151 1.0911114 1.1794586 1.2188109 #> [646,] 0.69162439 0.7439166 0.9022142 0.9147525 0.9972726 1.0179116 1.0717781 #> [647,] 0.58197751 0.7391057 0.8008340 0.9239508 0.9941564 1.0069490 1.0591957 #> [648,] 0.25940823 0.2881677 0.4069067 0.4167995 0.4490281 0.5004484 0.5369063 #> [649,] 0.43361530 0.4576543 0.5310053 0.5408687 0.5907444 0.6274188 0.6276467 #> [650,] 0.55397237 0.7664153 0.9252118 0.9803168 1.0258493 1.0333822 1.0508506 #> [651,] 0.25537990 0.3510083 0.3896725 0.4092183 0.4114427 0.5009519 0.5058887 #> [652,] 0.50856221 0.6573377 0.6668035 0.7638306 0.8564216 0.8851545 0.9487532 #> [653,] 0.72771956 0.7701128 0.8085942 0.8565908 0.8612755 0.8616950 0.9033424 #> [654,] 0.63004997 0.6790883 0.6930265 0.7471025 0.7796087 0.8001579 0.8162489 #> [655,] 0.62415853 0.6790883 0.8149223 0.8164451 0.8245908 0.8249625 0.8542619 #> [656,] 0.36371447 0.5336176 0.5416509 0.5799145 0.5927017 0.5963091 0.5968608 #> [657,] 0.26592251 0.3839797 0.5314992 0.5582578 0.6573676 0.6859789 0.6869186 #> [658,] 0.96860515 1.5596684 1.7214903 1.8390047 1.8732701 2.0095196 2.0340435 #> [659,] 0.33992786 0.6787343 0.7953610 0.7995270 0.8406601 0.8451771 0.8946463 #> [660,] 0.57850160 0.6149651 0.6331314 0.7958120 0.8032878 0.8500671 0.8728612 #> [661,] 0.59645976 0.6882759 0.7294277 0.7314181 0.7398650 0.7451284 0.7905992 #> [662,] 0.51562884 0.6948035 0.6954605 0.7612766 0.7820931 0.7821108 0.8110738 #> [663,] 0.63575836 0.6640708 0.7659304 0.7776300 0.8079934 0.8763540 0.9216423 #> [664,] 0.32642149 0.5204109 0.5661041 0.5980774 0.6062228 0.6408244 0.6936385 #> [665,] 0.34583666 0.5094248 0.5822296 0.6019879 0.6508259 0.6516076 0.6744814 #> [666,] 0.43381139 0.4546433 0.5788702 0.6756414 0.7471335 0.8011819 0.8455020 #> [667,] 0.53467357 0.5373530 0.5983490 0.5995905 0.6462057 0.7008084 0.7061636 #> [668,] 0.37654869 0.4283986 0.5094248 0.6696494 0.7168134 0.7303654 0.7825482 #> [669,] 0.45033714 0.5345327 0.5369502 0.5948893 0.5985664 0.6389778 0.6726457 #> [670,] 0.83931843 0.8628560 0.9748641 0.9761721 1.0343402 1.0928670 1.0943788 #> [671,] 0.36481889 0.3875701 0.4533006 0.4579549 0.5029217 0.5244214 0.5263868 #> [672,] 0.53423748 0.6205033 0.6237384 0.6597708 0.6637734 0.6859404 0.7042937 #> [673,] 0.22512765 0.3954141 0.4057245 0.4066714 0.5754289 0.5981497 0.6459042 #> [674,] 0.41796157 0.5450184 0.5485833 0.5590485 0.5908792 0.5971899 0.6076136 #> [675,] 0.41441336 0.4468249 0.4590581 0.4647662 0.5223277 0.5907237 0.6244787 #> [676,] 0.35057975 0.6053708 0.6305607 0.7109863 0.7912119 0.8047230 0.8227136 #> [677,] 0.47357398 0.6408472 0.7471467 0.7822333 0.8163211 0.8184313 0.8571742 #> [678,] 0.36291605 0.4614661 0.5432727 0.5830972 0.5858808 0.6132328 0.6351667 #> [679,] 0.34692452 0.4372083 0.5217926 0.5248431 0.5675716 0.5873715 0.6131153 #> [680,] 0.47956545 0.7061636 0.7550567 0.7569945 0.8113778 0.8205277 0.8486483 #> [681,] 0.69219525 0.7959432 0.7989554 0.8713739 0.8858275 0.8900467 0.9009966 #> [682,] 0.23817426 0.2808169 0.3592144 0.5177640 0.5288084 0.5930796 0.5983421 #> [683,] 0.38064751 0.4014603 0.4293245 0.4413397 0.5164612 0.5171589 0.5566918 #> [684,] 0.27374113 0.4327614 0.5156453 0.5587885 0.6472249 0.7160519 0.7567857 #> [685,] 0.36601515 0.3742301 0.4121055 0.5945311 0.7146722 0.7312741 0.7746565 #> [686,] 0.52802346 0.5513452 0.6314889 0.6345006 0.7183474 0.7496084 0.7939503 #> [687,] 0.50375515 0.5870666 0.6666116 0.7169169 0.7286017 0.8465621 0.8663543 #> [688,] 0.24611185 0.3370508 0.5064250 0.5171589 0.5526758 0.6160175 0.6205033 #> [689,] 0.41794591 0.6505936 0.6588649 0.7485899 0.7491659 0.7548487 0.8576855 #> [690,] 0.48897230 0.5338226 0.5631145 0.6876064 0.7249473 0.7610390 0.7665936 #> [691,] 0.25265749 0.3648189 0.4231851 0.4983350 0.5054447 0.5058957 0.5379967 #> [692,] 0.37194528 0.3896725 0.4518190 0.4888065 0.5286499 0.5368963 0.5411184 #> [693,] 0.42115831 0.5520766 0.5550128 0.9107576 0.9213139 0.9311899 1.0733480 #> [694,] 0.30665876 0.3717870 0.3719453 0.4997310 0.5480230 0.6036892 0.6316852 #> [695,] 0.27235215 0.3682785 0.3877919 0.4563907 0.4686757 0.4790775 0.5102164 #> [696,] 0.44798338 0.5244854 0.8689665 0.9423840 0.9442252 0.9472707 0.9680754 #> [697,] 0.27562530 0.3560464 0.6615503 0.7733899 0.7821108 0.7931469 0.8066735 #> [698,] 0.64271917 0.8104446 0.8518589 0.9819177 1.0527555 1.0704130 1.1284419 #> [699,] 0.37536730 0.4279793 0.5698067 0.6805403 0.6930791 0.7037863 0.7775509 #> [700,] 0.77560656 0.8556852 1.1021752 1.1159335 1.1821154 1.2422865 1.2640127 #> [701,] 0.54944796 0.6858087 0.7160519 0.7161151 0.7514740 0.8011942 0.8040033 #> [702,] 0.43725443 0.4518521 0.5293771 0.5417388 0.5617081 0.5693952 0.5809123 #> [703,] 0.27373125 0.3681418 0.5585375 0.5629239 0.6276305 0.6427449 0.6776797 #> [704,] 0.44727767 0.6573590 0.7268775 0.7276097 0.7664300 0.7956606 0.8151043 #> [705,] 0.48215564 0.7168258 0.7797592 0.8584295 0.8881306 0.9258788 1.1747701 #> [706,] 0.32987731 0.5539253 0.6512268 0.6805731 0.7051882 0.7770416 0.8028310 #> [707,] 0.34290313 0.3916555 0.4384343 0.4439449 0.4451151 0.4652345 0.4779779 #> [708,] 0.72429204 0.8485679 0.9030081 1.0299906 1.0379055 1.0509786 1.0511232 #> [709,] 0.26411020 0.5763736 0.5913644 0.6009726 0.6159768 0.6332756 0.6387827 #> [710,] 0.59236817 0.6317479 0.6552598 0.6905037 0.7620866 0.7751092 0.7835180 #> [711,] 0.49777424 0.4986955 0.5246994 0.6800699 0.6932700 0.7195649 0.7310794 #> [712,] 0.30585146 0.3569595 0.3610021 0.4310536 0.5617081 0.6139850 0.6173314 #> [713,] 0.22730905 0.3101792 0.3896564 0.6595335 0.6614731 0.6616534 0.7026276 #> [714,] 0.73113062 0.7404447 0.7598041 0.8894494 0.9218340 0.9772646 1.0149214 #> [715,] 0.53546582 0.6488153 0.6827288 0.7065292 0.7176423 0.7196598 0.7858885 #> [716,] 0.31706208 0.3824672 0.4997944 0.5211668 0.5414298 0.5734929 0.6104140 #> [717,] 0.29573626 0.5202421 0.5274417 0.5923872 0.6130515 0.6268764 0.6668035 #> [718,] 0.51787090 0.6681381 0.6921952 0.6945929 0.7616721 0.7728200 0.7767504 #> [719,] 0.44511515 0.4773816 0.5208903 0.5221296 0.5708846 0.6036892 0.6071663 #> [720,] 0.31021064 0.3667173 0.4148328 0.5036602 0.5388405 0.5443826 0.5743608 #> [721,] 0.76376970 1.4945173 1.5618344 1.6583313 1.7226438 1.7337409 1.8270387 #> [722,] 0.57910311 0.6115139 0.6685038 0.8223332 0.8940572 0.9323082 0.9573444 #> [723,] 0.40406787 0.4589491 0.5738974 0.6282063 0.6353663 0.6645386 0.6905037 #> [724,] 0.26361352 0.3612065 0.5829462 0.6409314 0.6512282 0.6560213 0.6666938 #> [725,] 0.37766750 0.4877192 0.4977504 0.6141899 0.6374305 0.6463524 0.7035879 #> [726,] 0.40235927 0.7095820 0.7211537 0.7390536 0.7565762 0.8014326 0.8093141 #> [727,] 0.25229778 0.5443461 0.5480230 0.5517974 0.5632433 0.6230362 0.6285004 #> [728,] 0.60180716 0.6345307 0.7319469 0.7498497 0.7510102 0.7522254 0.7795453 #> [729,] 0.79251236 0.9169129 0.9624720 1.0016268 1.1132133 1.1183533 1.1703586 #> [730,] 0.21651187 0.3307715 0.3902055 0.4048998 0.4300517 0.5080894 0.5970754 #> [731,] 0.66354613 0.8080975 0.8122482 0.8448919 0.8598953 0.8703998 0.8736572 #> [732,] 0.76793703 0.9746944 1.0603844 1.1809858 1.3134783 1.3312986 1.4184369 #> [733,] 1.41806022 1.4979673 1.5334406 1.6314486 1.6429539 1.7030105 1.7172150 #> [734,] 0.35753455 0.3949244 0.4214572 0.4281875 0.4408832 0.4810949 0.4826539 #> [735,] 0.16898732 0.4774863 0.4997761 0.5450184 0.5577090 0.5654247 0.5790945 #> [736,] 0.27779408 0.4117843 0.4624789 0.4869968 0.5152503 0.5926834 0.6532210 #> [737,] 0.45094915 0.7870609 0.8365102 0.9197372 0.9669580 0.9771254 1.0148486 #> [738,] 0.33982358 0.7319139 0.7776584 0.7905012 0.8014663 0.8392729 0.9160105 #> [739,] 0.39752836 0.4628165 0.5139286 0.5557389 0.6299496 0.6648775 0.6892268 #> [740,] 0.35502072 0.5015861 0.5160277 0.6083196 0.6489536 0.6874109 0.6959152 #> [741,] 0.35694620 0.4291263 0.5064923 0.6014621 0.6239316 0.6610865 0.6857948 #> [742,] 0.54794447 0.5909986 0.6224500 0.6278003 0.6351667 0.6394549 0.6607352 #> [743,] 0.53400936 0.5574238 0.7236006 0.7286656 0.7608778 0.7813525 0.8909473 #> [744,] 0.81247524 1.0111774 1.0421754 1.0457865 1.1062559 1.1693766 1.2517755 #> [745,] 0.33705076 0.3842782 0.4068962 0.5566918 0.5967728 0.6206061 0.6247646 #> [746,] 0.57869430 0.6643098 0.6963693 0.7499769 0.7532504 0.7787472 0.7840776 #> [747,] 0.34583666 0.3765487 0.6485353 0.6536423 0.6600611 0.6950010 0.7608483 #> [748,] 0.55392527 0.6011789 0.6107278 0.6565608 0.6666422 0.7067511 0.7295667 #> [749,] 1.28096940 1.3035623 1.4027108 1.4503325 1.5398309 1.6228690 1.6251855 #> [750,] 0.75682393 0.9759578 1.0086455 1.0171610 1.0781083 1.0961189 1.1095360 #> [751,] 0.47282196 0.5538007 0.6370667 0.7604574 0.8299728 0.8469233 0.8905465 #> [752,] 0.50321505 0.6012756 0.6440738 0.6763688 0.6811249 0.6878932 0.7024877 #> [753,] 1.12512471 1.5002459 1.6186691 1.6746142 1.6971242 1.7244525 1.7529364 #> [754,] 0.19126137 0.2539434 0.3530391 0.4211737 0.4610017 0.4735912 0.4812976 #> [755,] 0.74738137 0.8490869 0.8598790 0.8819419 0.8834692 0.8956083 0.9020044 #> [756,] 0.40863482 0.4201660 0.4875000 0.4977342 0.5139286 0.5250651 0.5548998 #> [757,] 0.59144499 0.6451118 0.6556036 0.6960641 0.7246572 0.7526997 0.7562335 #> [758,] 0.29034567 0.3153831 0.4148944 0.4420203 0.5036863 0.5100027 0.5967067 #> [759,] 0.24282195 0.5158832 0.5926552 0.6115119 0.6196056 0.6231284 0.6441286 #> [760,] 0.34646260 0.4576543 0.5116226 0.5403305 0.5449393 0.5790349 0.5962150 #> [761,] 0.28637481 0.5161356 0.6072845 0.6403226 0.6589856 0.6618140 0.6619228 #> [762,] 0.22663217 0.7094030 0.7527483 0.8047230 0.8173328 0.8380186 0.8391967 #> [763,] 0.34765312 0.4212806 0.4268198 0.4444787 0.6220212 0.6292556 0.6796196 #> [764,] 0.25529604 0.4244684 0.4925101 0.5098325 0.5315147 0.5866931 0.6092869 #> [765,] 0.63149423 0.6344471 0.8072431 0.8556852 0.9310300 0.9539625 0.9673221 #> [766,] 0.41353422 0.4800824 0.4997822 0.5004472 0.5308093 0.5517345 0.5817011 #> [767,] 0.17192229 0.3120374 0.3274886 0.3970135 0.4186993 0.4204261 0.4487641 #> [768,] 0.49960642 0.5680358 0.5989883 0.6187675 0.6281894 0.6901499 0.7043782 #> [769,] 0.53075473 0.5948165 0.7371089 0.7845360 0.8615314 0.8746930 0.9299736 #> [770,] 1.08831280 1.1290978 1.2708480 1.2778259 1.2814328 1.3589799 1.4092152 #> [771,] 0.84050232 1.2081018 1.3537660 1.3676314 1.3998444 1.4217575 1.4250399 #> [772,] 0.49874972 0.5116226 0.5348619 0.5606675 0.6284367 0.6302704 0.6997016 #> [773,] 1.01823009 1.1625109 1.1724161 1.2807257 1.3346802 1.3372022 1.3953495 #> [774,] 0.42107740 0.5228137 0.5782739 0.6344471 0.6408634 0.6552209 0.7278126 #> [775,] 1.44425777 1.5024005 1.5148908 1.5275841 1.5392507 1.5788080 1.6184525 #> [776,] 0.51100732 0.6797352 0.6979795 0.7239671 0.7271320 0.7607239 0.7625511 #> [777,] 0.27986585 0.6141458 0.6186548 0.6930265 0.6978360 0.7064858 0.7565171 #> [778,] 0.61968631 0.6495568 0.6692727 0.6764916 0.6782440 0.7079180 0.7088686 #> [779,] 0.45979711 0.4943986 0.5124604 0.5573640 0.5791735 0.6387119 0.6584954 #> [780,] 0.33118191 0.3463831 0.4397189 0.4891228 0.4944305 0.5297014 0.5306987 #> [781,] 0.31406018 0.3746252 0.4828882 0.5987444 0.6003784 0.6580683 0.6789879 #> [782,] 0.26858368 0.3672683 0.4606300 0.4823437 0.4895812 0.5083644 0.5182948 #> [783,] 0.80509624 0.9642723 0.9748641 0.9932183 1.0175262 1.0440972 1.0506605 #> [784,] 0.34632790 0.5812384 0.6068026 0.6817921 0.7782817 0.7818776 0.8135992 #> [785,] 0.95031389 1.1390276 1.1488276 1.1717946 1.1885282 1.2093484 1.2547582 #> [786,] 0.57850160 0.7428819 0.7626059 0.7664228 0.7746565 0.8124345 0.8786955 #> [787,] 0.23817426 0.3740172 0.4150215 0.5086507 0.5239781 0.5252724 0.5643148 #> [788,] 0.80083402 0.8688661 0.8952005 0.9025712 1.0016799 1.0082361 1.0213946 #> [789,] 0.47662769 0.5948893 0.6012587 0.7134555 0.7582208 0.8186225 0.8845991 #> [790,] 0.38039938 0.4066714 0.4787794 0.5510211 0.6025951 0.6032960 0.6046658 #> [791,] 0.52281365 0.6314942 0.6905425 0.7001828 0.7370280 0.7496531 0.8518589 #> [792,] 0.87344003 0.8786955 0.9130445 0.9356086 1.0061377 1.0099780 1.0406190 #> [793,] 0.39295645 0.6883166 0.7067511 0.7409552 0.7463260 0.9268664 0.9380089 #> [794,] 0.45337305 0.5127075 0.5475791 0.5705612 0.5791735 0.5867750 0.6463524 #> [795,] 0.38074638 0.5428344 0.7870609 0.8122835 0.8285331 0.9000570 1.0503715 #> [796,] 0.65741762 0.6595386 0.6786018 0.6805422 0.6911190 0.6945954 0.6964581 #> [797,] 0.42601353 0.6297133 0.8849188 0.9089493 0.9556250 0.9724911 1.0142959 #> [798,] 0.07900148 0.2134423 0.3366718 0.3990870 0.5151064 0.5494136 0.6121996 #> [799,] 0.75505674 0.7705614 0.7789857 0.8690916 0.9599744 0.9880098 1.0048817 #> [800,] 0.29071253 0.5367478 0.5514176 0.5993767 0.6653796 0.6892158 0.7155752 #> [801,] 0.45390851 0.4654537 0.4702363 0.4998753 0.5088901 0.5587419 0.5677993 #> [802,] 0.66430980 0.7860686 0.7917791 0.8035448 0.8512671 0.8943658 0.9688910 #> [803,] 0.43339983 0.4889723 0.5015533 0.5121367 0.5496062 0.5623404 0.6369267 #> [804,] 0.22663217 0.6737981 0.6864307 0.7741433 0.7760244 0.7914695 0.8054832 #> [805,] 0.34928645 0.3683386 0.3883146 0.5029217 0.5178635 0.5493735 0.5851822 #> [806,] 0.96860515 1.5474225 1.6916781 1.7508751 1.8336938 1.8432269 1.8601614 #> [807,] 0.81823719 0.8905984 0.9877874 1.0080292 1.0574307 1.0694985 1.0751693 #> [808,] 0.51274896 0.5794487 0.5989883 0.6016442 0.6227651 0.6675875 0.7007486 #> [809,] 0.41927666 0.4758018 0.5229936 0.6353158 0.6485353 0.7109808 0.7184761 #> [810,] 0.52007377 0.5829748 0.7459431 0.7847037 0.7953610 0.8120088 0.8905465 #> [811,] 0.52670130 0.5495123 0.5750012 0.5960382 0.6278278 0.6401071 0.6482104 #> [812,] 0.30912486 0.3221767 0.3445838 0.3679166 0.3921957 0.4167995 0.4364860 #> [813,] 0.26707323 0.3675232 0.3676174 0.4747191 0.5430170 0.5577346 0.5612849 #> [814,] 0.66246718 0.6764916 0.6870574 0.7056267 0.7653108 0.7741433 0.7975241 #> [815,] 0.46401044 0.6718409 0.6750776 0.6944161 0.8141279 0.8415446 0.8674535 #> [816,] 0.40585333 0.5158832 0.5310501 0.6237231 0.6283424 0.6308819 0.6735509 #> [817,] 0.51787090 0.7322052 0.7942737 0.8858275 0.8992017 0.9529758 0.9777157 #> [818,] 0.44878229 0.4693085 0.5348619 0.5403305 0.5507061 0.5780262 0.5890797 #> [819,] 0.40312556 0.4724889 0.5146832 0.6540016 0.6699648 0.7296882 0.7548694 #> [820,] 0.52243421 0.9528990 0.9566207 0.9690615 0.9959560 1.0041419 1.0048973 #> [821,] 0.61876751 0.6220356 0.6670638 0.7302815 0.7320587 0.7336890 0.7419142 #> [822,] 0.45033714 0.5105971 0.6909721 0.7200852 0.7301892 0.7582208 0.7617684 #> [823,] 0.27752292 0.3530391 0.3569595 0.4105468 0.4740533 0.5041885 0.5822294 #> [824,] 0.92369646 0.9935013 1.0084146 1.0603844 1.1688281 1.2238993 1.2536617 #> [825,] 0.93623884 0.9670659 0.9716891 1.0213059 1.0791829 1.0908452 1.2003763 #> [826,] 0.51547045 0.5428344 0.5513518 0.7418054 0.7596351 0.8207332 0.8464236 #> [827,] 0.36357445 0.3906010 0.4358474 0.4812976 0.4851370 0.5204109 0.5300283 #> [828,] 0.21918209 0.3515583 0.4460121 0.4735912 0.5015861 0.5055582 0.5928341 #> [829,] 0.47248888 0.7390052 0.7862303 0.7934345 0.8240780 0.8774079 0.8816378 #> [830,] 0.61443490 0.7276547 0.7430517 0.7568780 0.8004466 0.8797075 0.8800598 #> [831,] 0.33721916 0.4333998 0.4494589 0.5627313 0.5631145 0.6392648 0.6425977 #> [832,] 0.35584388 0.3608171 0.4589638 0.4647662 0.5355108 0.5409202 0.5661899 #> [833,] 0.31425204 0.3975284 0.5110073 0.6375903 0.6674449 0.7254397 0.7415100 #> [834,] 0.43093765 0.4821556 0.6053708 0.7107931 0.8125750 0.9832103 1.0043076 #> [835,] 0.84050232 1.4143479 1.5009154 1.6691936 1.6755054 1.6854697 1.8534532 #> [836,] 0.46890975 0.4922400 0.5605946 0.5643164 0.5666706 0.6025482 0.6248032 #> [837,] 0.29686347 0.4882768 0.6842423 0.7095820 0.7367931 0.8004466 0.8982905 #> [838,] 0.28297282 0.4104397 0.5606675 0.5922417 0.5930066 0.6435596 0.6506995 #> [839,] 0.66046375 0.7587216 0.7705614 0.7822333 0.8118788 0.9794113 0.9822419 #> [840,] 0.20603309 0.3096341 0.3153831 0.4322466 0.4852165 0.4985219 0.5298338 #> [841,] 0.51426500 0.5160277 0.5448443 0.6115975 0.6209217 0.6602422 0.6673354 #> [842,] 0.87682996 0.8871343 0.9657971 0.9972174 1.0056713 1.0089594 1.0226326 #> [843,] 0.53344981 0.5666706 0.5705612 0.5804025 0.6160059 0.6285987 0.6888110 #> [844,] 0.63329928 0.7035094 0.8909473 0.9090553 0.9450609 1.0034860 1.0322017 #> [845,] 0.59733251 0.9069941 1.0315972 1.0651302 1.1004546 1.1819033 1.2104695 #> [846,] 0.35584388 0.4053927 0.4569943 0.4801926 0.5726849 0.5907237 0.6562850 #> [847,] 0.44945895 0.4669321 0.4710597 0.5335359 0.5496062 0.5714739 0.5741004 #> [848,] 0.41227811 0.4813064 0.5127075 0.5322349 0.5396463 0.5524752 0.5573640 #> [849,] 0.60207570 0.6332993 0.7541340 0.7612766 0.8339584 0.8695335 0.9403258 #> [850,] 0.56667337 0.5690583 0.6806436 0.6841255 0.7057482 0.7196784 0.7266933 #> [851,] 0.60644755 0.6068605 0.6088823 0.6313599 0.6381212 0.7043648 0.8091027 #> [852,] 0.46309918 0.5503828 0.5671789 0.6737981 0.6893140 0.6897462 0.7094030 #> [853,] 0.54477441 0.7328934 0.7748304 0.7982394 0.8948322 0.9052024 0.9099494 #> [854,] 0.42219845 0.5288742 0.5313286 0.5314622 0.5489092 0.6208746 0.6326844 #> [855,] 0.36655786 0.4248826 0.5211668 0.5812758 0.6696843 0.6909388 0.7411261 #> [856,] 0.66906906 0.6929659 0.7029221 0.7363367 0.8576981 0.9298216 0.9509733 #> [857,] 0.10961117 0.5561111 0.5914676 0.6253939 0.6720140 0.6791187 0.6825467 #> [858,] 0.34458382 0.3564569 0.4068168 0.4490281 0.4817999 0.5008421 0.5009519 #> [859,] 0.53900013 0.7596351 0.7809893 0.7977962 0.8110831 0.8285331 0.8717266 #> [860,] 0.26056421 0.3320452 0.3675232 0.4011632 0.4244684 0.5245809 0.5277923 #> [861,] 0.53863757 0.5741677 0.7806849 0.9257462 0.9832158 1.0343402 1.0652415 #> [862,] 0.48019263 0.5409202 0.6188566 0.6728008 0.6845853 0.6976790 0.6983050 #> [863,] 0.16898732 0.4179616 0.4874333 0.5182454 0.5581737 0.5727718 0.5833047 #> [864,] 0.49674503 0.8623701 0.9361598 1.0285802 1.0919945 1.1454716 1.1467348 #> [865,] 0.74356670 0.8506358 0.8988180 0.9653260 0.9658914 1.0456813 1.1495489 #> [866,] 0.19274415 0.5890797 0.6656190 0.6929399 0.7092455 0.7130241 0.7300549 #> [867,] 0.82780332 0.8682733 0.8819994 0.9382707 0.9624720 1.0012975 1.0401675 #> [868,] 0.80504130 1.0132570 1.1390276 1.2647887 1.2893205 1.3519468 1.4066961 #> [869,] 0.38993068 0.4592070 0.4627760 0.4812428 0.4883416 0.5284553 0.5554485 #> [870,] 0.41300116 0.4726289 0.5145285 0.5831077 0.6027282 0.6196082 0.6260452 #> [871,] 0.35872808 0.5216523 0.5218097 0.5943798 0.8396827 0.8405865 0.8480663 #> [872,] 0.66061744 0.9751220 0.9887768 1.0250606 1.1462059 1.1488878 1.1533324 #> [873,] 0.55047599 0.9306545 0.9634239 1.0486355 1.0540035 1.1314857 1.1924546 #> [874,] 0.74044467 0.8490869 0.8839707 1.0353342 1.0634033 1.0914634 1.1081549 #> [875,] 0.36909222 0.3824672 0.4248826 0.4752431 0.5622268 0.6527913 0.7932064 #> [876,] 0.19126137 0.3194772 0.3331275 0.3635745 0.3842054 0.4105468 0.4783768 #> [877,] 0.52330958 0.5452436 0.6420042 0.6720140 0.7052971 0.7300549 0.7423220 #> [878,] 0.35754567 0.3647637 0.3856706 0.4204261 0.4580945 0.4684129 0.4723983 #> [879,] 0.48698634 0.5103997 0.5561728 0.5943264 0.6441364 0.6522988 0.6841255 #> [880,] 0.38075836 0.4420203 0.4485579 0.4658402 0.5140316 0.5446332 0.5554485 #> [881,] 0.17192229 0.2007145 0.2783166 0.3575457 0.4577139 0.4804332 0.4840932 #> [882,] 0.55742381 0.7035094 0.8815920 0.9407532 0.9665835 1.0070158 1.0256505 #> [883,] 1.06671323 1.1098776 1.1939526 1.2708891 1.3019662 1.3272026 1.3346704 #> [884,] 0.59122759 0.5924289 0.6878932 0.7469794 0.7701128 0.7939503 0.9124692 #> [885,] 0.26361352 0.4103122 0.5966234 0.6251264 0.6412155 0.6638039 0.6959499 #> [886,] 0.38142744 0.4702799 0.6183745 0.6692462 0.7132491 0.7992174 0.8603296 #> [887,] 0.41103442 0.4721603 0.4943986 0.5488501 0.5919320 0.6048693 0.6696642 #> [888,] 0.48463135 0.5583400 0.6188566 0.7207993 0.7371089 0.7768013 0.7927239 #> [889,] 0.20402766 0.2125101 0.5652156 0.6438647 0.6656291 0.6934208 0.7234980 #> [890,] 0.40644938 0.4742585 0.5110722 0.5993060 0.6140650 0.6602692 0.6758489 #> [891,] 0.31009228 0.5020218 0.5300283 0.5325700 0.5659865 0.6462933 0.6560148 #> [892,] 0.63783218 0.7207886 0.7319139 1.1488815 1.2363910 1.2394352 1.2466568 #> [893,] 0.51562884 0.6047001 0.6736678 0.7180052 0.8424851 0.8446939 0.8627030 #> [894,] 1.16058388 1.2652724 1.3307926 1.3720424 1.4087473 1.4230225 1.4442578 #> [895,] 0.76904320 0.8050931 1.0306609 1.0424817 1.0576121 1.1172876 1.1341513 #> [896,] 1.03405675 1.0807985 1.1629530 1.1726818 1.2371444 1.2431209 1.2989847 #> [897,] 0.26056421 0.2953271 0.3596759 0.3676174 0.4370006 0.4674089 0.4867114 #> [898,] 0.66295655 1.0524327 1.1454716 1.1938812 1.2348503 1.2659794 1.3541057 #> [899,] 0.26714363 0.4168866 0.4398471 0.4827427 0.4993616 0.5299611 0.5303869 #> [900,] 0.99383792 1.0207571 1.0337819 1.0365731 1.0417322 1.0553590 1.0669284 #> [901,] 0.41869934 0.4499811 0.4804332 0.4892405 0.5419171 0.5443461 0.5630225 #> [902,] 0.41001145 0.4336526 0.4356013 0.4651105 0.5229936 0.5439551 0.5553379 #> [903,] 0.44601213 0.4739712 0.4898238 0.5262443 0.5330906 0.5404534 0.5571109 #> [904,] 0.38729811 0.4356013 0.4758018 0.5605504 0.5629239 0.6072090 0.6208746 #> [905,] 0.43376457 0.6284165 0.6473181 0.6926234 0.7473814 0.7664153 0.8196974 #> [906,] 0.36889156 0.4181342 0.4353989 0.5443613 0.6925852 0.7820596 0.8175952 #> [907,] 0.35553709 0.3639759 0.4068168 0.4192504 0.4518190 0.4547488 0.4583266 #> [908,] 0.62373689 0.7183489 0.8926911 0.9374606 0.9533518 0.9709443 0.9932183 #> [909,] 0.47303215 0.5924289 0.6284165 0.6345006 0.6550931 0.6738575 0.6763688 #> [910,] 0.37272716 0.7574815 0.8620530 0.9235493 1.0293627 1.0346818 1.0506003 #> [911,] 0.29797572 0.4412957 0.5142650 0.5494740 0.6083196 0.6670661 0.6900840 #> [912,] 0.31680663 0.3592144 0.3740172 0.4725692 0.5161527 0.5982736 0.6788071 #> [913,] 0.40154240 0.5960382 0.6015735 0.6039505 0.6143347 0.6173411 0.6399215 #> [914,] 0.25529604 0.4657359 0.5176139 0.5277923 0.5430170 0.5472789 0.5484479 #> [915,] 0.52267751 0.5274417 0.7076093 0.7294261 0.7614309 0.7638306 0.7763127 #> [916,] 0.60183197 0.6690691 0.7574397 0.7667885 0.7775509 0.8601285 0.9643448 #> [917,] 0.27254531 0.3342411 0.4363043 0.4371461 0.4383080 0.4857113 0.5166758 #> [918,] 0.27820372 0.3134153 0.4121055 0.4625246 0.5752935 0.7428819 0.7835030 #> [919,] 0.22730905 0.3454661 0.4692602 0.5782066 0.7374615 0.7477163 0.7979180 #> [920,] 0.38219942 0.5233096 0.6548786 0.6736678 0.6747795 0.7224611 0.7872064 #> [921,] 0.35397953 0.4057245 0.4850417 0.5075179 0.5084299 0.5268116 0.5634698 #> [922,] 0.49108733 0.6237369 0.6983050 0.7207993 0.7317220 0.7329495 0.8043957 #> [923,] 0.57316599 0.7018844 0.7698621 0.9067110 0.9072002 0.9167711 0.9358035 #> [924,] 0.27779408 0.5067058 0.5474909 0.5619969 0.5990116 0.6007823 0.6651803 #> [925,] 0.28951401 0.3507199 0.6028122 0.6597689 0.6932175 0.7135412 0.7213954 #> [926,] 0.65566614 0.7996484 0.8013142 0.8076352 0.8094535 0.8484571 0.8515109 #> [927,] 0.27562530 0.5907578 0.6047001 0.6923461 0.6948035 0.7315810 0.7754890 #> [928,] 0.42319471 0.4619486 0.5357388 0.6964726 0.7374605 0.7408393 0.7504980 #> [929,] 0.38240555 0.6968206 0.7107931 0.7555991 0.7797592 1.0041571 1.0511232 #> [930,] 0.48905962 0.5091761 0.6165220 0.6183745 0.6802482 0.7362994 0.7480086 #> [931,] 0.46604194 0.5057079 0.5093474 0.6374305 0.6515160 0.6895867 0.7152979 #> [932,] 0.36022339 0.3990870 0.4527244 0.6065168 0.6401071 0.6408634 0.6579255 #> [933,] 0.27977522 0.3955108 0.4488479 0.4904569 0.4931540 0.5990700 0.6173134 #> [934,] 0.38153048 0.4648426 0.5635859 0.6371110 0.6375488 0.6404007 0.6428293 #> [935,] 0.44190042 0.4850204 0.5412125 0.5499718 0.5658402 0.5743258 