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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 @@ -152,18 +152,18 @@

Dimensionality red tof_preprocess() |> tof_reduce_dimensions(method = "tsne", augment = FALSE) #> # A tibble: 3,000 × 2 -#> .tsne1 .tsne2 -#> <dbl> <dbl> -#> 1 8.65 13.2 -#> 2 1.61 16.2 -#> 3 -9.86 38.4 -#> 4 -8.74 17.2 -#> 5 6.72 15.9 -#> 6 -4.57 24.6 -#> 7 0.840 17.9 -#> 8 -0.335 30.1 -#> 9 -3.15 24.4 -#> 10 4.96 8.83 +#> .tsne1 .tsne2 +#> <dbl> <dbl> +#> 1 9.10 10.6 +#> 2 -0.610 12.8 +#> 3 4.70 35.3 +#> 4 0.467 22.0 +#> 5 8.97 17.7 +#> 6 -5.29 23.7 +#> 7 -3.58 13.8 +#> 8 -11.6 23.4 +#> 9 -4.22 21.2 +#> 10 1.12 7.05 #> # ℹ 2,990 more rows

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

@@ -171,18 +171,18 @@

Dimensionality red phenograph_data |> tof_reduce_dimensions(method = "umap", augment = FALSE) #> # A tibble: 3,000 × 2 -#> .umap1 .umap2 -#> <dbl> <dbl> -#> 1 9.72 2.09 -#> 2 8.54 2.11 -#> 3 6.34 0.991 -#> 4 4.00 -2.02 -#> 5 9.69 1.81 -#> 6 1.18 -3.16 -#> 7 9.50 1.33 -#> 8 4.95 -1.21 -#> 9 4.90 1.34 -#> 10 9.49 4.33 +#> .umap1 .umap2 +#> <dbl> <dbl> +#> 1 9.68 4.59 +#> 2 8.90 3.55 +#> 3 3.06 -0.0897 +#> 4 2.76 -1.80 +#> 5 9.87 4.21 +#> 6 0.317 -2.52 +#> 7 9.92 3.76 +#> 8 2.23 -0.769 +#> 9 5.43 -0.381 +#> 10 8.30 5.75 #> # 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 a27856fea4355f7e6139f05a399af84860e578c4..95ef2e5c73869d07478078cbf3ad5cc8383ea01d 100644 GIT binary patch literal 497604 zcmc$`WmJ@57dA>G4bp7^(nvRw1|=cg-6`FTfP_f5bR*pzN+Tc*LpKcFaPE0ueb4v% z{P@nSg|mjm%skJ1?|biSU)Qz${6X#w7CIR^92^|hySEaGaB#@3aBztFk5IsGv=PR2 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zD@1J_lA-a0T$g5)njazd?c}gIRo_%y{RC3l-I!8Cy;67-gY$BWgBXB*2X|nDKS9hY z!eppKWOgG9i8YqYR}dG&5vTBzYfY=c9f*q~R6ZVf&D{JPrWBS_hS@NDq{zua4HPoH zu#$1-GwFzLOluUwjya|*lH~)AS#lw}^UD`6cHfcSMegc|(OJl-2U8o&(^{-*HH>YW zj9HGFUBm_$@8jMEzncaC@+wH{5VYeLsOJ;IZBnOp-jIp^INJ|*@vN_=Wr0Utb*6k= z3IZSMlryH@QPbeVSESQiu@&|5{dvswBnMR}Y~66Dsj-np#Wk2gUh{WZs!ean^S^G5 zLDk9SYL4$Ou=kw8T*iv=Axrw&uPz?2bE%ELIg*;1TGoh^NUC-LarzcA%$bRhni9bI z(q;`5vCBGqPK&74#3&jX8uEcz2zX!*u?!rqNpKTFNmCK4%yR=r7?zVTabIk887~{a zoECpMPostprocessing with tof_postprocess select(cd45, cd34, cd38) %>% head() #> # A tibble: 6 × 3 -#> cd45 cd34 cd38 -#> <dbl> <dbl> <dbl> -#> 1 130. 2.81 1.17 -#> 2 229. -0.800 10.6 -#> 3 292. 4.68 1.42 -#> 4 431. -0.406 13.2 +#> cd45 cd34 cd38 +#> <dbl> <dbl> <dbl> +#> 1 130. 3.18 0.862 +#> 2 229. -0.949 10.6 +#> 3 293. 4.93 1.35 +#> 4 431. 0.0843 13.0 #> # ℹ 2 more rows
    diff --git a/index.html b/index.html index 4df2870..a4e4bc2 100644 --- a/index.html +++ b/index.html @@ -91,8 +91,8 @@
    Sample-level operations
    -

    tidytof: A user-friendly framework for interactive and highly reproducible cytometry data analysis - +

    tidytof: A user-friendly framework for interactive and reproducible cytometry data analysis +

    @@ -127,17 +127,17 @@

    Installation
    -if(!require(devtools)) install.packages("devtools") 
+if (!require(devtools)) install.packages("devtools")
 devtools::install_github("keyes-timothy/tidytof")

    Once tidytof is installed, you can attach it to your current R session using the following code:

    In addition, we can install and load the other packages we need for this vignette:

    -if(!require(FlowSOM)) BiocManager::install("FlowSOM")
+if (!require(FlowSOM)) BiocManager::install("FlowSOM")
 library(FlowSOM)
 
-if(!require(tidyverse)) install.packages("tidyverse")
+if (!require(tidyverse)) install.packages("tidyverse")
 library(tidyverse)
    @@ -164,12 +164,12 @@

    Reading data with tof_read_dataUsing one of these directories (or any other directory containing cytometry data on your local machine), we can use tof_read_data to read cytometry data from raw files. Acceptable formats include .fcs files and .csv files. Importantly, tof_read_data is smart enough to read single .fcs/.csv files or multiple .fcs/.csv files depending on whether its first argument (path) leads to a single file or to a directory of files.

    Here, we can use tof_read_data to read in all of the .fcs files in the “phenograph” example dataset bundled into tidytof and store it in the phenograph variable.

    -phenograph <- 
-  tidytof_example_data("phenograph") |> 
-  tof_read_data()
+phenograph <-
+    tidytof_example_data("phenograph") |>
+    tof_read_data()
 
-phenograph |> 
-  class()
+phenograph |>
+    class()
 #> [1] "tof_tbl"    "tbl_df"     "tbl"        "data.frame"

    Regardless of its input format, tidytof reads data into an extended tibble called a tof_tbl (pronounced “tof tibble”), an S3 class identical to tbl_df, but with one additional attribute (“panel”). tidytof stores this additional attribute in tof_tbls because, in addition to analyzing cytometry data from individual experiments, cytometry users often want to compare panels between experiments to find common markers or to compare which metals are associated with particular markers across panels.

    A few notes about tof_tbls:

    @@ -181,18 +181,18 @@

    Reading data with tof_read_dataBecause tof_tbls inherit from the tbl_df class, all methods available to tibbles are also available to tof_tbls. For example, dplyr’s useful mutate method can be applied to our tof_tbl named phenograph above to convert the columns encoding the phenograph cluster ID and stimulation condition to which each cell belongs into character vectors (instead of their original numeric codes in the uncleaned dataset). 
    -phenograph <- 
-  phenograph |> 
-  # mutate the input tof_tbl
-  mutate(
-    PhenoGraph = as.character(PhenoGraph), 
-    Condition = as.character(Condition)
-  )
+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()
+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
 #>   <chr>                      <chr>      <chr>    
@@ -204,14 +204,14 @@ 
    Reading data with tof_read_data#> 6 H1_PhenoGraph_cluster1.fcs 12         12

    The tof_tbl class is preserved even after these transformations.

    -phenograph |> 
-  class()
+phenograph |>
+    class()
 #> [1] "tof_tbl"    "tbl_df"     "tbl"        "data.frame"

    Finally, to retrieve panel information from a tof_tbl, use tof_get_panel:

    -phenograph |> 
-  tof_get_panel() |> 
-  head()
+phenograph |>
+    tof_get_panel() |>
+    head()
 #> # A tibble: 6 × 2
 #>   metals      antigens   
 #>   <chr>       <chr>      
@@ -232,9 +232,9 @@ 
    Pre-processing with tof_prepro
 
    As an example, we can preprocess our phenograph tof_tibble above and see how our first few measurements change before and after.
    
 
     # before preprocessing
-phenograph |> 
-  select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> 
-  head()
+phenograph |>
+    select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |>
+    head()
 #> # A tibble: 6 × 3
 #>   `CD45|Sm154` `CD34|Nd148` `CD38|Er167`
 #>          <dbl>        <dbl>        <dbl>
@@ -246,14 +246,14 @@ 
    Pre-processing with tof_prepro
 #> 6        448.         2.69         11.1
    
 
     # perform preprocessing
-phenograph <- 
-  phenograph |> 
-  tof_preprocess()
+phenograph <-
+    phenograph |>
+    tof_preprocess()
 
 # inspect new values
-phenograph |> 
-  select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |> 
-  head()
+phenograph |>
+    select(`CD45|Sm154`, `CD34|Nd148`, `CD38|Er167`) |>
+    head()
 #> # A tibble: 6 × 3
 #>   `CD45|Sm154` `CD34|Nd148` `CD38|Er167`
 #>          <dbl>        <dbl>        <dbl>
@@ -275,8 +275,8 @@ 
    Downsampling with tof_downsample
 
     data(phenograph_data)
 
-phenograph_data |> 
-  count(phenograph_cluster)
+phenograph_data |>
+    count(phenograph_cluster)
 #> # A tibble: 3 × 2
 #>   phenograph_cluster     n
 #>   <chr>              <int>
@@ -285,15 +285,15 @@ 
    Downsampling with tof_downsample
 #> 3 cluster3            1000
    
 
    To randomly sample 200 cells per cluster, we can use tof_downsample using the “constant” method:
    
 
    -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)
+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
 #>   <chr>              <int>
@@ -302,15 +302,15 @@ 
    Downsampling with tof_downsample
 #> 3 cluster3             200
    
 
    Alternatively, if we wanted to sample 50% of the cells in each cluster, we could use the “prop” method:
    
 
    -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)
+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
 #>   <chr>              <int>
@@ -319,30 +319,30 @@ 
    Downsampling with tof_downsample
 #> 3 cluster3             500
    
 
    And finally, you might also be interested in taking a slightly different approach to downsampling that downsamples the number of cells not to a fixed constant or proportion, but to a fixed density in phenotypic space. For example, the following scatterplot demonstrates that there are certain areas of phenotypic density in phenograph_data that contain more cells than others along the cd34/cd38 axes:
    
 
    -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 |>
+    # 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()
    
 
    
 
    To reduce the number of cells in our dataset until the local density around each cell in our dataset is relatively constant, we can use the “density” method of tof_downsample:
    
 
    -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()
    
+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()

    For more details, check out the documentation for the 3 underlying members of the tof_downsample_* function family (which are wrapped by tof_downsample):

      @@ -357,16 +357,16 @@

      Writing data with tof_write_data

      Finally, users may wish to store single-cell data as .fcs or .csv files after transformation, concatenation, filtering, or other data processing steps such as dimensionality reduction and/or clustering (see below). To write single-cell data from a tof_tbl into .fcs or .csv files, use tof_write_data.

      -# when copying and pasting this code, feel free to change this path 
+# 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 |> + tof_write_data( + group_cols = phenograph_cluster, + out_path = my_path, + format = "fcs" + )

    tof_write_data’s trickiest argument is group_cols, the argument used to specify which columns in tof_tibble should be used to group cells (i.e. the rows of tof_tibble) into separate .fcs or .csv files. Simply put, this argument allows tof_write_data to create a single .fcs or .csv file for each unique combination of values in the columns specified by the user. In the example above, cells are grouped into 3 output .fcs files - one for each of the 3 clusters encoded by the phenograph_cluster column in phenograph_data. These files should have the following names (derived from the values in the phenograph_cluster column):

    • cluster1.fcs
      • @@ -375,15 +375,15 @@

      Writing data with tof_write_data

    However, suppose we wanted to write multiple files for each cluster by breaking cells into two groups: those that express high levels of pstat5 and those that express low levels of pstat5. We can use dplyr::mutate to create a new column in phenograph_data that breaks cells into high- and low-pstat5 expression groups, then add this column to our group_cols specification:

    -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 |> + # 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" + )

    This will write 6 files with the following names (derived from the values in phenograph_cluster and expression_group).

    • cluster1_low.fcs
      • @@ -407,23 +407,23 @@

      Identifying clusters with t

      To do so, we can use the tof_cluster verb. Several clustering methods are implemented in tidytof, including FlowSOM, PhenoGraph, k-means, and others.

      To demonstrate, we can apply the FlowSOM clustering algorithm to our phenograph_data from above. Note that phenograph_data contains 6000 total cells (2000 each from 3 clusters identified in the original PhenoGraph publication).

      -phenograph_clusters <- 
-  phenograph_data |> 
-  tof_preprocess() |> 
-  tof_cluster(method = "flowsom", cluster_cols = contains("cd"))
+phenograph_clusters <-
+    phenograph_data |>
+    tof_preprocess() |>
+    tof_cluster(method = "flowsom", cluster_cols = contains("cd"))
 
-phenograph_clusters |> 
-  select(sample_name, .flowsom_metacluster, everything()) |> 
-  head()
+phenograph_clusters |>
+    select(sample_name, .flowsom_metacluster, everything()) |>
+    head()
 #> # A tibble: 6 × 26
 #>   sample_name      .flowsom_metacluster phenograph_cluster    cd19 cd11b    cd34
 #>   <chr>            <chr>                <chr>                <dbl> <dbl>   <dbl>
-#> 1 H1_PhenoGraph_c… 3                    cluster1           -0.0336 2.46   0.608 
-#> 2 H1_PhenoGraph_c… 7                    cluster1            0.324  0.856 -0.116 
-#> 3 H1_PhenoGraph_c… 3                    cluster1            0.532  2.67   0.909 
-#> 4 H1_PhenoGraph_c… 2                    cluster1            0.0163 2.97   0.0725
-#> 5 H1_PhenoGraph_c… 4                    cluster1            0.144  2.98   0.128 
-#> 6 H1_PhenoGraph_c… 2                    cluster1            0.742  3.41   0.336 
+#> 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 <dbl>, cd123 <dbl>, cd33 <dbl>, cd47 <dbl>,
 #> #   cd7 <dbl>, cd44 <dbl>, cd38 <dbl>, cd3 <dbl>, cd117 <dbl>, cd64 <dbl>,
 #> #   cd41 <dbl>, pstat3 <dbl>, pstat5 <dbl>, pampk <dbl>, p4ebp1 <dbl>,
@@ -431,39 +431,39 @@ 
      Identifying clusters with t
 
      The output of tof_cluster is a tof_tbl identical to the input tibble, now with the addition of an additional column (“.flowsom_metacluster”) that encodes the cluster id for each cell in the input tof_tbl. Note that all output columns added to a tibble or tof_tbl by tidytof begin with a full-stop (“.”) to reduce the likelihood of collisions with existing column names.
      
 
      Because the output of tof_cluster is a tof_tbl, we can use dplyr’s count method to assess the accuracy of the FlowSOM clustering compared to the original clustering from the PhenoGraph paper.
      
 
      -phenograph_clusters |> 
-  count(phenograph_cluster, .flowsom_metacluster, sort = TRUE)
-#> # A tibble: 24 × 3
+phenograph_clusters |>
+    count(phenograph_cluster, .flowsom_metacluster, sort = TRUE)
+#> # A tibble: 23 × 3
 #>    phenograph_cluster .flowsom_metacluster     n
 #>    <chr>              <chr>                <int>
-#>  1 cluster2           13                     483
-#>  2 cluster3           18                     418
-#>  3 cluster3           11                     300
-#>  4 cluster2           20                     215
-#>  5 cluster1           3                      213
-#>  6 cluster3           12                     182
-#>  7 cluster1           4                      177
-#>  8 cluster1           1                      167
-#>  9 cluster1           2                      165
-#> 10 cluster2           19                     124
-#> # ℹ 14 more rows
      
+#>  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

      Here, we can see that the FlowSOM algorithm groups most cells from the same PhenoGraph cluster with one another (with a small number of mistakes per PhenoGraph cluster).

      To change which clustering algorithm tof_cluster uses, alter the method flag; to change the columns used to compute the clusters, change the cluster_cols flag. And finally, if you want to return a tibble that only includes the cluster labels (not the cluster labels added as a new column to the input tof_tbl), set augment to FALSE.

       # 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()
+phenograph_data |>
+    tof_preprocess() |>
+    tof_cluster(method = "flowsom", cluster_cols = contains("cd"), augment = FALSE) |>
+    head()
 #> # A tibble: 6 × 1
 #>   .flowsom_metacluster
 #>   <chr>               
-#> 1 11                  
-#> 2 7                   
-#> 3 11                  
-#> 4 16                  
-#> 5 4                   
-#> 6 16
      +#> 1 13 +#> 2 3 +#> 3 10 +#> 4 11 +#> 5 10 +#> 6 11

    Dimensionality reduction with tof_reduce_dimensions() @@ -473,42 +473,42 @@

    Dimensionality redu

    tidytof includes several algorithms commonly used by biologists for dimensionality reduction: Principal component analysis (PCA), t-distributed stochastic neighbor embedding (tSNE), and uniform manifold approximation and projection (UMAP). To apply these to a dataset, use tof_reduce_dimensions:

     # perform the dimensionality reduction
-phenograph_tsne <- 
-  phenograph_clusters |> 
-  tof_reduce_dimensions(method = "tsne")
+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()
+phenograph_tsne |>
+    select(contains("tsne")) |>
+    head()
 #> # A tibble: 6 × 2
-#>   .tsne_1 .tsne_2
-#>     <dbl>   <dbl>
-#> 1   7.44    -5.16
-#> 2   5.64    -9.25
-#> 3 -10.9    -25.6 
-#> 4   0.781  -17.2 
-#> 5   3.50    -7.82
-#> 6   2.82   -24.9
    +#> .tsne1 .tsne2 +#> <dbl> <dbl> +#> 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

    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 tof_tbl with new columns for each embedding dimension) as its result. To return only the features embeddings themselves, set augment to FALSE (as in tof_cluster).

    Regardless of the method used, reduced-dimension feature embeddings can be visualized using ggplot2 (or any graphics package):

     # plot the tsne embeddings using color to distinguish between clusters
-phenograph_tsne |> 
-  ggplot(aes(x = .tsne_1, y = .tsne_2, fill = phenograph_cluster)) + 
-  geom_point(shape = 21) + 
-  theme_bw() + 
-  labs(fill = NULL)
    +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 = .tsne_1, y = .tsne_2, fill = cd11b)) + 
-  geom_point(shape = 21) + 
-  scale_fill_viridis_c() +
-  theme_bw() + 
-  labs(fill = "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")

    Such visualizations can be helpful in qualitatively describing the phenotypic differences between the clusters in a dataset. For example, in the example above, we can see that one of the clusters has high CD11b expression, whereas the others have lower CD11b expression.

    @@ -520,33 +520,34 @@

    tidytof provides the tof_analyze_abundance and tof_analyze_expression verbs for differential abundance and differential expression analyses, respectively.

    To demonstrate how to use these verbs, we’ll first download a dataset originally collected for the development of the CITRUS algorithm. These data are available in the HDCytoData package, which is available on Bioconductor and can be downloaded with the following command:

    -if (!requireNamespace("BiocManager", quietly = TRUE))
+if (!requireNamespace("BiocManager", quietly = TRUE)) {
     install.packages("BiocManager")
+}
 
 BiocManager::install("HDCytoData")

    To load the CITRUS data into our current R session, we can call a function from the HDCytoData, which will provide it to us in a format from the {flowCore} package (called a “flowSet”). To convert this into a tidy tibble, we can use tidytof built-in method for converting flowCore objects into tof_tbl’s .

     citrus_raw <- HDCytoData::Bodenmiller_BCR_XL_flowSet()
 
-citrus_data <- 
-  citrus_raw |> 
-  as_tof_tbl(sep = "_")
    +citrus_data <- + citrus_raw |> + as_tof_tbl(sep = "_")

    Thus, we can see that citrus_data is a tof_tbl with 172791 cells (one in each row) and 39 pieces of information about each cell (one in each column).

    We can also extract some metadata from the raw data and join it with our single-cell data using some functions from the tidyverse:

    -citrus_metadata <- 
-  tibble(
-    file_name = as.character(flowCore::pData(citrus_raw)[[1]]), 
-    sample_id = 1:length(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 <-
+    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()
+    head()
 #> # A tibble: 6 × 4
 #>   file_name                          sample_id patient  stimulation
 #>   <chr>                                  <int> <chr>    <chr>      
@@ -559,21 +560,21 @@ 
    -citrus_data <- 
-  citrus_data |> 
-  left_join(citrus_metadata, by = "sample_id")
    +citrus_data <- + citrus_data |> + left_join(citrus_metadata, by = "sample_id")

    After these data cleaning steps, we now have citrus_data, a tof_tbl containing cells collected from 8 patients. Specifically, 2 samples were taken from each patient: one in which the cells’ B-cell receptors were stimulated (BCR-XL) and one in which they were not (Basal). In citrus_data, each cell’s patient of origin is stored in the patient column, and each cell’s stimulation condition is stored in the stimulation column. In addition, the population_id column stores information about cluster labels that were applied to each cell using a combination of FlowSOM clustering and manual merging (for details, run ?HDCytoData::Bodenmiller_BCR_XL in the R console).

    We might wonder if there are certain clusters that expand or deplete within patients between the two stimulation conditions described above - this is a question that requires differential abundance analysis (DAA). tidytof’s tof_analyze_abundance verb supports the use of 3 statistical approaches for performing DAA: diffcyt, generalized-linear mixed modeling (GLMMs), and simple t-tests. Because the setup described above uses a paired design and only has 2 experimental conditions of interest (Basal vs. BCR-XL), we can use the paired t-test method:

    -daa_result <- 
-  citrus_data |> 
-  tof_analyze_abundance(
-    cluster_col = population_id, 
-    effect_col = stimulation, 
-    group_cols = patient, 
-    test_type = "paired", 
-    method = "ttest"
-  )
+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
@@ -589,62 +590,62 @@ 
    #> 8 8             0.236    0.270   ""          -1.30      7  -0.00228   0.901

    Based on this output, we can see that 6 of our 8 clusters have statistically different abundance in our two stimulation conditions. Using tidytof easy integration with tidyverse packages, we can use this result to visualize the fold-changes of each cluster (within each patient) in the BCR-XL condition compared to the Basal condition using ggplot2:

    -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_
-      )
-  )
+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()
+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"
-  )
    +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" + )

    Importantly, the output of tof_analyze_abundance depends slightly on the underlying statistical method being used, and details can be found in the documentation for each tof_analyze_abundance_* function family member:

      @@ -654,26 +655,26 @@

      tidytof’s tof_analyze_expression verb. As above, we can use paired t-tests with multiple-hypothesis correction to to test for significant differences in each cluster’s expression of our signaling markers between stimulation conditions.

      -signaling_markers <- 
-  c(
-    "pNFkB_Nd142", "pStat5_Nd150", "pAkt_Sm152", "pStat1_Eu153", "pStat3_Gd158", 
-    "pSlp76_Dy164", "pBtk_Er166", "pErk_Er168", "pS6_Yb172", "pZap70_Gd156"
-  )
+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 <-
+    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()
+dea_result |>
+    head()
 #> # A tibble: 6 × 9
 #>   population_id marker   p_val   p_adj significant     t    df mean_diff mean_fc
 #>   <chr>         <chr>    <dbl>   <dbl> <chr>       <dbl> <dbl>     <dbl>   <dbl>
@@ -686,11 +687,11 @@ 
      tof_analyze_expression() also depends on the underlying test being used, we can see that the result above looks relatively similar to the output from tof_analyze_abundance(). Above, the output is a tibble in which each row represents the differential expression results from a single cluster-marker pair - for example, the first row represents the difference in expression of pS6 in cluster 1 between the BCR-XL and Basal conditions. Each row includes the raw p-value and multiple-hypothesis-corrected p-value for each cluster-marker pair.
      
 
      This result can be used to make a volcano plot to visualize the results for all cluster-marker pairs:
      
 
      -volcano_plot <- 
-  dea_result |> 
-  tof_plot_clusters_volcano(
-    use_ggrepel = TRUE
-  ) 
+volcano_plot <-
+    dea_result |>
+    tof_plot_clusters_volcano(
+        use_ggrepel = TRUE
+    )
 
 volcano_plot
      
 
      
@@ -709,17 +710,17 @@ 
      Feature extraction with First, we have tof_extract_proportion, which extracts the proportion of cells in each cluster within each sample (with samples defined using the group_cols argument):
      
 
       # preprocess the numeric columns in the citrus dataset
-citrus_data <- 
-  citrus_data |> 
-  mutate(cluster = str_c("cluster", population_id)) |> 
-  tof_preprocess()
+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()
+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`
 #>   <chr>    <chr>                 <dbl>           <dbl>           <dbl>
@@ -733,13 +734,13 @@ 
      Feature extraction with #> #   `prop@cluster6` <dbl>, `prop@cluster7` <dbl>, `prop@cluster8` <dbl>
      
 
      Like all members of the tof_extract_* function family, tof_extract_proportion() returns one row for each sample (defined as a unique combination of values of the group_cols) and one column for each extracted feature (above, one column for the proportion of each of the 8 clusters in citrus_data). These values can also be returned in “long” format by changing the format argument:
      
 
      -citrus_data |> 
-  tof_extract_proportion(
-    cluster_col = cluster, 
-    group_cols = c(patient, stimulation), 
-    format = "long"
-  ) |> 
-  head()
+citrus_data |>
+    tof_extract_proportion(
+        cluster_col = cluster,
+        group_cols = c(patient, stimulation),
+        format = "long"
+    ) |>
+    head()
 #> # A tibble: 6 × 4
 #>   patient  stimulation cluster     prop
 #>   <chr>    <chr>       <chr>      <dbl>
@@ -751,14 +752,14 @@ 
      Feature extraction with #> 6 patient1 Basal       cluster6 0.0759
      
 
      Another member of the same function family, tof_extract_central_tendency, computes the central tendency (e.g. mean or median) of user-specified markers in each cluster.
      
 
      -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()
+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`
 #>   <chr>    <chr>                          <dbl>                   <dbl>
@@ -777,14 +778,14 @@ 
      Feature extraction with #> #   `CD4_Nd145@cluster5_ct` <dbl>, `CD20_Sm147@cluster5_ct` <dbl>, …
      
 
      tof_extract_threshold is similar to tof_extract_central_tendency, but calculates the proportion of cells above a user-specified expression value for each marker instead of a measure of central tendency:
      
 
      -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()
+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
 #>   <chr>    <chr>                                 <dbl>                     <dbl>
@@ -804,15 +805,15 @@ 
      Feature extraction with The two final members of the tof_extract_* function family – tof_extract_emd and tof_extract_jsd are designed specifically for comparing distributions of marker expression between stimulation conditions. As such, they must be given a stimulation_col that identifies which stimulation condition each cell is in, and a basal_level that specifies the reference (i.e. unstimulated) condition within the stimulation_col. With these additional arguments, tof_extract_emd computes the Earth-mover’s distance between each marker’s distribution in the stimulation conditions (within each cluster) and the basal condition; similarly, tof_extract_jsd computes the Jensen-Shannon divergence index between the same distributions. Both of these values are ways to compare how different 2 distributions are to one another and are more computationally expensive (but also higher-resolution) than simply comparing measures of central tendency.
      
 
       # 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()
+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
 #>   <chr>                      <dbl>                  <dbl>                  <dbl>
@@ -831,15 +832,15 @@ 
      Feature extraction with #> #   `BCR-XL_CD4_Nd145@cluster4_emd` <dbl>, …
      
 
       # 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()
+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
 #>   <chr>                      <dbl>                  <dbl>                  <dbl>
@@ -858,17 +859,17 @@ 
      Feature extraction with #> #   `BCR-XL_CD4_Nd145@cluster4_jsd` <dbl>, …
      
 
      Finally, the tof_extract_features verb provides a wrapper to each of the members of its function family, allowing users to extract multiple features types at once. For example, the following code extracts the proportion of each cluster, median of several markers in each cluster, and EMD between the basal condition and stimulated condition in each cluster for all patients in citrus_data.
      
 
      -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()
      
+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()

      Outcomes modeling with tof_model @@ -880,19 +881,19 @@

      Outcomes modeling with tof_model 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" +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 <- + 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() +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 #> <chr> <chr> <chr> <chr> <chr> <chr> <chr> @@ -906,28 +907,28 @@

      Outcomes modeling with tof_model

      The data processing steps above result in the ddpr_patients tibble. The numeric columns in ddpr_patients represent aggregated cell population features for each sample (see Supplementary Table 5 in this paper for details). The non-numeric columns represent clinical metadata about each sample (run ?ddpr_metadata for more information).

      There are also a few preprocessing steps that we might want to perform now to save us some headaches when we’re fitting models later.

      -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)
-  )
      +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) + )

      Separating the training and validation cohorts

      In the original DDPR paper, some patients were used to fit the model and the rest were used to assess the model after it was tuned. We can separate our training and validation cohorts using the cohort variable in ddpr_patients

      -ddpr_training <- 
-  ddpr_patients |> 
-  dplyr::filter(cohort == "Training") 
+ddpr_training <-
+    ddpr_patients |>
+    dplyr::filter(cohort == "Training")
 
-ddpr_validation <- 
-  ddpr_patients |> 
-  dplyr::filter(cohort == "Validation")
      +ddpr_validation <- + ddpr_patients |> + dplyr::filter(cohort == "Validation")
       nrow(ddpr_training)
 #> [1] 49
@@ -941,8 +942,8 @@ 
      Building a classifier u
 
      First, we can build an elastic net classifier to predict which patients will relapse and which patients won’t (ignoring time-to-event data for now). For this, we can use the relapse_status column in ddpr_training as the outcome variable:
      
 
       # find how many of each outcome we have in our cohort
-ddpr_training |> 
-  dplyr::count(relapse_status)
+ddpr_training |>
+    dplyr::count(relapse_status)
 #> # A tibble: 2 × 2
 #>   relapse_status     n
 #>   <fct>          <int>
@@ -950,13 +951,13 @@ 
      Building a classifier u
 #> 2 Yes               18
      
 
      Specifically, we can use the tof_split_data function to split our cohort into a training and test set either once (a “simple” split) or multiple times (using either k-fold cross-validation or bootstrapping). In this case, we use 5-fold cross-validation, but reading the documentation of tof_split_data demonstrates how to use other methods.
      
 
      -training_split <- 
-  ddpr_training |> 
-  tof_split_data(
-    split_method = "k-fold", 
-    num_cv_folds = 5, 
-    strata = relapse_status
-  )
+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 
@@ -987,18 +988,18 @@ 
      Building a classifier u
 #> [1] "vfold_split" "rsplit"
      
 
      Note that you can use rsample::training and rsample::testing to return the training and test obeservations from each resampling:
      
 
      -my_resample |> 
-  rsample::training() |> 
-  head()
+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
 #>   <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
-#> 1 UPN1-Rx        0.0395    0.618     0.0634    0.572       2.93     0.944
-#> 2 UPN2           0.139     0.0662    0.0221    0.0825      2.25     0.454
-#> 3 UPN3           0.633     0.0234    0.0165    0.0327      2.25     0.226
-#> 4 UPN7           0.474     0.966     0.124     1.24        2.59     0.243
-#> 5 UPN8           0.951     0.958     0.161     0.556       3.18     0.556
-#> 6 UPN9          15.6       0.446     0.0445    0.163       2.86     0.434
+#> 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 <dbl>, CD179a_Pop1 <dbl>,
 #> #   CD179b_Pop1 <dbl>, IgMi_Pop1 <dbl>, IgMs_Pop1 <dbl>, TdT_Pop1 <dbl>,
 #> #   CD22_Pop1 <dbl>, tIkaros_Pop1 <dbl>, CD79b_Pop1 <dbl>, Ki67_Pop1 <dbl>,
@@ -1007,18 +1008,18 @@ 
      Building a classifier u
 #> #   HLADR_Pop1 <dbl>, p4EBP1_FC_Basal_Pop1 <dbl>, pSTAT5_FC_Basal_Pop1 <dbl>,
 #> #   pPLCg1_2_FC_Basal_Pop1 <dbl>, pAkt_FC_Basal_Pop1 <dbl>, …
 
-my_resample |> 
-  rsample::testing() |> 
-  head()
+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
 #>   <chr>           <dbl>     <dbl>     <dbl>     <dbl>     <dbl>     <dbl>
-#> 1 UPN1          3.06       0.583   0.00449      0.164      1.94     0.416
-#> 2 UPN6          5.62       0.550   0.00374      0.622      2.86     0.342
-#> 3 UPN10         0.00374    0.761   0.000696     0.829      3.19     0.886
-#> 4 UPN13         0.0634     0.0300  0.0219       0.109      2.34     0.314
-#> 5 UPN22         3.29       1.63    0.128        0.525      3.38     0.688
-#> 6 UPN22-Rx      0.0643     1.68    0.0804       1.56       3.06     0.529
+#> 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 <dbl>, CD179a_Pop1 <dbl>,
 #> #   CD179b_Pop1 <dbl>, IgMi_Pop1 <dbl>, IgMs_Pop1 <dbl>, TdT_Pop1 <dbl>,
 #> #   CD22_Pop1 <dbl>, tIkaros_Pop1 <dbl>, CD79b_Pop1 <dbl>, Ki67_Pop1 <dbl>,
@@ -1028,20 +1029,20 @@ 
      Building a classifier u
 #> #   pPLCg1_2_FC_Basal_Pop1 <dbl>, pAkt_FC_Basal_Pop1 <dbl>, …
      
 
      From here, we can feed training_split into the tof_train_model function to tune a logistic regression model that predicts the relapse_status of a leukemia patient. Be sure to check out the tof_create_grid documentation to learn how to make a hyperparameter search grid for model tuning (in this case, we limit the mixture parameter to a value of 1, which fits a sparse lasso model). Also note that for demonstration purposes, we include only the features that come from one cell population (“Population 2”) in the original dataset, which means that we probably shouldn’t expect our model to perform as well as the one in the original paper (which select from many more features).
      
 
      -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
-  )
      
+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
+    )

      The output of tof_train_model is a tof_model, an object containing information about the trained model (and that can be passed to the tof_predict and tof_assess_model verbs). When a tof_model is printed, some information about the optimal hyperparamters is printed, and so is a table of the nonzero model coefficients in the model.

       print(class_mod)
-#> A two-class `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e-10 
+#> 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
 #>    <chr>                     <dbl>
@@ -1058,14 +1059,14 @@ 
      Building a classifier u
 #> # ℹ 15 more rows

      We can then use the trained model to make predictions on the validation data that we set aside earlier:

      -class_predictions <- 
-  class_mod |> 
-  tof_predict(new_data = ddpr_validation, prediction_type = "class")
+class_predictions <-
+    class_mod |>
+    tof_predict(new_data = ddpr_validation, prediction_type = "class")
 
-class_predictions |> 
-  dplyr::mutate(
-    truth = ddpr_validation$relapse_status
-  )
+class_predictions |>
+    dplyr::mutate(
+        truth = ddpr_validation$relapse_status
+    )
 #> # A tibble: 12 × 2
 #>    .pred truth
 #>    <chr> <fct>
@@ -1086,9 +1087,9 @@ 
      Building a classifier u
 
       # calling the function with no new_data evaluates the
 # the nodel using its training data
-training_assessment <- 
-  class_mod |> 
-  tof_assess_model()
+training_assessment <-
+    class_mod |>
+    tof_assess_model()
 
 training_assessment
 #> $model_metrics
@@ -1128,15 +1129,15 @@ 
      Building a classifier u
 #> 4 Yes          Yes                             18
      
 
      And we can make an ROC curve using our metrics:
      
 
      -class_mod |> 
-  tof_plot_model() + 
-  labs(subtitle = "ROC Curve (Training data)")
      
+class_mod |>
+    tof_plot_model() +
+    labs(subtitle = "ROC Curve (Training data)")

      We can then assess the model on the validation data…

      -validation_assessment <- 
-  class_mod |> 
-  tof_assess_model(new_data = ddpr_validation)
+validation_assessment <-
+    class_mod |>
+    tof_assess_model(new_data = ddpr_validation)
 
 validation_assessment
 #> $model_metrics
@@ -1178,9 +1179,9 @@ 
      Building a classifier u
 #> 3 Yes          No                               4
 #> 4 Yes          Yes                              2
      -class_mod |> 
-  tof_plot_model(new_data = ddpr_validation) + 
-  labs(subtitle = "ROC Curve (Validation data)")
      +class_mod |> + tof_plot_model(new_data = ddpr_validation) + + labs(subtitle = "ROC Curve (Validation data)")

      @@ -1221,26 +1222,26 @@

      3. Use {tidyto 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 - ) +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 + )

      As shown above, stringing together tidytof verbs creates a pipeline that can be read easily from left-to-right and top-to-bottom – this means that it will be relatively easy for you to return to this code later (to modify it, or to write a methods section for your next high-impact manuscript!) or, perhaps more importantly, for one of your colleagues to return to it later when they want to recreate your analysis.

      @@ -1302,7 +1303,6 @@

      Dev status

    • R-CMD-check-bioc
    • Lifecycle: experimental
    • Codecov test coverage
      • -
    • R-CMD-check

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-#> 3 15 -#> 4 2 -#> 5 20 -#> 6 10 -#> 7 3 -#> 8 19 +#> 1 16 +#> 2 13 +#> 3 9 +#> 4 19 +#> 5 19 +#> 6 9 +#> 7 9 +#> 8 9 #> 9 20 -#> 10 16 +#> 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 12 -#> 2 15 +#> 1 9 +#> 2 6 #> 3 18 -#> 4 8 -#> 5 13 -#> 6 19 -#> 7 15 -#> 8 15 -#> 9 2 -#> 10 8 +#> 4 12 +#> 5 5 +#> 6 6 +#> 7 6 +#> 8 2 +#> 9 14 +#> 10 19 #> # ℹ 990 more rows diff --git a/reference/tof_cluster_phenograph.html b/reference/tof_cluster_phenograph.html index bb056b9..0bde9da 100644 --- a/reference/tof_cluster_phenograph.html +++ b/reference/tof_cluster_phenograph.html @@ -147,31 +147,31 @@

    Examples#> # A tibble: 1,000 × 1 #> .phenograph_cluster #> <chr> -#> 1 2 -#> 2 10 -#> 3 10 -#> 4 4 -#> 5 3 -#> 6 9 -#> 7 1 -#> 8 8 -#> 9 11 -#> 10 4 +#> 1 1 +#> 2 7 +#> 3 3 +#> 4 2 +#> 5 1 +#> 6 8 +#> 7 4 +#> 8 1 +#> 9 2 +#> 10 6 #> # 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 2 -#> 3 7 -#> 4 12 -#> 5 10 -#> 6 5 -#> 7 7 -#> 8 1 -#> 9 13 -#> 10 5 +#> 1 5 +#> 2 6 +#> 3 2 +#> 4 2 +#> 5 3 +#> 6 10 +#> 7 10 +#> 8 9 +#> 9 1 +#> 10 12 #> # ℹ 990 more rows diff --git a/reference/tof_downsample.html b/reference/tof_downsample.html index eddded1..6dbc801 100644 --- a/reference/tof_downsample.html +++ b/reference/tof_downsample.html @@ -146,18 +146,18 @@

    Examples method = "constant" ) #> # A tibble: 200 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 0.124 -0.0156 -2.05 0.0134 g -#> 2 1.65 -1.39 -0.180 -0.710 c -#> 3 0.636 -0.170 0.778 -1.97 a -#> 4 -1.44 0.340 0.241 1.63 n -#> 5 -0.876 -1.04 0.109 0.403 i -#> 6 -2.78 -0.871 -0.725 0.717 d -#> 7 0.809 -0.349 -0.243 -0.735 w -#> 8 -1.10 -0.263 -2.10 -1.32 j -#> 9 1.95 -0.584 -0.993 -0.651 p -#> 10 1.99 -0.220 -1.03 -2.58 a +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.0692 0.285 1.75 -0.343 a +#> 2 0.302 2.12 0.830 0.868 s +#> 3 0.564 -0.337 -0.804 1.05 i +#> 4 -0.732 0.0620 -0.747 0.463 z +#> 5 -0.280 1.33 0.405 -2.20 v +#> 6 0.891 1.15 -0.613 0.358 r +#> 7 -0.285 0.192 -0.505 -0.123 t +#> 8 -1.28 0.837 0.399 0.329 t +#> 9 0.532 1.61 -0.373 0.693 b +#> 10 -0.391 -1.59 1.16 -0.363 y #> # ℹ 190 more rows # sample 10% of all cells from the input data @@ -167,18 +167,18 @@

    Examples method = "prop" ) #> # A tibble: 100 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 0.797 0.755 1.77 0.876 r -#> 2 -0.570 0.751 -1.00 -0.810 n -#> 3 -0.450 -0.998 -0.986 1.78 w -#> 4 -0.311 1.98 -0.719 -1.06 z -#> 5 -0.669 -0.263 0.830 1.43 f -#> 6 0.661 -0.696 -0.728 -0.639 l -#> 7 -0.165 0.176 -0.203 0.0940 f -#> 8 -0.770 0.292 0.309 0.132 t -#> 9 0.490 1.07 -0.408 -0.541 a -#> 10 0.874 0.445 1.02 -0.806 u +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 0.416 -0.0549 -0.695 0.226 t +#> 2 -0.811 0.802 0.674 -0.247 d +#> 3 1.13 -0.218 -1.17 -0.354 l +#> 4 0.514 -0.853 1.11 -0.959 j +#> 5 -0.127 -0.115 0.840 0.696 p +#> 6 0.00882 1.18 0.463 0.0690 l +#> 7 -0.483 0.728 -0.210 -0.456 n +#> 8 1.59 -1.13 0.00641 0.909 y +#> 9 0.315 0.0125 -0.150 0.00140 s +#> 10 0.147 0.00315 -1.83 0.743 j #> # ℹ 90 more rows # sample ~10% of cells from the input data using density dependence @@ -187,20 +187,20 @@

    Examples target_prop_cells = 0.1, method = "density" ) -#> # A tibble: 101 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 2.02 -0.234 1.02 -0.727 i -#> 2 1.52 -0.652 2.83 -0.0350 q -#> 3 0.281 -0.616 -0.00816 -1.07 k -#> 4 1.79 -0.490 0.426 1.61 y -#> 5 1.99 -0.220 -1.03 -2.58 a -#> 6 1.43 1.35 0.391 -0.886 x -#> 7 -0.335 1.80 -0.470 -0.747 r -#> 8 -1.03 -0.145 -1.36 -1.08 c -#> 9 0.630 -0.491 -0.990 -1.11 z -#> 10 -0.110 0.501 0.903 -1.36 n -#> # ℹ 91 more rows +#> # A tibble: 95 × 5 +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -1.18 -0.355 -2.25 -0.514 g +#> 2 -1.39 -0.266 0.671 -0.916 r +#> 3 -0.0662 -0.249 0.407 -0.0341 s +#> 4 0.324 0.379 0.697 -0.186 a +#> 5 -2.17 -2.24 -0.895 0.114 a +#> 6 -0.532 1.68 0.507 0.445 c +#> 7 -0.848 -0.836 0.340 -0.572 h +#> 8 -0.805 -1.24 0.00874 0.480 r +#> 9 -0.298 -1.97 1.40 -0.383 z +#> 10 -0.921 0.747 1.42 -1.29 w +#> # ℹ 85 more rows diff --git a/reference/tof_downsample_constant.html b/reference/tof_downsample_constant.html index 36ffab4..605efc7 100644 --- a/reference/tof_downsample_constant.html +++ b/reference/tof_downsample_constant.html @@ -128,18 +128,18 @@

    Examples num_cells = 500L ) #> # A tibble: 500 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -0.0579 -1.72 -1.42 0.289 x -#> 2 2.73 0.172 0.177 1.18 h -#> 3 0.538 -2.43 0.701 -0.222 n -#> 4 -1.16 -0.397 1.38 -0.903 a -#> 5 -0.210 0.404 -1.36 0.656 m -#> 6 1.08 -0.877 0.165 -2.33 t -#> 7 0.216 -1.20 0.776 -1.04 b -#> 8 0.670 2.26 -0.317 0.340 s -#> 9 0.468 0.819 0.0918 0.853 s -#> 10 2.01 0.284 -0.721 -1.49 y +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.299 -1.19 0.223 1.02 m +#> 2 0.726 1.12 0.860 0.970 a +#> 3 -0.361 1.01 0.00652 -0.159 b +#> 4 -0.947 0.396 -0.836 -0.952 k +#> 5 1.51 2.26 0.961 0.164 j +#> 6 -0.209 0.126 -1.25 -1.08 e +#> 7 1.22 1.00 -1.42 -0.400 u +#> 8 0.534 2.94 -0.222 0.675 j +#> 9 0.143 0.236 -1.29 -1.27 r +#> 10 0.238 0.709 -0.00808 1.59 a #> # ℹ 490 more rows # sample 20 cells per cluster from the input data @@ -149,18 +149,18 @@

    Examples num_cells = 20L ) #> # A tibble: 520 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -1.16 -0.397 1.38 -0.903 a -#> 2 -0.135 -0.177 -0.800 0.947 h -#> 3 0.0699 -1.46 1.82 -0.283 g -#> 4 0.840 1.47 0.305 -0.474 n -#> 5 1.08 -0.877 0.165 -2.33 t -#> 6 1.80 1.07 1.60 0.549 q -#> 7 2.01 0.284 -0.721 -1.49 y -#> 8 0.0317 -0.919 -0.970 0.168 t -#> 9 -0.884 -0.334 0.0589 0.366 l -#> 10 -1.09 -0.656 -1.48 -1.57 v +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 0.392 1.62 -1.94 1.01 c +#> 2 0.726 1.12 0.860 0.970 a +#> 3 -0.920 -1.39 -0.800 -0.920 j +#> 4 0.0644 -0.297 -1.60 0.520 z +#> 5 -0.587 0.152 -1.27 3.11 o +#> 6 -1.23 0.513 -1.32 0.973 d +#> 7 0.615 1.89 -1.57 -0.552 t +#> 8 -0.707 1.13 0.536 1.08 l +#> 9 1.22 1.00 -1.42 -0.400 u +#> 10 0.534 2.94 -0.222 0.675 j #> # ℹ 510 more rows diff --git a/reference/tof_downsample_density.html b/reference/tof_downsample_density.html index 4cbb3d8..9f2f2bd 100644 --- a/reference/tof_downsample_density.html +++ b/reference/tof_downsample_density.html @@ -192,20 +192,20 @@

    Examples target_prop_cells = 0.5, density_estimation_method = "spade" ) -#> # A tibble: 514 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 -1.76 -0.130 0.750 0.569 -#> 2 -0.325 0.995 1.42 -1.90 -#> 3 0.139 -0.0432 -0.569 1.27 -#> 4 -0.221 -1.13 1.43 0.595 -#> 5 1.82 0.0462 0.937 -0.0783 -#> 6 1.47 0.909 -1.03 -1.34 -#> 7 -0.287 -1.11 0.295 -0.0843 -#> 8 -0.467 -0.421 -0.487 -1.22 -#> 9 0.311 -0.00684 0.877 -0.194 -#> 10 1.41 0.107 -0.0705 0.292 -#> # ℹ 504 more rows +#> # A tibble: 509 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 -1.00 0.353 0.283 -1.57 +#> 2 -0.886 0.199 1.64 -0.417 +#> 3 0.386 1.87 0.0963 -0.325 +#> 4 -1.28 -1.74 1.02 -1.64 +#> 5 -0.428 1.97 -0.960 -0.886 +#> 6 -0.696 -0.0458 0.825 0.594 +#> 7 -0.0576 1.67 0.984 -0.0994 +#> 8 1.39 0.920 1.87 0.798 +#> 9 2.40 0.000711 1.10 -0.262 +#> 10 -2.04 -1.19 -0.184 -1.20 +#> # ℹ 499 more rows tof_downsample_density( tof_tibble = sim_data, @@ -213,20 +213,20 @@

    Examples target_num_cells = 200L, density_estimation_method = "spade" ) -#> # A tibble: 201 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.325 0.995 1.42 -1.90 -#> 2 -1.48 -1.45 0.0642 -0.158 -#> 3 1.82 0.0462 0.937 -0.0783 -#> 4 0.311 -0.00684 0.877 -0.194 -#> 5 0.118 0.233 -1.21 -0.548 -#> 6 0.648 -1.34 -0.0295 -0.718 -#> 7 -0.403 -1.06 0.418 -0.470 -#> 8 1.14 -1.03 -0.990 0.589 -#> 9 -0.105 -0.0490 0.514 -0.102 -#> 10 0.0821 0.844 1.40 -0.0105 -#> # ℹ 191 more rows +#> # A tibble: 200 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 -1.00 0.353 0.283 -1.57 +#> 2 -0.428 1.97 -0.960 -0.886 +#> 3 -2.04 -1.19 -0.184 -1.20 +#> 4 -1.52 -0.327 -0.195 0.177 +#> 5 -0.398 -0.112 -1.63 0.182 +#> 6 -0.643 0.493 -0.672 0.266 +#> 7 -1.02 1.37 -1.63 -0.311 +#> 8 2.26 -0.788 -1.67 -0.281 +#> 9 0.180 -0.00392 -1.88 0.582 +#> 10 -0.529 0.870 2.02 -0.0415 +#> # ℹ 190 more rows tof_downsample_density( tof_tibble = sim_data, @@ -234,20 +234,20 @@

    Examples target_num_cells = 200L, density_estimation_method = "mean_distance" ) -#> # A tibble: 226 × 4 -#> cd45 cd38 cd34 cd19 -#> <dbl> <dbl> <dbl> <dbl> -#> 1 0.113 -2.21 0.328 0.912 -#> 2 -0.221 -1.13 1.43 0.595 -#> 3 1.28 2.66 1.32 0.302 -#> 4 0.648 -1.34 -0.0295 -0.718 -#> 5 -0.508 0.647 0.783 0.965 -#> 6 -0.382 -0.949 0.423 0.316 -#> 7 1.29 -0.837 -0.445 -0.362 -#> 8 0.118 -0.781 0.707 -1.72 -#> 9 0.368 0.897 -2.19 0.497 -#> 10 1.37 0.563 -0.343 0.968 -#> # ℹ 216 more rows +#> # A tibble: 190 × 4 +#> cd45 cd38 cd34 cd19 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 -0.315 0.160 1.03 0.820 +#> 2 -1.34 0.138 1.64 -0.306 +#> 3 -1.28 -1.74 1.02 -1.64 +#> 4 0.487 -1.20 -0.728 -2.00 +#> 5 1.39 0.920 1.87 0.798 +#> 6 -0.0217 0.359 0.876 -0.866 +#> 7 1.11 0.197 -0.411 0.936 +#> 8 0.145 1.44 -0.984 1.05 +#> 9 0.310 -1.39 0.195 1.41 +#> 10 -0.281 0.265 1.11 -0.438 +#> # ℹ 180 more rows diff --git a/reference/tof_downsample_prop.html b/reference/tof_downsample_prop.html index 6bc05a5..4eee77f 100644 --- a/reference/tof_downsample_prop.html +++ b/reference/tof_downsample_prop.html @@ -129,18 +129,18 @@

    Examples prop_cells = 0.1 ) #> # A tibble: 100 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 -2.68 -0.366 0.00593 -0.150 x -#> 2 -0.0284 0.659 0.234 0.469 g -#> 3 -1.97 -0.272 -1.01 -0.245 h -#> 4 -0.139 0.326 0.497 -1.61 q -#> 5 -0.830 -0.881 -0.198 0.854 u -#> 6 -0.971 1.23 -0.347 -0.173 m -#> 7 1.53 0.522 0.843 2.24 t -#> 8 1.43 1.73 -0.129 0.411 d -#> 9 1.79 -1.61 0.990 1.02 g -#> 10 -0.716 0.200 0.856 -0.306 e +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 0.644 -1.20 -0.232 -1.17 v +#> 2 2.04 1.48 -0.209 -1.32 j +#> 3 0.452 -0.0795 0.0191 1.38 x +#> 4 -0.886 -0.0508 0.359 -0.399 i +#> 5 0.0550 -0.366 0.696 0.765 p +#> 6 -0.747 -1.26 0.764 0.225 k +#> 7 1.18 0.667 0.981 -0.288 j +#> 8 0.870 -0.572 0.431 -0.0517 y +#> 9 1.35 0.408 0.328 0.775 x +#> 10 0.144 1.40 0.0902 -0.249 o #> # ℹ 90 more rows # sample 10% of all cells from each cluster in the input data @@ -150,18 +150,18 @@

    Examples prop_cells = 0.1 ) #> # A tibble: 90 × 5 -#> cd45 cd38 cd34 cd19 cluster_id -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 2.00 0.505 1.01 1.17 a -#> 2 -0.239 -0.817 0.556 -0.381 a -#> 3 -0.148 2.25 0.850 1.25 a -#> 4 0.658 -2.70 -0.985 -1.30 a -#> 5 -1.62 -0.0323 -1.14 1.48 b -#> 6 0.300 -1.15 -1.44 1.39 b -#> 7 1.22 -1.28 0.340 -1.08 b -#> 8 0.128 -0.844 1.38 0.218 b -#> 9 0.561 0.619 0.395 -0.0378 b -#> 10 0.0478 1.47 -0.618 -1.26 c +#> cd45 cd38 cd34 cd19 cluster_id +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -0.350 -1.94 -0.824 1.21 a +#> 2 -0.302 0.423 -0.871 0.0792 a +#> 3 -1.29 -0.273 -1.44 0.806 a +#> 4 -0.376 -0.750 -0.0136 -1.13 a +#> 5 0.187 -1.31 0.439 -1.15 b +#> 6 0.422 -0.801 0.251 -1.35 b +#> 7 -0.397 -0.394 -2.26 -0.0954 b +#> 8 0.839 0.631 -0.724 2.08 b +#> 9 -0.260 -1.09 1.92 -1.23 c +#> 10 -0.534 -0.521 1.15 1.12 c #> # ℹ 80 more rows diff --git a/reference/tof_estimate_density.html b/reference/tof_estimate_density.html index e9c4209..7b14592 100644 --- a/reference/tof_estimate_density.html +++ b/reference/tof_estimate_density.html @@ -155,18 +155,18 @@

    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 -0.406 -0.0145 -1.41 -0.484 1 -#> 2 -0.0872 0.00235 -0.843 -0.796 1 -#> 3 0.901 -0.100 -1.44 -1.02 1 -#> 4 -0.793 -2.06 0.599 1.23 1 -#> 5 1.60 -1.71 -0.553 0.595 1 -#> 6 -0.579 0.516 -0.501 -0.0455 1 -#> 7 0.0192 -0.216 0.352 -0.482 1 -#> 8 -0.00891 1.54 -0.209 -0.767 1 -#> 9 0.936 1.95 -0.224 -0.195 1 -#> 10 1.23 0.389 -0.0372 0.565 1 +#> cd45 cd38 cd34 cd19 .spade_density +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -1.36 0.411 0.548 1.12 1 +#> 2 -0.0788 -1.01 2.17 1.62 1 +#> 3 -0.0631 -0.366 0.203 1.89 1 +#> 4 0.225 0.467 -1.29 0.559 1 +#> 5 1.20 1.28 1.03 0.828 1 +#> 6 -0.786 -0.349 -0.575 0.980 1 +#> 7 -0.562 1.67 0.808 -0.668 1 +#> 8 0.0671 -1.00 1.24 -0.923 1 +#> 9 1.45 -1.51 -1.56 -1.52 1 +#> 10 1.48 0.414 1.22 -0.0824 1 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around @@ -177,18 +177,18 @@

    Examples method = "spade" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .spade_density -#> <dbl> <dbl> <dbl> <dbl> <dbl> -#> 1 -0.406 -0.0145 -1.41 -0.484 1 -#> 2 -0.0872 0.00235 -0.843 -0.796 1 -#> 3 0.901 -0.100 -1.44 -1.02 1 -#> 4 -0.793 -2.06 0.599 1.23 0 -#> 5 1.60 -1.71 -0.553 0.595 0.4 -#> 6 -0.579 0.516 -0.501 -0.0455 1 -#> 7 0.0192 -0.216 0.352 -0.482 1 -#> 8 -0.00891 1.54 -0.209 -0.767 1 -#> 9 0.936 1.95 -0.224 -0.195 1 -#> 10 1.23 0.389 -0.0372 0.565 1 +#> cd45 cd38 cd34 cd19 .spade_density +#> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 -1.36 0.411 0.548 1.12 0.6 +#> 2 -0.0788 -1.01 2.17 1.62 0.2 +#> 3 -0.0631 -0.366 0.203 1.89 0.9 +#> 4 0.225 0.467 -1.29 0.559 1 +#> 5 1.20 1.28 1.03 0.828 0.4 +#> 6 -0.786 -0.349 -0.575 0.980 1 +#> 7 -0.562 1.67 0.808 -0.668 0.6 +#> 8 0.0671 -1.00 1.24 -0.923 1 +#> 9 1.45 -1.51 -1.56 -1.52 0 +#> 10 1.48 0.414 1.22 -0.0824 0.4 #> # ℹ 990 more rows diff --git a/reference/tof_extract_central_tendency.html b/reference/tof_extract_central_tendency.html index e7fc160..5606de4 100644 --- a/reference/tof_extract_central_tendency.html +++ b/reference/tof_extract_central_tendency.html @@ -192,8 +192,8 @@

    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 -0.0756 -0.0766 0.725 0.0444 -0.172 -#> 2 mario 0.144 -0.0654 -0.389 -0.189 0.265 +#> 1 kirby 0.324 0.0821 -0.108 0.0729 0.0187 +#> 2 mario 0.174 0.0791 -0.352 0.162 0.000622 #> # 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>, @@ -212,16 +212,16 @@