0.5858808 #> [936,] 0.47357398 0.5110472 0.5623404 0.6583454 0.6729948 0.7008084 0.7540306 #> [937,] 0.61987271 0.7960216 0.9158306 1.0644285 1.1518480 1.1549978 1.1687427 #> [938,] 0.29207978 0.5936938 0.5990116 0.6335023 0.6814437 0.6869994 0.7562474 #> [939,] 0.10961117 0.5477442 0.5794918 0.6271646 0.6420042 0.6697815 0.6785323 #> [940,] 0.37616048 0.5179263 0.5733939 0.6280572 0.6450097 0.7735499 0.7956606 #> [941,] 0.59060294 0.5975474 0.6558207 0.6976132 0.7076012 0.7717883 0.8033412 #> [942,] 0.21344226 0.2370228 0.3082354 0.3653168 0.4527244 0.5664058 0.6008899 #> [943,] 0.37666715 0.4403305 0.4726289 0.4837092 0.5482435 0.5557389 0.5831863 #> [944,] 0.26497430 0.6674207 0.7481556 0.7606691 0.9812453 1.0048817 1.0092092 #> [945,] 0.38219942 0.5879447 0.6173491 0.6611236 0.6726969 0.6757738 0.7159151 #> [946,] 0.49533234 0.6346750 0.6884615 0.7041224 0.7872879 0.8081023 0.8233290 #> [947,] 0.62918180 0.7968490 0.8299728 0.8488415 0.9636785 0.9671748 0.9727649 #> [948,] 0.19062462 0.3290349 0.3639759 0.4651105 0.5008421 0.5286499 0.5410208 #> [949,] 0.47084221 0.6654162 0.7048069 0.7374605 0.7551687 0.8144466 0.8210441 #> [950,] 0.40585333 0.6090162 0.6438393 0.6645667 0.6893189 0.7167928 0.7535016 #> [951,] 1.71535420 1.7710182 1.8012323 1.9644858 2.0306185 2.1379828 2.1948562 #> [952,] 0.94719990 1.1703586 1.2716053 1.2792180 1.2884615 1.3356232 1.3512644 #> [953,] 0.24524756 0.3722042 0.4048998 0.4403305 0.4920482 0.5248431 0.5715971 #> [954,] 0.59171612 0.6484736 0.6593648 0.7002319 0.7342629 0.7738593 0.7823798 #> [955,] 0.36726831 0.3876206 0.3916555 0.4122333 0.4146105 0.4408832 0.4436452 #> [956,] 0.28197372 0.2951427 0.2970063 0.3015421 0.3331275 0.4610017 0.4851370 #> [957,] 0.75137243 0.7668919 0.8383914 0.8551747 0.8874871 0.8942799 0.9154840 #> [958,] 0.43970503 0.4590166 0.4673949 0.4807027 0.4878093 0.4942333 0.5067848 #> [959,] 0.21563769 0.4584524 0.4785825 0.4826764 0.4827427 0.5608112 0.5656248 #> [960,] 0.34159301 0.3900757 0.4015424 0.4751257 0.5479820 0.6047128 0.6139697 #> [961,] 1.64730746 1.7606569 1.8298994 1.8558945 1.8647541 1.9069203 2.0772323 #> [962,] 0.46116246 0.6403444 0.6522988 0.7392183 0.7429670 0.7526534 0.8085424 #> [963,] 0.20071453 0.3274886 0.3432095 0.3856706 0.4592138 0.4892405 0.5513524 #> [964,] 0.60683629 0.6517776 0.8600679 0.9081630 0.9218340 0.9547859 0.9884917 #> [965,] 0.53546582 0.5653624 0.5850334 0.5880933 0.5956964 0.6276467 0.6442174 #> [966,] 0.21651187 0.2317734 0.3859907 0.4146105 0.4910142 0.4920482 0.5625149 #> [967,] 0.41103442 0.4597971 0.5235230 0.5307719 0.5510616 0.6118213 0.6688656 #> [968,] 0.39860107 0.4380599 0.4533006 0.5529875 0.5604020 0.5783303 0.5932555 #> [969,] 0.39210704 0.4336153 0.5295156 0.6056370 0.6156885 0.6515893 0.6693918 #> [970,] 0.38757011 0.4032872 0.4681296 0.5233900 0.5238755 0.5789343 0.5793320 #> [971,] 0.21918209 0.2444457 0.4211737 0.4739712 0.4783768 0.4932683 0.5504818 #> [972,] 0.61758497 0.6874442 0.8036485 0.8796937 0.8933293 0.9167823 0.9520005 #> [973,] 0.37950826 0.4977742 0.5460506 0.5654979 0.6470833 0.7371090 0.7557208 #> [974,] 0.13267935 0.2428452 0.3835312 0.4100114 0.4443748 0.4630358 0.4857113 #> [975,] 0.29137886 0.3808558 0.4494418 0.5510616 0.5705460 0.5788722 0.5844677 #> [976,] 0.57887022 0.8547462 0.8845617 0.8948754 0.9142141 0.9572253 1.0449902 #> [977,] 0.53467357 0.6050027 0.6217932 0.6930354 0.8113778 0.8167004 0.8336929 #> [978,] 0.24855464 0.3429031 0.4231851 0.4459950 0.5169359 0.5208903 0.5263868 #> [979,] 0.38428962 0.4476540 0.4706032 0.4717548 0.4817677 0.5750238 0.5753789 #> [980,] 0.38831457 0.4681296 0.5244214 0.5359329 0.5656454 0.6005207 0.6054502 #> [981,] 0.32642149 0.4353507 0.4688051 0.5189561 0.5746850 0.5750395 0.5897147 #> [982,] 0.39295645 0.6280775 0.7546048 0.8182106 0.8942453 0.9120783 0.9266390 #> [983,] 0.51525033 0.6593257 0.6665253 0.6819764 0.6948617 0.7044144 0.7242251 #> [984,] 0.93081475 0.9929918 1.0686850 1.0714018 1.0977995 1.1447031 1.1805190 #> [985,] 0.37853720 0.4998753 0.6013005 0.6443655 0.6587918 0.6663823 0.6978056 #> [986,] 0.27237342 0.4337646 0.5060788 0.6272943 0.6577665 0.6708163 0.6836464 #> [987,] 1.38856912 1.6361155 1.7030627 1.7118549 1.7578300 1.7924477 1.8974918 #> [988,] 0.66347900 0.7011247 0.7657464 0.7867163 0.8439553 0.8543112 0.8612993 #> [989,] 0.25283902 0.4725692 0.6412155 0.6530337 0.6666938 0.6822739 0.6852734 #> [990,] 0.34537301 0.4943825 0.5348730 0.6260920 0.6302965 0.6686640 0.6707097 #> [991,] 0.19274415 0.6253939 0.6271646 0.6384520 0.7050826 0.7361423 0.7423220 #> [992,] 0.68535263 0.7449855 0.8332449 0.8448919 0.8546296 0.8701522 0.9151551 #> [993,] 0.29573626 0.5085622 0.5222549 0.5714498 0.5996214 0.6150778 0.6439832 #> [994,] 0.34324427 0.3871573 0.4821855 0.4984314 0.5288742 0.5620869 0.6308612 #> [995,] 0.38428962 0.5359329 0.5577090 0.5581737 0.5796832 0.6007823 0.6205757 #> [996,] 0.24855464 0.3755687 0.4384343 0.4606300 0.4865361 0.5041361 0.5170806 #> [997,] 0.71485709 1.0349787 1.1488815 1.1793684 1.1988625 1.2671123 1.2780407 #> [998,] 0.47264023 0.5073235 0.5280320 0.5308094 0.5817286 0.6269897 0.6459467 #> [999,] 0.34558386 0.4790775 0.5313286 0.5317383 0.5337046 0.5827102 0.5840487 #> [1000,] 0.34397845 0.3794034 0.4114427 0.4779854 0.5226127 0.5297184 0.5677993 #> [,8] [,9] [,10] #> [1,] 1.5243659 1.5808169 1.6309819 #> [2,] 0.9611154 0.9978109 1.0056389 #> [3,] 0.8207332 0.8635637 0.8753390 #> [4,] 0.9403258 0.9432942 0.9694713 #> [5,] 0.7043648 0.7952674 0.8274985 #> [6,] 0.5439281 0.5770422 0.5982568 #> [7,] 0.7036611 0.7609579 0.8734171 #> [8,] 0.5276847 0.5397691 0.6196082 #> [9,] 0.6723708 0.7324915 0.7489528 #> [10,] 0.6932124 0.7214650 0.7540280 #> [11,] 1.0114782 1.0153096 1.0194461 #> [12,] 0.6912918 0.6960641 0.7493430 #> [13,] 0.7598048 0.8036485 0.8177474 #> [14,] 0.6430809 0.6726969 0.6822545 #> [15,] 1.0414107 1.0553590 1.0591613 #> [16,] 0.8700759 0.8739138 0.8938110 #> [17,] 0.7917640 0.7923931 0.8010192 #> [18,] 0.8303925 0.8822084 0.8877890 #> [19,] 0.7475963 0.7657464 0.7864254 #> [20,] 0.5630225 0.5770422 0.6040373 #> [21,] 0.6078729 0.6186548 0.6461841 #> [22,] 0.8864625 0.9761193 0.9881459 #> [23,] 0.5940433 0.5999559 0.6055215 #> [24,] 0.6131153 0.6777096 0.7524123 #> [25,] 0.6602214 0.6994899 0.7164263 #> [26,] 0.8288068 0.9339935 0.9397074 #> [27,] 0.6342003 0.6379932 0.6517197 #> [28,] 1.1203890 1.1337488 1.2100997 #> [29,] 0.5249234 0.6053305 0.6375590 #> [30,] 0.7140120 0.7154912 0.7488831 #> [31,] 0.4942333 0.5761194 0.5855809 #> [32,] 0.7520886 0.8040873 0.8396827 #> [33,] 0.8323721 0.8906242 0.9136292 #> [34,] 0.6641804 0.6717917 0.6872239 #> [35,] 1.0939944 1.1142197 1.1249202 #> [36,] 0.5219579 0.5337046 0.5643711 #> [37,] 0.9828091 1.0030337 1.0086629 #> [38,] 0.6305399 0.6523084 0.6545695 #> [39,] 0.8966548 0.9142811 0.9187279 #> [40,] 0.6468075 0.7282995 0.7573701 #> [41,] 1.0466836 1.0745694 1.0879764 #> [42,] 0.8402753 0.8565728 0.8618696 #> [43,] 0.6616534 0.6651803 0.6820772 #> [44,] 0.8484498 0.8826854 0.9821245 #> [45,] 1.6800755 1.6845896 1.7288681 #> [46,] 0.8054544 0.8615314 0.8707869 #> [47,] 0.6353663 0.6555521 0.6722421 #> [48,] 0.9178182 0.9200243 0.9927841 #> [49,] 0.5457032 0.5663475 0.5790349 #> [50,] 0.4860429 0.5075179 0.5379716 #> [51,] 0.7611719 0.7637640 0.7995270 #> [52,] 1.2980012 1.3192358 1.3233202 #> [53,] 0.5446332 0.5478312 0.5535708 #> [54,] 0.6638055 0.6651148 0.6797352 #> [55,] 0.5976980 0.6141899 0.6375299 #> [56,] 0.8755020 0.8962753 0.9500238 #> [57,] 1.2146188 1.2815139 1.4096943 #> [58,] 1.0638888 1.1074634 1.1145257 #> [59,] 0.7242678 0.7270035 0.7293509 #> [60,] 1.0101676 1.0546030 1.0602406 #> [61,] 0.6618560 0.6654939 0.6703510 #> [62,] 0.7501233 0.7567534 0.8105553 #> [63,] 0.7102408 0.7406132 0.7604855 #> [64,] 0.6923005 0.7421846 0.7520886 #> [65,] 1.2056942 1.2894449 1.2991575 #> [66,] 1.0340444 1.0554123 1.0610237 #> [67,] 1.3881422 1.3955733 1.4387810 #> [68,] 0.7332208 0.7571446 0.7770250 #> [69,] 1.0857663 1.1366361 1.1438864 #> [70,] 0.7315282 0.7459833 0.7725486 #> [71,] 0.6440949 0.6446191 0.6983985 #> [72,] 1.2722014 1.2976706 1.3110711 #> [73,] 1.3203683 1.3419641 1.3451671 #> [74,] 1.6801885 1.6853579 1.7120924 #> [75,] 0.5693952 0.5750238 0.5821031 #> [76,] 1.0838494 1.1065840 1.1169843 #> [77,] 0.8086745 0.8116027 0.8389967 #> [78,] 0.6819764 0.7163205 0.7215572 #> [79,] 0.7950445 0.8059375 0.8063845 #> [80,] 0.7012542 0.7164036 0.7310572 #> [81,] 2.1642842 2.2000876 2.2331132 #> [82,] 0.8300079 0.8348768 0.8955452 #> [83,] 0.7831751 0.8878947 0.8923720 #> [84,] 0.7421833 0.7430611 0.7604475 #> [85,] 1.1437027 1.2023561 1.3051234 #> [86,] 0.7069953 0.7164263 0.7207612 #> [87,] 0.5002889 0.5212093 0.5277063 #> [88,] 1.0414267 1.0564780 1.1446718 #> [89,] 0.7087140 0.7210939 0.7300471 #> [90,] 0.6453419 0.6832413 0.7144920 #> [91,] 0.6620797 0.6756653 0.7069087 #> [92,] 1.1947872 1.2003763 1.2043890 #> [93,] 0.8512671 0.8513194 0.9902324 #> [94,] 0.8398371 0.8401201 0.8559189 #> [95,] 0.6780016 0.6897462 0.7036488 #> [96,] 0.5746850 0.5875314 0.5921158 #> [97,] 0.8658725 0.9338371 0.9690399 #> [98,] 1.0853816 1.1052694 1.1294021 #> [99,] 0.6692727 0.6825616 0.7028998 #> [100,] 1.1663184 1.1840966 1.1903360 #> [101,] 0.6834033 0.7182481 0.7783143 #> [102,] 0.7084309 0.7091938 0.7659119 #> [103,] 0.6449446 0.6462933 0.6719392 #> [104,] 0.9233463 0.9835241 0.9947733 #> [105,] 1.1891001 1.2249966 1.2384766 #> [106,] 1.1099235 1.1355901 1.1725647 #> [107,] 0.8123415 0.8325188 0.8671570 #> [108,] 0.8365915 0.8835575 0.9009966 #> [109,] 0.6326446 0.6574176 0.6704237 #> [110,] 1.3458911 1.3519802 1.3581095 #> [111,] 0.6545029 0.6869348 0.6881646 #> [112,] 1.0572347 1.0772545 1.0896860 #> [113,] 0.7767504 0.8143436 0.8462347 #> [114,] 0.9217313 0.9487040 0.9488802 #> [115,] 0.7756343 0.8557102 0.8710254 #> [116,] 0.7891744 0.8032448 0.8230275 #> [117,] 0.8484029 0.8488996 0.8612993 #> [118,] 0.8383914 0.8446840 0.8528961 #> [119,] 0.8192676 0.9261510 0.9664756 #> [120,] 0.7897595 0.8170575 0.8347737 #> [121,] 0.9853501 1.0255424 1.0457865 #> [122,] 0.6665253 0.7115648 0.7229703 #> [123,] 0.7270004 0.7878174 0.7915487 #> [124,] 1.8305911 1.9078020 1.9944160 #> [125,] 0.5928401 0.6368901 0.6445785 #> [126,] 0.7807276 0.7853142 0.8001476 #> [127,] 0.7061602 0.7241037 0.7882429 #> [128,] 0.6003784 0.6088547 0.6383166 #> [129,] 0.7535734 0.7603356 0.7929358 #> [130,] 0.9012099 0.9060264 0.9358829 #> [131,] 0.6657764 0.6753620 0.6839478 #> [132,] 0.9469076 0.9695313 1.0316372 #> [133,] 0.9726798 1.0643520 1.0705907 #> [134,] 0.9120428 0.9666811 1.0409997 #> [135,] 1.5699467 1.5947757 1.6392738 #> [136,] 0.8182372 0.8191019 0.8344704 #> [137,] 0.6936492 0.7024877 0.7115648 #> [138,] 0.6849311 0.7284462 0.7367644 #> [139,] 0.8675932 0.8743178 0.8836714 #> [140,] 0.5796756 0.5953103 0.6121718 #> [141,] 0.5783303 0.5966800 0.6036024 #> [142,] 0.6007914 0.6438789 0.6502767 #> [143,] 0.6620525 0.6806436 0.6867477 #> [144,] 0.8598790 0.8724186 0.9304678 #> [145,] 0.7014700 0.7119572 0.7208976 #> [146,] 0.8137668 0.8385605 0.8450309 #> [147,] 1.8041595 1.8161606 1.8558945 #> [148,] 1.1210947 1.1285060 1.1598155 #> [149,] 0.6452655 0.6583454 0.6593995 #> [150,] 1.3792715 1.4307066 1.5478979 #> [151,] 1.0860630 1.1100208 1.1332205 #> [152,] 0.6403226 0.6421274 0.6443655 #> [153,] 1.1906040 1.2140674 1.2141235 #> [154,] 1.0057026 1.0182671 1.0256053 #> [155,] 1.0403373 1.0696501 1.0827458 #> [156,] 1.1766876 1.2167468 1.2247420 #> [157,] 0.6424917 0.6529798 0.6937404 #> [158,] 0.7134555 0.7617684 0.8326278 #> [159,] 0.7298838 0.7843733 0.8009879 #> [160,] 0.5654880 0.5663867 0.5886008 #> [161,] 0.5649241 0.5693824 0.5812760 #> [162,] 0.8142111 0.8256681 0.8272541 #> [163,] 0.8281454 0.8328057 0.8483943 #> [164,] 0.9857413 1.0088831 1.0380476 #> [165,] 0.5506159 0.5767945 0.5830846 #> [166,] 0.8656360 0.8914866 0.9326425 #> [167,] 0.5233900 0.5292069 0.5416509 #> [168,] 0.5578011 0.5643164 0.5944404 #> [169,] 0.7386633 0.7662297 0.9012099 #> [170,] 1.1387468 1.1658656 1.1679701 #> [171,] 0.9562380 0.9585808 0.9690550 #> [172,] 0.7958698 0.8491134 0.8656282 #> [173,] 0.7329002 0.7548487 0.7614309 #> [174,] 0.8888326 0.9138311 1.0139692 #> [175,] 0.8472938 0.8731073 0.9566480 #> [176,] 0.9184091 0.9184174 0.9258757 #> [177,] 1.2139058 1.2432262 1.2583021 #> [178,] 0.9205091 0.9403023 0.9821884 #> [179,] 1.6050450 1.6194342 1.6253970 #> [180,] 0.7539741 0.7678168 0.7904194 #> [181,] 0.7747172 0.8280436 0.8445258 #> [182,] 0.7610390 0.7624164 0.7719599 #> [183,] 0.8098735 0.8116027 0.9110454 #> [184,] 1.1174933 1.1827852 1.2005486 #> [185,] 0.5391978 0.5443826 0.5470348 #> [186,] 0.6459826 0.6600433 0.6793389 #> [187,] 0.5965657 0.6096712 0.6342035 #> [188,] 0.7144528 0.7317062 0.7411022 #> [189,] 0.6665549 0.7036611 0.8455571 #> [190,] 0.6707097 0.6855123 0.7104163 #> [191,] 0.6880536 0.6951885 0.7146643 #> [192,] 0.6223099 0.6273539 0.6390304 #> [193,] 0.6342512 0.6388741 0.6752632 #> [194,] 0.6327771 0.6469318 0.6602149 #> [195,] 1.3646138 1.4342775 1.4604686 #> [196,] 0.5524353 0.5610534 0.5743608 #> [197,] 0.8344419 0.8614908 0.8924273 #> [198,] 0.9935013 1.0102038 1.0604318 #> [199,] 0.9424759 0.9766749 0.9793582 #> [200,] 0.8082735 0.8481908 0.8727487 #> [201,] 0.9090066 0.9098650 0.9413194 #> [202,] 0.5442527 0.5662443 0.5758904 #> [203,] 0.8915143 0.9333763 0.9400642 #> [204,] 0.8024589 0.8144466 0.8153928 #> [205,] 1.4519661 1.6070510 1.6374261 #> [206,] 1.1985378 1.2227509 1.2647286 #> [207,] 0.5022303 0.5100027 0.5204578 #> [208,] 0.5768237 0.5828092 0.6070402 #> [209,] 0.8405143 0.8704515 0.8730958 #> [210,] 0.5245977 0.5732797 0.5897997 #> [211,] 0.6331277 0.6618434 0.6851041 #> [212,] 0.8040936 0.8173328 0.8276968 #> [213,] 0.6421244 0.6618434 0.6669356 #> [214,] 0.8695335 0.8838543 0.9361518 #> [215,] 0.8780073 0.9322287 0.9854342 #> [216,] 0.6316852 0.6329600 0.6541418 #> [217,] 0.7409552 0.7749358 0.7765259 #> [218,] 0.9633232 0.9700862 0.9714272 #> [219,] 1.0734137 1.1260716 1.2022306 #> [220,] 0.7089577 0.7958406 0.8049686 #> [221,] 0.8087226 0.8820261 0.8979552 #> [222,] 0.8423325 0.8752929 0.8792610 #> [223,] 1.1291117 1.1304449 1.1406933 #> [224,] 0.8237598 0.8477494 0.8525389 #> [225,] 0.8003785 0.8120694 0.8328057 #> [226,] 0.7349225 0.7711570 0.7720764 #> [227,] 0.6475480 0.6542009 0.6584288 #> [228,] 0.7708681 0.7972449 0.8793620 #> [229,] 0.6023254 0.6616765 0.7303278 #> [230,] 0.7363367 0.7637374 0.7667885 #> [231,] 0.5180424 0.5335359 0.5597946 #> [232,] 0.6078622 0.6278613 0.6333816 #> [233,] 0.5992267 0.6017974 0.6051926 #> [234,] 0.7275223 0.7328934 0.7858247 #> [235,] 0.8318334 0.8319812 0.8925846 #> [236,] 0.6797679 0.6834770 0.6946250 #> [237,] 0.6653248 0.7276097 0.7601208 #> [238,] 0.7668919 0.7835613 0.7921361 #> [239,] 0.7590097 0.7645542 0.7747119 #> [240,] 1.4700593 1.4715203 1.4952204 #> [241,] 1.2063140 1.2099781 1.2246481 #> [242,] 1.1757867 1.2450534 1.2567412 #> [243,] 0.5949664 0.6131437 0.6576266 #> [244,] 1.8735486 1.8739523 1.9042581 #> [245,] 0.4875000 0.5057847 0.5298338 #> [246,] 0.8673738 0.8834692 0.9245452 #> [247,] 0.9667485 0.9776315 0.9832663 #> [248,] 0.9853571 1.0121351 1.0502786 #> [249,] 0.5453723 0.5486410 0.5569566 #> [250,] 0.6523133 0.7128838 0.7176423 #> [251,] 1.0271251 1.0428790 1.0444198 #> [252,] 0.8376048 0.8393177 0.9233463 #> [253,] 0.7022452 0.7049319 0.7232298 #> [254,] 0.9378070 0.9465619 0.9539625 #> [255,] 0.7323271 0.8051645 0.8157303 #> [256,] 0.8054544 0.8320862 0.8332449 #> [257,] 0.8221198 0.8385605 0.8713739 #> [258,] 1.5881133 1.6214879 1.6598806 #> [259,] 0.4608005 0.4680016 0.4993466 #> [260,] 0.5204578 0.5257055 0.5325700 #> [261,] 0.8619552 0.8927870 0.9265075 #> [262,] 0.9361687 0.9668368 0.9751466 #> [263,] 0.6995904 0.7329333 0.7573726 #> [264,] 0.7285425 0.7669078 0.7967487 #> [265,] 0.6361679 0.6626872 0.6931054 #> [266,] 0.8464808 0.9042451 0.9361598 #> [267,] 0.7958406 0.8504945 0.8575893 #> [268,] 0.9130299 0.9142488 0.9184519 #> [269,] 0.7023902 0.7036488 0.7190904 #> [270,] 1.0134838 1.0375951 1.0953411 #> [271,] 0.9033689 0.9500238 0.9779554 #> [272,] 0.7569841 0.7713455 0.7743237 #> [273,] 0.8281020 0.8404081 0.8997864 #> [274,] 0.6021311 0.6329169 0.6451058 #> [275,] 0.6116902 0.6584954 0.6982418 #> [276,] 0.6766993 0.6804542 0.6970547 #> [277,] 1.1671591 1.1940877 1.2013685 #> [278,] 0.5385578 0.5524752 0.5540552 #> [279,] 0.9806397 0.9903009 1.0124594 #> [280,] 0.7733261 0.7757703 0.7814405 #> [281,] 1.6651384 1.7565781 1.7573194 #> [282,] 0.6233547 0.6381087 0.6790567 #> [283,] 0.8320862 0.8486365 0.8659943 #> [284,] 0.6353006 0.6440505 0.6491302 #> [285,] 1.2464875 1.2563855 1.3767573 #> [286,] 0.9741064 0.9807108 1.0087750 #> [287,] 0.6533300 0.7024012 0.7046043 #> [288,] 0.9766857 1.0721776 1.0851837 #> [289,] 0.6582334 0.6715584 0.7489660 #> [290,] 0.5736358 0.6016753 0.6031855 #> [291,] 0.9047764 0.9416227 0.9437803 #> [292,] 0.6534406 0.6535046 0.6689339 #> [293,] 0.7839534 0.7906479 0.7915487 #> [294,] 0.6527325 0.7207140 0.7265732 #> [295,] 0.8515109 0.8821759 0.8829943 #> [296,] 1.0891309 1.1238643 1.1280101 #> [297,] 0.6817921 0.7024531 0.7885705 #> [298,] 0.7650493 0.7830500 0.7850708 #> [299,] 0.9253394 0.9618571 0.9675878 #> [300,] 0.8019273 0.8136202 0.8417530 #> [301,] 0.8982530 0.9216886 0.9237611 #> [302,] 0.6387119 0.6701486 0.6924840 #> [303,] 0.8194054 0.8245683 0.8276968 #> [304,] 0.8163358 0.8968895 0.9190604 #> [305,] 0.6911725 0.7333086 0.7364530 #> [306,] 0.6273922 0.6494594 0.6758489 #> [307,] 0.6020250 0.6032960 0.6141436 #> [308,] 1.2462559 1.2627624 1.2734046 #> [309,] 1.0961978 1.1436299 1.1709643 #> [310,] 0.6557014 0.6608235 0.6910342 #> [311,] 0.6901804 0.7042251 0.7121510 #> [312,] 0.7725283 0.8024589 0.8167136 #> [313,] 1.3093299 1.3269674 1.3276929 #> [314,] 0.7285425 0.7598048 0.7688160 #> [315,] 0.8906903 0.9014016 0.9082654 #> [316,] 0.6597708 0.6716182 0.7138331 #> [317,] 1.3444230 1.3969292 1.4297278 #> [318,] 1.0851945 1.1559070 1.1757867 #> [319,] 0.6329600 0.6380099 0.6387574 #> [320,] 0.5443672 0.5488831 0.5624516 #> [321,] 1.0981134 1.1148162 1.1424652 #> [322,] 0.6027873 0.6314889 0.6915544 #> [323,] 0.9562376 1.0873696 1.1439529 #> [324,] 1.1809562 1.1949260 1.2121301 #> [325,] 1.2758465 1.2764101 1.3314925 #> [326,] 0.8488415 0.8858925 0.9049453 #> [327,] 0.7816092 0.8003785 0.8061216 #> [328,] 1.5466154 1.6159929 1.6380888 #> [329,] 1.5863012 1.5911419 1.7342734 #> [330,] 0.9494797 0.9970640 1.0387814 #> [331,] 0.5663867 0.5977062 0.6327771 #> [332,] 0.7786057 0.7926984 0.8472351 #> [333,] 0.5780262 0.5830670 0.6025930 #> [334,] 0.8014663 0.8420080 0.8437626 #> [335,] 0.5649241 0.5889128 0.6072000 #> [336,] 0.8751113 0.8806370 0.9049909 #> [337,] 0.9169476 0.9258788 0.9786166 #> [338,] 1.0001497 1.1000918 1.1152972 #> [339,] 1.1983213 1.2084839 1.2476221 #> [340,] 0.8387845 0.8578511 0.8625806 #> [341,] 1.0503849 1.0581414 1.0981878 #> [342,] 0.5831863 0.5897049 0.6166476 #> [343,] 0.8909072 0.8952005 0.9274179 #> [344,] 0.9235493 0.9288125 0.9731136 #> [345,] 2.8094835 2.8886078 2.9410917 #> [346,] 0.5564435 0.5892740 0.6000514 #> [347,] 0.7551687 0.8334741 0.8581683 #> [348,] 0.7263683 0.7687818 0.7934345 #> [349,] 0.6638946 0.7025544 0.7051790 #> [350,] 1.6618546 1.6642624 1.7089094 #> [351,] 1.0794372 1.1290306 1.1451370 #> [352,] 0.6767724 0.7167063 0.7404281 #> [353,] 0.8472273 0.8487451 0.8598339 #> [354,] 0.9188042 0.9250145 0.9784457 #> [355,] 0.6427334 0.6523133 0.6591489 #> [356,] 0.5867750 0.6096996 0.6609022 #> [357,] 0.6504095 0.6693203 0.6915823 #> [358,] 1.0662185 1.0938059 1.1255470 #> [359,] 0.6849131 0.7253001 0.7567343 #> [360,] 0.8253925 0.8469022 0.8507445 #> [361,] 1.2972732 1.3129135 1.3208724 #> [362,] 0.5900256 0.5966234 0.6255330 #> [363,] 0.8098279 0.8405189 0.8419364 #> [364,] 0.8382100 0.8417069 0.8511844 #> [365,] 0.9920899 0.9990774 1.0466645 #> [366,] 0.5600479 0.6000714 0.6015735 #> [367,] 0.8771099 0.8793941 0.8808018 #> [368,] 1.1420320 1.2435833 1.2576645 #> [369,] 0.8582952 0.8599602 0.8648614 #> [370,] 0.6438789 0.6464029 0.6594544 #> [371,] 0.8132519 0.8348768 0.8718345 #> [372,] 0.8886290 0.8980959 0.9182436 #> [373,] 0.6728326 0.6859789 0.7359366 #> [374,] 0.7909004 0.8103030 0.8672868 #> [375,] 1.2857568 1.3381113 1.3387273 #> [376,] 0.7944559 0.8013535 0.8304747 #> [377,] 1.0194842 1.0607650 1.1032316 #> [378,] 0.7188675 0.8235059 0.8276617 #> [379,] 1.0211121 1.1530092 1.1705561 #> [380,] 0.9923883 1.0221610 1.0783806 #> [381,] 0.6207979 0.6427334 0.6639098 #> [382,] 1.2759019 1.2801700 1.3483353 #> [383,] 0.7151393 0.7507690 0.7514525 #> [384,] 0.7303102 0.7447334 0.7563054 #> [385,] 0.7729818 0.7752801 0.8192156 #> [386,] 0.5042462 0.5132578 0.5347960 #> [387,] 0.6685003 0.7032316 0.7095077 #> [388,] 1.2940043 1.3116532 1.3305883 #> [389,] 0.4817999 0.4878093 0.5410208 #> [390,] 0.7331032 0.7444346 0.7671677 #> [391,] 0.7537042 0.7746594 0.7845776 #> [392,] 0.9354164 0.9916784 1.0038588 #> [393,] 1.1592034 1.1789048 1.1881525 #> [394,] 0.6467058 0.6565775 0.6923005 #> [395,] 0.7310763 0.7552492 0.7594214 #> [396,] 0.8986675 0.9143464 0.9276981 #> [397,] 0.8486483 0.8508795 0.8601285 #> [398,] 0.9976411 1.0011695 1.0739424 #> [399,] 0.8745644 0.8782130 0.9043587 #> [400,] 0.9098650 1.0092030 1.0362576 #> [401,] 0.5597946 0.5927760 0.6031826 #> [402,] 0.7700191 0.7817334 0.7818776 #> [403,] 0.9737559 1.0240651 1.0344264 #> [404,] 0.5936955 0.5965183 0.6031826 #> [405,] 0.5359840 0.5426235 0.5494953 #> [406,] 0.4984314 0.5700570 0.5701457 #> [407,] 0.6150030 0.6689186 0.6761037 #> [408,] 1.2397482 1.3744765 1.4820356 #> [409,] 1.1517586 1.1595079 1.1607152 #> [410,] 0.8970618 0.9174193 0.9612071 #> [411,] 0.6916650 0.6981241 0.7041224 #> [412,] 0.6471527 0.6816065 0.6878781 #> [413,] 0.7327581 0.7406132 0.7452370 #> [414,] 0.6648266 0.6678141 0.6744941 #> [415,] 0.6697815 0.6791187 0.6953316 #> [416,] 0.8168176 0.8194116 0.8211489 #> [417,] 0.6078729 0.6152754 0.6421274 #> [418,] 1.7155196 1.7207718 1.7415727 #> [419,] 0.9481335 0.9612071 0.9820950 #> [420,] 0.6800200 0.6808957 0.6992552 #> [421,] 0.6046658 0.6209275 0.6402169 #> [422,] 0.6835824 0.7585124 0.7881878 #> [423,] 0.7491987 0.7522365 0.7601292 #> [424,] 0.5020218 0.5030537 0.5178701 #> [425,] 2.2932696 2.3723662 2.4223384 #> [426,] 0.7645542 0.8054434 0.8494835 #> [427,] 1.4016451 1.4382258 1.4433517 #> [428,] 0.8341936 0.8344704 0.8387845 #> [429,] 0.6107487 0.6735196 0.6762099 #> [430,] 0.7438620 0.7674429 0.7714117 #> [431,] 0.8219423 0.8234266 0.8463815 #> [432,] 0.7341805 0.7534588 0.7727361 #> [433,] 0.6377760 0.6462234 0.6638736 #> [434,] 0.9333763 0.9422343 0.9488802 #> [435,] 1.1427459 1.1572862 1.1624329 #> [436,] 0.7498497 0.7768013 0.7789587 #> [437,] 0.7549707 0.7669871 0.7987205 #> [438,] 1.0791655 1.0973817 1.1052959 #> [439,] 0.4825606 0.5023097 0.5102164 #> [440,] 0.7093803 0.7365695 0.8450230 #> [441,] 0.8186225 0.8575886 0.8617306 #> [442,] 1.1939267 1.2530121 1.2676821 #> [443,] 1.0690217 1.0938534 1.1098776 #> [444,] 0.7602211 0.7900737 0.8484472 #> [445,] 0.7305650 0.7666968 0.7783143 #> [446,] 0.7870018 0.8203685 0.8417069 #> [447,] 0.7270035 0.7565922 0.7755959 #> [448,] 0.8279811 0.8490338 0.8573882 #> [449,] 0.7283879 0.7563002 0.7800571 #> [450,] 0.7829672 0.7968122 0.8093141 #> [451,] 0.9289359 0.9913643 1.0041851 #> [452,] 0.8580262 0.8603759 0.8625607 #> [453,] 0.7599411 0.8054832 0.8322838 #> [454,] 0.5303881 0.5404534 0.5506574 #> [455,] 0.8750339 0.8754412 0.8896112 #> [456,] 0.9700862 0.9895414 1.0009251 #> [457,] 0.7884800 0.7985785 0.8309672 #> [458,] 0.7219261 0.7259888 0.7685295 #> [459,] 1.5169999 1.5208390 1.5669053 #> [460,] 0.9304993 1.0034544 1.0204791 #> [461,] 0.9252409 0.9413701 1.0006566 #> [462,] 0.7252742 0.7520877 0.7681750 #> [463,] 0.5873715 0.5940268 0.6009576 #> [464,] 1.4102500 1.4254980 1.4289465 #> [465,] 0.9724693 0.9892153 1.0053203 #> [466,] 0.6776049 0.6793633 0.7444877 #> [467,] 0.7234270 0.7373352 0.7395970 #> [468,] 0.7312486 0.7620866 0.7834036 #> [469,] 1.9449903 1.9997245 2.0342805 #> [470,] 1.8673973 1.9165614 1.9322301 #> [471,] 0.7849593 0.7867677 0.8164522 #> [472,] 0.8077282 0.8113149 0.8162749 #> [473,] 0.8092988 0.8227479 0.8581683 #> [474,] 0.7886554 0.8040224 0.8216809 #> [475,] 0.9568295 0.9668492 0.9772124 #> [476,] 0.7496162 0.7635732 0.7935786 #> [477,] 1.1293331 1.1469452 1.1471375 #> [478,] 0.6020250 0.6364563 0.6564804 #> [479,] 0.8828625 0.9178182 0.9357569 #> [480,] 0.8551747 0.8561162 0.8696071 #> [481,] 0.7271914 0.7889064 0.8276440 #> [482,] 0.9167345 0.9432508 0.9734263 #> [483,] 0.8877356 0.8877721 0.9464073 #> [484,] 0.6820772 0.6901804 0.7073480 #> [485,] 0.6032926 0.6355482 0.6519567 #> [486,] 0.7694448 0.7867677 0.7921361 #> [487,] 0.8669934 0.8987052 0.9039787 #> [488,] 0.5386993 0.5510784 0.5615143 #> [489,] 0.9526243 0.9738814 1.0211781 #> [490,] 0.5797474 0.5819609 0.5829153 #> [491,] 1.3827639 1.4192413 1.4736857 #> [492,] 0.6208781 0.6852571 0.7241467 #> [493,] 0.5825832 0.6224511 0.6276391 #> [494,] 1.4299967 1.4388615 1.4452256 #> [495,] 0.8014326 0.8063517 0.8796086 #> [496,] 1.0511263 1.0638448 1.0654817 #> [497,] 0.6331116 0.6969062 0.6987263 #> [498,] 0.5942840 0.6013444 0.6100400 #> [499,] 0.8186754 0.8388021 0.8446939 #> [500,] 1.0481851 1.1458741 1.1547490 #> [501,] 0.6127121 0.6392152 0.6698288 #> [502,] 0.7746160 0.8066735 0.8549282 #> [503,] 0.7550132 0.8247882 0.8276536 #> [504,] 0.7007891 0.7760447 0.7927290 #> [505,] 0.6207979 0.6369267 0.7119260 #> [506,] 0.8450073 0.8506358 0.8947993 #> [507,] 0.4527564 0.4929968 0.5031262 #> [508,] 0.8210360 0.8356232 0.8370369 #> [509,] 0.6196056 0.7501435 0.8137668 #> [510,] 0.6689186 0.6801213 0.7140120 #> [511,] 1.6786398 1.6801571 1.6809669 #> [512,] 1.0270393 1.0378446 1.0830380 #> [513,] 1.0686446 1.0745639 1.0884353 #> [514,] 1.2383352 1.3142746 1.3165361 #> [515,] 0.6923461 0.7222090 0.7349476 #> [516,] 1.6999924 1.7366141 1.7710182 #> [517,] 0.7227302 0.7333383 0.7438428 #> [518,] 0.6412565 0.6487788 0.6527705 #> [519,] 0.7007891 0.7166611 0.7211109 #> [520,] 1.0286506 1.1018465 1.1064026 #> [521,] 0.7539787 0.7646527 0.7749358 #> [522,] 0.6460914 0.6592550 0.6777096 #> [523,] 0.6011296 0.6099995 0.6267533 #> [524,] 0.7527483 0.7889473 0.7951141 #> [525,] 1.4603744 1.6534632 1.6554812 #> [526,] 0.6993946 0.7225583 0.7272496 #> [527,] 1.4180602 1.4306645 1.4584196 #> [528,] 0.8336929 0.8786696 0.8990421 #> [529,] 0.8049686 0.9500680 0.9775793 #> [530,] 1.5558516 1.5605163 1.6140382 #> [531,] 0.5470348 0.5673608 0.5755022 #> [532,] 0.7040888 0.7044367 0.7181985 #> [533,] 0.5600479 0.5693824 0.5705460 #> [534,] 0.7481863 0.7556583 0.7578103 #> [535,] 1.6764237 1.6891714 1.6925196 #> [536,] 0.6892268 0.7104893 0.7386475 #> [537,] 0.6605804 0.6822545 0.6859765 #> [538,] 0.6724188 0.6760600 0.6805757 #> [539,] 0.9236578 0.9779554 0.9825563 #> [540,] 1.5450570 1.5982529 1.6469659 #> [541,] 0.7671446 0.7686223 0.7913083 #> [542,] 0.7749350 0.7760432 0.7970975 #> [543,] 0.9972164 1.0454226 1.0554706 #> [544,] 0.9734595 0.9762631 0.9798293 #> [545,] 1.2253034 1.2347755 1.2356855 #> [546,] 0.5308653 0.5433494 0.5648950 #> [547,] 0.6845853 0.6870117 0.7195420 #> [548,] 0.6836239 0.7093803 0.7112369 #> [549,] 0.6624672 0.7044367 0.7271320 #> [550,] 0.5930796 0.6009576 0.6332259 #> [551,] 1.0512387 1.0828803 1.0834900 #> [552,] 1.0671486 1.0812531 1.0817206 #> [553,] 1.0822813 1.0942597 1.1059081 #> [554,] 0.4990195 0.5265139 0.5447722 #> [555,] 0.6851350 0.6876713 0.6926234 #> [556,] 0.6995904 0.7367393 0.7459699 #> [557,] 1.0643377 1.0757091 1.0772153 #> [558,] 0.5876273 0.5937526 0.6021311 #> [559,] 0.6316512 0.6322469 0.6470051 #> [560,] 0.8883228 0.9206066 0.9323522 #> [561,] 0.6731440 0.6860345 0.6885872 #> [562,] 0.6638946 0.7372376 0.8081303 #> [563,] 0.8289467 0.8349365 0.8653335 #> [564,] 0.7583382 0.7673915 0.8239604 #> [565,] 0.7240913 0.7753555 0.7841834 #> [566,] 0.5998774 0.6128027 0.6591489 #> [567,] 1.6908154 1.7030627 1.7225823 #> [568,] 0.7425193 0.7489660 0.7500161 #> [569,] 0.7170007 0.8089017 0.8122410 #> [570,] 0.6774092 0.6799657 0.7025544 #> [571,] 0.4820388 0.5136056 0.5213306 #> [572,] 0.5566345 0.5922417 0.5935245 #> [573,] 0.7110430 0.7135245 0.7384764 #> [574,] 0.8450073 0.8713145 0.8943658 #> [575,] 0.5573569 0.5840487 0.6198596 #> [576,] 0.5361367 0.5540552 0.5557784 #> [577,] 0.5574661 0.5752938 0.5902023 #> [578,] 0.7813525 0.8041972 0.8239604 #> [579,] 0.7566974 0.8184313 0.8656965 #> [580,] 1.1771946 1.1826661 1.1970214 #> [581,] 1.0231257 1.1010580 1.1155543 #> [582,] 0.9441800 1.0828442 1.1472337 #> [583,] 0.5396463 0.5420004 0.5607260 #> [584,] 1.2029668 1.2605534 1.3054052 #> [585,] 0.6278613 0.6442174 0.6732055 #> [586,] 0.8234069 0.8244759 0.8432501 #> [587,] 0.6341530 0.6353158 0.6772174 #> [588,] 0.7814405 0.8528844 0.8812575 #> [589,] 0.6008158 0.6372487 0.6381665 #> [590,] 0.8535271 0.8584035 0.8604812 #> [591,] 0.6475480 0.6583511 0.6692462 #> [592,] 0.6862504 0.7064858 0.7096041 #> [593,] 0.7141790 0.7540948 0.7743470 #> [594,] 0.6790748 0.7063940 0.7134847 #> [595,] 0.8243222 0.8426470 0.8441713 #> [596,] 0.4841514 0.5657912 0.5755022 #> [597,] 0.7452370 0.7575772 0.7952843 #> [598,] 1.4963948 1.5203882 1.6024957 #> [599,] 1.1383098 1.1702267 1.2238629 #> [600,] 1.0364890 1.0783860 1.0881871 #> [601,] 0.9747691 0.9784879 0.9891670 #> [602,] 0.6325404 0.6790821 0.7024531 #> [603,] 0.6043368 0.6424482 0.6583511 #> [604,] 0.5864847 0.6016442 0.6022500 #> [605,] 0.5472789 0.5627529 0.6346750 #> [606,] 1.0111774 1.0255424 1.0621965 #> [607,] 0.8565637 0.8674535 0.8724976 #> [608,] 1.0922215 1.1643951 1.1807064 #> [609,] 0.9794582 1.0258198 1.0537422 #> [610,] 1.8744437 1.8869722 1.9029999 #> [611,] 0.9482153 1.0703472 1.0738687 #> [612,] 0.5826571 0.6483689 0.7058580 #> [613,] 0.7554312 0.7697521 0.7769445 #> [614,] 0.7722574 0.7785427 0.7852635 #> [615,] 0.7480086 0.8008770 0.8052328 #> [616,] 0.4987497 0.5915236 0.6025436 #> [617,] 1.1530540 1.1624329 1.1696436 #> [618,] 0.6675875 0.6696642 0.6964581 #> [619,] 1.9258524 1.9589473 1.9616130 #> [620,] 0.7694448 0.7945205 0.8112945 #> [621,] 0.6608919 0.7044144 0.7183474 #> [622,] 0.8357592 0.8851545 0.9266689 #> [623,] 1.4453892 1.5597602 1.5799055 #> [624,] 0.6013444 0.6160422 0.6277272 #> [625,] 0.6735509 0.7061602 0.7103009 #> [626,] 0.7594411 0.7819699 0.8162749 #> [627,] 0.6639936 0.6704440 0.7070445 #> [628,] 1.4792002 1.4858616 1.5553256 #> [629,] 0.6801713 0.6803374 0.6807494 #> [630,] 0.7838042 0.8330158 0.8551235 #> [631,] 0.5658423 0.5659957 0.5706138 #> [632,] 0.8275516 0.8398715 0.8894340 #> [633,] 1.8945030 1.9278525 1.9424763 #> [634,] 0.7367904 0.7715897 0.7772676 #> [635,] 0.5245977 0.5419030 0.5470238 #> [636,] 0.7606916 0.7766755 0.7873526 #> [637,] 0.8667406 0.9232460 0.9668317 #> [638,] 0.7093980 0.7179996 0.8043226 #> [639,] 0.7107465 0.7365392 0.7509218 #> [640,] 0.6474838 0.6752632 0.7081496 #> [641,] 0.7407793 0.7601183 0.7669871 #> [642,] 0.5757339 0.5988755 0.6056370 #> [643,] 0.8543398 0.9490901 0.9616271 #> [644,] 0.7037034 0.7089464 0.7225416 #> [645,] 1.2614159 1.3394622 1.4055323 #> [646,] 1.0875151 1.1115924 1.1613842 #> [647,] 1.2023717 1.2228821 1.2469979 #> [648,] 0.5538165 0.5571109 0.5848674 #> [649,] 0.6731897 0.6863041 0.7436029 #> [650,] 1.0723583 1.0829244 1.0888367 #> [651,] 0.5360911 0.5439281 0.5514637 #> [652,] 0.9823087 1.0061961 1.0161166 #> [653,] 0.9615576 0.9772413 1.0220572 #> [654,] 0.8567612 0.8835575 0.9073879 #> [655,] 0.8739169 0.9189904 0.9469421 #> [656,] 0.6440738 0.6553880 0.6621010 #> [657,] 0.6948833 0.7239879 0.7303132 #> [658,] 2.0539740 2.0902739 2.1038077 #> [659,] 0.9031826 0.9270452 0.9469395 #> [660,] 0.8741287 0.8839707 0.9087493 #> [661,] 0.8132943 0.8224501 0.8247882 #> [662,] 0.8186754 0.8330141 0.8343830 #> [663,] 0.9810485 0.9927890 1.0106749 #> [664,] 0.7029838 0.7151743 0.7163519 #> [665,] 0.6817316 0.7015655 0.7115043 #> [666,] 0.8458111 0.9004967 0.9803732 #> [667,] 0.7187057 0.7246572 0.7471467 #> [668,] 0.7927652 0.7950445 0.8107627 #> [669,] 0.6911190 0.7413602 0.8334453 #> [670,] 1.1509168 1.1776238 1.1924359 #> [671,] 0.5475349 0.5505027 0.5639221 #> [672,] 0.7212985 0.7231728 0.7255583 #> [673,] 0.6546189 0.6678590 0.6745093 #> [674,] 0.6236229 0.6316512 0.6448085 #> [675,] 0.6976790 0.6988070 0.7022311 #> [676,] 0.8647881 0.8881306 0.9042629 #> [677,] 0.8584035 0.9732872 0.9841871 #> [678,] 0.6459826 0.7084309 0.7096870 #> [679,] 0.6269285 0.6510310 0.7190904 #> [680,] 0.9532322 0.9611154 0.9822419 #> [681,] 0.9136462 0.9197724 0.9385553 #> [682,] 0.6151094 0.6664986 0.6852734 #> [683,] 0.6181234 0.6336101 0.6550931 #> [684,] 0.8281020 0.8915035 0.9109314 #> [685,] 0.7855720 0.8112534 0.8486313 #> [686,] 0.7968281 0.7993908 0.8096249 #> [687,] 0.8684122 0.8933293 0.9039735 #> [688,] 0.6518934 0.6784733 0.7134594 #> [689,] 0.9237826 0.9400302 0.9471953 #> [690,] 0.8141279 0.8358715 0.8369607 #> [691,] 0.5529875 0.5601840 0.5644245 #> [692,] 0.5517974 0.5693161 0.5873525 #> [693,] 1.0859972 1.1167549 1.1855703 #> [694,] 0.6327384 0.6403465 0.6522010 #> [695,] 0.5369063 0.5844722 0.6025021 #> [696,] 0.9683329 0.9684100 1.0204332 #> [697,] 0.8111384 0.8424851 0.8542503 #> [698,] 1.1814480 1.2263634 1.2384766 #> [699,] 0.7927652 0.8077602 0.8295842 #> [700,] 1.2702424 1.2908346 1.3338531 #> [701,] 0.8291931 0.8403914 0.8738022 #> [702,] 0.6434446 0.6671026 0.6860087 #> [703,] 0.7074719 0.7149534 0.7375093 #> [704,] 0.8169586 0.8272137 0.8283524 #> [705,] 1.2313260 1.2356855 1.2885794 #> [706,] 0.8154469 0.8226472 0.8487451 #> [707,] 0.4983350 0.5262098 0.5327186 #> [708,] 1.0624992 1.0715707 1.1079968 #> [709,] 0.6789879 0.7242251 0.7460334 #> [710,] 0.7968163 0.8044401 0.8081303 #> [711,] 0.8103030 0.8407096 0.8612372 #> [712,] 0.6796196 0.7025482 0.7212138 #> [713,] 0.7080254 0.7608227 0.8142425 #> [714,] 1.0440521 1.0512421 1.0767011 #> [715,] 0.8151947 0.8285825 0.8582232 #> [716,] 0.6284367 0.7223476 0.7408906 #> [717,] 0.7046757 0.7136841 0.7604333 #> [718,] 0.8838055 0.9219829 0.9919327 #> [719,] 0.6121718 0.6380099 0.6388043 #> [720,] 0.5953103 0.6349862 0.6359090 #> [721,] 1.8820315 1.9001787 1.9088798 #> [722,] 0.9795572 0.9823719 1.0485794 #> [723,] 0.7339828 0.7342629 0.7421833 #> [724,] 0.7866264 0.8289504 0.8772428 #> [725,] 0.7079180 0.7419142 0.7869214 #> [726,] 0.8201243 0.8342268 0.8486467 #> [727,] 0.6288299 0.6441286 0.6508259 #> [728,] 0.7837983 0.8149223 0.8295842 #> [729,] 1.1908658 1.2187422 1.2412926 #> [730,] 0.6217570 0.6510310 0.6630560 #> [731,] 0.9160105 0.9928598 1.1118426 #> [732,] 1.4420573 1.4974156 1.5126267 #> [733,] 1.7203979 1.7855192 1.8151633 #> [734,] 0.4929968 0.5297014 0.5530272 #> [735,] 0.5965755 0.6003747 0.6095929 #> [736,] 0.6981032 0.7104502 0.7132787 #> [737,] 1.0985167 1.1041007 1.1451672 #> [738,] 0.9223074 1.0153096 1.0387814 #> [739,] 0.7026276 0.7146490 0.7245649 #> [740,] 0.7207847 0.7329248 0.7348654 #> [741,] 0.6898966 0.7134847 0.7154010 #> [742,] 0.6608039 0.7080254 0.7310347 #> [743,] 0.9439670 1.0013745 1.1012426 #> [744,] 1.3015360 1.3296636 1.3845750 #> [745,] 0.6325404 0.6648266 0.6837852 #> [746,] 0.8505685 0.8759186 0.8890348 #> [747,] 0.7840299 0.8194621 0.8238080 #> [748,] 0.7545404 0.7563807 0.8210554 #> [749,] 1.6518706 1.6901572 1.7073382 #> [750,] 1.1184452 1.1456799 1.1546014 #> [751,] 0.9202416 0.9760870 0.9904668 #> [752,] 0.7601292 0.7789707 0.7888291 #> [753,] 1.8041411 1.8399981 1.9228155 #> [754,] 0.5117631 0.5189561 0.5379454 #> [755,] 0.9188856 0.9264988 0.9320816 #> [756,] 0.6017029 0.6238293 0.6410903 #> [757,] 0.7847037 0.7996425 0.8101178 #> [758,] 0.6032702 0.6093081 0.6393244 #> [759,] 0.6645667 0.6992010 0.7103009 #> [760,] 0.6381541 0.7110158 0.7136425 #> [761,] 0.6806684 0.6858037 0.8278033 #> [762,] 0.8708349 0.9128123 0.9241553 #> [763,] 0.6832413 0.6860087 0.7341805 #> [764,] 0.6157924 0.6343072 0.6558207 #> [765,] 1.0118846 1.0134499 1.0171610 #> [766,] 0.5975777 0.6000514 0.6011296 #> [767,] 0.4531303 0.4674339 0.4860429 #> [768,] 0.7399173 0.7863414 0.7904919 #> [769,] 0.9369696 0.9517156 0.9656858 #> [770,] 1.4471992 1.4562042 1.4820356 #> [771,] 1.4930419 1.5148422 1.5548330 #> [772,] 0.7373352 0.7443812 0.7529223 #> [773,] 1.3959402 1.4042072 1.4333654 #> [774,] 0.7428682 0.7751488 0.7923230 #> [775,] 1.6821274 1.7425683 1.8219406 #> [776,] 0.7952674 0.8616408 0.8749683 #> [777,] 0.7886363 0.7969842 0.8028407 #> [778,] 0.7563054 0.7619809 0.7651778 #> [779,] 0.6701998 0.6882292 0.7097852 #> [780,] 0.5524353 0.5626330 0.6274188 #> [781,] 0.6867477 0.6981032 0.7169447 #> [782,] 0.5279354 0.5476963 0.5488831 #> [783,] 1.0564007 1.0920713 1.1147534 #> [784,] 0.8546296 0.9293673 0.9503139 #> [785,] 1.2862190 1.3487894 1.3838227 #> [786,] 0.8884697 0.8903167 0.9072002 #> [787,] 0.5993060 0.6125669 0.6208781 #> [788,] 1.0283774 1.0756508 1.0760060 #> [789,] 0.9727410 0.9951119 0.9999906 #> [790,] 0.6117980 0.6456994 0.6477592 #> [791,] 0.9312645 0.9560108 0.9569102 #> [792,] 1.0465306 1.0909766 1.0914729 #> [793,] 0.9404725 0.9429739 0.9568295 #> [794,] 0.6590509 0.6658791 0.6763955 #> [795,] 1.0802310 1.0822813 1.1103753 #> [796,] 0.7007486 0.7014731 0.7070516 #> [797,] 1.0239352 1.0372344 1.0721517 #> [798,] 0.6339919 0.6431744 0.7286949 #> [799,] 1.0520687 1.0530698 1.1656796 #> [800,] 0.7208518 0.7217458 0.7246355 #> [801,] 0.5717532 0.5975777 0.6159771 #> [802,] 0.9789055 1.0506605 1.1064540 #> [803,] 0.6408472 0.7736341 0.8250772 #> [804,] 0.8274985 0.8491893 0.8821759 #> [805,] 0.5870801 0.5927017 0.6470051 #> [806,] 1.9180341 1.9459520 2.0220995 #> [807,] 1.1019302 1.1048826 1.1452958 #> [808,] 0.7080196 0.7107465 0.7237772 #> [809,] 0.7306420 0.7346647 0.7392183 #> [810,] 0.9619491 0.9759745 0.9819215 #> [811,] 0.7134353 0.7410821 0.7671390 #> [812,] 0.4397050 0.5120447 0.5158111 #> [813,] 0.5866931 0.6096429 0.6152754 #> [814,] 0.7999641 0.8117286 0.8357712 #> [815,] 0.8746795 0.9115486 0.9335750 #> [816,] 0.7146225 0.8258685 0.8431029 #> [817,] 1.0041419 1.0242532 1.0347041 #> [818,] 0.6384520 0.6638735 0.7109461 #> [819,] 0.7700035 0.7865491 0.8122098 #> [820,] 1.0418785 1.0676253 1.1292646 #> [821,] 0.7532504 0.7747172 0.8473076 #> [822,] 0.9227195 0.9487566 0.9581983 #> [823,] 0.5875314 0.5942840 0.5965755 #> [824,] 1.2553628 1.2694406 1.3343760 #> [825,] 1.2048136 1.3095050 1.3428537 #> [826,] 0.9132950 0.9771254 1.0106245 #> [827,] 0.5661952 0.5750395 0.5822294 #> [828,] 0.6576110 0.6597499 0.6673354 #> [829,] 0.9136389 0.9287249 0.9310701 #> [830,] 0.8903167 0.9201335 0.9323082 #> [831,] 0.6718409 0.6869043 0.6991622 #> [832,] 0.6859404 0.7035090 0.7048176 #> [833,] 0.7420225 0.7588907 0.7750704 #> [834,] 1.0100578 1.0138136 1.0163078 #> [835,] 1.8535687 1.9005632 1.9730804 #> [836,] 0.6506995 0.6590509 0.6599847 #> [837,] 0.9098862 0.9364781 0.9552696 #> [838,] 0.6858191 0.6927322 0.7062499 #> [839,] 1.0220518 1.0238183 1.0621965 #> [840,] 0.5401356 0.5557784 0.5687479 #> [841,] 0.6776225 0.6874784 0.6990156 #> [842,] 1.0768269 1.1514249 1.1546014 #> [843,] 0.6942737 0.7040666 0.7097852 #> [844,] 1.0846216 1.1308978 1.1327691 #> [845,] 1.2357066 1.2465444 1.2791774 #> [846,] 0.6924840 0.6987541 0.7768013 #> [847,] 0.5996629 0.6371110 0.6737094 #> [848,] 0.5578011 0.5624516 0.5842370 #> [849,] 0.9665835 0.9976055 1.0352665 #> [850,] 0.7457151 0.7990490 0.8223862 #> [851,] 0.8717785 0.9060917 0.9801779 #> [852,] 0.7719812 0.7912119 0.7975241 #> [853,] 0.9266390 0.9300174 0.9645604 #> [854,] 0.6398836 0.6412565 0.6775025 #> [855,] 0.7485899 0.7781986 0.8268827 #> [856,] 0.9786673 1.0056389 1.0444198 #> [857,] 0.6893189 0.6929399 0.7584337 #> [858,] 0.5513524 0.5553564 0.5569566 #> [859,] 0.8832566 0.9021617 0.9851522 #> [860,] 0.5454917 0.5477646 0.5658402 #> [861,] 1.1027024 1.1274677 1.1693164 #> [862,] 0.7058580 0.8086243 0.8343830 #> [863,] 0.5877672 0.5956524 0.5991496 #> [864,] 1.2654594 1.3005064 1.3382089 #> [865,] 1.1711535 1.1903278 1.2141117 #> [866,] 0.7920784 0.7935786 0.8320460 #> [867,] 1.0431132 1.0607671 1.0757144 #> [868,] 1.4160604 1.4465604 1.4920582 #> [869,] 0.5573569 0.5675909 0.5761090 #> [870,] 0.6336309 0.6358192 0.6776049 #> [871,] 0.8684122 0.8849601 0.8927541 #> [872,] 1.1902981 1.2012097 1.2430482 #> [873,] 1.2633157 1.2769944 1.2891117 #> [874,] 1.1157276 1.1936167 1.1995324 #> [875,] 0.8294768 0.8344419 0.8362827 #> [876,] 0.5117953 0.5192327 0.5420004 #> [877,] 0.8030448 0.8216809 0.8369158 #> [878,] 0.5102163 0.5178139 0.6454089 #> [879,] 0.6869348 0.6993587 0.7524557 #> [880,] 0.5584951 0.5687479 0.5827281 #> [881,] 0.4919623 0.4922360 0.5161984 #> [882,] 1.1207087 1.1468858 1.1637005 #> [883,] 1.4398054 1.4933724 1.5107034 #> [884,] 0.9321315 0.9481810 0.9830945 #> [885,] 0.7053919 0.7086441 0.7171960 #> [886,] 0.8671570 0.8687603 0.8753464 #> [887,] 0.6857948 0.7112517 0.7573017 #> [888,] 0.8283524 0.8703998 0.9551885 #> [889,] 0.7289802 0.8437187 0.8561901 #> [890,] 0.6797562 0.6992552 0.6998319 #> [891,] 0.6658791 0.6701998 0.6801707 #> [892,] 1.3072786 1.3114265 1.3533247 #> [893,] 0.8628560 0.9132747 0.9165879 #> [894,] 1.5536419 1.5676981 1.5715458 #> [895,] 1.1455775 1.1833642 1.2430949 #> [896,] 1.3007429 1.3456632 1.3779056 #> [897,] 0.5183205 0.5743258 0.6032926 #> [898,] 1.3950150 1.4845992 1.5350760 #> [899,] 0.5721419 0.5732198 0.6285374 #> [900,] 1.0721517 1.1149484 1.1474075 #> [901,] 0.5700570 0.5922966 0.6117980 #> [902,] 0.5699897 0.5910700 0.6163551 #> [903,] 0.5701457 0.5879070 0.5888147 #> [904,] 0.6237141 0.6314061 0.6545029 #> [905,] 0.8616950 0.8821900 0.8904956 #> [906,] 0.8366904 0.8441713 0.8561901 #> [907,] 0.4796330 0.5170806 0.5385851 #> [908,] 0.9966891 1.0304489 1.0453836 #> [909,] 0.6767015 0.7246652 0.7910346 #> [910,] 1.0512462 1.0634887 1.1017909 #> [911,] 0.7690241 0.7835180 0.7842613 #> [912,] 0.6977815 0.7157679 0.7306703 #> [913,] 0.6552209 0.6862504 0.7353109 #> [914,] 0.5776104 0.6021255 0.7304606 #> [915,] 0.7863488 0.8475656 0.8720974 #> [916,] 1.0116410 1.0413067 1.0591309 #> [917,] 0.5229253 0.5461736 0.5666734 #> [918,] 0.7958120 0.8136202 0.8537410 #> [919,] 0.7987205 0.8064646 0.8194054 #> [920,] 0.8142671 0.8244194 0.8358189 #> [921,] 0.6044993 0.6127121 0.6399026 #> [922,] 0.8370144 0.8698289 0.9121935 #> [923,] 0.9604526 0.9776841 1.0087750 #> [924,] 0.6765533 0.7119572 0.7163205 #> [925,] 0.7317220 0.7530499 0.7623217 #> [926,] 0.8837417 0.8855182 0.9463822 #> [927,] 0.7803086 0.8170575 0.8442191 #> [928,] 0.7599411 0.7889473 0.7904919 #> [929,] 1.1081530 1.2327271 1.2911965 #> [930,] 0.7506026 0.8026476 0.8214476 #> [931,] 0.7451284 0.7653108 0.7774373 #> [932,] 0.6848409 0.7093980 0.7568239 #> [933,] 0.6515893 0.6556246 0.6686640 #> [934,] 0.6678590 0.6910904 0.7055392 #> [935,] 0.5962874 0.6394549 0.6976758 #> [936,] 0.7912512 0.8118788 0.8307374 #> [937,] 1.1736185 1.2208004 1.2446415 #> [938,] 0.7667661 0.7940193 0.8452377 #> [939,] 0.6827288 0.7092455 0.7464431 #> [940,] 0.8604242 0.8726374 0.8772334 #> [941,] 0.8093763 0.8289961 0.8344334 #> [942,] 0.6301110 0.6482104 0.6788163 #> [943,] 0.5850961 0.6228291 0.6632456 #> [944,] 1.0421754 1.0446976 1.0731588 #> [945,] 0.7180052 0.7315810 0.7349476 #> [946,] 0.8506424 0.8683091 0.9293002 #> [947,] 1.0121738 1.0359470 1.0360698 #> [948,] 0.5552995 0.5578163 0.5626172 #> [949,] 0.8668694 0.8669934 0.8724862 #> [950,] 0.7558192 0.7727489 0.7882429 #> [951,] 2.2518509 2.2876735 2.3032924 #> [952,] 1.3832696 1.4011508 1.4280204 #> [953,] 0.5844173 0.6092434 0.6248032 #> [954,] 0.8162019 0.8250067 0.8313569 #> [955,] 0.5101946 0.5432098 0.5471188 #> [956,] 0.5132561 0.5241539 0.5257055 #> [957,] 1.0073150 1.0311136 1.0359089 #> [958,] 0.5265139 0.5461736 0.5519625 #> [959,] 0.6008707 0.6280089 0.6345307 #> [960,] 0.6509555 0.7058413 0.7246035 #> [961,] 2.1073527 2.1123186 2.1230725 #> [962,] 0.8408857 0.8688455 0.8930393 #> [963,] 0.5797989 0.5860902 0.5995586 #> [964,] 1.0524327 1.0525541 1.0626364 #> [965,] 0.6757378 0.6832628 0.7136841 #> [966,] 0.5765295 0.5861157 0.5992267 #> [967,] 0.6782984 0.6786018 0.6802397 #> [968,] 0.6391293 0.6621010 0.6837852 #> [969,] 0.7136425 0.7223476 0.7272496 #> [970,] 0.5855896 0.5870801 0.5888147 #> [971,] 0.5590485 0.5654247 0.5661557 #> [972,] 0.9690550 0.9994436 1.0083670 #> [973,] 0.7691601 0.7728200 0.7825482 #> [974,] 0.4869863 0.5158111 0.5624893 #> [975,] 0.6556246 0.6693918 0.6756131 #> [976,] 1.0606498 1.0695690 1.0897265 #> [977,] 0.8508795 0.8643554 0.9045820 #> [978,] 0.5277032 0.5386993 0.5486410 #> [979,] 0.5850961 0.5925284 0.5991496 #> [980,] 0.6278819 0.6315640 0.6414373 #> [981,] 0.5928978 0.6146377 0.6159974 #> [982,] 0.9526243 0.9714016 1.0005670 #> [983,] 0.7373603 0.7435158 0.7604729 #> [984,] 1.1850158 1.2176165 1.2439219 #> [985,] 0.7098652 0.7231843 0.7240913 #> [986,] 0.7024012 0.7333383 0.7539410 #> [987,] 1.8988976 1.9063222 1.9955230 #> [988,] 0.8699772 0.9439670 0.9859146 #> [989,] 0.7045640 0.8301865 0.8381168 #> [990,] 0.6898966 0.6935705 0.7925446 #> [991,] 0.7808682 0.8238527 0.8348792 #> [992,] 0.9184891 0.9223074 0.9293687 #> [993,] 0.7294261 0.8360314 0.8809900 #> [994,] 0.6341530 0.6459071 0.6519416 #> [995,] 0.6254579 0.6277272 0.6626872 #> [996,] 0.5238755 0.5315577 0.5379967 #> [997,] 1.3163073 1.3237790 1.3668184 #> [998,] 0.6553880 0.6724235 0.7223482 #> [999,] 0.6011789 0.6055215 0.6515013 #> [1000,] 0.5779394 0.5873525 0.5902023 #>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"Compute log-rank test p-value difference two survival curves obtained splitting dataset \"low\" \"high\" risk group using possible relative-risk thresholds.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"tof_find_log_rank_threshold(input_data, relative_risk_col, time_col, event_col)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"input_data tbl_df data.frame observation row. relative_risk_col unquote column name indicating column contains relative-risk estimates observation. time_col unquoted column name indicating column contains true time--event information observation. event_col unquoted column name indicating column contains outcome (event censorship). Must binary column - values either 0 1 (1 indicating adverse event 0 indicating censorship) FALSE TRUE (TRUE indicating adverse event FALSE indicating censorship).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_log_rank_threshold.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"tibble 3 columns: \"candidate_thresholds\" (relative-risk threshold used log-rank test), \"log_rank_p_val\" (p-values log-rank tests) \"is_best\" (logical value indicating candidate threshold gave optimal, .e. smallest, p-value).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":null,"dir":"Reference","previous_headings":"","what":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"Using character vectors obtained `name` `desc` columns parameters data flowFrame, figure high-dimensional cytometry panel used collect data return tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"","code":"tof_find_panel_info(input_flowFrame)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"input_flowFrame raw flowFrame (just read .fcs file) high-dimensional cytometry panel extracted","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_find_panel_info.