    Examples#> # A tibble: 208 × 4 #> patient cluster_id channel values #> <chr> <chr> <chr> <dbl> -#> 1 kirby a cd45 -0.0756 -#> 2 kirby a cd38 -0.0766 -#> 3 kirby a cd34 0.725 -#> 4 kirby a cd19 0.0444 -#> 5 kirby b cd45 -0.172 -#> 6 kirby b cd38 0.238 -#> 7 kirby b cd34 -0.0607 -#> 8 kirby b cd19 0.0159 -#> 9 kirby c cd45 0.247 -#> 10 kirby c cd38 -0.143 +#> 1 kirby a cd45 0.324 +#> 2 kirby a cd38 0.0821 +#> 3 kirby a cd34 -0.108 +#> 4 kirby a cd19 0.0729 +#> 5 kirby b cd45 0.0187 +#> 6 kirby b cd38 -0.102 +#> 7 kirby b cd34 0.0402 +#> 8 kirby b cd19 -0.408 +#> 9 kirby c cd45 -0.389 +#> 10 kirby c cd38 0.410 #> # ℹ 198 more rows diff --git a/reference/tof_extract_emd.html b/reference/tof_extract_emd.html index a8a7504..ca89726 100644 --- a/reference/tof_extract_emd.html +++ b/reference/tof_extract_emd.html @@ -209,17 +209,17 @@

    Examples reference_level = "basal" ) #> # A tibble: 2 × 105 -#> patient `stim_cd45@t_emd` `stim_cd38@t_emd` `stim_cd34@t_emd` +#> patient `stim_cd45@q_emd` `stim_cd38@q_emd` `stim_cd34@q_emd` #> <chr> <dbl> <dbl> <dbl> -#> 1 kirby 9.33 9.77 6.04 -#> 2 mario NA NA NA -#> # ℹ 101 more variables: `stim_cd19@t_emd` <dbl>, `stim_cd45@d_emd` <dbl>, -#> # `stim_cd38@d_emd` <dbl>, `stim_cd34@d_emd` <dbl>, `stim_cd19@d_emd` <dbl>, -#> # `stim_cd45@s_emd` <dbl>, `stim_cd38@s_emd` <dbl>, `stim_cd34@s_emd` <dbl>, -#> # `stim_cd19@s_emd` <dbl>, `stim_cd45@i_emd` <dbl>, `stim_cd38@i_emd` <dbl>, -#> # `stim_cd34@i_emd` <dbl>, `stim_cd19@i_emd` <dbl>, `stim_cd45@l_emd` <dbl>, -#> # `stim_cd38@l_emd` <dbl>, `stim_cd34@l_emd` <dbl>, `stim_cd19@l_emd` <dbl>, -#> # `stim_cd45@j_emd` <dbl>, `stim_cd38@j_emd` <dbl>, … +#> 1 mario NA NA NA +#> 2 kirby NA NA NA +#> # ℹ 101 more variables: `stim_cd19@q_emd` <dbl>, `stim_cd45@z_emd` <dbl>, +#> # `stim_cd38@z_emd` <dbl>, `stim_cd34@z_emd` <dbl>, `stim_cd19@z_emd` <dbl>, +#> # `stim_cd45@n_emd` <dbl>, `stim_cd38@n_emd` <dbl>, `stim_cd34@n_emd` <dbl>, +#> # `stim_cd19@n_emd` <dbl>, `stim_cd45@i_emd` <dbl>, `stim_cd38@i_emd` <dbl>, +#> # `stim_cd34@i_emd` <dbl>, `stim_cd19@i_emd` <dbl>, `stim_cd45@h_emd` <dbl>, +#> # `stim_cd38@h_emd` <dbl>, `stim_cd34@h_emd` <dbl>, `stim_cd19@h_emd` <dbl>, +#> # `stim_cd45@d_emd` <dbl>, `stim_cd38@d_emd` <dbl>, … # extract emd of each cluster (using the "basal" stim # condition as a reference) in long format @@ -233,16 +233,16 @@

    Examples#> # A tibble: 104 × 4 #> cluster_id marker stimulation emd #> <chr> <chr> <chr> <dbl> -#> 1 t cd45 stim 9.18 -#> 2 t cd38 stim 3.88 -#> 3 t cd34 stim 7.49 -#> 4 t cd19 stim 10.4 -#> 5 d cd45 stim 7.16 -#> 6 d cd38 stim 4.41 -#> 7 d cd34 stim 4.79 -#> 8 d cd19 stim 5.43 -#> 9 s cd45 stim 14.1 -#> 10 s cd38 stim 5.16 +#> 1 q cd45 stim 8.56 +#> 2 q cd38 stim 14.1 +#> 3 q cd34 stim 7.86 +#> 4 q cd19 stim 17.9 +#> 5 z cd45 stim 5.38 +#> 6 z cd38 stim 8.33 +#> 7 z cd34 stim 9.05 +#> 8 z cd19 stim 4.89 +#> 9 n cd45 stim 8.50 +#> 10 n cd38 stim 4.99 #> # ℹ 94 more rows diff --git a/reference/tof_extract_features.html b/reference/tof_extract_features.html index 5adc935..05979aa 100644 --- a/reference/tof_extract_features.html +++ b/reference/tof_extract_features.html @@ -232,8 +232,8 @@

    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 0.0234 0.0331 0.0507 0.0273 0.0409 0.0409 0.0331 -#> 2 mario 0.0370 0.0390 0.0349 0.0513 0.0370 0.0370 0.0287 +#> 1 kirby 0.0365 0.0385 0.0325 0.0385 0.0588 0.0487 0.0264 +#> 2 mario 0.0138 0.0493 0.0335 0.0237 0.0256 0.0434 0.0296 #> # 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>, @@ -262,8 +262,8 @@

    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 0.0234 0.0331 0.0507 0.0273 0.0409 0.0409 0.0331 -#> 2 mario 0.0370 0.0390 0.0349 0.0513 0.0370 0.0370 0.0287 +#> 1 kirby 0.0365 0.0385 0.0325 0.0385 0.0588 0.0487 0.0264 +#> 2 mario 0.0138 0.0493 0.0335 0.0237 0.0256 0.0434 0.0296 #> # 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 dd98eb2..dce2f18 100644 --- a/reference/tof_extract_jsd.html +++ b/reference/tof_extract_jsd.html @@ -202,17 +202,17 @@

    Examples reference_level = "basal" ) #> # A tibble: 2 × 105 -#> patient `stim_cd45@y_jsd` `stim_cd38@y_jsd` `stim_cd34@y_jsd` +#> patient `stim_cd45@e_jsd` `stim_cd38@e_jsd` `stim_cd34@e_jsd` #> <chr> <dbl> <dbl> <dbl> #> 1 kirby NA NA NA #> 2 mario NA NA NA -#> # ℹ 101 more variables: `stim_cd19@y_jsd` <dbl>, `stim_cd45@a_jsd` <dbl>, -#> # `stim_cd38@a_jsd` <dbl>, `stim_cd34@a_jsd` <dbl>, `stim_cd19@a_jsd` <dbl>, -#> # `stim_cd45@r_jsd` <dbl>, `stim_cd38@r_jsd` <dbl>, `stim_cd34@r_jsd` <dbl>, -#> # `stim_cd19@r_jsd` <dbl>, `stim_cd45@t_jsd` <dbl>, `stim_cd38@t_jsd` <dbl>, -#> # `stim_cd34@t_jsd` <dbl>, `stim_cd19@t_jsd` <dbl>, `stim_cd45@g_jsd` <dbl>, -#> # `stim_cd38@g_jsd` <dbl>, `stim_cd34@g_jsd` <dbl>, `stim_cd19@g_jsd` <dbl>, -#> # `stim_cd45@m_jsd` <dbl>, `stim_cd38@m_jsd` <dbl>, … +#> # ℹ 101 more variables: `stim_cd19@e_jsd` <dbl>, `stim_cd45@j_jsd` <dbl>, +#> # `stim_cd38@j_jsd` <dbl>, `stim_cd34@j_jsd` <dbl>, `stim_cd19@j_jsd` <dbl>, +#> # `stim_cd45@q_jsd` <dbl>, `stim_cd38@q_jsd` <dbl>, `stim_cd34@q_jsd` <dbl>, +#> # `stim_cd19@q_jsd` <dbl>, `stim_cd45@h_jsd` <dbl>, `stim_cd38@h_jsd` <dbl>, +#> # `stim_cd34@h_jsd` <dbl>, `stim_cd19@h_jsd` <dbl>, `stim_cd45@c_jsd` <dbl>, +#> # `stim_cd38@c_jsd` <dbl>, `stim_cd34@c_jsd` <dbl>, `stim_cd19@c_jsd` <dbl>, +#> # `stim_cd45@i_jsd` <dbl>, `stim_cd38@i_jsd` <dbl>, … # extract jsd of each cluster (using the "basal" stim # condition as a reference) in long format @@ -226,16 +226,16 @@

    Examples#> # A tibble: 104 × 4 #> cluster_id marker stimulation jsd #> <chr> <chr> <chr> <dbl> -#> 1 y cd45 stim 0.788 -#> 2 y cd38 stim 0.937 -#> 3 y cd34 stim 0.812 -#> 4 y cd19 stim 0.915 -#> 5 a cd45 stim 0.813 -#> 6 a cd38 stim 0.690 -#> 7 a cd34 stim 0.923 -#> 8 a cd19 stim 0.741 -#> 9 r cd45 stim 0.737 -#> 10 r cd38 stim 0.769 +#> 1 e cd45 stim 0.763 +#> 2 e cd38 stim 0.764 +#> 3 e cd34 stim 0.730 +#> 4 e cd19 stim 0.764 +#> 5 j cd45 stim 0.680 +#> 6 j cd38 stim 0.893 +#> 7 j cd34 stim 0.840 +#> 8 j cd19 stim 0.924 +#> 9 q cd45 stim 0.862 +#> 10 q cd38 stim 0.862 #> # ℹ 94 more rows diff --git a/reference/tof_extract_proportion.html b/reference/tof_extract_proportion.html index f212832..b5b9a4b 100644 --- a/reference/tof_extract_proportion.html +++ b/reference/tof_extract_proportion.html @@ -160,8 +160,8 @@

    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 0.0281 0.0281 0.0561 0.0421 0.0341 0.0261 0.0561 -#> 2 mario 0.0419 0.0379 0.0479 0.0379 0.0419 0.0220 0.0559 +#> 1 kirby 0.05 0.042 0.03 0.032 0.028 0.036 0.04 +#> 2 mario 0.054 0.04 0.038 0.034 0.04 0.036 0.042 #> # 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>, @@ -176,18 +176,18 @@

    Examples format = "long" ) #> # A tibble: 52 × 3 -#> patient cluster_id prop -#> <chr> <chr> <dbl> -#> 1 kirby a 0.0281 -#> 2 kirby b 0.0281 -#> 3 kirby c 0.0561 -#> 4 kirby d 0.0421 -#> 5 kirby e 0.0341 -#> 6 kirby f 0.0261 -#> 7 kirby g 0.0561 -#> 8 kirby h 0.0301 -#> 9 kirby i 0.0321 -#> 10 kirby j 0.0401 +#> patient cluster_id prop +#> <chr> <chr> <dbl> +#> 1 kirby a 0.05 +#> 2 kirby b 0.042 +#> 3 kirby c 0.03 +#> 4 kirby d 0.032 +#> 5 kirby e 0.028 +#> 6 kirby f 0.036 +#> 7 kirby g 0.04 +#> 8 kirby h 0.032 +#> 9 kirby i 0.052 +#> 10 kirby j 0.038 #> # ℹ 42 more rows diff --git a/reference/tof_extract_threshold.html b/reference/tof_extract_threshold.html index ef10ba2..f8c41fe 100644 --- a/reference/tof_extract_threshold.html +++ b/reference/tof_extract_threshold.html @@ -189,8 +189,8 @@

    Examples#> # A tibble: 2 × 105 #> patient `cd45@a_threshold` `cd38@a_threshold` `cd34@a_threshold` #> <chr> <dbl> <dbl> <dbl> -#> 1 kirby 0 0.0769 0.0769 -#> 2 mario 0 0.0667 0 +#> 1 kirby 0 0.0769 0.0769 +#> 2 mario 0.0714 0 0.143 #> # 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>, @@ -212,13 +212,13 @@

    Examples#> 1 kirby a cd45 0 #> 2 kirby a cd38 0.0769 #> 3 kirby a cd34 0.0769 -#> 4 kirby a cd19 0.0769 -#> 5 kirby b cd45 0.0769 -#> 6 kirby b cd38 0 -#> 7 kirby b cd34 0.0385 -#> 8 kirby b cd19 0.115 +#> 4 kirby a cd19 0 +#> 5 kirby b cd45 0.111 +#> 6 kirby b cd38 0.148 +#> 7 kirby b cd34 0.259 +#> 8 kirby b cd19 0.0741 #> 9 kirby c cd45 0 -#> 10 kirby c cd38 0.0833 +#> 10 kirby c cd38 0.0588 #> # ℹ 198 more rows diff --git a/reference/tof_find_knn.html b/reference/tof_find_knn.html index f4f6406..aef4080 100644 --- a/reference/tof_find_knn.html +++ b/reference/tof_find_knn.html @@ -137,3010 +137,3010 @@