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use tidytof's opinionated heuristic for extracted a high-dimensional cytometry panel's metal-antigen pairs\nfrom a flowFrame (read from a .fcs file.) — tof_find_panel_info","text":"tibble 2 columns (`metals` `antigens`) correspond metals antigens high-dimensional cytometry panel used data acquisition.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":null,"dir":"Reference","previous_headings":"","what":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"function trains glmnet model training set rsplit object, calculates performance metrics model validation/holdout set combinations mixture penalty hyperparameters provided hyperparameter grid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"","code":"tof_fit_split( split_data, prepped_recipe, hyperparameter_grid, model_type, outcome_colnames )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"split_data `rsplit` object rsample package. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe trained recipe hyperparameter_grid tibble containing hyperparameter values tune. Can created using tof_create_grid model_type string representing type glmnet model fit. outcome_colnames Quoted column names indicating columns data fit represent outcome variables (others assumed predictors).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"tibble number rows input hyperparameter grid. row represents combination mixture penalty, column contains performance metric fitted glmnet model `split_data`'s holdout set. specific performance metrics depend type model fit: \"linear\" mean-squared error (`mse`) mean absolute error (`mae`) \"two-class\" binomial deviance (`binomial_deviance`); misclassification error rate `misclassification_error`; area receiver-operating curve (`roc_auc`); `mse` `mse` \"multiclass\" multinomial deviance (`multinomial_deviance`); misclassification error rate `misclassification_error`; area receiver-operating curve (`roc_auc`) computed using Hand-Till method roc_auc; `mse` `mse` \"survival\" negative log2-transformed partial likelihood (`neg_log_partial_likelihood`) Harrel's concordance index (often simply called \"C\"; `concordance_index`)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_fit_split.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Fit a glmnet model and calculate performance metrics using a single rsplit object — tof_fit_split","text":"Harrel Jr, F. E. Lee, K. L. Mark, D. B. (1996) Tutorial biostatistics: multivariable prognostic models: issues developing models, evaluating assumptions adequacy, measuring reducing error, Statistics Medicine, 15, pages 361–387.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":null,"dir":"Reference","previous_headings":"","what":"Generate a color palette using tidytof. — tof_generate_palette","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"function generates color palette based color palette author's favorite pokemon.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"","code":"tof_generate_palette(num_colors)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"num_colors integer specifying number colors like generate.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"character vector hex codes specifying colors palette.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_generate_palette.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Generate a color palette using tidytof. — tof_generate_palette","text":"","code":"tof_generate_palette(num_colors = 5L) #> [1] \"#D86020\" \"#28A8B8\" \"#F89040\" \"#D0D0D0\" \"#903000\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"Get `tof_model`'s optimal mixture (alpha) value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"","code":"tof_get_model_mixture(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"numeric value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_mixture.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s optimal mixture (alpha) value — tof_get_model_mixture","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_mixture(regression_model) #> [1] 1"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"Get `tof_model`'s outcome variable name(s)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"","code":"tof_get_model_outcomes(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"character vector","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_outcomes.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s outcome variable name(s) — tof_get_model_outcomes","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_outcomes(regression_model) #> [1] \"outcome\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"Get `tof_model`'s optimal penalty (lambda) value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"","code":"tof_get_model_penalty(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"numeric value","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_penalty.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s optimal penalty (lambda) value — tof_get_model_penalty","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_penalty(regression_model) #> [1] 0.003162278"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s training data — tof_get_model_training_data","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"Get `tof_model`'s training data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"","code":"tof_get_model_training_data(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"tibble (non-preprocessed) training data used fit model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_training_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s training data — tof_get_model_training_data","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_training_data(regression_model) #> # A tibble: 100 × 9 #> sample cd45 pstat5 cd34 outcome class multiclass event time_to_event #> #> 1 28 0.691 0.785 0.439 5.97 class1 class1 0 13.3 #> 2 55 0.857 0.819 0.811 6.65 class1 class2 1 11.9 #> 3 16 0.695 0.106 0.627 1.49 class2 class1 0 7.10 #> 4 11 0.0496 0.115 0.372 0.193 class2 class1 0 10.8 #> 5 67 0.112 0.227 0.784 2.74 class2 class3 1 9.07 #> 6 23 0.355 0.796 0.815 4.99 class1 class1 0 9.64 #> 7 89 0.407 0.191 0.282 2.69 class2 class3 1 12.0 #> 8 21 0.0873 0.348 0.944 2.32 class2 class1 0 16.2 #> 9 26 0.672 0.420 0.183 2.89 class2 class1 0 10.8 #> 10 20 0.708 0.504 0.430 3.69 class1 class1 0 10.6 #> # ℹ 90 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s model type — tof_get_model_type","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"Get `tof_model`'s model type","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"","code":"tof_get_model_type(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"string","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_type.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s model type — tof_get_model_type","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_type(regression_model) #> [1] \"linear\""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"Get `tof_model`'s processed predictor matrix (glmnet)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"","code":"tof_get_model_x(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"x value formatted glmnet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_x.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s processed predictor matrix (for glmnet) — tof_get_model_x","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_x(regression_model) #> cd45 pstat5 cd34 #> [1,] 0.59528468 1.44049135 -0.83714724 #> [2,] 1.54466688 -0.22824576 1.06582350 #> [3,] -0.12522254 -1.08701937 1.13310841 #> [4,] -0.76518793 -0.15699067 0.77008930 #> [5,] 1.12500430 -0.59512774 1.49386093 #> [6,] 0.62293193 0.42242236 -1.15306393 #> [7,] 0.95787530 0.69707908 -1.53772848 #> [8,] -1.51437535 -1.26969989 0.71432329 #> [9,] 0.71836259 -0.16856184 1.23580735 #> [10,] -0.17814153 -0.99433548 1.60091909 #> [11,] -0.17615269 -0.31469660 -0.71168946 #> [12,] 1.49710327 -0.49062257 -1.09733437 #> [13,] 0.60861274 -0.90280890 -0.02380285 #> [14,] -0.97021357 -1.66032177 0.97382990 #> [15,] -1.12931291 1.04706206 0.98000587 #> [16,] 1.36839304 1.30151098 -0.77030751 #> [17,] -1.00794017 -1.24661367 -0.07513261 #> [18,] 1.57525546 0.89661144 -0.24023615 #> [19,] -1.37678241 0.26408240 0.82416993 #> [20,] 0.46284376 -1.74386667 0.59860382 #> [21,] 1.53204332 0.75290827 -0.39625612 #> [22,] -0.54577299 1.39097230 0.16327280 #> [23,] 0.86148076 -0.52197611 -0.37413485 #> [24,] 0.90588132 0.42532148 1.26113010 #> [25,] -0.11179582 -0.80614650 -1.19038292 #> [26,] -0.17196734 0.74510860 -0.81294208 #> [27,] -1.01322479 -0.07601260 -1.09422140 #> [28,] -0.21067085 -1.67342491 1.44540044 #> [29,] 0.06241313 -1.75624527 -1.13753404 #> [30,] 1.35966090 0.91896495 0.38898307 #> [31,] -0.42218411 0.43046533 1.33648001 #> [32,] 0.29022671 -0.16514078 0.19730942 #> [33,] -0.85397979 1.44423593 0.25040494 #> [34,] 0.57594925 -0.93603822 1.39197131 #> [35,] 0.66512784 -0.66210884 0.21891150 #> [36,] 1.29937014 0.26633988 -1.06070517 #> [37,] 0.47025773 0.60766544 1.20720669 #> [38,] 1.74216883 1.29773790 -0.92743740 #> [39,] -0.05671371 -0.21378531 -0.44117363 #> [40,] 0.90578150 1.36117276 -0.69817984 #> [41,] -0.30202868 1.40239907 -0.72668353 #> [42,] -1.52848056 1.51200754 -1.15051494 #> [43,] 1.37175592 -0.85830784 0.33996543 #> [44,] 0.10292442 1.47296414 1.54534688 #> [45,] 0.87231483 -0.66998719 -0.75471131 #> [46,] 0.59973216 1.52335287 0.88083634 #> [47,] 0.03172790 0.88023205 0.25640078 #> [48,] -0.93905443 -1.76333291 -1.50980937 #> [49,] 0.68597932 1.01875328 0.54721470 #> [50,] -1.34154855 -1.47231339 -1.23182257 #> [51,] -1.19759029 1.11769827 1.37274402 #> [52,] -0.77676834 -0.24984412 1.28737788 #> [53,] 1.57761598 -0.80905492 -1.46397424 #> [54,] -1.46234273 0.53946793 0.83992867 #> [55,] -1.44160006 -0.92726804 -0.72762774 #> [56,] -1.12277586 -0.44741260 -1.23962862 #> [57,] -1.53422704 -1.75313915 -1.40023083 #> [58,] -1.52850730 -0.09200885 -1.26759899 #> [59,] 0.25170208 -1.66114265 -1.43033089 #> [60,] 1.41142859 0.06305778 0.03403432 #> [61,] 1.61192397 1.53805764 0.20902184 #> [62,] 0.81832934 0.05420090 1.13949815 #> [63,] -1.48387133 -0.94285481 0.50179554 #> [64,] -0.29774219 1.41597976 -0.13233003 #> [65,] -0.11098242 0.33902408 0.53157601 #> [66,] 1.60395232 -0.14373650 -1.30828061 #> [67,] 1.37038390 -0.95215370 -1.07137337 #> [68,] -1.08615289 -0.33296319 0.99702174 #> [69,] 0.96390776 0.70442885 -0.52280306 #> [70,] -0.92393107 1.26050968 -1.25652288 #> [71,] 0.82670256 1.01989361 0.96623132 #> [72,] -0.12792110 0.61884228 -0.94373227 #> [73,] -0.03773112 -0.36445157 1.30015191 #> [74,] 0.53737663 1.15735483 -1.28048161 #> [75,] -0.43960698 -0.57800199 -1.07667595 #> [76,] -1.16969056 -1.49595434 -1.37999938 #> [77,] -0.74519037 -1.35082752 -1.47184025 #> [78,] -0.49350483 0.12421461 0.95061511 #> [79,] 0.06086406 -0.34898140 -0.32547597 #> [80,] 1.10918997 0.06044862 1.54905017 #> [81,] -0.86538695 -0.61392745 1.27208174 #> [82,] 1.14162107 1.17549231 0.56257925 #> [83,] 1.23388738 0.16430646 1.59824413 #> [84,] -1.50351537 -0.04168292 1.44026256 #> [85,] -0.68300922 0.71941312 0.24602419 #> [86,] -1.08604708 -1.61860127 -0.62076171 #> [87,] -1.52548098 -0.06910424 -0.43377568 #> [88,] 1.22128478 0.09523296 -0.03669461 #> [89,] -0.85895991 1.11803238 -0.36124931 #> [90,] -0.46602823 -0.60026305 0.43627494 #> [91,] -0.06529655 -1.23539097 -0.79265925 #> [92,] -0.65848390 -1.13040670 1.20447999 #> [93,] -1.29812461 0.25648772 1.26211649 #> [94,] -0.64842523 -1.01460888 -1.51738573 #> [95,] -1.14939283 0.99348930 0.17886208 #> [96,] 0.16810639 0.16164857 0.18501520 #> [97,] -1.12685763 1.54704470 -0.82246960 #> [98,] 1.64024070 1.31257818 1.21617554 #> [99,] -0.41100871 -1.27935540 -1.29617247 #> [100,] 0.10725702 1.41010101 0.03048824"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":null,"dir":"Reference","previous_headings":"","what":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"Get `tof_model`'s processed outcome variable matrix (glmnet)","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"","code":"tof_get_model_y(tof_model)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"tof_model tof_model","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"y value formatted glmnet","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_model_y.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get a `tof_model`'s processed outcome variable matrix (for glmnet) — tof_get_model_y","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) tof_get_model_y(regression_model) #> [1] 2.5193384 4.5149299 4.0560885 5.9611669 4.5580430 3.8717242 2.0115023 #> [8] 2.3287751 1.2558387 2.7106398 8.0353582 3.1109667 0.0956430 3.8845026 #> [15] 2.2893241 2.2838848 6.6738549 4.3610705 2.6677311 1.2832516 5.0359106 #> [22] 5.3490989 3.5828511 4.2151462 3.9590472 3.5700904 2.8944736 5.6814454 #> [29] 1.5711894 4.9576279 5.2051262 2.9132805 0.7297537 5.1965211 1.1214608 #> [36] 1.7391494 2.4467109 3.9139769 5.0194642 2.6534669 2.7843119 6.1812425 #> [43] 4.3700452 1.5681085 4.5762836 4.9653675 6.3941393 1.4707955 6.8116263 #> [50] 2.5860246 3.1838962 3.8527498 3.6197843 0.3490233 6.4851868 1.8348396 #> [57] 1.9694412 3.2396971 3.0333514 2.4014812 5.1867053 0.6855628 5.7730632 #> [64] 3.4127052 5.0764523 2.9876150 3.6740342 3.8951042 3.8605096 2.4412410 #> [71] 6.9218832 1.6564309 2.7859638 1.6670116 4.8367688 4.9466905 5.3100786 #> [78] 5.4125562 1.9711748 4.9535322 5.8852148 2.2287108 4.1071774 4.6672257 #> [85] 5.6143727 5.9681251 1.7989624 3.9874215 6.6877886 3.3037816 2.2971176 #> [92] 3.9744913 3.5935729 0.8340110 8.2184186 2.9458438 4.9067439 0.5873825 #> [99] 3.1074093 5.5015267"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":null,"dir":"Reference","previous_headings":"","what":"Get panel information from a tof_tibble — tof_get_panel","title":"Get panel information from a tof_tibble — tof_get_panel","text":"Get panel information tof_tibble","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Get panel information from a tof_tibble — tof_get_panel","text":"","code":"tof_get_panel(tof_tibble)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Get panel information from a tof_tibble — tof_get_panel","text":"tof_tibble `tof_tbl`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Get panel information from a tof_tibble — tof_get_panel","text":"tibble containing information CyTOF panel used data acquisition data contained `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_get_panel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Get panel information from a tof_tibble — tof_get_panel","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) tof_get_panel(tof_tibble) #> # A tibble: 59 × 2 #> metals antigens #> #> 1 Time Time #> 2 Event_length Event_length #> 3 Y89 CD45 #> 4 Pd102 empty #> 5 Pd104 empty #> 6 Pd105 empty #> 7 Pd106 empty #> 8 Pd108 empty #> 9 Pd110 empty #> 10 In113 CD61 #> # ℹ 49 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":null,"dir":"Reference","previous_headings":"","what":"Find if a vector is numeric — tof_is_numeric","title":"Find if a vector is numeric — tof_is_numeric","text":"function takes input vector `.vec` checks either integer double (.e. type vector might encode high-dimensional cytometry measurements).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Find if a vector is numeric — tof_is_numeric","text":"","code":"tof_is_numeric(.vec)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Find if a vector is numeric — tof_is_numeric","text":".vec vector.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_is_numeric.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find if a vector is numeric — tof_is_numeric","text":"boolean value indicating .vec type integer double.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"function uses distances cell K nearest neighbors estimate local density cell `tof_tbl` `tibble` containing high-dimensional cytometry data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"","code":"tof_knn_density( tof_tibble, distance_cols = where(tof_is_numeric), num_neighbors = min(15L, nrow(tof_tibble)), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), estimation_method = c(\"mean_distance\", \"sum_distance\"), normalize = TRUE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. num_neighbors integer indicating number nearest neighbors use estimating local density cell. Defaults minimum 15 number rows `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". estimation_method string indicating relative density cell calculated distances k nearest neighbors. Options \"mean_distance\" (default; estimates relative density cell's neighborhood taking negative average distances nearest neighbors) \"sum_distance\" (estimates relative density cell's neighborhood taking negative sum distances nearest neighbors). normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional optional arguments pass tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_knn_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate cells' local densities using K-nearest-neighbor density estimation — tof_knn_density","text":"tibble single column named \".knn_density\" containing local density estimates input cell `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"Compute log-rank test p-value difference two survival curves obtained splitting dataset \"low\" \"high\" risk group using given relative-risk threshold.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"tof_log_rank_test( input_data, relative_risk_col, time_col, event_col, threshold )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"input_data tbl_df data.frame observation row. relative_risk_col unquote column name indicating column contains relative-risk estimates observation. time_col unquoted column name indicating column contains true time--event information observation. event_col unquoted column name indicating column contains outcome (event censorship). Must binary column - values either 0 1 (1 indicating adverse event 0 indicating censorship) FALSE TRUE (TRUE indicating adverse event FALSE indicating censorship). threshold numeric value indicating relative-risk threshold used split observations low- high-risk groups.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"numeric value <1, p-value log-rank test.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_log_rank_test.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute the log-rank test p-value for the difference between the two survival\ncurves obtained by splitting a dataset into a ","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":null,"dir":"Reference","previous_headings":"","what":"Title — tof_make_knn_graph","title":"Title — tof_make_knn_graph","text":"Title","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Title — tof_make_knn_graph","text":"","code":"tof_make_knn_graph( tof_tibble, knn_cols, num_neighbors, distance_function = c(\"euclidean\", \"cosine\"), graph_type = c(\"weighted\", \"unweighted\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Title — tof_make_knn_graph","text":"tof_tibble tibble tof_tbl. knn_cols Unquoted column names indicating columns tof_tibble used KNN calculation. num_neighbors integer number neighbors find cell ( including ). distance_function string indicating distance function use nearest-neighbor calculation. Options include \"euclidean\" (default) \"cosine\" distances. graph_type string indicating graph's edges weights (\"weighted\"; default) (\"unweighted\"). ... Optional additional arguments pass tof_find_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Title — tof_make_knn_graph","text":"tbl_graph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_knn_graph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Title — tof_make_knn_graph","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":null,"dir":"Reference","previous_headings":"","what":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"Compute receiver-operating curve (ROC) two-class multiclass dataset","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"","code":"tof_make_roc_curve(input_data, truth_col, prob_cols)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"input_data tof_tbl, tbl_df, data.frame row observation. truth_col unquoted column name indicating column `input_data` contains true class labels observation. Must factor. prob_cols Unquoted column names indicating columns `input_data` contain probability estimates class `truth_col`. columns must specified order factor levels `truth_col`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"tibble can used plot ROC classification task. candidate probability threshold, following reported: specificity, sensitivity, true-positive rate (tpr), false-positive rate (fpr).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_make_roc_curve.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Compute a receiver-operating curve (ROC) for a two-class or multiclass dataset — tof_make_roc_curve","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a logistic regression classifier log_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) # make predictions predictions <- tof_predict( log_model, new_data = feature_tibble, prediction_type = \"response\" ) prediction_tibble <- dplyr::tibble( truth = feature_tibble$class, prediction = predictions$.pred ) # make ROC curve tof_make_roc_curve( input_data = prediction_tibble, truth_col = truth, prob_cols = prediction )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data. — tof_metacluster","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"function wrapper around tidytof's tof_metacluster_* function family. performs metaclustering CyTOF data using user-specified method (5 choices) method's corresponding input parameters.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"","code":"tof_metacluster( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, ..., augment = TRUE, method = c(\"consensus\", \"hierarchical\", \"kmeans\", \"phenograph\", \"flowsom\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. ... Additional arguments pass `tof_metacluster_*` function family member corresponding chosen `method`. augment boolean value indicating output column-bind metacluster ids cell new column `tof_tibble` (TRUE; default) single-column tibble including metacluster ids returned (FALSE). method string indicating clustering method used. Valid values include \"consensus\", \"hierarchical\", \"kmeans\", \"phenograph\", \"flowsom\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding metacluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding metacluster ids.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data. — tof_metacluster","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"consensus\", method = \"flowsom\" ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster #> #> 1 -1.58 -0.768 -0.195 0.900 e 4 #> 2 -2.39 0.525 -2.68 1.29 v 2 #> 3 0.664 -1.03 -0.206 -0.00204 t 6 #> 4 1.04 -0.160 -0.922 0.622 y 2 #> 5 -0.350 0.344 0.463 -0.365 l 1 #> 6 -2.19 -0.237 0.670 0.159 r 3 #> 7 -0.365 -0.370 0.445 -0.568 d 1 #> 8 0.512 -0.625 1.01 -1.68 f 1 #> 9 -1.08 -0.935 0.582 -0.970 x 4 #> 10 0.352 -0.135 0.276 -0.193 e 4 #> # ℹ 990 more rows tof_metacluster( tof_tibble = sim_data, cluster_col = cluster_id, method = \"phenograph\" ) #> # A tibble: 1,000 × 6 #> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster #> #> 1 -1.58 -0.768 -0.195 0.900 e 1 #> 2 -2.39 0.525 -2.68 1.29 v 2 #> 3 0.664 -1.03 -0.206 -0.00204 t 1 #> 4 1.04 -0.160 -0.922 0.622 y 2 #> 5 -0.350 0.344 0.463 -0.365 l 3 #> 6 -2.19 -0.237 0.670 0.159 r 2 #> 7 -0.365 -0.370 0.445 -0.568 d 3 #> 8 0.512 -0.625 1.01 -1.68 f 3 #> 9 -1.08 -0.935 0.582 -0.970 x 1 #> 10 0.352 -0.135 0.276 -0.193 e 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"function performs consensus metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See ConsensusClusterPlus additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"","code":"tof_metacluster_consensus( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, proportion_clusters = 0.9, proportion_features = 1, num_reps = 20L, clustering_algorithm = c(\"hierarchical\", \"pam\", \"kmeans\"), distance_function = c(\"euclidean\", \"minkowski\", \"pearson\", \"spearman\", \"maximum\", \"binary\", \"canberra\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. proportion_clusters numeric value 0 1 indicating proportion clusters subsample (total number clusters `cluster_col`) iteration consensus clustering. Defaults 0.9 proportion_features numeric value 0 1 indicating proportion features (.e. proportion columns specified `metacluster_cols`) subsample iteration consensus clustering. Defaults 1 (features included). num_reps integer indicating many subsampled replicates run consensus clustering. Defaults 20. clustering_algorithm string indicating clustering algorithm ConsensusClusterPlus use metacluster subsampled clusters resampling. Options \"hierarchical\" (default), \"pam\" (partitioning around medoids), \"kmeans\". distance_function string indicating distance function used compute distances clusters consensus clustering. Options \"euclidean\" (default), \"manhattan\", \"minkowski\", \"pearson\", \"spearman\", \"maximum\", \"binary\", \"canberra\". See ConsensusClusterPlus. ... Optional additional arguments pass ConsensusClusterPlus.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"tibble single column (`.consensus_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_consensus.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using consensus clustering — tof_metacluster_consensus","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_consensus(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .consensus_metacluster #> #> 1 4 #> 2 10 #> 3 4 #> 4 3 #> 5 1 #> 6 4 #> 7 4 #> 8 7 #> 9 3 #> 10 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"function performs metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. takes advantage FlowSOM package's built-functionality automatically detecting number metaclusters can use several strategies adapted FlowSOM team: consensus metaclustering, hierarchical metaclustering, k-means metaclustering, metaclustering using FlowSOM algorithm . See MetaClustering additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"","code":"tof_metacluster_flowsom( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, clustering_algorithm = c(\"consensus\", \"hierarchical\", \"kmeans\", \"som\"), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating maximum number clusters returned. Defaults 10. Note function, output may provide small number metaclusters requested. MetaClustering uses \"Elbow method\" automatically detect optimal number metaclusters. clustering_algorithm string indicating clustering algorithm MetaClustering use perform metaclustering. Options \"consensus\" (default), \"hierarchical\", \"kmeans\", \"som\" (.e. self-organizing map; FlowSOM algorithm ). ... Optional additional arguments pass MetaClustering.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"tibble single column (`.flowsom_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_flowsom.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using FlowSOM's built-in metaclustering algorithm — tof_metacluster_flowsom","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_flowsom( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"consensus\" ) #> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> #> 1 1 #> 2 1 #> 3 1 #> 4 1 #> 5 1 #> 6 1 #> 7 1 #> 8 1 #> 9 1 #> 10 2 #> # ℹ 990 more rows tof_metacluster_flowsom( tof_tibble = sim_data, cluster_col = cluster_id, clustering_algorithm = \"som\" ) #> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> #> 1 4 #> 2 4 #> 3 6 #> 4 2 #> 5 1 #> 6 1 #> 7 2 #> 8 4 #> 9 4 #> 10 5 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"function performs hierarchical metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See hclust.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"","code":"tof_metacluster_hierarchical( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, distance_function = c(\"euclidean\", \"manhattan\", \"minkowski\", \"maximum\", \"canberra\", \"binary\"), agglomeration_method = c(\"complete\", \"single\", \"average\", \"median\", \"centroid\", \"ward.D\", \"ward.D2\", \"mcquitty\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. distance_function string indicating distance function used compute distances clusters hierarchical metaclustering. Options \"euclidean\" (default), \"manhattan\", \"minkowski\", \"maximum\", \"canberra\", \"binary\". See dist additional details. agglomeration_method string indicating agglomeration algorithm used hierarchical cluster combination. Options \"complete\" (default), \"single\", \"average\", \"median\", \"centroid\", \"ward.D\", \"ward.D2\", \"mcquitty\". See hclust details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"tibble single column (`.hierarchical_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_hierarchical.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using hierarchical agglomerative clustering — tof_metacluster_hierarchical","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_hierarchical(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .hierarchical_metacluster #> #> 1 4 #> 2 3 #> 3 1 #> 4 9 #> 5 10 #> 6 1 #> 7 6 #> 8 7 #> 9 3 #> 10 5 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"function performs k-means metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements number desired metaclusters. See hclust.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"","code":"tof_metacluster_kmeans( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_metaclusters = 10L, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_metaclusters integer indicating number clusters returned. Defaults 10. ... Optional additional method specifications pass tof_cluster_kmeans.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"tibble single column (`.kmeans_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_kmeans.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using k-means clustering — tof_metacluster_kmeans","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_kmeans(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .kmeans_metacluster #> #> 1 1 #> 2 10 #> 3 10 #> 4 2 #> 5 7 #> 6 8 #> 7 1 #> 8 9 #> 9 1 #> 10 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":null,"dir":"Reference","previous_headings":"","what":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"function performs PhenoGraph metaclustering `tof_tbl` containing CyTOF data using user-specified selection input variables/CyTOF measurements. number metaclusters automatically detected PhenoGraph algorithm. See tof_cluster_phenograph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"","code":"tof_metacluster_phenograph( tof_tibble, cluster_col, metacluster_cols = where(tof_is_numeric), central_tendency_function = stats::median, num_neighbors = 5L, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. metacluster_cols Unquoted column names indicating columns `tof_tibble` use computing metaclusters. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. central_tendency_function function used calculate measurement central tendency cluster metaclustering. function used compute summary statistic input cluster `cluster_col` across columns specified `metacluster_cols`, resulting vector (one cluster) used input metaclustering. Defaults median. num_neighbors integer indicating number neighbors use constructing PhenoGraph's k-nearest-neighbor graph. Smaller values emphasize local graph structure; larger values emphasize global graph structure (add time computation). Defaults 5. ... Optional additional method specifications pass tof_cluster_phenograph.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"tibble single column (`.phenograph_metacluster`) number rows input `tof_tibble`. entry column indicates metacluster label assigned row `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_metacluster_phenograph.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Metacluster clustered CyTOF data using PhenoGraph clustering — tof_metacluster_phenograph","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) tof_metacluster_phenograph(tof_tibble = sim_data, cluster_col = cluster_id) #> # A tibble: 1,000 × 1 #> .phenograph_metacluster #> #> 1 3 #> 2 4 #> 3 2 #> 4 1 #> 5 1 #> 6 1 #> 7 2 #> 8 3 #> 9 4 #> 10 4 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot marker expression density plots — tof_plot_cells_density","title":"Plot marker expression density plots — tof_plot_cells_density","text":"function plots marker expression density plots user-specified column tof_tbl. Optionally, cells can grouped plot multiple vertically-arranged density plots","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot marker expression density plots — tof_plot_cells_density","text":"","code":"tof_plot_cells_density( tof_tibble, marker_col, group_col, num_points = 512, theme = ggplot2::theme_bw(), use_ggridges = FALSE, scale = 1, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot marker expression density plots — tof_plot_cells_density","text":"tof_tibble `tof_tbl` `tibble`. marker_col unquoted column name representing column `tof_tibble` (.e. CyTOF protein measurement) included feature extraction calculation. group_col Unquoted column names representing column `tof_tibble` used break rows `tof_tibble` subgroups plotted separate histograms. Defaults plotting without subgroups. num_points number points along full range `marker_col` density calculated theme ggplot2 theme plot. Defaults theme_bw use_ggridges boolean value indicting geom_ridgeline used plot overlain histograms. Defaults FALSE. TRUE, ggridges package must installed. scale Use set `scale` argument geom_ridgeline, controls far apart (vertically) density plots arranged along y-axis. Defaults 1. ... Additional optional arguments send geom_ridgeline.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot marker expression density plots — tof_plot_cells_density","text":"ggplot object","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot marker expression density plots — tof_plot_cells_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(c(\"a\", \"b\"), size = 1000, replace = TRUE) ) density_plot <- tof_plot_cells_density( tof_tibble = sim_data, marker_col = cd45, group_col = cluster_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"function makes scatterplots using single-cell data embedded low-dimensional space (generated tof_reduce_dimensions, point colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"","code":"tof_plot_cells_embedding( tof_tibble, embedding_cols, color_col, facet_cols, compute_embedding_cols = where(tof_is_numeric), embedding_method = c(\"pca\", \"tsne\", \"umap\"), embedding_args = list(), theme = ggplot2::theme_bw(), ..., method = c(\"ggplot2\", \"scattermore\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"tof_tibble `tof_tbl` `tibble`. embedding_cols Unquoted column names indicating columns `tof_tibble` used x y axes scatterplot. Supports tidyselect helpers. Must select exactly 2 columns. provided, feature embedding can computed scratch using method provided using `embedding_method` argument tof_reduce_dimensions arguments passed `embedding_args`. color_col unquoted column name specifying column `tof_tibble` used color point scatterplot. facet_cols unquoted column name specifying column `tof_tibble` used break scatterplot facets using facet_wrap. compute_embedding_cols Unquoted column names indicating columns 'tof_tibble' use computing embeddings method specified `embedding_method`. Defaults numeric columns 'tof_tibble'. Supports tidyselect helpers. embedding_method string indicating method used feature embedding (`embedding_cols` provided). Options (passed tof_reduce_dimensions) \"pca\" (default), \"tsne\", \"umap\". embedding_args Optional additional arguments pass tof_reduce_dimensions. example, `method = \"tsne\"`, might include `num_comp`, `perplexity`, `theta`. theme ggplot2 theme apply scatterplot. Defaults theme_bw. ... Optional additional arguments pass tof_plot_cells_scatter. method string indicating plotting engine used. Valid values include \"ggplot2\" (default) \"scattermore\" (recommended 100K cells plotted). Note method = \"scattermore\" requires scattermore package installed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_embedding.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplots of single-cell data using low-dimensional feature embeddings — tof_plot_cells_embedding","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # embed with pca pca_plot <- tof_plot_cells_embedding( tof_tibble = sim_data, color_col = cd38, embedding_method = \"pca\", compute_embedding_cols = starts_with(\"cd\") ) # embed with tsne tsne_plot <- tof_plot_cells_embedding( tof_tibble = sim_data, color_col = cluster_id, embedding_method = \"tsne\", compute_embedding_cols = starts_with(\"cd\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"function makes force-directed layouts using single-cell data embedded 2-dimensional space representing k-nearest-neighbor graph constructed using cell--cell similarities. node force-directed layout represents single cell colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"","code":"tof_plot_cells_layout( tof_tibble, knn_cols = where(tof_is_numeric), color_col, facet_cols, num_neighbors = 5, graph_type = c(\"weighted\", \"unweighted\"), graph_layout = \"fr\", distance_function = c(\"euclidean\", \"cosine\"), edge_alpha = 0.25, node_size = 2, theme = ggplot2::theme_void(), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"tof_tibble `tof_tbl` `tibble`. knn_cols Unquoted column names indicating columns `tof_tibble` used compute cell--cell distances used construct k-nearest-neighbor graph. Supports tidyselect helpers. Defaults numeric columns. color_col Unquoted column name indicating column `tof_tibble` used color nodes force-directed layout. facet_cols Unquoted column names indicating columns `tof_tibble` used separate nodes different force-directed layouts. num_neighbors integer specifying many neighbors used construct k-nearest neighbor graph. graph_type string specifying k-nearest neighbor graph \"weighted\" (default) \"unweighted\". graph_layout string specifying algorithm used compute force-directed layout. Passed ggraph. Defaults \"fr\", Fruchterman-Reingold algorithm. examples include \"nicely\", \"gem\", \"kk\", many others. See layout_tbl_graph_igraph examples. distance_function string indicating distance function use computing cell--cell distances. Valid options include \"euclidean\" (default) \"cosine\". edge_alpha numeric value 0 1 specifying transparency edges drawn force-directed layout. Defaults 0.25. node_size numeric value specifying size nodes force-directed layout. Defaults 2. theme ggplot2 theme apply force-directed layout. Defaults theme_void ... hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"ggraph/ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_layout.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot force-directed layouts of single-cell data — tof_plot_cells_layout","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) ) # make a layout colored by a marker layout_cd38 <- tof_plot_cells_layout( tof_tibble = sim_data, color_col = cd38 ) # make a layout colored by cluster id layout_cluster <- tof_plot_cells_layout( tof_tibble = sim_data, color_col = cluster_id, )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"function makes scatterplots single-cell data using user-specified x- y-axes. Additionally, point scatterplot can colored using user-specified variable.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"","code":"tof_plot_cells_scatter( tof_tibble, x_col, y_col, color_col, facet_cols, theme = ggplot2::theme_bw(), ..., method = c(\"ggplot2\", \"scattermore\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"tof_tibble `tof_tbl` `tibble`. x_col unquoted column name specifying column `tof_tibble` used x-axis. y_col unquoted column name specifying column `tof_tibble` used y-axis. color_col unquoted column name specifying column `tof_tibble` used color point scatterplot. facet_cols unquoted column name specifying column `tof_tibble` used break scatterplot facets using facet_wrap. theme ggplot2 theme apply scatterplot. Defaults theme_bw. ... Optional additional arguments pass geom_point method = \"ggplot2\" geom_scattermore method = \"scattermore\". method string indicating plotting engine used. Valid values include \"ggplot2\" (default) \"scattermore\" (recommended 100K cells plotted). Note method = \"scattermore\" requires scattermore package installed.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"ggplot object.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_cells_scatter.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot scatterplots of single-cell data. — tof_plot_cells_scatter","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = c(rnorm(n = 500), rnorm(n = 500, mean = 2)), cd34 = c(rnorm(n = 500), rnorm(n = 500, mean = 4)), cd19 = rnorm(n = 1000), cluster_id = c(rep(\"a\", 500), rep(\"b\", 500)) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"function makes heatmap cluster--cluster marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap cluster IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"","code":"tof_plot_clusters_heatmap( tof_tibble, cluster_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_clusterwise = FALSE, cluster_markers = TRUE, cluster_clusters = TRUE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_clusterwise boolean value indicating heatmap rescale rows heatmap maximum value cluster 1 minimum value 0. Defaults FALSE. cluster_markers boolean value indicating heatmap order columns (.e. markers) using hierarchical clustering. Defaults TRUE. cluster_clusters boolean value indicating heatmap order rows (.e. clusters) using hierarchical clustering. Defaults TRUE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing cluster marker expression patterns in CyTOF data — tof_plot_clusters_heatmap","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) heatmap <- tof_plot_clusters_heatmap( tof_tibble = sim_data, cluster_col = cluster_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":null,"dir":"Reference","previous_headings":"","what":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"function plots minimum-spanning tree using clustered single-cell data order summarize cluster-level characteristics. node MST represents single cluster colored using user-specified variable (either continuous discrete).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"","code":"tof_plot_clusters_mst( tof_tibble, cluster_col, knn_cols = where(tof_is_numeric), color_col, num_neighbors = 5L, graph_type = c(\"unweighted\", \"weighted\"), graph_layout = \"nicely\", central_tendency_function = stats::median, distance_function = c(\"euclidean\", \"cosine\"), edge_alpha = 0.4, node_size = \"cluster_size\", theme = ggplot2::theme_void(), ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"tof_tibble `tof_tbl` `tibble`. cluster_col unquoted column name indicating column `tof_tibble` stores cluster ids cluster cell belongs. Cluster labels can produced via method user chooses - including manual gating, functions `tof_cluster_*` function family, method. knn_cols Unquoted column names indicating columns `tof_tibble` used compute cluster--cluster distances used construct k-nearest-neighbor graph. Supports tidyselect helpers. Defaults numeric columns. color_col Unquoted column name indicating column `tof_tibble` used color nodes MST. num_neighbors integer specifying many neighbors used construct k-nearest neighbor graph. graph_type string specifying k-nearest neighbor graph \"weighted\" (default) \"unweighted\". graph_layout argument specifies layout MST one two ways. Option 1: Provide string specifying algorithm used compute force-directed layout. Passed ggraph. Defaults \"nicely\", tries automatically select visually-appealing layout. examples include \"fr\", \"gem\", \"kk\", many others. See layout_tbl_graph_igraph examples. Option 2: Provide ggraph object previously generated function. layout used plot ggraph object used template new plot. Using option, number clusters (labels) must identical template. option useful want make multiple plots tof_tibble colored different protein markers, example. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. distance_function string indicating distance function use computing cluster--clusters distances constructing MST. Valid options include \"euclidean\" (default) \"cosine\". edge_alpha numeric value 0 1 specifying transparency edges drawn force-directed layout. Defaults 0.25. node_size Either numeric value specifying size nodes MST string \"cluster_size\", case size node representing cluster scaled according number cells cluster (default). theme ggplot2 theme apply force-directed layout. Defaults theme_void ... Optional additional arguments hnsw_knn","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"ggraph/ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_mst.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Visualize clusters in CyTOF data using a minimum spanning tree (MST). — tof_plot_clusters_mst","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE) ) # make a layout colored by a marker layout_cd38 <- tof_plot_clusters_mst( tof_tibble = sim_data, cluster_col = cluster_id, color_col = cd38 ) # use the same layout as the plot above to color the same # tree using a different marker layout_cd45 <- tof_plot_clusters_mst( tof_tibble = sim_data, cluster_col = cluster_id, color_col = cd45, graph_layout = layout_cd38 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":null,"dir":"Reference","previous_headings":"","what":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"function makes volcano plot using results differential expression analysis (DEA) produced one `tof_dea_*` verbs. point volcano plot represents single cluster-marker pair, colored significance level direction marker expression difference.