    Examples) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] -#> [1,] 992 435 985 238 66 939 919 181 825 995 -#> [2,] 153 154 787 895 289 432 114 784 379 774 -#> [3,] 945 35 553 884 360 7 956 357 86 562 -#> [4,] 556 616 499 964 211 688 89 761 155 805 -#> [5,] 86 553 637 377 7 624 242 751 283 3 -#> [6,] 830 941 940 569 678 256 323 356 199 851 -#> [7,] 956 624 553 86 714 3 945 360 132 232 -#> [8,] 822 604 76 493 740 229 547 514 222 578 -#> [9,] 619 378 531 527 732 975 585 728 549 589 -#> [10,] 266 575 291 863 469 900 268 376 838 638 -#> [11,] 972 153 154 570 157 245 895 121 746 529 -#> [12,] 600 235 911 331 617 468 367 615 134 23 -#> [13,] 464 284 978 495 117 89 706 211 189 702 -#> [14,] 325 555 879 622 812 799 433 672 123 176 -#> [15,] 690 152 420 907 701 492 35 981 657 86 -#> [16,] 140 64 162 164 143 552 230 366 243 167 -#> [17,] 386 502 643 891 413 551 120 567 876 257 -#> [18,] 341 909 533 598 995 340 487 351 724 66 -#> [19,] 488 909 92 340 322 341 18 855 486 183 -#> [20,] 372 856 507 943 72 315 309 22 136 655 -#> [21,] 280 90 689 680 307 778 189 436 702 359 -#> [22,] 72 118 943 309 507 796 856 824 20 470 -#> [23,] 373 327 843 844 676 134 936 12 699 617 -#> [24,] 29 742 122 826 334 847 756 178 382 955 -#> [25,] 921 169 916 344 866 225 991 500 930 700 -#> [26,] 380 93 196 758 452 911 158 78 77 891 -#> [27,] 285 312 69 292 36 223 362 237 661 180 -#> [28,] 440 62 52 226 908 241 677 71 217 878 -#> [29,] 742 122 24 382 331 816 955 847 826 334 -#> [30,] 68 855 461 974 952 735 183 472 614 345 -#> [31,] 893 589 787 531 52 62 595 975 850 908 -#> [32,] 205 835 375 477 79 315 953 151 136 713 -#> [33,] 463 221 179 653 361 479 95 871 938 529 -#> [34,] 971 539 1000 864 870 95 938 685 136 267 -#> [35,] 3 884 562 360 553 690 945 751 86 511 -#> [36,] 69 285 292 98 237 931 912 27 223 661 -#> [37,] 40 756 214 982 178 632 130 334 780 640 -#> [38,] 904 953 437 799 107 622 433 533 669 128 -#> [39,] 719 223 285 777 292 661 795 246 193 362 -#> [40,] 37 214 705 756 982 809 242 780 178 983 -#> [41,] 723 496 661 385 50 36 755 519 98 312 -#> [42,] 204 107 437 290 602 159 460 540 622 401 -#> [43,] 71 878 391 440 62 28 106 914 764 247 -#> [44,] 830 740 493 434 456 941 295 449 766 294 -#> [45,] 793 883 329 780 239 338 442 195 268 384 -#> [46,] 427 458 844 807 794 740 23 373 60 820 -#> [47,] 129 75 245 162 278 918 234 54 972 570 -#> [48,] 221 776 915 361 379 405 168 55 950 359 -#> [49,] 587 438 534 538 168 796 856 545 792 22 -#> [50,] 231 254 524 255 98 280 464 689 397 897 -#> [51,] 818 920 178 990 113 847 982 813 576 91 -#> [52,] 62 908 28 440 217 241 31 589 709 595 -#> [53,] 966 94 335 82 431 454 116 189 469 579 -#> [54,] 408 849 652 554 278 948 230 856 374 572 -#> [55,] 950 776 959 789 801 171 842 582 663 317 -#> [56,] 529 34 95 642 1000 938 354 539 630 245 -#> [57,] 914 347 245 570 642 47 234 56 864 971 -#> [58,] 949 937 601 220 870 462 829 267 862 630 -#> [59,] 434 119 456 528 708 505 949 684 267 277 -#> [60,] 794 218 699 515 373 516 514 763 294 508 -#> [61,] 875 451 579 963 116 865 915 958 335 860 -#> [62,] 52 440 28 908 31 241 217 589 878 709 -#> [63,] 788 797 836 655 546 372 194 518 477 984 -#> [64,] 366 140 552 16 143 243 126 490 85 164 -#> [65,] 628 902 520 763 518 514 836 880 910 186 -#> [66,] 425 181 825 598 533 472 726 182 18 390 -#> [67,] 578 278 938 95 849 141 836 984 880 76 -#> [68,] 952 345 461 735 974 183 156 370 30 567 -#> [69,] 36 237 27 285 170 912 292 180 342 443 -#> [70,] 676 711 820 806 775 78 93 750 843 465 -#> [71,] 391 43 28 677 878 534 587 440 62 792 -#> [72,] 22 309 943 631 20 824 372 507 856 135 -#> [73,] 699 314 877 277 294 917 665 119 508 327 -#> [74,] 519 292 719 755 362 661 557 39 618 285 -#> [75,] 374 641 572 47 948 999 54 960 422 230 -#> [76,] 880 578 141 980 836 858 902 67 822 984 -#> [77,] 932 939 610 452 196 506 891 26 935 158 -#> [78,] 93 617 158 321 777 465 373 26 989 750 -#> [79,] 390 32 954 151 477 833 181 953 643 835 -#> [80,] 498 679 19 488 486 81 416 402 92 712 -#> [81,] 435 561 322 486 612 710 1 238 995 939 -#> [82,] 94 365 933 694 963 431 53 743 363 900 -#> [83,] 263 798 819 796 300 647 526 834 22 206 -#> [84,] 517 281 525 248 378 687 233 549 355 673 -#> [85,] 366 162 64 164 16 490 918 143 302 980 -#> [86,] 553 5 7 714 956 3 35 624 232 360 -#> [87,] 829 566 483 339 862 523 88 182 630 220 -#> [88,] 802 957 924 871 194 463 339 568 630 829 -#> [89,] 443 170 947 211 482 4 706 556 499 603 -#> [90,] 778 280 689 359 21 436 524 399 950 931 -#> [91,] 813 197 818 586 990 535 983 51 674 468 -#> [92,] 488 19 30 855 542 909 183 857 351 487 -#> [93,] 78 617 158 26 321 634 196 380 911 465 -#> [94,] 82 365 53 694 933 963 431 363 61 900 -#> [95,] 938 67 121 245 653 871 278 971 33 129 -#> [96,] 326 680 861 970 922 791 21 654 853 307 -#> [97,] 610 733 841 901 103 932 304 77 723 452 -#> [98,] 254 223 285 36 661 292 931 69 39 795 -#> [99,] 627 618 768 444 109 550 930 237 823 916 -#> [100,] 370 754 183 935 977 345 729 952 68 156 -#> [101,] 284 117 640 453 231 397 611 334 706 332 -#> [102,] 299 450 738 809 945 571 536 831 790 3 -#> [103,] 901 97 733 841 610 913 898 355 723 304 -#> [104,] 287 848 228 236 804 336 960 573 313 374 -#> [105,] 722 190 876 407 257 107 120 602 437 146 -#> [106,] 497 247 471 986 289 532 633 635 874 489 -#> [107,] 437 42 602 290 460 38 204 105 707 190 -#> [108,] 139 714 522 330 212 771 232 377 860 215 -#> [109,] 823 478 627 99 490 973 143 768 444 393 -#> [110,] 946 259 367 12 854 235 191 753 600 844 -#> [111,] 834 693 782 349 633 532 538 819 746 83 -#> [112,] 353 419 629 811 536 563 639 313 667 175 -#> [113,] 920 847 658 178 332 51 146 982 310 762 -#> [114,] 432 436 778 969 774 359 895 784 90 379 -#> [115,] 857 351 176 487 662 879 669 14 485 855 -#> [116,] 875 61 579 189 335 702 451 727 964 668 -#> [117,] 284 640 544 101 445 704 249 978 837 335 -#> [118,] 593 545 22 306 943 961 438 470 72 796 -#> [119,] 456 294 708 434 277 218 699 917 59 73 -#> [120,] 551 190 658 17 876 386 502 105 643 257 -#> [121,] 938 871 129 95 245 856 278 234 534 49 -#> [122,] 29 24 178 334 847 742 756 816 586 813 -#> [123,] 404 672 325 42 205 929 894 540 799 107 -#> [124,] 163 523 506 181 192 829 786 541 954 872 -#> [125,] 369 734 512 674 983 265 40 720 817 632 -#> [126,] 973 143 64 366 552 288 140 671 243 490 -#> [127,] 667 336 520 745 252 573 852 552 515 313 -#> [128,] 559 833 473 217 182 405 350 381 803 38 -#> [129,] 278 162 245 121 938 95 583 67 849 164 -#> [130,] 632 615 756 262 334 753 122 367 596 265 -#> [131,] 500 137 169 659 858 832 736 618 944 296 -#> [132,] 146 297 826 624 227 409 956 7 441 876 -#> [133,] 770 906 594 186 721 508 264 308 457 737 -#> [134,] 844 373 967 617 344 308 264 955 235 331 -#> [135,] 309 631 943 470 72 581 507 810 22 824 -#> [136,] 194 477 655 984 315 20 871 462 797 802 -#> [137,] 131 500 786 659 736 937 944 858 541 169 -#> [138,] 675 821 614 348 645 602 783 574 415 437 -#> [139,] 108 798 216 714 420 152 771 681 701 537 -#> [140,] 552 143 243 64 16 745 520 490 366 252 -#> [141,] 880 980 76 578 490 788 836 910 67 628 -#> [142,] 759 544 730 146 722 297 624 958 956 105 -#> [143,] 140 243 552 973 64 16 823 126 478 109 -#> [144,] 415 411 485 662 138 614 979 675 204 783 -#> [145,] 693 269 647 188 798 253 526 83 263 208 -#> [146,] 722 132 544 142 759 297 876 737 658 826 -#> [147,] 407 737 906 457 696 789 594 713 388 887 -#> [148,] 282 406 491 283 637 705 869 982 576 242 -#> [149,] 503 358 370 381 260 355 951 281 524 324 -#> [150,] 346 762 453 332 977 754 413 371 742 621 -#> [151,] 721 835 713 457 810 594 257 79 205 32 -#> [152,] 681 657 300 216 907 690 15 798 981 389 -#> [153,] 154 11 895 2 379 529 972 33 850 221 -#> [154,] 153 11 895 157 2 391 972 289 570 529 -#> [155,] 616 603 588 4 392 596 185 556 428 396 -#> [156,] 726 345 567 472 935 183 413 598 68 260 -#> [157,] 746 11 972 154 570 224 391 633 934 153 -#> [158,] 321 196 465 93 78 891 251 634 26 380 -#> [159,] 401 792 204 677 42 538 460 290 923 241 -#> [160,] 859 967 994 716 344 867 866 700 428 398 -#> [161,] 692 405 796 460 538 819 168 602 545 105 -#> [162,] 16 129 278 164 85 918 583 849 230 64 -#> [163,] 124 523 506 825 181 992 260 87 192 829 -#> [164,] 16 478 823 162 143 583 490 109 140 141 -#> [165,] 845 986 532 494 247 187 291 782 838 743 -#> [166,] 840 312 392 27 715 170 428 814 69 89 -#> [167,] 606 387 504 521 455 745 961 288 451 243 -#> [168,] 538 796 792 49 405 534 587 241 315 48 -#> [169,] 500 131 25 820 858 244 944 137 921 916 -#> [170,] 443 69 482 89 342 706 912 36 947 556 -#> [171,] 959 950 605 55 317 801 249 702 875 958 -#> [172,] 271 582 568 768 560 399 924 698 926 463 -#> [173,] 960 374 228 903 287 104 997 175 613 804 -#> [174,] 637 200 576 406 305 873 144 148 415 751 -#> [175,] 613 903 804 229 353 515 173 997 667 429 -#> [176,] 857 879 351 433 669 487 585 783 115 799 -#> [177,] 371 193 621 932 453 231 385 254 661 452 -#> [178,] 756 982 122 51 847 113 920 214 334 24 -#> [179,] 653 33 463 479 924 361 221 95 279 938 -#> [180,] 795 237 285 69 706 892 223 27 393 342 -#> [181,] 825 182 66 425 124 163 79 523 260 726 -#> 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522 431 212 -#> [482,] 443 702 791 170 706 189 912 556 89 211 -#> [483,] 709 862 87 620 566 803 531 425 595 220 -#> [484,] 808 310 735 827 503 370 952 268 249 821 -#> [485,] 662 411 979 316 144 325 14 415 555 799 -#> [486,] 322 710 251 416 402 712 843 911 612 909 -#> [487,] 669 351 533 341 18 598 176 909 724 857 -#> [488,] 19 92 855 909 351 487 18 30 341 857 -#> [489,] 289 694 784 900 471 774 303 106 895 986 -#> [490,] 980 141 823 109 880 143 478 140 627 164 -#> [491,] 442 282 406 283 869 645 785 148 481 446 -#> [492,] 15 501 152 337 657 962 389 690 419 420 -#> [493,] 941 740 830 547 760 456 434 8 554 708 -#> [494,] 276 838 165 614 348 491 291 986 874 144 -#> [495,] 13 329 464 513 284 89 239 231 338 255 -#> [496,] 362 723 519 661 312 41 74 304 27 719 -#> [497,] 106 874 471 893 986 635 31 289 589 187 -#> [498,] 679 91 197 80 402 651 468 486 813 818 -#> [499,] 4 688 805 791 922 211 89 556 964 947 -#> [500,] 169 131 858 137 944 25 820 921 244 659 -#> [501,] 492 337 430 389 657 641 962 599 15 152 -#> [502,] 17 413 695 386 567 382 120 876 441 643 -#> [503,] 149 370 484 808 358 273 381 454 551 863 -#> [504,] 521 387 167 644 606 773 451 206 306 584 -#> [505,] 685 59 608 760 988 949 267 34 862 864 -#> [506,] 124 872 523 163 560 192 932 77 825 260 -#> [507,] 470 20 22 290 796 72 943 309 835 315 -#> [508,] 186 518 917 810 763 594 721 133 218 457 -#> [509,] 479 853 279 918 529 2 153 207 179 33 -#> [510,] 641 572 188 999 962 145 948 569 269 960 -#> [511,] 562 772 459 356 597 690 884 35 907 360 -#> [512,] 632 37 262 130 588 40 590 125 983 756 -#> [513,] 13 495 211 89 978 922 947 464 189 680 -#> [514,] 902 628 921 65 763 794 218 880 858 910 -#> [515,] 804 516 229 313 127 60 794 667 767 218 -#> [516,] 691 515 767 60 319 313 678 357 563 804 -#> [517,] 248 84 687 281 358 525 969 913 233 951 -#> [518,] 508 186 943 309 520 372 628 65 836 749 -#> [519,] 362 74 661 719 292 496 39 285 312 755 -#> [520,] 65 518 628 763 961 127 388 660 943 745 -#> [521,] 504 387 167 644 584 606 451 288 773 480 -#> [522,] 330 771 692 860 212 108 626 730 481 161 -#> [523,] 124 163 506 260 324 381 350 825 872 182 -#> [524,] 399 778 358 90 897 689 436 50 280 254 -#> [525,] 687 376 969 549 517 84 784 774 378 248 -#> [526,] 894 656 929 274 208 540 631 83 72 290 -#> [527,] 585 619 9 732 975 589 974 176 638 378 -#> [528,] 277 321 465 989 390 119 612 59 79 890 -#> [529,] 479 245 33 938 95 153 56 642 279 121 -#> [530,] 202 311 58 937 870 949 354 630 220 137 -#> [531,] 9 31 566 378 549 787 893 589 233 975 -#> [532,] 986 471 187 635 782 923 111 401 933 159 -#> [533,] 598 669 904 18 724 341 487 909 66 340 -#> [534,] 587 391 49 538 168 792 677 234 226 241 -#> [535,] 990 227 409 873 868 91 360 200 51 818 -#> [536,] 691 357 563 102 811 419 981 35 299 313 -#> [537,] 771 648 301 782 625 365 522 108 139 828 -#> [538,] 792 168 49 587 534 923 796 159 161 677 -#> [539,] 1000 34 864 971 803 226 862 595 267 607 -#> [540,] 929 894 42 204 526 232 123 215 401 290 -#> [541,] 786 829 659 954 462 937 194 192 124 924 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960 -#> [642,] 245 479 529 938 95 311 56 279 278 302 -#> [643,] 386 17 257 473 713 663 350 954 502 120 -#> [644,] 480 521 504 451 889 387 773 206 167 61 -#> [645,] 675 785 821 138 212 574 920 658 576 283 -#> [646,] 623 262 219 611 392 312 275 101 371 231 -#> [647,] 693 798 145 83 420 215 540 263 701 526 -#> [648,] 537 625 301 771 834 365 782 363 963 828 -#> [649,] 660 745 591 961 881 455 474 476 210 261 -#> [650,] 765 295 383 44 416 612 905 843 449 81 -#> [651,] 873 679 410 535 91 200 990 734 751 498 -#> [652,] 749 408 849 54 554 372 824 943 72 309 -#> [653,] 179 95 33 311 924 938 630 463 870 88 -#> [654,] 288 395 521 326 504 791 167 387 688 973 -#> [655,] 984 477 836 136 63 372 194 462 20 788 -#> [656,] 526 894 631 208 72 290 507 20 274 205 -#> [657,] 389 426 681 152 981 216 591 300 976 881 -#> [658,] 920 120 821 876 113 146 190 502 310 722 -#> [659,] 541 786 444 137 829 910 858 192 141 937 -#> [660,] 649 961 745 455 881 591 474 476 520 606 -#> [661,] 223 285 519 98 39 292 362 74 36 27 -#> [662,] 485 979 144 614 411 783 316 799 14 669 -#> [663,] 926 842 789 906 737 407 317 271 643 386 -#> [664,] 44 295 997 941 414 493 830 6 383 940 -#> [665,] 423 868 877 917 314 800 810 73 270 151 -#> [666,] 769 925 424 705 195 790 831 301 704 837 -#> [667,] 127 852 252 429 313 552 811 243 336 745 -#> [668,] 725 964 418 717 958 210 445 213 875 451 -#> [669,] 487 799 533 724 904 176 598 783 38 351 -#> [670,] 584 964 395 668 717 688 116 211 521 725 -#> [671,] 429 398 252 667 552 126 243 973 225 994 -#> [672,] 123 404 325 400 979 744 996 592 14 724 -#> [673,] 728 281 355 619 998 542 472 248 609 260 -#> [674,] 586 983 813 734 265 91 632 636 122 615 -#> [675,] 645 138 574 415 576 821 200 409 920 658 -#> [676,] 70 806 843 78 93 820 23 373 617 710 -#> [677,] 792 208 587 159 226 534 538 28 290 894 -#> [678,] 767 270 800 356 236 459 516 581 877 104 -#> [679,] 498 402 651 91 410 535 468 990 712 818 -#> [680,] 861 326 791 189 21 96 922 482 702 947 -#> [681,] 216 976 300 564 426 152 389 881 657 206 -#> [682,] 729 258 100 977 754 888 935 183 998 219 -#> [683,] 757 338 255 454 808 827 863 231 503 484 -#> [684,] 851 400 199 205 996 896 749 32 656 548 -#> [685,] 347 608 505 864 971 34 760 547 414 554 -#> [686,] 448 368 410 651 886 936 449 679 744 328 -#> [687,] 549 969 525 774 358 951 517 248 784 566 -#> [688,] 556 791 439 499 4 973 717 964 213 288 -#> [689,] 931 280 90 21 912 399 524 359 950 702 -#> [690,] 15 511 35 907 562 772 420 152 86 701 -#> [691,] 357 516 767 882 319 884 747 313 563 536 -#> [692,] 522 161 330 212 626 460 105 860 707 933 -#> [693,] 647 145 798 111 83 263 269 188 834 962 -#> [694,] 900 82 94 365 933 489 469 471 291 774 -#> [695,] 502 724 441 151 904 533 658 567 382 17 -#> [696,] 887 333 842 906 926 317 737 927 663 147 -#> [697,] 225 629 398 577 994 429 160 671 25 967 -#> [698,] 399 560 568 957 924 172 463 403 192 339 -#> [699,] 73 294 218 60 119 373 877 277 508 314 -#> [700,] 991 866 930 916 344 892 867 444 921 910 -#> [701,] 215 420 798 377 232 647 108 139 86 690 -#> [702,] 189 482 280 464 171 706 443 116 284 689 -#> [703,] 761 239 185 588 329 603 965 396 155 616 -#> [704,] 837 445 240 195 117 978 640 730 727 705 -#> [705,] 195 769 780 40 790 214 704 148 282 242 -#> [706,] 443 284 180 170 482 702 69 464 254 98 -#> [707,] 602 190 460 437 821 107 348 559 105 120 -#> [708,] 119 456 218 890 294 434 989 514 902 766 -#> [709,] 483 908 803 217 52 620 862 595 425 62 -#> [710,] 612 843 486 251 322 528 158 465 93 995 -#> [711,] 70 815 465 676 820 612 736 775 304 137 -#> [712,] 402 314 744 592 400 486 665 277 996 449 -#> [713,] 257 457 835 721 594 151 643 407 810 147 -#> [714,] 232 7 108 86 216 860 212 564 956 215 -#> [715,] 166 814 840 343 428 312 731 392 362 27 -#> [716,] 994 160 859 293 885 596 967 753 616 428 -#> [717,] 213 210 418 455 725 476 606 668 320 649 -#> [718,] 210 418 725 639 591 717 811 584 396 426 -#> [719,] 39 777 750 519 74 246 362 452 292 661 -#> [720,] 839 734 203 265 3 945 102 357 536 35 -#> [721,] 457 594 713 151 810 835 257 917 133 508 -#> [722,] 737 146 876 105 407 142 605 759 544 789 -#> [723,] 41 496 733 661 519 177 385 304 362 932 -#> [724,] 695 592 533 669 341 904 151 487 799 909 -#> [725,] 418 210 668 717 213 476 591 474 649 964 -#> [726,] 156 567 345 551 472 260 17 182 825 381 -#> [727,] 978 240 837 335 966 116 579 189 211 964 -#> [728,] 324 260 281 951 673 732 248 472 825 619 -#> [729,] 977 682 754 100 219 346 332 935 453 177 -#> [730,] 759 142 297 624 544 212 785 146 283 958 -#> [731,] 312 392 623 246 600 859 362 750 331 27 -#> [732,] 585 589 728 974 9 527 549 433 425 619 -#> [733,] 97 103 723 610 304 932 496 901 841 898 -#> [734,] 674 839 720 983 91 586 265 813 535 873 -#> [735,] 461 68 974 952 821 310 484 345 190 348 -#> [736,] 304 137 557 131 296 74 192 519 901 500 -#> [737,] 906 876 722 147 696 663 826 407 594 887 -#> [738,] 450 102 299 396 185 536 596 811 369 831 -#> [739,] 198 328 548 383 115 14 316 352 272 879 -#> [740,] 766 493 434 456 8 708 794 218 44 229 -#> [741,] 350 473 381 582 568 872 801 899 551 271 -#> [742,] 29 24 346 122 847 382 611 380 758 150 -#> [743,] 365 942 431 446 846 828 82 481 626 845 -#> [744,] 410 672 543 328 996 712 402 979 404 400 -#> [745,] 649 660 243 455 961 606 591 252 881 552 -#> [746,] 157 224 972 111 11 570 363 834 633 188 -#> [747,] 571 955 235 319 264 753 580 308 265 615 -#> [748,] 550 832 627 342 126 109 973 393 99 814 -#> [749,] 631 309 652 943 372 270 72 518 20 135 -#> [750,] 777 246 719 362 758 39 78 731 93 519 -#> [751,] 873 35 360 3 562 553 5 637 839 86 -#> [752,] 475 368 751 411 701 637 421 174 690 420 -#> [753,] 615 235 293 265 747 130 571 596 367 331 -#> [754,] 977 935 150 100 183 413 453 346 567 729 -#> [755,] 74 36 292 618 519 237 931 912 69 661 -#> [756,] 178 214 122 37 982 334 24 847 130 113 -#> [757,] 683 338 255 454 863 231 50 827 808 575 -#> [758,] 380 623 611 600 26 742 331 452 371 346 -#> [759,] 544 142 730 146 958 249 722 297 624 605 -#> [760,] 547 554 408 493 941 984 608 849 864 685 -#> [761,] 4 616 185 603 396 703 155 964 211 805 -#> [762,] 150 453 346 621 413 382 742 371 899 386 -#> [763,] 388 628 65 186 902 520 508 991 910 514 -#> [764,] 440 878 62 52 975 28 43 607 709 908 -#> [765,] 905 612 238 650 919 59 528 710 995 843 -#> [766,] 740 434 708 858 989 456 514 493 8 76 -#> [767,] 678 516 563 691 313 800 270 236 581 884 -#> [768,] 99 618 627 172 444 237 931 912 109 271 -#> [769,] 790 705 925 831 5 553 86 242 283 195 -#> [770,] 264 133 826 308 955 594 906 457 737 441 -#> [771,] 860 522 537 819 834 579 108 963 865 648 -#> [772,] 511 356 459 597 907 256 562 236 690 581 -#> [773,] 206 438 306 504 593 118 387 545 167 976 -#> [774,] 784 273 289 787 687 893 549 969 31 471 -#> [775,] 70 304 750 343 806 820 711 519 676 736 -#> [776,] 55 789 48 950 407 801 663 959 473 797 -#> [777,] 246 719 750 39 866 344 78 991 452 617 -#> [778,] 436 90 524 358 359 969 399 568 280 689 -#> [779,] 467 307 286 897 21 970 403 524 436 96 -#> [780,] 195 883 705 239 37 704 40 282 148 640 -#> [781,] 928 233 339 913 394 517 248 354 403 87 -#> [782,] 537 111 532 349 771 828 648 365 834 633 -#> [783,] 348 614 799 138 622 669 433 437 461 176 -#> [784,] 774 289 893 687 489 525 114 969 787 31 -#> [785,] 283 645 297 212 242 730 869 624 146 920 -#> [786,] 541 829 659 937 954 124 601 192 137 944 -#> [787,] 850 595 31 566 774 379 531 217 549 273 -#> [788,] 63 836 546 910 880 797 141 655 628 518 -#> [789,] 407 776 663 55 926 842 801 147 722 887 -#> [790,] 831 769 445 925 624 553 705 956 809 102 -#> [791,] 326 482 922 680 688 211 499 443 189 947 -#> [792,] 159 538 677 241 168 587 290 534 923 796 -#> [793,] 45 268 883 384 338 827 942 683 575 329 -#> [794,] 60 229 514 218 515 902 763 921 516 740 -#> [795,] 842 237 180 292 223 285 39 926 317 930 -#> [796,] 507 22 168 49 290 545 470 538 161 118 -#> [797,] 63 194 315 788 833 473 136 477 776 546 -#> [798,] 420 647 83 263 215 701 139 693 232 714 -#> [799,] 622 669 38 783 904 433 42 348 437 107 -#> [800,] 270 459 423 877 917 581 678 665 810 631 -#> [801,] 605 317 55 899 959 789 663 741 551 842 -#> [802,] 88 194 871 803 957 833 924 136 473 797 -#> [803,] 217 595 802 850 833 709 128 267 182 315 -#> [804,] 515 313 104 229 127 563 336 667 516 573 -#> [805,] 499 4 885 89 428 688 616 556 603 761 -#> [806,] 676 70 911 93 26 561 78 12 617 710 -#> [807,] 458 46 886 448 259 516 629 319 203 327 -#> [808,] 484 370 863 503 310 268 683 827 575 735 -#> [809,] 214 945 299 624 956 242 571 102 297 756 -#> [810,] 917 457 721 835 508 270 713 151 594 205 -#> [811,] 852 299 667 261 319 313 127 536 252 571 -#> [812,] 953 879 14 375 592 620 622 325 433 205 -#> [813,] 586 197 91 983 674 818 51 178 636 122 -#> [814,] 840 428 867 342 859 27 312 166 393 362 -#> [815,] 711 70 820 676 244 775 736 612 137 765 -#> [816,] 334 122 742 29 332 412 847 636 911 219 -#> [817,] 734 498 983 125 674 91 197 854 651 586 -#> [818,] 51 197 990 91 813 920 200 535 178 576 -#> [819,] 834 545 206 860 83 865 771 438 161 976 -#> [820,] 169 70 500 676 244 25 944 711 775 78 -#> [821,] 190 461 658 138 707 735 645 310 120 602 -#> [822,] 578 8 604 76 547 554 67 311 760 880 -#> [823,] 478 109 143 164 490 99 788 444 627 910 -#> [824,] 72 22 300 309 943 230 881 118 573 135 -#> [825,] 260 181 66 182 163 726 472 523 425 324 -#> [826,] 770 580 737 24 132 441 876 906 146 955 -#> [827,] 454 484 249 335 284 808 683 310 966 605 -#> [828,] 968 365 743 782 537 625 648 845 82 301 -#> [829,] 541 786 462 194 937 58 924 954 124 601 -#> [830,] 941 493 294 456 6 851 678 218 119 44 -#> [831,] 790 769 553 86 956 102 981 925 624 945 -#> [832,] 550 627 131 618 99 748 755 768 916 74 -#> [833,] 473 128 315 350 802 803 797 643 182 194 -#> [834,] 819 111 771 83 889 49 438 538 206 161 -#> [835,] 713 32 205 810 721 457 151 507 257 315 -#> [836,] 655 880 788 63 984 628 141 518 902 372 -#> [837,] 240 704 445 727 978 668 579 117 335 730 -#> [838,] 291 900 986 575 471 469 638 974 626 863 -#> [839,] 720 734 3 751 945 873 35 553 357 203 -#> [840,] 428 814 342 166 27 867 69 312 170 392 -#> [841,] 610 97 901 163 992 355 77 103 932 825 -#> [842,] 926 663 317 795 696 271 899 582 887 789 -#> [843,] 710 612 676 23 416 486 989 78 528 806 -#> [844,] 134 373 967 23 60 617 344 921 25 319 -#> [845,] 165 743 986 968 900 532 828 291 365 782 -#> [846,] 481 942 446 431 626 869 330 743 522 283 -#> [847,] 332 113 334 122 742 346 178 150 920 24 -#> [848,] 287 236 104 228 256 960 374 336 569 824 -#> [849,] 408 54 554 278 652 67 372 856 578 836 -#> [850,] 595 787 803 379 566 217 802 241 88 871 -#> [851,] 199 684 896 987 941 548 996 608 400 414 -#> [852,] 811 667 261 127 252 319 313 320 515 429 -#> [853,] 432 114 307 861 96 21 680 94 509 359 -#> [854,] 946 367 636 12 191 412 110 586 936 674 -#> [855,] 351 598 472 857 487 30 18 176 533 909 -#> [856,] 20 22 372 72 507 943 796 315 49 656 -#> [857,] 115 351 176 487 855 669 879 662 783 585 -#> [858,] 76 500 514 880 980 944 659 921 141 169 -#> [859,] 160 716 867 428 731 344 994 967 814 246 -#> [860,] 865 771 522 819 564 216 958 714 579 142 -#> [861,] 680 189 326 791 116 21 96 702 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304 932 -#> [902,] 628 65 514 763 880 836 518 218 910 520 -#> [903,] 613 175 604 173 366 64 804 229 353 997 -#> [904,] 38 953 799 533 669 437 724 107 695 433 -#> [905,] 919 238 985 620 765 995 1 862 220 601 -#> [906,] 737 133 594 663 696 147 926 770 876 457 -#> [907,] 236 772 573 581 981 884 690 511 300 152 -#> [908,] 52 217 433 62 709 589 622 440 28 241 -#> [909,] 341 18 340 598 533 487 322 724 995 251 -#> [910,] 546 444 788 628 930 880 991 141 763 902 -#> [911,] 26 380 93 816 758 12 617 251 29 600 -#> [912,] 931 237 36 69 768 443 170 689 482 292 -#> [913,] 403 517 248 355 781 281 358 103 841 233 -#> [914,] 878 347 1000 570 539 57 43 864 391 234 -#> [915,] 963 48 933 889 61 405 161 545 776 168 -#> [916,] 930 700 991 866 867 444 344 921 627 25 -#> [917,] 810 270 508 800 721 665 457 835 314 277 -#> [918,] 162 129 164 85 245 583 278 654 479 47 -#> [919,] 601 905 238 220 985 620 862 949 181 267 -#> [920,] 113 658 51 132 146 847 178 382 645 675 -#> [921,] 991 866 25 344 514 916 930 700 763 944 -#> [922,] 947 791 326 499 680 482 89 970 443 211 -#> [923,] 635 241 538 792 471 405 159 168 161 217 -#> [924,] 88 957 463 339 698 802 172 194 829 568 -#> [925,] 769 377 869 108 790 5 283 831 86 139 -#> [926,] 663 842 696 906 789 271 546 795 317 887 -#> [927,] 333 476 887 261 320 696 474 213 892 388 -#> [928,] 781 354 394 87 233 220 339 630 483 58 -#> [929,] 540 232 526 894 123 459 656 574 42 290 -#> [930,] 916 700 444 991 866 910 627 99 867 344 -#> [931,] 912 689 36 98 237 768 69 897 292 443 -#> [932,] 77 452 610 304 506 193 371 177 621 872 -#> [933,] 963 915 82 692 363 365 694 94 161 61 -#> [934,] 633 349 224 247 106 157 111 746 253 782 -#> [935,] 754 183 567 413 156 345 100 977 726 150 -#> [936,] 416 23 468 886 327 946 12 201 843 486 -#> [937,] 58 949 601 462 829 786 541 870 659 220 -#> [938,] 95 121 245 67 278 971 871 653 129 33 -#> [939,] 196 77 561 322 992 340 935 251 26 158 -#> [940,] 6 987 356 256 896 323 569 597 543 851 -#> [941,] 830 493 199 851 760 414 547 6 554 456 -#> 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599 693 647 798 139 701 145 152 15 -#> [963,] 933 915 61 889 363 82 771 579 94 834 -#> [964,] 668 211 213 4 717 556 725 688 418 670 -#> [965,] 185 603 596 588 155 761 616 703 293 396 -#> [966,] 53 335 189 727 978 454 827 464 702 116 -#> [967,] 134 319 160 308 844 344 264 747 235 955 -#> [968,] 828 845 743 365 165 782 625 537 446 82 -#> [969,] 687 358 436 778 525 549 273 774 517 114 -#> [970,] 947 912 286 326 443 931 680 170 922 482 -#> [971,] 864 34 938 95 67 1000 849 278 121 539 -#> [972,] 11 570 746 234 49 157 121 889 153 534 -#> [973,] 143 126 439 109 243 288 393 688 140 552 -#> [974,] 735 68 461 585 348 732 345 783 707 821 -#> [975,] 9 31 589 893 62 52 531 874 908 440 -#> [976,] 564 681 216 881 206 300 118 426 865 649 -#> [977,] 754 150 346 453 729 332 935 100 183 219 -#> [978,] 727 837 13 240 966 117 704 335 211 464 -#> [979,] 662 415 485 404 672 799 669 123 487 144 -#> [980,] 141 490 880 76 578 910 858 788 836 444 -#> [981,] 881 907 657 573 591 426 681 152 564 336 -#> [982,] 178 242 756 214 113 51 406 920 37 785 -#> [983,] 674 813 586 734 197 91 178 632 982 756 -#> [984,] 655 477 136 836 462 194 870 63 372 20 -#> [985,] 1 919 992 435 238 905 66 220 620 181 -#> [986,] 471 532 187 635 874 838 497 900 923 291 -#> [987,] 896 199 548 851 417 414 608 6 274 684 -#> [988,] 607 220 862 505 620 539 483 267 34 1000 -#> [989,] 944 890 708 373 528 78 321 119 921 218 -#> [990,] 535 227 51 409 818 91 868 441 920 873 -#> [991,] 866 700 930 344 916 921 444 910 763 546 -#> [992,] 1 939 163 238 825 181 66 985 435 77 -#> [993,] 769 637 148 705 5 751 839 174 40 925 -#> [994,] 716 885 160 398 859 697 428 629 671 429 -#> [995,] 390 18 592 341 238 909 612 322 251 340 -#> [996,] 400 543 800 449 877 684 665 672 459 270 -#> [997,] 613 173 323 175 903 228 804 104 960 287 -#> [998,] 355 673 841 281 682 542 258 610 100 97 -#> [999,] 572 641 188 263 300 206 773 374 83 824 -#> [1000,] 539 34 864 971 226 803 878 595 850 28 +#> [1,] 442 230 784 689 53 137 330 836 996 69 +#> [2,] 874 179 540 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[865,] 285 556 883 347 302 956 151 536 863 21 +#> [866,] 529 320 348 254 38 857 694 792 693 559 +#> [867,] 192 498 402 360 257 627 165 465 261 835 +#> [868,] 221 983 346 630 26 829 429 211 560 404 +#> [869,] 347 761 217 372 180 964 295 779 186 698 +#> [870,] 258 578 385 81 444 817 944 656 962 308 +#> [871,] 250 847 41 361 849 555 470 890 513 760 +#> [872,] 307 771 571 253 103 511 575 123 933 991 +#> [873,] 366 632 681 105 887 85 738 361 740 943 +#> [874,] 540 2 179 168 223 720 978 528 15 951 +#> [875,] 291 76 550 804 912 801 582 862 900 406 +#> [876,] 802 475 637 831 160 496 457 997 877 267 +#> [877,] 831 169 548 639 267 802 24 457 160 796 +#> [878,] 581 477 813 924 79 579 748 509 980 920 +#> [879,] 748 215 532 500 534 233 558 50 79 924 +#> [880,] 163 329 957 200 750 722 710 290 996 408 +#> [881,] 960 326 453 463 998 488 718 306 807 727 +#> [882,] 427 567 521 115 170 592 183 23 860 763 +#> [883,] 151 556 382 865 347 991 148 956 273 677 +#> [884,] 433 469 305 525 467 8 514 6 173 921 +#> [885,] 614 249 432 139 179 399 353 218 375 809 +#> [886,] 908 667 519 111 938 174 685 946 446 323 +#> [887,] 943 366 738 873 105 717 858 632 675 555 +#> [888,] 819 818 771 372 549 510 594 48 779 575 +#> [889,] 565 683 920 35 509 337 477 307 980 379 +#> [890,] 485 41 871 649 250 978 178 555 375 849 +#> [891,] 811 236 659 194 986 384 695 546 114 149 +#> [892,] 522 44 634 387 942 468 899 653 793 386 +#> [893,] 15 112 375 585 969 178 967 617 405 259 +#> [894,] 805 187 791 473 193 843 360 737 362 202 +#> [895,] 274 612 517 98 78 932 314 423 150 420 +#> [896,] 819 647 510 724 277 199 527 549 888 395 +#> [897,] 827 56 96 244 300 564 984 412 338 414 +#> [898,] 134 184 537 8 833 525 542 428 332 108 +#> [899,] 44 522 419 146 437 761 964 180 610 892 +#> [900,] 727 912 153 212 664 807 992 434 550 582 +#> [901,] 393 716 388 17 371 990 631 570 317 916 +#> [902,] 387 386 776 633 973 919 658 892 914 246 +#> [903,] 450 563 370 381 282 16 363 82 636 289 +#> [904,] 256 225 139 583 506 616 658 399 973 633 +#> [905,] 505 920 581 424 715 924 878 452 337 672 +#> [906,] 995 476 11 173 216 786 232 13 413 349 +#> [907,] 143 37 318 562 797 474 531 762 925 982 +#> [908,] 886 519 938 667 685 946 111 691 411 820 +#> [909,] 741 838 36 697 357 567 654 343 427 80 +#> [910,] 640 816 599 798 927 325 177 861 311 354 +#> [911,] 769 579 676 509 980 477 79 337 188 104 +#> [912,] 153 632 664 582 219 105 900 423 150 738 +#> [913,] 409 45 93 299 252 598 562 762 75 264 +#> [914,] 265 981 591 733 999 552 97 410 892 386 +#> [915,] 823 968 166 765 352 728 604 662 508 25 +#> [916,] 570 458 441 176 512 707 226 129 750 680 +#> [917,] 407 100 309 822 661 125 71 851 815 839 +#> [918,] 58 551 778 89 694 668 780 478 459 62 +#> [919,] 387 504 942 653 963 902 246 892 973 699 +#> [920,] 581 565 878 813 337 477 509 980 905 683 +#> [921,] 552 455 410 97 344 266 6 591 3 514 +#> [922,] 378 92 775 403 461 657 862 406 677 863 +#> [923,] 222 931 926 197 355 488 673 553 323 679 +#> [924,] 878 672 581 813 806 748 789 715 879 532 +#> [925,] 797 808 294 531 474 907 373 37 327 319 +#> [926,] 323 673 777 953 923 519 843 111 222 744 +#> [927,] 599 589 177 856 910 426 120 640 656 189 +#> [928,] 631 620 279 17 662 739 590 32 621 946 +#> [929,] 603 262 569 641 213 773 834 6 471 711 +#> [930,] 677 57 310 151 314 437 76 895 804 419 +#> [931,] 355 331 923 488 673 197 718 255 222 208 +#> [932,] 437 705 274 895 423 643 105 742 98 522 +#> [933,] 481 229 818 991 382 714 260 774 273 771 +#> [934,] 859 216 749 703 13 454 308 476 906 88 +#> [935,] 782 480 297 517 219 479 801 90 615 418 +#> [936,] 29 227 853 172 18 675 272 796 71 548 +#> [937,] 816 768 414 861 107 244 359 68 910 412 +#> [938,] 588 519 908 886 371 685 777 323 111 926 +#> [939,] 530 682 242 744 486 794 790 240 430 380 +#> [940,] 170 730 998 231 115 370 586 121 719 313 +#> [941,] 587 692 484 728 49 949 604 109 401 156 +#> [942,] 653 504 919 892 77 387 246 20 419 468 +#> [943,] 555 740 717 887 366 649 681 871 849 361 +#> [944,] 126 308 551 753 416 385 668 870 918 767 +#> [945,] 511 422 577 379 657 862 35 42 376 864 +#> [946,] 519 240 621 691 958 323 32 886 667 908 +#> [947,] 129 767 780 725 499 731 63 803 436 626 +#> [948,] 505 715 608 905 766 787 83 672 194 806 +#> [949,] 501 622 941 692 823 604 765 405 613 726 +#> [950,] 593 695 126 336 669 478 243 12 944 293 +#> [951,] 795 432 528 485 890 649 464 369 451 624 +#> [952,] 772 315 953 186 362 277 642 539 510 647 +#> [953,] 673 642 926 952 323 843 341 285 536 958 +#> [954,] 520 43 128 468 733 524 148 97 273 275 +#> [955,] 589 177 670 445 927 157 856 354 426 599 +#> [956,] 372 217 844 347 991 869 865 285 883 151 +#> [957,] 163 880 200 996 589 189 329 120 836 837 +#> [958,] 21 691 536 667 162 443 4 347 946 240 +#> [959,] 822 71 309 100 407 917 18 788 661 851 +#> [960,] 881 392 998 727 302 807 463 19 745 453 +#> [961,] 150 423 340 801 420 517 612 76 297 78 +#> [962,] 684 660 979 502 495 702 573 335 210 706 +#> [963,] 246 919 942 653 387 902 419 481 310 504 +#> [964,] 295 180 761 146 899 698 869 98 105 610 +#> [965,] 52 483 283 696 132 318 91 137 201 143 +#> [966,] 239 740 366 681 105 5 295 267 873 964 +#> [967,] 708 15 292 969 764 994 178 397 280 893 +#> [968,] 716 662 393 166 915 701 823 388 84 279 +#> [969,] 112 284 617 238 967 259 708 893 261 327 +#> [970,] 69 689 133 298 710 339 112 238 441 7 +#> [971,] 851 815 839 313 90 730 398 106 661 550 +#> [972,] 644 684 704 783 578 440 203 979 694 67 +#> [973,] 633 699 902 591 658 387 776 225 386 506 +#> [974,] 627 850 135 446 318 397 257 131 201 360 +#> [975,] 9 358 394 333 526 828 48 199 888 845 +#> [976,] 59 33 5 848 350 993 523 465 845 86 +#> [977,] 367 721 561 19 545 377 392 355 42 55 +#> [978,] 890 375 485 178 617 871 555 41 250 46 +#> [979,] 210 706 684 495 660 335 962 502 233 247 +#> [980,] 260 382 911 477 683 878 769 337 151 991 +#> [981,] 410 552 733 999 678 914 265 921 72 954 +#> [982,] 762 651 37 174 264 162 562 205 497 913 +#> [983,] 830 404 705 364 812 786 868 274 386 221 +#> [984,] 56 312 842 827 713 415 435 190 289 897 +#> [985,] 207 311 10 81 258 159 381 23 282 418 +#> [986,] 117 799 833 236 134 678 659 811 8 425 +#> [987,] 606 826 535 841 634 280 674 218 574 144 +#> [988,] 645 603 711 855 431 596 803 305 616 142 +#> [989,] 703 597 13 216 147 172 60 88 29 272 +#> [990,] 140 570 901 198 388 533 916 17 458 611 +#> [991,] 382 883 956 556 307 151 229 933 260 185 +#> [992,] 664 212 153 119 732 900 244 912 632 482 +#> [993,] 14 59 5 724 976 496 376 123 239 48 +#> [994,] 292 708 280 535 356 618 764 826 847 342 +#> [995,] 906 476 173 232 11 786 600 195 30 404 +#> [996,] 836 189 957 722 837 880 137 47 329 290 +#> [997,] 438 160 831 125 876 815 877 407 917 802 +#> [998,] 730 940 370 170 960 881 313 418 745 115 +#> [999,] 733 981 410 840 806 914 552 97 273 954 +#> [1000,] 429 424 80 251 209 829 683 452 211 630 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] -#> [1,] 0.37440774 0.5196210 0.6317683 0.6323538 0.8849260 0.8892560 0.9006937 -#> [2,] 0.71849076 0.8712022 0.8970041 0.9249884 0.9328638 0.9559555 0.9912308 -#> [3,] 0.33816647 0.3531914 0.4294108 0.5196530 0.5297383 0.5577980 0.6443834 -#> [4,] 0.54020961 0.6153700 0.6328276 0.6360577 0.6708464 0.6854089 0.7296972 -#> [5,] 0.39339787 0.4755666 0.6066615 0.7330094 0.7611787 0.7646775 0.7696904 -#> [6,] 0.81778517 0.8393568 0.8864097 0.8899425 0.8930137 0.9004203 0.9592937 -#> [7,] 0.31336098 0.4734230 0.4790972 0.5125694 0.5484259 0.5577980 0.5839837 -#> [8,] 0.69429370 0.6999343 0.8040632 0.8374759 0.8581814 0.8620620 0.9037422 -#> [9,] 0.49870522 0.5159316 0.5161472 0.6504423 0.7226073 0.7458232 0.8078439 -#> [10,] 0.43286308 0.6523323 0.8222796 0.8646028 0.9897671 1.0654198 1.0962131 -#> [11,] 0.41437972 0.5560717 0.6561674 0.7609491 0.7669037 0.8590798 0.8752580 -#> [12,] 0.67937879 0.7172980 0.7480645 0.7544614 0.8050573 0.8083640 0.8481635 -#> [13,] 0.52070502 0.6684482 0.7688076 0.7823320 0.8670848 0.9316208 0.9584593 -#> [14,] 0.41137800 0.5708362 0.5865694 0.6090094 0.6456530 0.6700576 0.7213540 -#> [15,] 0.59022782 0.7694994 0.8918625 1.0814758 1.1290545 1.1519124 1.1534854 -#> [16,] 0.45092441 0.4958946 0.5104549 0.5122970 0.5530641 0.6073729 0.6617331 -#> [17,] 0.21549985 0.3255899 0.3544750 0.4664532 0.4715509 0.4860027 0.4879230 -#> [18,] 0.28419387 0.3691577 0.5267358 0.5449690 0.5967973 0.5985593 0.6283001 -#> [19,] 0.60423508 0.9040169 0.9165124 1.0119967 1.0878704 1.1063906 1.1405633 -#> [20,] 0.28229287 0.3660425 0.4033861 0.4280612 0.4487782 0.4534048 0.4866329 -#> [21,] 0.53505778 0.5400066 0.6122105 0.7306741 0.7555545 0.8497530 0.8610543 -#> [22,] 0.21870056 0.3441216 0.3792610 0.4279712 0.4352831 0.4734599 0.4760026 -#> [23,] 0.69971236 0.7776224 0.7876056 0.7994573 0.8586295 0.8605815 0.8819863 -#> [24,] 0.38762218 0.4285535 0.4381970 0.5334751 0.6623435 0.6643578 0.6768378 -#> [25,] 0.53268027 0.6801124 0.6909970 0.7254717 0.7283049 0.7764582 0.7892815 -#> [26,] 0.43329051 0.5178387 0.5360601 0.5642680 0.5666451 0.5944099 0.6307465 -#> [27,] 0.30426516 0.3859418 0.4251838 0.4719293 0.5115982 0.5455742 0.5471404 -#> [28,] 0.42629419 0.4520204 0.4723838 0.5216885 0.5984226 0.6227925 0.6259317 -#> [29,] 0.35403636 0.3840472 0.3876222 0.5544435 0.5753758 0.6441560 0.6696159 -#> [30,] 0.70399445 0.8185857 0.8728922 0.8887481 0.9390512 0.9504278 0.9534198 -#> [31,] 0.54620478 0.5678647 0.5794177 0.5859331 0.6016578 0.6631736 0.6973421 -#> [32,] 0.32203562 0.3870360 0.4510068 0.4565310 0.5181098 0.5219980 0.5803326 -#> [33,] 0.59842366 0.6252209 0.6259422 0.6469902 0.6731264 0.6884798 0.6987678 -#> [34,] 0.42295739 0.4799947 0.4822970 0.5100279 0.7521974 0.7531811 0.7617941 -#> [35,] 0.35319143 0.4240218 0.5113439 0.5796505 0.5815733 0.6434926 0.6531255 -#> [36,] 0.28798006 0.3680010 0.4005744 0.4271074 0.4391532 0.4795506 0.4806273 -#> [37,] 0.46960421 0.5704743 0.5713747 0.7433508 0.8263683 0.9032159 0.9188813 -#> [38,] 0.16379892 0.4343415 0.4634704 0.4812532 0.5262166 0.5456857 0.5900957 -#> [39,] 0.32942541 0.3488723 0.4544843 0.4706264 0.4737524 0.5055472 0.5182899 -#> [40,] 0.46960421 0.5094448 0.8000923 0.8405120 0.8940577 0.9118995 0.9520868 -#> [41,] 0.56070439 0.8039226 0.8458749 0.9259973 0.9706053 1.0019942 1.0221179 -#> [42,] 0.26218164 0.3335003 0.4401235 0.4973035 0.5241576 0.5242244 0.5406525 -#> [43,] 0.63571193 0.7382393 0.9638590 1.0666145 1.0770948 1.0874487 1.0945852 -#> [44,] 0.98042680 0.9936126 1.0111360 1.0134749 1.0909802 1.1755655 1.2292283 -#> [45,] 0.96471043 1.1360815 1.2717714 1.5203067 1.5482659 1.7086648 1.7576296 -#> [46,] 0.92897311 1.0798204 1.2356634 1.2538734 1.2887295 1.3175664 1.3497319 -#> [47,] 0.89238862 0.9257327 0.9620712 0.9739201 0.9943237 1.1340280 1.1505984 -#> [48,] 0.44108247 0.4464113 0.4928736 0.5033910 0.5245417 0.5959135 0.6397210 -#> [49,] 0.39469461 0.3966216 0.4374424 0.4706606 0.4847577 0.4929153 0.6373957 -#> [50,] 0.62219362 0.6576409 0.7388219 0.7729828 0.7943559 0.8368663 0.8794120 -#> [51,] 0.30483279 0.4510571 0.5991109 0.6021361 0.6431851 0.6820095 0.6822274 -#> [52,] 0.16342217 0.3812533 0.4723838 0.4917449 0.5672412 0.6012372 0.6016578 -#> [53,] 0.47146486 0.6398191 0.6982818 0.7187265 0.7796118 0.9114307 0.9247849 -#> [54,] 0.32811395 0.4597374 0.5698973 0.6545683 0.7488184 0.7636551 0.8007657 -#> [55,] 0.26351243 0.2898434 0.3279429 0.3904213 0.4334976 0.4823644 0.5486852 -#> [56,] 0.77615810 0.8922126 0.9410711 0.9435230 0.9454779 0.9498508 0.9649450 -#> [57,] 1.01660350 1.0576989 1.0578482 1.0948736 1.1998130 1.2010383 1.2067120 -#> [58,] 0.31354966 0.3359187 0.4454083 0.5013944 0.5523149 0.5649255 0.6207553 -#> [59,] 0.54093924 0.6735608 0.6915943 0.7171179 0.7925492 0.8258154 0.8404956 -#> [60,] 0.39442631 0.5581936 0.6069573 0.6558298 0.6593543 0.6951015 0.7095590 -#> [61,] 0.42184296 0.4722252 0.4810669 0.5213441 0.5457783 0.5978246 0.6324206 -#> [62,] 0.16342217 0.4269498 0.4520204 0.5193114 0.6631736 0.7057671 0.7128491 -#> [63,] 0.22126147 0.3532780 0.4414033 0.4531874 0.4647006 0.5107906 0.5602252 -#> [64,] 0.33416754 0.4265877 0.4359787 0.4958946 0.5433790 0.6881546 0.7001640 -#> [65,] 0.23679109 0.2815989 0.3487280 0.4295142 0.5025708 0.5573627 0.6129167 -#> [66,] 0.44637144 0.5003019 0.5529245 0.6283844 0.6367535 0.6652489 0.7487575 -#> [67,] 0.33537141 0.5092862 0.5898424 0.5930201 0.6001093 0.6728713 0.6872553 -#> [68,] 0.37747986 0.4014896 0.4185361 0.4482289 0.5682722 0.5703633 0.6131069 -#> [69,] 0.28798006 0.3927377 0.4251838 0.4288953 0.4558640 0.4906690 0.5028235 -#> [70,] 0.54611205 0.6926403 0.7215123 0.7658092 0.8311058 0.9560666 1.0400445 -#> [71,] 0.47206866 0.6357119 0.6360646 0.6926937 0.6989833 0.7644924 0.7893722 -#> [72,] 0.21870056 0.2927399 0.2999043 0.3591081 0.4487782 0.4585332 0.4617932 -#> [73,] 0.30035462 0.5001202 0.5593482 0.5631557 0.6212020 0.6350773 0.6398905 -#> [74,] 0.30645224 0.5000964 0.5348828 0.5447165 0.5564983 0.5910982 0.6571445 -#> [75,] 0.73137409 0.7876294 0.7927559 0.9257327 0.9287029 0.9333650 0.9570342 -#> [76,] 0.40150325 0.4310171 0.4984940 0.5758994 0.5810228 0.5926704 0.7203417 -#> [77,] 0.42677834 0.5655940 0.5780670 0.5869566 0.5955668 0.6814462 0.6902135 -#> [78,] 0.24603251 0.4255550 0.5281018 0.6320025 0.6766829 0.6816221 0.7057718 -#> [79,] 0.44042104 0.5181098 0.5632566 0.5853790 0.5921010 0.6301329 0.6573707 -#> [80,] 1.17838407 1.4621528 1.4891457 1.6146337 1.6568477 1.6983617 1.7513378 -#> [81,] 0.97605309 1.0511801 1.0518557 1.0868473 1.1142342 1.1686700 1.1819750 -#> [82,] 0.30897320 0.4068803 0.5589393 0.5719344 0.6460923 0.6503385 0.7187265 -#> [83,] 0.29770340 0.5268685 0.5810016 0.5937661 0.6260777 0.6646483 0.6767827 -#> [84,] 0.73278181 0.8382064 0.8712902 0.9346618 0.9439012 1.1061611 1.2219257 -#> [85,] 0.63396681 0.7252764 0.8155012 0.8245226 0.8380611 0.9923770 0.9993802 -#> [86,] 0.32458341 0.3933979 0.5125694 0.6410320 0.6680742 0.6691817 0.6752053 -#> [87,] 0.66478616 0.7107108 0.7159460 0.7237704 0.7282900 0.7332213 0.7434692 -#> [88,] 0.30996952 0.3251140 0.3526699 0.5128921 0.5429316 0.6395256 0.6586995 -#> [89,] 0.57281868 0.5758453 0.7006440 0.7207689 0.7258189 0.7296972 0.7965332 -#> [90,] 0.39852216 0.4400132 0.4526669 0.5161778 0.5400066 0.5461164 0.6188844 -#> [91,] 0.48933920 0.6051880 0.6382770 0.6502117 0.6693098 0.7108046 0.7692118 -#> [92,] 0.62260206 0.9165124 1.3085745 1.3806910 1.5870406 1.6285594 1.6554045 -#> [93,] 0.24603251 0.3878538 0.4591714 0.5178387 0.6585143 0.6621358 0.6810870 -#> [94,] 0.30897320 0.6290901 0.6398191 0.6496431 0.7823336 0.7843597 0.8580708 -#> [95,] 0.04497083 0.5930201 0.6119075 0.6270330 0.6429569 0.6600823 0.6656487 -#> [96,] 0.62583943 0.7505155 0.9832066 1.0235993 1.0636559 1.1201775 1.1666406 -#> [97,] 0.35144018 0.6382641 0.7079532 0.7095187 0.7659251 0.8091797 0.8513275 -#> [98,] 0.31801358 0.3401989 0.4208660 0.4271074 0.5054884 0.5429898 0.5665091 -#> [99,] 0.21601832 0.3354368 0.3484150 0.3856758 0.5485465 0.5505203 0.5557009 -#> [100,] 0.41366461 0.5126158 0.5594749 0.5739571 0.6100644 0.6774000 0.7450978 -#> [101,] 0.66441475 0.7549090 0.7557675 0.7765707 0.7936885 0.7990604 0.8064913 -#> [102,] 0.46871727 0.5296746 0.6837906 0.7008518 0.7655323 0.8326505 0.8770413 -#> [103,] 0.76556108 0.7659251 0.7682346 0.9744743 1.0745172 1.1330448 1.1399362 -#> [104,] 0.30557984 0.3708418 0.3794128 0.5570809 0.5645912 0.6157789 0.6886539 -#> [105,] 0.42014793 0.4929323 0.5075120 0.5230954 0.5328842 0.5333565 0.5456871 -#> [106,] 0.60144678 0.9305805 0.9306688 0.9501760 0.9659964 1.0453325 1.0542669 -#> [107,] 0.26191941 0.3335003 0.3864874 0.4739008 0.4901712 0.5262166 0.5277461 -#> [108,] 0.53526753 0.5824204 0.5919049 0.6908379 0.6932066 0.6949184 0.7062489 -#> [109,] 0.32366596 0.3816655 0.5062977 0.5485465 0.6035720 0.6073536 0.6347685 -#> [110,] 0.65787838 0.8523714 1.1802698 1.3560210 1.3630859 1.3759184 1.3968075 -#> [111,] 0.50862957 0.6866783 0.7279623 0.7948560 0.8165282 0.8374849 0.8375440 -#> [112,] 1.55852407 1.5673626 1.6632869 1.6701479 1.7246864 1.7747710 1.7934094 -#> [113,] 0.36644829 0.4296335 0.5638167 0.6098110 0.6319255 0.6431851 0.6599731 -#> [114,] 0.27515565 0.7833691 0.7984154 0.8626899 0.8700936 0.8907433 0.9139923 -#> [115,] 0.38214516 0.6842062 0.7184307 0.8236605 0.8753309 0.9271065 0.9900302 -#> [116,] 0.45027271 0.5457783 0.6602727 0.6741195 0.6863318 0.7091045 0.7172088 -#> [117,] 0.56160326 0.6429118 0.7370743 0.7549090 0.7710974 0.7860382 0.7889454 -#> [118,] 0.23376156 0.3276499 0.3441216 0.3508093 0.5015056 0.5053651 0.5176712 -#> [119,] 0.31583768 0.3737276 0.4558761 0.5437083 0.5458634 0.5974658 0.6560051 -#> [120,] 0.34765690 0.3987382 0.4601763 0.4879230 0.5018809 0.5067646 0.5416199 -#> [121,] 0.58701147 0.5952777 0.6049412 0.6119075 0.6268535 0.6821388 0.7100069 -#> [122,] 0.38404719 0.4381970 0.4623692 0.4922980 0.5168948 0.5305939 0.5628925 -#> [123,] 0.35215632 0.3951595 0.5682765 0.5992893 0.6292636 0.6469049 0.6536885 -#> [124,] 0.37061007 0.3711900 0.3871380 0.5807237 0.5986405 0.6415935 0.6531730 -#> [125,] 1.16275048 1.2245416 1.2926636 1.3602846 1.3698820 1.4084729 1.4638888 -#> [126,] 0.50990188 0.6099109 0.7001640 0.7341859 0.7767447 0.7813376 0.7953612 -#> [127,] 0.38172029 0.5355710 0.5596657 0.5633162 0.5823183 0.5929949 0.6202899 -#> [128,] 0.32678210 0.4734693 0.5506425 0.5621268 0.5665520 0.5690209 0.6215555 -#> [129,] 0.32240302 0.5219120 0.5762402 0.6049412 0.6920584 0.7313320 0.7474944 -#> [130,] 0.49883170 0.6053445 0.7167148 0.7190791 0.7574800 0.7613518 0.7938191 -#> [131,] 0.53564742 0.6130721 0.6764559 0.7958312 0.8108554 0.8286614 0.8786131 -#> [132,] 0.42579509 0.5151876 0.5341619 0.5671062 0.5755747 0.5903237 0.6075006 -#> [133,] 0.35147778 0.3636692 0.3647389 0.4423911 0.5608529 0.5691907 0.5779306 -#> [134,] 0.39681078 0.4251424 0.5178433 0.5361624 0.6805308 0.6867334 0.7135838 -#> [135,] 0.30942474 0.4231522 0.4267138 0.5000046 0.5050926 0.5430369 0.5739390 -#> [136,] 0.41118245 0.4267375 0.4318495 0.4339700 0.4768650 0.5465379 0.5586927 -#> [137,] 0.61307211 0.6772350 0.7043720 0.7118153 0.8149391 0.8453201 0.8556896 -#> [138,] 0.43229124 0.5113181 0.5293639 0.5827702 0.5852424 0.5925780 0.6027970 -#> [139,] 0.53526753 0.6705211 0.7233836 0.7638404 0.7897678 0.7912746 0.8126103 -#> [140,] 0.25761193 0.3248417 0.3820281 0.4265877 0.4509244 0.6123456 0.6371633 -#> [141,] 0.21551060 0.2478560 0.4984940 0.5043320 0.5153898 0.5377246 0.5380901 -#> [142,] 0.23215070 0.3977784 0.4000386 0.4393166 0.4936062 0.5116403 0.5226528 -#> [143,] 0.32484169 0.4430272 0.4500779 0.4606841 0.5433790 0.5530641 0.5891766 -#> [144,] 0.70360030 0.7115000 0.7115202 0.7263464 0.7909070 0.8401681 0.8523100 -#> [145,] 0.53097493 0.5426041 0.5660205 0.8089592 0.9680723 0.9860777 1.0241528 -#> [146,] 0.36684834 0.4257951 0.4369957 0.4393166 0.4547368 0.5026818 0.5330085 -#> [147,] 0.34840672 0.4549819 0.4564327 0.5024490 0.5335413 0.5341757 0.5377761 -#> [148,] 0.68439506 0.7249098 0.8863247 0.9094872 0.9198462 0.9435541 0.9756199 -#> [149,] 0.48461201 0.6281582 0.7102363 0.7320435 0.7869123 0.8343406 0.8597976 -#> [150,] 0.27767287 0.2814221 0.2841270 0.4878354 0.4954478 0.4975521 0.5313888 -#> [151,] 0.37046721 0.4650588 0.4816673 0.4861472 0.5433768 0.5727964 0.5752032 -#> [152,] 0.56256667 0.5770576 0.6789019 0.7157721 0.7351366 0.7589517 0.7694994 -#> [153,] 0.54053002 0.5560717 0.5999067 0.7184908 0.7479200 0.7761110 0.8660131 -#> [154,] 0.54053002 0.6561674 0.8307043 0.8541981 0.8712022 0.9810323 0.9903409 -#> [155,] 0.37489759 0.4336603 0.7790019 0.8323242 0.8554640 0.8577206 0.9099627 -#> [156,] 0.30569236 0.3262743 0.3579049 0.3630452 0.4926383 0.5405824 0.6052169 -#> [157,] 0.64667460 0.7669037 0.8257021 0.8541981 0.9243470 0.9655508 1.0201939 -#> [158,] 0.37740552 0.4020145 0.4190839 0.4591714 0.5281018 0.5550570 0.5788570 -#> [159,] 0.33547237 0.3463819 0.4977218 0.5180707 0.5242244 0.5635180 0.5817520 -#> [160,] 0.35829338 0.5718766 0.5747591 0.5779698 0.6522162 0.7144527 0.7876369 -#> [161,] 0.50669338 0.5422210 0.5631950 0.5689447 0.6092181 0.6320838 0.6627306 -#> [162,] 0.51045490 0.5219120 0.5858995 0.5937850 0.7252764 0.8000330 0.8418996 -#> [163,] 0.37061007 0.4042716 0.5416616 0.6091344 0.6365370 0.7418901 0.7538221 -#> [164,] 0.51229703 0.5239113 0.5903342 0.5937850 0.6369687 0.6395027 0.7197476 -#> [165,] 0.54908168 0.9813251 1.1372137 1.1695009 1.2031595 1.2249379 1.2830620 -#> [166,] 0.61272048 0.7611578 0.7883182 0.8482370 0.8973138 0.8973912 0.9098426 -#> [167,] 0.24308049 0.2554877 0.3695717 0.4638763 0.4841923 0.5672204 0.5678705 -#> [168,] 0.38041963 0.4770930 0.4780638 0.4847577 0.5092416 0.5473634 0.5519264 -#> [169,] 0.26265001 0.6764559 0.6801124 0.7182474 0.8034945 0.8243029 0.9143408 -#> [170,] 0.26578713 0.4558640 0.5643588 0.5758453 0.6031906 0.6038029 0.6317046 -#> [171,] 0.17370456 0.4308061 0.4805783 0.4823644 0.5499564 0.5587297 0.6249264 -#> [172,] 0.26941305 0.4017908 0.5415747 0.5470445 0.5757414 0.5872002 0.6124346 -#> [173,] 0.87618145 0.9526264 0.9565228 0.9713093 1.0146814 1.0413455 1.1054185 -#> [174,] 0.61152392 0.7089670 0.7760067 0.9366138 0.9987533 1.0345146 1.0764891 -#> [175,] 0.55117629 0.6955637 0.8672199 0.9175538 1.0857738 1.0870652 1.1226844 -#> [176,] 0.51603535 0.5265768 0.5388508 0.5712551 0.5801553 0.6707166 0.7064780 -#> [177,] 0.57667920 0.6744389 0.7106979 0.7830243 0.8084727 0.8349234 0.8471915 -#> [178,] 0.35086757 0.4558084 0.4623692 0.5991109 0.5998957 0.6098110 0.6568489 -#> [179,] 0.59394151 0.6259422 0.6412760 0.7072447 0.9181475 0.9528680 0.9805333 -#> [180,] 0.45973796 0.5236926 0.5413020 0.5590320 0.5625617 0.5949277 0.6129280 -#> [181,] 0.48466368 0.4966760 0.5003019 0.5597563 0.5807237 0.6365370 0.6573707 -#> [182,] 0.49667604 0.5257725 0.5446453 0.5665520 0.5922374 0.5939369 0.6009061 -#> [183,] 0.44863069 0.4890375 0.4972940 0.5129245 0.5339640 0.5405824 0.5594749 -#> [184,] 1.54969454 1.6058166 1.7903249 1.8486250 1.9415717 1.9861202 1.9921299 -#> [185,] 0.46840057 0.5643122 0.7830058 0.8577187 0.9074240 0.9099627 0.9585011 -#> [186,] 0.21503155 0.4423911 0.4427498 0.4470150 0.4800804 0.4852145 0.5140889 -#> [187,] 0.54488025 0.5484068 0.6032080 0.6201989 0.6812469 0.6997092 0.7071064 -#> [188,] 0.55211975 0.5650773 0.6230229 0.7717706 0.7985339 0.8089592 0.8240324 -#> [189,] 0.38410060 0.6421988 0.6741195 0.6748141 0.7148040 0.7160836 0.7192255 -#> [190,] 0.29678858 0.3855973 0.3987382 0.4023448 0.4417458 0.4929323 0.5541087 -#> [191,] 0.83653302 0.9011914 1.1703316 1.1948539 1.3102195 1.3296849 1.3600533 -#> [192,] 0.25389733 0.3532200 0.5986405 0.6218608 0.6532183 0.6823172 0.6910984 -#> [193,] 0.22947858 0.3398413 0.4375047 0.5370750 0.5849706 0.5936046 0.5978990 -#> [194,] 0.40798941 0.4111824 0.4547087 0.4821675 0.5014282 0.5111314 0.5392919 -#> [195,] 0.42611375 0.5618296 0.6964333 0.8322004 0.9590741 0.9636064 0.9893164 -#> [196,] 0.40201451 0.4776085 0.5035358 0.5344354 0.5360601 0.5955668 0.6228103 -#> [197,] 0.46147713 0.6051880 0.6178127 0.7489781 0.7982087 0.8318041 0.9819342 -#> [198,] 0.90254645 1.1476636 1.2435210 1.2555075 1.2713360 1.2953993 1.2964965 -#> [199,] 0.35295760 0.4725503 0.6273540 0.6553937 0.6742267 0.7099813 0.7272521 -#> [200,] 0.22003989 0.6339161 0.6577880 0.7028651 0.7089670 0.7099221 0.7364444 -#> [201,] 1.30424483 1.3801370 1.6572063 1.6867596 1.7135375 1.7356769 1.7962741 -#> [202,] 0.96348909 1.1220217 1.2021038 1.2176376 1.3685976 1.4039036 1.4407800 -#> [203,] 0.72611264 0.8111915 0.8342606 0.8372628 0.8585798 0.8709668 1.0290069 -#> [204,] 0.26218164 0.3876053 0.4977218 0.5012997 0.5277461 0.5384173 0.5414872 -#> [205,] 0.32203562 0.4016753 0.4300057 0.5546442 0.5858126 0.5875525 0.6082550 -#> [206,] 0.37711935 0.4536032 0.5418304 0.5571568 0.5603790 0.5993868 0.6085266 -#> [207,] 1.29870348 1.4759336 1.5193207 1.6065617 1.6335442 1.6481263 1.6744164 -#> [208,] 0.41828293 0.4505434 0.5149747 0.5504608 0.5652963 0.6662338 0.6896558 -#> [209,] 1.09602517 1.3455591 1.3968001 1.4076033 1.4311583 1.4532473 1.4562446 -#> [210,] 0.23259817 0.2557204 0.3945140 0.4285865 0.5073673 0.5450982 0.5976732 -#> [211,] 0.57035503 0.6708464 0.6737630 0.7192255 0.7207689 0.7496445 0.7887201 -#> [212,] 0.39122971 0.4981192 0.5229825 0.5269430 0.5292013 0.5795555 0.5996315 -#> [213,] 0.35637992 0.4327384 0.4632784 0.4996657 0.5418139 0.5668559 0.5799450 -#> [214,] 0.50944477 0.5110197 0.5355926 0.5386034 0.5713747 0.5859775 0.6705592 -#> [215,] 0.34791594 0.5351012 0.5943828 0.6316720 0.6639791 0.7338786 0.7368381 -#> [216,] 0.27087776 0.2839025 0.3817311 0.5293472 0.5908624 0.6269716 0.6411617 -#> [217,] 0.39744238 0.4157032 0.4207887 0.5008248 0.5621268 0.5672412 0.6119784 -#> [218,] 0.33093201 0.5025979 0.5581936 0.5705094 0.5770479 0.5935215 0.5974658 -#> [219,] 0.47130002 0.4807848 0.5290914 0.6324410 0.6944847 0.7013259 0.7344657 -#> [220,] 0.50139444 0.6117694 0.6213244 0.6379400 0.6655542 0.7627878 0.7797618 -#> [221,] 0.17049041 0.4410825 0.4612374 0.4953889 0.5724994 0.6252209 0.7118564 -#> [222,] 0.95693748 1.1132546 1.1244440 1.1303904 1.2668734 1.3012856 1.3358036 -#> [223,] 0.28983255 0.3401989 0.3488723 0.4446433 0.4599618 0.4971177 0.5089164 -#> [224,] 0.68358791 0.7398657 0.8153097 0.9655508 1.0165439 1.0306387 1.0955916 -#> [225,] 0.52787810 0.6537160 0.7764582 0.8385543 0.8581661 0.9558473 0.9584774 -#> [226,] 0.47700844 0.5216885 0.5275571 0.5504608 0.6699999 0.6742696 0.6864118 -#> [227,] 0.40229399 0.4330564 0.4653143 0.4910223 0.5192757 0.5500262 0.5755747 -#> [228,] 0.16765161 0.3794128 0.3972535 0.4516268 0.5227325 0.5984751 0.7192160 -#> [229,] 0.56725251 0.5680787 0.5804492 0.6965240 0.7132296 0.7339512 0.7397689 -#> [230,] 0.53481399 0.6018809 0.6580955 0.6617331 0.6902225 0.7393891 0.7903442 -#> [231,] 0.56754181 0.6221936 0.7386678 0.7643205 0.7936885 0.8099410 0.8349234 -#> [232,] 0.33706501 0.5086939 0.5351012 0.5672952 0.6229590 0.6575454 0.7062489 -#> [233,] 0.51643360 0.5335565 0.5571488 0.5878117 0.7780923 0.8003594 0.8174984 -#> [234,] 0.38920400 0.6969675 0.7000426 0.7243613 0.7523700 0.7669118 0.7962175 -#> [235,] 0.37712295 0.3991980 0.5946666 0.6124585 0.6698246 0.7125412 0.7172980 -#> [236,] 0.34385430 0.3561176 0.5080554 0.5570809 0.5827833 0.6707290 0.6944958 -#> [237,] 0.35853973 0.3927377 0.4391532 0.4562070 0.4633042 0.4707987 0.5236926 -#> [238,] 0.59462859 0.6323538 0.6356349 0.6532442 0.7442849 0.7569891 0.8079612 -#> [239,] 0.59284156 0.6264562 0.8839687 0.8899777 0.9636064 0.9918935 1.0633670 -#> [240,] 0.24728549 0.5359625 0.5978341 0.6676988 0.7735319 0.7764392 0.7795528 -#> [241,] 0.39744238 0.4338809 0.4520274 0.5868525 0.6012372 0.6065631 0.6152847 -#> [242,] 0.37716013 0.4836585 0.5110197 0.5421546 0.5715196 0.6801882 0.6905674 -#> [243,] 0.28394795 0.3820281 0.3876899 0.3974501 0.4430272 0.5167141 0.5183246 -#> [244,] 0.82430292 0.9597073 0.9680445 1.3547966 1.3548550 1.3607090 1.3921615 -#> [245,] 0.57624017 0.5876477 0.6268535 0.6270330 0.6747779 0.6848974 0.7931200 -#> [246,] 0.31299205 0.5034560 0.5043867 0.5419253 0.5535397 0.5575974 0.7085888 -#> [247,] 0.64262965 0.8921008 0.9305805 0.9339677 1.0069366 1.0193305 1.1414821 -#> [248,] 0.48782602 0.5189966 0.5340295 0.5627590 0.5878117 0.7211326 0.7482774 -#> [249,] 0.29951135 0.4259536 0.4972859 0.6099927 0.6159928 0.6249264 0.6333368 -#> [250,] 1.20210381 1.5519966 1.7618684 1.9120597 1.9633872 2.0200774 2.0272466 -#> [251,] 0.35031851 0.4776085 0.5788570 0.6083226 0.6146147 0.6818975 0.7005643 -#> [252,] 0.32958296 0.3974501 0.4641185 0.4644079 0.4666262 0.5781133 0.5823183 -#> [253,] 0.64262965 0.7900258 0.9860777 0.9909285 1.0919575 1.1173512 1.1255470 -#> [254,] 0.31801358 0.3946047 0.5089164 0.6378590 0.6576409 0.6856637 0.6870499 -#> [255,] 0.56754181 0.6402904 0.7009357 0.7173158 0.7729828 0.9631997 1.0913812 -#> [256,] 0.34385430 0.5192776 0.6300583 0.6377344 0.7117716 0.7386289 0.7809774 -#> [257,] 0.22952545 0.3700009 0.4237522 0.4269215 0.4446646 0.4706503 0.4720348 -#> [258,] 0.44150652 0.8000399 0.9923017 1.1193029 1.2113795 1.2947642 1.3275814 -#> [259,] 0.85237136 1.0701032 1.2366975 1.4143776 1.5372939 1.5401955 1.6497181 -#> [260,] 0.34800816 0.4439292 0.4792568 0.5645975 0.5678585 0.5823287 0.5922374 -#> [261,] 0.35396396 0.4361154 0.4656768 0.4666262 0.5121672 0.5249327 0.5773453 -#> [262,] 0.71907907 0.7288427 0.7508510 0.7767205 0.7890183 0.9310220 0.9435470 -#> [263,] 0.29770340 0.4141110 0.5676076 0.6408758 0.6567339 0.6658709 0.7265516 -#> [264,] 0.19402179 0.2678304 0.3317761 0.5779306 0.5793657 0.5922482 0.6011110 -#> [265,] 0.52654089 0.5694851 0.7143330 0.7409058 0.7444994 0.7695044 0.8202393 -#> [266,] 0.43286308 0.6539635 0.7156137 1.0338277 1.0880982 1.1014870 1.2061600 -#> [267,] 0.36675682 0.5501011 0.6080251 0.6144638 0.6671930 0.6740908 0.6979966 -#> [268,] 0.59586692 0.7958265 0.8402377 0.8576936 0.9461251 0.9771705 1.0383945 -#> [269,] 0.54260412 0.5650773 0.6896558 0.7013672 0.8024806 0.8243659 0.8619546 -#> [270,] 0.22500973 0.4578056 0.5132158 0.5347966 0.5573906 0.5618274 0.5656646 -#> [271,] 0.26941305 0.2701207 0.4742903 0.5017003 0.5199536 0.5307726 0.5483506 -#> [272,] 0.48405150 0.5934796 0.7977922 0.8339237 0.8528246 0.9053046 0.9145482 -#> [273,] 0.50643364 0.6628639 0.7460642 0.7497779 0.7905453 0.8047521 0.8092070 -#> [274,] 0.56440130 0.6220324 0.6527405 0.7055652 0.7417904 0.7430240 0.7638724 -#> [275,] 0.64111107 0.7362176 0.7411347 0.8071888 0.9011914 0.9139025 0.9205592 -#> [276,] 0.48583868 1.1128216 1.1280921 1.2256428 1.2970298 1.3186694 1.3719151 -#> [277,] 0.27111546 0.5458634 0.5522195 0.5631557 0.6328689 0.6480226 0.6719849 -#> [278,] 0.32240302 0.5092862 0.5285431 0.5858995 0.6302899 0.6656487 0.6848974 -#> [279,] 0.54272310 0.9510885 1.0333731 1.0378200 1.0842766 1.0886630 1.1258774 -#> [280,] 0.44001319 0.4482383 0.5350578 0.5680075 0.7093066 0.7229251 0.7477676 -#> [281,] 0.48782602 0.5382450 0.6441751 0.6856915 0.7746998 0.8234598 0.8382064 -#> [282,] 0.63216176 0.6843951 0.7033122 0.7206454 0.8457100 0.8556248 0.9346566 -#> [283,] 0.20119578 0.4330050 0.5292013 0.6444889 0.6596934 0.6674573 0.7008368 -#> [284,] 0.41791948 0.5500002 0.5616033 0.6644147 0.6684482 0.7115284 0.7714425 -#> [285,] 0.28122955 0.2898325 0.3042652 0.3680010 0.4208660 0.4288953 0.4544843 -#> [286,] 0.83117402 0.9536993 1.0759830 1.0794063 1.0797320 1.1290180 1.2017439 -#> [287,] 0.16765161 0.2524951 0.3055798 0.4410636 0.4944418 0.5827833 0.6699247 -#> [288,] 0.56499395 0.6104109 0.6494149 0.6533586 0.6589826 0.6912301 0.7058457 -#> [289,] 0.56128240 0.6060546 0.6891124 0.7835165 0.8987008 0.9107711 0.9328638 -#> [290,] 0.44254775 0.4739008 0.4938839 0.4973035 0.5154688 0.5364357 0.5900000 -#> [291,] 0.43114166 0.5037232 0.7502026 0.8022048 0.8222796 0.9218647 0.9456269 -#> [292,] 0.28122955 0.3585397 0.4005744 0.4599618 0.4719293 0.4737524 0.4941077 -#> [293,] 0.45982563 0.4627462 0.6790274 0.7352688 0.7444994 0.7550201 0.8146329 -#> [294,] 0.33093201 0.3737276 0.4075892 0.4965954 0.5459429 0.6212020 0.6413108 -#> [295,] 1.22922834 1.6228459 1.7320149 1.8213842 1.9268269 2.0307088 2.0696552 -#> [296,] 0.69835149 0.7256993 0.7417198 0.8211896 0.8380895 0.8780206 0.8932582 -#> [297,] 0.24047378 0.3771601 0.4302289 0.4714800 0.5026818 0.5079815 0.5116403 -#> [298,] 0.97717053 1.0341550 1.1530179 1.1615413 1.1964910 1.2109834 1.2236306 -#> [299,] 0.46871727 0.5940345 0.6718302 0.6963547 0.7474774 0.7541661 0.7727392 -#> [300,] 0.40337394 0.4141110 0.4921739 0.4987909 0.5418304 0.6260777 0.6305279 -#> [301,] 0.38832700 0.5591449 0.7470331 0.8280040 0.9345298 1.0007794 1.0097257 -#> [302,] 1.05544757 1.0864902 1.2096848 1.2278592 1.2559593 1.3363969 1.3384323 -#> [303,] 1.05931979 1.4083979 1.5475604 1.5779953 1.5884435 1.6557957 1.7377046 -#> [304,] 0.61747662 0.6944256 0.7058491 0.7141267 0.7232051 0.7310167 0.7342439 -#> [305,] 0.99875331 1.1196969 1.1629325 1.1712476 1.1934590 1.2020231 1.2147471 -#> [306,] 0.16044299 0.3508093 0.3586839 0.3947949 0.4432703 0.5215421 0.5766561 -#> [307,] 0.75555445 0.7985300 0.9673132 1.0086205 1.0165799 1.0217857 1.0825259 -#> [308,] 0.19402179 0.3555067 0.4337352 0.5723201 0.5901790 0.6238143 0.6588093 -#> [309,] 0.15739874 0.2927399 0.3094247 0.3420135 0.4140120 0.4279712 0.4617395 -#> [310,] 0.42526236 0.5833116 0.6245978 0.6534410 0.6637959 0.6728224 0.7151564 -#> [311,] 0.65948086 0.7622319 0.7696701 0.7931384 0.8242023 0.8477518 0.9194493 -#> [312,] 0.38594183 0.4376983 0.4930348 0.6038293 0.6202930 0.6476761 0.6973202 -#> [313,] 0.29619568 0.5594014 0.5688117 0.5864253 0.5870340 0.6421234 0.6713917 -#> [314,] 0.27111546 0.5001202 0.5686945 0.6167773 0.6353030 0.7064299 0.7158896 -#> [315,] 0.45340478 0.4768650 0.4864367 0.4908308 0.4909328 0.5154688 0.5219980 -#> [316,] 0.62039678 0.7758929 0.7894831 0.7977922 0.8005508 0.8373456 0.8664193 -#> [317,] 0.34725973 0.4317180 0.4657972 0.4728753 0.4784125 0.4947778 0.5146067 -#> [318,] 0.66774590 1.0664273 1.2667554 1.2998875 1.3524285 1.3923727 1.4089270 -#> [319,] 0.53683528 0.6458406 0.6819790 0.6968820 0.7221679 0.7954932 0.8013005 -#> [320,] 0.27692957 0.3295830 0.4315167 0.4361154 0.4632784 0.5194180 0.5731871 -#> [321,] 0.37740552 0.5298873 0.5674285 0.6048902 0.6227255 0.6320025 0.6411027 -#> [322,] 0.35031851 0.5399719 0.6108232 0.6269043 0.6609895 0.7051456 0.7055877 -#> [323,] 0.95372941 0.9592937 0.9818004 0.9823114 1.0534956 1.0553641 1.0968400 -#> [324,] 0.18607074 0.4439292 0.5175486 0.5340295 0.5501744 0.5571488 0.5711820 -#> [325,] 0.41137800 0.5682765 0.5934796 0.6069523 0.7628360 0.7758584 0.7772044 -#> [326,] 0.49029828 0.5084776 0.5933649 0.6258394 0.8065527 0.8477603 0.8576452 -#> [327,] 0.67399602 0.6808892 0.7301544 0.7776224 0.7818618 0.7955500 0.8297262 -#> [328,] 0.83159959 0.9046579 1.3424826 1.3942088 1.3985994 1.4262390 1.4263432 -#> [329,] 0.62645619 0.8139930 1.0637702 1.1379397 1.2330201 1.2445042 1.2717714 -#> [330,] 0.34718697 0.3912297 0.4194307 0.4439014 0.5228171 0.5727556 0.6406330 -#> [331,] 0.26035703 0.5753758 0.5946666 0.6105969 0.6638629 0.6697832 0.6705406 -#> [332,] 0.30855503 0.3710229 0.4878354 0.5026781 0.5290914 0.5319935 0.5828565 -#> [333,] 0.32922558 0.3842350 0.4455320 0.5087297 0.5693583 0.5817674 0.6002576 -#> [334,] 0.43580695 0.4922980 0.5026781 0.5831288 0.5879639 0.6053585 0.6735620 -#> [335,] 0.57875264 0.6839511 0.6853032 0.6863318 0.6982818 0.7301646 0.7401596 -#> [336,] 0.23416207 0.4904582 0.5355710 0.5864253 0.5963909 0.6157789 0.6580955 -#> [337,] 0.56117283 0.9724704 1.0097257 1.2080045 1.3264815 1.4421302 1.4433116 -#> [338,] 0.46558168 0.4841030 0.6402904 0.9160282 0.9315429 0.9379916 1.0990903 -#> [339,] 0.56536588 0.6463297 0.6586995 0.6940481 0.7130196 0.7237704 0.7773880 -#> [340,] 0.40700908 0.5399719 0.5425732 0.5478941 0.5985593 0.6146147 0.6517178 -#> [341,] 0.23253009 0.2841939 0.5425732 0.5526583 0.5707032 0.6098153 0.6189824 -#> [342,] 0.33335252 0.4853275 0.5666667 0.5674679 0.5784783 0.5826087 0.5944991 -#> [343,] 0.91191200 0.9235948 0.9993258 1.0262315 1.0356451 1.0573300 1.0660545 -#> [344,] 0.21286690 0.3670160 0.4329317 0.5043867 0.5509312 0.5822610 0.6361994 -#> [345,] 0.32627428 0.4014896 0.4233858 0.4694141 0.4925961 0.4972940 0.5446308 -#> [346,] 0.27767287 0.2783384 0.3710229 0.3929441 0.4713000 0.4934612 0.4958311 -#> [347,] 0.49907726 0.6222739 0.7091526 0.8132572 0.8963111 0.9075966 1.0341483 -#> [348,] 0.21792988 0.5361539 0.5793089 0.5827702 0.5841615 0.5937395 0.6079173 -#> [349,] 0.49561025 0.7515701 0.7900258 0.7948560 0.8526947 0.8921008 0.9249440 -#> [350,] 0.15272180 0.3451188 0.5312809 0.5427308 0.5455492 0.5483506 0.5682192 -#> [351,] 0.50111140 0.5290405 0.5388508 0.5638882 0.6842062 0.6856766 0.7081862 -#> [352,] 0.48405150 0.9997366 1.0063590 1.0186303 1.0258218 1.0370866 1.0842196 -#> [353,] 0.85506147 0.9796234 0.9997460 1.0331354 1.0857738 1.1596754 1.1766655 -#> [354,] 0.33886686 0.7598023 0.7773880 0.8075970 0.8530453 0.8797843 0.9533309 -#> [355,] 0.53824498 0.7482774 0.8041277 0.8343406 0.8527889 0.8808184 0.9057698 -#> [356,] 0.39460346 0.4735638 0.5339004 0.5406958 0.6300583 0.6759686 0.6760014 -#> [357,] 0.36786814 0.4943045 0.5858722 0.6555239 0.6883619 0.7011929 0.7140176 -#> [358,] 0.54467172 0.6008991 0.6133631 0.6281582 0.7045401 0.7285143 0.7305726 -#> [359,] 0.49538891 0.5077185 0.5161778 0.6036898 0.6470808 0.6509040 0.6822214 -#> [360,] 0.33360306 0.3951510 0.5297383 0.5445057 0.5796505 0.6006658 0.6055141 -#> [361,] 0.17049041 0.5033910 0.5077185 0.6048211 0.6167268 0.6264364 0.6731264 -#> [362,] 0.30195725 0.4376983 0.5229335 0.5471404 0.5548141 0.5564983 0.5812133 -#> [363,] 0.60005117 0.6010209 0.6738717 0.7179053 0.8279851 0.8281021 0.8510822 -#> [364,] 0.66774590 1.3163298 1.4704140 1.4948077 1.4982230 1.5405538 1.5528732 -#> [365,] 0.40688031 0.5753531 0.6290901 0.6897839 0.7227850 0.7229793 0.7529551 -#> [366,] 0.33416754 0.6339668 0.7072508 0.7096127 0.7341859 0.7492985 0.7689684 -#> [367,] 0.77150740 0.8281860 0.8365330 0.8415089 0.8481635 0.8571622 0.8613339 -#> [368,] 0.93525318 0.9501049 1.0214541 1.0440251 1.0497149 1.0786428 1.1421249 -#> [369,] 0.75502011 0.8643716 0.8916613 0.8925923 0.9750515 1.1627505 1.1896890 -#> [370,] 0.41366461 0.5810245 0.6492271 0.6670795 0.7102363 0.7141773 0.7190354 -#> [371,] 0.22947858 0.4005975 0.4329269 0.4377666 0.4958311 0.5414789 0.5570704 -#> [372,] 0.28229287 0.3060863 0.4140120 0.4617932 0.4860057 0.4901277 0.5107906 -#> [373,] 0.42234308 0.4251424 0.6440649 0.6532035 0.6593543 0.6997124 0.7057718 -#> [374,] 0.35705946 0.4944418 0.5227325 0.6337435 0.7313741 0.7448075 0.7467064 -#> [375,] 0.43000568 0.4510068 0.4537805 0.4770084 0.4908308 0.6091098 0.6605649 -#> [376,] 0.70550896 0.8658663 1.0162033 1.1059807 1.1066440 1.1351071 1.1641759 -#> [377,] 0.73231115 0.7330094 0.7646062 0.7711267 0.8413085 0.8651759 0.8653694 -#> [378,] 0.51593162 0.6416642 0.7694242 0.8225500 0.8955663 0.9084808 0.9439012 -#> [379,] 0.46123736 0.5245417 0.6048211 0.6248637 0.6372920 0.7045773 0.7268233 -#> [380,] 0.41684967 0.4332905 0.5386192 0.5518292 0.5708686 0.5978990 0.6011880 -#> [381,] 0.47919111 0.4792568 0.4871539 0.5175486 0.5446453 0.5682192 0.6344623 -#> [382,] 0.48667127 0.5048615 0.5272385 0.5503752 0.5544435 0.5757508 0.6004304 -#> [383,] 1.39237913 1.4389659 1.4775812 1.5511146 1.6401882 1.6666768 1.7071872 -#> [384,] 1.11081929 1.1862048 1.2363080 1.3240156 1.3580938 1.3619310 1.3934562 -#> [385,] 0.84719154 0.9257695 0.9259973 0.9631997 1.0528749 1.1507484 1.2607130 -#> [386,] 0.21549985 0.2927903 0.4528098 0.4554006 0.4847586 0.5067646 0.5147386 -#> [387,] 0.25548771 0.2627143 0.3012077 0.3369672 0.5852312 0.6533586 0.6665172 -#> [388,] 0.38313595 0.4852145 0.5667683 0.5705758 0.5723201 0.5816681 0.5817390 -#> [389,] 0.36776757 0.3820516 0.5654415 0.6478635 0.6581621 0.7580703 0.7614613 -#> [390,] 0.44042104 0.4462403 0.4861098 0.6844823 0.6898711 0.6920427 0.7149602 -#> [391,] 0.37755185 0.4720687 0.4748450 0.6676814 0.6830605 0.7232436 0.7517080 -#> [392,] 0.49303475 0.6282661 0.6352669 0.7280407 0.7822611 0.7883182 0.8423544 -#> [393,] 0.24156076 0.3333525 0.4894152 0.4907465 0.6046438 0.6096974 0.6198758 -#> [394,] 0.75980226 0.9182698 1.0389936 1.0786253 1.0842766 1.1125976 1.1204943 -#> [395,] 0.79131716 0.8450893 0.8701179 0.9600946 0.9620011 1.1613341 1.1650136 -#> [396,] 0.46840057 0.6900040 0.7541661 0.7634109 0.8923103 0.9210998 0.9261443 -#> [397,] 0.39460470 0.4769464 0.4993423 0.5605158 0.5964247 0.6108362 0.6189705 -#> [398,] 0.54175410 0.6101697 0.6214295 0.7347792 0.7857347 0.8037321 0.8482199 -#> [399,] 0.42443846 0.4946278 0.4988318 0.4991808 0.5728891 0.5872002 0.6422448 -#> [400,] 0.40589754 0.5994650 0.6405285 0.6597749 0.7341665 0.7384536 0.7440294 -#> [401,] 0.33547237 0.3876053 0.5448803 0.5742499 0.5754628 0.6805831 0.7048972 -#> [402,] 0.52774869 0.7536813 0.7796129 0.8243505 0.8382165 0.8643576 0.9220884 -#> [403,] 0.61366148 0.6602462 0.7427637 0.7735705 0.8246115 0.8442161 0.8613048 -#> [404,] 0.35215632 0.4564511 0.6322821 0.6395455 0.7042088 0.7094070 0.7266282 -#> [405,] 0.50924156 0.5422210 0.5544217 0.5690209 0.5959135 0.6045659 0.6229129 -#> [406,] 0.57640862 0.7099221 0.7166143 0.7249098 0.7888289 0.8557293 0.8600432 -#> [407,] 0.27916916 0.3484067 0.4446646 0.4531318 0.4875835 0.5116676 0.5139554 -#> [408,] 0.32811395 0.3350727 0.3440006 0.4969256 0.6380791 0.6789776 0.7619706 -#> [409,] 0.33360306 0.4789346 0.4905156 0.5192757 0.5432577 0.5903237 0.6025242 -#> [410,] 0.51734179 0.8135626 0.9046579 0.9546533 0.9682437 1.0339429 1.0568210 -#> [411,] 0.51230855 0.7115000 0.7758929 0.7828679 0.9054288 1.0238655 1.1780441 -#> [412,] 0.56052284 0.6773700 0.9139025 0.9216663 1.0707815 1.0758797 1.1157012 -#> [413,] 0.27429430 0.4174707 0.4715509 0.4769901 0.5313888 0.5681010 0.5757508 -#> [414,] 0.65431994 0.7272521 0.7962587 0.8683901 0.8706834 1.0330764 1.0452828 -#> [415,] 0.52130015 0.5345360 0.6173324 0.6322821 0.6335443 0.7028651 0.7036003 -#> [416,] 0.80280506 0.8307399 0.8473081 0.9751862 0.9957300 1.0787663 1.2395073 -#> [417,] 0.63880966 0.6527405 0.7013672 0.7099813 0.7667526 0.7864823 0.8593760 -#> [418,] 0.17091574 0.2557204 0.4154745 0.4707840 0.4996657 0.5305223 0.6249691 -#> [419,] 0.91355647 0.9407524 1.1901579 1.2092175 1.2215077 1.2843890 1.2951655 -#> [420,] 0.44077352 0.4423909 0.6316720 0.6967569 0.7555210 0.7897678 0.7911129 -#> [421,] 1.18129626 1.1820783 1.2192175 1.2663894 1.4015970 1.4078856 1.5088903 -#> [422,] 0.60188092 0.7306779 0.7614894 0.8201824 0.8507603 0.8563820 0.8624440 -#> [423,] 0.37105317 0.4343819 0.4415877 0.4975265 0.4978055 0.5155221 0.6055141 -#> [424,] 1.03306172 1.0416197 1.0467315 1.1375541 1.2039755 1.2894666 1.2903858 -#> [425,] 0.44637144 0.5257725 0.5597563 0.6685950 0.6792135 0.7737699 0.7910788 -#> [426,] 0.38205163 0.4427894 0.5293472 0.5416102 0.5452154 0.5486471 0.6135617 -#> [427,] 0.92897311 1.4607150 1.7194306 1.7638615 1.8088267 1.8113200 1.8456745 -#> [428,] 0.36893463 0.5189327 0.5674679 0.6249000 0.6716065 0.6980939 0.7404938 -#> [429,] 0.31315190 0.6597764 0.7347792 0.8168133 0.8300385 0.8385543 0.8550615 -#> [430,] 0.56117283 0.9280705 1.0769471 1.0922780 1.2842654 1.3766206 1.4703591 -#> [431,] 0.35991731 0.6071260 0.6503385 0.6590381 0.6897839 0.7125915 0.7392944 -#> [432,] 0.27515565 0.6780247 0.7875097 0.8063710 0.8114488 0.8716991 0.8797149 -#> [433,] 0.32404141 0.4805979 0.5681275 0.5712551 0.5734678 0.5900957 0.6468046 -#> [434,] 0.33579434 0.5409392 0.5437083 0.5716376 0.7179293 0.7368908 0.7704097 -#> [435,] 0.51962096 0.8417451 0.8673370 0.9462979 0.9760531 1.0758839 1.1064294 -#> [436,] 0.35551633 0.5461164 0.6081071 0.7107542 0.7305726 0.7630830 0.7731162 -#> [437,] 0.22473774 0.2619194 0.3630418 0.3737517 0.4401235 0.4417458 0.4634704 -#> [438,] 0.39662159 0.4432703 0.4436377 0.4566741 0.5134027 0.5176712 0.5475328 -#> [439,] 0.24156076 0.4339481 0.4853275 0.5150846 0.5453835 0.5731871 0.6445660 -#> [440,] 0.42629419 0.4269498 0.4917449 0.5967956 0.7033093 0.7925975 0.8220188 -#> [441,] 0.47387022 0.4973497 0.5272385 0.5500262 0.5524280 0.5579955 0.5641639 -#> [442,] 0.69234816 0.8231293 0.8600432 0.8741934 0.8783775 0.9141506 0.9162641 -#> [443,] 0.26578713 0.3455166 0.5271173 0.5728187 0.6090574 0.6193783 0.6266733 -#> [444,] 0.30916496 0.3856758 0.4017785 0.4945967 0.5464797 0.5544855 0.5725951 -#> [445,] 0.50248079 0.5973751 0.6198012 0.7106098 0.7710974 0.7735319 0.7790543 -#> [446,] 0.30696916 0.4064382 0.4439416 0.5047480 0.6406330 0.6714506 0.7422157 -#> [447,] 0.94100371 1.0331354 1.0410825 1.0532556 1.1293918 1.1827213 1.2545704 -#> [448,] 0.85654824 1.1190720 1.1199056 1.1775246 1.1937552 1.2525599 1.2540978 -#> [449,] 0.72588120 0.9358353 0.9492661 1.0811068 1.0912420 1.1134560 1.1234979 -#> [450,] 0.49268324 0.5296746 0.7474774 0.7683373 0.8557396 0.8643716 0.9074240 -#> [451,] 0.40730826 0.4722252 0.4829929 0.5956886 0.6168834 0.6456856 0.6482295 -#> [452,] 0.43750473 0.4411643 0.5386192 0.5570704 0.5666451 0.5785084 0.5869566 -#> [453,] 0.27833841 0.2841270 0.3683553 0.4005975 0.4819972 0.5281680 0.5319935 -#> [454,] 0.55997084 0.6393230 0.7498951 0.7731162 0.8337882 0.8384616 0.8678513 -#> [455,] 0.39223456 0.4003260 0.4009120 0.4256820 0.4335879 0.4368795 0.4841923 -#> [456,] 0.31583768 0.3357943 0.4075892 0.4683504 0.6180513 0.6915943 0.7676689 -#> [457,] 0.19718617 0.2507500 0.2952804 0.4110020 0.4269215 0.4505302 0.4861472 -#> [458,] 0.79403055 0.9079105 0.9744997 1.0245793 1.0396396 1.0684175 1.0798204 -#> [459,] 0.41279177 0.4735638 0.4978055 0.5009526 0.5194236 0.5210512 0.5656646 -#> [460,] 0.18365652 0.3565099 0.3737517 0.4901712 0.5012997 0.5406525 0.5541087 -#> [461,] 0.33777507 0.4185361 0.4500143 0.5213931 0.5732926 0.5733587 0.6311623 -#> [462,] 0.34000005 0.4547087 0.4803415 0.4979181 0.4981605 0.5139271 0.5324105 -#> [463,] 0.50958632 0.5724994 0.5762256 0.5984237 0.6036898 0.6167249 0.6167268 -#> [464,] 0.41791948 0.5207050 0.6310681 0.6748141 0.6753137 0.7997849 0.8257319 -#> [465,] 0.41908392 0.5298873 0.6748081 0.6816221 0.7071701 0.7271169 0.7385645 -#> [466,] 1.34494781 1.5233323 1.5635876 1.6090008 1.7593662 1.7745993 1.8503743 -#> [467,] 0.79853002 0.8112394 0.9556263 1.0278128 1.0292884 1.0529251 1.0797320 -#> [468,] 0.67399602 0.8083640 0.8330092 0.8502555 0.8880128 0.8915497 0.9213053 -#> [469,] 0.63932303 0.6410085 0.7169540 0.8022048 0.8101539 0.8853278 0.8998839 -#> [470,] 0.35517167 0.5000046 0.5068350 0.5085941 0.5143304 0.5251669 0.5482755 -#> [471,] 0.36491652 0.3965035 0.5752258 0.6001995 0.6997092 0.7815906 0.8311178 -#> [472,] 0.36304515 0.5643803 0.5915972 0.6311051 0.6366774 0.6652489 0.6658170 -#> [473,] 0.15272180 0.3878751 0.4335768 0.4719275 0.5506425 0.5845838 0.5928785 -#> [474,] 0.43303048 0.4391350 0.4656768 0.5288773 0.5328568 0.5817674 0.5961361 -#> [475,] 0.53830065 1.1780441 1.1802566 1.1812963 1.3166412 1.3192053 1.3597177 -#> [476,] 0.27692957 0.3539640 0.3934326 0.4327384 0.4391350 0.4641185 0.4707840 -#> [477,] 0.30784366 0.4136345 0.4267375 0.4565310 0.5014282 0.5324105 0.5731336 -#> [478,] 0.14874895 0.3816655 0.5239113 0.6164456 0.6900380 0.6975582 0.7122184 -#> [479,] 0.54272310 0.6060152 0.6884798 0.7072447 0.7911542 0.7989374 0.8566173 -#> [480,] 0.43637557 0.7652942 0.7878777 0.8075332 0.8276442 0.9581094 0.9721621 -#> [481,] 0.30696916 0.3199699 0.3438252 0.4263777 0.4439014 0.5274743 0.6674573 -#> [482,] 0.34551656 0.4346415 0.5532540 0.5643588 0.6144827 0.6421988 0.6648602 -#> [483,] 0.50575352 0.7093871 0.7159460 0.7826749 0.8296502 0.8301152 0.8694828 -#> [484,] 0.39196996 0.4252624 0.6739281 0.7272578 0.7433350 0.7891056 0.8303995 -#> [485,] 0.32267120 0.5123086 0.5447285 0.6203968 0.7115202 0.8486390 0.8656488 -#> [486,] 0.61082321 0.6598523 0.7005643 0.8473081 0.8643576 0.9271097 0.9627590 -#> [487,] 0.37131494 0.5290405 0.6032977 0.6206803 0.6283001 0.6460744 0.6707166 -#> [488,] 0.60423508 0.6226021 1.0455526 1.1612867 1.2450324 1.2661239 1.2846899 -#> [489,] 0.60605455 0.7563084 0.8980437 0.9233367 1.0435580 1.0566689 1.0593198 -#> [490,] 0.38529405 0.5153898 0.5944632 0.6035720 0.6498777 0.6666415 0.6975582 -#> [491,] 0.69234816 0.7206454 0.7888289 0.8132450 0.8274062 0.8489513 0.8564503 -#> [492,] 1.15191239 1.3899942 1.4500010 1.4885976 1.5228824 1.5852860 1.6965329 -#> [493,] 0.56315141 0.6642650 0.6965077 0.6999140 0.7645853 0.7683081 0.7897385 -#> [494,] 0.48583868 1.1109211 1.1695009 1.3133883 1.3257371 1.3295063 1.3357156 -#> [495,] 0.78233201 1.0637702 1.1451020 1.2800477 1.3045123 1.3432057 1.3498040 -#> [496,] 0.58121331 0.5842185 0.5934716 0.7158673 0.7288453 0.8039226 0.8781543 -#> [497,] 0.60144678 0.6523766 0.7815906 0.7855782 0.7883097 0.9350030 0.9439458 -#> [498,] 0.58608188 0.9931059 1.1399638 1.1783841 1.2097972 1.2181288 1.2365478 -#> [499,] 0.63282757 0.6682973 0.6743829 0.7578613 0.7671347 0.7887201 0.8602626 -#> [500,] 0.26265001 0.5356474 0.6421611 0.6772350 0.7457229 0.7951561 0.8453708 -#> [501,] 1.38999419 1.5992446 1.8790500 2.3113869 2.3168782 2.3637341 2.3922669 -#> [502,] 0.32558987 0.4174707 0.4371221 0.4554006 0.4772252 0.5048615 0.5416199 -#> [503,] 0.48461201 0.7215688 0.7433350 0.7617940 0.7680742 0.7905453 0.7961259 -#> [504,] 0.22486970 0.2627143 0.3695717 0.4947433 0.5551474 0.5586764 0.5956886 -#> [505,] 0.68271114 0.8258154 0.8675523 0.9214783 0.9267411 0.9584324 0.9606637 -#> [506,] 0.38713800 0.4160170 0.4167471 0.5416616 0.6201974 0.6218608 0.6513026 -#> [507,] 0.35517167 0.4033861 0.4352831 0.4425477 0.4630983 0.4665478 0.4729722 -#> [508,] 0.21503155 0.4364501 0.5066574 0.5068288 0.5470138 0.5629381 0.5653868 -#> [509,] 1.35540675 1.3651020 1.4498864 1.4697750 1.4866551 1.6000226 1.6029319 -#> [510,] 1.12729374 1.1422187 1.1849896 1.2738890 1.2847577 1.2913047 1.3467737 -#> [511,] 0.33004290 0.3838357 0.5194236 0.5339004 0.5774565 0.6389957 0.6516153 -#> [512,] 0.87751687 1.0572218 1.1211695 1.1232132 1.2340452 1.2617497 1.2899045 -#> [513,] 1.26501290 1.2800477 1.3927728 1.4072841 1.4812533 1.4894414 1.5189249 -#> [514,] 0.32349377 0.4353077 0.5567052 0.5573627 0.5938147 0.5940007 0.6354609 -#> [515,] 0.46681568 0.4981513 0.5804492 0.5870340 0.6382426 0.6558298 0.6613072 -#> [516,] 0.48176089 0.4981513 0.6124427 0.6951015 0.6968820 0.7107325 0.7603987 -#> [517,] 0.56275897 0.7327818 0.7415040 0.7746998 0.7948028 0.8078147 0.8275757 -#> [518,] 0.43645014 0.4427498 0.4582420 0.4617395 0.4696348 0.4860057 0.4869942 -#> [519,] 0.30195725 0.3064522 0.4834773 0.5319833 0.5844000 0.5934716 0.6602826 -#> [520,] 0.34872796 0.4696348 0.5094419 0.5328879 0.5508058 0.5596657 0.5667683 -#> [521,] 0.22486970 0.3012077 0.4638763 0.4827970 0.5581506 0.5857969 0.6168834 -#> [522,] 0.34718697 0.4734484 0.4759376 0.4821902 0.4981192 0.5919049 0.6765002 -#> [523,] 0.37119003 0.4042716 0.4167471 0.6327125 0.6424517 0.6451651 0.6544454 -#> [524,] 0.49883178 0.5564145 0.6133631 0.6188844 0.6548673 0.6671413 0.7107542 -#> [525,] 0.51487828 0.7055090 0.7121909 0.7625581 0.8078147 0.8712902 0.9196127 -#> [526,] 0.26233166 0.3301814 0.5477472 0.5644013 0.5652963 0.6095633 0.6419645 -#> [527,] 0.44657063 0.5469588 0.6504423 0.7273693 0.9348490 0.9960069 1.0127452 -#> [528,] 0.55221954 0.6227255 0.6748081 0.6885028 0.6898711 0.6936241 0.6971507 -#> [529,] 0.60601516 0.6747779 0.7274967 0.7710522 0.7746791 0.7761110 0.7761581 -#> [530,] 0.96348909 1.0005127 1.1531303 1.1819835 1.2705730 1.2881013 1.3097788 -#> [531,] 0.51614723 0.5859331 0.6091953 0.6416642 0.6484656 0.7493636 0.7608141 -#> [532,] 0.52734415 0.5752258 0.6201989 0.6509948 0.7475855 0.7848703 0.8374849 -#> [533,] 0.22768949 0.4755288 0.5001598 0.5267358 0.5417933 0.5707032 0.6032977 -#> [534,] 0.17308916 0.3775519 0.4374424 0.4842991 0.5473634 0.5522140 0.5544129 -#> [535,] 0.27366290 0.4910223 0.5432577 0.5697910 0.6618646 0.7108046 0.7424222 -#> [536,] 0.82986354 0.8525027 0.8694440 0.8770413 0.8785766 0.9407524 0.9698302 -#> [537,] 0.50957678 0.5401574 0.5591449 0.5853550 0.6122867 0.8077789 0.8343363 -#> [538,] 0.36353777 0.3804196 0.4706606 0.4781166 0.4842991 0.5207731 0.5435039 -#> [539,] 0.12432811 0.4799947 0.7927098 0.8045342 0.8104586 0.8335825 0.8565542 -#> [540,] 0.39267342 0.5104084 0.5539836 0.5571402 0.6095633 0.6229590 0.6637908 -#> [541,] 0.28212285 0.2891919 0.4649396 0.5648903 0.5664279 0.5829420 0.6284229 -#> [542,] 0.99208071 1.0640377 1.1408177 1.2567472 1.2610097 1.2973438 1.3444573 -#> [543,] 0.48996733 0.5606936 0.5728755 0.6760014 0.7544363 0.7715961 0.7818113 -#> [544,] 0.22723214 0.3977784 0.4259536 0.4369957 0.5030719 0.5087297 0.5148810 -#> [545,] 0.32764986 0.3947949 0.3989610 0.4436377 0.5049733 0.5212736 0.5482755 -#> [546,] 0.38903127 0.4299702 0.4647006 0.4800804 0.5080255 0.5473492 0.6005693 -#> [547,] 0.22075267 0.3000063 0.6380791 0.6999140 0.7695203 0.7772380 0.7839562 -#> [548,] 0.54444892 0.7026799 0.7865930 0.8482936 0.8593760 0.8771458 0.9025465 -#> [549,] 0.41975680 0.5425809 0.6484656 0.6700281 0.6740985 0.7462301 0.7625581 -#> [550,] 0.44410572 0.4786823 0.5505203 0.6208388 0.7245477 0.7528892 0.8241554 -#> [551,] 0.34765690 0.4860027 0.5374429 0.5498394 0.5611247 0.5674918 0.5913260 -#> [552,] 0.25761193 0.2839479 0.4359787 0.4500779 0.5492644 0.6073729 0.6101469 -#> [553,] 0.32458341 0.4294108 0.4755666 0.4790972 0.5430447 0.5497086 0.5698945 -#> [554,] 0.30000634 0.3440006 0.3895970 0.5069029 0.6480851 0.6545683 0.7117059 -#> [555,] 0.57083623 0.6741173 0.7033093 0.7443693 0.7894831 0.7964902 0.8458878 -#> [556,] 0.51508459 0.5199321 0.5402096 0.5418139 0.5784783 0.6096974 0.6549053 -#> [557,] 0.35322004 0.4095795 0.6571445 0.7186039 0.7256993 0.7310167 0.7931390 -#> [558,] 1.73915157 2.0895044 2.1799612 2.2052621 2.2889129 2.3213603 2.3241489 -#> [559,] 0.32678210 0.5064336 0.5544217 0.6344623 0.6354370 0.6742242 0.6952326 -#> [560,] 0.25389733 0.4095795 0.4867599 0.5728891 0.5757414 0.6128420 0.6201974 -#> [561,] 0.57920643 0.6609895 0.7130723 0.7608231 0.8280994 0.8915774 0.9119757 -#> [562,] 0.33004290 0.3951510 0.4803121 0.5113439 0.5922088 0.6058663 0.6105139 -#> [563,] 0.29619568 0.6414745 0.6818945 0.7300882 0.7494630 0.7524761 0.8068922 -#> [564,] 0.28390248 0.3352527 0.4368724 0.4797075 0.5452154 0.5546065 0.5663818 -#> [565,] 1.70387685 1.8756984 1.9238217 1.9351767 2.0168327 2.0358703 2.0389652 -#> [566,] 0.50368519 0.5164336 0.5711820 0.6091953 0.6135240 0.6179733 0.6527461 -#> [567,] 0.27429430 0.3579049 0.4233858 0.4472829 0.4561306 0.4772252 0.4890375 -#> [568,] 0.36176548 0.4923406 0.4991808 0.5343824 0.5415747 0.5455492 0.5481736 -#> [569,] 0.58372918 0.5984751 0.6508299 0.6699247 0.7298699 0.7735197 0.8480297 -#> [570,] 0.38920400 0.6251328 0.7517080 0.7609491 0.7827158 0.8434251 0.8563041 -#> [571,] 0.44136721 0.5160107 0.5731316 0.5940345 0.6567246 0.6588093 0.6591535 -#> [572,] 0.33373156 0.4814404 0.5521197 0.7448075 0.7741427 0.7927559 0.8362222 -#> [573,] 0.23416207 0.4356454 0.5688117 0.5912199 0.5929949 0.5965354 0.6701969 -#> [574,] 0.46386001 0.4905156 0.5672952 0.6115767 0.6319799 0.6335443 0.6418135 -#> [575,] 0.44102183 0.5958669 0.6523323 0.6539635 0.7169540 0.7502026 0.7989667 -#> [576,] 0.22003989 0.5764086 0.5800283 0.6960439 0.6992767 0.7734542 0.7760067 -#> [577,] 0.52787810 0.8656258 1.0886843 1.1303904 1.1652766 1.1992359 1.2040200 -#> [578,] 0.33537141 0.4310171 0.4855791 0.5043320 0.5506841 0.6286606 0.6449115 -#> [579,] 0.48106688 0.6400865 0.6602727 0.6610452 0.6643364 0.6676988 0.6725714 -#> [580,] 0.40229399 0.4831555 0.5020988 0.5160107 0.5657945 0.5793657 0.5832154 -#> [581,] 0.35648607 0.5155221 0.5430369 0.5512025 0.5672378 0.5931377 0.6105139 -#> [582,] 0.27012072 0.4017908 0.4559433 0.4892858 0.4946278 0.5128846 0.5350863 -#> [583,] 0.35868385 0.4425961 0.6264364 0.6395027 0.6446911 0.6611604 0.6894536 -#> [584,] 0.55815062 0.5852312 0.6701959 0.6814269 0.7058457 0.7367348 0.7549490 -#> [585,] 0.44657063 0.4577985 0.7049280 0.7064780 0.7427919 0.7677238 0.7866158 -#> [586,] 0.33284522 0.3968305 0.5929588 0.6385466 0.6502117 0.6619225 0.7842259 -#> [587,] 0.17308916 0.3946946 0.4523318 0.4748450 0.4781166 0.5063630 0.5519264 -#> [588,] 0.77900189 0.7890183 0.8378151 0.8851613 0.8899777 0.9409221 1.0251435 -#> [589,] 0.56786474 0.5713880 0.6050779 0.6742909 0.6922978 0.7159223 0.7236839 -#> [590,] 1.28990446 1.4368071 1.4372491 1.6684753 1.6883546 1.7540976 1.8215611 -#> [591,] 0.34060381 0.3857767 0.4285865 0.4323910 0.5243204 0.5416102 0.5619075 -#> [592,] 0.44624031 0.4964189 0.6007497 0.6597749 0.6793760 0.7046408 0.7064299 -#> [593,] 0.16044299 0.2337616 0.3989610 0.4425961 0.4827286 0.5134027 0.5305207 -#> [594,] 0.23433208 0.2507500 0.3647389 0.3863603 0.4060251 0.4638805 0.4706503 -#> [595,] 0.17597548 0.4429149 0.4458199 0.5008248 0.6135240 0.6417925 0.6768346 -#> [596,] 0.46274619 0.5643122 0.7634109 0.7683373 0.8060857 0.8275630 0.8577206 -#> [597,] 0.48996733 0.5210512 0.5406958 0.5489483 0.5774565 0.7836787 0.8296072 -#> [598,] 0.22768949 0.5449690 0.5478941 0.5777244 0.5915972 0.6030622 0.6073392 -#> [599,] 0.83371889 0.8694239 0.8819580 0.9786088 0.9887986 1.0007794 1.1333100 -#> [600,] 0.26035703 0.5574171 0.6283340 0.6793788 0.6816118 0.7125412 0.7249124 -#> [601,] 0.37701435 0.3873969 0.4454083 0.5852220 0.6213244 0.6464179 0.6551963 -#> [602,] 0.18365652 0.2247377 0.2577553 0.3864874 0.4023448 0.5241576 0.5414872 -#> [603,] 0.43366031 0.6522712 0.8378151 0.8617993 0.8700834 0.9045392 1.0198042 -#> [604,] 0.69993429 0.7284301 0.9402911 1.1137788 1.1158997 1.1169389 1.1244440 -#> [605,] 0.29951135 0.3365124 0.4805783 0.4852801 0.4947778 0.5059454 0.5593715 -#> [606,] 0.24308049 0.3369672 0.4299477 0.4368795 0.5183246 0.5551474 0.5619075 -#> [607,] 0.44525949 0.8932878 0.9821860 1.0011803 1.0103385 1.0981862 1.1486406 -#> [608,] 0.59405249 0.6543199 0.6553937 0.7091526 0.8328801 0.8486597 0.8609763 -#> [609,] 1.02977260 1.2619621 1.2667554 1.4606998 1.5334449 1.5560651 1.5957530 -#> [610,] 0.35144018 0.5686196 0.5780670 0.6654444 0.8031539 0.8919497 0.8937318 -#> [611,] 0.39294411 0.4329269 0.4807848 0.4819972 0.4849148 0.5227979 0.5640177 -#> [612,] 0.46886255 0.5946286 0.6971507 0.7394289 0.7854985 0.8576131 0.9304374 -#> [613,] 0.51490569 0.5511763 0.9546355 1.1132546 1.1169389 1.1330129 1.2553011 -#> [614,] 0.44559324 0.5293639 0.5361539 0.5733587 0.7285486 0.7346014 0.7350405 -#> [615,] 0.33760424 0.3771229 0.5694851 0.6053445 0.6638629 0.7151035 0.7352688 -#> [616,] 0.37489759 0.6153700 0.6522712 0.6900040 0.7691259 0.7830058 0.8490819 -#> [617,] 0.38785383 0.4255550 0.5361624 0.6440649 0.6580717 0.6705406 0.6816118 -#> [618,] 0.33543684 0.3541546 0.4724177 0.5725951 0.6208388 0.6248876 0.6351238 -#> [619,] 0.49870522 0.5469588 0.7579067 0.7694242 0.7866158 0.8033215 0.9437138 -#> [620,] 0.45415534 0.6671930 0.6792135 0.6862691 0.7155112 0.7678281 0.7826749 -#> [621,] 0.33984133 0.4188059 0.4377666 0.4584733 0.4993423 0.5206577 0.5281680 -#> [622,] 0.32404141 0.4348363 0.5456857 0.5670290 0.5897818 0.6090094 0.6204706 -#> [623,] 0.43463282 0.4849148 0.6283340 0.6380633 0.6411111 0.6772921 0.7280407 -#> [624,] 0.24047378 0.2949672 0.4734230 0.4836585 0.4855279 0.5226528 0.5671062 -#> [625,] 0.38832700 0.6122867 0.6906560 0.8819580 0.9070997 0.9280705 0.9724704 -#> [626,] 0.31996990 0.4064382 0.4194307 0.5095882 0.6187073 0.6682280 0.6765002 -#> [627,] 0.21601832 0.4441057 0.4724177 0.4945967 0.5062977 0.5134793 0.5488952 -#> [628,] 0.17402163 0.2367911 0.4004923 0.4353077 0.4847487 0.4869942 0.4950905 -#> [629,] 0.80130051 0.8168133 0.8360590 0.9012890 0.9719824 0.9892314 1.0326790 -#> [630,] 0.33886686 0.6463297 0.6651778 0.7060388 0.7318452 0.7538065 0.7692197 -#> [631,] 0.34201350 0.3591081 0.4149334 0.4231522 0.4760790 0.4992524 0.5132158 -#> [632,] 0.49883170 0.7197396 0.7218012 0.7508510 0.7842259 0.7848501 0.7930338 -#> [633,] 0.40473610 0.4956102 0.7398657 0.8165282 0.9339677 0.9609719 1.0370421 -#> [634,] 0.48667127 0.5674285 0.5851370 0.6052577 0.6083226 0.6202031 0.6234698 -#> [635,] 0.32803844 0.3965035 0.5484068 0.5868525 0.6253015 0.6509948 0.6716849 -#> [636,] 0.56052284 0.6385466 0.6993452 0.7218012 0.7321719 0.8379912 0.8791882 -#> [637,] 0.60666145 0.6115239 0.6960439 0.7364444 0.7907865 0.8651759 0.9106117 -#> [638,] 0.90824995 0.9976108 1.0257840 1.0490677 1.0991771 1.1008939 1.1014870 -#> [639,] 0.74307655 0.9445222 0.9865465 1.0185952 1.0381068 1.0908697 1.1027061 -#> [640,] 0.64291176 0.6669140 0.7011364 0.7486184 0.7557675 0.8058338 0.8103042 -#> [641,] 0.35605581 0.4814404 0.7876294 0.7985339 0.8573493 0.9576553 0.9659746 -#> [642,] 0.79311997 0.7989374 0.8668125 0.8767005 0.8896757 0.9194493 0.9435230 -#> [643,] 0.29279031 0.3544750 0.3700009 0.4719275 0.5045860 0.5201948 0.5427308 -#> [644,] 0.43637557 0.4827970 0.4947433 0.7024931 0.7082306 0.7102383 0.7503552 -#> [645,] 0.38434723 0.4441885 0.5685905 0.5852424 0.5996315 0.6418135 0.6847756 -#> [646,] 0.75806006 0.7767205 0.7881615 0.8075945 0.8449039 0.9816106 0.9991894 -#> [647,] 0.34015775 0.4648615 0.5660205 0.6646483 0.6967569 0.7338786 0.7546331 -#> [648,] 0.54015739 0.6906560 0.7470331 0.7691937 0.8596595 0.8851544 0.8855184 -#> [649,] 0.21401924 0.3291718 0.3406038 0.4113986 0.4169955 0.4256820 0.4330305 -#> [650,] 1.21571383 1.6228459 1.7779071 1.8151949 1.8291248 1.8450162 1.9125968 -#> [651,] 0.73862833 0.7855138 0.8135626 0.9202073 0.9247456 1.0629530 1.1103865 -#> [652,] 0.48695987 0.4969256 0.5372878 0.5698973 0.6480851 0.6495004 0.6571974 -#> [653,] 0.59394151 0.6429569 0.6469902 0.6594809 0.6669274 0.6801128 0.7318452 -#> [654,] 0.71059489 0.7913172 0.8570968 0.9106875 0.9401968 0.9611286 0.9756189 -#> [655,] 0.19780375 0.3078437 0.3579763 0.4318495 0.4531874 0.5124364 0.5570000 -#> [656,] 0.33018138 0.3567649 0.4992524 0.5149747 0.5353460 0.5900000 0.6121609 -#> [657,] 0.36776757 0.4427894 0.5663868 0.5770576 0.6664361 0.7138950 0.7603336 -#> [658,] 0.39142869 0.4601763 0.4863964 0.5127744 0.5638167 0.5713538 0.5737622 -#> [659,] 0.46493959 0.5561894 0.7030695 0.7118153 0.7135524 0.7341962 0.7462063 -#> [660,] 0.21401924 0.2281317 0.3397789 0.4009120 0.4742309 0.5243204 0.5328568 -#> [661,] 0.44464332 0.4574371 0.4834773 0.5054884 0.5055472 0.5161832 0.5229335 -#> [662,] 0.32267120 0.5252603 0.7263464 0.7772791 0.7828679 0.7894459 0.8005508 -#> [663,] 0.23163085 0.3125665 0.3891389 0.4232646 0.4854022 0.5116676 0.5146067 -#> [664,] 1.68507968 1.7320149 1.8154012 1.8993829 1.9175140 1.9931019 2.0122906 -#> [665,] 0.37105317 0.3822649 0.4080247 0.5637485 0.5686945 0.5851032 0.6203053 -#> [666,] 1.26596347 1.2987503 1.3163879 1.3481555 1.4350144 1.4667450 1.5794637 -#> [667,] 0.38172029 0.5221992 0.6277827 0.6597764 0.6713917 0.6715206 0.7095495 -#> [668,] 0.41705624 0.4903407 0.5305223 0.5740756 0.5962643 0.5976732 0.6198012 -#> [669,] 0.37131494 0.4369859 0.4755288 0.5471576 0.5577912 0.5801553 0.6030622 -#> [670,] 0.68142692 0.7803132 0.8450893 0.9206161 0.9284356 0.9300096 0.9300310 -#> [671,] 0.31315190 0.5417541 0.7687094 0.7741071 0.8012836 0.8145714 0.8155763 -#> [672,] 0.39515951 0.4564511 0.6069523 0.6405285 0.7031803 0.7632962 0.7708018 -#> [673,] 0.66039001 