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"","code":"tof_plot_clusters_volcano( dea_result, num_top_pairs = 10L, alpha = 0.05, point_size = 2, label_size = 3, nudge_x = 0, nudge_y = 0.25, increase_color = \"#207394\", decrease_color = \"#cd5241\", insignificant_color = \"#cdcdcd\", use_ggrepel = FALSE, theme = ggplot2::theme_bw() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"dea_result tibble containing differential expression analysis (DEA) results produced one members `tof_dea_*` function family. num_top_pairs integer representing number significant cluster-marker pairs labeled volcano plot. alpha numeric value 0 1 representing significance level p-value considered statistically significant. Defaults 0.05. point_size numeric value specifying size points volcano plot. label_size numeric value specifying size text labeling cluster-marker pairs. nudge_x numeric value specifying far cluster-marker pair labels adjusted left (`nudge_x` negative) right (`nudge_x` positive) avoid overlap plotted points. Passed geom_text, ignored `use_ggrepel` = TRUE. Defaults 0. nudge_y numeric value specifying far cluster-marker pair labels adjusted downwards (`nudge_y` negative) upwards (`nudge_y` positive) avoid overlap plotted points. Passed geom_text, ignored `use_ggrepel` = TRUE. Defaults 0.25. increase_color hex code specifying fill color used points corresponding cluster-marker pairs significant increases detected. decrease_color hex code specifying fill color used points corresponding cluster-marker pairs significant decreases detected. insignificant_color hex code specifying fill color used points corresponding cluster-marker pairs significant differences detected. use_ggrepel boolean value indicting geom_text_repel used plot labels cluster-marker pairs. Defaults FALSE. TRUE, ggrepel package must installed. theme ggplot2 theme apply volcano plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_clusters_volcano.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Create a volcano plot from differential expression analysis results — tof_plot_clusters_volcano","text":"","code":"# create a mock differential expression analysis result sim_dea_result <- dplyr::tibble( cluster_id = rep(letters, 2), marker = rep(c(\"cd45\", \"cd34\"), times = length(letters)), p_adj = runif(n = 2 * length(letters), min = 0, max = 0.5), mean_fc = runif(n = 2 * length(letters), min = 0.01, max = 10), significant = dplyr::if_else(p_adj < 0.05, \"*\", \"\") ) attr(sim_dea_result, which = \"dea_method\") <- \"t_unpaired\" # create the volcano plot volcano <- tof_plot_clusters_volcano(dea_result = sim_dea_result)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"function makes heatmap group--group marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap groups plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"","code":"tof_plot_heatmap( tof_tibble, y_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_ywise = FALSE, cluster_markers = TRUE, cluster_groups = TRUE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"tof_tibble `tof_tbl` `tibble`. y_col unquoted column name indicating column `tof_tibble` stores ids group cell belongs. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated cluster cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_ywise boolean value indicating heatmap rescale rows heatmap maximum value group 1 minimum value 0. Defaults FALSE. cluster_markers boolean value indicating heatmap order columns (.e. markers) using hierarchical clustering. Defaults TRUE. cluster_groups boolean value indicating heatmap order rows (.e. groups) using hierarchical clustering. Defaults TRUE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing group marker expression patterns in high-dimensional cytometry data — tof_plot_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"Plot results glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"","code":"tof_plot_model(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"ggplot object. `tof_model` linear model, scatterplot predicted outcome vs. true outcome returned. `tof_model` two-class model, ROC curve returned. `tof_model` multiclass model, one-versus-ROC curve returned class. `tof_model` survival model, Kaplan-Meier curve returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Plot the results of a glmnet model fit on sample-level data. — tof_plot_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # make the plot plot_1 <- tof_plot_model(tof_model = regression_model, new_data = new_tibble) # train a logistic regression classifier logistic_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) # make the plot plot_2 <- tof_plot_model(tof_model = logistic_model, new_data = new_tibble)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"Plot results linear glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"","code":"tof_plot_model_linear(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_linear.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a linear glmnet model fit on sample-level data. — tof_plot_model_linear","text":"ggplot object. Specifically, scatterplot predicted outcome vs. true outcome returned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"Plot results two-class glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"","code":"tof_plot_model_logistic(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_logistic.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a two-class glmnet model fit on sample-level data. — tof_plot_model_logistic","text":"ggplot object. Specifically, ROC curve..","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"Plot results multiclass glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"","code":"tof_plot_model_multinomial(tof_model, new_data, theme = ggplot2::theme_bw())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. theme ggplot2 theme apply plot. Defaults theme_bw.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_multinomial.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a multiclass glmnet model fit on sample-level data. — tof_plot_model_multinomial","text":"ggplot object. Specifically, one-versus-ROC curve (one class).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":null,"dir":"Reference","previous_headings":"","what":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"Plot results survival glmnet model fit sample-level data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"","code":"tof_plot_model_survival( tof_model, new_data, censor_size = 2.5, theme = ggplot2::theme_bw() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations plot made. new_data provided, plot made using training data used fit model. Alternatively, string \"tuning_data\" can provided, plot generated using predictions generated model tuning. censor_size numeric value indicating large plot tick marks representing censored values Kaplan-Meier curve. theme ggplot2 theme apply plot. Defaults theme_bw","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_model_survival.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Plot the results of a survival glmnet model fit on sample-level data. — tof_plot_model_survival","text":"ggplot object. Specifically, Kaplan-Meier curve.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"function makes heatmap sample--sample marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap sample IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"","code":"tof_plot_sample_features( feature_tibble, sample_col, feature_cols = where(tof_is_numeric), scale_featurewise = FALSE, scale_samplewise = FALSE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"feature_tibble tbl_df data.frame aggregated sample-level features, generated tof_extract_features. sample_col unquoted column name indicating column `tof_tibble` stores IDs sample. sample IDs present, numeric ID assigned row `feature_tibble` based row index. feature_cols Unquoted column names indicating column `feature_tibble` interpreted features plotted along x-axis heatmap. Supports tidyselect helpers. scale_featurewise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_samplewise boolean value indicating heatmap rescale rows heatmap maximum value sample 1 minimum value 0. Defaults FALSE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_features.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_features","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), cluster_id = sample(letters, size = 1000, replace = TRUE), sample_id = sample(paste0(\"sample\", 1:5), size = 1000, replace = TRUE) ) # extract cluster proportions in each simulated patient feature_data <- tof_extract_proportion( tof_tibble = sim_data, cluster_col = cluster_id, group_cols = sample_id ) # plot the heatmap heatmap <- tof_plot_sample_features(feature_tibble = feature_data)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":null,"dir":"Reference","previous_headings":"","what":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"function makes heatmap sample--sample marker expression patterns single-cell data. Markers plotted along horizontal (x-) axis heatmap sample IDs plotted along vertical (y-) axis heatmap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"","code":"tof_plot_sample_heatmap( tof_tibble, sample_col, marker_cols = where(tof_is_numeric), central_tendency_function = stats::median, scale_markerwise = FALSE, scale_samplewise = FALSE, line_width = 0.25, theme = ggplot2::theme_minimal() )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"tof_tibble `tof_tbl` `tibble`. sample_col unquoted column name indicating column `tof_tibble` stores ids sample cell belongs. marker_cols Unquoted column names indicating column `tof_tibble` interpreted markers plotted along x-axis heatmap. Supports tidyselect helpers. central_tendency_function function use computing measure central tendency aggregated sample cluster_col. Defaults median. scale_markerwise boolean value indicating heatmap rescale columns heatmap maximum value marker 1 minimum value 0. Defaults FALSE. scale_samplewise boolean value indicating heatmap rescale rows heatmap maximum value sample 1 minimum value 0. Defaults FALSE. line_width numeric value indicating thick lines separating tiles heatmap . Defaults 0.25. theme ggplot2 theme apply heatmap. Defaults theme_minimal","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"ggplot object.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_plot_sample_heatmap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Make a heatmap summarizing sample marker expression patterns in CyTOF data — tof_plot_sample_heatmap","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000), sample_id = sample(paste0(\"sample\", 1:5), size = 1000, replace = TRUE) ) heatmap <- tof_plot_sample_heatmap( tof_tibble = sim_data, sample_col = sample_id )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":null,"dir":"Reference","previous_headings":"","what":"Post-process transformed CyTOF data. — tof_postprocess","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"function transforms `tof_tibble` transformed ion counts mass cytometer back something looks like .fcs file Fluidigm software generates.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"","code":"tof_postprocess( tof_tibble = NULL, channel_cols = where(tof_is_numeric), redo_noise = FALSE, transform_fun = function(x) rev_asinh(x, shift_factor = 0, scale_factor = 0.2) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"tof_tibble `tof_tibble` `tibble`. channel_cols vector non-quoted column names indicating columns `tof_tibble` contain protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. redo_noise boolean value indicating whether add uniform noise CyTOF measurement aesthetic visualization purposes. See paper. Defaults FALSE transform_fun vectorized function apply column specified `channel_cols` post-processing. Defaults rev_asinh transformation (cofactor 5).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun` (noise added removed depending `redo_noise`).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_postprocess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Post-process transformed CyTOF data. — tof_postprocess","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 preprocessed_tof_tibble <- tof_preprocess(tof_tibble) # postprocess all numeric columns to reverse the preprocessing tof_postprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 Inf 22215276. 6.00e224 8.82 1411688. Inf #> 2 Inf 446205752. Inf 135. 859679. Inf #> 3 Inf 22215276. Inf 2634. 321. 8.51e277 #> 4 Inf 22215276. 2.24e254 3.47 1383. 8.29e254 #> 5 Inf 164149923. Inf 127. 38726. 3.70e280 #> 6 Inf 446205752. 1.14e211 163. 116. 3.29e272 #> 7 Inf 22215276. 2.62e219 79.1 175. 1.82e246 #> 8 Inf 164149923. 5.35e246 1876. 291228. 5.64e305 #> 9 Inf 446205752. Inf 55.3 24727. Inf #> 10 Inf 22215276. 2.38e236 5.05 2010273. Inf #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":null,"dir":"Reference","previous_headings":"","what":"Use a trained elastic net model to predict fitted values from new data — tof_predict","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"function uses trained `tof_model` make predictions new data.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"","code":"tof_predict( tof_model, new_data, prediction_type = c(\"response\", \"class\", \"link\", \"survival curve\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"tof_model `tof_model` trained using tof_train_model new_data tibble new observations predictions made. new_data provided, predictions made training data used fit model. prediction_type string indicating type prediction provided model: \"response\" (default) \"linear\" models, predicted response observation. \"two-class\" \"multiclass\" models, fitted probabilities class observation. \"survival\" models, fitted relative-risk observation. \"class\" applies \"two-class\" \"multiclass\" models. , class label corresponding class maximum fitted probability. \"link\" linear predictions model (output link function model family.) \"survival curve\" applies \"survival\" models. Returns tibble indicating patient's probability survival (1 - probability(event)) timepoint dataset. Obtained using survfit function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"tibble single column (`.pred`) containing predictions , multiclass models `prediction_type` == \"response\", tibble one column class. row output corresponds row `new_data` ( , `new_data` provided, row `tof_model`'s training data). latter case, sure check `tof_model$training_data` confirm order observations, resampling procedure can change ordering relative original input data.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_predict.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Use a trained elastic net model to predict fitted values from new data — tof_predict","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100) ) new_tibble <- dplyr::tibble( sample = as.character(1:20), cd45 = runif(n = 20), pstat5 = runif(n = 20), cd34 = runif(n = 20), outcome = (3 * cd45) + (4 * pstat5) + rnorm(20) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model regression_model <- tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) # apply the model to new data tof_predict(tof_model = regression_model, new_data = new_tibble) #> # A tibble: 20 × 1 #> .pred #> #> 1 3.90 #> 2 4.77 #> 3 3.33 #> 4 0.388 #> 5 2.43 #> 6 3.07 #> 7 3.38 #> 8 2.59 #> 9 5.27 #> 10 4.30 #> 11 0.824 #> 12 0.987 #> 13 2.44 #> 14 5.04 #> 15 3.86 #> 16 5.88 #> 17 4.41 #> 18 4.80 #> 19 4.44 #> 20 3.14"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":null,"dir":"Reference","previous_headings":"","what":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"Train recipe list recipes preprocessing sample-level cytometry data","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"","code":"tof_prep_recipe(split_data, unprepped_recipe)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. easiest way generate use tof_split_data. Alternatively, unsplit tbl_df, though recommended. unprepped_recipe recipe object (`split_data` `rsplit` object `tbl_df`) list recipes (`split_data` `rset` object).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_prep_recipe.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train a recipe or list of recipes for preprocessing sample-level cytometry data — tof_prep_recipe","text":"split_data \"rsplit\" \"tbl_df\" object, return single prepped recipe. split_data \"rset\" object, return list prepped recipes specific fold resampling procedure.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":null,"dir":"Reference","previous_headings":"","what":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"function transforms `tof_tbl` raw ion counts, reads, fluorescence intensity units directly measured cytometer using user-provided function. can used perform standard pre-processing steps (.e. arcsinh transformation) cytometry data analysis.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"","code":"tof_preprocess( tof_tibble = NULL, channel_cols = where(tof_is_numeric), undo_noise = FALSE, transform_fun = function(x) asinh(x/5) )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. undo_noise boolean value indicating whether remove uniform noise Fluidigm software adds CyTOF measurements aesthetic visualization purposes. See paper. Defaults FALSE. transform_fun vectorized function apply protein value variance stabilization. Defaults asinh transformation (co-factor 5).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun` (noise removed removed depending `undo_noise`).","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_preprocess.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Preprocess raw high-dimensional cytometry data. — tof_preprocess","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 tof_preprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 15.3 1.88 5.33 0.263 1.70 5.85 #> 2 14.9 2.05 5.83 0.731 1.67 5.71 #> 3 15.2 1.88 5.70 1.13 0.861 5.54 #> 4 13.7 1.88 5.45 0.129 1.06 5.46 #> 5 15.2 1.99 5.73 0.721 1.41 5.55 #> 6 14.4 2.05 5.27 0.760 0.708 5.52 #> 7 13.9 1.88 5.31 0.645 0.771 5.42 #> 8 14.2 1.99 5.42 1.09 1.58 5.64 #> 9 15.6 2.05 6.03 0.586 1.37 5.83 #> 10 9.75 1.88 5.38 0.177 1.73 5.78 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , … # preprocess all numeric columns using the log base 10 tranformation tof_preprocess(tof_tibble, transform_fun = log10) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 7.04 1.20 2.71 0.125 1.12 2.94 #> 2 6.85 1.28 2.93 0.601 1.11 2.88 #> 3 6.99 1.20 2.87 0.843 0.686 2.81 #> 4 6.36 1.20 2.77 -0.189 0.800 2.77 #> 5 6.98 1.26 2.89 0.594 0.984 2.81 #> 6 6.65 1.28 2.69 0.621 0.584 2.80 #> 7 6.44 1.20 2.70 0.539 0.628 2.75 #> 8 6.57 1.26 2.75 0.821 1.07 2.85 #> 9 7.18 1.28 3.02 0.491 0.964 2.93 #> 10 4.63 1.20 2.74 -0.0515 1.13 2.91 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"Read high-dimensional cytometry data .csv file tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"","code":"tof_read_csv(file_path = NULL, panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"file_path file path single .csv file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from a .csv file into a tidy tibble. — tof_read_csv","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition). panel information obvious data read .csv file, information must provided manually user (unlike `tof_read_fcs`).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"Read data .fcs/.csv file directory .fcs/.csv files.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"","code":"tof_read_data(path = NULL, sep = \"|\", panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"path file path single file directory files. valid file types .fcs files .csv files containing high-dimensional cytometry data. sep Optional. string use separate antigen name associated metal column names output tibble. Defaults \"|\". used input file .fcs file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\". used input file .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"[c m+1] tibble row represents single cell (c total dataset) column represents high-dimensional cytometry measurement (m total dataset). one .fcs read , last column tibble (`file_name`) represent file name .fcs file cell read.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Read data from an .fcs/.csv file or a directory of .fcs/.csv files. — tof_read_data","text":"","code":"input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_read_data(input_file) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 11021370 16 517. 1.33 13.2 865. #> 2 7112446. 19 850. 3.99 12.7 756. #> 3 9722098 16 747. 6.96 4.85 639. #> 4 2267279. 16 585. 0.648 6.32 586. #> 5 9624729 18 773. 3.93 9.65 645. #> 6 4439897 19 485. 4.18 3.84 627. #> 7 2762526. 16 504. 3.46 4.25 566. #> 8 3746682. 18 567. 6.62 11.7 703. #> 9 15214280 19 1043. 3.10 9.20 853. #> 10 42699. 16 543. 0.888 13.6 813. #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"function reads high-dimensional cytometry data single .fcs file tidy data structure called `tof_tbl` (\"tof_tibble\"). tof_tibbles identical normal tibbles except additional attribute (\"panel\") stores information high-dimensional cytometry panel used data acquisition.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"","code":"tof_read_fcs(file_path = NULL, sep = \"|\")"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"file_path file path single .fcs file. sep string use separate antigen name associated metal column names output tibble. Defaults \"|\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_fcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from an .fcs file into a tidy tibble. — tof_read_fcs","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":null,"dir":"Reference","previous_headings":"","what":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"Read high-dimensional cytometry data single .fcs .csv file tidy tibble.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"","code":"tof_read_file(file_path = NULL, sep = \"|\", panel_info = dplyr::tibble())"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"file_path file path single .fcs .csv file. sep string use separate antigen name associated metal column names output tibble. Defaults \"|\". used input file .fcs file. panel_info Optional. tibble data.frame containing information panel used high-dimensional cytometry data acquisition. Two columns required: \"metals\" \"antigens\". used input file .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_read_file.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Read high-dimensional cytometry data from a single .fcs or .csv file into a tidy tibble. — tof_read_file","text":"`tof_tbl` row represents single cell column represents high-dimensional cytometry antigen channel. `tof_tbl` S3 class extends \"tibble\" class storing one additional attribute: \"panel\" (tibble storing information panel used data acquisition). panel information obvious data read .csv file, information must provided manually user.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"function wrapper around tidytof's tof_reduce_* function family. performs dimensionality reduction single-cell data using user-specified method (3 choices) method's corresponding input parameters","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"","code":"tof_reduce_dimensions( tof_tibble, ..., augment = TRUE, method = c(\"pca\", \"tsne\", \"umap\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"tof_tibble `tof_tbl` `tibble`. ... Arguments passed tof_reduce_* function corresponding embedding method. See tof_reduce_pca, tof_reduce_tsne, tof_reduce_umap. augment boolean value indicating output column-bind dimensionality-reduced embedding vectors cell new column `tof_tibble` (TRUE, default) tibble including low-dimensionality embeddings returned (FALSE). method method dimensionality reduction. Currently, PCA, tSNE, UMAP embedding supported.