0.6856915 0.8041277 0.9437138 0.9860807 0.9920807 1.0049737 -#> [674,] 0.33284522 0.5010170 0.6251130 0.6716530 0.7409058 0.7854566 0.7930338 -#> [675,] 0.38434723 0.4322912 0.4638600 0.5213002 0.5800283 0.6553089 0.6577880 -#> [676,] 0.54611205 0.6339835 0.7537169 0.7589348 0.8409048 0.8528875 0.8586295 -#> [677,] 0.36503326 0.4182829 0.4523318 0.5180707 0.5275571 0.5544129 0.6218384 -#> [678,] 0.24648209 0.5618274 0.5625347 0.6759686 0.7198735 0.7292775 0.7603987 -#> [679,] 0.58608188 0.7796129 0.7855138 0.8454423 0.9682437 1.0820030 1.0851767 -#> [680,] 0.48666539 0.4902983 0.6471791 0.7160836 0.7306741 0.7505155 0.7845608 -#> [681,] 0.27087776 0.3374291 0.4033739 0.4368724 0.5486471 0.5625667 0.5654415 -#> [682,] 0.57372655 0.8000399 0.9717136 0.9758373 1.0200868 1.0596932 1.1610597 -#> [683,] 0.45429280 0.4655817 0.7009357 0.7498951 0.8086566 0.8632858 0.8719987 -#> [684,] 0.46307271 0.5994650 0.6273540 0.7221424 0.7638539 0.7792963 0.8033483 -#> [685,] 0.49907726 0.5940525 0.6827111 0.7004509 0.8277587 0.8280008 0.8723819 -#> [686,] 1.50596458 1.5657095 1.5754641 1.6338610 1.7295999 1.7931603 1.8113775 -#> [687,] 0.41975680 0.4354461 0.5148783 0.7302572 0.7369999 0.7374324 0.7415040 -#> [688,] 0.51993210 0.6507086 0.6613144 0.6682973 0.6854089 0.6890310 0.7054623 -#> [689,] 0.41315968 0.4482383 0.4526669 0.6122105 0.6367922 0.6531001 0.6671413 -#> [690,] 0.59022782 0.6389957 0.6434926 0.7062934 0.7356794 0.7406443 0.7555210 -#> [691,] 0.36786814 0.4817609 0.6667033 0.6692368 0.7221679 0.7474076 0.7715780 -#> [692,] 0.47593764 0.5066934 0.5228171 0.6133590 0.6682280 0.6710433 0.6933420 -#> [693,] 0.34015775 0.5309749 0.6754314 0.6866783 0.7454285 0.8355015 0.8627002 -#> [694,] 0.44287676 0.5719344 0.6496431 0.7229793 0.7558851 0.7563084 0.8101539 -#> [695,] 0.43712215 0.4553386 0.5641639 0.6392469 0.6439245 0.6870271 0.7060563 -#> [696,] 0.14706566 0.4455320 0.4473325 0.4511470 0.4564653 0.4728753 0.4769149 -#> [697,] 0.65371596 0.8360590 0.8482199 0.8656258 0.8673346 0.9124285 0.9252556 -#> [698,] 0.42443846 0.4867599 0.5343824 0.5512543 0.5709348 0.6297882 0.6391369 -#> [699,] 0.30035462 0.4965954 0.5705094 0.6069573 0.6560051 0.7111043 0.7248677 -#> [700,] 0.23566961 0.2962470 0.3037289 0.3804423 0.4329317 0.4957784 0.5218996 -#> [701,] 0.34791594 0.4423909 0.6339192 0.7711267 0.8043722 0.8176418 0.8193722 -#> [702,] 0.38410060 0.4346415 0.5680075 0.6310681 0.6459038 0.6606776 0.6892405 -#> [703,] 0.89461960 1.1036450 1.1944454 1.2430173 1.2445042 1.3393180 1.3762091 -#> [704,] 0.36525783 0.5024808 0.5359625 0.6964333 0.7860382 0.8233114 0.8387448 -#> [705,] 0.56182959 0.5877482 0.7154758 0.8000923 0.8829692 0.9316102 0.9406988 -#> [706,] 0.52711728 0.5500002 0.5625617 0.6038029 0.6144827 0.6606776 0.6733218 -#> [707,] 0.25775535 0.2967886 0.3565099 0.3630418 0.5500049 0.5858786 0.5937395 -#> [708,] 0.45587612 0.4683504 0.5025979 0.5197292 0.5459429 0.5716376 0.5886034 -#> [709,] 0.50575352 0.5658166 0.5931351 0.6119784 0.6813854 0.6862691 0.7420654 -#> [710,] 0.46886255 0.5376504 0.6598523 0.6818975 0.7356749 0.7961273 0.8070495 -#> [711,] 0.69264027 0.7333815 0.9616452 1.1140323 1.1178551 1.1265248 1.1347521 -#> [712,] 0.52774869 0.7639499 0.8649104 0.8873985 0.8907783 0.9271097 0.9462811 -#> [713,] 0.22952545 0.2952804 0.2981954 0.3316853 0.4060251 0.4816673 0.5045860 -#> [714,] 0.33706501 0.5484259 0.5824204 0.6410320 0.6418896 0.6458494 0.6616116 -#> [715,] 0.89731375 1.0537978 1.0872180 1.1281765 1.2874463 1.3430162 1.4573941 -#> [716,] 0.45879487 0.5779698 0.6209997 0.6790274 0.7966283 0.8060857 0.8867858 -#> [717,] 0.35637992 0.3945140 0.4154745 0.4335879 0.4409417 0.5278541 0.5740247 -#> [718,] 0.66195643 0.6783787 0.7290759 0.7430766 0.8105196 0.8730697 0.9213143 -#> [719,] 0.32942541 0.3510939 0.5217597 0.5319833 0.5348828 0.5535397 0.5548141 -#> [720,] 0.28226619 0.7007640 0.8111915 0.8873127 0.9021124 0.9370842 0.9768958 -#> [721,] 0.19718617 0.2343321 0.3316853 0.3704672 0.4115678 0.4458681 0.4720348 -#> [722,] 0.36214779 0.3668483 0.3811909 0.4201479 0.4531318 0.4936062 0.5059454 -#> [723,] 0.56070439 0.5842185 0.7960508 0.8029320 0.8727750 0.9239359 0.9257695 -#> [724,] 0.45533863 0.4964189 0.5417933 0.5471576 0.5526583 0.6159372 0.6252936 -#> [725,] 0.17091574 0.2325982 0.4170562 0.4409417 0.5799450 0.5900520 0.6431570 -#> [726,] 0.30569236 0.4472829 0.4694141 0.5611247 0.5643803 0.5823287 0.6055500 -#> [727,] 0.49841470 0.5978341 0.6058621 0.7401596 0.7509420 0.7776361 0.8039289 -#> [728,] 0.55017438 0.5645975 0.6441751 0.6565390 0.6603900 0.6950564 0.7211326 -#> [729,] 0.56164157 0.5737265 0.6881103 0.7450978 0.7895455 0.9096213 0.9724750 -#> [730,] 0.34414173 0.4000386 0.4302289 0.4855279 0.5030719 0.5269430 0.5926604 -#> [731,] 0.62029301 0.6352669 0.6380633 0.7147159 0.7249124 0.7291427 0.7433481 -#> [732,] 0.45779847 0.6922978 0.6950564 0.7096315 0.7226073 0.7273693 0.7833891 -#> [733,] 0.63826410 0.7682346 0.7960508 0.8937318 1.0084275 1.1145695 1.1332786 -#> [734,] 0.67165298 0.6951862 0.7007640 0.7114757 0.8941862 0.9199336 0.9738467 -#> [735,] 0.33777507 0.4482289 0.5056059 0.5320780 0.5597726 0.6534410 0.6739281 -#> [736,] 0.69442557 0.8149391 0.8167236 0.8786131 0.9513195 0.9557853 0.9811198 -#> [737,] 0.26835689 0.3334680 0.3621478 0.4549819 0.4769149 0.4854022 0.4865940 -#> [738,] 0.49268324 0.6837906 0.9234186 0.9441428 1.0225519 1.1003096 1.1280842 -#> [739,] 1.29539934 1.8722235 1.9284252 2.0010740 2.0750523 2.1162269 2.1169820 -#> [740,] 0.51734193 0.6642650 0.7704097 0.8122535 0.8581814 0.8711888 0.8950791 -#> [741,] 0.34511877 0.3878751 0.4791911 0.4892858 0.4923406 0.4952032 0.5455918 -#> [742,] 0.35403636 0.4285535 0.5277485 0.5305939 0.5498010 0.5503752 0.5640177 -#> [743,] 0.57535311 0.6563266 0.6590381 0.6714506 0.7532099 0.8052437 0.8501118 -#> [744,] 0.51734179 0.7632962 0.8085228 0.8315996 0.8591507 0.8649104 0.9220884 -#> [745,] 0.32917180 0.3397789 0.3876899 0.3922346 0.4001949 0.4299477 0.4323910 -#> [746,] 0.64667460 0.6835879 0.6979654 0.8804967 0.9374665 0.9778351 0.9948617 -#> [747,] 0.44136721 0.5503266 0.6124585 0.6458406 0.6666177 0.6790456 0.6853860 -#> [748,] 0.82415540 0.8868312 1.0763748 1.1582749 1.1680500 1.2156539 1.2208195 -#> [749,] 0.41493342 0.4700809 0.4869599 0.5400945 0.5436498 0.5573906 0.5594085 -#> [750,] 0.46799293 0.5034560 0.5217597 0.6771774 0.7306261 0.7400928 0.7437343 -#> [751,] 0.58434221 0.6646158 0.6969564 0.7237858 0.7355400 0.7586271 0.7755287 -#> [752,] 0.53830065 1.3152491 1.4967682 1.5136034 1.5573514 1.6377122 1.6517498 -#> [753,] 0.33760424 0.3991980 0.4598256 0.5265409 0.6790456 0.7613518 0.8217289 -#> [754,] 0.23008354 0.3640111 0.4975521 0.5126158 0.5339640 0.5841814 0.6017285 -#> [755,] 0.54471650 0.6192503 0.6313910 0.6351238 0.7059582 0.7568730 0.7921023 -#> [756,] 0.35086757 0.5386034 0.5628925 0.5704743 0.5749911 0.5831288 0.6768378 -#> [757,] 0.45429280 0.4841030 0.7173158 1.0579114 1.1550930 1.1706918 1.2055242 -#> [758,] 0.41684967 0.4346328 0.5227979 0.5574171 0.5642680 0.5748597 0.6105969 -#> [759,] 0.22723214 0.2321507 0.3441417 0.4547368 0.4606442 0.4972859 0.5295162 -#> [760,] 0.22075267 0.3895970 0.6789776 0.7645853 0.7901658 0.8013009 0.8486597 -#> [761,] 0.81532947 0.8531185 0.8577187 0.8617993 0.8923103 0.8946196 0.9825679 -#> [762,] 0.28142206 0.3683553 0.4934612 0.5206577 0.5777682 0.6004304 0.6115999 -#> [763,] 0.38313595 0.4004923 0.4295142 0.4470150 0.4973108 0.5328879 0.5470138 -#> [764,] 0.85570464 0.8876872 0.8948302 1.0199801 1.0230997 1.0844723 1.1143924 -#> [765,] 0.92943659 0.9304374 1.0307765 1.2157138 1.2584467 1.2722371 1.3115574 -#> [766,] 0.51734193 0.7179293 0.7347693 0.8160029 0.8686874 0.8732938 0.9418266 -#> [767,] 0.24648209 0.6124427 0.6414745 0.6667033 0.6792825 0.7024752 0.7242682 -#> [768,] 0.34841500 0.3541546 0.5134793 0.5470445 0.6012403 0.6016000 0.6136057 -#> [769,] 0.52454319 0.5877482 0.6497930 0.6978403 0.8646325 0.9219122 0.9685776 -#> [770,] 0.33177611 0.3514778 0.4325350 0.4337352 0.4599327 0.4638805 0.4926394 -#> [771,] 0.46283211 0.4734484 0.5095768 0.6294922 0.6707089 0.6725714 0.6949184 -#> [772,] 0.38383571 0.3946035 0.5009526 0.5489483 0.5762502 0.6377344 0.6440527 -#> [773,] 0.37711935 0.4566741 0.5215421 0.5586764 0.5766874 0.6164888 0.6767031 -#> [774,] 0.38993683 0.6628639 0.6891124 0.7015497 0.7302572 0.7341874 0.7989318 -#> [775,] 0.83110582 0.9107652 0.9549498 1.0660545 1.0960889 1.1519532 1.1663112 -#> [776,] 0.28984342 0.3565893 0.4464113 0.4694526 0.5826878 0.5859595 0.5972087 -#> [777,] 0.31299205 0.3510939 0.4679929 0.4706264 0.5949997 0.6361994 0.6766829 -#> [778,] 0.35551633 0.3985222 0.5564145 0.6008991 0.6470808 0.6550018 0.6857016 -#> [779,] 0.95562635 1.2624698 1.4753414 1.7136959 1.7248187 1.7823646 1.8136329 -#> [780,] 0.42611375 0.6624952 0.7154758 0.8839687 0.9812302 0.9990435 1.0114281 -#> [781,] 0.79814003 0.9909280 1.0555764 1.0578797 1.1204943 1.1938586 1.1983328 -#> [782,] 0.58535499 0.7279623 0.7475855 0.8526947 0.8637983 0.8680231 0.8855184 -#> [783,] 0.21792988 0.4455932 0.4849461 0.6027970 0.6261556 0.6593693 0.6599585 -#> [784,] 0.38993683 0.5612824 0.7160182 0.8346796 0.8980437 0.9196127 0.9264201 -#> [785,] 0.43300503 0.4441885 0.4714800 0.5229825 0.5421546 0.5926604 0.6107581 -#> [786,] 0.28212285 0.4773151 0.5561894 0.5812558 0.6418716 0.6531730 0.6551963 -#> [787,] 0.38701647 0.4429149 0.5794177 0.6179733 0.7015497 0.7045773 0.7493636 -#> [788,] 0.22126147 0.4290754 0.4299702 0.4498610 0.5164665 0.5368604 0.5377246 -#> [789,] 0.27916916 0.3565893 0.3891389 0.3904213 0.4800280 0.5232374 0.5258567 -#> [790,] 0.36237544 0.5245432 0.7106098 0.8368973 0.8374019 0.8462018 0.8829692 -#> [791,] 0.50847764 0.5532540 0.5913410 0.6471791 0.6507086 0.6737630 0.7578613 -#> [792,] 0.34638187 0.3635378 0.3650333 0.4520274 0.4780638 0.5063630 0.5364357 -#> [793,] 0.96471043 1.1238617 1.1670605 1.2363080 1.3438715 1.4656662 1.5202666 -#> [794,] 0.39442631 0.5672525 0.5940007 0.6562407 0.6613072 0.7771895 0.8676490 -#> [795,] 0.44218243 0.4562070 0.4597380 0.4941077 0.4971177 0.5149583 0.5182899 -#> [796,] 0.46309833 0.4734599 0.4770930 0.4929153 0.4938839 0.5049733 0.5068350 -#> [797,] 0.35327797 0.4821675 0.5343740 0.5368604 0.5838635 0.5845838 0.5888128 -#> [798,] 0.44077352 0.4648615 0.5268685 0.5676076 0.5943828 0.6339192 0.6705211 -#> [799,] 0.43483633 0.4369859 0.4812532 0.4849461 0.4924158 0.5734678 0.5745182 -#> [800,] 0.22500973 0.4127918 0.4415877 0.5176093 0.5338640 0.5512025 0.5625347 -#> [801,] 0.33651243 0.4317180 0.4334976 0.4602431 0.4944497 0.5258567 0.5421749 -#> [802,] 0.30996952 0.4079894 0.4082440 0.5007269 0.5075303 0.5519626 0.5956112 -#> [803,] 0.42078874 0.4458199 0.5007269 0.5594309 0.5710308 0.5931351 0.6654464 -#> [804,] 0.46681568 0.5594014 0.5645912 0.5680787 0.7353619 0.7524761 0.7629788 -#> [805,] 0.67438290 0.8576244 0.8710689 1.0787180 1.0813341 1.1490374 1.1719507 -#> [806,] 0.63398347 0.7658092 0.8245560 0.8568136 0.8746828 0.9278684 0.9469909 -#> [807,] 0.79403055 1.2538734 1.3409049 1.3886341 1.4143776 1.6039930 1.6385793 -#> [808,] 0.39196996 0.6492271 0.7006185 0.7617940 0.7707938 0.7958265 0.8086566 -#> [809,] 0.53559259 0.5987499 0.6718302 0.6761828 0.6896680 0.6905674 0.6947258 -#> [810,] 0.21910513 0.4110020 0.4115678 0.4192902 0.5068288 0.5347966 0.5386177 -#> [811,] 0.31452038 0.6963547 0.7095495 0.7133073 0.7954932 0.8097445 0.8705029 -#> [812,] 0.54548829 0.5467555 0.6456530 0.6605649 0.7046408 0.7678281 0.7826580 -#> [813,] 0.39683053 0.4614771 0.4893392 0.5376430 0.6251130 0.6571221 0.7005399 -#> [814,] 0.42103663 0.5189327 0.5921624 0.7932110 0.7953206 0.8013409 0.8744982 -#> [815,] 0.73338145 1.1091642 1.3315277 1.5215019 1.5316756 1.5636798 1.5704113 -#> [816,] 0.58796392 0.6011233 0.6173346 0.6441560 0.6747761 0.6773700 0.6797189 -#> [817,] 1.36065079 1.5137655 1.5193820 1.5240171 1.5498771 1.5958677 1.6121502 -#> [818,] 0.30483279 0.6178127 0.6380806 0.6382770 0.6571221 0.7243450 0.8326125 -#> [819,] 0.40273042 0.5212736 0.5603790 0.5628116 0.5810016 0.5887656 0.6294922 -#> [820,] 0.71824740 0.7215123 0.8453708 0.8528875 0.9597073 0.9738452 1.1058711 -#> [821,] 0.38559728 0.4500143 0.4863964 0.5113181 0.5500049 0.5597726 0.5685905 -#> [822,] 0.62866059 0.6942937 0.7284301 0.7386602 0.7695203 0.8087480 0.8161729 -#> [823,] 0.14874895 0.3236660 0.5891766 0.5903342 0.5944632 0.6515879 0.6564730 -#> [824,] 0.45853317 0.4877218 0.4921739 0.5230083 0.5250451 0.5348140 0.5552367 -#> [825,] 0.34800816 0.4846637 0.5529245 0.6009061 0.6091344 0.6167648 0.6366774 -#> [826,] 0.43253503 0.4831555 0.4865940 0.5334751 0.5341619 0.5524280 0.5628177 -#> [827,] 0.55997084 0.7272578 0.7378849 0.7621492 0.8044038 0.8407370 0.8632858 -#> [828,] 0.68204381 0.7227850 0.8052437 0.8680231 0.8800067 0.9070997 0.9344276 -#> [829,] 0.28919190 0.4773151 0.4803415 0.5392919 0.5550533 0.6207553 0.6225913 -#> [830,] 0.55774317 0.6965077 0.7545314 0.7733977 0.8177852 0.9057978 0.9184425 -#> [831,] 0.36237544 0.6978403 0.7546528 0.7967500 0.8791270 0.8803966 0.8834623 -#> [832,] 0.47868229 0.7783767 0.8286614 0.8366864 0.8746035 0.8868312 0.9465115 -#> [833,] 0.43357679 0.4734693 0.4909328 0.5312809 0.5519626 0.5710308 0.5838635 -#> [834,] 0.40273042 0.5086296 0.6707089 0.6912846 0.7190484 0.7190719 0.7273207 -#> [835,] 0.29819539 0.3870360 0.4016753 0.4192902 0.4458681 0.4505302 0.4650588 -#> [836,] 0.35797635 0.3874699 0.4290754 0.4414033 0.4415019 0.4950905 0.5380901 -#> [837,] 0.24728549 0.3652578 0.5973751 0.6058621 0.6714878 0.7875077 0.8330189 -#> [838,] 0.43114166 0.7145558 0.7729599 0.7989667 0.8771213 0.8853278 0.9082500 -#> [839,] 0.28226619 0.6951862 0.7774018 0.8110518 0.8782227 0.8891517 0.9318547 -#> [840,] 0.36893463 0.4210366 0.5944991 0.6127205 0.6323531 0.6557683 0.7007896 -#> [841,] 0.66544443 0.7079532 0.7227111 0.8612166 0.8741109 0.8808184 0.9161539 -#> [842,] 0.23240441 0.3125665 0.3472597 0.4421824 0.4473325 0.4742903 0.5013366 -#> [843,] 0.53765037 0.7394289 0.7537169 0.7876056 0.8028051 0.9627590 1.0137468 -#> [844,] 0.39681078 0.4223431 0.6354693 0.7994573 0.8189176 0.8444658 0.8649676 -#> [845,] 0.54908168 0.8796845 0.9251165 0.9912498 1.0151577 1.0183992 1.0188981 -#> [846,] 0.34382525 0.3694933 0.4439416 0.6071260 0.6187073 0.6506722 0.6954510 -#> [847,] 0.30855503 0.4296335 0.4358069 0.5168948 0.5498010 0.5910771 0.5998957 -#> [848,] 0.25249506 0.3561176 0.3708418 0.3972535 0.5192776 0.5754905 0.6337435 -#> [849,] 0.33507272 0.4597374 0.5069029 0.5285431 0.5372878 0.6001093 0.6130547 -#> [850,] 0.17597548 0.3870165 0.5594309 0.6248637 0.6527461 0.6582989 0.6609336 -#> [851,] 0.35295760 0.4630727 0.5233075 0.7415390 0.7522670 0.7865930 0.8056994 -#> [852,] 0.31452038 0.5221992 0.5249327 0.6202899 0.6636930 0.6819790 0.7412960 -#> [853,] 1.06356035 1.0736661 1.0825259 1.0962366 1.2298611 1.2869292 1.2916757 -#> [854,] 0.96573690 1.1756648 1.1910772 1.2641272 1.3102195 1.3528209 1.3630859 -#> [855,] 0.56388819 0.6959618 0.7599807 0.7856879 0.8119880 0.8185857 0.8418481 -#> [856,] 0.36604246 0.4760026 0.4901277 0.4980567 0.6008453 0.6148009 0.6341309 -#> [857,] 0.38214516 0.5011114 0.5160354 0.6922482 0.7856879 0.8467618 0.9077031 -#> [858,] 0.59267044 0.6421611 0.6810884 0.7180966 0.7294620 0.7447693 0.7462063 -#> [859,] 0.35829338 0.6209997 0.6765025 0.6980939 0.7291427 0.7454981 0.7605660 -#> [860,] 0.35107872 0.4628321 0.4821902 0.5628116 0.5663818 0.5908624 0.6366756 -#> [861,] 0.48666539 0.7148040 0.8065527 0.8443996 0.8621241 0.9785859 0.9832066 -#> [862,] 0.36675682 0.4541553 0.6117694 0.7093871 0.7282900 0.7284700 0.7376806 -#> [863,] 0.44102183 0.7006185 0.7156137 0.8526129 0.8576936 0.8646028 0.8719987 -#> [864,] 0.25530869 0.5100279 0.6222739 0.7004509 0.7518164 0.7927098 0.8247377 -#> [865,] 0.35107872 0.4073083 0.4727952 0.4797075 0.5887656 0.5978246 0.6011425 -#> [866,] 0.20711353 0.2128669 0.2962470 0.4271706 0.4383742 0.5015894 0.5419253 -#> [867,] 0.49074646 0.5218996 0.5402898 0.5666667 0.5851182 0.5921624 0.6165753 -#> [868,] 0.38226491 0.4330564 0.4343819 0.4789346 0.4973497 0.6238698 0.6618646 -#> [869,] 0.20119578 0.5274743 0.5727556 0.5795555 0.6107581 0.6321618 0.6506722 -#> [870,] 0.34000005 0.5523149 0.5835526 0.5923980 0.6091881 0.6279973 0.6840545 -#> [871,] 0.40824403 0.5111314 0.5128921 0.5586927 0.5952777 0.6600823 0.6684693 -#> [872,] 0.41601699 0.4297828 0.4584733 0.4952032 0.5350863 0.5674021 0.5813493 -#> [873,] 0.56979095 0.5843422 0.6339161 0.6882297 0.7151055 0.7386283 0.7734542 -#> [874,] 0.65237655 0.6812469 0.7236839 0.7454451 0.8110938 0.8311178 0.9366361 -#> [875,] 0.42184296 0.4502727 0.4504206 0.4829929 0.6192943 0.6400865 0.6467567 -#> [876,] 0.33346801 0.3811909 0.4237522 0.4528098 0.4738702 0.5018809 0.5075120 -#> [877,] 0.40802474 0.4975265 0.5176093 0.5593482 0.6650764 0.6870879 0.6948454 -#> [878,] 0.67952156 0.6989833 0.7085963 0.7382393 0.7438888 0.8188065 0.8575808 -#> [879,] 0.52657678 0.5467555 0.5681275 0.5865694 0.7248411 0.7300095 0.7356286 -#> [880,] 0.21551060 0.3874699 0.3986045 0.4015032 0.4855791 0.5016559 0.5164665 -#> [881,] 0.38577668 0.4169955 0.4356454 0.4523469 0.4742309 0.4904582 0.5262045 -#> [882,] 0.56579449 0.5672378 0.5858722 0.5922482 0.6274278 0.6352016 0.6591535 -#> [883,] 0.59284156 0.6624952 0.8139930 0.8322004 0.9945305 1.0395810 1.1360815 -#> [884,] 0.35648607 0.4240218 0.4803121 0.4943045 0.5196530 0.5445057 0.6082788 -#> [885,] 0.53011276 0.6101697 0.6716065 0.7966283 0.8490819 0.8710689 0.9369868 -#> [886,] 0.68088921 0.8565482 0.8652427 0.9079105 0.9379848 0.9565635 0.9650364 -#> [887,] 0.14706566 0.3842350 0.4784125 0.5095337 0.5187470 0.5218255 0.5586692 -#> [888,] 0.44150652 1.0596932 1.2575503 1.3015299 1.3514940 1.3968001 1.4491465 -#> [889,] 0.54753283 0.5853666 0.6010209 0.6263943 0.7082306 0.7190484 0.7254835 -#> [890,] 0.51972920 0.5291272 0.5473492 0.5569819 0.5688599 0.6242691 0.6411027 -#> [891,] 0.46645317 0.5035358 0.5147386 0.5518292 0.5550570 0.5851370 0.6037221 -#> [892,] 0.43151667 0.4339481 0.4894152 0.4957784 0.5787041 0.5879778 0.5921479 -#> [893,] 0.54620478 0.7160182 0.7341874 0.7608141 0.7634351 0.7855782 0.8240498 -#> [894,] 0.26233166 0.3567649 0.4505434 0.5104084 0.5520312 0.6066468 0.6306655 -#> [895,] 0.59990667 0.6372920 0.6738717 0.6780247 0.7835165 0.8307043 0.8752580 -#> [896,] 0.44276402 0.4725503 0.5233075 0.5444489 0.6388097 0.7417904 0.7792963 -#> [897,] 0.61366148 0.6422448 0.6548673 0.6628541 0.7373350 0.8047808 0.8112394 -#> [898,] 1.13993621 1.2238417 1.2944929 1.4138356 1.4223752 1.5587307 1.5715766 -#> [899,] 0.41880586 0.4297828 0.4559433 0.4602431 0.4657972 0.4769464 0.5013366 -#> [900,] 0.44287676 0.5037232 0.6410085 0.7145558 0.8573915 0.8691765 0.8887671 -#> [901,] 0.70951869 0.7227111 0.7655611 0.9049409 1.1103051 1.1436977 1.1671287 -#> [902,] 0.17402163 0.2815989 0.3234938 0.4973108 0.5245936 0.5454948 0.5846517 -#> [903,] 0.51490569 0.6955637 0.9402911 0.9713093 0.9992002 1.0926981 1.1465395 -#> [904,] 0.16379892 0.4782800 0.4924158 0.5001598 0.5577912 0.5829803 0.6159372 -#> [905,] 0.64480070 0.7569891 0.9153377 0.9287700 0.9294366 1.0306542 1.1035669 -#> [906,] 0.26835689 0.3636692 0.3863603 0.4232646 0.4511470 0.4564327 0.4722120 -#> [907,] 0.50805535 0.5762502 0.5965354 0.6315937 0.6394524 0.6589275 0.7062934 -#> [908,] 0.38125326 0.4157032 0.4805979 0.5193114 0.5658166 0.5713880 0.5897818 -#> [909,] 0.23253009 0.3691577 0.4070091 0.5777244 0.6250401 0.6715531 0.7055877 -#> [910,] 0.38903127 0.4017785 0.4498610 0.4847487 0.4936195 0.5238938 0.5465308 -#> [911,] 0.59440990 0.6989068 0.6995980 0.7242203 0.7455946 0.7480645 0.7795465 -#> [912,] 0.29872606 0.4707987 0.4806273 0.4906690 0.6176954 0.6193783 0.6317046 -#> [913,] 0.82461152 0.8591310 0.8731408 0.9164328 1.0578797 1.0834695 1.1278420 -#> [914,] 0.67952156 0.9075966 0.9496742 0.9526848 1.0082962 1.0166035 1.1136135 -#> [915,] 0.44171162 0.4928736 0.5012118 0.6263943 0.6324206 0.6440154 0.6997098 -#> [916,] 0.29134698 0.3804423 0.4118020 0.4271706 0.5402898 0.5544855 0.5822610 -#> [917,] 0.21910513 0.4578056 0.5066574 0.5338640 0.5411180 0.5637485 0.5884009 -#> [918,] 0.80003296 0.8520190 0.8595147 0.9993802 1.0713708 1.0777662 1.0786178 -#> [919,] 0.58522196 0.6448007 0.6532442 0.6655542 0.7092594 0.7155112 0.7459836 -#> [920,] 0.36644829 0.3914287 0.4510571 0.6330261 0.6349593 0.6370497 0.6568489 -#> [921,] 0.43640664 0.5015894 0.5326803 0.5509312 0.5567052 0.5991453 0.6388802 -#> [922,] 0.50561703 0.5913410 0.5933649 0.7671347 0.7845608 0.9111170 0.9142453 -#> [923,] 0.32803844 0.4338809 0.5207731 0.5649189 0.6001995 0.6045659 0.6381594 -#> [924,] 0.35266986 0.3835013 0.5095863 0.5653659 0.5709348 0.5956112 0.6124346 -#> [925,] 0.64979301 0.7646062 0.8164882 0.8276377 0.8368973 0.8421748 0.8771384 -#> [926,] 0.23163085 0.2324044 0.4564653 0.4722120 0.4800280 0.5017003 0.5080255 -#> [927,] 0.32922558 0.3934326 0.5095337 0.5121672 0.5194180 0.5259880 0.5288773 -#> [928,] 0.79814003 0.9533309 1.0389936 1.1709526 1.1749098 1.1781384 1.2001907 -#> [929,] 0.39267342 0.5086939 0.5477472 0.5520312 0.6469049 0.6839461 0.6940983 -#> [930,] 0.29134698 0.3037289 0.3091650 0.3305156 0.4383742 0.4936195 0.5488952 -#> [931,] 0.29872606 0.4131597 0.4795506 0.5665091 0.5997251 0.6136057 0.6261643 -#> [932,] 0.42677834 0.4411643 0.5686196 0.6174766 0.6513026 0.6731724 0.7777964 -#> [933,] 0.39206430 0.5012118 0.5589393 0.7086554 0.7179053 0.7529551 0.7558851 -#> [934,] 0.40473610 0.7515701 0.8153097 1.0193305 1.1129096 1.1454856 1.2042223 -#> [935,] 0.36401105 0.4486307 0.4561306 0.4769901 0.4926383 0.5446308 0.5739571 -#> [936,] 0.83073988 0.8819863 1.0641047 1.0782516 1.0969681 1.1447650 1.2193489 -#> [937,] 0.33591867 0.3466916 0.3873969 0.5139271 0.5550533 0.5812558 0.5829420 -#> [938,] 0.04497083 0.5870115 0.5876477 0.5898424 0.6302899 0.6714246 0.6754866 -#> [939,] 0.53443537 0.5655940 0.5792064 0.7051456 0.7086461 0.7153976 0.8056811 -#> [940,] 0.88640969 1.1744302 1.2819133 1.3042470 1.3137897 1.4205330 1.4232624 -#> [941,] 0.55774317 0.5631514 0.7511748 0.7522670 0.7901658 0.7962587 0.8298927 -#> [942,] 0.35991731 0.3694933 0.4263777 0.5047480 0.5095882 0.6563266 0.7308714 -#> [943,] 0.15739874 0.2999043 0.3060863 0.3792610 0.4267138 0.4280612 0.4582420 -#> [944,] 0.46932208 0.5688599 0.6562525 0.7224982 0.7271169 0.7447693 0.7451598 -#> [945,] 0.33816647 0.5002608 0.5497086 0.5832154 0.5839837 0.5987499 0.6531255 -#> [946,] 0.65787838 0.9657369 0.9883514 1.0701032 1.1374992 1.1447650 1.2451739 -#> [947,] 0.50561703 0.6266733 0.6672829 0.6746814 0.7006440 0.7663563 0.7955146 -#> [948,] 0.58372918 0.7636551 0.8544198 0.9077602 0.9096541 0.9287029 0.9601853 -#> [949,] 0.31354966 0.3466916 0.3770144 0.4981605 0.5835526 0.6080251 0.6379400 -#> [950,] 0.26351243 0.3076413 0.4308061 0.4694526 0.5942378 0.6482221 0.6509040 -#> [951,] 0.18607074 0.4871539 0.5036852 0.5189966 0.5335565 0.5425809 0.5678585 -#> [952,] 0.37747986 0.4925961 0.5129245 0.5213931 0.5320780 0.6816017 0.7071654 -#> [953,] 0.43434151 0.4537805 0.4782800 0.5454883 0.5546442 0.5803326 0.6204706 -#> [954,] 0.54966756 0.5632566 0.5648903 0.6048902 0.6158045 0.6161821 0.6182972 -#> [955,] 0.26783037 0.3555067 0.4599327 0.5020988 0.5503266 0.5731316 0.5883047 -#> [956,] 0.29496717 0.3133610 0.5002608 0.5079815 0.5430447 0.5891322 0.5961361 -#> [957,] 0.32511397 0.3617655 0.3835013 0.5075303 0.5512543 0.5762256 0.5950332 -#> [958,] 0.45042063 0.4606442 0.4727952 0.5148810 0.5263058 0.5962643 0.6366756 -#> [959,] 0.17370456 0.3076413 0.3279429 0.4852801 0.4944497 0.5166436 0.5732934 -#> [960,] 0.35705946 0.4410636 0.4516268 0.5754905 0.6508299 0.6886539 0.8302582 -#> [961,] 0.22813174 0.4001949 0.4003260 0.4113986 0.4827286 0.5053651 0.5508058 -#> [962,] 0.79111290 0.8337189 0.9096423 0.9653220 0.9693479 1.0392179 1.0942301 -#> [963,] 0.39206430 0.4417116 0.5213441 0.5853666 0.6000512 0.6460923 0.6988536 -#> [964,] 0.49034069 0.5703550 0.6110425 0.6360577 0.6465991 0.6549053 0.6917995 -#> [965,] 1.03453276 1.0574077 1.1989866 1.2401557 1.2488760 1.2885182 1.3480125 -#> [966,] 0.47146486 0.5787526 0.7349696 0.7509420 0.8018245 0.8678513 0.8806975 -#> [967,] 0.51784333 0.5368353 0.5718766 0.6238143 0.6354693 0.6705881 0.7379584 -#> [968,] 0.68204381 0.9912498 1.0265272 1.2226328 1.3495796 1.3968221 1.4309520 -#> [969,] 0.43544614 0.5446717 0.6081071 0.6550018 0.7121909 0.7462301 0.8047521 -#> [970,] 0.67468144 0.8115864 0.8311740 0.8576452 0.8817724 0.8970071 0.9092255 -#> [971,] 0.25530869 0.4229574 0.6714246 0.6844769 0.7469655 0.7584307 0.7610679 -#> [972,] 0.41437972 0.6251328 0.6979654 0.7523700 0.8096269 0.8257021 0.8363396 -#> [973,] 0.46068409 0.5099019 0.5453835 0.6073536 0.6115334 0.6494149 0.6606911 -#> [974,] 0.50560586 0.5682722 0.5732926 0.7049280 0.7061830 0.7096315 0.7706464 -#> [975,] 0.74582316 0.7609546 0.8046738 0.8240498 0.8241563 0.8281400 0.8356967 -#> [976,] 0.33525273 0.3374291 0.3817311 0.4523469 0.4536032 0.4987909 0.6120957 -#> [977,] 0.23008354 0.4954478 0.5046153 0.5614417 0.5616416 0.5828565 0.5882024 -#> [978,] 0.49841470 0.6714878 0.7688076 0.7764392 0.8018245 0.8105688 0.8233114 -#> [979,] 0.52526029 0.5345360 0.5447285 0.6395455 0.7031803 0.7376332 0.7483666 -#> [980,] 0.24785601 0.3852941 0.3986045 0.5758994 0.6449115 0.6810014 0.7294620 -#> [981,] 0.63397320 0.6394524 0.6664361 0.6701969 0.6802607 0.7022022 0.7301337 -#> [982,] 0.45580842 0.5715196 0.5749911 0.5859775 0.6681737 0.6822274 0.7166143 -#> [983,] 0.50101698 0.5376430 0.5929588 0.7114757 0.7489781 0.7692118 0.7773591 -#> [984,] 0.19780375 0.4136345 0.4339700 0.4415019 0.4979181 0.6075412 0.6091881 -#> [985,] 0.63176827 0.7092594 0.8033928 0.8673370 0.9140608 0.9153377 1.0441789 -#> [986,] 0.36491652 0.5273441 0.6032080 0.6253015 0.7454451 0.7729599 0.7883097 -#> [987,] 0.44276402 0.6742267 0.7026799 0.7415390 0.7864823 0.8683901 0.9982356 -#> [988,] 0.44525949 0.8690679 0.8875176 0.9267411 1.0588732 1.0614233 1.0699795 -#> [989,] 0.46932208 0.5569819 0.5886034 0.6532035 0.6885028 0.7242253 0.7372330 -#> [990,] 0.27366290 0.4653143 0.6021361 0.6025242 0.6380806 0.6693098 0.6744915 -#> [991,] 0.20711353 0.2356696 0.3305156 0.3670160 0.4118020 0.4364066 0.5464797 -#> [992,] 0.37440774 0.7086461 0.7418901 0.7442849 0.7787907 0.7836854 0.7855291 -#> [993,] 1.04193676 1.0992335 1.1503974 1.1741563 1.2870854 1.3807208 1.4045472 -#> [994,] 0.45879487 0.5301128 0.5747591 0.6214295 0.7605660 0.8673346 0.8709480 -#> [995,] 0.48610978 0.5967973 0.6007497 0.6189824 0.6356349 0.7741783 0.7854985 -#> [996,] 0.40589754 0.5606936 0.6975627 0.7258812 0.7362571 0.7638539 0.7670559 -#> [997,] 0.95463549 1.1054185 1.1069747 1.1304412 1.2374547 1.4004799 1.4737834 -#> [998,] 0.85278886 0.9860807 1.0845824 1.2091892 1.2122146 1.2567472 1.3275814 -#> [999,] 0.33373156 0.3560558 0.6230229 0.6408758 0.6742086 0.7076926 0.8252286 -#> [1000,] 0.12432811 0.4822970 0.7518164 0.7584307 0.8035997 0.8186917 0.8575808 +#> [1,] 0.75123324 0.7728336 0.8535100 0.9610233 0.9988456 1.0241624 1.0346625 +#> [2,] 0.72794121 0.8192607 1.0118221 1.0396370 1.0447104 1.0736597 1.1356458 +#> [3,] 0.31837803 0.4564968 0.4829120 0.5219559 0.5329834 0.6032451 0.6036069 +#> [4,] 0.31047166 0.3921811 0.4326476 0.4630233 0.5398038 0.5555416 0.6151065 +#> [5,] 0.46734788 0.4760593 0.5466231 0.5606464 0.5640889 0.5726195 0.5783608 +#> [6,] 0.51152215 0.5275568 0.5497865 0.5556941 0.5624806 0.6122315 0.6278000 +#> [7,] 0.29359887 0.5086648 0.5542773 0.5716974 0.5869704 0.5940340 0.5959630 +#> [8,] 0.26143033 0.4718084 0.5603310 0.5707832 0.6882329 0.7308097 0.7341715 +#> [9,] 0.24984831 0.5609735 0.8632232 0.9219492 1.0170726 1.0442705 1.0520745 +#> [10,] 0.28986430 0.4537154 0.5204006 0.5421002 0.5423238 0.5463735 0.5587194 +#> [11,] 0.46652531 0.5084248 0.5263341 0.5266163 0.5446352 0.5495054 0.5850168 +#> [12,] 0.37170190 0.4381048 0.5241100 0.5423993 0.5424052 0.5655038 0.6071722 +#> [13,] 0.57222011 0.6124874 0.6357935 0.6396935 0.6462147 0.6708131 0.7391421 +#> [14,] 0.34717912 0.5838188 0.5962627 0.6141551 0.6745910 0.8141714 0.8167852 +#> [15,] 0.58059369 0.6224053 0.6662234 0.6803393 0.7407235 0.8389967 0.8429282 +#> [16,] 0.63314368 0.8630664 0.8939270 1.0213772 1.0458141 1.0663115 1.1116785 +#> [17,] 0.54835213 0.7198504 0.7467861 0.8400073 0.8529885 0.9064278 0.9411035 +#> [18,] 0.66924590 0.6764030 0.8256855 0.8928497 0.9098371 0.9177645 0.9264857 +#> [19,] 0.47391219 0.5187272 0.6039699 0.6313239 0.7718839 0.8130187 0.8617744 +#> [20,] 0.15081454 0.3758961 0.3943047 0.4202105 0.4597621 0.5058134 0.5526436 +#> [21,] 0.26227964 0.3306440 0.4625359 0.4748135 0.5735866 0.5903636 0.6051760 +#> [22,] 0.43233503 0.5294653 0.5807848 0.6182965 0.6219620 0.6649769 0.6820446 +#> [23,] 0.51569242 0.5551573 0.6019943 0.6874485 0.7129012 0.7578704 0.7810091 +#> [24,] 0.26564505 0.8384672 0.9027206 0.9060224 0.9245897 0.9398366 0.9480771 +#> [25,] 0.58665844 0.8659828 0.8981276 0.9708535 1.0050467 1.0329852 1.0364884 +#> [26,] 0.19951012 0.3092625 0.3686001 0.4240887 0.6186630 0.6479357 0.7211515 +#> [27,] 0.36583627 0.4208790 0.4291136 0.5052316 0.5424372 0.6643204 0.6768602 +#> [28,] 0.63749376 0.6643837 0.7336474 0.8632298 0.9630081 1.0143946 1.0585654 +#> [29,] 0.22773632 0.2810413 0.3024392 0.6371490 0.7398366 0.7736565 0.7775577 +#> [30,] 0.41843597 0.4328745 0.4340449 0.5067279 0.5268015 0.5302476 0.5624027 +#> [31,] 0.44109626 0.5183360 0.6860934 0.9339130 0.9629930 1.0579500 1.0943981 +#> [32,] 0.51867312 0.5645729 0.6016788 0.6357531 0.6454604 0.6971910 0.7115832 +#> [33,] 0.47605929 0.5257607 0.5566021 0.5824304 0.6490195 0.7135536 0.7156801 +#> [34,] 0.97263621 1.1064219 1.1444155 1.1538665 1.1596231 1.2692750 1.3001680 +#> [35,] 0.98260936 1.1123529 1.1596231 1.1717316 1.1850187 1.2225809 1.2601779 +#> [36,] 0.49461149 0.5933300 0.6646068 0.6683092 0.8323921 0.8352585 0.8584428 +#> [37,] 0.33752748 0.3668717 0.3733893 0.3738390 0.4858712 0.5329507 0.5359664 +#> [38,] 0.63792553 0.6805158 0.8052105 0.8253294 0.9434459 0.9543999 0.9622925 +#> [39,] 0.79115486 0.9026125 1.0002906 1.0816304 1.1440999 1.2319974 1.2718061 +#> [40,] 0.43594864 0.7188591 0.7189500 0.8269726 1.0044478 1.0197690 1.0395219 +#> [41,] 0.29776484 0.3110705 0.3169707 0.3448560 0.3540405 0.3739873 0.4252863 +#> [42,] 0.56407060 0.8340099 0.9236600 0.9486578 0.9507656 0.9726362 0.9826094 +#> [43,] 0.35070927 0.4212043 0.4399540 0.5016230 0.5245879 0.5408275 0.5485840 +#> [44,] 0.13879613 0.2449583 0.3716588 0.3857744 0.4513943 0.4539900 0.4953717 +#> [45,] 0.27674962 0.3248874 0.3792540 0.4061885 0.4685934 0.5096766 0.5394649 +#> [46,] 0.70931322 0.7762396 0.7874432 0.8142466 0.8157303 0.8707487 0.8863619 +#> [47,] 0.34194145 0.6358041 0.6792359 0.6991985 0.7115954 0.7373865 0.8197065 +#> [48,] 0.54036550 0.6094105 0.6267325 0.6674362 0.7317710 0.7325700 0.7442621 +#> [49,] 0.53167438 0.6307679 0.7248237 0.7447617 0.7648825 0.7757223 0.7758032 +#> [50,] 0.34302240 0.4088468 0.4949305 0.6649502 0.6736528 0.6801958 0.6810368 +#> [51,] 0.44016974 0.5076383 0.5175767 0.7000366 0.7683283 0.7880880 0.8114595 +#> [52,] 0.34683737 0.4084745 0.5982558 0.5990630 0.6392620 0.6435954 0.6734625 +#> [53,] 0.43617718 0.4965451 0.5327786 0.5778905 0.5869704 0.5978329 0.6719906 +#> [54,] 0.30248329 0.3537647 0.3890210 0.5052213 0.5101134 0.5352680 0.5394586 +#> [55,] 0.52308478 0.5402685 0.5593313 0.5874356 0.7559557 0.7821695 0.7822892 +#> [56,] 0.69637689 0.7825293 0.8849706 0.9317456 0.9860083 1.0912192 1.1467084 +#> [57,] 0.16587260 0.2957155 0.3413110 0.4116207 0.4205946 0.5540923 0.6107552 +#> [58,] 0.21674498 0.2655099 0.3790429 0.3932341 0.4436496 0.5844425 0.6218011 +#> [59,] 0.40454188 0.4800348 0.5403655 0.5566021 0.5771780 0.6141551 0.6305519 +#> [60,] 0.75561149 0.7588936 0.7819681 0.7975871 0.8945171 0.9152283 0.9162163 +#> [61,] 0.30100470 0.4964479 0.5864232 0.5975842 0.6878447 0.6894267 0.6965180 +#> [62,] 0.16282452 0.3341115 0.4328745 0.4532430 0.4765493 0.5213406 0.5403077 +#> [63,] 0.27072483 0.3851949 0.4612409 0.5142720 0.5167208 0.5294653 0.6030569 +#> [64,] 0.49644789 0.5186731 0.5261169 0.5533974 0.5655843 0.6290988 0.6358013 +#> [65,] 0.35509133 0.5000259 0.5016800 0.5273160 0.5482631 0.6101186 0.6657547 +#> [66,] 0.51826095 0.5183360 0.6878115 0.9025787 0.9928195 1.0014826 1.0029237 +#> [67,] 0.20842890 0.3411604 0.3568485 0.4068925 0.4214355 0.4377764 0.4642517 +#> [68,] 0.47750098 0.6644961 0.8175037 0.8371260 0.8424111 0.8428174 0.8493960 +#> [69,] 0.45928747 0.4965451 0.5542773 0.5917991 0.6237566 0.6343305 0.6550373 +#> [70,] 1.00949093 1.1650926 1.2078393 1.2169444 1.2341341 1.2526122 1.3587940 +#> [71,] 0.22038706 0.3698291 0.5063805 0.6682706 0.6764030 0.6803568 0.7025122 +#> [72,] 0.41824000 0.4184360 0.4325937 0.4377125 0.4848735 0.5098943 0.5213406 +#> [73,] 0.36595529 0.4150283 0.4230495 0.4715471 0.4820203 0.4893759 0.5093840 +#> [74,] 0.48657110 0.5810022 0.7735514 0.8210664 0.8910608 0.8927631 0.9176643 +#> [75,] 0.40020124 0.4232959 0.4436496 0.4596097 0.5149030 0.5152366 0.5463399 +#> [76,] 0.48817965 0.4927598 0.5107277 0.5623835 0.5859066 0.5935301 0.5946564 +#> [77,] 0.15081454 0.4335340 0.4363955 0.5182849 0.5205824 0.5418517 0.5425802 +#> [78,] 0.30995948 0.3273770 0.4254990 0.4672448 0.5121810 0.5136565 0.5154224 +#> [79,] 0.47970446 0.4947340 0.6647554 0.7263034 0.7535873 0.7609947 0.7625186 +#> [80,] 0.79618626 0.8698408 0.8936291 0.8975484 0.9456737 0.9510369 0.9690743 +#> [81,] 0.47623620 0.4877438 0.4971451 0.5032684 0.5146468 0.5204006 0.5501698 +#> [82,] 0.93583477 1.0071221 1.0885044 1.1050823 1.1755327 1.1912636 1.2374069 +#> [83,] 0.70018889 0.8879985 0.9946745 1.0809744 1.1726914 1.2589149 1.3267449 +#> [84,] 0.85574145 0.9550328 0.9788586 0.9906871 1.0431189 1.0460363 1.0573611 +#> [85,] 0.32254407 0.4354345 0.4755552 0.4913438 0.5488552 0.5558120 0.6032168 +#> [86,] 0.58625836 0.7660761 0.7706474 0.7710468 0.7778610 0.7782860 0.7874432 +#> [87,] 0.39066227 0.4763041 0.5491078 0.6551946 0.6561217 0.6865512 0.7407140 +#> [88,] 0.40030939 0.5722201 0.6381720 0.6390878 0.6411925 0.6611606 0.6664912 +#> [89,] 0.46587098 0.5052316 0.5139742 0.5400055 0.5485160 0.5844425 0.5922445 +#> [90,] 0.19996637 0.2139283 0.3522130 0.3596801 0.5070188 0.5268149 0.5578403 +#> [91,] 0.34578357 0.3713855 0.5607744 0.5739698 0.5832266 0.5948363 0.6034775 +#> [92,] 0.13118949 0.4307515 0.5089294 0.6219634 0.7261603 0.7798434 0.8711605 +#> [93,] 0.15515797 0.2767496 0.3113777 0.3347892 0.4146456 0.4380970 0.4573444 +#> [94,] 0.40949008 0.7662003 0.8612254 0.8767726 0.8964731 1.1548959 1.1752675 +#> [95,] 0.81376542 0.8750154 0.8946274 0.8990536 0.9284389 0.9383646 0.9488876 +#> [96,] 0.36398921 0.7536512 0.7638342 0.8374410 0.8986590 0.9269765 0.9570827 +#> [97,] 0.40763037 0.4564968 0.4906205 0.4921939 0.4925928 0.5062591 0.5259640 +#> [98,] 0.27744439 0.3237234 0.4171864 0.4533024 0.4574603 0.5073590 0.5079862 +#> [99,] 0.44986007 0.5442689 0.6053123 0.6070115 0.6822141 0.6850527 0.8025247 +#> [100,] 0.36217182 0.4002402 0.5479470 0.6148331 0.6682706 0.6987118 0.7176815 +#> [101,] 1.16034211 1.1830457 1.4589260 1.4813706 1.5482614 1.6614958 1.7219734 +#> [102,] 0.20121330 0.2754540 0.3096327 0.3457012 0.3905825 0.3975459 0.4304232 +#> [103,] 1.20255514 1.2330142 1.2589149 1.3426098 1.3481100 1.5117781 1.6069093 +#> [104,] 0.44558674 0.5320156 0.5873528 0.5957944 0.6836550 0.6969275 0.7006954 +#> [105,] 0.28454807 0.4706502 0.4755460 0.5303708 0.5371465 0.5512903 0.5558524 +#> [106,] 0.47750098 0.4851378 0.4973154 0.5868762 0.7283071 0.8416151 0.8593591 +#> [107,] 0.98216529 1.0064208 1.1040333 1.2141759 1.2774960 1.2957623 1.3022217 +#> [108,] 0.47869309 0.8343755 0.9840538 1.0110834 1.0577510 1.0926077 1.1350025 +#> [109,] 0.49710326 0.5006748 0.5058134 0.5114418 0.5418517 0.5850060 0.5912959 +#> [110,] 0.19702653 0.3113777 0.3457012 0.4007505 0.4532430 0.4683849 0.4685934 +#> [111,] 0.36008810 0.4174495 0.4408895 0.4548463 0.4828149 0.5061000 0.5141196 +#> [112,] 0.57458749 0.6909275 0.7177089 0.7218150 0.7744658 0.8237228 0.8288619 +#> [113,] 0.57147892 0.6049098 0.6478061 0.6816491 0.7262376 0.7536512 0.8159034 +#> [114,] 0.53953399 0.6014050 0.6576091 0.6786399 0.6947844 0.7110033 0.7216347 +#> [115,] 0.48599759 0.5154224 0.5558413 0.5789364 0.6354666 0.6547929 0.6603546 +#> [116,] 0.33628768 0.4650064 0.5263157 0.7069239 0.7447617 0.7891283 0.8259153 +#> [117,] 0.69958193 0.9283156 1.0939061 1.1216403 1.1250323 1.1288179 1.1515432 +#> [118,] 0.36398921 0.5714789 0.6894734 0.7014631 0.7260693 0.7619641 0.8240498 +#> [119,] 0.29785763 0.3904106 0.5147426 0.5658674 0.5798745 0.5887206 0.5896853 +#> [120,] 0.35909924 0.4613995 0.4725982 0.5263326 0.5385190 0.5893542 0.6825840 +#> [121,] 0.35748401 0.4455867 0.5154027 0.6343369 0.6851376 0.7147886 0.7230207 +#> [122,] 0.26383426 0.2754540 0.2988481 0.2991627 0.3411604 0.4419143 0.4423288 +#> [123,] 0.62395483 0.6685404 0.7950016 0.8067952 0.8221489 0.8340085 0.8765175 +#> [124,] 0.82937244 0.8707487 0.9551027 1.0103346 1.0519770 1.0832535 1.2253165 +#> [125,] 0.55221468 0.5857830 0.6497453 0.6719993 0.6832759 0.6859977 0.7828531 +#> [126,] 0.45402224 0.6171313 0.6190679 0.7087893 0.7216347 0.7515560 0.7782790 +#> [127,] 0.27028389 0.3572052 0.3993061 0.4557764 0.4626339 0.4726576 0.4993475 +#> [128,] 0.28309416 0.4212043 0.4574603 0.4870235 0.5599778 0.5800733 0.5826591 +#> [129,] 0.25146438 0.4186988 0.5370477 0.5654464 0.5749203 0.6088727 0.6190746 +#> [130,] 0.49033543 0.5993018 0.6014718 0.6776866 0.7389885 0.7588429 0.7662003 +#> [131,] 0.35233048 0.4090712 0.5369264 0.6189582 0.6317574 0.6684878 0.6798811 +#> [132,] 0.48681494 0.5408715 0.6034775 0.6051837 0.6162894 0.6389791 0.6392017 +#> [133,] 0.20289628 0.5917991 0.7040529 0.8219994 0.8745047 0.9693476 0.9888539 +#> [134,] 0.86087471 0.8734834 0.9566091 1.0393017 1.0486299 1.1115831 1.1149204 +#> [135,] 0.27865399 0.2969847 0.3897075 0.4253274 0.4470561 0.4950304 0.5887206 +#> [136,] 0.73790913 0.7727693 0.7880136 0.8494167 0.9279067 1.0006806 1.0803707 +#> [137,] 0.45981635 0.4838382 0.5119779 0.5679398 0.5857583 0.5990630 0.6031911 +#> [138,] 0.30248329 0.3120646 0.3843879 0.3865320 0.4880859 0.4925018 0.5331744 +#> [139,] 0.36139975 0.3821192 0.3884705 0.4467783 0.6854213 0.6990176 0.7259507 +#> [140,] 0.79370421 1.1505867 1.1890757 1.1910424 1.2222432 1.3167299 1.3355435 +#> [141,] 0.19951012 0.2417104 0.2781614 0.4680106 0.6250949 0.6525452 0.6603980 +#> [142,] 0.37893352 0.4015229 0.4064958 0.4696581 0.4792169 0.4909026 0.5502156 +#> [143,] 0.31849457 0.3427354 0.3941537 0.4860365 0.5329507 0.5679398 0.5687805 +#> [144,] 0.36318027 0.4053421 0.4399584 0.4597621 0.5413375 0.5425802 0.5480974 +#> [145,] 0.38772521 0.4192009 0.4871528 0.5026201 0.5073320 0.6164275 0.6390106 +#> [146,] 0.26642362 0.3237234 0.4394007 0.4495239 0.4681588 0.4948503 0.5472648 +#> [147,] 0.61755802 0.6381720 0.6557861 0.7506527 0.7529128 0.7540028 0.7591031 +#> [148,] 0.18634156 0.5009155 0.5485840 0.5599778 0.5931087 0.5965084 0.6349871 +#> [149,] 0.12046345 0.5411184 0.6786399 0.6798061 0.6833299 0.7009866 0.7406080 +#> [150,] 0.17188245 0.3712625 0.3743934 0.3987000 0.4416277 0.5023142 0.5079862 +#> [151,] 0.37547110 0.4474790 0.4828926 0.5041523 0.6416367 0.6856432 0.7161804 +#> [152,] 0.79937319 0.8241613 0.8907011 1.0241436 1.1297508 1.1573210 1.1658958 +#> [153,] 0.19419128 0.2061225 0.3007250 0.4605015 0.5260537 0.5295352 0.5749598 +#> [154,] 0.30085979 0.3841699 0.5687805 0.5778905 0.6377990 0.6564521 0.6819878 +#> [155,] 0.19513800 0.4827132 0.5439209 0.5786936 0.5950256 0.6442066 0.6687645 +#> [156,] 0.46868378 0.5114418 0.5316744 0.5526436 0.5607080 0.6290993 0.6300735 +#> [157,] 0.59045114 0.9869900 1.1535666 1.1582809 1.1780062 1.2135801 1.2585351 +#> [158,] 0.24545491 0.3333003 0.3969425 0.4088468 0.4721369 0.4748135 0.5520819 +#> [159,] 0.28986430 0.3544125 0.3764880 0.3992212 0.5491586 0.5680241 0.5835483 +#> [160,] 0.43780840 0.4498638 0.7033036 0.7877849 0.8172419 0.8357316 0.8577205 +#> [161,] 0.40582631 0.5054017 0.7030924 0.7243780 0.7407140 0.7705307 0.7930608 +#> [162,] 0.35234103 0.4147042 0.4795022 0.4952720 0.4962613 0.4989207 0.5185135 +#> [163,] 0.54300594 0.5583139 0.6795859 0.8095291 0.8206264 0.9728522 1.1327863 +#> [164,] 0.52367020 0.5885379 0.8717503 0.8939719 0.9026125 0.9614319 1.0410679 +#> [165,] 0.96143186 1.0199945 1.0232013 1.0551349 1.0902843 1.1010699 1.1045643 +#> [166,] 0.61571818 0.7040191 0.7058041 0.7656191 0.7734775 0.7824822 0.8215516 +#> [167,] 0.35229033 0.3921811 0.5471192 0.5639015 0.5672174 0.5735262 0.5753703 +#> [168,] 0.96825642 1.0396370 1.1898986 1.2800371 1.2881848 1.3270543 1.3435666 +#> [169,] 0.66673361 0.6783771 0.6894816 0.7395991 0.7798434 0.8020988 0.8409993 +#> [170,] 0.28743920 0.4859976 0.6151464 0.6426209 0.6501716 0.6633979 0.7000754 +#> [171,] 0.31719653 0.4426525 0.4802058 0.5162458 0.5402894 0.5484489 0.5699656 +#> [172,] 0.08045996 0.2277363 0.4479699 0.6083513 0.7456291 0.7558175 0.7626539 +#> [173,] 0.26393459 0.3593245 0.3717283 0.3820295 0.4015229 0.4740713 0.5161408 +#> [174,] 0.30338965 0.3445281 0.3752012 0.3833929 0.4174495 0.4185740 0.5224324 +#> [175,] 0.60837870 0.6760452 0.7997734 0.8205199 0.8496036 0.8767713 0.8946601 +#> [176,] 0.61829653 0.6366517 0.6997558 0.7136063 0.7383797 0.7610103 0.8476542 +#> [177,] 0.29403821 0.3544237 0.5263326 0.5589544 0.6405376 0.6407043 0.6920594 +#> [178,] 0.30753941 0.4389056 0.4819657 0.5267271 0.5626776 0.5839383 0.6590841 +#> [179,] 0.61451410 0.8065479 0.8074916 0.8192607 0.8541983 0.8731405 0.9437775 +#> [180,] 0.26642362 0.3097676 0.3796937 0.3842518 0.5073590 0.5130520 0.5358143 +#> [181,] 0.47013970 0.5230848 0.5470321 0.8496036 0.8674290 0.9467661 0.9817323 +#> [182,] 0.36021158 0.4470561 0.4515219 0.4767623 0.5241343 0.5364844 0.5865542 +#> [183,] 0.53317440 0.6307292 0.6375269 0.6506767 0.6550839 0.6692890 0.6958393 +#> [184,] 0.59828158 0.7636470 0.8197293 0.8356096 0.8501639 0.8775427 0.9832344 +#> [185,] 0.51230390 0.5204844 0.5648963 0.7084205 0.7865361 0.7996885 0.8246749 +#> [186,] 0.38596645 0.4145507 0.5691145 0.5989056 0.6002112 0.6328652 0.6405012 +#> [187,] 0.96030647 1.0761704 1.0808862 1.0870889 1.1122201 1.1279417 1.1959206 +#> [188,] 0.49473402 0.6299515 0.6832576 0.6942725 0.6958393 0.7062105 0.7064561 +#> [189,] 0.35909924 0.4255819 0.5056986 0.5103887 0.6192480 0.7221886 0.7384616 +#> [190,] 0.48660327 0.7114507 0.7246401 0.7313637 0.8284993 1.0153069 1.0305508 +#> [191,] 0.65050082 0.6816491 0.6842797 0.7600037 0.7705307 0.7733659 0.8202470 +#> [192,] 0.81182782 0.8194373 0.8417935 1.1055276 1.1253849 1.1289979 1.1556449 +#> [193,] 0.66753911 0.7257133 0.7266951 0.7743539 0.8705561 0.9086395 0.9237531 +#> [194,] 0.62045966 0.7111853 0.7449372 0.8068628 0.8219124 0.8335637 0.8411046 +#> [195,] 0.32084307 0.3593245 0.3889879 0.4064958 0.4534542 0.5020450 0.5061745 +#> [196,] 0.59162905 0.6261190 0.6519473 0.6742100 0.7273983 0.7396244 0.7504098 +#> [197,] 0.25774459 0.3922203 0.4871528 0.5731440 0.5860091 0.6188489 0.6274038 +#> [198,] 0.55964953 0.6301278 0.7006146 0.7704925 0.8103618 0.8187889 0.8411695 +#> [199,] 0.56526701 0.5919769 0.7933394 0.8006608 0.8031460 0.8174900 0.9500898 +#> [200,] 0.63850522 0.6481022 0.7466410 0.8095291 0.8236840 0.8689609 0.8885308 +#> [201,] 0.27865399 0.2923046 0.5241343 0.5313052 0.5860547 0.5909128 0.5959543 +#> [202,] 0.42631331 0.5904569 0.5998869 0.7362744 0.7605896 0.8071532 0.8210664 +#> [203,] 0.20842890 0.2428933 0.2988481 0.3402142 0.3697308 0.3865320 0.3890210 +#> [204,] 0.47013970 0.5402685 0.6083787 0.7876943 0.8457159 0.8897081 0.8939270 +#> [205,] 0.34090425 0.4108805 0.4379580 0.5384481 0.5431463 0.5516274 0.5550702 +#> [206,] 0.52132482 0.5291135 0.5549221 0.6991985 0.7043297 0.7597526 0.7809424 +#> [207,] 0.21587317 0.5501698 0.5944061 0.6019943 0.6036912 0.6121399 0.6232368 +#> [208,] 0.19508862 0.3493520 0.4058071 0.5062397 0.5209490 0.5405174 0.5476367 +#> [209,] 0.34835901 0.3519596 0.4355776 0.7052760 0.7261054 0.7961863 0.7980408 +#> [210,] 0.15472873 0.2741477 0.3276143 0.3874743 0.4512306 0.4848735 0.5456915 +#> [211,] 0.30083629 0.5030022 0.5802170 0.6049625 0.6124386 0.6855360 0.7587768 +#> [212,] 0.50905999 0.5763424 0.6077715 0.6494028 0.6571812 0.7116446 0.7209059 +#> [213,] 0.31837803 0.3607279 0.5027575 0.5151727 0.5491726 0.5556941 0.6165135 +#> [214,] 0.58913260 0.8071999 0.9856087 0.9869900 1.0638696 1.1631375 1.1634441 +#> [215,] 0.31003120 0.3585382 0.3890705 0.3975790 0.4177120 0.5668593 0.5714364 +#> [216,] 0.40446900 0.4795265 0.5715888 0.5716924 0.6813370 0.7271226 0.7320339 +#> [217,] 0.41890859 0.4878582 0.5088305 0.5258547 0.5416196 0.5602363 0.5909218 +#> [218,] 0.40846562 0.4148849 0.4390372 0.4579331 0.4686838 0.5092006 0.5149004 +#> [219,] 0.31027223 0.3580854 0.4416277 0.4557797 0.4635054 0.4980985 0.5223489 +#> [220,] 0.67821809 0.7269518 0.7884421 0.8981276 0.9696410 1.0095566 1.0242894 +#> [221,] 0.29113310 0.4240887 0.4680106 0.5413932 0.5551246 0.6033871 0.6445696 +#> [222,] 0.31200656 0.3922203 0.5089802 0.6606336 0.6940500 0.7074970 0.7195269 +#> [223,] 0.80654788 0.8376506 0.9906952 1.0447104 1.0594308 1.1200518 1.1231975 +#> [224,] 0.29785763 0.3634108 0.3946379 0.4253274 0.5136025 0.5313052 0.5568903 +#> [225,] 0.51517267 0.6075172 0.6088218 0.6133793 0.6376472 0.6498677 0.6685887 +#> [226,] 0.41347323 0.4914823 0.5701759 0.5940340 0.6190746 0.6238119 0.6348328 +#> [227,] 0.30243922 0.4479699 0.4755517 0.5638175 0.6171614 0.6514300 0.7090876 +#> [228,] 0.90555460 0.9918885 1.1684544 1.1782701 1.2056914 1.2159078 1.2336533 +#> [229,] 0.51230390 0.6631301 0.6653664 0.7335557 0.7518248 0.8405305 0.9252294 +#> [230,] 0.77283362 0.8079880 0.8467161 0.8528566 0.8555573 0.9325761 0.9362719 +#> [231,] 0.59961280 0.6343369 0.7535997 0.7794280 0.8462224 0.8493717 0.8586241 +#> [232,] 0.26393459 0.2679328 0.2736122 0.3208431 0.3571812 0.4792169 0.4811862 +#> [233,] 0.17875079 0.3975790 0.4053346 0.4628703 0.5497720 0.5765510 0.6245413 +#> [234,] 0.67821809 0.6819256 0.6971910 0.7084205 0.7768582 0.8004708 0.8130695 +#> [235,] 0.35229033 0.4113023 0.4561031 0.4946848 0.4971033 0.5271013 0.5398038 +#> [236,] 0.62906531 0.7234641 0.7449372 0.7810325 0.8642700 0.8649101 0.8839147 +#> [237,] 0.20395837 0.3844797 0.4306182 0.5561102 0.5694909 0.5874698 0.5900798 +#> [238,] 0.57458749 0.5959630 0.6238756 0.6348328 0.6590748 0.6690772 0.7566615 +#> [239,] 0.24032959 0.5017250 0.5050407 0.5692543 0.5727376 0.5783608 0.6350611 +#> [240,] 0.41672649 0.4448618 0.4721369 0.5199025 0.5258675 0.5286509 0.5563468 +#> [241,] 0.98405382 1.1375119 1.1976571 1.2046299 1.2651680 1.3673162 1.3923991 +#> [242,] 0.57243631 0.9198506 1.0032346 1.1074054 1.1169257 1.1289227 1.1497500 +#> [243,] 0.55313581 0.6545472 0.7726849 0.8180935 0.8738729 0.8899513 0.9580921 +#> [244,] 0.57634239 0.6158662 0.6329802 0.6512504 0.6656699 0.6795726 0.7843879 +#> [245,] 0.40030939 0.4047201 0.6347717 0.6518593 0.6594240 0.6682516 0.6796197 +#> [246,] 0.20957563 0.5535769 0.5850788 0.5888863 0.5982422 0.6195995 0.6572079 +#> [247,] 0.32538741 0.3764248 0.4068925 0.4827132 0.5377097 0.5388781 0.5564219 +#> [248,] 0.07991648 0.3969090 0.4554043 0.5958814 0.6328125 0.6462949 0.6542727 +#> [249,] 0.40846562 0.5515295 0.6300735 0.6669118 0.6803393 0.7171484 0.7440268 +#> [250,] 0.24531337 0.2920796 0.3045802 0.3529613 0.3733389 0.3739873 0.4389056 +#> [251,] 0.30083629 0.3895963 0.5889364 0.6204556 0.6560988 0.6840995 0.7052760 +#> [252,] 0.35356658 0.4333924 0.4380970 0.4901973 0.5463399 0.5705713 0.5717453 +#> [253,] 0.71555951 0.8531945 0.9589535 1.0147257 1.0754897 1.0893189 1.0893488 +#> [254,] 0.32560846 0.3436373 0.5602555 0.6310238 0.7119669 0.7121295 0.8242852 +#> [255,] 0.19508862 0.3335111 0.3359325 0.4334802 0.4795022 0.5193626 0.5880298 +#> [256,] 0.33217500 0.5334556 0.5916505 0.7259507 0.7293199 0.7529128 0.7547055 +#> [257,] 0.34844429 0.3698551 0.4515219 0.6835393 0.7039375 0.7097591 0.7388971 +#> [258,] 0.48774381 0.5034437 0.6036912 0.6462597 0.6528830 0.6738408 0.7292561 +#> [259,] 0.50387183 0.5885379 0.8716588 0.9163334 0.9436734 0.9756567 0.9843683 +#> [260,] 0.31083119 0.5392876 0.5584831 0.6788038 0.6856432 0.6939049 0.7587768 +#> [261,] 0.50540167 0.6094466 0.6106162 0.7100869 0.7474043 0.7548418 0.7657958 +#> [262,] 0.36072787 0.4829120 0.6355625 0.6428138 0.6685887 0.7080839 0.7910339 +#> [263,] 0.39463790 0.3948480 0.5147426 0.5186912 0.5888827 0.6300291 0.6367832 +#> [264,] 0.39988625 0.4002021 0.4185740 0.4483313 0.4727237 0.4871645 0.4962613 +#> [265,] 0.23993803 0.5224575 0.5456090 0.5960820 0.6028731 0.6078198 0.6157228 +#> [266,] 0.37926665 0.4468730 0.4925928 0.4942371 0.5473633 0.5825448 0.6047881 +#> [267,] 0.46645854 0.5050407 0.6158712 0.7140490 0.7180839 0.7395991 0.7456979 +#> [268,] 0.38487972 0.4326476 0.4426525 0.4727237 0.4775865 0.4810028 0.5031708 +#> [269,] 1.06380439 1.1005776 1.1676122 1.1770550 1.2246484 1.2639915 1.3374345 +#> [270,] 0.35760660 0.5306228 0.6001879 0.6131775 0.6140907 0.6375269 0.6378469 +#> [271,] 0.37007701 0.3978038 0.4291071 0.5187272 0.5306961 0.5880864 0.6199867 +#> [272,] 0.08045996 0.2810413 0.4755517 0.5850168 0.7445571 0.7490401 0.7683102 +#> [273,] 0.48999353 0.5196985 0.5392876 0.6215960 0.6416367 0.6426553 0.6913845 +#> [274,] 0.24010441 0.3874158 0.4525542 0.4539625 0.4949872 0.5261021 0.5265085 +#> [275,] 0.30100470 0.5029816 0.5127194 0.5510996 0.5639015 0.5655843 0.6384658 +#> [276,] 0.54449025 1.0617308 1.1690400 1.2214387 1.2731221 1.2739128 1.4960939 +#> [277,] 0.29910942 0.5714456 0.5963457 0.6705824 0.6787581 0.7043262 0.7580649 +#> [278,] 0.35718116 0.4193687 0.4433985 0.4534542 0.4665253 0.5451624 0.5502156 +#> [279,] 0.41394639 0.6325459 0.6699654 0.7836029 0.7851510 0.8182959 0.8425422 +#> [280,] 0.22475057 0.3396683 0.4055814 0.4175321 0.4377923 0.4471095 0.4585823 +#> [281,] 0.29444451 0.4113023 0.4141784 0.4404892 0.4630233 0.5031708 0.5333971 +#> [282,] 0.31644694 0.5551573 0.5961214 0.6221072 0.6550789 0.7101610 0.7283993 +#> [283,] 0.34578357 0.3468282 0.3687117 0.4835465 0.5648596 0.6162894 0.6381854 +#> [284,] 0.50387183 0.5236702 0.7701056 0.7911549 0.8739263 0.8836861 0.9411378 +#> [285,] 0.41119252 0.5622579 0.6023753 0.6042996 0.6207918 0.6479959 0.6823808 +#> [286,] 0.85567071 1.0204548 1.0805443 1.1364532 1.3088280 1.3428671 1.3523249 +#> [287,] 0.52439153 0.7499065 0.9440280 0.9675250 1.0153069 1.2163210 1.2222432 +#> [288,] 0.54239075 0.6429094 0.8806943 1.3620115 1.4205182 1.4246796 1.4566494 +#> [289,] 0.94204478 0.9724045 0.9750135 1.0133426 1.1141167 1.1172216 1.1293275 +#> [290,] 0.34194145 0.4542825 0.5073444 0.5291135 0.7688035 0.7853064 0.8207269 +#> [291,] 0.26594121 0.4145992 0.5107277 0.5382576 0.5775870 0.6428034 0.6433268 +#> [292,] 0.33966834 0.3705808 0.4968724 0.5614452 0.5912959 0.6030132 0.6323524 +#> [293,] 0.54240523 0.5685634 0.6545472 0.7480650 0.7893958 0.7894660 0.8047075 +#> [294,] 0.26927678 0.4298212 0.4530965 0.4804884 0.5015095 0.5394649 0.5716974 +#> [295,] 0.26343145 0.2979331 0.4463409 0.5502562 0.5519391 0.5640889 0.5888744 +#> [296,] 0.28309416 0.3568485 0.4377125 0.4419143 0.4674355 0.4870335 0.5484208 +#> [297,] 0.23681564 0.3086668 0.4232261 0.4378483 0.4828435 0.5027134 0.5113881 +#> [298,] 0.44123648 0.4935362 0.5216645 0.5916291 0.6151879 0.7155653 0.7452075 +#> [299,] 0.15515797 0.1970265 0.3792540 0.3921772 0.4304232 0.4333924 0.4406006 +#> [300,] 0.35029758 0.3859824 0.6158662 0.6386173 0.6545420 0.6789222 0.6912502 +#> [301,] 0.17672129 0.2920796 0.3487168 0.4119493 0.4148849 0.4666492 0.5515295 +#> [302,] 0.53302955 0.5382576 0.5505231 0.6596260 0.6688616 0.6746336 0.6863087 +#> [303,] 1.26360735 1.2688591 1.4489720 1.5091963 1.5111890 1.6452195 1.7570417 +#> [304,] 0.32254407 0.3594234 0.4605015 0.4645395 0.5425355 0.5434271 0.5556632 +#> [305,] 0.24866835 0.3789335 0.3792134 0.4561757 0.5161408 0.5266475 0.5673960 +#> [306,] 0.26740534 0.3208248 0.4667387 0.4988816 0.5023142 0.5210939 0.5911413 +#> [307,] 0.68948956 0.8591377 0.8632621 0.8758765 0.9509291 0.9731328 1.0001691 +#> [308,] 0.51152798 0.7055470 0.7202057 0.7660243 0.7819818 0.7912739 0.8258615 +#> [309,] 0.50638053 0.5179364 0.5468375 0.6046618 0.6985545 0.7881233 0.8091576 +#> [310,] 0.16587260 0.3356574 0.3871199 0.4471608 0.4613834 0.5982422 0.6546082 +#> [311,] 0.43717069 0.4465631 0.5035108 0.6308780 0.6550789 0.6917758 0.7570848 +#> [312,] 0.35346968 0.4866033 0.4911228 0.9278780 0.9597156 0.9675250 0.9823241 +#> [313,] 0.46884574 0.4715928 0.4735928 0.5765781 0.5976214 0.6209378 0.6795702 +#> [314,] 0.18842874 0.2774444 0.3743934 0.4975694 0.5050988 0.5495635 0.5568096 +#> [315,] 0.48747026 0.5115595 0.5776815 0.5810022 0.5833092 0.5961116 0.6199867 +#> [316,] 0.71882339 0.7298102 0.7980408 0.8027938 0.8137303 0.8317879 0.8445150 +#> [317,] 0.42087897 0.4312480 0.5634303 0.6422605 0.6566718 0.7048257 0.7623149 +#> [318,] 0.34683737 0.4090368 0.4860365 0.5359664 0.5383853 0.6108532 0.6336967 +#> [319,] 0.37138546 0.4117716 0.4785496 0.4868149 0.5997890 0.6381854 0.6638874 +#> [320,] 0.46517599 0.5368974 0.6422321 0.6776026 0.6805158 0.7277149 0.7588429 +#> [321,] 0.09697109 0.3934919 0.3992212 0.4013733 0.4841724 0.5219711 0.6044576 +#> [322,] 0.61354902 0.7331312 0.8358797 0.8843709 0.9381280 0.9839040 0.9903867 +#> [323,] 0.37300207 0.3944722 0.4408895 0.5156462 0.6216648 0.6234677 0.6260663 +#> [324,] 0.30975111 0.5447037 0.5730397 0.5768426 0.6028126 0.6794550 0.6901274 +#> [325,] 0.49046188 0.5118977 0.5345808 0.5487884 0.6182018 0.6263767 0.6269757 +#> [326,] 0.48914700 0.5917555 0.6135490 0.8236793 0.8388834 0.8661062 0.8718881 +#> [327,] 0.47466248 0.6094466 0.6433676 0.6557585 0.6625708 0.6628817 0.6690772 +#> [328,] 0.73136370 0.8106643 0.8197065 0.9443775 1.0060133 1.0388432 1.0488026 +#> [329,] 0.56057729 0.5963301 0.6683562 0.6795859 0.7329504 0.8120086 0.8243287 +#> [330,] 0.27949333 0.4054250 0.4598163 0.4782392 0.5327786 0.6019534 0.6318225 +#> [331,] 0.33351112 0.3356392 0.3441288 0.4058071 0.4617185 0.4678582 0.5969929 +#> [332,] 0.50641307 0.5115280 0.5247714 0.5806314 0.6723671 0.7161356 0.7278448 +#> [333,] 0.52374859 0.5609209 0.5919769 0.7004560 0.7470308 0.7624804 0.7892986 +#> [334,] 0.42795110 0.4530965 0.4612184 0.4931298 0.5000259 0.5240484 0.5290541 +#> [335,] 0.32761430 0.3475464 0.3843879 0.3949626 0.4205965 0.4415721 0.4535849 +#> [336,] 0.50451422 0.8163552 1.0484713 1.0540369 1.1106780 1.1204779 1.1334821 +#> [337,] 0.56262031 0.7564921 0.8078945 0.8326922 0.8352566 0.8653057 0.8771891 +#> [338,] 0.35029758 0.6987542 0.7735514 0.8113368 0.8188095 0.8228033 0.8238807 +#> [339,] 0.49353624 0.6602226 0.7272707 0.7396244 0.7642973 0.8857255 0.9054262 +#> [340,] 0.26385543 0.4138580 0.4591137 0.4841722 0.4971807 0.5357776 0.5859066 +#> [341,] 0.25334228 0.4108805 0.4143892 0.4465461 0.5185135 0.5862135 0.6011670 +#> [342,] 0.23535424 0.3844714 0.3873494 0.4871865 0.5377632 0.5901825 0.6002112 +#> [343,] 0.49333622 0.5099957 0.5933300 0.6932422 0.7805514 0.7853750 0.9455148 +#> [344,] 0.42777114 0.4942371 0.4996934 0.5374261 0.5378361 0.5491287 0.5572177 +#> [345,] 0.43543452 0.4731234 0.5097656 0.5154537 0.5501798 0.5601731 0.5648554 +#> [346,] 0.27816143 0.2911331 0.3092625 0.4558091 0.6182421 0.6550839 0.6832576 +#> [347,] 0.31133296 0.5416196 0.5924440 0.5951297 0.6042996 0.6043597 0.6051760 +#> [348,] 0.32560846 0.4348900 0.5081217 0.5956861 0.7277149 0.8389705 0.8713819 +#> [349,] 0.27110632 0.2736122 0.3717283 0.4193687 0.4696581 0.5124989 0.5188817 +#> [350,] 0.46804512 0.5862584 0.6132736 0.6300291 0.6931258 0.7347957 0.8070378 +#> [351,] 0.59930178 0.6040936 0.6243774 0.7537008 0.8512212 0.8618755 0.8767726 +#> [352,] 0.72695182 0.8925216 1.0102435 1.0211662 1.0279754 1.0329852 1.2980993 +#> [353,] 0.31170307 0.6374938 0.7231658 0.8318084 0.9315260 0.9454788 1.0235594 +#> [354,] 0.47014037 0.5311183 0.5807093 0.5893542 0.6192480 0.6478061 0.6894734 +#> [355,] 0.31605501 0.5687865 0.6450369 0.6562205 0.6602688 0.6698618 0.6733962 +#> [356,] 0.21566331 0.3844714 0.4404892 0.4961319 0.5078930 0.5369264 0.5770353 +#> [357,] 0.44777486 0.5099957 0.5576428 0.6046208 0.6613447 0.6683092 0.6720861 +#> [358,] 0.44599817 0.5609735 0.7096210 0.8861194 0.8862931 0.8908547 0.9431895 +#> [359,] 0.59854713 0.7551560 0.7975922 0.8340979 0.9488876 0.9678762 1.0104143 +#> [360,] 0.69343238 0.8115715 0.8518886 0.8595648 0.8660315 0.9372885 1.0002448 +#> [361,] 0.18945147 0.3203283 0.3361130 0.3448560 0.3493174 0.3777877 0.4662042 +#> [362,] 0.48657110 0.6705820 0.7001198 0.7362744 0.8745113 0.9834923 0.9946699 +#> [363,] 0.31644694 0.5156924 0.6910260 0.7869916 0.8203473 0.8254108 0.8631331 +#> [364,] 0.57077554 0.6313865 0.6719993 0.6959316 0.7819634 0.8589755 0.8680502 +#> [365,] 0.58735280 0.5874356 0.6407729 0.6911030 0.7000366 0.7373071 0.7389704 +#> [366,] 0.28454807 0.3498404 0.3726540 0.3987329 0.4167784 0.4767417 0.4883674 +#> [367,] 0.24909537 0.6217602 0.8683734 0.9142125 0.9621563 0.9622823 0.9941056 +#> [368,] 0.20121330 0.2632861 0.2991627 0.3697308 0.4134017 0.4259888 0.5201796 +#> [369,] 0.24136134 0.6594240 0.6920199 0.7117756 0.7880136 0.8037376 0.8159284 +#> [370,] 0.49568273 0.6052306 0.6221072 0.6234920 0.6700591 0.7000754 0.7869916 +#> [371,] 0.47399983 0.6625547 0.7281672 0.7694525 0.7743539 0.8051282 0.8220837 +#> [372,] 0.36567019 0.4189086 0.5234874 0.5771078 0.5944776 0.5951297 0.7530624 +#> [373,] 0.18460255 0.2060190 0.3782162 0.3791834 0.4426923 0.4612184 0.4794289 +#> [374,] 0.46500636 0.5511406 0.5911722 0.5958814 0.6242367 0.6417307 0.6562524 +#> [375,] 0.30753941 0.5299665 0.5805937 0.5926426 0.6214401 0.6687676 0.7171484 +#> [376,] 0.58633486 0.6239548 0.6599681 0.6648714 0.6745910 0.7043262 0.7398995 +#> [377,] 0.50763829 0.5663951 0.6228037 0.7147886 0.7307823 0.7430718 0.7822892 +#> [378,] 0.30361897 0.3430907 0.5306958 0.5863829 0.6143961 0.6843397 0.7215748 +#> [379,] 0.62529285 0.9373032 0.9589361 0.9839291 1.0569027 1.1136162 1.1334750 +#> [380,] 0.31004980 0.6990266 0.9175304 1.0122607 1.1262807 1.2133086 1.2242808 +#> [381,] 0.49568273 0.5961214 0.6232368 0.6734077 0.8202901 0.8360364 0.8509438 +#> [382,] 0.31083119 0.4828926 0.4899935 0.5448074 0.5461205 0.5871392 0.7828199 +#> [383,] 0.33628768 0.5511406 0.5918048 0.6436058 0.6527433 0.6718151 0.7163910 +#> [384,] 0.44077776 0.5411184 0.6801610 0.7706525 0.7962910 0.8294079 0.8335637 +#> [385,] 0.15604009 0.4762362 0.5680078 0.5814557 0.6462597 0.6486402 0.6494302 +#> [386,] 0.38532828 0.4359686 0.5209098 0.5689635 0.5757741 0.5904509 0.5991853 +#> [387,] 0.33470661 0.3647560 0.5047202 0.5630189 0.5689635 0.5829721 0.5891104 +#> [388,] 0.65667184 0.6750756 0.6775991 0.7616078 0.7726849 0.9088996 0.9411035 +#> [389,] 0.31575585 0.5786936 0.6070519 0.6149352 0.7121295 0.7450731 0.7560740 +#> [390,] 0.40636897 0.4379580 0.4465461 0.5390755 0.5909128 0.5998869 0.6395035 +#> [391,] 0.71637391 0.9092624 0.9917192 1.0071221 1.0974428 1.1297508 1.1602162 +#> [392,] 0.51834711 0.5505231 0.5846558 0.5926610 0.7513978 0.7774382 0.7794280 +#> [393,] 0.14605421 0.6075121 0.6775991 0.7048257 0.7444487 0.7576389 0.8104532 +#> [394,] 0.46638959 0.5294214 0.5480739 0.6234146 0.6267325 0.6959634 0.7060311 +#> [395,] 1.04920444 1.1822441 1.2197848 1.3257101 1.3857820 1.3957540 1.3989331 +#> [396,] 0.50074816 0.9368811 1.0471965 1.0540369 1.0659909 1.0825885 1.0936585 +#> [397,] 0.35233048 0.3636660 0.4969829 0.5377632 0.5908810 0.6138392 0.7085501 +#> [398,] 0.33018724 0.5976214 0.6068353 0.6865432 0.7154680 0.7209059 0.7564411 +#> [399,] 0.32685154 0.3821192 0.5760403 0.6261190 0.6692866 0.7112562 0.7409202 +#> [400,] 0.99219177 1.0147922 1.1050029 1.1439902 1.2163210 1.2301288 1.2718061 +#> [401,] 0.50067478 0.5271013 0.5735262 0.6820610 0.6876870 0.7728214 0.7903175 +#> [402,] 0.61061623 0.6870153 0.7093132 0.7347957 0.8417935 0.9099727 0.9207612 +#> [403,] 0.13118949 0.5519855 0.5731555 0.6801153 0.8020988 0.8270089 0.9160206 +#> [404,] 0.35945348 0.3929751 0.4128428 0.4949872 0.5282306 0.6033871 0.6078198 +#> [405,] 0.47216259 0.6429094 0.8313961 0.9559077 1.1019036 1.1677363 1.2491498 +#> [406,] 0.52840771 0.5306958 0.5440199 0.5557236 0.5803419 0.5863349 0.5922188 +#> [407,] 0.18051066 0.4002402 0.5468375 0.5576100 0.5873958 0.6803568 0.7861184 +#> [408,] 0.42982120 0.4931298 0.5219711 0.5226653 0.5355817 0.5618288 0.6011145 +#> [409,] 0.23307038 0.5096766 0.5400055 0.5411882 0.5479479 0.5729807 0.5868046 +#> [410,] 0.22690537 0.3102786 0.4061678 0.4906205 0.5086148 0.5583424 0.6063374 +#> [411,] 0.59354400 0.6070115 0.6145340 0.6739434 0.6935096 0.6939493 0.7555520 +#> [412,] 0.45311652 0.4835465 0.6051837 0.6545420 0.6795726 0.6883190 0.7681392 +#> [413,] 0.52874860 0.5485678 0.5557123 0.5716924 0.6452023 0.7073426 0.7129012 +#> [414,] 0.59854713 0.7433678 0.7931495 1.1149152 1.1309300 1.2472189 1.2511145 +#> [415,] 1.00915273 1.0397144 1.0531656 1.0897849 1.1299244 1.2105582 1.2705682 +#> [416,] 0.44685385 0.4802813 0.4841724 0.5951493 0.6011145 0.6486402 0.6837553 +#> [417,] 0.39066227 0.4058263 0.5686761 0.5893919 0.7694337 0.7836191 0.8194804 +#> [418,] 0.30577483 0.4465631 0.5118977 0.5633698 0.5763036 0.6474377 0.6518129 +#> [419,] 0.33565738 0.3413110 0.3634738 0.3796179 0.3857744 0.4868420 0.5030333 +#> [420,] 0.26385543 0.3050787 0.3099595 0.4493096 0.5142780 0.5426927 0.5432273 +#> [421,] 0.72981016 0.8191095 1.0391618 1.0635243 1.1439234 1.1497564 1.1579309 +#> [422,] 0.77901084 0.8141714 0.8880066 0.9686581 0.9776988 0.9981969 1.0372120 +#> [423,] 0.17188245 0.3102722 0.4068494 0.4948228 0.5050988 0.5092787 0.5166929 +#> [424,] 0.58021695 0.6560988 0.8278941 0.8310321 0.8902744 0.9046550 0.9497534 +#> [425,] 0.50074816 0.7472879 0.7886271 0.8055432 0.8072300 0.8322109 0.8544600 +#> [426,] 0.65841128 0.7551560 0.8793542 0.8990536 0.9343049 0.9647801 1.0091527 +#> [427,] 0.40360403 0.4477749 0.4521097 0.5402954 0.5465505 0.6198224 0.6646068 +#> [428,] 0.47869309 0.9571049 1.1259877 1.1363119 1.2431542 1.2535680 1.2883587 +#> [429,] 0.34027172 0.3519596 0.3895963 0.5030022 0.5741917 0.5803987 0.7337276 +#> [430,] 0.21863794 0.5792501 0.5799310 0.6055756 0.6098908 0.6619151 0.7052326 +#> [431,] 0.42262902 0.5161931 0.5211340 0.5809020 0.6295294 0.6742100 0.7002335 +#> [432,] 0.57267127 0.5730397 0.6557861 0.6854213 0.6911311 0.6957793 0.7067249 +#> [433,] 0.24866835 0.2996319 0.4200978 0.4258462 0.4310830 0.5603310 0.5667096 +#> [434,] 0.39780376 0.4386172 0.4797447 0.4874703 0.4933392 0.5682878 0.6303169 +#> [435,] 0.49112284 0.6390106 0.6759421 0.7114507 0.7823791 0.7902742 0.7998991 +#> [436,] 0.36738405 0.4135458 0.5167208 0.5334669 0.5473633 0.5482934 0.5957012 +#> [437,] 0.29571554 0.3634738 0.3637507 0.3814911 0.3871199 0.3876728 0.4539900 +#> [438,] 0.49119061 0.7828531 0.8027938 0.8172419 0.9485036 1.0184482 1.0185377 +#> [439,] 0.83437551 0.9571049 1.0803707 1.1735669 1.1976571 1.2111689 1.3843671 +#> [440,] 0.24289334 0.3120646 0.3537647 0.3949626 0.4134017 0.4214355 0.4319502 +#> [441,] 0.20289628 0.6237566 0.6844604 0.7369896 0.8385844 0.8803578 0.8962578 +#> [442,] 0.75123324 0.9219082 1.0496610 1.1107187 1.1215952 1.1425865 1.1637278 +#> [443,] 0.24545491 0.4625359 0.4660731 0.4949305 0.5009155 0.5330862 0.5507820 +#> [444,] 0.67768660 0.7038044 0.7892187 0.8172974 0.8343469 0.8612254 0.8907011 +#> [445,] 1.22623347 1.2420842 1.3854303 1.3953893 1.4621698 1.5357017 1.5569942 +#> [446,] 0.47289305 0.6145340 0.6684878 0.6760726 0.6819878 0.6879263 0.6969249 +#> [447,] 1.45228667 1.6043613 1.6490182 1.8686264 1.8838568 1.8952957 1.9006795 +#> [448,] 0.57243631 0.7067615 0.8611921 1.0199524 1.0782641 1.1621953 1.1898859 +#> [449,] 0.24863800 0.3598209 0.3796937 0.4948503 0.5142888 0.5173598 0.5306004 +#> [450,] 0.44366791 0.7192560 1.2657779 1.2739660 1.3403521 1.3811454 1.5902023 +#> [451,] 0.50167997 0.5237082 0.5300692 0.5317664 0.5451624 0.5490896 0.5717995 +#> [452,] 0.64144872 0.8653057 1.1236420 1.2908359 1.3073813 1.3171347 1.3352933 +#> [453,] 0.40963634 0.4797447 0.5306961 0.6045138 0.6223325 0.6235444 0.6778300 +#> [454,] 0.57965193 0.7163739 0.7299407 0.7894505 0.9358348 1.0104482 1.0190099 +#> [455,] 0.33731955 0.3678879 0.3792666 0.4171944 0.5020450 0.5062591 0.5226281 +#> [456,] 0.09697109 0.3223750 0.3544125 0.4270109 0.4468538 0.5355817 0.5816812 +#> [457,] 0.26564505 0.7653629 0.8236488 0.9174340 0.9267543 0.9272176 0.9374222 +#> [458,] 0.48470066 0.5052092 0.5596495 0.6027456 0.6594966 0.6607136 0.6997558 +#> [459,] 0.16282452 0.2891480 0.4182400 0.4340449 0.4996934 0.5051723 0.5113368 +#> [460,] 0.44060061 0.4449831 0.4557764 0.4573444 0.4681588 0.5077163 0.5246272 +#> [461,] 0.27996485 0.5602452 0.6143961 0.6219634 0.6801153 0.7140490 0.7973198 +#> [462,] 0.70380443 0.8241613 1.1755510 1.1950636 1.2795708 1.3081447 1.3256072 +#> [463,] 0.26740534 0.3117459 0.4585147 0.4678582 0.5209490 0.5859090 0.5880765 +#> [464,] 0.52166448 0.5273160 0.6314860 0.6347717 0.6591848 0.6602226 0.6829279 +#> [465,] 0.46804512 0.6870153 0.7545626 0.7706474 0.7762396 0.8118278 0.9270370 +#> [466,] 0.31004980 0.7137102 0.8195100 0.8730117 0.9593647 1.0354425 1.1030984 +#> [467,] 0.29292073 0.3314803 0.4310830 0.4425622 0.4561757 0.5497865 0.5655038 +#> [468,] 0.27981317 0.3631803 0.3758961 0.4363955 0.4510979 0.4921939 0.5126823 +#> [469,] 0.34390429 0.3668414 0.4200978 0.5247714 0.5707832 0.5720249 0.6123558 +#> [470,] 0.21164504 0.2930111 0.3169707 0.3361130 0.3613120 0.3687756 0.3733389 +#> [471,] 0.20417561 0.4786287 0.5084755 0.5140385 0.5410413 0.5428946 0.6918404 +#> [472,] 0.83587974 0.8890259 0.9255049 0.9664203 1.0821634 1.0852990 1.1843941 +#> [473,] 0.76058958 0.9090353 0.9256838 0.9697771 1.0202062 1.0394324 1.0615033 +#> [474,] 0.40542497 0.4449027 0.4514099 0.5119779 0.5300852 0.5545604 0.5947904 +#> [475,] 0.87266945 1.0586150 1.0839485 1.0860132 1.1040333 1.1843674 1.3202047 +#> [476,] 0.39915058 0.4044690 0.4656943 0.5084248 0.5287486 0.5500657 0.6266317 +#> [477,] 0.51479463 0.5694424 0.6092113 0.6469373 0.6859729 0.6948786 0.6948917 +#> [478,] 0.33332358 0.4540222 0.6165999 0.6311446 0.6354071 0.6525144 0.6576091 +#> [479,] 0.32348784 0.4167936 0.4622053 0.4756200 0.4828435 0.4856521 0.5458660 +#> [480,] 0.20774666 0.2368156 0.3148796 0.3318808 0.4167936 0.5171208 0.5223489 +#> [481,] 0.64265530 0.6615948 0.6939049 0.7691026 0.7837144 0.8313751 0.8374920 +#> [482,] 0.31191358 0.5115595 0.5186912 0.5568903 0.5658674 0.5805367 0.6526331 +#> [483,] 0.36871174 0.4752569 0.4946232 0.5832266 0.6778089 0.6828681 0.7059491 +#> [484,] 0.61434841 0.6643837 0.7710334 0.8202840 0.8318084 1.0382065 1.1255773 +#> [485,] 0.32282065 0.4160175 0.4522604 0.4870973 0.5622388 0.5837745 0.6021089 +#> [486,] 0.69902660 0.9255049 0.9593647 0.9603065 1.0848989 1.1964572 1.2806124 +#> [487,] 0.36583627 0.4312480 0.5921655 0.6179367 0.6290988 0.6688491 0.6798061 +#> [488,] 0.48914700 0.5809788 0.5860091 0.6228037 0.6670446 0.7151664 0.7331312 +#> [489,] 0.54426886 0.5818935 0.6092135 0.6802616 0.7296064 0.7703605 0.7863003 +#> [490,] 0.64144872 1.1515432 1.2574863 1.3068810 1.3621837 1.4400532 1.5037535 +#> [491,] 0.68609343 0.6878115 1.0190392 1.1752675 1.2500793 1.4017630 1.4175909 +#> [492,] 0.19996637 0.2641698 0.4474902 0.4658086 0.5312450 0.5522147 0.5635916 +#> [493,] 0.55034352 0.6327033 0.7231658 0.9630081 1.1152965 1.1166193 1.2305216 +#> [494,] 0.44016974 0.7307823 0.8092806 0.8182231 0.8593062 0.8993036 0.9097510 +#> [495,] 0.27017635 0.3699957 0.4096830 0.4512306 0.4612165 0.4628703 0.4941352 +#> [496,] 0.73232324 0.7710468 0.8167852 0.8925772 0.9230566 0.9272176 0.9380741 +#> [497,] 0.35942336 0.4049898 0.4334802 0.4913438 0.5031696 0.5197681 0.5405174 +#> [498,] 1.03943243 1.0721897 1.0905577 1.1089035 1.1314227 1.1912541 1.2253874 +#> [499,] 0.27072483 0.3865848 0.4292685 0.5141644 0.5370477 0.5482934 0.5624287 +#> [500,] 0.37642480 0.3978315 0.3998387 0.5439209 0.5668593 0.6262270 0.6268746 +#> [501,] 0.47216259 0.5423907 0.7833552 0.9727131 1.1413017 1.1811073 1.2372009 +#> [502,] 0.34754640 0.3576066 0.3862040 0.3874743 0.4431694 0.4952917 0.5221971 +#> [503,] 1.25593536 1.3176629 1.3585019 1.3851334 1.3869811 1.4232511 1.4684821 +#> [504,] 0.41890633 0.4389962 0.4798121 0.6254812 0.6718151 0.6731796 0.7411969 +#> [505,] 0.78545275 0.8847553 1.0815974 1.1519782 1.3383411 1.4112341 1.5026801 +#> [506,] 0.37644458 0.5334556 0.5487723 0.7187771 0.7560823 0.7745370 0.9103913 +#> [507,] 0.70927902 0.7398905 0.8395667 0.8904261 0.8994460 0.9188252 0.9655937 +#> [508,] 0.51459128 0.6009983 0.6357531 0.6474427 0.6819256 0.7059153 0.7111395 +#> [509,] 0.43697204 0.4958141 0.5626203 0.5694424 0.5959871 0.6326815 0.7707595 +#> [510,] 0.40476741 0.5652670 0.5761168 0.6490482 0.6917122 0.7084974 0.7534034 +#> [511,] 0.82162781 0.9675018 0.9981969 1.0832153 1.2422757 1.2847455 1.2862323 +#> [512,] 0.30085979 0.4361772 0.5251689 0.5338096 0.5823034 0.6650514 0.7381164 +#> [513,] 0.29301114 0.3731118 0.4421991 0.4486907 0.4543662 0.4699835 0.4710620 +#> [514,] 0.29292073 0.4098502 0.4798583 0.5378361 0.5423993 0.5514421 0.5667096 +#> [515,] 0.26793281 0.2711063 0.3820295 0.3889879 0.4171944 0.4261615 0.4433985 +#> [516,] 0.27028389 0.3415438 0.3734846 0.3840070 0.4426923 0.4609179 0.4715471 +#> [517,] 0.30866684 0.3234878 0.3318808 0.4254990 0.4446930 0.4488586 0.4648980 +#> [518,] 0.07991648 0.4038128 0.4762512 0.5911722 0.6350611 0.6912811 0.6981930 +#> [519,] 0.37025403 0.3944722 0.4546167 0.5061000 0.5270976 0.5509830 0.5810267 +#> [520,] 0.27981317 0.4053421 0.4202105 0.4315446 0.5017654 0.5127194 0.5205824 +#> [521,] 0.34512046 0.4042546 0.4521097 0.5453375 0.5888239 0.6046208 0.6074047 +#> [522,] 0.13879613 0.3419732 0.3665608 0.4178758 0.4448724 0.4893759 0.5030333 +#> [523,] 0.54363676 0.5771780 0.5962627 0.7278883 0.8893138 0.9051578 0.9270370 +#> [524,] 0.33184739 0.3507093 0.3598209 0.5017654 0.5346923 0.5475948 0.5485160 +#> [525,] 0.26143033 0.3439043 0.3928074 0.4258462 0.5064131 0.5837491 0.7095298 +#> [526,] 0.28883648 0.4871865 0.5237486 0.5480739 0.6635628 0.6831837 0.6882478 +#> [527,] 0.77901084 1.1327007 1.2185269 1.2789434 1.2862323 1.3177358 1.4660522 +#> [528,] 0.38801771 0.3950953 0.4755505 0.5452861 0.5768426 0.6079400 0.6224556 +#> [529,] 0.27912903 0.3436373 0.4348900 0.6630490 0.6776026 0.7560740 0.8175933 +#> [530,] 0.45669225 0.5905829 0.6098908 0.8362491 0.8714916 0.8802147 0.9061382 +#> [531,] 0.36687170 0.4117716 0.4755552 0.5197681 0.5227071 0.5425355 0.5607744 +#> [532,] 0.33962879 0.3890705 0.4053346 0.5261169 0.6142398 0.6576187 0.6897535 +#> [533,] 0.73738653 0.8103618 0.8189857 0.8284993 0.8510004 0.8795685 0.8951109 +#> [534,] 0.17875079 0.3396288 0.3585382 0.5533974 0.5907733 0.6179367 0.6273748 +#> [535,] 0.32500550 0.3266515 0.3547841 0.3792344 0.4175321 0.4422283 0.4517481 +#> [536,] 0.33064403 0.3969425 0.5079901 0.5199025 0.5263975 0.5330862 0.5579990 +#> [537,] 0.86476367 0.9477399 0.9832344 1.0586136 1.2534981 1.3615438 1.3869811 +#> [538,] 1.51838574 1.6109467 1.7708302 1.8085940 1.9709012 1.9897454 2.1130770 +#> [539,] 0.31191358 0.4145507 0.4342199 0.5136025 0.5556632 0.5776815 0.5888827 +#> [540,] 0.48520405 0.6145141 0.9431510 0.9827255 1.0118221 1.0709103 1.1359482 +#> [541,] 0.40472014 0.4864877 0.6390878 0.6591848 0.7155856 0.7660701 0.7727693 +#> [542,] 0.76364702 0.7853750 0.8647637 1.0036165 1.0303411 1.0931007 1.0985984 +#> [543,] 0.58070930 0.6279492 0.6657246 0.6752513 0.6872129 0.7148179 0.7352546 +#> [544,] 0.61975077 0.9142557 0.9437180 1.0027772 1.0378565 1.1022966 1.2146529 +#> [545,] 0.60396993 0.8556707 0.8925672 0.9236600 0.9622823 0.9663578 0.9743262 +#> [546,] 0.12046345 0.4407778 0.6910868 0.7058085 0.7183874 0.7288252 0.7548969 +#> [547,] 1.17888285 1.2565012 1.3092816 1.3485371 1.3592511 1.3868103 1.3965006 +#> [548,] 0.55249129 0.5894451 0.6794550 0.6825160 0.7403855 0.7499781 0.7685453 +#> [549,] 0.40476741 0.5235680 0.5698808 0.6055756 0.6478767 0.7933394 0.8048888 +#> [550,] 0.59574307 0.6550834 0.7081091 0.8176899 0.8303848 0.8308750 0.8356224 +#> [551,] 0.36883720 0.3790429 0.4196418 0.4813874 0.6354071 0.7087893 0.7210488 +#> [552,] 0.22690537 0.3318503 0.3474324 0.3919076 0.5672823 0.5812286 0.5816418 +#> [553,] 0.25774459 0.3120066 0.5073320 0.6759421 0.6994948 0.7042752 0.7059491 +#> [554,] 0.48465152 0.5241100 0.5428946 0.5505425 0.5685634 0.5802675 0.6282014 +#> [555,] 0.28688126 0.3224021 0.4224186 0.4696288 0.4751730 0.4799773 0.4841708 +#> [556,] 0.42682207 0.4577456 0.5079901 0.5520819 0.6207918 0.6501106 0.6740800 +#> [557,] 0.51817417 0.5442637 0.5868762 0.6314365 0.6477322 0.6781473 0.6986159 +#> [558,] 0.19513800 0.3253874 0.3978315 0.5390598 0.5714364 0.5780151 0.6273748 +#> [559,] 0.60147181 0.6422321 0.6654582 0.7315526 0.7750984 0.8343469 0.8389705 +#> [560,] 0.58039868 0.6049625 0.6840995 0.7140941 0.7261054 0.7437403 0.7692705 +#> [561,] 0.56407060 0.5846558 0.8028055 0.8462224 0.8719151 0.8746693 0.8830011 +#> [562,] 0.34273538 0.3841699 0.4105118 0.4383739 0.4858712 0.5065422 0.5251689 +#> [563,] 0.44366791 0.7238057 1.1969462 1.3358588 1.3543267 1.3886413 1.5250261 +#> [564,] 0.63184141 0.7544464 0.7638342 0.8534673 0.9591824 0.9823241 0.9860083 +#> [565,] 0.53892468 0.6336688 0.8073962 0.8078945 0.9509165 0.9673514 1.0400164 +#> [566,] 1.40517342 1.4799383 1.5337433 1.6619145 1.6645287 1.6742392 1.6770461 +#> [567,] 0.40425462 0.5054819 0.5465505 0.6699691 0.6847809 0.7834679 0.7970432 +#> [568,] 0.29230462 0.2969847 0.3602116 0.3634108 0.3904106 0.5878294 0.6034387 +#> [569,] 0.41354584 0.4359867 0.4786287 0.5156658 0.5329834 0.5480443 0.5491726 +#> [570,] 0.40415075 0.6027456 0.6301278 0.6499022 0.6650514 0.7953267 0.8599447 +#> [571,] 1.01472574 1.1123529 1.1923692 1.3073333 1.3876238 1.5133920 1.5342320 +#> [572,] 1.41605561 1.5498688 1.7465202 1.7507080 1.8019820 1.8112516 1.8627146 +#> [573,] 0.59337182 0.6013404 0.6559592 0.6794917 0.7073037 0.7472879 0.7559327 +#> [574,] 0.20601898 0.2138112 0.2836252 0.3734846 0.3993061 0.4641697 0.5026104 +#> [575,] 0.56988075 0.5799310 0.6264804 0.6338821 0.7155595 0.7585791 0.8802147 +#> [576,] 0.24010441 0.3451244 0.3776825 0.4448724 0.5067279 0.5093840 0.5153017 +#> [577,] 0.44522840 0.4637061 0.5803419 0.6279280 0.7888915 0.8112898 0.8401171 +#> [578,] 0.46177516 0.5032684 0.5560926 0.6384115 0.6558610 0.6738408 0.6795696 +#> [579,] 0.25183425 0.4369720 0.4405283 0.4724317 0.6092113 0.6395914 0.6836550 +#> [580,] 0.55034352 0.8932676 0.9048609 0.9122109 0.9204655 1.0020373 1.0966724 +#> [581,] 0.27957885 0.4029978 0.5986835 0.6859729 0.7006915 0.8743741 0.9495803 +#> [582,] 0.46009541 0.5295352 0.5377479 0.5922188 0.5985136 0.6594093 0.6648714 +#> [583,] 0.16973124 0.3884705 0.4261615 0.4953801 0.5302664 0.6032451 0.6088218 +#> [584,] 0.36096082 0.4292685 0.4315726 0.5161931 0.5861869 0.6151879 0.6519473 +#> [585,] 0.62240532 0.8313961 0.8539695 1.0598134 1.0899057 1.1185382 1.1891441 +#> [586,] 0.53355768 0.6586069 0.7373932 0.8106935 0.8137303 0.8560083 0.8586241 +#> [587,] 0.61434841 0.6742504 0.7758032 0.8632298 0.9866009 0.9946745 1.0171249 +#> [588,] 0.50994700 0.7137102 0.7281672 0.8292282 0.8709165 0.9152130 0.9175304 +#> [589,] 0.35442371 0.4167385 0.4613995 0.4975055 0.5103887 0.7466410 0.7591619 +#> [590,] 0.54835213 0.7573177 0.7731458 0.7745561 0.7955375 0.8407062 0.8494256 +#> [591,] 0.48960202 0.5124989 0.5754902 0.5816418 0.5968568 0.6040564 0.6065801 +#> [592,] 0.40807386 0.5852049 0.5944061 0.6151464 0.6305765 0.6354666 0.6405491 +#> [593,] 0.50451422 0.5435610 0.8748761 0.9055023 1.0413266 1.0492853 1.1193411 +#> [594,] 0.60941048 0.8286666 0.8362473 0.8475980 0.8652686 0.9248949 0.9401446 +#> [595,] 1.02764981 1.0675792 1.1726157 1.1737706 1.2117283 1.2122693 1.2136554 +#> [596,] 0.48648770 0.6071182 0.6077914 0.6451965 0.6664912 0.6796197 0.6990176 +#> [597,] 0.31537054 0.4401888 0.7049388 0.7611104 0.7926868 0.8169729 0.8347674 +#> [598,] 0.33478924 0.3535666 0.3921772 0.3934919 0.4061885 0.4270109 0.4683849 +#> [599,] 0.29403821 0.4011936 0.4167385 0.4725982 0.5336573 0.5487884 0.5762095 +#> [600,] 0.34154377 0.3776825 0.4150283 0.4241536 0.4626339 0.4811862 0.5238636 +#> [601,] 0.29062669 0.3969090 0.4038128 0.6562524 0.6739732 0.6791804 0.7069239 +#> [602,] 0.36985508 0.4767623 0.6505008 0.6659052 0.6908772 0.6949283 0.7074873 +#> [603,] 0.46510675 0.5777555 0.6102530 0.6860259 0.6954669 0.8290772 0.8351042 +#> [604,] 0.58189352 0.6589000 0.6686780 0.6893716 0.8203466 0.8461130 0.8599066 +#> [605,] 0.28459575 0.3376415 0.4192009 0.5731440 0.6105425 0.6149352 0.6214294 +#> [606,] 0.11608719 0.3638735 0.3792344 0.3799525 0.3879428 0.4390372 0.4585823 +#> [607,] 0.80719987 0.8293724 1.0997222 1.2354086 1.3177878 1.3927904 1.4089550 +#> [608,] 0.70018889 1.2503321 1.2780366 1.3383411 1.4643526 1.5313353 1.5387897 +#> [609,] 0.54703206 0.6326815 0.7559557 0.8459306 0.8506859 0.8917204 0.9307966 +#> [610,] 0.27361090 0.3943047 0.4335340 0.5142888 0.5265425 0.5371743 0.5416264 +#> [611,] 0.82697259 0.8951109 0.9285000 0.9448010 1.0888297 1.2039308 1.2067116 +#> [612,] 0.30507871 0.3273770 0.4344419 0.4591137 0.4751239 0.4870335 0.5101134 +#> [613,] 0.54105950 0.7512295 0.8366602 0.9077201 1.0226334 1.0241518 1.1789045 +#> [614,] 0.32685154 0.4467783 0.6437903 0.6957793 0.7111202 0.7504098 0.7703679 +#> [615,] 0.21392826 0.2641698 0.4380816 0.4557797 0.5332089 0.5424867 0.5470235 +#> [616,] 0.16973124 0.3613998 0.4432428 0.4559979 0.5624052 0.5760403 0.6043345 +#> [617,] 0.54910784 0.5686761 0.6911297 0.6945931 0.7177089 0.7198688 0.7597752 +#> [618,] 0.21566331 0.3873494 0.4141784 0.4209737 0.4946848 0.5121898 0.5441918 +#> [619,] 0.31174585 0.3208248 0.3302222 0.3356392 0.3359325 0.3493520 0.4049898 +#> [620,] 0.48416998 0.6688491 0.6699654 0.7330502 0.7655117 0.7961905 0.8147810 +#> [621,] 0.54243723 0.5634303 0.5645729 0.5921655 0.5974787 0.6009983 0.6831728 +#> [622,] 0.44986007 0.6802616 0.6893716 0.8791343 0.8939719 0.9821576 0.9837626 +#> [623,] 0.58665844 0.8052953 0.8582436 0.9003027 0.9672961 0.9803843 1.0155728 +#> [624,] 0.24136134 0.5790625 0.6168309 0.6571811 0.6682516 0.7423945 0.7494334 +#> [625,] 0.52631572 0.6946062 0.8203172 0.8286666 0.8862931 1.0503166 1.0956282 +#> [626,] 0.28362515 0.3572052 0.3782162 0.4301535 0.4609179 0.4736896 0.4951976 +#> [627,] 0.34844429 0.4708596 0.4950304 0.5364844 0.5959543 0.6394717 0.6509156 +#> [628,] 0.70116622 0.8738729 0.9232706 0.9257615 0.9500668 1.0094909 1.0283225 +#> [629,] 0.41787351 0.5240484 0.6101186 0.6395927 0.6518593 0.6559082 0.6670681 +#> [630,] 0.34027172 0.3483590 0.5775658 0.5876252 0.6204556 0.6855360 0.7085435 +#> [631,] 0.48183801 0.7126242 0.7467861 0.7836029 0.8784556 0.8980112 0.8987570 +#> [632,] 0.19419128 0.2839768 0.3802278 0.4279197 0.4645395 0.4706502 0.4918564 +#> [633,] 0.23167235 0.4764203 0.4957492 0.5487723 0.5527083 0.5540839 0.6585077 +#> [634,] 0.37995251 0.4259055 0.4356021 0.4399584 0.4510979 0.5149004 0.6219822 +#> [635,] 0.39363928 0.4841722 0.5142780 0.5335577 0.5623835 0.5888239 0.6495236 +#> [636,] 0.63314368 1.0503016 1.1153360 1.1235266 1.1364532 1.1380188 1.1802990 +#> [637,] 0.85546966 0.8595941 0.8765175 0.8949732 0.9058662 0.9230566 0.9535782 +#> [638,] 0.54449025 1.2893829 1.4274326 1.4313215 1.4664327 1.6344595 1.6742548 +#> [639,] 0.46645854 0.4802830 0.6528180 0.6667336 0.6717098 0.6732536 0.6761374 +#> [640,] 0.35965794 0.4701404 0.5471553 0.5762095 0.6405376 0.6763833 0.6825840 +#> [641,] 0.43598666 0.4381048 0.5115222 0.5120130 0.5410413 0.5505425 0.5846763 +#> [642,] 0.25334228 0.5324512 0.5390755 0.5392326 0.5592968 0.5691145 0.5746819 +#> [643,] 0.36595529 0.3844797 0.4811879 0.5233490 0.5263663 0.5361204 0.5486290 +#> [644,] 0.34235251 0.5245471 0.5290194 0.5560926 0.5857627 0.6764634 0.6833624 +#> [645,] 0.36347243 0.4590754 0.6295294 0.6487658 0.6860259 0.7038720 0.7096475 +#> [646,] 0.20395837 0.4657923 0.4935208 0.5361204 0.5476945 0.5932247 0.6263767 +#> [647,] 0.29910942 0.4783190 0.5909218 0.6377302 0.6388986 0.7084974 0.7317710 +#> [648,] 0.38519494 0.4323350 0.5596796 0.6020497 0.6978972 0.7114342 0.7876461 +#> [649,] 0.35376145 0.4160175 0.4224186 0.4543662 0.4552867 0.4556376 0.4574192 +#> [650,] 1.26516795 1.2810324 1.5369662 1.5829576 1.5865118 1.6985075 1.7013157 +#> [651,] 0.30338965 0.3409042 0.3426225 0.3523410 0.3687144 0.4049237 0.4143892 +#> [652,] 0.38772521 0.4232959 0.5335816 0.5549221 0.5569106 0.6105425 0.6243774 +#> [653,] 0.37150479 0.4389962 0.4868420 0.4953717 0.5373714 0.5376232 0.5427501 +#> [654,] 0.78346792 0.7997734 0.8752845 0.9425318 0.9635824 0.9930162 0.9941969 +#> [655,] 0.49333622 0.4946115 0.5402954 0.5576428 0.6074047 0.7881233 0.8216602 +#> [656,] 0.81634704 0.8325586 0.9138814 0.9159168 0.9335997 0.9660048 1.0039897 +#> [657,] 0.30361897 0.5440199 0.5652880 0.6127630 0.6279280 0.6685404 0.7063750 +#> [658,] 0.37644458 0.4764203 0.5026760 0.5916505 0.6111725 0.6610760 0.7162576 +#> [659,] 0.46121647 0.4937272 0.6014050 0.6290653 0.6328639 0.7201901 0.7545655 +#> [660,] 0.27017635 0.4135228 0.4148534 0.4937272 0.5395340 0.5418916 0.5552143 +#> [661,] 0.58739582 0.6725229 0.7993370 0.9048243 0.9329313 0.9839407 1.0221971 +#> [662,] 0.41394639 0.6016788 0.6157182 0.6422605 0.6474427 0.6831728 0.7126242 +#> [663,] 0.18460255 0.2138112 0.3822591 0.3840070 0.3862574 0.4230495 0.4736896 +#> [664,] 0.30072497 0.3464240 0.3802278 0.3995926 0.6015810 0.6372212 0.6519142 +#> [665,] 0.30975111 0.3880177 0.4556376 0.5139233 0.5483299 0.5894451 0.6325165 +#> [666,] 0.43594864 0.5407081 0.8323526 0.9285000 0.9896595 1.0405765 1.0770914 +#> [667,] 0.34452809 0.3600881 0.3892187 0.4049237 0.4952720 0.5409081 0.5506858 +#> [668,] 0.25073339 0.2655099 0.3688372 0.4002012 0.5335816 0.5430112 0.5562233 +#> [669,] 0.55313581 0.8070900 0.8268404 0.9176893 0.9838827 1.0484415 1.0492853 +#> [670,] 0.77684925 0.7780350 0.8705382 0.8738428 0.9277626 0.9402255 0.9558444 +#> [671,] 0.62196198 0.6366517 0.6978972 0.8322545 0.8497819 0.9580972 1.0241518 +#> [672,] 0.30084255 0.5388117 0.5648963 0.5803478 0.7518248 0.7807733 0.8681542 +#> [673,] 0.43749453 0.5505227 0.5672890 0.5687865 0.5735866 0.5894259 0.5947715 +#> [674,] 0.24863800 0.3318474 0.4301535 0.4310850 0.4422283 0.4810028 0.4870022 +#> [675,] 0.53320894 0.6171614 0.6680559 0.6984509 0.7053176 0.7166298 0.7309727 +#> [676,] 0.39983873 0.4405283 0.4797045 0.5156670 0.5388781 0.5728042 0.6942725 +#> [677,] 0.35368533 0.4474790 0.4496334 0.4678426 0.6593369 0.7176423 0.7300486 +#> [678,] 0.39190757 0.4899965 0.5306228 0.5583424 0.6397865 0.6539415 0.6601470 +#> [679,] 0.42631331 0.4291071 0.5682878 0.6235444 0.6862337 0.7036078 0.7612428 +#> [680,] 0.40020213 0.4387530 0.4914823 0.5162458 0.5411882 0.5538449 0.5839871 +#> [681,] 0.27824019 0.3971149 0.4167784 0.4673479 0.4958828 0.5184400 0.5692543 +#> [682,] 0.44486184 0.5100506 0.5699179 0.5792501 0.5905829 0.6962281 0.6983024 +#> [683,] 0.63366877 0.7138283 0.8833182 0.9951903 0.9986345 1.0016824 1.0486325 +#> [684,] 0.32908268 0.3829368 0.4572627 0.4617752 0.5499103 0.5675280 0.5688572 +#> [685,] 0.47289305 0.4739998 0.6328005 0.6521120 0.6720328 0.6739434 0.7266951 +#> [686,] 0.35509133 0.4265314 0.6051751 0.6168309 0.6865512 0.7016635 0.7183189 +#> [687,] 0.71885913 0.8323526 0.9055023 0.9176893 0.9448010 1.1334821 1.1536165 +#> [688,] 0.51403851 0.5219559 0.5413375 0.5480443 0.6355625 0.6555136 0.6644830 +#> [689,] 0.61265766 0.6874574 0.7786590 0.8613076 0.9503610 0.9610233 1.0182848 +#> [690,] 0.42653138 0.4625601 0.4763041 0.5893919 0.6858187 0.6941150 0.6977133 +#> [691,] 0.29444451 0.3104717 0.4775865 0.5029744 0.5991155 0.6026409 0.6093745 +#> [692,] 0.69054547 0.8296649 0.8948670 0.9504654 1.0171249 1.0282789 1.0300253 +#> [693,] 0.51757671 0.5368974 0.7315526 0.7389885 0.8092806 0.8729183 0.8828692 +#> [694,] 0.21674498 0.2507334 0.4196418 0.4596097 0.5467713 0.5569106 0.6194824 +#> [695,] 0.69062254 0.8392936 1.1106780 1.1193411 1.1366575 1.1374524 1.1464533 +#> [696,] 0.34682822 0.3859824 0.4531165 0.4946232 0.5408715 0.5927076 0.5948363 +#> [697,] 0.95620290 1.1272844 1.1292652 1.3601725 1.4219230 1.4411477 1.4704535 +#> [698,] 0.26343145 0.4435184 0.5219604 0.5413950 0.5726195 0.6162140 0.6336426 +#> [699,] 0.42701073 0.5527083 0.8069478 0.8241123 0.8512284 0.8574372 0.8688199 +#> [700,] 0.36336045 0.4141241 0.4178735 0.4315726 0.5211340 0.6684754 0.6770869 +#> [701,] 0.54105950 0.9021926 0.9023072 1.1646467 1.1690400 1.1740738 1.2017255 +#> [702,] 0.33332358 0.4941352 0.5491287 0.5514421 0.5823703 0.6171313 0.6328639 +#> [703,] 0.31537054 0.4176534 0.6411925 0.6462147 0.7403294 0.7454633 0.7899675 +#> [704,] 0.26328614 0.2638343 0.3096327 0.3402142 0.4377764 0.4409665 0.5062397 +#> [705,] 0.38149111 0.3966306 0.4026726 0.4178758 0.4513943 0.4525542 0.5153017 +#> [706,] 0.15472873 0.3533348 0.3699957 0.3862040 0.4205965 0.4325937 0.5418916 +#> [707,] 0.43000173 0.5073444 0.5338096 0.5381579 0.6499022 0.6594966 0.6792359 +#> [708,] 0.33323683 0.4416356 0.4746625 0.4969829 0.5978585 0.6048488 0.6156391 +#> [709,] 0.60490980 0.6197508 0.6389791 0.6671744 0.6842797 0.8041659 0.8240498 +#> [710,] 0.44123648 0.5963301 0.6770869 0.6782984 0.7436061 0.7591996 0.7596100 +#> [711,] 0.36738405 0.4432428 0.4953801 0.5027575 0.5266475 0.5653288 0.5719470 +#> [712,] 0.44109626 0.5182610 0.8114595 0.8182231 0.8704664 0.8718881 0.8750923 +#> [713,] 0.35346968 0.7246401 0.7499065 0.7823791 0.8305809 0.9931664 1.0404170 +#> [714,] 0.58893642 0.6124386 0.6788038 0.7691026 0.7834769 0.8118543 0.8310321 +#> [715,] 0.66340973 0.8016984 0.8089124 0.8649101 0.8843676 0.9499100 1.0330265 +#> [716,] 0.14605421 0.6347718 0.6725681 0.7590693 0.7616078 0.7722215 0.7821952 +#> [717,] 0.25735799 0.3537615 0.3695420 0.3777877 0.4214002 0.4252863 0.4281232 +#> [718,] 0.33022224 0.3441288 0.4096363 0.4386172 0.4585147 0.5031696 0.5193626 +#> [719,] 0.65860686 0.8196058 0.8914782 0.9086368 0.9839407 1.0014516 1.0230460 +#> [720,] 0.39509527 0.5139233 0.5447037 0.5524913 0.8080131 0.8155186 0.8215988 +#> [721,] 0.55933129 0.6217602 0.6965583 0.7067615 0.7703424 0.8276266 0.8368484 +#> [722,] 0.63580413 0.7688035 0.8106643 0.9246408 0.9380414 0.9439554 1.0680439 +#> [723,] 0.46256011 0.4801519 0.5622388 0.6051751 0.6279492 0.6551946 0.6571811 +#> [724,] 0.34717912 0.4045419 0.5436368 0.6388986 0.7033968 0.7442621 0.7580649 +#> [725,] 0.51181402 0.5664642 0.6071722 0.6465218 0.6607136 0.6631087 0.6820446 +#> [726,] 0.75122951 0.7833552 0.8497819 0.8599066 0.8806943 0.9128641 0.9215414 +#> [727,] 0.22735807 0.5369339 0.5880864 0.6313239 0.6365637 0.6494028 0.6978221 +#> [728,] 0.69858196 0.8308600 0.8988011 0.9483479 0.9529792 0.9557612 0.9866009 +#> [729,] 0.58913260 0.5904511 1.0997222 1.2397709 1.3674834 1.3693199 1.3854303 +#> [730,] 0.47359279 0.5291050 0.5563820 0.6234920 0.6501716 0.6651793 0.6910260 +#> [731,] 0.38658483 0.4641697 0.4951976 0.5142720 0.5381997 0.5654464 0.5733830 +#> [732,] 0.53774224 0.6493739 0.6512504 0.6671744 0.7118943 0.7749027 0.7782724 +#> [733,] 0.40440349 0.4300163 0.5196985 0.5397259 0.6101514 0.6157228 0.6182368 +#> [734,] 1.35351303 1.6109467 1.7051863 1.7236095 1.8262364 1.9428414 2.0175106 +#> [735,] 0.29359887 0.4279511 0.4804884 0.5369971 0.5482631 0.5865848 0.5953025 +#> [736,] 0.90613818 0.9500898 0.9713147 1.0095566 1.0338680 1.1879064 1.2013942 +#> [737,] 0.52439153 0.6675391 0.8305809 1.0501612 1.0567316 1.1273738 1.1840653 +#> [738,] 0.42791970 0.4785496 0.5145920 0.5260537 0.5268112 0.5434271 0.5488552 +#> [739,] 0.77256093 0.8926279 1.0544813 1.1382764 1.1418979 1.1830457 1.2970655 +#> [740,] 0.27824019 0.3109608 0.3987329 0.4258963 0.5017250 0.5105818 0.5888744 +#> [741,] 0.27773200 0.8213555 0.8559930 0.9525178 0.9838940 1.0723668 1.1206739 +#> [742,] 0.38767281 0.4026726 0.4205946 0.4471608 0.4802830 0.5507127 0.5815310 +#> [743,] 0.32237498 0.3764880 0.4013733 0.4537154 0.4802813 0.4904619 0.5476945 +#> [744,] 0.49446897 0.5258675 0.5579990 0.5600066 0.5672890 0.5699179 0.6562205 +#> [745,] 0.35748401 0.5123131 0.5290194 0.5663951 0.5957944 0.6198647 0.6897391 +#> [746,] 0.31719653 0.3848797 0.4561031 0.4979489 0.5333971 0.5371761 0.5391688 +#> [747,] 0.67604518 0.9275340 1.0278988 1.0327197 1.2267464 1.2375629 1.3199692 +#> [748,] 0.12222005 0.4177120 0.6268746 0.7180089 0.7371032 0.7431611 0.7436420 +#> [749,] 0.74546332 0.7926868 0.8942326 1.0656954 1.1363119 1.1613837 1.1821319 +#> [750,] 0.43000173 0.5052092 0.5965068 0.6683562 0.7105456 0.7366041 0.7704925 +#> [751,] 0.28459575 0.3694457 0.5026201 0.6070519 0.6188489 0.6194824 0.6218011 +#> [752,] 0.70730375 0.7993732 0.8869694 0.9236479 0.9618237 1.0278988 1.0328210 +#> [753,] 0.45428253 0.5213248 0.5381579 0.5430112 0.6351318 0.6863909 0.6964509 +#> [754,] 0.73790913 0.9146707 0.9268745 0.9441675 0.9728522 0.9886703 1.0041557 +#> [755,] 0.51540272 0.5320156 0.5852049 0.6104892 0.6833624 0.6897391 0.6911030 +#> [756,] 0.41347323 0.4387530 0.4678809 0.4871645 0.4934944 0.5402894 0.5618440 +#> [757,] 1.17827006 1.2534981 1.2886819 1.3553739 1.3561492 1.4709502 1.4736376 +#> [758,] 0.48695844 0.5510996 0.5864232 0.6654570 0.6918404 0.6931311 0.6985820 +#> [759,] 1.60098152 1.6043613 1.6645287 1.8161045 1.8887888 1.9565765 2.0145514 +#> [760,] 0.17672129 0.2947138 0.3361387 0.3529613 0.4579331 0.4654775 0.5653037 +#> [761,] 0.27361090 0.3842518 0.3946160 0.4463409 0.4822301 0.4970682 0.5173598 +#> [762,] 0.09831649 0.3687144 0.3738390 0.3752012 0.4383739 0.4483313 0.4989207 +#> [763,] 0.40807386 0.6692890 0.7407087 0.7445498 0.7617625 0.7812317 0.8029511 +#> [764,] 0.33323683 0.3636660 0.4090712 0.4678809 0.5384396 0.5770353 0.6342586 +#> [765,] 0.74149780 0.7884421 0.8924413 0.8925216 1.0852243 1.1068269 1.1279884 +#> [766,] 0.88475525 1.0307034 1.1031474 1.1288179 1.1767783 1.2611665 1.3072379 +#> [767,] 0.39276221 0.4141241 0.4186988 0.5460700 0.5804832 0.5861869 0.5965068 +#> [768,] 0.60683531 0.6239664 0.6329802 0.6571812 0.7014294 0.7681392 0.9043524 +#> [769,] 0.25183425 0.3287616 0.4958141 0.5156670 0.6469373 0.7240607 0.7927622 +#> [770,] 0.81951004 1.0033135 1.0122607 1.1505867 1.1603421 1.1781983 1.1982659 +#> [771,] 0.63388207 0.7708441 0.8405305 0.8498749 0.9045846 0.9466005 0.9803991 +#> [772,] 0.38596645 0.4342199 0.4684409 0.5602363 0.5714456 0.5805367 0.5833092 +#> [773,] 0.63764715 0.7092790 0.8200161 0.8512293 0.8517123 0.9066578 0.9243237 +#> [774,] 0.29062669 0.4554043 0.4762512 0.6821908 0.6890077 0.7307151 0.7315235 +#> [775,] 0.27996485 0.5089294 0.5409551 0.5731555 0.6783771 0.7118041 0.7215748 +#> [776,] 0.38532828 0.4681975 0.5026760 0.5540839 0.5630189 0.6028126 0.6175580 +#> [777,] 0.51411964 0.5156462 0.5420390 0.5666462 0.6606336 0.6987629 0.7096344 +#> [778,] 0.28914798 0.3341115 0.3905825 0.4007505 0.4423288 0.4925744 0.5092038 +#> [779,] 0.44351835 0.5441918 0.5751142 0.5771078 0.5850060 0.6032333 0.6109984 +#> [780,] 0.42911358 0.4658710 0.4704423 0.5636624 0.5749203 0.6282014 0.6493615 +#> [781,] 0.58078479 0.6287389 0.6727315 0.7011662 0.7128726 0.7296091 0.7383797 +#> [782,] 0.17515967 0.3148796 0.4622053 0.4935208 0.5181742 0.5225545 0.5405678 +#> [783,] 0.34235251 0.4319502 0.4692709 0.4880859 0.5052213 0.5123131 0.5347047 +#> [784,] 0.27949333 0.5545604 0.5978329 0.6031911 0.6126577 0.6343305 0.7437618 +#> [785,] 0.24171039 0.3686001 0.4558091 0.5551246 0.6074524 0.6198224 0.6720896 +#> [786,] 0.39297509 0.3943971 0.4756200 0.5476847 0.5500657 0.5557123 0.5801731 +#> [787,] 0.62045966 0.6634097 0.6801610 0.7152110 0.7696425 0.8052953 0.8215516 +#> [788,] 0.54426365 0.5638175 0.6749506 0.6987118 0.7195750 0.7425097 0.7472007 +#> [789,] 0.30084255 0.5204844 0.5390357 0.6601272 0.6631301 0.7861197 0.7917998 +#> [790,] 0.21863794 0.4566923 0.5100506 0.6264804 0.6478767 0.7233856 0.7629658 +#> [791,] 0.69343238 0.7916039 0.8174900 0.8209361 0.8597213 0.8956150 0.9191062 +#> [792,] 0.31575585 0.3376415 0.3694457 0.6310238 0.6630490 0.6687645 0.6753017 +#> [793,] 0.33731955 0.3451244 0.4241536 0.4726576 0.4765648 0.4850090 0.5104808 +#> [794,] 0.33330030 0.3430224 0.4944690 0.5286509 0.5507820 0.5691711 0.6740800 +#> [795,] 0.48015193 0.4870973 0.5434333 0.5681859 0.5790625 0.6224556 0.6920199 +#> [796,] 0.91545188 0.9851428 0.9985390 1.2401699 1.2706049 1.4116211 1.4267028 +#> [797,] 0.26927678 0.3941537 0.4221518 0.4449027 0.4632372 0.4782392 0.5086648 +#> [798,] 0.30577483 0.4371707 0.5345808 0.6105168 0.6122402 0.6865432 0.7719839 +#> [799,] 0.85802249 0.9283156 1.2431804 1.2509933 1.2636073 1.2909028 1.3068810 +#> [800,] 0.60499899 0.6353063 0.6588731 0.6686780 0.6783649 0.7031266 0.7059153 +#> [801,] 0.35808541 0.3596801 0.4380816 0.4387298 0.4474902 0.4715928 0.4920686 +#> [802,] 0.42497290 0.4498638 0.7653629 0.8186734 0.8834345 0.8955751 0.9027206 +#> [803,] 0.36096082 0.3633604 0.4226290 0.5460700 0.5624287 0.6155368 0.6287389 +#> [804,] 0.41459923 0.4496334 0.4880470 0.4927598 0.4971807 0.5330295 0.5530242 +#> [805,] 0.87209495 1.2580143 1.5146902 1.5192367 1.6207223 1.7483121 1.7926297 +#> [806,] 0.32580181 0.5388117 0.5390357 0.6096010 0.6983498 0.7022300 0.7152110 +#> [807,] 0.22735807 0.3700770 0.4739122 0.4933392 0.6045138 0.6468782 0.7561484 +#> [808,] 0.36954200 0.3791834 0.3862574 0.4063097 0.4711711 0.4919237 0.4926647 +#> [809,] 0.31170307 0.6327033 0.7336474 0.9043976 0.9048609 0.9850208 1.0023837 +#> [810,] 0.42322607 0.4259888 0.4446164 0.4449831 0.4667387 0.4983489 0.5171208 +#> [811,] 0.47742160 0.7927973 0.8392936 0.9368811 0.9712860 1.0107862 1.0526832 +#> [812,] 0.57426260 0.5857830 0.6128149 0.6252175 0.6313865 0.6448859 0.6608414 +#> [813,] 0.40299783 0.5424989 0.6948786 0.7302712 0.8294974 0.9501195 0.9531711 +#> [814,] 0.72727075 0.8829206 0.9873766 1.0244392 1.0398980 1.2170482 1.2480076 +#> [815,] 0.24949429 0.4851378 0.4852129 0.5202777 0.6531028 0.7033036 0.7176815 +#> [816,] 0.51507672 0.5471553 0.5615487 0.6850255 0.7931495 0.7975922 0.8424111 +#> [817,] 0.15604009 0.4431694 0.5146468 0.5624673 0.5727703 0.6123558 0.6131775 +#> [818,] 0.65828602 0.6739732 0.6864811 0.6890077 0.7181508 0.7231773 0.7530624 +#> [819,] 0.51564803 0.5235680 0.5761168 0.7585791 0.8006608 0.8498749 0.9355411 +#> [820,] 0.51459128 0.5371761 0.5935440 0.6053123 0.6092135 0.6686627 0.6725681 +#> [821,] 0.33148026 0.3717019 0.4098502 0.5120130 0.5334669 0.5664642 0.6158515 +#> [822,] 0.22038706 0.2836823 0.5179364 0.5479470 0.5576100 0.6203619 0.8928497 +#> [823,] 0.65890004 0.6900357 0.7414978 0.8366602 0.9023072 0.9483216 0.9959242 +#> [824,] 0.29471381 0.3487168 0.4474640 0.4799773 0.4854754 0.4999563 0.5302813 +#> [825,] 0.20417561 0.4846515 0.5846763 0.5975842 0.6272176 0.6513768 0.6654570 +#> [826,] 0.22475057 0.2403118 0.3006863 0.3547841 0.3638735 0.4968724 0.4979489 +#> [827,] 0.33702873 0.8849706 1.0227250 1.2455886 1.2472189 1.2625984 1.2728276 +#> [828,] 0.23535424 0.2888365 0.4209737 0.5078930 0.5609209 0.6032333 0.6234146 +#> [829,] 0.43557756 0.5741917 0.5876252 0.6720861 0.6932422 0.7299574 0.8358283 +#> [830,] 0.34242776 0.3594535 0.3943971 0.6445696 0.6479357 0.6531994 0.6720896 +#> [831,] 0.42497290 0.4378084 0.6613781 0.6717098 0.7180839 0.8036945 0.8090444 +#> [832,] 0.18842874 0.3712625 0.4171864 0.4988816 0.5338943 0.5859090 0.6002829 +#> [833,] 0.59828158 0.6140907 0.6609668 0.6916844 0.7095298 0.7533940 0.7615608 +#> [834,] 0.85086686 0.8693708 1.0402638 1.0913809 1.1162744 1.1580728 1.1882179 +#> [835,] 0.39484804 0.5377422 0.5896853 0.5998148 0.6132736 0.7345074 0.7400159 +#> [836,] 0.28200459 0.4838382 0.5056986 0.7097586 0.7118269 0.7154967 0.7260693 +#> [837,] 0.28200459 0.4255819 0.5311183 0.5385190 0.5857583 0.6752513 0.7014631 +#> [838,] 0.72854123 0.8213555 1.2479845 1.2509933 1.3578865 1.3883013 1.4704535 +#> [839,] 0.35221305 0.4658086 0.4688457 0.4852129 0.5470235 0.5481457 0.5696647 +#> [840,] 0.32580181 0.4869584 0.5029816 0.5929361 0.6505765 0.6601272 0.6815772 +#> [841,] 0.24031182 0.3266515 0.3289196 0.3879428 0.4055814 0.4310850 0.4802058 +#> [842,] 0.63184141 0.7825293 0.9278780 0.9812174 0.9830406 1.0397144 1.0555835 +#> [843,] 0.40636897 0.5420390 0.5550702 0.5862135 0.5904569 0.6071488 0.6836963 +#> [844,] 0.47831904 0.4992977 0.5258547 0.5944776 0.5963457 0.6599681 0.7640140 +#> [845,] 0.46638959 0.5044446 0.5824304 0.6530868 0.6635628 0.6810039 0.7583725 +#> [846,] 0.18634156 0.4660731 0.5016230 0.5377097 0.5390598 0.5907733 0.6081748 +#> [847,] 0.29038507 0.3045802 0.3361387 0.3493174 0.3687756 0.3766028 0.4119493 +#> [848,] 0.50444460 0.5294214 0.6075915 0.7424908 0.8023538 0.8194373 0.9051578 +#> [849,] 0.18945147 0.2116450 0.2573580 0.3110705 0.3223047 0.3731118 0.3766028 +#> [850,] 0.38970748 0.5717071 0.5865542 0.5878294 0.5908949 0.6282007 0.6367832 +#> [851,] 0.24949429 0.4835535 0.4973154 0.5481457 0.6148331 0.7302801 0.7861184 +#> [852,] 0.46124088 0.5084755 0.5156658 0.5596796 0.5802675 0.6023530 0.6047881 +#> [853,] 0.66924590 0.7049388 0.8096001 0.8637338 0.8647272 0.8857966 0.9629770 +#> [854,] 0.40949008 0.4903354 0.6040936 0.6654582 0.7892187 0.9190275 0.9911303 +#> [855,] 0.45907545 0.5777555 0.5780089 0.7002335 0.7745468 0.8503036 0.8679628 +#> [856,] 0.60555636 0.7423913 0.7591619 0.7960978 0.9138814 0.9284428 1.1086736 +#> [857,] 0.54070810 0.5956861 0.7189500 0.7750984 0.7991863 0.8518376 0.8815974 +#> [858,] 0.43061825 0.4657923 0.4811879 0.5268112 0.5396213 0.5876804 0.5909889 +#> [859,] 0.47952646 0.5485678 0.5796519 0.7068683 0.7246722 0.7839269 0.8756713 +#> [860,] 0.34512046 0.3936393 0.5426927 0.5617833 0.5775658 0.6252175 0.6699691 +#> [861,] 0.33018724 0.6105168 0.6239664 0.6644961 0.6850255 0.7567525 0.7708239 +#> [862,] 0.44522840 0.4481359 0.5284077 0.5530242 0.5652880 0.5863829 0.6606663 +#> [863,] 0.44813593 0.4637061 0.4678426 0.4880470 0.5557236 0.6843397 0.7063750 +#> [864,] 0.62529285 0.8191095 1.0646795 1.0720833 1.0783879 1.1276875 1.1329767 +#> [865,] 0.41119252 0.4577456 0.5452479 0.6297758 0.6929260 0.7024502 0.7180998 +#> [866,] 0.27912903 0.4651760 0.5081217 0.5602555 0.6379255 0.7991863 0.8322765 +#> [867,] 1.15661048 1.3462438 1.4471441 1.5645124 1.5808967 1.6063300 1.6320500 +#> [868,] 0.54139322 0.6773988 0.7105605 0.7149278 0.7211515 0.7299574 0.7337276 +#> [869,] 0.31133296 0.4822301 0.5088305 0.5234874 0.5358143 0.5401556 0.5946605 +#> [870,] 0.50344372 0.6795696 0.7893724 0.8130988 0.8172974 0.8690556 0.8829630 +#> [871,] 0.24531337 0.2903851 0.2977648 0.3203283 0.3223047 0.3224021 0.3613120 +#> [872,] 1.09268104 1.1382408 1.1923692 1.2465492 1.3481100 1.4001927 1.4189594 +#> [873,] 0.37265397 0.4918564 0.4958828 0.5303708 0.5321874 0.5558120 0.5829550 +#> [874,] 0.48520405 0.7279412 0.8074916 0.9682564 1.1254497 1.1566882 1.1824327 +#> [875,] 0.26594121 0.5953002 0.5957431 0.6172237 0.6716399 0.6896890 0.6984653 +#> [876,] 0.81867341 0.8726695 0.9058662 0.9245956 0.9318064 1.0447502 1.0583478 +#> [877,] 0.66137812 0.6894816 0.8342098 0.8775902 0.9044680 0.9185322 0.9245897 +#> [878,] 0.27957885 0.5147946 0.5424989 0.5528173 0.7625186 0.7667228 0.7693490 +#> [879,] 0.12222005 0.3100312 0.6142398 0.6262270 0.6349211 0.6857133 0.7149717 +#> [880,] 0.55831393 0.5605773 0.6047171 0.6481022 0.9314802 0.9439554 0.9520623 +#> [881,] 0.49736419 0.5917555 0.6223325 0.6957458 0.7322672 0.7716297 0.7745400 +#> [882,] 0.40360403 0.5054819 0.5453375 0.5558413 0.6633979 0.7233839 0.7326161 +#> [883,] 0.37547110 0.4268221 0.5448074 0.5452479 0.6043597 0.6078415 0.6495392 +#> [884,] 0.29963193 0.3668414 0.3792134 0.3928074 0.4425622 0.4718084 0.4798583 +#> [885,] 0.64379029 0.7468705 0.8206195 0.8262232 0.8541983 0.8743703 0.9315260 +#> [886,] 0.30096399 0.3892187 0.4546167 0.4548463 0.5983123 0.6060355 0.6328005 +#> [887,] 0.46703537 0.4767417 0.5145920 0.5321874 0.5712901 0.5777134 0.5909889 +#> [888,] 0.51564803 0.6582860 0.7708441 0.7788900 0.8357769 0.8599289 0.8652686 +#> [889,] 0.53892468 1.0486325 1.1644871 1.1717316 1.2046336 1.2400584 1.2450309 +#> [890,] 0.32282065 0.3540405 0.4008547 0.4574192 0.4597129 0.4749997 0.4819657 +#> [891,] 0.47742160 0.7234641 0.8592318 1.0554974 1.0620862 1.1109065 1.1366575 +#> [892,] 0.34197322 0.3716588 0.4356021 0.5047202 0.5166129 0.5323596 0.5344388 +#> [893,] 0.66622344 0.6909275 0.8534496 0.8539695 0.9764982 0.9863673 0.9984615 +#> [894,] 0.87209495 1.0808862 1.3254115 1.3404295 1.4891477 1.5381128 1.5951257 +#> [895,] 0.38741583 0.4344419 0.4488586 0.4533024 0.4672448 0.4848637 0.4975694 +#> [896,] 1.03245278 1.1925389 1.2063668 1.2165550 1.2345554 1.2772759 1.3177358 +#> [897,] 0.33702873 0.9317456 1.1587109 1.2196853 1.2244513 1.2387571 1.2509943 +#> [898,] 0.86087471 0.8775427 1.0586136 1.2316273 1.2486592 1.3250910 1.3474000 +#> [899,] 0.24495826 0.3665608 0.3796179 0.4394007 0.4699607 0.4970682 0.4973593 +#> [900,] 0.53693393 0.5509196 0.5758092 0.6077715 0.6372212 0.6468782 0.6495503 +#> [901,] 0.60751210 0.6347718 0.6750756 0.8400073 0.8639459 0.8938192 0.8987570 +#> [902,] 0.36475602 0.4359686 0.4681975 0.4957492 0.5509920 0.6065373 0.6610760 +#> [903,] 0.71925597 0.7238057 1.4242708 1.4808959 1.6356863 1.7811930 1.8049915 +#> [904,] 0.33217500 0.6075172 0.7308439 0.7573067 0.7745370 0.8331341 0.8373647 +#> [905,] 1.08159736 1.0956074 1.1598090 1.2341912 1.2433489 1.2582241 1.2708966 +#> [906,] 0.29083287 0.3991506 0.5263341 0.5313566 0.5715888 0.5801731 0.5866417 +#> [907,] 0.31849457 0.3375275 0.4090368 0.4105118 0.4221518 0.4514099 0.5643394 +#> [908,] 0.30096399 0.5509830 0.5541190 0.6393489 0.6521120 0.6928918 0.7301782 +#> [909,] 0.27773200 0.7285412 0.9501224 0.9562029 0.9740056 1.0353836 1.1208545 +#> [910,] 0.35965794 0.5150767 0.5336573 0.6122402 0.6531913 0.6848237 0.6920594 +#> [911,] 0.32876163 0.4724317 0.5728042 0.5959871 0.6017204 0.7424823 0.7535873 +#> [912,] 0.20612255 0.2839768 0.3995926 0.4600954 0.4980985 0.5371465 0.5509196 +#> [913,] 0.23307038 0.3248874 0.4146456 0.4839062 0.4901973 0.5347778 0.5501868 +#> [914,] 0.23993803 0.5254370 0.6040564 0.6101514 0.6314974 0.6320861 0.6400916 +#> [915,] 0.69003570 0.8739171 0.8805306 0.8924413 1.0279754 1.0449738 1.1265626 +#> [916,] 0.40415075 0.4847007 0.6844604 0.7136063 0.7381164 0.7421462 0.7458784 +#> [917,] 0.18051066 0.3621718 0.6046618 0.6203619 0.6725229 0.6859977 0.7025122 +#> [918,] 0.39323414 0.4813874 0.5092038 0.5139742 0.5467713 0.5562233 0.5636624 +#> [919,] 0.33470661 0.4189063 0.4808973 0.5373714 0.5594251 0.6065373 0.6195995 +#> [920,] 0.70069148 0.8073962 0.8207193 0.8294974 0.8352566 0.8714288 0.9656345 +#> [921,] 0.34743241 0.3678879 0.4061678 0.4076304 0.4277711 0.4468730 0.5275568 +#> [922,] 0.34309068 0.4307515 0.5409551 0.5519855 0.5602452 0.6127630 0.7134870 +#> [923,] 0.50898016 0.5401785 0.6252212 0.6393644 0.6450369 0.6670446 0.6746208 +#> [924,] 0.55281735 0.5803478 0.5986835 0.7302712 0.7642574 0.7886514 0.7917998 +#> [925,] 0.46323720 0.4711711 0.5015095 0.5227071 0.5300852 0.6057881 0.6281770 +#> [926,] 0.37300207 0.5505227 0.5666462 0.5839332 0.6252212 0.6690963 0.6836963 +#> [927,] 0.40119359 0.4975055 0.5589544 0.6055564 0.6531913 0.6584113 0.7604565 +#> [928,] 0.48183801 0.4841700 0.6325459 0.7198504 0.7318016 0.7725609 0.7955375 +#> [929,] 0.61025299 0.7080839 0.8064294 0.8291200 0.8344719 0.8517123 0.8693708 +#> [930,] 0.35368533 0.4116207 0.4613834 0.5041523 0.5568096 0.5683251 0.6135434 +#> [931,] 0.31605501 0.4617185 0.5401785 0.5809788 0.5947715 0.6274038 0.6835825 +#> [932,] 0.36375067 0.3966306 0.4539625 0.4848637 0.5092787 0.5233490 0.5512903 +#> [933,] 0.66159483 0.6653664 0.7231773 0.7462066 0.7828199 0.7834769 0.8125567 +#> [934,] 0.72467222 0.8892709 0.8942326 0.9342293 1.0019782 1.0190099 1.0817150 +#> [935,] 0.17515967 0.2077467 0.4378483 0.4446930 0.4635054 0.4856521 0.4920686 +#> [936,] 0.63714896 0.6514300 0.8096001 0.8222615 0.8256855 0.8450948 0.8645342 +#> [937,] 0.56154873 0.7014294 0.7433678 0.8551267 1.0064208 1.0101535 1.0104143 +#> [938,] 0.50994700 0.5270976 0.5541190 0.5983123 0.6625547 0.6720328 0.6987629 +#> [939,] 1.13746210 1.1554039 1.1709452 1.2012700 1.3270357 1.3400856 1.4142670 +#> [940,] 0.28743920 0.5563820 0.5856901 0.5996128 0.6547929 0.6700591 0.7373932 +#> [941,] 0.67425040 0.6905455 0.7710334 0.9557612 0.9796315 1.0197606 1.1882176 +#> [942,] 0.37150479 0.4798121 0.4808973 0.5166129 0.5182849 0.5829721 0.5888863 +#> [943,] 0.28688126 0.4258963 0.4281232 0.4670354 0.4883674 0.5060454 0.5184400 +#> [944,] 0.77827903 0.7819818 0.8017048 0.8162861 0.8181842 0.8197966 0.8473799 +#> [945,] 0.82162781 1.0372120 1.1002160 1.1334750 1.2121846 1.2270154 1.2946210 +#> [946,] 0.37025403 0.4167265 0.5974787 0.6093745 0.6213680 0.6260663 0.6454604 +#> [947,] 0.25146438 0.3927622 0.4704423 0.5118140 0.5141644 0.5981667 0.6030569 +#> [948,] 0.78545275 1.4532707 1.5313353 1.5427409 1.6086276 1.7504291 1.7885411 +#> [949,] 0.97271309 0.9821576 1.0197606 1.0300253 1.0468094 1.0555918 1.2018503 +#> [950,] 0.54356099 0.6906225 0.7515560 0.8163552 0.8268404 1.0246197 1.0835439 +#> [951,] 0.54343326 0.5726713 0.6079400 0.6362282 0.6411489 0.6882258 0.7099575 +#> [952,] 0.46844091 0.5961116 0.6552774 0.6681492 0.6705820 0.6705824 0.6773838 +#> [953,] 0.43749453 0.5324512 0.5839332 0.6552774 0.6928143 0.7366193 0.7452899 +#> [954,] 0.43154461 0.4399540 0.4870235 0.5126823 0.5397259 0.5775316 0.5931087 +#> [955,] 1.02021825 1.1881607 1.1982660 1.2262335 1.2503180 1.2585351 1.2634868 +#> [956,] 0.36567019 0.4878582 0.4992977 0.5924440 0.6344151 0.6589955 0.7024502 +#> [957,] 0.54300594 0.6047171 0.6385052 0.8357751 0.8688679 0.8910615 0.9210669 +#> [958,] 0.26227964 0.5029744 0.5263975 0.5506858 0.5875720 0.5980812 0.6151065 +#> [959,] 0.28368227 0.3698291 0.6985545 0.7824819 0.8126316 0.8950014 0.9177645 +#> [960,] 0.49736419 0.5183471 0.6872977 0.7351015 0.7555208 0.7721703 0.8106311 +#> [961,] 0.39870005 0.4068494 0.4138580 0.4387298 0.4493096 0.4648980 0.4751239 +#> [962,] 0.32908268 0.4148534 0.4663373 0.4952917 0.5148448 0.5823703 0.5933718 +#> [963,] 0.20957563 0.5594251 0.6219183 0.6602812 0.7078393 0.7360118 0.7703560 +#> [964,] 0.29793315 0.3097676 0.3946160 0.4495239 0.4973593 0.5219604 0.5401556 +#> [965,] 0.40847446 0.4752569 0.5648596 0.5927076 0.6392017 0.6626822 0.6950428 +#> [966,] 0.24032959 0.3109608 0.3498404 0.3971149 0.4755460 0.5466231 0.5519391 +#> [967,] 0.83996762 0.8429282 0.8430529 0.8506495 0.8805370 0.9030809 0.9297668 +#> [968,] 0.75906932 0.7979691 0.8549952 0.8591549 0.8739171 0.9021926 0.9483216 +#> [969,] 0.72181504 0.7701056 0.8012313 0.8261697 0.8506495 0.9436734 0.9631076 +#> [970,] 0.45928747 0.6874574 0.7040529 0.7155653 0.7596100 0.7642973 0.8288619 +#> [971,] 0.48355347 0.5202777 0.5696647 0.6209378 0.8020739 0.8293285 0.8423353 +#> [972,] 0.52454708 0.5688572 0.5876460 0.5896110 0.6384115 0.6425840 0.6848581 +#> [973,] 0.23167235 0.4270107 0.5509920 0.6065801 0.6111725 0.6277603 0.6409072 +#> [974,] 0.47085961 0.6588610 0.6596290 0.6879263 0.7065132 0.7088534 0.7781482 +#> [975,] 0.24984831 0.4459982 0.7060311 0.8481894 0.8836024 0.9899705 0.9995362 +#> [976,] 0.48003483 0.5257607 0.5606464 0.6075915 0.6931258 0.7270252 0.7278883 +#> [977,] 0.24909537 0.6965583 0.8719151 0.8899695 0.8925672 0.9524394 0.9771197 +#> [978,] 0.47499973 0.5926426 0.6021089 0.6590841 0.6911297 0.7376685 0.7610334 +#> [979,] 0.27414771 0.3533348 0.3829368 0.4096830 0.4135228 0.4415721 0.4663373 +#> [980,] 0.55848306 0.5871392 0.6017204 0.6948917 0.7138283 0.7796337 0.8115816 +#> [981,] 0.31027863 0.3318503 0.4300163 0.4788421 0.4899965 0.5254370 0.5960820 +#> [982,] 0.09831649 0.3426225 0.3733893 0.3833929 0.3998862 0.4147042 0.5065422 +#> [983,] 0.34242776 0.4128428 0.5373668 0.5707755 0.5742626 0.6556826 0.6773988 +#> [984,] 0.69637689 0.9597156 0.9830406 1.0227250 1.0404170 1.0897849 1.1604625 +#> [985,] 0.21587317 0.5035108 0.5587194 0.6152778 0.6528830 0.6680267 0.6734077 +#> [986,] 0.69958193 0.8580225 0.8754899 0.9156674 0.9566091 0.9670229 0.9763510 +#> [987,] 0.11608719 0.3006863 0.3250055 0.3289196 0.4259055 0.4377923 0.4870022 +#> [988,] 0.36347243 0.4651067 0.5653288 0.5780089 0.5809020 0.6451965 0.6682168 +#> [989,] 0.41765340 0.4401888 0.6357935 0.6813370 0.7506527 0.7766402 0.7819681 +#> [990,] 0.79370421 0.8864215 0.8938192 0.9058586 1.0523116 1.1082587 1.1725493 +#> [991,] 0.54612053 0.6078415 0.6344151 0.6866979 0.6894896 0.7161804 0.7335557 +#> [992,] 0.34642398 0.5090600 0.5749598 0.5798745 0.6493739 0.6495503 0.6656699 +#> [993,] 0.58381883 0.6305519 0.6584598 0.7033968 0.7270252 0.7323232 0.7672032 +#> [994,] 0.37058080 0.4416356 0.4471095 0.4517481 0.4961319 0.5121898 0.5384396 +#> [995,] 0.29083287 0.4656943 0.4740713 0.5162538 0.5446352 0.5476847 0.5501051 +#> [996,] 0.71182690 0.8015365 0.8357751 0.9246408 0.9344621 1.0045098 1.0209222 +#> [997,] 0.49119061 0.8981545 1.0922723 1.0947023 1.1007515 1.1540649 1.1801942 +#> [998,] 0.52910495 0.5856901 0.6052306 0.6426209 0.6872977 0.7322672 0.7591426 +#> [999,] 0.40440349 0.4788421 0.5086148 0.5929361 0.6096010 0.6314974 0.6784615 +#> [1000,] 0.95906486 0.9779007 0.9891183 0.9967242 1.0371942 1.0655567 1.1123559 #> [,8] [,9] [,10] -#> [1,] 1.0220903 1.0506990 1.0647866 -#> [2,] 1.0159804 1.0628464 1.0808911 -#> [3,] 0.6555239 0.6691817 0.6939634 -#> [4,] 0.8153295 0.8323242 0.8576244 -#> [5,] 0.7755287 0.8105826 0.8419308 -#> [6,] 0.9605567 0.9751774 0.9853637 -#> [7,] 0.6006658 0.6082166 0.6575454 -#> [8,] 0.9434125 0.9569375 0.9573127 -#> [9,] 0.8578887 0.8690163 0.9022197 -#> [10,] 1.1059807 1.1271065 1.2717197 -#> [11,] 0.9275638 0.9374665 0.9629951 -#> [12,] 0.8683344 0.8833672 0.8889993 -#> [13,] 0.9806743 0.9860205 1.0262920 -#> [14,] 0.8045830 0.8120747 0.8122041 -#> [15,] 1.1613124 1.1683864 1.1694879 -#> [16,] 0.7096127 0.7521758 0.7795502 -#> [17,] 0.5288156 0.5698295 0.5967761 -#> [18,] 0.6856766 0.7122816 0.7510326 -#> [19,] 1.1743609 1.2663257 1.2730675 -#> [20,] 0.5036007 0.5465379 0.5710962 -#> [21,] 0.8614368 0.8647220 0.9100972 -#> [22,] 0.4877218 0.5036007 0.5085941 -#> [23,] 0.8889993 0.9078457 0.9205416 -#> [24,] 0.6778038 0.6823220 0.6916917 -#> [25,] 0.7951561 0.9031122 0.9116083 -#> [26,] 0.7183941 0.7444101 0.7563863 -#> [27,] 0.5881498 0.6069905 0.6132131 -#> [28,] 0.6360646 0.6735662 0.7085963 -#> [29,] 0.6846481 0.6876163 0.7291948 -#> [30,] 1.0284256 1.0312844 1.0480669 -#> [31,] 0.7609546 0.7702795 0.8113416 -#> [32,] 0.5915577 0.6097883 0.6270160 -#> [33,] 0.7015258 0.7143286 0.7274967 -#> [34,] 0.8280008 0.8410305 0.8838150 -#> [35,] 0.6646158 0.6752053 0.6759780 -#> [36,] 0.5115982 0.5658013 0.6014117 -#> [37,] 0.9767749 0.9812302 0.9813567 -#> [38,] 0.6544361 0.6599681 0.6723698 -#> [39,] 0.5575974 0.5998258 0.6160434 -#> [40,] 1.0114281 1.0269782 1.0689315 -#> [41,] 1.0221192 1.0405471 1.0802871 -#> [42,] 0.5539836 0.5670290 0.5742499 -#> [43,] 1.1136135 1.1143924 1.1565844 -#> [44,] 1.2811282 1.3083940 1.3553939 -#> [45,] 1.7795953 1.7843617 1.8282038 -#> [46,] 1.3636859 1.3862933 1.4477444 -#> [47,] 1.1656563 1.1768936 1.2009966 -#> [48,] 0.6516211 0.6789018 0.6822214 -#> [49,] 0.6571013 0.6922736 0.7112233 -#> [50,] 0.9127270 0.9160051 0.9327613 -#> [51,] 0.7005399 0.7818339 0.7852307 -#> [52,] 0.6050779 0.6813854 0.7484952 -#> [53,] 0.9623091 0.9637074 0.9640488 -#> [54,] 0.9004985 0.9043681 0.9113352 -#> [55,] 0.5764813 0.5861439 0.6036196 -#> [56,] 0.9721654 0.9896678 1.0213423 -#> [57,] 1.2183686 1.2607060 1.2665570 -#> [58,] 0.6979966 0.7376806 0.7692197 -#> [59,] 0.9177779 0.9393185 0.9394270 -#> [60,] 0.7310089 0.7778650 0.8020409 -#> [61,] 0.6713180 0.7570872 0.7576333 -#> [62,] 0.7238832 0.7438888 0.7741449 -#> [63,] 0.5693800 0.5731336 0.6171722 -#> [64,] 0.8134684 0.8155012 0.8486851 -#> [65,] 0.6314431 0.6620528 0.6695307 -#> [66,] 0.7487702 0.7510326 0.7665815 -#> [67,] 0.7037280 0.7139507 0.7341633 -#> [68,] 0.6670795 0.7039944 0.7103590 -#> [69,] 0.5590320 0.5826087 0.6090574 -#> [70,] 1.0705625 1.0839160 1.1077849 -#> [71,] 0.7925975 0.8267848 0.8600750 -#> [72,] 0.4665478 0.4980567 0.5050926 -#> [73,] 0.6832122 0.7127399 0.7301544 -#> [74,] 0.6706489 0.7036290 0.7122281 -#> [75,] 0.9944253 1.0938387 1.1284889 -#> [76,] 0.7341633 0.7386602 0.7543114 -#> [77,] 0.7444101 0.7878845 0.8116886 -#> [78,] 0.7183941 0.7242253 0.7437343 -#> [79,] 0.6669422 0.6838606 0.7015809 -#> [80,] 1.7852066 1.9035128 1.9593996 -#> [81,] 1.2074716 1.2937300 1.3053031 -#> [82,] 0.8501118 0.8510822 0.8887671 -#> [83,] 0.6912846 0.7224415 0.7439148 -#> [84,] 1.2312541 1.2640422 1.2865493 -#> [85,] 1.0518943 1.0554476 1.0708872 -#> [86,] 0.6896308 0.7412201 0.7649269 -#> [87,] 0.7517473 0.7538065 0.7627878 -#> [88,] 0.6607499 0.6651778 0.6755015 -#> [89,] 0.8049486 0.8602626 0.9045392 -#> [90,] 0.7128878 0.7728106 0.8510530 -#> [91,] 0.7852307 0.7854566 0.8330092 -#> [92,] 1.7256766 1.7263170 1.7344674 -#> [93,] 0.6820320 0.6995980 0.7385645 -#> [94,] 0.9136147 1.0265890 1.0361744 -#> [95,] 0.6844769 0.6987678 0.7313320 -#> [96,] 1.1858038 1.2298611 1.2433319 -#> [97,] 0.9149957 1.1553283 1.1715203 -#> [98,] 0.6350549 0.6468977 0.6569167 -#> [99,] 0.6095392 0.6515879 0.7125169 -#> [100,] 0.7548809 0.7640128 0.7852657 -#> [101,] 0.8339963 0.8746430 0.8802706 -#> [102,] 0.8803966 0.9150893 0.9655447 -#> [103,] 1.3828986 1.3844780 1.4077336 -#> [104,] 0.6973268 0.7098145 0.7467064 -#> [105,] 0.5697776 0.6048633 0.6226070 -#> [106,] 1.0555791 1.0815560 1.0834790 -#> [107,] 0.5333565 0.5858786 0.5920383 -#> [108,] 0.7323111 0.7431155 0.7739945 -#> [109,] 0.6553638 0.7204231 0.7339759 -#> [110,] 1.4211427 1.5612817 1.5691502 -#> [111,] 0.8804201 0.8804967 0.9133936 -#> [112,] 1.8300943 1.8462969 1.8554780 -#> [113,] 0.6681737 0.7201891 0.7234314 -#> [114,] 0.9264201 0.9446354 0.9898591 -#> [115,] 1.0305274 1.0873706 1.1108897 -#> [116,] 0.7776361 0.8285550 0.8445627 -#> [117,] 0.8105688 0.8334105 0.8344281 -#> [118,] 0.5251669 0.5258499 0.5761898 -#> [119,] 0.6680991 0.6735608 0.6832122 -#> [120,] 0.5456871 0.5893277 0.5902274 -#> [121,] 0.7243613 0.7391918 0.7430023 -#> [122,] 0.6011233 0.6619225 0.7385597 -#> [123,] 0.6637908 0.6920283 0.7221301 -#> [124,] 0.6622352 0.6793424 0.6797925 -#> [125,] 1.4978961 1.5240171 1.5591992 -#> [126,] 0.8145714 0.8294044 0.8933134 -#> [127,] 0.6213495 0.6382426 0.6421234 -#> [128,] 0.6497090 0.6654464 0.6723698 -#> [129,] 0.7529188 0.7531056 0.8286505 -#> [130,] 0.8415089 0.8776250 0.8833272 -#> [131,] 0.9066364 0.9705027 0.9867259 -#> [132,] 0.6082166 0.6088717 0.6183626 -#> [133,] 0.5901790 0.5906260 0.6049659 -#> [134,] 0.7160267 0.7525095 0.7664416 -#> [135,] 0.5871855 0.5888006 0.5958864 -#> [136,] 0.5596206 0.5888128 0.5969488 -#> [137,] 0.8632859 0.8702170 0.9241328 -#> [138,] 0.6115767 0.6173324 0.6180640 -#> [139,] 0.8219311 0.8441528 0.8611271 -#> [140,] 0.7012192 0.7072508 0.7099819 -#> [141,] 0.5743078 0.6728713 0.6747774 -#> [142,] 0.5263058 0.5891322 0.6317845 -#> [143,] 0.6099109 0.6164456 0.6347685 -#> [144,] 0.8930163 0.9479502 0.9620478 -#> [145,] 1.0256061 1.0524403 1.0587543 -#> [146,] 0.5566784 0.5713538 0.5788591 -#> [147,] 0.5543984 0.5705758 0.5721490 -#> [148,] 0.9795626 1.0067947 1.0458673 -#> [149,] 0.8637182 0.9008321 0.9043360 -#> [150,] 0.5414789 0.5788055 0.5862132 -#> [151,] 0.5853790 0.5875525 0.5915577 -#> [152,] 0.7714320 0.7798431 0.7872667 -#> [153,] 0.9014666 0.9705633 0.9777831 -#> [154,] 1.0157654 1.0779330 1.0913184 -#> [155,] 0.9316542 0.9658182 0.9670866 -#> [156,] 0.6073392 0.6131069 0.6592461 -#> [157,] 1.1114114 1.1454856 1.1555352 -#> [158,] 0.6052577 0.6307465 0.7290489 -#> [159,] 0.6285511 0.6381594 0.6443042 -#> [160,] 0.7880060 0.8260032 0.8534824 -#> [161,] 0.6633763 0.6657410 0.6797596 -#> [162,] 0.8476128 0.8624028 0.8771263 -#> [163,] 0.7845580 0.8266930 0.8318053 -#> [164,] 0.7392637 0.7512895 0.8111425 -#> [165,] 1.2922349 1.3063313 1.3084353 -#> [166,] 0.9128304 0.9454870 1.0354152 -#> [167,] 0.6104109 0.6456856 0.6493743 -#> [168,] 0.6152847 0.6210218 0.6397210 -#> [169,] 0.9241328 0.9419218 1.1478319 -#> [170,] 0.6543718 0.6672829 0.7638711 -#> [171,] 0.6459038 0.6709559 0.6813143 -#> [172,] 0.6297882 0.6436494 0.6455486 -#> [173,] 1.1226844 1.1330129 1.2040133 -#> [174,] 1.0910784 1.1286312 1.1740047 -#> [175,] 1.1304412 1.1901069 1.1943255 -#> [176,] 0.7143797 0.7184307 0.7481196 -#> [177,] 0.8513361 0.8603842 0.8674875 -#> [178,] 0.6705592 0.6735620 0.6778038 -#> [179,] 1.0194385 1.0333731 1.0398635 -#> [180,] 0.6132131 0.6198758 0.6648093 -#> [181,] 0.6900043 0.7318831 0.7641128 -#> [182,] 0.6079576 0.6144660 0.6170095 -#> [183,] 0.5681010 0.5703633 0.6560972 -#> [184,] 2.0221354 2.0532234 2.0724232 -#> [185,] 1.0225519 1.0345328 1.0356459 -#> [186,] 0.5291272 0.5727759 0.5803860 -#> [187,] 0.8187429 0.8696613 0.8934775 -#> [188,] 0.8677932 0.8900682 0.9412017 -#> [189,] 0.7229251 0.7349696 0.7951147 -#> [190,] 0.5737622 0.5920383 0.6537043 -#> [191,] 1.3769688 1.3968075 1.4065190 -#> [192,] 0.6929701 0.6971881 0.6983515 -#> [193,] 0.5998258 0.6034515 0.6136679 -#> [194,] 0.5429316 0.5570000 0.5602252 -#> [195,] 1.0188878 1.0793454 1.0858714 -#> [196,] 0.6269043 0.6810870 0.6868403 -#> [197,] 1.0075614 1.0431346 1.0821745 -#> [198,] 1.3594604 1.4146364 1.4263432 -#> [199,] 0.7430240 0.7511748 0.8771458 -#> [200,] 0.7863389 0.8066169 0.8143462 -#> [201,] 1.8213842 1.8912685 1.9962653 -#> [202,] 1.4548162 1.4980519 1.4988507 -#> [203,] 1.0292585 1.0564403 1.0715925 -#> [204,] 0.5571402 0.6248315 0.6380658 -#> [205,] 0.6196099 0.6290472 0.6292636 -#> [206,] 0.6378416 0.6567339 0.6771992 -#> [207,] 1.6953200 1.7081563 1.7206302 -#> [208,] 0.7497781 0.7546189 0.7557900 -#> [209,] 1.5233936 1.5288099 1.5962986 -#> [210,] 0.6239921 0.6247975 0.6265817 -#> [211,] 0.7915838 0.8227246 0.8271605 -#> [212,] 0.6133590 0.6334791 0.6504994 -#> [213,] 0.5879778 0.5889366 0.6110425 -#> [214,] 0.7508885 0.8324732 0.8941450 -#> [215,] 0.7468176 0.7739945 0.8493845 -#> [216,] 0.6418896 0.6420441 0.6478635 -#> [217,] 0.6582989 0.6735662 0.7013804 -#> [218,] 0.6180513 0.6354609 0.6562407 -#> [219,] 0.7390813 0.7445928 0.7881615 -#> [220,] 0.8158097 0.8690679 0.8797843 -#> [221,] 0.7647663 0.7783410 0.7837751 -#> [222,] 1.3820083 1.3966081 1.4695749 -#> [223,] 0.5455742 0.5658013 0.6034515 -#> [224,] 1.1583985 1.2959913 1.3045000 -#> [225,] 0.9930158 1.0073708 1.0326790 -#> [226,] 0.7177860 0.7245656 0.7300216 -#> [227,] 0.6152128 0.6768162 0.7500130 -#> [228,] 0.8081437 0.8210160 0.8341311 -#> [229,] 0.8173428 0.8390229 0.8620620 -#> [230,] 0.8007657 0.8012049 0.8112083 -#> [231,] 0.9177369 0.9279086 0.9315429 -#> [232,] 0.7094825 0.7412201 0.7487212 -#> [233,] 0.8801390 0.8919684 0.9048960 -#> [234,] 0.8240324 0.8333244 0.8385498 -#> [235,] 0.7525095 0.7695044 0.7708732 -#> [236,] 0.7133467 0.7192160 0.7198735 -#> [237,] 0.5881498 0.5997251 0.6016000 -#> [238,] 0.8105593 0.9042581 0.9140608 -#> [239,] 1.0796850 1.0905094 1.0981334 -#> [240,] 0.8508098 0.8635917 0.8980599 -#> [241,] 0.6227925 0.6280889 0.6417925 -#> [242,] 0.7067708 0.7184563 0.7377581 -#> [243,] 0.6115334 0.6430333 0.6456477 -#> [244,] 1.4331381 1.4607150 1.5316756 -#> [245,] 0.8501881 0.8563041 0.8590798 -#> [246,] 0.7118094 0.7147159 0.7300108 -#> [247,] 1.1565844 1.1967436 1.2031595 -#> [248,] 0.7812252 0.7929947 0.8109086 -#> [249,] 0.6637959 0.6669140 0.6801039 -#> [250,] 2.0397549 2.0483508 2.0556776 -#> [251,] 0.7355141 0.7445453 0.7960024 -#> [252,] 0.6012655 0.6101469 0.6277827 -#> [253,] 1.1820783 1.1844585 1.1852437 -#> [254,] 0.7059918 0.7131183 0.7172770 -#> [255,] 1.1761711 1.1954909 1.2139322 -#> [256,] 0.8081437 0.8302582 0.8359470 -#> [257,] 0.4847586 0.4983993 0.5328842 -#> [258,] 1.3630821 1.3645951 1.4483778 -#> [259,] 1.6683581 1.6846586 1.6916542 -#> [260,] 0.6327125 0.6592461 0.6797594 -#> [261,] 0.5816681 0.6651170 0.6719349 -#> [262,] 1.0199230 1.0306691 1.0465887 -#> [263,] 0.7328701 0.7366519 0.7488794 -#> [264,] 0.6567246 0.6632021 0.6666177 -#> [265,] 0.8342606 0.8833272 0.8873127 -#> [266,] 1.2187290 1.2362250 1.3522488 -#> [267,] 0.7023748 0.7102512 0.7225791 -#> [268,] 1.0880982 1.0941461 1.0960332 -#> [269,] 0.8627002 0.8743276 0.8751708 -#> [270,] 0.6198851 0.6314126 0.6475378 -#> [271,] 0.5674021 0.5693480 0.5928785 -#> [272,] 0.9341928 0.9833019 0.9973478 -#> [273,] 0.8096329 0.8292255 0.8555027 -#> [274,] 0.7891404 0.8243659 0.8278745 -#> [275,] 0.9317763 0.9409960 0.9466711 -#> [276,] 1.3790276 1.3802968 1.4076604 -#> [277,] 0.6844823 0.7143535 0.7407388 -#> [278,] 0.7100069 0.7488184 0.7600098 -#> [279,] 1.1506401 1.1908013 1.2663376 -#> [280,] 0.7630830 0.7659436 0.7761110 -#> [281,] 0.8431615 0.8637182 0.9226522 -#> [282,] 0.9590741 1.0251275 1.0283219 -#> [283,] 0.7033122 0.7052669 0.7377581 -#> [284,] 0.8044038 0.8099410 0.8531779 -#> [285,] 0.4574371 0.4633042 0.5149583 -#> [286,] 1.2142199 1.2325390 1.2486723 -#> [287,] 0.7117716 0.7845475 0.8023944 -#> [288,] 0.7105949 0.7146610 0.7506698 -#> [289,] 0.9633728 0.9659964 1.0147951 -#> [290,] 0.6066468 0.6082550 0.6091098 -#> [291,] 1.0277905 1.0338277 1.0357108 -#> [292,] 0.5000964 0.5028235 0.5161832 -#> [293,] 0.8557396 0.8838206 0.9363732 -#> [294,] 0.7162112 0.7261345 0.7368908 -#> [295,] 2.1098008 2.1263382 2.1662556 -#> [296,] 0.9174849 0.9391519 0.9513195 -#> [297,] 0.5151876 0.5552198 0.6246004 -#> [298,] 1.2236423 1.2574848 1.2584744 -#> [299,] 0.8009335 0.8341694 0.8629700 -#> [300,] 0.6411617 0.6742086 0.6789019 -#> [301,] 1.0222484 1.0651726 1.0824499 -#> [302,] 1.4780679 1.4931978 1.4998941 -#> [303,] 1.7793757 1.8106507 1.8345273 -#> [304,] 0.8031539 0.8513275 0.8791171 -#> [305,] 1.2292597 1.2357294 1.2826214 -#> [306,] 0.6505470 0.6771992 0.6788451 -#> [307,] 1.1818239 1.1945705 1.2083771 -#> [308,] 0.6593763 0.6723912 0.6867334 -#> [309,] 0.4700809 0.4816955 0.4866329 -#> [310,] 0.7201891 0.7371411 0.7627039 -#> [311,] 0.9326344 1.0005127 1.0038540 -#> [312,] 0.7288453 0.7482867 0.7527413 -#> [313,] 0.6792825 0.7098145 0.7107325 -#> [314,] 0.7188043 0.7305566 0.7341665 -#> [315,] 0.5343740 0.5371986 0.6196099 -#> [316,] 1.0993493 1.1180550 1.1213501 -#> [317,] 0.5166436 0.5418754 0.5499564 -#> [318,] 1.4359835 1.4469369 1.4834774 -#> [319,] 0.8046689 0.8057893 0.8264896 -#> [320,] 0.5838682 0.5972898 0.6249691 -#> [321,] 0.6478044 0.6480226 0.6585143 -#> [322,] 0.7356749 0.7797624 0.8027854 -#> [323,] 1.0977021 1.1011990 1.1069747 -#> [324,] 0.6170095 0.6424517 0.6700281 -#> [325,] 0.7829631 0.7942367 0.7964902 -#> [326,] 0.8682310 0.9106875 1.0473610 -#> [327,] 0.8372628 0.8838178 0.9024265 -#> [328,] 1.4275131 1.5188548 1.5214910 -#> [329,] 1.2825670 1.3081612 1.3743889 -#> [330,] 0.6444889 0.6908379 0.6954510 -#> [331,] 0.6772921 0.7085888 0.7115589 -#> [332,] 0.6055590 0.6319255 0.6535943 -#> [333,] 0.6089178 0.6171698 0.6420984 -#> [334,] 0.7013259 0.7197396 0.7201731 -#> [335,] 0.7554972 0.7570872 0.7621492 -#> [336,] 0.6903874 0.7099542 0.7263274 -#> [337,] 1.4731754 1.4770385 1.4885976 -#> [338,] 1.1171090 1.1343383 1.1866288 -#> [339,] 0.7904959 0.8211896 0.8407411 -#> [340,] 0.6571768 0.7153976 0.7344173 -#> [341,] 0.6206803 0.7202563 0.7536813 -#> [342,] 0.6031906 0.6348537 0.6648093 -#> [343,] 1.0684635 1.1281765 1.2701446 -#> [344,] 0.6379043 0.6522162 0.6533374 -#> [345,] 0.5810245 0.5867504 0.5913260 -#> [346,] 0.5046153 0.5277485 0.5910771 -#> [347,] 1.0505311 1.0576989 1.0992925 -#> [348,] 0.6311623 0.6560215 0.6822626 -#> [349,] 1.0165439 1.1333100 1.1340170 -#> [350,] 0.5813493 0.5950332 0.6079576 -#> [351,] 0.7300095 0.7602339 0.7629245 -#> [352,] 1.1053641 1.1180550 1.1312491 -#> [353,] 1.1837643 1.1988522 1.2302067 -#> [354,] 0.9533387 0.9564164 0.9649450 -#> [355,] 0.9164328 0.9176535 1.0082143 -#> [356,] 0.7133467 0.7360781 0.7797316 -#> [357,] 0.7177315 0.7849657 0.7935783 -#> [358,] 0.7369999 0.7680742 0.7718746 -#> [359,] 0.6829276 0.7062740 0.7828293 -#> [360,] 0.6238698 0.6319799 0.6712264 -#> [361,] 0.8012627 0.8229591 0.8455997 -#> [362,] 0.6160434 0.6178926 0.6240917 -#> [363,] 0.9001376 0.9136147 0.9147746 -#> [364,] 1.5932991 1.6157023 1.6168954 -#> [365,] 0.8077789 0.8595672 0.8851544 -#> [366,] 0.8105764 0.9519390 0.9627244 -#> [367,] 0.8791882 0.8820581 0.9409960 -#> [368,] 1.1542080 1.2095336 1.2174031 -#> [369,] 1.2001517 1.2151301 1.2367852 -#> [370,] 0.7215688 0.7504660 0.7612260 -#> [371,] 0.5766792 0.6133128 0.6378261 -#> [372,] 0.5124364 0.5342045 0.5436498 -#> [373,] 0.7111043 0.7964800 0.8074443 -#> [374,] 0.7735197 0.7903442 0.8573493 -#> [375,] 0.6717405 0.6987919 0.7056988 -#> [376,] 1.1960716 1.2061600 1.2549521 -#> [377,] 0.9120929 0.9420186 1.0101190 -#> [378,] 0.9728332 0.9873418 0.9990933 -#> [379,] 0.7385341 0.7460797 0.7479200 -#> [380,] 0.6228103 0.6252471 0.6378261 -#> [381,] 0.6451651 0.6466886 0.6497090 -#> [382,] 0.6164454 0.6313534 0.6431640 -#> [383,] 1.7215563 1.7413460 1.7505421 -#> [384,] 1.4146987 1.4767661 1.5423798 -#> [385,] 1.2790766 1.3318983 1.3342449 -#> [386,] 0.5253829 0.5374429 0.5478135 -#> [387,] 0.6767031 0.6980473 0.7102383 -#> [388,] 0.6058627 0.6468854 0.6582067 -#> [389,] 0.7614894 0.7666488 0.7872667 -#> [390,] 0.7636327 0.7665815 0.7671899 -#> [391,] 0.7542396 0.7742806 0.8188065 -#> [392,] 0.8449039 0.8477306 0.8554640 -#> [393,] 0.6606911 0.6652344 0.7106932 -#> [394,] 1.1621956 1.1644541 1.1986978 -#> [395,] 1.1677274 1.2597720 1.2879303 -#> [396,] 0.9441428 0.9562852 0.9670866 -#> [397,] 0.6461569 0.6517059 0.6642091 -#> [398,] 0.8534824 0.8593124 0.9181514 -#> [399,] 0.6507469 0.6531001 0.6857016 -#> [400,] 0.7627499 0.8010999 0.8162060 -#> [401,] 0.7167615 0.7590209 0.7663757 -#> [402,] 0.9385822 0.9546533 0.9735454 -#> [403,] 0.9174849 0.9597572 0.9760358 -#> [404,] 0.7314190 0.7399096 0.7420626 -#> [405,] 0.6440154 0.6769841 0.6813038 -#> [406,] 0.9042870 0.9199815 0.9366138 -#> [407,] 0.5143304 0.5230954 0.5826878 -#> [408,] 0.7751121 0.8150130 0.8160969 -#> [409,] 0.6680155 0.6764425 0.6882297 -#> [410,] 1.0683585 1.0714703 1.1421249 -#> [411,] 1.2147471 1.2663894 1.2727458 -#> [412,] 1.1390125 1.1560421 1.1667422 -#> [413,] 0.5777682 0.5841814 0.5867504 -#> [414,] 1.0770330 1.1461043 1.1826790 -#> [415,] 0.7190763 0.7826114 0.8346353 -#> [416,] 1.2734965 1.3180883 1.3209671 -#> [417,] 0.8609763 0.8951846 0.9389049 -#> [418,] 0.6299388 0.6377190 0.6472083 -#> [419,] 1.3202222 1.3234820 1.3480002 -#> [420,] 0.7911226 0.8918625 0.9113162 -#> [421,] 1.5099517 1.5879799 1.6275022 -#> [422,] 0.8648793 0.8801506 0.8962246 -#> [423,] 0.6058663 0.6082788 0.6314126 -#> [424,] 1.3163879 1.3328708 1.3662731 -#> [425,] 0.7955620 0.7975097 0.8114964 -#> [426,] 0.6418109 0.6934614 0.7022022 -#> [427,] 1.8529715 1.9275538 1.9412448 -#> [428,] 0.8037321 0.8180881 0.8260032 -#> [429,] 0.8748160 0.9124285 0.9167675 -#> [430,] 1.4781769 1.5195083 1.5757886 -#> [431,] 0.7765806 0.7796118 0.7852608 -#> [432,] 0.9012644 0.9333909 0.9559555 -#> [433,] 0.6599585 0.6928193 0.6981878 -#> [434,] 0.7897385 0.8571447 0.8957822 -#> [435,] 1.1936272 1.2030167 1.2407876 -#> [436,] 0.7833691 0.8614368 0.8945441 -#> [437,] 0.5384173 0.5829803 0.5841615 -#> [438,] 0.5571568 0.6174222 0.6316854 -#> [439,] 0.6609872 0.6613144 0.7115270 -#> [440,] 0.8451830 0.8557046 0.8650737 -#> [441,] 0.5728784 0.5745183 0.5839120 -#> [442,] 1.0467757 1.0550746 1.0574801 -#> [443,] 0.6892405 0.7332718 0.7333728 -#> [444,] 0.5805887 0.6012403 0.6268490 -#> [445,] 0.7912182 0.8435330 0.8641570 -#> [446,] 0.7852608 0.8762724 0.9075501 -#> [447,] 1.3676645 1.3747721 1.4162996 -#> [448,] 1.2725432 1.2803105 1.2833201 -#> [449,] 1.1295200 1.2154808 1.2187803 -#> [450,] 0.9210998 0.9405099 1.0160491 -#> [451,] 0.6643364 0.6665172 0.6964813 -#> [452,] 0.6314404 0.6404495 0.6684119 -#> [453,] 0.5370750 0.5614417 0.6017285 -#> [454,] 0.9114307 0.9131999 0.9218992 -#> [455,] 0.5167141 0.5323207 0.5668559 -#> [456,] 0.7683081 0.7733977 0.8122535 -#> [457,] 0.5024490 0.5412091 0.5569929 -#> [458,] 1.0857416 1.1236817 1.1830759 -#> [459,] 0.5728755 0.5922088 0.5931377 -#> [460,] 0.5689447 0.5754628 0.5817520 -#> [461,] 0.6513352 0.7065268 0.7117218 -#> [462,] 0.5501011 0.5596206 0.5649255 -#> [463,] 0.6391369 0.6395256 0.6412760 -#> [464,] 0.8306194 0.8751047 0.8794120 -#> [465,] 0.7393981 0.8423412 0.8437964 -#> [466,] 1.8836910 1.9095367 1.9135119 -#> [467,] 1.1950243 1.2333716 1.2435436 -#> [468,] 0.9667808 0.9857054 0.9916732 -#> [469,] 0.9461251 0.9463880 0.9540307 -#> [470,] 0.5763679 0.5815961 0.5851288 -#> [471,] 0.8321809 0.8519922 0.8573915 -#> [472,] 0.6797594 0.6974174 0.7011072 -#> [473,] 0.6045314 0.6086948 0.6161821 -#> [474,] 0.6178535 0.6265817 0.6299388 -#> [475,] 1.4563943 1.4668533 1.4956531 -#> [476,] 0.4759897 0.5278541 0.5323207 -#> [477,] 0.5904797 0.5921010 0.6182972 -#> [478,] 0.7475949 0.7595601 0.7750158 -#> [479,] 0.8676342 0.9958579 1.0082914 -#> [480,] 0.9988466 1.0379648 1.0873266 -#> [481,] 0.6811404 0.7125915 0.7438543 -#> [482,] 0.7233252 0.7258189 0.7496445 -#> [483,] 0.8908971 0.9220001 0.9393187 -#> [484,] 0.8402377 0.8782716 0.8924074 -#> [485,] 0.8752551 0.9683506 0.9777102 -#> [486,] 0.9860885 1.0097891 1.0138331 -#> [487,] 0.6715531 0.6862316 0.6922482 -#> [488,] 1.2949801 1.3199758 1.3314084 -#> [489,] 1.0834790 1.1111410 1.1434177 -#> [490,] 0.7012192 0.7035310 0.7197476 -#> [491,] 0.8863247 0.9151983 0.9861853 -#> [492,] 1.7159364 1.7248158 1.7670427 -#> [493,] 0.8374759 0.9264355 0.9493804 -#> [494,] 1.3369391 1.3572066 1.3602157 -#> [495,] 1.3663135 1.3747328 1.3915746 -#> [496,] 0.9388876 0.9787855 0.9818573 -#> [497,] 0.9633728 0.9716273 1.0105809 -#> [498,] 1.2406684 1.2583409 1.3507659 -#> [499,] 0.8820550 1.0146170 1.0150288 -#> [500,] 0.9055686 0.9680445 0.9941518 -#> [501,] 2.4145756 2.4420108 2.4464436 -#> [502,] 0.5520771 0.5745183 0.5826584 -#> [503,] 0.8337882 0.8505912 0.8526129 -#> [504,] 0.6882318 0.6964999 0.7367348 -#> [505,] 0.9813650 0.9874835 1.0472607 -#> [506,] 0.6814462 0.7060224 0.7076732 -#> [507,] 0.4816955 0.4847935 0.4864367 -#> [508,] 0.5691907 0.5770479 0.5780491 -#> [509,] 1.6335442 1.6385179 1.6535956 -#> [510,] 1.3528960 1.3563794 1.3611355 -#> [511,] 0.6759780 0.7094116 0.7247117 -#> [512,] 1.2926636 1.3023587 1.3749284 -#> [513,] 1.5299030 1.5399956 1.6611706 -#> [514,] 0.6710257 0.6810884 0.6950817 -#> [515,] 0.7584446 0.8062355 0.8130625 -#> [516,] 0.8178128 0.8178424 0.8205612 -#> [517,] 0.8591310 0.8919684 0.9240162 -#> [518,] 0.5025708 0.5418103 0.5693054 -#> [519,] 0.6895236 0.6973202 0.7059582 -#> [520,] 0.5773875 0.5787767 0.6029737 -#> [521,] 0.7146610 0.7643505 0.7878777 -#> [522,] 0.6803957 0.6811404 0.7170140 -#> [523,] 0.6578455 0.6659730 0.6749460 -#> [524,] 0.7388219 0.7477676 0.7486448 -#> [525,] 0.9893545 1.0141225 1.0483922 -#> [526,] 0.6767827 0.7060023 0.7315015 -#> [527,] 1.0208764 1.0257840 1.0330603 -#> [528,] 0.7171179 0.7334094 0.7448059 -#> [529,] 0.8668125 0.9510885 0.9550453 -#> [530,] 1.3518969 1.3648092 1.3849989 -#> [531,] 0.7892308 0.8003594 0.8356967 -#> [532,] 0.8611332 0.8613864 0.9164915 -#> [533,] 0.6250401 0.6367535 0.6517178 -#> [534,] 0.7000426 0.7177860 0.7378278 -#> [535,] 0.7863389 0.8349013 0.8376880 -#> [536,] 0.9794036 1.0058872 1.0216113 -#> [537,] 0.8364121 0.8611271 0.8800067 -#> [538,] 0.5635180 0.6092181 0.6218384 -#> [539,] 0.8904440 0.8921551 0.8932878 -#> [540,] 0.6639791 0.7167615 0.7429688 -#> [541,] 0.6532183 0.6622352 0.6784005 -#> [542,] 1.3483684 1.3507839 1.3894962 -#> [543,] 0.8085228 0.8488790 0.8680777 -#> [544,] 0.5340517 0.5593715 0.6246004 -#> [545,] 0.5689344 0.6011425 0.6378416 -#> [546,] 0.6056311 0.6089128 0.6109068 -#> [547,] 0.7942207 0.8298927 0.8602639 -#> [548,] 0.9145482 0.9155776 0.9157779 -#> [549,] 0.7833891 0.7929947 0.7977212 -#> [550,] 0.8923763 0.8936716 0.8943902 -#> [551,] 0.6252989 0.6302890 0.6325989 -#> [552,] 0.6213495 0.6715206 0.6738913 -#> [553,] 0.5815733 0.6928156 0.7067708 -#> [554,] 0.7766909 0.7895843 0.8063112 -#> [555,] 0.8607480 0.8663636 0.8850935 -#> [556,] 0.6819663 0.7233252 0.7428187 -#> [557,] 0.8135751 0.8167236 0.8241794 -#> [558,] 2.3432253 2.3690277 2.3950798 -#> [559,] 0.7093489 0.7243632 0.7538692 -#> [560,] 0.6790146 0.6813491 0.6987252 -#> [561,] 0.9278684 0.9670014 0.9923017 -#> [562,] 0.6440527 0.6939634 0.6946025 -#> [563,] 0.8178424 0.8214247 0.8249273 -#> [564,] 0.5854172 0.6030661 0.6703721 -#> [565,] 2.2018303 2.2170551 2.2715903 -#> [566,] 0.6740985 0.6961154 0.7107108 -#> [567,] 0.5288156 0.6289047 0.6432439 -#> [568,] 0.6086948 0.6167249 0.6182271 -#> [569,] 0.8899425 0.8907170 0.9248265 -#> [570,] 0.8781574 0.9243470 0.9436126 -#> [571,] 0.6611107 0.6947258 0.7779487 -#> [572,] 0.8544198 0.8607628 0.9113352 -#> [573,] 0.6818945 0.6944958 0.6973268 -#> [574,] 0.6450755 0.7042088 0.7045850 -#> [575,] 0.8500746 0.9282655 1.0189408 -#> [576,] 0.7818339 0.7826114 0.8231869 -#> [577,] 1.2586485 1.2649063 1.4049734 -#> [578,] 0.6757237 0.6791727 0.7117059 -#> [579,] 0.6788451 0.6853032 0.7137189 -#> [580,] 0.6333447 0.6695882 0.6853860 -#> [581,] 0.6198851 0.6315937 0.6712264 -#> [582,] 0.5481736 0.5764813 0.6090256 -#> [583,] 0.6900380 0.6917397 0.7118564 -#> [584,] 0.8031552 0.8159384 0.8325545 -#> [585,] 0.8078439 0.8962927 0.9024955 -#> [586,] 0.7863477 0.8318041 0.8382579 -#> [587,] 0.6263667 0.6662338 0.6693745 -#> [588,] 1.0664086 1.0867883 1.1595318 -#> [589,] 0.7238832 0.7291073 0.7634351 -#> [590,] 1.8472453 1.9284869 1.9455458 -#> [591,] 0.6030661 0.6150027 0.6178535 -#> [592,] 0.7067891 0.7202563 0.7385543 -#> [593,] 0.5766874 0.6071901 0.6722267 -#> [594,] 0.5037597 0.5140889 0.5278857 -#> [595,] 0.6973421 0.7460797 0.7484519 -#> [596,] 0.8587832 0.8776250 0.8925923 -#> [597,] 0.8990254 0.8998505 0.9043764 -#> [598,] 0.6098153 0.6283844 0.6289047 -#> [599,] 1.1384414 1.1628123 1.1670567 -#> [600,] 0.7396622 0.7411347 0.7429837 -#> [601,] 0.6981834 0.7385372 0.7571183 -#> [602,] 0.5697776 0.5925780 0.6079173 -#> [603,] 1.0356459 1.0574077 1.0919283 -#> [604,] 1.1836077 1.2198348 1.2271238 -#> [605,] 0.6046276 0.6299998 0.6461569 -#> [606,] 0.5649939 0.5740247 0.5826055 -#> [607,] 1.1519656 1.1540375 1.1572590 -#> [608,] 0.8675523 0.8853132 0.9111472 -#> [609,] 1.6049527 1.6168954 1.6674373 -#> [610,] 0.9049409 