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated embedding space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply dimensionality reduction to a single-cell dataset. — tof_reduce_dimensions","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 100), cd38 = rnorm(n = 100), cd34 = rnorm(n = 100), cd19 = rnorm(n = 100) ) # calculate pca tof_reduce_dimensions(tof_tibble = sim_data, method = \"pca\") #> # A tibble: 100 × 8 #> cd45 cd38 cd34 cd19 .pc1 .pc2 .pc3 .pc4 #> #> 1 -0.561 -0.259 2.01 0.845 1.12 0.283 1.46 1.16 #> 2 0.332 -1.31 1.39 0.389 1.53 0.121 0.00464 0.920 #> 3 -2.33 1.63 0.112 -0.119 -2.14 0.519 1.72 0.914 #> 4 -1.18 1.42 0.597 -0.744 -1.48 -0.628 1.14 0.854 #> 5 0.666 0.469 -0.181 -0.449 -0.306 -0.736 -0.256 -0.428 #> 6 1.70 -1.30 1.42 0.239 2.22 -0.718 -0.496 0.184 #> 7 -0.474 0.138 -1.46 0.995 -0.717 1.50 0.0360 -0.949 #> 8 -0.0121 -1.38 -0.248 0.624 0.656 1.11 -0.614 0.183 #> 9 0.728 -1.33 -0.111 0.147 0.870 0.315 -1.00 0.0103 #> 10 1.03 0.181 -0.171 -0.463 0.0189 -0.830 -0.546 -0.520 #> # ℹ 90 more rows # calculate tsne tof_reduce_dimensions(tof_tibble = sim_data, method = \"tsne\") #> # A tibble: 100 × 6 #> cd45 cd38 cd34 cd19 .tsne1 .tsne2 #> #> 1 -0.561 -0.259 2.01 0.845 -1.12 -3.02 #> 2 0.332 -1.31 1.39 0.389 -3.26 -1.49 #> 3 -2.33 1.63 0.112 -0.119 5.46 -0.0530 #> 4 -1.18 1.42 0.597 -0.744 4.78 0.0140 #> 5 0.666 0.469 -0.181 -0.449 2.38 -1.80 #> 6 1.70 -1.30 1.42 0.239 -3.61 -2.86 #> 7 -0.474 0.138 -1.46 0.995 1.38 2.17 #> 8 -0.0121 -1.38 -0.248 0.624 -3.08 0.895 #> 9 0.728 -1.33 -0.111 0.147 -3.65 0.923 #> 10 1.03 0.181 -0.171 -0.463 2.36 -2.19 #> # ℹ 90 more rows # calculate umap tof_reduce_dimensions(tof_tibble = sim_data, method = \"umap\") #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 100 × 6 #> cd45 cd38 cd34 cd19 .umap1 .umap2 #> #> 1 -0.561 -0.259 2.01 0.845 -0.133 -0.963 #> 2 0.332 -1.31 1.39 0.389 -4.09 0.285 #> 3 -2.33 1.63 0.112 -0.119 4.83 -1.72 #> 4 -1.18 1.42 0.597 -0.744 4.61 -1.28 #> 5 0.666 0.469 -0.181 -0.449 2.49 1.01 #> 6 1.70 -1.30 1.42 0.239 -3.36 0.151 #> 7 -0.474 0.138 -1.46 0.995 -0.808 -2.59 #> 8 -0.0121 -1.38 -0.248 0.624 -5.92 -0.166 #> 9 0.728 -1.33 -0.111 0.147 -4.87 0.746 #> 10 1.03 0.181 -0.171 -0.463 2.25 0.798 #> # ℹ 90 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform principal component analysis on single-cell data — tof_reduce_pca","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"function calculates principal components using single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"","code":"tof_reduce_pca( tof_tibble, pca_cols = where(tof_is_numeric), num_comp = 5, threshold = NA, center = TRUE, scale = TRUE, return_recipe = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"tof_tibble `tof_tbl` `tibble`. pca_cols Unquoted column names indicating columns `tof_tibble` use computing principal components. Defaults numeric columns. Supports tidyselect helpers. num_comp number PCA components calculate. Defaults 5. See step_pca. threshold double 0 1 representing fraction total variance covered components returned output. See step_pca. center boolean value indicating column centered mean 0 PCA analysis. Defaults TRUE. scale boolean value indicating column scaled standard deviation = 1 PCA analysis. Defaults TRUE. return_recipe boolean value indicating instead UMAP result, prepped recipe object containing PCA embedding returned. Set option TRUE want create PCA embedding using one dataset also want project new observations onto embedding space later.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated principal component space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_pca.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform principal component analysis on single-cell data — tof_reduce_pca","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) new_data <- dplyr::tibble( cd45 = rnorm(n = 50), cd38 = rnorm(n = 50), cd34 = rnorm(n = 50), cd19 = rnorm(n = 50) ) # calculate pca tof_reduce_pca(tof_tibble = sim_data, num_comp = 2) #> # A tibble: 200 × 2 #> .pc1 .pc2 #> #> 1 -0.244 1.51 #> 2 -0.553 0.502 #> 3 -0.503 -1.25 #> 4 -0.538 1.31 #> 5 -0.528 -1.23 #> 6 -2.61 0.0402 #> 7 -1.39 1.82 #> 8 0.298 0.459 #> 9 -1.15 0.779 #> 10 1.21 0.0252 #> # ℹ 190 more rows # return recipe instead of embeddings pca_recipe <- tof_reduce_pca(tof_tibble = sim_data, return_recipe = TRUE) # apply recipe to new data recipes::bake(pca_recipe, new_data = new_data) #> # A tibble: 50 × 4 #> PC1 PC2 PC3 PC4 #> #> 1 -0.370 -0.725 -0.491 0.695 #> 2 -1.58 -1.37 -1.03 2.04 #> 3 1.00 0.601 0.390 -1.64 #> 4 -0.617 1.70 0.101 -1.45 #> 5 0.549 0.832 0.827 -0.329 #> 6 0.808 0.344 1.62 0.295 #> 7 -0.761 -0.428 0.360 -1.21 #> 8 -0.758 -0.632 -1.11 0.153 #> 9 -0.851 0.897 -0.937 0.828 #> 10 0.154 -0.190 -1.40 -0.915 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":null,"dir":"Reference","previous_headings":"","what":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"function calculates tSNE embedding using single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"","code":"tof_reduce_tsne( tof_tibble, tsne_cols = where(tof_is_numeric), num_comp = 2, perplexity = 30, theta = 0.5, max_iterations = 1000, verbose = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"tof_tibble `tof_tbl` `tibble`. tsne_cols Unquoted column names indicating columns `tof_tibble` use computing tSNE embedding. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_comp number tSNE components calculate embedding. Defaults 2. perplexity positive numeric value represents represents rough balance input data’s local global structure emphasized embedding. Smaller values emphasize local structure; larger values emphasize global structure. recommended range generally 5-50. Defaults 30. theta numeric value representing speed/accuracy tradeoff embedding. Set 0 exact tSNE; increase faster approximation. Defaults 0.5 max_iterations integer number iterations use embedding calculation. Defaults 1000. verbose boolean value indicating whether progress updates printed embedding calculation. Default FALSE. ... Additional arguments pass Rtsne.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated tSNE space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_tsne.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Perform t-distributed stochastic neighborhood embedding on single-cell data — tof_reduce_tsne","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) # calculate tsne tof_reduce_tsne(tof_tibble = sim_data) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 7.65 4.09 #> 2 -5.58 -6.48 #> 3 0.309 -5.97 #> 4 -6.72 -6.01 #> 5 2.42 -0.467 #> 6 0.0232 1.01 #> 7 -4.22 -3.00 #> 8 7.07 3.76 #> 9 6.67 1.84 #> 10 -5.70 2.25 #> # ℹ 190 more rows # calculate tsne with only 2 columns tof_reduce_tsne(tof_tibble = sim_data, tsne_cols = c(cd34, cd38)) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 -4.88 -2.18 #> 2 2.85 3.32 #> 3 8.34 -0.916 #> 4 -0.846 6.80 #> 5 -5.65 1.18 #> 6 7.16 -1.99 #> 7 -2.02 -2.87 #> 8 -3.53 -5.04 #> 9 -2.12 -4.92 #> 10 -4.89 8.78 #> # ℹ 190 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":null,"dir":"Reference","previous_headings":"","what":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"function calculates UMAP embedding single-cell data `tof_tibble`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"","code":"tof_reduce_umap( tof_tibble, umap_cols = where(tof_is_numeric), num_comp = 2, neighbors = 5, min_dist = 0.01, learn_rate = 1, epochs = NULL, verbose = FALSE, n_threads = 1, return_recipe = FALSE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"tof_tibble `tof_tbl` `tibble`. umap_cols Unquoted column names indicating columns `tof_tibble` use computing UMAP embedding. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_comp integer number UMAP components. neighbors integer number nearest neighbors used construct target simplicial set. min_dist effective minimum distance embedded points. learn_rate Positive number learning rate optimization process. epochs Number iterations neighbor optimization. See umap details. verbose boolean indicating run details logged console. Defaults FALSE. n_threads Number threads use UMAP calculation. Defaults 1. return_recipe boolean value indicating instead UMAP result, prepped recipe object containing UMAP embedding returned. Set option TRUE want create UMAP embedding using one dataset also want project new observations onto embedding space later. ... Optional. options passed arguments umap.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"tibble number rows `tof_tibble`, representing single cell. `num_comp` columns represents cell's embedding calculated UMAP space.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_reduce_umap.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Apply uniform manifold approximation and projection (UMAP) to single-cell data — tof_reduce_umap","text":"","code":"# simulate single-cell data sim_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200) ) new_data <- dplyr::tibble( cd45 = rnorm(n = 50), cd38 = rnorm(n = 50), cd34 = rnorm(n = 50), cd19 = rnorm(n = 50) ) # calculate umap tof_reduce_umap(tof_tibble = sim_data) #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 200 × 2 #> .umap1 .umap2 #> #> 1 -0.671 2.77 #> 2 -0.398 1.34 #> 3 -2.67 -1.34 #> 4 -0.473 -0.356 #> 5 -0.327 3.20 #> 6 -2.18 2.84 #> 7 4.18 -2.06 #> 8 -0.190 -0.0974 #> 9 4.08 -2.51 #> 10 -2.04 0.786 #> # ℹ 190 more rows # calculate umap with only 2 columns tof_reduce_tsne(tof_tibble = sim_data, umap_cols = c(cd34, cd38)) #> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> #> 1 8.31 -4.23 #> 2 3.03 -3.13 #> 3 -2.08 6.18 #> 4 -4.30 1.17 #> 5 7.53 -2.50 #> 6 5.96 0.366 #> 7 -8.31 -2.83 #> 8 1.13 -4.12 #> 9 -6.24 -2.37 #> 10 4.00 -0.209 #> # ℹ 190 more rows # return recipe umap_recipe <- tof_reduce_umap(tof_tibble = sim_data, return_recipe = TRUE) #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> Found more than one class \"dist\" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ # apply recipe to new data recipes::bake(umap_recipe, new_data = new_data) #> # A tibble: 50 × 2 #> UMAP1 UMAP2 #> #> 1 1.47 -1.53 #> 2 2.64 0.782 #> 3 1.63 -4.23 #> 4 2.06 1.96 #> 5 -1.47 -1.47 #> 6 0.206 -1.21 #> 7 -1.75 3.22 #> 8 2.88 1.60 #> 9 -2.78 -0.962 #> 10 -0.322 -1.91 #> # ℹ 40 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":null,"dir":"Reference","previous_headings":"","what":"Set panel information from a tof_tibble — tof_set_panel","title":"Set panel information from a tof_tibble — tof_set_panel","text":"Set panel information tof_tibble","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Set panel information from a tof_tibble — tof_set_panel","text":"","code":"tof_set_panel(tof_tibble, panel)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Set panel information from a tof_tibble — tof_set_panel","text":"tof_tibble `tof_tbl`. panel tibble containing two columns (`metals` `antigens`) representing information panel","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Set panel information from a tof_tibble — tof_set_panel","text":"`tof_tibble` containing information CyTOF panel used data acquisition data contained input `tof_tibble`. Two columns required: \"metals\" \"antigens\".","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_set_panel.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Set panel information from a tof_tibble — tof_set_panel","text":"","code":"# get current panel from an .fcs file input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) current_panel <- tof_get_panel(tof_tibble) # create a new panel (remove empty channels) new_panel <- dplyr::filter(current_panel, antigens != \"empty\") tof_set_panel(tof_tibble = tof_tibble, panel = new_panel) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 11021370 16 517. 1.33 13.2 865. #> 2 7112446. 19 850. 3.99 12.7 756. #> 3 9722098 16 747. 6.96 4.85 639. #> 4 2267279. 16 585. 0.648 6.32 586. #> 5 9624729 18 773. 3.93 9.65 645. #> 6 4439897 19 485. 4.18 3.84 627. #> 7 2762526. 16 504. 3.46 4.25 566. #> 8 3746682. 18 567. 6.62 11.7 703. #> 9 15214280 19 1043. 3.10 9.20 853. #> 10 42699. 16 543. 0.888 13.6 813. #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"function uses algorithm described Qiu et al., (2011) estimate local density cell `tof_tbl` `tibble` containing high-dimensional cytometry data. Briefly, algorithm involves counting number neighboring cells within sphere radius alpha surrounding cell. , using nn2 function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"","code":"tof_spade_density( tof_tibble, distance_cols = where(tof_is_numeric), distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\"), num_alpha_cells = 2000L, alpha_multiplier = 5, max_neighbors = round(0.01 * nrow(tof_tibble)), normalize = TRUE, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"tof_tibble `tof_tbl` `tibble`. distance_cols Unquoted names columns `tof_tibble` use calculating cell--cell distances local density estimation cell. Defaults numeric columns `tof_tibble`. distance_function string indicating distance function use calculating cell--cell distances local density estimation. Options include \"euclidean\" (default) \"cosine\". num_alpha_cells integer indicating many cells `tof_tibble` randomly sampled `tof_tibble` order estimate `alpha`, radius sphere constructed around cell local density estimation. Alpha calculated taking median nearest-neighbor distance `num_alpha_cells` randomly-sampled cells multiplying `alpha_multiplier`. Defaults 2000. alpha_multiplier numeric value indicating multiplier used calculating `alpha`, radius sphere constructed around cell local density estimation. Alpha calculated taking median nearest-neighbor distance `num_alpha_cells` cells randomly-sampled `tof_tibble` multiplying `alpha_multiplier`. Defaults 5. max_neighbors integer indicating maximum number neighbors can counted within sphere surrounding given cell. Implemented reduce density estimation procedure's speed memory requirements. Defaults 1% number rows `tof_tibble`. normalize boolean value indicating vector local density estimates normalized values 0 1. Defaults TRUE. ... Additional optional arguments pass tof_find_knn.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"tibble single column named \".spade_density\" containing local density estimates input cell `tof_tibble`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_spade_density.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate cells' local densities as done in Spanning-tree Progression Analysis of Density-normalized Events (SPADE) — tof_spade_density","text":"","code":"sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) # perform the density estimation tof_spade_density(tof_tibble = sim_data) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 1 #> 2 1 #> 3 1 #> 4 1 #> 5 1 #> 6 1 #> 7 1 #> 8 1 #> 9 1 #> 10 1 #> # ℹ 990 more rows # perform the density estimation using cosine distance tof_spade_density( tof_tibble = sim_data, distance_function = \"cosine\", alpha_multiplier = 2 ) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 0 #> 2 0.143 #> 3 0.286 #> 4 0.143 #> 5 0.286 #> 6 0.286 #> 7 0.143 #> 8 0.143 #> 9 0.429 #> 10 0 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around # each cell tof_spade_density( tof_tibble = sim_data, alpha_multiplier = 2 ) #> # A tibble: 1,000 × 1 #> .spade_density #> #> 1 0.7 #> 2 1 #> 3 1 #> 4 1 #> 5 0.4 #> 6 1 #> 7 1 #> 8 1 #> 9 1 #> 10 1 #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Split high-dimensional cytometry data into a training and test set — tof_split_data","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"Split high-dimensional cytometry data training test set","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"","code":"tof_split_data( feature_tibble, split_method = c(\"k-fold\", \"bootstrap\", \"simple\"), split_col, simple_prop = 3/4, num_cv_folds = 10, num_cv_repeats = 1L, num_bootstraps = 10, strata = NULL, ... )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"feature_tibble tibble row represents sample- patient- level observation, produced tof_extract_features. split_method Either string logical vector specifying perform split. string, valid options include k-fold cross validation (\"k-fold\"; default), bootstrapping (\"bootstrap\"), single binary split (\"simple\"). logical vector, contain one entry row `feature_tibble` indicating row included training set (TRUE) excluded validation/test set (FALSE). Ignored entirely `split_col` specified. split_col unquoted column name logical column `feature_tibble` indicating row included training set (TRUE) excluded validation/test set (FALSE). simple_prop numeric value 0 1 indicating proportion data used training. Defaults 3/4. Ignored split_method \"simple\". num_cv_folds integer indicating many cross-validation folds used. Defaults 10. Ignored split_method \"k-fold\". num_cv_repeats integer indicating many independent cross-validation replicates used (.e. many num_cv_fold splits performed). Defaults 1. Ignored split_method \"k-fold\". num_bootstraps integer indicating many independent bootstrap replicates used. Defaults 25. Ignored split_method \"bootstrap\". strata unquoted column name representing column feature_tibble used stratify data splitting. Defaults NULL (stratification). ... Optional additional arguments pass vfold_cv k-fold cross validation, bootstraps bootstrapping, initial_split simple splitting.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"k-fold cross validation bootstrapping, \"rset\" object; simple splitting, \"rsplit\" object. details, see rsample.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split high-dimensional cytometry data into a training and test set — tof_split_data","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 50), rep(1, times = 50)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) # split the dataset into 10 CV folds tof_split_data( feature_tibble = feature_tibble, split_method = \"k-fold\" ) #> # 10-fold cross-validation #> # A tibble: 10 × 2 #> splits id #> #> 1 Fold01 #> 2 Fold02 #> 3 Fold03 #> 4 Fold04 #> 5 Fold05 #> 6 Fold06 #> 7 Fold07 #> 8 Fold08 #> 9 Fold09 #> 10 Fold10 # split the dataset into 10 bootstrap resamplings tof_split_data( feature_tibble = feature_tibble, split_method = \"bootstrap\" ) #> # Bootstrap sampling #> # A tibble: 10 × 2 #> splits id #> #> 1 Bootstrap01 #> 2 Bootstrap02 #> 3 Bootstrap03 #> 4 Bootstrap04 #> 5 Bootstrap05 #> 6 Bootstrap06 #> 7 Bootstrap07 #> 8 Bootstrap08 #> 9 Bootstrap09 #> 10 Bootstrap10 # split the dataset into a single training/test set # stratified by the \"class\" column tof_split_data( feature_tibble = feature_tibble, split_method = \"simple\", strata = class ) #> #> <74/26/100>"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":null,"dir":"Reference","previous_headings":"","what":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"Split dimensionality reduction data tidytof combines SingleCellExperiment conversion","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"","code":"tof_split_tidytof_reduced_dimensions(sce)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"sce SingleCellExperiment entry named \"tidytof_reduced_dimensions\" reducedDims slot.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"SingleCellExperiment separate entries named \"tidytof_pca\", \"tidytof_umap\", \"tidytof_tsne\" reducedDims slots (one dimensionality reduction methods tidytof native support).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_split_tidytof_reduced_dimensions.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Split the dimensionality reduction data that tidytof combines during SingleCellExperiment conversion — tof_split_tidytof_reduced_dimensions","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":null,"dir":"Reference","previous_headings":"","what":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"function uses training set/test set paradigm tune fit elastic net model using variety user-specified details. Tuning can performed using either simple training vs. test set split, k-fold cross-validation, bootstrapping, multiple preprocessing options available.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"","code":"tof_train_model( split_data, unsplit_data, predictor_cols, response_col = NULL, time_col = NULL, event_col = NULL, model_type = c(\"linear\", \"two-class\", \"multiclass\", \"survival\"), hyperparameter_grid = tof_create_grid(), standardize_predictors = TRUE, remove_zv_predictors = FALSE, impute_missing_predictors = FALSE, optimization_metric = \"tidytof_default\", best_model_type = c(\"best\", \"best with sparsity\"), num_cores = 1 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"split_data `rsplit` `rset` object rsample package containing sample-level data use modeling. easiest way generate use tof_split_data. unsplit_data tibble containing sample-level data use modeling without resampling. using resampling method advised, argument provides interface fit model without using cross-validation bootstrap resampling. Ignored split_data provided. predictor_cols Unquoted column names indicating columns data contained `split_data` used predictors elastic net model. Supports tidyselect helpers. response_col Unquoted column name indicating column data contained `split_data` used outcome \"two-class\", \"multiclass\", \"linear\" elastic net model. Must factor \"two-class\" \"multiclass\" models must numeric \"linear\" models. Ignored `model_type` \"survival\". time_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must numeric. Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". event_col Unquoted column name indicating column data contained `split_data` represents time--event outcome \"survival\" elastic net model. Must binary column - values either 0 1 (1 indicating adverse event) FALSE TRUE (TRUE indicating adverse event). Ignored `model_type` \"two-class\", \"multiclass\", \"linear\". model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. hyperparameter_grid hyperparameter grid indicating values elastic net penalty (lambda) elastic net mixture (alpha) hyperparamters used model tuning. Generate grid using tof_create_grid. standardize_predictors logical value indicating numeric predictor columns standardized (centered scaled) model fitting, standard practice elastic net regularization. Defaults TRUE. remove_zv_predictors logical value indicating predictor columns near-zero variance removed model fitting using step_nzv. Defaults FALSE. impute_missing_predictors logical value indicating predictor columns missing values imputed using k-nearest neighbors model fitting (see step_impute_knn). Imputation performed using observation's 5 nearest-neighbors. Defaults FALSE. optimization_metric string indicating optimization metric used hyperparameter selection model tuning. Valid values depend model_type. \"linear\" models, choices \"mse\" (mean squared error predictions; default) \"mae\" (mean absolute error predictions). \"two-class\" models, choices \"roc_auc\" (area Receiver-Operating Curve classification; default), \"misclassification error\" (proportion misclassified observations), \"binomial_deviance\" (see deviance.glmnet), \"mse\" (mean squared error logit function), \"mae\" (mean absolute error logit function). \"multiclass\" models, choices \"roc_auc\" (area Receiver-Operating Curve classification using Hand-Till generalization ROC AUC multiclass models roc_auc; default), \"misclassification error\" (proportion misclassified observations), \"multinomial_deviance\" (see deviance.glmnet), \"mse\" \"mae\" . \"survival\" models, choices \"concordance_index\" (Harrel's C index; see deviance.glmnet) \"partial_likelihood_deviance\" (see deviance.glmnet). best_model_type Currently unused. num_cores Integer indicating many cores used parallel processing fitting multiple models. Defaults 1. Overhead separate models across multiple cores can high, significant speedup unlikely observed unless many large models fit.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"`tof_model`, S3 class includes elastic net model best performance (assessed via cross-validation, bootstrapping, simple splitting depending `split_data`) across tested hyperparameter value combinations. `tof_models` store following information: model final elastic net (\"glmnet\") model, chosen selecting elastic net hyperparameters best `optimization_metric` performance validation sets resample used train model (average) recipe recipe used data preprocessing mixture optimal mixture hyperparameter (alpha) glmnet model penalty optimal penalty hyperparameter (lambda) glmnet model model_type string indicating type glmnet model fit outcome_colnames character vector representing names columns training data modeled outcome variables training_data tibble containing (preprocessed) data used train model tuning_metrics tibble containing validation set performance metrics (model predictions) resample fold model tuning. log_rank_thresholds survival models , tibble containing information relative-risk thresholds can used split training data 2 risk groups (low- high-risk) based final model's predictions. relative-risk threshold, log-rank test p-value indicator threshold gives significant separation provided. best_log_rank_threshold survival models , numeric value representing relative-risk threshold yields significant log-rank test separating training data low- high-risk groups.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_train_model.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Train an elastic net model to predict sample-level phenomena using high-dimensional cytometry data. — tof_train_model","text":"","code":"feature_tibble <- dplyr::tibble( sample = as.character(1:100), cd45 = runif(n = 100), pstat5 = runif(n = 100), cd34 = runif(n = 100), outcome = (3 * cd45) + (4 * pstat5) + rnorm(100), class = as.factor( dplyr::if_else(outcome > median(outcome), \"class1\", \"class2\") ), multiclass = as.factor( c(rep(\"class1\", 30), rep(\"class2\", 30), rep(\"class3\", 40)) ), event = c(rep(0, times = 30), rep(1, times = 70)), time_to_event = rnorm(n = 100, mean = 10, sd = 2) ) split_data <- tof_split_data(feature_tibble, split_method = \"simple\") # train a regression model tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = outcome, model_type = \"linear\" ) #> A linear `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 4 × 2 #> feature coefficient #> #> 1 (Intercept) 3.51 #> 2 pstat5 1.28 #> 3 cd45 0.836 #> 4 cd34 0.123 # train a logistic regression classifier tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), response_col = class, model_type = \"two-class\" ) #> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-03 #> # A tibble: 4 × 2 #> feature coefficient #> #> 1 pstat5 -2.31 #> 2 cd45 -1.19 #> 3 cd34 -0.275 #> 4 (Intercept) -0.0137 # train a cox regression survival model tof_train_model( split_data = split_data, predictor_cols = c(cd45, pstat5, cd34), time_col = time_to_event, event_col = event, model_type = \"survival\" ) #> A survival `tof_model` with a mixture parameter (alpha) of 0 and a penalty parameter (lambda) of 1e+00 #> # A tibble: 3 × 2 #> feature coefficient #> #> 1 pstat5 0.0633 #> 2 cd45 -0.0608 #> 3 cd34 0.0493"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":null,"dir":"Reference","previous_headings":"","what":"Transform raw high-dimensional cytometry data. — tof_transform","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"function transforms `tof_tbl` raw ion counts, reads, fluorescence intensity units directly measured cytometer using user-provided function.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"","code":"tof_transform( tof_tibble = NULL, channel_cols = where(tof_is_numeric), transform_fun )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"tof_tibble `tof_tbl` `tibble`. channel_cols Unquoted column names representing columns contain single-cell protein measurements. Supports tidyselect helpers. nothing specified, default transform numeric columns. transform_fun vectorized function apply protein value variance stabilization.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"`tof_tbl` identical dimensions input `tof_tibble`, columns specified channel_cols transformed using `transform_fun`.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_transform.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Transform raw high-dimensional cytometry data. — tof_transform","text":"","code":"# read in an example .fcs file from tidytof's internal datasets input_file <- dir(tidytof_example_data(\"aml\"), full.names = TRUE)[[1]] tof_tibble <- tof_read_data(input_file) # preprocess all numeric columns with default behavior # arcsinh transformation with a cofactor of 5 tof_preprocess(tof_tibble) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 15.3 1.88 5.33 0.263 1.70 5.85 #> 2 14.9 2.05 5.83 0.731 1.67 5.71 #> 3 15.2 1.88 5.70 1.13 0.861 5.54 #> 4 13.7 1.88 5.45 0.129 1.06 5.46 #> 5 15.2 1.99 5.73 0.721 1.41 5.55 #> 6 14.4 2.05 5.27 0.760 0.708 5.52 #> 7 13.9 1.88 5.31 0.645 0.771 5.42 #> 8 14.2 1.99 5.42 1.09 1.58 5.64 #> 9 15.6 2.05 6.03 0.586 1.37 5.83 #> 10 9.75 1.88 5.38 0.177 1.73 5.78 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , … # preprocess all numeric columns using the log base 10 tranformation tof_preprocess(tof_tibble, transform_fun = log10) #> # A tibble: 100 × 59 #> Time Event_length `CD45|Y89` `empty|Pd102` `empty|Pd104` `empty|Pd105` #> #> 1 7.04 1.20 2.71 0.125 1.12 2.94 #> 2 6.85 1.28 2.93 0.601 1.11 2.88 #> 3 6.99 1.20 2.87 0.843 0.686 2.81 #> 4 6.36 1.20 2.77 -0.189 0.800 2.77 #> 5 6.98 1.26 2.89 0.594 0.984 2.81 #> 6 6.65 1.28 2.69 0.621 0.584 2.80 #> 7 6.44 1.20 2.70 0.539 0.628 2.75 #> 8 6.57 1.26 2.75 0.821 1.07 2.85 #> 9 7.18 1.28 3.02 0.491 0.964 2.93 #> 10 4.63 1.20 2.74 -0.0515 1.13 2.91 #> # ℹ 90 more rows #> # ℹ 53 more variables: `empty|Pd106` , `empty|Pd108` , #> # `empty|Pd110` , `CD61|In113` , `CD99|In115` , #> # `empty|I127` , `CD45RA|La139` , `CD93|Ce140` , #> # `CD3_CD19|Pr141` , `CCR2|Nd142` , `CD117|Nd143` , #> # `CD123|Nd144` , `CD64|Nd145` , `CD90|Nd146` , #> # `CD38|Sm147` , `CD34|Nd148` , `CEBPa|Sm149` , …"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":null,"dir":"Reference","previous_headings":"","what":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"Tune elastic net model's hyperparameters multiple resamples","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"","code":"tof_tune_glmnet( split_data, prepped_recipe, hyperparameter_grid, model_type, outcome_cols, optimization_metric = \"tidytof_default\", num_cores = 1 )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"split_data `rsplit` `rset` object rsample package. easiest way generate use tof_split_data. Alternatively, unsplit tbl_df can provided, though recommended. prepped_recipe Either single recipe object (`split_data` `rsplit` object `tbl_df`) list recipes (`split_data` `rset` object) entry list corresponds resample `split_data`. hyperparameter_grid hyperparameter grid indicating values elastic net penalty (lambda) elastic net mixture (alpha) hyperparameters used model tuning. Generate grid using tof_create_grid. model_type string indicating kind elastic net model build. continuous response predicted, use \"linear\" linear regression; categorical response 2 classes predicted, use \"two-class\" logistic regression; categorical response 2 levels predicted, use \"multiclass\" multinomial regression; time--event outcome predicted, use \"survival\" Cox regression. outcome_cols Unquoted column name(s) indicating column(s) data contained `split_data` used outcome elastic net model. survival models, two columns selected; others, one column selected. optimization_metric string indicating optimization metric used hyperparameter selection model tuning. Valid values depend model_type. num_cores Integer indicating many cores used parallel processing fitting multiple models. Defaults 1. Overhead separate models across multiple cores can high, significant speedup unlikely observed unless many large models fit.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_tune_glmnet.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Tune an elastic net model's hyperparameters over multiple resamples — tof_tune_glmnet","text":"tibble containing summary model's performance resampling iteration across hyperparameter combinations. contain 3 columns: \"splits\" (list-col containing resampling iteration's `rsplit` object), \"id\" (name resampling iteration), \"performance_metrics\" (list-col containing performance metrics resampling iteration. row \"performance_metrics\" tibble columns \"mixture\" \"penalty\" several additional columns containing performance metrics model mixture/penalty combination). See tof_fit_split additional details.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"function performs distance-based upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). calculating distance (either mahalanobis, cosine, pearson) cell `tof_tibble` centroid cluster `reference_tibble`, sorting cells cluster corresponding closest centroid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"","code":"tof_upsample( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), ..., augment = TRUE, method = c(\"distance\", \"neighbor\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. ... Additional arguments pass `tof_upsample_*` function family member corresponding chosen method. augment boolean value indicating output column-bind cluster ids cell new column `tof_tibble` (TRUE, default) single-column tibble including cluster ids returned (FALSE). method string indicating clustering methods used. Valid values include \"distance\" (default) \"neighbor\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"`tof_tbl` `tibble` augment = FALSE, single column encoding upsampled cluster ids cell `tof_tibble`. augment = TRUE, ncol(tof_tibble) + 1 columns: (unaltered) columns `tof_tibble` plus additional column encoding cluster ids.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using distance to cluster centroids tof_upsample( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, method = \"distance\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .upsample_cluster #> #> 1 1.88 -0.666 -0.879 -1.05 b #> 2 0.717 -1.30 0.351 -0.598 b #> 3 -0.175 0.328 1.06 -0.521 a #> 4 -0.600 0.0668 -0.331 -0.147 a #> 5 0.452 0.359 -3.45 -0.699 a #> 6 0.585 -0.176 2.05 -0.112 a #> 7 0.701 1.20 1.74 0.252 a #> 8 -1.77 -2.10 -0.554 -0.625 a #> 9 1.99 -0.590 1.45 -0.588 b #> 10 0.807 0.0548 1.46 0.435 a #> # ℹ 990 more rows # upsample using distance to nearest neighbor tof_upsample( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, method = \"neighbor\" ) #> # A tibble: 1,000 × 5 #> cd45 cd38 cd34 cd19 .upsample_cluster #> #> 1 1.88 -0.666 -0.879 -1.05 b #> 2 0.717 -1.30 0.351 -0.598 a #> 3 -0.175 0.328 1.06 -0.521 b #> 4 -0.600 0.0668 -0.331 -0.147 b #> 5 0.452 0.359 -3.45 -0.699 a #> 6 0.585 -0.176 2.05 -0.112 b #> 7 0.701 1.20 1.74 0.252 b #> 8 -1.77 -2.10 -0.554 -0.625 b #> 9 1.99 -0.590 1.45 -0.588 a #> 10 0.807 0.0548 1.46 0.435 b #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"function performs distance-based upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). calculating distance (either mahalanobis, cosine, pearson) cell `tof_tibble` centroid cluster `reference_tibble`, sorting cells cluster corresponding closest centroid.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"","code":"tof_upsample_distance( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), parallel_cols, distance_function = c(\"mahalanobis\", \"cosine\", \"pearson\"), num_cores = 1L, return_distances = FALSE )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. parallel_cols Optional. Unquoted column names indicating columns `tof_tibble` use breaking data order parallelize upsampling using `foreach` `doParallel` backend. Supports tidyselect helpers. distance_function string indicating distance function used perform upsampling. Options \"mahalanobis\" (default), \"cosine\", \"pearson\". num_cores integer indicating number CPU cores used parallelize classification. Defaults 1 (single core). return_distances boolean value indicating whether returned result include one column, cluster ids corresponding row `tof_tibble` (return_distances = FALSE, default), returned result include additional columns representing distance row `tof_tibble` reference subpopulation centroids (return_distances = TRUE).","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"`return_distances = FALSE`, tibble one column named `.upsample_cluster`, character vector length `nrow(tof_tibble)` indicating id reference cluster cell (.e. row) `tof_tibble` assigned. `return_distances = TRUE`, tibble `nrow(tof_tibble)` rows num_clusters + 1 columns, num_clusters number clusters `reference_tibble`. row represents cell `tof_tibble`, num_clusters columns represent distance cell reference subpopulations' cluster centroids. final column represents cluster id reference subpopulation minimum distance cell represented row.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_distance.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the closest cluster in a reference dataset — tof_upsample_distance","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using mahalanobis distance tof_upsample_distance( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 b #> 2 b #> 3 b #> 4 b #> 5 b #> 6 b #> 7 b #> 8 b #> 9 b #> 10 b #> # ℹ 990 more rows # upsample using cosine distance tof_upsample_distance( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, distance_function = \"cosine\" ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 a #> 2 a #> 3 a #> 4 a #> 5 a #> 6 a #> 7 a #> 8 b #> 9 a #> 10 a #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":null,"dir":"Reference","previous_headings":"","what":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"function performs upsampling CyTOF data sorting single cells (passed function `tof_tibble`) phenotypically similar cell subpopulation reference dataset (passed function `reference_tibble`). finding cell `tof_tibble`'s nearest neighbor `reference_tibble` assigning cluster nearest neighbor belongs. nearest neighbor calculation can performed either euclidean cosine distance.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"","code":"tof_upsample_neighbor( tof_tibble, reference_tibble, reference_cluster_col, upsample_cols = where(tof_is_numeric), num_neighbors = 1L, distance_function = c(\"euclidean\", \"cosine\", \"l2\", \"ip\") )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"tof_tibble `tibble` `tof_tbl` containing cells upsampled nearest reference subpopulation. reference_tibble `tibble` `tof_tibble` containing cells already clustered manually gated subpopulations. reference_cluster_col unquoted column name indicating column `reference_tibble` contains subpopulation label (cluster id) cell `reference_tibble`. upsample_cols Unquoted column names indicating columns `tof_tibble` use computing distances used upsampling. Defaults numeric columns `tof_tibble`. Supports tidyselect helpers. num_neighbors integer indicating many neighbors used nearest neighbor calculation. Clusters assigned based majority vote. distance_function string indicating distance function used perform upsampling. Options \"euclidean\" (default) \"cosine\".","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"tibble one column named `.upsample_cluster`, character vector length `nrow(tof_tibble)` indicating id reference cluster cell (.e. row) `tof_tibble` assigned.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_upsample_neighbor.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Upsample cells into the cluster of their nearest neighbor a reference dataset — tof_upsample_neighbor","text":"","code":"# simulate single-cell data (and reference data with clusters to upsample # into sim_data <- dplyr::tibble( cd45 = rnorm(n = 1000), cd38 = rnorm(n = 1000), cd34 = rnorm(n = 1000), cd19 = rnorm(n = 1000) ) reference_data <- dplyr::tibble( cd45 = rnorm(n = 200), cd38 = rnorm(n = 200), cd34 = rnorm(n = 200), cd19 = rnorm(n = 200), cluster_id = c(rep(\"a\", times = 100), rep(\"b\", times = 100)) ) # upsample using euclidean distance tof_upsample_neighbor( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 a #> 2 b #> 3 b #> 4 a #> 5 a #> 6 a #> 7 b #> 8 a #> 9 b #> 10 b #> # ℹ 990 more rows # upsample using cosine distance tof_upsample_neighbor( tof_tibble = sim_data, reference_tibble = reference_data, reference_cluster_col = cluster_id, distance_function = \"cosine\" ) #> # A tibble: 1,000 × 1 #> .upsample_cluster #> #> 1 b #> 2 b #> 3 b #> 4 b #> 5 a #> 6 a #> 7 a #> 8 b #> 9 a #> 10 b #> # ℹ 990 more rows"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":null,"dir":"Reference","previous_headings":"","what":"Write a series of .csv files from a tof_tbl — tof_write_csv","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"function takes given `tof_tbl` writes single-cell data contains .csv files within directory located `out_path`. `group_cols` argument specifies rows `tof_tbl` (cell) broken separate .csv files","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"","code":"tof_write_csv(tof_tibble, group_cols, out_path, sep = \"_\", file_name)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"tof_tibble `tof_tbl` `tibble`. group_cols Optional. Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults NULL (cells written single file). out_path system path indicating directory output .csv files saved. directory exist, created. sep Delimiter used values `group_cols` create output .csv file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_csv.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write a series of .csv files from a tof_tbl — tof_write_csv","text":"function return anything. Instead, side-effect saving .csv files `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":null,"dir":"Reference","previous_headings":"","what":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"Write data (form `tof_tbl`) either .csv .fcs file storage.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"","code":"tof_write_data( tof_tibble = NULL, group_cols, out_path = NULL, format = c(\"fcs\", \"csv\"), sep = \"_\", file_name )"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"tof_tibble `tof_tbl` `tibble`. group_cols Optional. Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults grouping (cells written single file). out_path Path directory output files saved. format format files written. Currently supports .csv .fcs files sep Delimiter used values `group_cols` create output .csv/.fcs file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"function explicitly return values. Instead, writes .csv /.fcs files specified `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_data.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write high-dimensional cytometry data to a file or to a directory of files — tof_write_data","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":null,"dir":"Reference","previous_headings":"","what":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"function takes given `tof_tbl` writes single-cell data contains .fcs files within directory located `out_path`. `group_cols` argument specifies rows `tof_tbl` (cell) broken separate .fcs files","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"","code":"tof_write_fcs(tof_tibble, group_cols, out_path, sep = \"_\", file_name)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"tof_tibble `tof_tbl` `tibble`. group_cols Unquoted names columns `tof_tibble` used group cells separate files. Supports tidyselect helpers. Defaults NULL (cells written single file). out_path system path indicating directory output .csv files saved. directory exist, created. sep Delimiter used values `group_cols` create output .fcs file names. Defaults \"_\". file_name `group_cols` specified, name (without extension) used saved .csv file.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"function return anything. Instead, side-effect saving .fcs files `out_path`.","code":""},{"path":[]},{"path":"https://keyes-timothy.github.io/tidytof/reference/tof_write_fcs.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Write a series of .fcs files from a tof_tbl — tof_write_fcs","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":null,"dir":"Reference","previous_headings":"","what":"Select variables with a function — where","title":"Select variables with a function — where","text":"copy , selection helper selects variables predicate function returns TRUE. See language details tidyselection.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Select variables with a function — where","text":"","code":"where(fn)"},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Select variables with a function — where","text":"fn function returns TRUE FALSE (technically, predicate function). Can also purrr-like formula.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Select variables with a function — where","text":"predicate can used select columns data.frame.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Select variables with a function — where","text":"help file replicated verbatim tidyselect-package.","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Select variables with a function — where","text":"Lionel Henry Hadley Wickham (2021). tidyselect: Select Set Strings. R package version 1.1.1. https://CRAN.R-project.org/package=tidyselect","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/where.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Select variables with a function — where","text":"","code":"NULL #> NULL"},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0995","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.5","title":"tidytof 0.99.5","text":"NEW FEATURES Update dependencies. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0994","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.4","title":"tidytof 0.99.4","text":"NEW FEATURES Update tests. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0993","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.3","title":"tidytof 0.99.3","text":"NEW FEATURES Update docs. SIGNIFICANT USER-VISIBLE CHANGES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0992","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.2","title":"tidytof 0.99.2","text":"NEW FEATURES Update docs. SIGNIFICANT USER-VISIBLE CHANGES None BUG FIXES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0992-1","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.2","title":"tidytof 0.99.2","text":"NEW FEATURES Removed unnecessary files caused build fail. SIGNIFICANT USER-VISIBLE CHANGES None BUG FIXES None","code":""},{"path":"https://keyes-timothy.github.io/tidytof/news/index.html","id":"tidytof-0990","dir":"Changelog","previous_headings":"","what":"tidytof 0.99.0","title":"tidytof 0.99.0","text":"NEW FEATURES Added NEWS.md file track changes package. SIGNIFICANT USER-VISIBLE CHANGES Submitted Bioconductor BUG FIXES None","code":""}]