0.9094752 1.0089479 -#> [611,] 0.5936046 0.6057138 0.6595870 -#> [612,] 0.9681270 0.9843482 1.0041864 -#> [613,] 1.2830695 1.3338333 1.3386482 -#> [614,] 0.7772791 0.8072681 0.8344979 -#> [615,] 0.7472657 0.7642566 0.7715074 -#> [616,] 0.8531185 0.8587832 0.8961581 -#> [617,] 0.7300108 0.7336099 0.7580219 -#> [618,] 0.6519833 0.7036290 0.7411941 -#> [619,] 0.9758230 1.0551752 1.0664273 -#> [620,] 0.8801867 0.8923898 0.9039039 -#> [621,] 0.5862132 0.6693059 0.6782872 -#> [622,] 0.6219328 0.6248315 0.6261556 -#> [623,] 0.7478602 0.7580601 0.7706583 -#> [624,] 0.5698945 0.6299090 0.6403390 -#> [625,] 1.0167930 1.0453051 1.1333771 -#> [626,] 0.7392944 0.7548943 0.7563227 -#> [627,] 0.6061947 0.6288226 0.6932874 -#> [628,] 0.5016559 0.5094419 0.5727759 -#> [629,] 1.0443008 1.0609972 1.0629319 -#> [630,] 0.7998817 0.8195488 0.8678657 -#> [631,] 0.5547715 0.6030755 0.6154857 -#> [632,] 0.8144859 0.8281860 0.8697099 -#> [633,] 1.0451144 1.0542669 1.1114114 -#> [634,] 0.6419922 0.6432989 0.6447714 -#> [635,] 0.6742909 0.7286320 0.7541595 -#> [636,] 0.9607071 0.9640626 0.9819342 -#> [637,] 0.9198462 0.9676332 0.9754811 -#> [638,] 1.1200604 1.2439004 1.2668609 -#> [639,] 1.1293918 1.1544637 1.1925736 -#> [640,] 0.8213356 0.8387448 0.8433437 -#> [641,] 0.9677572 0.9888431 1.0394407 -#> [642,] 1.0378200 1.0572936 1.0864902 -#> [643,] 0.5496676 0.5826584 0.5893277 -#> [644,] 0.8202058 0.8503474 0.8628839 -#> [645,] 0.6876549 0.6992767 0.7008368 -#> [646,] 1.0006761 1.0891220 1.1000991 -#> [647,] 0.7692970 0.8176418 0.8722259 -#> [648,] 0.9001376 0.9306692 0.9344276 -#> [649,] 0.4759897 0.5073673 0.5773453 -#> [650,] 1.9382943 1.9416419 2.0260886 -#> [651,] 1.1290313 1.2173869 1.2181288 -#> [652,] 0.6658469 0.6770162 0.6778106 -#> [653,] 0.7356093 0.7722727 0.7758287 -#> [654,] 1.0047432 1.0131991 1.0317723 -#> [655,] 0.5662782 0.5710962 0.5811230 -#> [656,] 0.6184569 0.6220324 0.6290472 -#> [657,] 0.7677005 0.7693379 0.7703628 -#> [658,] 0.5899925 0.6245978 0.6298449 -#> [659,] 0.7469655 0.7567931 0.7701006 -#> [660,] 0.5644498 0.5773875 0.6264687 -#> [661,] 0.5910982 0.6014117 0.6069905 -#> [662,] 0.8325475 0.8360327 0.8718518 -#> [663,] 0.5199536 0.5201948 0.5253829 -#> [664,] 2.0411730 2.0795131 2.1723752 -#> [665,] 0.6398905 0.7198262 0.7426566 -#> [666,] 1.6206125 1.6538794 1.6952439 -#> [667,] 0.7155574 0.7265504 0.7561248 -#> [668,] 0.6236368 0.6674390 0.7730710 -#> [669,] 0.6593693 0.6599681 0.7081862 -#> [670,] 0.9392153 0.9818740 0.9936594 -#> [671,] 0.8417960 0.8581661 0.9167841 -#> [672,] 0.8022114 0.8045830 0.8129684 -#> [673,] 1.0238475 1.0297726 1.0514701 -#> [674,] 0.8379912 0.8492909 0.8665540 -#> [675,] 0.6764425 0.7128808 0.7414976 -#> [676,] 0.9237796 0.9572670 0.9769117 -#> [677,] 0.6259317 0.6287526 0.6306655 -#> [678,] 0.7745990 0.7968260 0.8289013 -#> [679,] 1.1319135 1.1391750 1.1587812 -#> [680,] 0.7995431 0.8797833 0.9001406 -#> [681,] 0.5658163 0.5663868 0.6085266 -#> [682,] 1.1814759 1.2122146 1.2527892 -#> [683,] 0.9574515 0.9917246 1.0476483 -#> [684,] 0.8202589 0.8405467 0.8482936 -#> [685,] 1.0408116 1.0770330 1.0790551 -#> [686,] 1.8179036 1.8340617 1.9116460 -#> [687,] 0.8240114 0.8346796 0.8540282 -#> [688,] 0.7111981 0.7168102 0.7506698 -#> [689,] 0.7062740 0.7083143 0.7332891 -#> [690,] 0.7589517 0.7814489 0.8743718 -#> [691,] 0.7887940 0.8068922 0.8298635 -#> [692,] 0.7042882 0.7073357 0.7086554 -#> [693,] 0.8677932 0.9081009 0.9096423 -#> [694,] 0.9426249 0.9456269 0.9996745 -#> [695,] 0.7261435 0.7369679 0.7431326 -#> [696,] 0.5259880 0.5325244 0.5335413 -#> [697,] 0.9357843 1.0164852 1.1235415 -#> [698,] 0.6602462 0.6971881 0.7130196 -#> [699,] 0.7407388 0.7441478 0.7587615 -#> [700,] 0.5805887 0.6411091 0.6493704 -#> [701,] 0.8441528 0.8526117 0.8743718 -#> [702,] 0.7091045 0.7115284 0.7332891 -#> [703,] 1.4329723 1.4998115 1.5321308 -#> [704,] 0.8716388 0.9324892 0.9406988 -#> [705,] 0.9435541 1.0315119 1.0640330 -#> [706,] 0.6753137 0.7172770 0.7210186 -#> [707,] 0.6354370 0.6485246 0.6687828 -#> [708,] 0.7013456 0.7131056 0.7347693 -#> [709,] 0.7484519 0.7737699 0.7741449 -#> [710,] 0.8423412 0.8555198 0.8749579 -#> [711,] 1.1663112 1.2098689 1.2143511 -#> [712,] 0.9481154 0.9482776 0.9492661 -#> [713,] 0.5139554 0.5386177 0.5543984 -#> [714,] 0.6937074 0.7245582 0.7468176 -#> [715,] 1.4709076 1.4824762 1.4912133 -#> [716,] 0.8951693 0.8961581 0.9878208 -#> [717,] 0.5740756 0.5972898 0.6142427 -#> [718,] 0.9250145 0.9261443 0.9285668 -#> [719,] 0.5785084 0.6069989 0.6116374 -#> [720,] 0.9843897 1.0550667 1.0561660 -#> [721,] 0.5411180 0.5608529 0.5653868 -#> [722,] 0.5295162 0.5340517 0.5555209 -#> [723,] 0.9688074 1.0018221 1.0154645 -#> [724,] 0.6862316 0.7080334 0.7092927 -#> [725,] 0.6652081 0.6720352 0.6917995 -#> [726,] 0.6144660 0.6167648 0.6466886 -#> [727,] 0.8975746 0.9026949 0.9269203 -#> [728,] 0.7297279 0.7441841 0.7579067 -#> [729,] 0.9753392 0.9793578 0.9902628 -#> [730,] 0.6466303 0.6596934 0.6713759 -#> [731,] 0.7510750 0.8031048 0.8497506 -#> [732,] 0.7844187 0.7910788 0.8033215 -#> [733,] 1.1436977 1.2593077 1.2944929 -#> [734,] 1.0233959 1.0988194 1.1192609 -#> [735,] 0.6888786 0.7709872 0.8026905 -#> [736,] 1.1020634 1.1103051 1.1116794 -#> [737,] 0.4875835 0.5037597 0.5218255 -#> [738,] 1.2087302 1.2367852 1.2630820 -#> [739,] 2.1226628 2.1489737 2.1847682 -#> [740,] 0.9699873 0.9936126 0.9981753 -#> [741,] 0.5541666 0.5674918 0.5693480 -#> [742,] 0.5708686 0.5748597 0.5788055 -#> [743,] 0.8565868 0.8744701 0.8796845 -#> [744,] 0.9290540 0.9548992 0.9762425 -#> [745,] 0.4644079 0.5262045 0.5492644 -#> [746,] 1.0158253 1.0370421 1.0656568 -#> [747,] 0.7036279 0.7143330 0.7151035 -#> [748,] 1.2295782 1.2701114 1.3213964 -#> [749,] 0.5693054 0.6197711 0.6784878 -#> [750,] 0.7510750 0.7788458 0.7880611 -#> [751,] 0.7907865 0.8110518 0.8268999 -#> [752,] 1.6528810 1.6852090 1.7012686 -#> [753,] 0.8275630 0.8613339 0.8736702 -#> [754,] 0.6022206 0.6873235 0.6881103 -#> [755,] 0.7941230 0.7957952 0.7978296 -#> [756,] 0.7081964 0.7167148 0.7362297 -#> [757,] 1.2384498 1.2410470 1.3512977 -#> [758,] 0.6404495 0.7010819 0.7160734 -#> [759,] 0.5552198 0.6299090 0.6299998 -#> [760,] 0.8529619 0.8699727 0.8723819 -#> [761,] 1.1095331 1.1378732 1.1933431 -#> [762,] 0.6133128 0.6134790 0.6152627 -#> [763,] 0.5848662 0.5881791 0.5938147 -#> [764,] 1.2015164 1.2033767 1.2357261 -#> [765,] 1.3644598 1.3664599 1.3791541 -#> [766,] 0.9682310 0.9753498 0.9834251 -#> [767,] 0.7501720 0.7553391 0.7784621 -#> [768,] 0.6176954 0.6553638 0.7037984 -#> [769,] 0.9799616 0.9971581 1.0188878 -#> [770,] 0.6067608 0.6166483 0.6215483 -#> [771,] 0.6988536 0.7143979 0.7691937 -#> [772,] 0.6707290 0.7406443 0.7925577 -#> [773,] 0.6920620 0.7192525 0.7378939 -#> [774,] 0.8061181 0.8335395 0.8519922 -#> [775,] 1.2012728 1.2145928 1.2264780 -#> [776,] 0.6036117 0.6045314 0.6074887 -#> [777,] 0.7134724 0.7330610 0.7336099 -#> [778,] 0.7357161 0.7659436 0.7765781 -#> [779,] 1.9690753 1.9715492 1.9898267 -#> [780,] 1.0283219 1.0732000 1.1302308 -#> [781,] 1.2927882 1.2994474 1.3193296 -#> [782,] 0.8892492 0.9349566 0.9609719 -#> [783,] 0.7018778 0.7117218 0.7143797 -#> [784,] 0.9308265 0.9329245 0.9864876 -#> [785,] 0.6700494 0.6884271 0.7247373 -#> [786,] 0.6910984 0.7043720 0.7224982 -#> [787,] 0.8368802 0.8438580 0.8626579 -#> [788,] 0.5811230 0.6050018 0.6342684 -#> [789,] 0.5341757 0.5555209 0.5730101 -#> [790,] 0.8837296 0.8878474 0.9150893 -#> [791,] 0.7885298 0.7951147 0.7955146 -#> [792,] 0.5522140 0.5649189 0.5940605 -#> [793,] 1.5337373 1.5347884 1.5472385 -#> [794,] 0.8744181 0.8762210 0.8950791 -#> [795,] 0.5354529 0.6159843 0.6497799 -#> [796,] 0.5435039 0.5631950 0.5761898 -#> [797,] 0.5904797 0.6074887 0.6143992 -#> [798,] 0.6754314 0.7094825 0.7628576 -#> [799,] 0.5793089 0.5914720 0.6272817 -#> [800,] 0.5851032 0.6079851 0.6545558 -#> [801,] 0.5455918 0.5498394 0.5578871 -#> [802,] 0.5969488 0.6414602 0.6594972 -#> [803,] 0.7023748 0.7029518 0.7169213 -#> [804,] 0.8185188 0.8205612 0.8326591 -#> [805,] 1.1750752 1.1903984 1.1933431 -#> [806,] 1.0302284 1.0537445 1.0748683 -#> [807,] 1.6709016 1.7005440 1.7394570 -#> [808,] 0.8407370 0.8500746 0.8637478 -#> [809,] 0.7008518 0.7150811 0.7800171 -#> [810,] 0.5433768 0.5853286 0.5858126 -#> [811,] 0.8785766 0.8927469 0.9212768 -#> [812,] 0.7942367 0.8017567 0.8151381 -#> [813,] 0.7228474 0.7321719 0.7385597 -#> [814,] 0.9128304 0.9264948 0.9299598 -#> [815,] 1.5803471 1.5953455 1.6277466 -#> [816,] 0.6993452 0.7242203 0.7344657 -#> [817,] 1.6124134 1.6636003 1.6760532 -#> [818,] 0.8376880 0.8426910 0.8453352 -#> [819,] 0.6316854 0.6320838 0.6329795 -#> [820,] 1.1178551 1.1519532 1.1632556 -#> [821,] 0.5833116 0.6029864 0.6135050 -#> [822,] 0.9326344 0.9529245 0.9951438 -#> [823,] 0.6772256 0.6932874 0.7187178 -#> [824,] 0.5820719 0.5912199 0.5958864 -#> [825,] 0.6578455 0.6685950 0.6729128 -#> [826,] 0.5650308 0.5788591 0.5883047 -#> [827,] 0.8792121 0.8806975 0.8816077 -#> [828,] 1.0188981 1.0945970 1.1559622 -#> [829,] 0.6354521 0.6415935 0.6464179 -#> [830,] 0.9306057 0.9731270 0.9804268 -#> [831,] 0.8952237 0.9297569 0.9763403 -#> [832,] 1.0843053 1.0897857 1.1173875 -#> [833,] 0.5930187 0.5939369 0.6069079 -#> [834,] 0.7465824 0.7697630 0.7820416 -#> [835,] 0.4847935 0.4983993 0.5371986 -#> [836,] 0.5418103 0.5454948 0.5471028 -#> [837,] 0.8334105 0.8618845 0.9283201 -#> [838,] 0.9496958 1.0138397 1.0735338 -#> [839,] 0.9777460 1.0175556 1.0292585 -#> [840,] 0.7527413 0.7735569 0.7822611 -#> [841,] 0.9744743 1.0102749 1.0125469 -#> [842,] 0.5128846 0.5187470 0.5232374 -#> [843,] 1.0283224 1.0680810 1.0750131 -#> [844,] 0.9416318 0.9482930 0.9629695 -#> [845,] 1.0277905 1.0347361 1.1459346 -#> [846,] 0.7532099 0.8083029 0.8129840 -#> [847,] 0.6244790 0.6370497 0.6643578 -#> [848,] 0.7263274 0.7298699 0.7523639 -#> [849,] 0.6670763 0.6757237 0.7488167 -#> [850,] 0.7400483 0.7407983 0.7678197 -#> [851,] 0.8328801 0.8593535 0.8706834 -#> [852,] 0.8042118 0.8181203 0.8300385 -#> [853,] 1.3269187 1.3651020 1.3995892 -#> [854,] 1.4041430 1.4212342 1.4334688 -#> [855,] 0.8489212 0.8509315 0.9226693 -#> [856,] 0.6371847 0.6373957 0.6489260 -#> [857,] 0.9479864 1.0184980 1.0239902 -#> [858,] 0.7565456 0.7837856 0.8034945 -#> [859,] 0.7948078 0.7953206 0.8008431 -#> [860,] 0.6458494 0.6610452 0.6853779 -#> [861,] 0.9921289 1.0741951 1.0819476 -#> [862,] 0.7420654 0.7459836 0.7656647 -#> [863,] 0.9134277 0.9463880 0.9774547 -#> [864,] 0.8385498 0.8457286 0.8462907 -#> [865,] 0.6143098 0.6192943 0.6269716 -#> [866,] 0.5851182 0.5949997 0.6690769 -#> [867,] 0.6249000 0.6323681 0.6533374 -#> [868,] 0.6744915 0.6873026 0.6948454 -#> [869,] 0.7422157 0.7559815 0.7563227 -#> [870,] 0.6857204 0.7060388 0.7225791 -#> [871,] 0.6754866 0.7015258 0.7165442 -#> [872,] 0.6624938 0.6659730 0.6665198 -#> [873,] 0.7918862 0.8899351 0.9106117 -#> [874,] 0.9897825 0.9967091 0.9998667 -#> [875,] 0.6674390 0.6709559 0.6722478 -#> [876,] 0.5127744 0.5200857 0.5278857 -#> [877,] 0.7248677 0.7362571 0.7574972 -#> [878,] 0.8650737 0.8673128 0.8876872 -#> [879,] 0.8082818 0.8188058 0.8420042 -#> [880,] 0.5238938 0.5245936 0.6314431 -#> [881,] 0.5546065 0.5552367 0.5658163 -#> [882,] 0.6593763 0.6692368 0.6819575 -#> [883,] 1.1362660 1.1670605 1.1814908 -#> [884,] 0.6274278 0.6516153 0.6589275 -#> [885,] 0.9445222 0.9758457 0.9935428 -#> [886,] 1.0196759 1.0782516 1.1500725 -#> [887,] 0.5611785 0.5721490 0.5730101 -#> [888,] 1.4525418 1.5335511 1.5650556 -#> [889,] 0.7290624 0.7626622 0.7701266 -#> [890,] 0.6575866 0.6631294 0.6709890 -#> [891,] 0.6369631 0.6478044 0.6552745 -#> [892,] 0.5949277 0.6268538 0.6411613 -#> [893,] 0.8987008 0.8996465 0.9122463 -#> [894,] 0.6536885 0.7169877 0.7448199 -#> [895,] 0.8754574 0.8961878 0.9137037 -#> [896,] 0.8990254 0.9600782 0.9845505 -#> [897,] 0.8324243 0.8734084 0.8839719 -#> [898,] 1.6232812 1.6329324 1.6843737 -#> [899,] 0.5307726 0.5541666 0.5542382 -#> [900,] 0.9233367 0.9341166 0.9619989 -#> [901,] 1.1674370 1.2091835 1.2888790 -#> [902,] 0.5935215 0.6133391 0.6145858 -#> [903,] 1.1552582 1.1596754 1.2374547 -#> [904,] 0.6360963 0.6439245 0.6468046 -#> [905,] 1.1296944 1.1306698 1.1445650 -#> [906,] 0.4926394 0.5200857 0.5569929 -#> [907,] 0.7094116 0.7272964 0.7351366 -#> [908,] 0.5967956 0.5984226 0.6065631 -#> [909,] 0.7092927 0.7741783 0.8215954 -#> [910,] 0.5743078 0.5881791 0.6133391 -#> [911,] 0.7960024 0.8147715 0.8201958 -#> [912,] 0.6367922 0.6648602 0.7074269 -#> [913,] 1.1330448 1.1393728 1.2083914 -#> [914,] 1.1261638 1.1363303 1.1381319 -#> [915,] 0.7265642 0.7372435 0.7600814 -#> [916,] 0.5991453 0.6288226 0.6909970 -#> [917,] 0.5936259 0.6167773 0.6328689 -#> [918,] 1.0889671 1.1266647 1.1340280 -#> [919,] 0.8134718 0.8773818 0.8794205 -#> [920,] 0.6634449 0.6847756 0.7128808 -#> [921,] 0.6411091 0.6529900 0.6562525 -#> [922,] 0.9428644 0.9479833 0.9766401 -#> [923,] 0.6546695 0.7623191 0.7831897 -#> [924,] 0.6176913 0.6225913 0.6360304 -#> [925,] 0.8952237 0.8954024 0.9522548 -#> [926,] 0.5354529 0.5418754 0.5586692 -#> [927,] 0.5889366 0.6452530 0.6895276 -#> [928,] 1.2360886 1.2645463 1.4156511 -#> [929,] 0.7045850 0.7102461 0.7254271 -#> [930,] 0.5557009 0.6323681 0.6379043 -#> [931,] 0.6628541 0.7176973 0.7332718 -#> [932,] 0.7830243 0.7958926 0.8087172 -#> [933,] 0.7823336 0.7935952 0.8060961 -#> [934,] 1.2170469 1.3016234 1.3293415 -#> [935,] 0.5882024 0.6785070 0.6851937 -#> [936,] 1.3042448 1.3067202 1.3290534 -#> [937,] 0.5923980 0.7701006 0.7797618 -#> [938,] 0.6801128 0.6920584 0.7143286 -#> [939,] 0.8133878 0.8762922 0.8792833 -#> [940,] 1.4887429 1.5084606 1.5273402 -#> [941,] 0.8393568 0.9422893 0.9868203 -#> [942,] 0.8230795 0.8939918 0.9083788 -#> [943,] 0.4729722 0.4760790 0.5015056 -#> [944,] 0.7457229 0.7612536 0.8136419 -#> [945,] 0.6536390 0.6611107 0.6819575 -#> [946,] 1.2655733 1.3786466 1.4154325 -#> [947,] 0.8477603 0.9001406 0.9536993 -#> [948,] 0.9881205 1.0120315 1.0276484 -#> [949,] 0.7025475 0.7284700 0.8108278 -#> [950,] 0.6722478 0.6789018 0.6876039 -#> [951,] 0.6565390 0.6916976 0.7045401 -#> [952,] 0.7141773 0.7169669 0.7371411 -#> [953,] 0.6559141 0.6639398 0.6669422 -#> [954,] 0.6337316 0.6354521 0.6418716 -#> [955,] 0.6696159 0.6698246 0.6916917 -#> [956,] 0.6075006 0.6443834 0.6680742 -#> [957,] 0.6603293 0.6623050 0.6940481 -#> [958,] 0.6436329 0.6482295 0.6713180 -#> [959,] 0.6036117 0.6117387 0.6139310 -#> [960,] 0.8303042 0.8761815 0.9232052 -#> [961,] 0.5678705 0.5766561 0.5903886 -#> [962,] 1.1215965 1.1605183 1.2426790 -#> [963,] 0.7745578 0.7843597 0.8036236 -#> [964,] 0.7111981 0.7140241 0.7803132 -#> [965,] 1.3762091 1.4262877 1.4529862 -#> [966,] 0.9081192 0.9342811 0.9352012 -#> [967,] 0.7595652 0.7708732 0.7737753 -#> [968,] 1.4909861 1.5724724 1.5742275 -#> [969,] 0.8061181 0.8275757 0.8626899 -#> [970,] 0.9417043 0.9428644 0.9652038 -#> [971,] 0.7672127 0.7708118 0.8045342 -#> [972,] 0.8512705 0.8660131 0.8675326 -#> [973,] 0.6890310 0.7102459 0.7356957 -#> [974,] 0.8293702 0.8737719 0.8771729 -#> [975,] 0.9366361 0.9594017 1.0164096 -#> [976,] 0.6135617 0.6143098 0.6145980 -#> [977,] 0.6100644 0.6560972 0.6944847 -#> [978,] 0.8658190 0.9287271 0.9562004 -#> [979,] 0.8026439 0.8061461 0.8523100 -#> [980,] 0.7544792 0.7700905 0.7944546 -#> [981,] 0.7798431 0.7893466 0.7930837 -#> [982,] 0.7172235 0.7433508 0.7481295 -#> [983,] 0.8697099 0.8845862 0.8919917 -#> [984,] 0.6171722 0.6253003 0.6681443 -#> [985,] 1.0713757 1.0957445 1.1245306 -#> [986,] 0.8691765 0.8912487 0.9218647 -#> [987,] 1.0751697 1.0866799 1.0975572 -#> [988,] 1.1608268 1.1729700 1.1819343 -#> [989,] 0.7698354 0.7771256 0.8079784 -#> [990,] 0.6881899 0.7428363 0.7918862 -#> [991,] 0.5465308 0.5848662 0.6901507 -#> [992,] 0.8033928 0.8417451 0.8513532 -#> [993,] 1.4140997 1.4184929 1.4390561 -#> [994,] 0.9012890 0.9167841 0.9214295 -#> [995,] 0.8027854 0.8349415 0.8488035 -#> [996,] 0.7708018 0.7850795 0.8009255 -#> [997,] 1.5251204 1.5339383 1.5403993 -#> [998,] 1.3328246 1.3609737 1.3649186 -#> [999,] 0.8731058 0.8916354 0.9132031 -#> [1000,] 0.8604964 0.8725707 0.9429566 +#> [1,] 1.1277723 1.1816461 1.1990002 +#> [2,] 1.2216679 1.2352019 1.2608456 +#> [3,] 0.6189972 0.6357971 0.6409345 +#> [4,] 0.6563752 0.6593882 0.6812084 +#> [5,] 0.6451487 0.6575858 0.6584598 +#> [6,] 0.6787011 0.7148674 0.7266524 +#> [7,] 0.6318225 0.6663029 0.6779481 +#> [8,] 0.7533940 0.7807431 0.7992978 +#> [9,] 1.0873969 1.0961940 1.1964236 +#> [10,] 0.5822407 0.5883231 0.5917679 +#> [11,] 0.6083513 0.6091392 0.6192702 +#> [12,] 0.7107465 0.7228639 0.7490380 +#> [13,] 0.7498239 0.7559947 0.7591031 +#> [14,] 0.8340085 0.8789927 0.8985272 +#> [15,] 0.8731405 0.8742264 0.9712348 +#> [16,] 1.1797889 1.1845552 1.2039234 +#> [17,] 1.0033135 1.0354425 1.0443826 +#> [18,] 0.9330399 0.9576537 0.9757561 +#> [19,] 0.8899695 0.9015966 0.9232722 +#> [20,] 0.5672174 0.5912759 0.6029007 +#> [21,] 0.6501106 0.6602688 0.6823808 +#> [22,] 0.7445577 0.7545314 0.7877731 +#> [23,] 0.7894505 0.8406640 0.8509438 +#> [24,] 0.9551027 0.9654747 1.0030288 +#> [25,] 1.0730757 1.0733707 1.0883246 +#> [26,] 0.7280532 0.7556115 0.7615608 +#> [27,] 0.7040471 0.7180793 0.7558149 +#> [28,] 1.1124655 1.1726914 1.2164293 +#> [29,] 0.7843067 0.7848351 0.8214750 +#> [30,] 0.5764293 0.6088673 0.6187806 +#> [31,] 1.1153360 1.1448984 1.1843941 +#> [32,] 0.7166056 0.7172308 0.7263161 +#> [33,] 0.7250852 0.7477290 0.7512148 +#> [34,] 1.3742790 1.4484635 1.4505247 +#> [35,] 1.2946210 1.2946745 1.3529488 +#> [36,] 0.9501224 0.9525178 0.9781398 +#> [37,] 0.5908949 0.6201243 0.6238170 +#> [38,] 0.9860467 1.0113623 1.0792367 +#> [39,] 1.3823259 1.4800667 1.4810435 +#> [40,] 1.0405158 1.0413266 1.0484415 +#> [41,] 0.4522604 0.4552867 0.4983990 +#> [42,] 1.0056548 1.0083335 1.0214761 +#> [43,] 0.5572177 0.5785664 0.6033729 +#> [44,] 0.5246025 0.5514685 0.5659403 +#> [45,] 0.5520987 0.5618288 0.5626486 +#> [46,] 0.9168416 0.9505614 0.9540991 +#> [47,] 0.9491927 0.9748667 1.0007042 +#> [48,] 0.7468701 0.7627787 0.7740428 +#> [49,] 0.8202840 0.8203177 0.8369207 +#> [50,] 0.7371032 0.7728698 0.7842856 +#> [51,] 0.8279323 0.8695652 0.8741601 +#> [52,] 0.6908772 0.7031286 0.7333217 +#> [53,] 0.6937080 0.7369896 0.7374129 +#> [54,] 0.5789364 0.5883231 0.5903267 +#> [55,] 0.8279323 0.8611921 0.8671132 +#> [56,] 1.2105582 1.3609712 1.4209254 +#> [57,] 0.6228348 0.6295301 0.6422188 +#> [58,] 0.6590690 0.6728996 0.6740843 +#> [59,] 0.6575858 0.6959634 0.7385297 +#> [60,] 0.9164001 0.9352856 0.9474459 +#> [61,] 0.6986781 0.7031266 0.7040471 +#> [62,] 0.5624673 0.5656930 0.5680078 +#> [63,] 0.6356963 0.6507316 0.6567545 +#> [64,] 0.6932837 0.7119049 0.7338189 +#> [65,] 0.6698017 0.6710056 0.6977133 +#> [66,] 1.0568114 1.0584024 1.0628807 +#> [67,] 0.4925018 0.5051723 0.5192192 +#> [68,] 0.8972152 0.9264264 1.0154784 +#> [69,] 0.7329504 0.7436061 0.7528723 +#> [70,] 1.3642570 1.4449321 1.4667406 +#> [71,] 0.8690166 0.9903226 1.0250856 +#> [72,] 0.5239980 0.5479711 0.5800733 +#> [73,] 0.5115099 0.5237082 0.5635509 +#> [74,] 0.9470885 0.9779822 1.0129587 +#> [75,] 0.5990171 0.6098017 0.6144892 +#> [76,] 0.5953002 0.6128149 0.6135434 +#> [77,] 0.5607080 0.6527787 0.6877265 +#> [78,] 0.5357776 0.5617833 0.5903267 +#> [79,] 0.7854694 0.8062140 0.8121136 +#> [80,] 0.9891183 0.9941969 0.9993140 +#> [81,] 0.5521847 0.5686514 0.5835483 +#> [82,] 1.2409497 1.2821540 1.3338516 +#> [83,] 1.3557836 1.3559887 1.3816546 +#> [84,] 1.1189773 1.1394716 1.2007114 +#> [85,] 0.6464739 0.6472974 0.6520667 +#> [86,] 0.8236488 0.8243628 0.8298920 +#> [87,] 0.7494334 0.7898359 0.8762251 +#> [88,] 0.7067036 0.7517859 0.7558175 +#> [89,] 0.6324472 0.6384841 0.6393787 +#> [90,] 0.5765781 0.6253036 0.6467292 +#> [91,] 0.6217369 0.6738939 0.6883190 +#> [92,] 0.9049822 0.9530349 1.0050902 +#> [93,] 0.5503597 0.5701566 0.5904144 +#> [94,] 1.1755510 1.2042861 1.2316526 +#> [95,] 0.9724045 0.9812174 0.9884258 +#> [96,] 0.9592701 0.9728378 1.0266564 +#> [97,] 0.5480974 0.5812286 0.6118587 +#> [98,] 0.5246272 0.5265085 0.5475677 +#> [99,] 0.8461130 0.8477330 0.8717503 +#> [100,] 0.7628803 0.7824819 0.7993370 +#> [101,] 1.7962282 1.8124344 1.8204522 +#> [102,] 0.4446164 0.4626767 0.4765493 +#> [103,] 1.6171545 1.6189861 1.6381932 +#> [104,] 0.7240607 0.7821695 0.8235358 +#> [105,] 0.5712901 0.5985136 0.6067016 +#> [106,] 0.8766191 0.8782562 0.8872883 +#> [107,] 1.3221504 1.3258106 1.3595579 +#> [108,] 1.2497242 1.2797769 1.2847999 +#> [109,] 0.6307679 0.6548610 0.6825140 +#> [110,] 0.5077163 0.5152366 0.5201796 +#> [111,] 0.5897513 0.6775438 0.7249468 +#> [112,] 0.8716588 0.9054262 0.9855289 +#> [113,] 0.8448758 0.8714976 0.9142557 +#> [114,] 0.7288252 0.8052105 0.8151615 +#> [115,] 0.6718406 0.6722277 0.6948328 +#> [116,] 0.8939690 0.9073273 0.9459113 +#> [117,] 1.1866277 1.2049009 1.2131600 +#> [118,] 0.9652541 0.9756351 0.9942206 +#> [119,] 0.5998649 0.6091943 0.6123165 +#> [120,] 0.6872129 0.7097586 0.7301151 +#> [121,] 0.7390389 0.7604702 0.7630935 +#> [122,] 0.4938862 0.4983489 0.5466885 +#> [123,] 0.9209192 0.9381286 0.9578028 +#> [124,] 1.2576783 1.3457942 1.3543061 +#> [125,] 0.8000203 0.8167809 0.8301323 +#> [126,] 0.8340372 0.8514435 0.8515045 +#> [127,] 0.5103176 0.5475948 0.5882427 +#> [128,] 0.5827694 0.5828899 0.5951991 +#> [129,] 0.6340862 0.6482273 0.6507316 +#> [130,] 0.7738277 0.8869694 0.8988091 +#> [131,] 0.6822141 0.6939493 0.6992247 +#> [132,] 0.6574931 0.7422702 0.7461058 +#> [133,] 0.9973896 1.0184821 1.0237132 +#> [134,] 1.1486590 1.1613028 1.1837634 +#> [135,] 0.6238170 0.6395035 0.6520195 +#> [136,] 1.0973044 1.1709569 1.2332039 +#> [137,] 0.6108532 0.6429611 0.6630001 +#> [138,] 0.5521847 0.5843494 0.6012981 +#> [139,] 0.7308439 0.7434359 0.7581942 +#> [140,] 1.3928817 1.4068608 1.4439310 +#> [141,] 0.6609668 0.6850597 0.7299244 +#> [142,] 0.5915916 0.6050565 0.6077914 +#> [143,] 0.5823034 0.5947904 0.6019534 +#> [144,] 0.5553071 0.5645280 0.6446181 +#> [145,] 0.6940500 0.7282091 0.7310340 +#> [146,] 0.5495635 0.5514685 0.5826591 +#> [147,] 0.7611104 0.8040045 0.8043343 +#> [148,] 0.6495392 0.6776919 0.6854624 +#> [149,] 0.7480650 0.8191044 0.8823637 +#> [150,] 0.5411869 0.5502896 0.5583409 +#> [151,] 0.7180998 0.7336379 0.7341086 +#> [152,] 1.1912636 1.2095629 1.2352239 +#> [153,] 0.5758092 0.6116671 0.6165203 +#> [154,] 0.7524017 0.7819264 0.7953267 +#> [155,] 0.6733962 0.6810368 0.6899465 +#> [156,] 0.6521463 0.6840823 0.7100448 +#> [157,] 1.3620943 1.3688559 1.3708892 +#> [158,] 0.5600066 0.6737385 0.6776919 +#> [159,] 0.5901161 0.5951493 0.6357927 +#> [160,] 0.8658120 0.8981545 0.9256894 +#> [161,] 0.7964822 0.7980622 0.8108124 +#> [162,] 0.5224324 0.5541192 0.5592968 +#> [163,] 1.1540350 1.1687959 1.1734590 +#> [164,] 1.0675792 1.1022426 1.1054776 +#> [165,] 1.1229310 1.1440999 1.1454136 +#> [166,] 0.8308600 0.8457055 0.8557414 +#> [167,] 0.5969554 0.5991155 0.6028723 +#> [168,] 1.3838868 1.3842514 1.3956961 +#> [169,] 0.8712192 0.9154238 0.9272356 +#> [170,] 0.7390389 0.7535997 0.7970432 +#> [171,] 0.6023530 0.6238119 0.6355553 +#> [172,] 0.7766402 0.7777657 0.7899675 +#> [173,] 0.5313566 0.5754902 0.5769897 +#> [174,] 0.5431463 0.5839871 0.6055436 +#> [175,] 0.9994798 1.0213772 1.0685104 +#> [176,] 0.8997026 0.9091246 0.9232706 +#> [177,] 0.7352546 0.7384616 0.7423913 +#> [178,] 0.6937898 0.6945931 0.7143432 +#> [179,] 1.0741032 1.0937999 1.0980578 +#> [180,] 0.5416264 0.5501798 0.5502562 +#> [181,] 0.9843336 1.0336066 1.0590858 +#> [182,] 0.6091943 0.6363898 0.6373057 +#> [183,] 0.7003897 0.7075189 0.7136140 +#> [184,] 0.9884055 1.0074228 1.0174219 +#> [185,] 0.8916240 0.8993724 0.9282640 +#> [186,] 0.6490195 0.6628685 0.6681492 +#> [187,] 1.2156100 1.2544759 1.2724148 +#> [188,] 0.7837702 0.8218098 0.8225141 +#> [189,] 0.7619641 0.8015365 0.8407601 +#> [190,] 1.0560959 1.1273738 1.1434620 +#> [191,] 0.8261643 0.8521884 0.9099727 +#> [192,] 1.1566105 1.1855532 1.1886661 +#> [193,] 0.9440280 0.9661984 0.9921918 +#> [194,] 0.8645134 0.8670063 0.9061379 +#> [195,] 0.5188817 0.5238636 0.5719127 +#> [196,] 0.8252084 0.8782798 0.8829206 +#> [197,] 0.6393644 0.7216211 0.7395698 +#> [198,] 0.9058586 0.9194339 0.9787950 +#> [199,] 0.9836911 1.0023737 1.0260539 +#> [200,] 0.8919486 0.9284428 0.9431832 +#> [201,] 0.5982558 0.6336967 0.6477662 +#> [202,] 0.8238807 0.8295729 0.8532570 +#> [203,] 0.3975459 0.4535849 0.4674355 +#> [204,] 0.8965174 0.8982999 0.8993036 +#> [205,] 0.5746819 0.5860547 0.5897513 +#> [206,] 0.7902742 0.8046735 0.8447648 +#> [207,] 0.6917758 0.7283993 0.7666928 +#> [208,] 0.5911413 0.5921035 0.5962793 +#> [209,] 0.8112749 0.8424908 0.8805676 +#> [210,] 0.5499103 0.5552143 0.5724341 +#> [211,] 0.7664883 0.7766420 0.8158615 +#> [212,] 0.7401987 0.7662884 0.7797529 +#> [213,] 0.6575542 0.6580014 0.6768431 +#> [214,] 1.2166165 1.2253165 1.2261671 +#> [215,] 0.6186600 0.6671991 0.6732922 +#> [216,] 0.7398366 0.7403294 0.7425097 +#> [217,] 0.5989056 0.6023753 0.6490482 +#> [218,] 0.5521809 0.5585820 0.6238076 +#> [219,] 0.5268149 0.5624969 0.5636207 +#> [220,] 1.1284222 1.1794205 1.1963549 +#> [221,] 0.6613447 0.6957399 0.7197768 +#> [222,] 0.7376104 0.7534260 0.7571064 +#> [223,] 1.1254497 1.1310411 1.1359482 +#> [224,] 0.5717071 0.5998148 0.6373057 +#> [225,] 0.7136713 0.7231501 0.7289041 +#> [226,] 0.6541174 0.7063086 0.7362559 +#> [227,] 0.7185699 0.7271226 0.7680247 +#> [228,] 1.2394236 1.2587553 1.3192218 +#> [229,] 0.9253467 0.9658028 0.9916496 +#> [230,] 0.9420330 0.9539283 0.9658795 +#> [231,] 0.8630664 0.8804020 0.8901090 +#> [232,] 0.4850090 0.5162538 0.5226281 +#> [233,] 0.6355172 0.6559131 0.6857133 +#> [234,] 0.8232688 0.8362491 0.8615428 +#> [235,] 0.5794818 0.5823488 0.5878504 +#> [236,] 0.8986594 0.9156674 0.9355886 +#> [237,] 0.6070168 0.6146959 0.6385568 +#> [238,] 0.7800092 0.7919962 0.8261697 +#> [239,] 0.6446008 0.6517779 0.6528180 +#> [240,] 0.5903636 0.6649502 0.6650598 +#> [241,] 1.5181657 1.5191653 1.6806529 +#> [242,] 1.1709452 1.2108411 1.2212352 +#> [243,] 0.9794785 1.0148312 1.0588020 +#> [244,] 0.8228033 0.9406653 0.9412112 +#> [245,] 0.7355959 0.7427091 0.8169518 +#> [246,] 0.6605395 0.7010432 0.7101375 +#> [247,] 0.5724341 0.5965084 0.6013598 +#> [248,] 0.6581380 0.6864811 0.7007856 +#> [249,] 0.7468705 0.7483195 0.7560462 +#> [250,] 0.4474640 0.4590751 0.4597129 +#> [251,] 0.8388607 0.9121455 0.9395272 +#> [252,] 0.6237672 0.6324957 0.6378056 +#> [253,] 1.1115367 1.1695811 1.2465492 +#> [254,] 0.8507018 0.8815974 0.9280725 +#> [255,] 0.5880765 0.6234843 0.6359189 +#> [256,] 0.7837060 0.7840352 0.8021130 +#> [257,] 0.7432229 0.7474043 0.7781482 +#> [258,] 0.8157246 0.8163470 0.8207238 +#> [259,] 1.0071526 1.0514318 1.0816304 +#> [260,] 0.7723188 0.8073125 0.8125567 +#> [261,] 0.7733659 0.7765029 0.7781799 +#> [262,] 0.8045442 0.8443363 0.8512293 +#> [263,] 0.6691929 0.6805346 0.7250852 +#> [264,] 0.5409081 0.5479479 0.5533832 +#> [265,] 0.6240639 0.6376946 0.6383766 +#> [266,] 0.6140820 0.6224795 0.6236054 +#> [267,] 0.7967967 0.8110177 0.8293811 +#> [268,] 0.5538449 0.5592717 0.5692131 +#> [269,] 1.3894109 1.3983734 1.4073322 +#> [270,] 0.6532931 0.6535509 0.6603980 +#> [271,] 0.6954934 0.8071532 0.8544645 +#> [272,] 0.7997930 0.8001390 0.8008286 +#> [273,] 0.7323858 0.7352067 0.7820764 +#> [274,] 0.5457597 0.5458660 0.5603155 +#> [275,] 0.6593882 0.6684101 0.6983498 +#> [276,] 1.5282590 1.5432321 1.5518024 +#> [277,] 0.7831023 0.8821525 0.8906814 +#> [278,] 0.5610538 0.5769897 0.6043345 +#> [279,] 0.8457055 0.8582436 0.9107608 +#> [280,] 0.5585820 0.5978585 0.6342586 +#> [281,] 0.5753703 0.6876870 0.7038946 +#> [282,] 0.7290462 0.8648736 0.8648975 +#> [283,] 0.6778300 0.6789222 0.7318722 +#> [284,] 0.9411683 0.9855289 1.0050375 +#> [285,] 0.7043195 0.7432437 0.7502960 +#> [286,] 1.3651561 1.3892015 1.3943802 +#> [287,] 1.2771909 1.2880192 1.3741400 +#> [288,] 1.5075022 1.5866471 1.6241959 +#> [289,] 1.1508820 1.2054700 1.2060725 +#> [290,] 0.8510004 0.8512387 0.8942729 +#> [291,] 0.6606663 0.6876140 0.7072640 +#> [292,] 0.6391744 0.6506912 0.6697097 +#> [293,] 0.8054279 0.8070900 0.8415829 +#> [294,] 0.6004037 0.6064343 0.6525837 +#> [295,] 0.5946605 0.6109984 0.6217659 +#> [296,] 0.5564219 0.5682669 0.5785664 +#> [297,] 0.5121810 0.5405678 0.5407712 +#> [298,] 0.8108245 0.8151554 0.8167585 +#> [299,] 0.4839062 0.4925744 0.5149030 +#> [300,] 0.7401987 0.7627323 0.7641001 +#> [301,] 0.5626776 0.6190075 0.6316696 +#> [302,] 0.6929260 0.7291426 0.7316196 +#> [303,] 1.9051118 1.9100229 1.9334947 +#> [304,] 0.5817847 0.5997890 0.6207748 +#> [305,] 0.5720249 0.5837491 0.5898708 +#> [306,] 0.5965174 0.6005517 0.6137788 +#> [307,] 1.0034240 1.0314873 1.0320467 +#> [308,] 0.8265704 0.8445410 0.8636050 +#> [309,] 0.8943133 0.9098371 0.9781398 +#> [310,] 0.6571992 0.6586063 0.6722925 +#> [311,] 0.8053866 0.8088441 0.8141323 +#> [312,] 0.9904840 1.0488026 1.0567316 +#> [313,] 0.6881557 0.7582715 0.7591426 +#> [314,] 0.5568581 0.5827694 0.6204041 +#> [315,] 0.6862337 0.7001198 0.7561484 +#> [316,] 0.8492696 0.8711605 0.8899702 +#> [317,] 0.7655117 0.7722215 0.7753759 +#> [318,] 0.6363898 0.6404232 0.6509156 +#> [319,] 0.6670985 0.6672640 0.6962625 +#> [320,] 0.8233239 0.8242852 0.8484660 +#> [321,] 0.6495988 0.6508068 0.6542615 +#> [322,] 1.0180832 1.0250659 1.0266634 +#> [323,] 0.6837625 0.6928143 0.6936870 +#> [324,] 0.7342534 0.7540028 0.7679974 +#> [325,] 0.6308780 0.6407043 0.6508068 +#> [326,] 0.9350917 0.9420448 0.9533092 +#> [327,] 0.6695906 0.6926156 0.7533284 +#> [328,] 1.0668279 1.0853428 1.2035339 +#> [329,] 0.8267822 0.8279529 0.8500662 +#> [330,] 0.6328065 0.6525837 0.6550373 +#> [331,] 0.6137788 0.6349687 0.6698618 +#> [332,] 0.7303193 0.7322579 0.7341715 +#> [333,] 0.8000697 0.8059705 0.8269345 +#> [334,] 0.5379070 0.5381997 0.5402068 +#> [335,] 0.4572627 0.4642517 0.4692709 +#> [336,] 1.1938257 1.2105804 1.2646468 +#> [337,] 0.8833182 0.8913455 0.9097912 +#> [338,] 0.8483925 0.8485289 0.8739361 +#> [339,] 0.9268745 0.9301437 0.9494924 +#> [340,] 0.5926610 0.6433268 0.6688616 +#> [341,] 0.6328652 0.6697167 0.6775438 +#> [342,] 0.6189582 0.6336426 0.6530868 +#> [343,] 0.9829755 0.9838940 1.0288515 +#> [344,] 0.5624027 0.5672823 0.5751450 +#> [345,] 0.5785100 0.5880912 0.6032128 +#> [346,] 0.7105605 0.7254459 0.7285652 +#> [347,] 0.6198076 0.6297758 0.6598254 +#> [348,] 0.8729183 0.8793803 0.9190275 +#> [349,] 0.5209098 0.5302664 0.5624052 +#> [350,] 0.8863619 0.9420280 0.9459780 +#> [351,] 0.8880786 0.9228884 0.9664693 +#> [352,] 1.3276280 1.3284202 1.3549191 +#> [353,] 1.0438588 1.0442103 1.0701039 +#> [354,] 0.7461058 0.7690134 0.7864186 +#> [355,] 0.7151664 0.7450731 0.7689244 +#> [356,] 0.5878504 0.5908810 0.6090971 +#> [357,] 0.6847809 0.8065705 0.8067604 +#> [358,] 1.0136404 1.0154122 1.0282789 +#> [359,] 1.0531656 1.0646987 1.1138957 +#> [360,] 1.0397803 1.0461697 1.0537380 +#> [361,] 0.4926647 0.5154537 0.5237127 +#> [362,] 0.9955914 1.0392933 1.0474345 +#> [363,] 0.8972945 0.8996744 0.9027318 +#> [364,] 0.9159105 0.9435594 0.9511380 +#> [365,] 0.7430718 0.7604702 0.7980560 +#> [366,] 0.5674692 0.5727376 0.6627259 +#> [367,] 1.0199524 1.0204548 1.0471335 +#> [368,] 0.5353742 0.5394586 0.5463735 +#> [369,] 0.8494756 0.8770611 0.8840677 +#> [370,] 0.9045793 0.9141658 0.9328294 +#> [371,] 0.8484185 0.8639459 0.9237728 +#> [372,] 0.7616295 0.7788900 0.8071625 +#> [373,] 0.5097656 0.5103176 0.5450248 +#> [374,] 0.6674362 0.6946062 0.8056381 +#> [375,] 0.7198688 0.7292259 0.7533332 +#> [376,] 0.7404467 0.7665664 0.7672032 +#> [377,] 0.8388834 0.8573182 0.8683734 +#> [378,] 0.7261603 0.7888915 0.8065603 +#> [379,] 1.1768365 1.2174035 1.2453905 +#> [380,] 1.2598092 1.3246854 1.3305341 +#> [381,] 0.8631331 0.8777154 0.8879424 +#> [382,] 0.8118543 0.8243492 0.8374920 +#> [383,] 0.7757223 0.7796205 0.8203172 +#> [384,] 0.8415829 0.8425422 0.8427918 +#> [385,] 0.6887134 0.6924916 0.6935413 +#> [386,] 0.6301571 0.6443765 0.6467538 +#> [387,] 0.6254812 0.6277603 0.6707994 +#> [388,] 0.9504140 0.9787950 0.9788155 +#> [389,] 0.7573177 0.7574468 0.7613383 +#> [390,] 0.6926296 0.7004906 0.7667403 +#> [391,] 1.1676943 1.2179838 1.2325915 +#> [392,] 0.8188247 0.8241835 0.8339341 +#> [393,] 0.8329418 0.8386294 0.8460252 +#> [394,] 0.7096210 0.7292739 0.7584514 +#> [395,] 1.4799383 1.5064008 1.5235157 +#> [396,] 1.1022143 1.1110085 1.1184343 +#> [397,] 0.7088534 0.7127603 0.7214720 +#> [398,] 0.7625693 0.7998142 0.8176899 +#> [399,] 0.7830381 0.8207924 0.8252420 +#> [400,] 1.2736809 1.3238208 1.3259648 +#> [401,] 0.7904562 0.8157185 0.8233814 +#> [402,] 0.9497575 0.9665054 0.9835361 +#> [403,] 0.9377258 1.0256769 1.0350779 +#> [404,] 0.6182421 0.6186630 0.6250949 +#> [405,] 1.3379349 1.3868488 1.4096641 +#> [406,] 0.6810388 0.7326497 0.7436179 +#> [407,] 0.8000203 0.8126316 0.8261912 +#> [408,] 0.6668383 0.6772600 0.7011845 +#> [409,] 0.5904144 0.6055436 0.6144892 +#> [410,] 0.6182368 0.6483972 0.6787011 +#> [411,] 0.7589513 0.7703605 0.7758075 +#> [412,] 0.7763655 0.7924663 0.8046182 +#> [413,] 0.7299407 0.7307987 0.7472007 +#> [414,] 1.2705682 1.3039116 1.3173147 +#> [415,] 1.3978243 1.4099353 1.4368023 +#> [416,] 0.7055470 0.7556795 0.7571401 +#> [417,] 0.8557944 0.9016194 0.9258674 +#> [418,] 0.6795702 0.6936138 0.7203690 +#> [419,] 0.5507127 0.5616786 0.5664522 +#> [420,] 0.5946564 0.6271226 0.6589521 +#> [421,] 1.2660488 1.2935256 1.3210175 +#> [422,] 1.0777358 1.1552969 1.2204435 +#> [423,] 0.5313030 0.5338943 0.5407712 +#> [424,] 0.9779007 1.0178157 1.0591399 +#> [425,] 0.8950220 0.9454731 0.9560139 +#> [426,] 1.0097005 1.0485973 1.1099787 +#> [427,] 0.6957399 0.7095744 0.7805514 +#> [428,] 1.3067943 1.3501308 1.3673162 +#> [429,] 0.8278941 0.8936291 0.9025688 +#> [430,] 0.7547116 0.7996885 0.8211158 +#> [431,] 0.7154578 0.7296091 0.7660701 +#> [432,] 0.7421226 0.7538467 0.8155186 +#> [433,] 0.5806314 0.5915916 0.6548079 +#> [434,] 0.6349687 0.6365637 0.6500784 +#> [435,] 0.8473934 0.8574420 0.9175749 +#> [436,] 0.6155368 0.6198042 0.6409345 +#> [437,] 0.4699607 0.5248791 0.5486645 +#> [438,] 1.0221971 1.0283199 1.0576939 +#> [439,] 1.4077905 1.4078477 1.4427959 +#> [440,] 0.4409665 0.4626767 0.4938862 +#> [441,] 0.9048603 0.9087658 0.9091246 +#> [442,] 1.1669433 1.1732456 1.1790484 +#> [443,] 0.5563468 0.5780151 0.5950256 +#> [444,] 0.9618237 0.9843987 1.0340714 +#> [445,] 1.6385503 1.6417239 1.6554494 +#> [446,] 0.7878675 0.7962567 0.7979834 +#> [447,] 2.0097095 2.0525425 2.0587405 +#> [448,] 1.1924949 1.2178915 1.2492241 +#> [449,] 0.5403095 0.5631900 0.5737795 +#> [450,] 1.6583900 1.7281786 1.7370262 +#> [451,] 0.6314860 0.6396309 0.6518001 +#> [452,] 1.3654383 1.3918685 1.4056749 +#> [453,] 0.6978221 0.7011060 0.7079638 +#> [454,] 1.0720183 1.1228689 1.1266475 +#> [455,] 0.5374261 0.5787567 0.5882427 +#> [456,] 0.5917679 0.6269757 0.6475247 +#> [457,] 1.0103346 1.0583478 1.0754355 +#> [458,] 0.7366074 0.7696810 0.8113648 +#> [459,] 0.5130750 0.5307737 0.5408275 +#> [460,] 0.5350491 0.5520000 0.5657748 +#> [461,] 0.8358193 0.8409993 0.8453590 +#> [462,] 1.3948239 1.4562316 1.4921572 +#> [463,] 0.5976927 0.6159258 0.6270187 +#> [464,] 0.7016635 0.7099575 0.7111202 +#> [465,] 0.9276422 0.9977400 1.0455170 +#> [466,] 1.1230235 1.1773242 1.1921464 +#> [467,] 0.5719470 0.6084708 0.6584985 +#> [468,] 0.5323596 0.5371743 0.6082670 +#> [469,] 0.6305895 0.6475206 0.6699390 +#> [470,] 0.3822591 0.4214002 0.4794289 +#> [471,] 0.6965180 0.7162136 0.7932884 +#> [472,] 1.2037354 1.2412579 1.2781834 +#> [473,] 1.1528347 1.1575442 1.1691273 +#> [474,] 0.6404232 0.6628817 0.6633204 +#> [475,] 1.3680536 1.3806925 1.4502315 +#> [476,] 0.7013151 0.7044657 0.7068683 +#> [477,] 0.7424823 0.8617070 0.8710939 +#> [478,] 0.6833299 0.7013946 0.7057739 +#> [479,] 0.5670320 0.5874698 0.6266317 +#> [480,] 0.5411971 0.5717583 0.5764336 +#> [481,] 0.8700075 0.8761098 0.8879985 +#> [482,] 0.6665749 0.6856635 0.6897393 +#> [483,] 0.7095349 0.7123296 0.7411007 +#> [484,] 1.2568529 1.3388399 1.3498595 +#> [485,] 0.6030901 0.6106986 0.6362282 +#> [486,] 1.3270357 1.3446570 1.3753723 +#> [487,] 0.6944722 0.6986781 0.7058085 +#> [488,] 0.7442717 0.7716297 0.8258617 +#> [489,] 0.8303598 0.8338579 0.8443118 +#> [490,] 1.5111890 1.5445189 1.5564387 +#> [491,] 1.4287770 1.4338115 1.4534610 +#> [492,] 0.6028583 0.6085813 0.6364568 +#> [493,] 1.2444039 1.2724466 1.2782419 +#> [494,] 0.9186961 0.9288731 0.9339130 +#> [495,] 0.5148448 0.5639078 0.6331913 +#> [496,] 0.9704159 0.9718047 0.9869484 +#> [497,] 0.5541192 0.5699598 0.5739698 +#> [498,] 1.2294258 1.2405399 1.2632070 +#> [499,] 0.5804832 0.6020497 0.6649769 +#> [500,] 0.6395914 0.6407729 0.6647554 +#> [501,] 1.2427244 1.2891885 1.3755788 +#> [502,] 0.5479711 0.5639078 0.5675280 +#> [503,] 1.5388021 1.6372228 1.6697375 +#> [504,] 0.7529697 0.7724098 0.7991795 +#> [505,] 1.5642651 1.5753041 1.5896113 +#> [506,] 0.9122109 0.9163889 0.9183538 +#> [507,] 0.9718225 0.9727489 0.9946086 +#> [508,] 0.7348817 0.7541810 0.7824822 +#> [509,] 0.8368484 0.8441140 0.8712227 +#> [510,] 0.7831023 0.8224706 0.8269345 +#> [511,] 1.2972052 1.2995585 1.3610271 +#> [512,] 0.7433959 0.7674406 0.8095525 +#> [513,] 0.4820203 0.4914494 0.5091375 +#> [514,] 0.5673960 0.6052295 0.6122315 +#> [515,] 0.4559979 0.4896020 0.4909026 +#> [516,] 0.4765648 0.5061745 0.5290541 +#> [517,] 0.5166929 0.5225545 0.5312450 +#> [518,] 0.7012764 0.7025821 0.7181508 +#> [519,] 0.6690963 0.6722143 0.7096344 +#> [520,] 0.5259640 0.5403095 0.5471192 +#> [521,] 0.7425617 0.7567662 0.7822832 +#> [522,] 0.5248791 0.5320507 0.5325069 +#> [523,] 0.9687019 0.9696667 1.0605354 +#> [524,] 0.5592717 0.5775316 0.6013149 +#> [525,] 0.7134244 0.7180454 0.7746114 +#> [526,] 0.6894268 0.6917122 0.7388943 +#> [527,] 1.5320355 1.5359497 1.5481314 +#> [528,] 0.6239519 0.6647352 0.6698996 +#> [529,] 0.8253294 0.8426068 0.8512405 +#> [530,] 0.9175670 0.9586984 0.9696410 +#> [531,] 0.5643394 0.6282007 0.6373988 +#> [532,] 0.7024892 0.7180089 0.7402136 +#> [533,] 0.9412042 0.9443775 0.9501321 +#> [534,] 0.6331913 0.6349211 0.6712309 +#> [535,] 0.4731234 0.5026104 0.5306004 +#> [536,] 0.5622579 0.5691711 0.5894259 +#> [537,] 1.3938269 1.4201260 1.4538800 +#> [538,] 2.1562114 2.1712210 2.1791550 +#> [539,] 0.5998649 0.6303169 0.6841752 +#> [540,] 1.2021338 1.2431104 1.2434704 +#> [541,] 0.8252084 0.8266875 0.8453320 +#> [542,] 1.1119488 1.1136882 1.1416424 +#> [543,] 0.7668405 0.7668689 0.7780350 +#> [544,] 1.2257260 1.2418458 1.3271819 +#> [545,] 0.9903867 1.0414513 1.0425481 +#> [546,] 0.7894660 0.7979414 0.8440595 +#> [547,] 1.4748032 1.5288034 1.6200355 +#> [548,] 0.8342098 0.8576379 0.8589755 +#> [549,] 0.8357769 0.8472275 0.8531945 +#> [550,] 0.8899300 0.9027397 0.9051167 +#> [551,] 0.7655653 0.7879630 0.8017048 +#> [552,] 0.6278000 0.6282079 0.6320861 +#> [553,] 0.7442717 0.7569825 0.7942681 +#> [554,] 0.6768602 0.6894267 0.7283503 +#> [555,] 0.4914494 0.5066574 0.5105818 +#> [556,] 0.6791070 0.6866979 0.7237962 +#> [557,] 0.7090876 0.7166298 0.7232070 +#> [558,] 0.6349871 0.6720933 0.6736528 +#> [559,] 0.8838416 0.8964731 0.9543999 +#> [560,] 0.7819956 0.8213843 0.8433024 +#> [561,] 0.8987376 0.9224726 0.9437928 +#> [562,] 0.5383853 0.5501868 0.5729807 +#> [563,] 1.6153224 1.6803922 1.7070157 +#> [564,] 0.9931664 1.0353998 1.0435389 +#> [565,] 1.1575457 1.2387429 1.2800923 +#> [566,] 1.7779091 1.7815292 1.8143845 +#> [567,] 0.8014217 0.8219595 0.8323921 +#> [568,] 0.6558496 0.6684397 0.6691929 +#> [569,] 0.5976967 0.6356963 0.6428138 +#> [570,] 0.8780852 0.8807945 0.8864215 +#> [571,] 1.5694029 1.6226836 1.6559844 +#> [572,] 1.8867064 1.8946499 1.8951040 +#> [573,] 0.7954502 0.8146452 0.8159977 +#> [574,] 0.5106542 0.5251450 0.5402068 +#> [575,] 0.9253467 0.9494333 0.9523070 +#> [576,] 0.5224575 0.5246025 0.5282306 +#> [577,] 0.8574534 0.8758765 0.9156594 +#> [578,] 0.6972932 0.7002176 0.7072410 +#> [579,] 0.7263034 0.7667228 0.7703424 +#> [580,] 1.1658215 1.2123779 1.2285181 +#> [581,] 0.9509007 0.9569566 0.9901120 +#> [582,] 0.6984653 0.7123291 0.7231011 +#> [583,] 0.6165135 0.6549497 0.6617059 +#> [584,] 0.6559082 0.6567545 0.6708039 +#> [585,] 1.2372009 1.2431104 1.3414206 +#> [586,] 0.8837551 0.8870166 0.8986792 +#> [587,] 1.1300497 1.1378241 1.1534381 +#> [588,] 0.9341930 0.9854642 0.9955070 +#> [589,] 0.7817602 0.7950334 0.8359405 +#> [590,] 0.8507018 0.8532815 0.8706052 +#> [591,] 0.6070573 0.6123936 0.6133793 +#> [592,] 0.6506767 0.6857493 0.6886219 +#> [593,] 1.1799360 1.2041982 1.2203544 +#> [594,] 0.9431895 1.0075567 1.0447846 +#> [595,] 1.2599315 1.2657402 1.2736123 +#> [596,] 0.7112562 0.7154578 0.7300252 +#> [597,] 0.9327264 0.9697106 0.9757561 +#> [598,] 0.5226653 0.5347778 0.6004037 +#> [599,] 0.7221886 0.7933000 0.7960978 +#> [600,] 0.5261021 0.5263663 0.5302476 +#> [601,] 0.7163910 0.7248237 0.7679930 +#> [602,] 0.7257560 0.7548418 0.7964255 +#> [603,] 0.8378586 0.8414736 0.8446556 +#> [604,] 0.9077201 0.9923359 1.0106972 +#> [605,] 0.6994948 0.7045753 0.7119669 +#> [606,] 0.5645280 0.5647955 0.5910950 +#> [607,] 1.4204439 1.4740305 1.4971383 +#> [608,] 1.5498688 1.6069093 1.7384498 +#> [609,] 0.9435022 0.9635824 0.9656355 +#> [610,] 0.5616786 0.5708692 0.6117274 +#> [611,] 1.2172076 1.2886264 1.3355435 +#> [612,] 0.5113881 0.5568581 0.5676932 +#> [613,] 1.1811073 1.2101745 1.2565012 +#> [614,] 0.8288751 0.8812426 0.9096677 +#> [615,] 0.5957393 0.6231754 0.6320876 +#> [616,] 0.6050565 0.6071182 0.6480847 +#> [617,] 0.7711614 0.7980622 0.8012313 +#> [618,] 0.5614452 0.6487696 0.6725718 +#> [619,] 0.5817847 0.6002829 0.6276241 +#> [620,] 0.8364844 0.8494256 0.8512405 +#> [621,] 0.7005655 0.7149314 0.7576389 +#> [622,] 0.9959242 1.0071526 1.0788781 +#> [623,] 1.0235715 1.0478058 1.0535272 +#> [624,] 0.7768493 0.7793272 0.8205517 +#> [625,] 1.1115848 1.1248822 1.1475012 +#> [626,] 0.5346923 0.5503597 0.5520000 +#> [627,] 0.6558496 0.6659052 0.7765029 +#> [628,] 1.0708415 1.1446938 1.2266482 +#> [629,] 0.6829279 0.7051407 0.7155856 +#> [630,] 0.7140941 0.7149278 0.7381929 +#> [631,] 0.9039229 0.9152130 0.9270557 +#> [632,] 0.5377479 0.5674692 0.6032168 +#> [633,] 0.6707994 0.6877516 0.7379386 +#> [634,] 0.6339956 0.6650379 0.6674789 +#> [635,] 0.6655739 0.6722275 0.7188234 +#> [636,] 1.2039409 1.2538158 1.3004335 +#> [637,] 0.9567400 0.9805564 1.0181206 +#> [638,] 1.7680531 1.7727131 1.8282199 +#> [639,] 0.7118041 0.7131544 0.7225123 +#> [640,] 0.7567525 0.7756439 0.7817602 +#> [641,] 0.5957012 0.6084708 0.6490912 +#> [642,] 0.6071488 0.6660500 0.6773838 +#> [643,] 0.5670320 0.5772271 0.6008826 +#> [644,] 0.6868794 0.6977937 0.7150017 +#> [645,] 0.7461080 0.7604850 0.7808844 +#> [646,] 0.6266361 0.6314365 0.6341940 +#> [647,] 0.7385297 0.7404467 0.7519838 +#> [648,] 0.8369020 0.8398624 0.8476542 +#> [649,] 0.4740971 0.4755505 0.5060454 +#> [650,] 1.7220624 1.7519612 1.7616198 +#> [651,] 0.4828149 0.5392326 0.5533832 +#> [652,] 0.6740843 0.6871881 0.7243692 +#> [653,] 0.5535769 0.5727801 0.5891104 +#> [654,] 1.0375054 1.0590858 1.0765221 +#> [655,] 0.8332197 0.8422317 0.8527906 +#> [656,] 1.0206574 1.0517808 1.0649530 +#> [657,] 0.7398995 0.8453590 0.8595941 +#> [658,] 0.8261406 0.8307582 0.8373647 +#> [659,] 0.7806629 0.7927973 0.7979414 +#> [660,] 0.5940258 0.6013404 0.6245413 +#> [661,] 1.0329767 1.0371129 1.0452618 +#> [662,] 0.7318016 0.7900017 0.7961905 +#> [663,] 0.4782290 0.4993475 0.5221172 +#> [664,] 0.6718837 0.6856635 0.7067344 +#> [665,] 0.6518001 0.6655306 0.6815493 +#> [666,] 1.1155896 1.1403134 1.1439860 +#> [667,] 0.5810267 0.5871655 0.6011670 +#> [668,] 0.7238108 0.7438533 0.7550931 +#> [669,] 1.0504722 1.0550247 1.0723984 +#> [670,] 0.9731496 0.9735136 0.9877512 +#> [671,] 1.0529481 1.0845242 1.1297322 +#> [672,] 0.8843676 0.8887155 0.9268679 +#> [673,] 0.6216648 0.6479959 0.6746208 +#> [674,] 0.5251450 0.5284033 0.5484489 +#> [675,] 0.7403855 0.7486979 0.7514374 +#> [676,] 0.6969275 0.7127841 0.7689411 +#> [677,] 0.7326497 0.7373563 0.7384600 +#> [678,] 0.6684066 0.6699390 0.6867790 +#> [679,] 0.7660027 0.7884803 0.7889959 +#> [680,] 0.6324472 0.6806701 0.6925097 +#> [681,] 0.6062568 0.6252554 0.6853151 +#> [682,] 0.7331707 0.7955418 0.8179501 +#> [683,] 1.0878071 1.1123559 1.1526806 +#> [684,] 0.5940258 0.6229879 0.6271196 +#> [685,] 0.8574649 0.8976148 0.9387797 +#> [686,] 0.7263605 0.7401842 0.7438612 +#> [687,] 1.3116721 1.3278361 1.3876646 +#> [688,] 0.6817233 0.6931311 0.7211141 +#> [689,] 1.0200061 1.0265487 1.0418415 +#> [690,] 0.7148179 0.7231723 0.7243780 +#> [691,] 0.6315268 0.6431302 0.6574496 +#> [692,] 1.0381903 1.0520745 1.0933699 +#> [693,] 0.9025787 0.9038915 0.9198327 +#> [694,] 0.6214294 0.6753017 0.7139887 +#> [695,] 1.1763199 1.1795062 1.2006747 +#> [696,] 0.6034387 0.6123165 0.6987542 +#> [697,] 1.4813297 1.6121141 1.7260634 +#> [698,] 0.6417307 0.6438171 0.6462949 +#> [699,] 0.8838049 0.9152433 0.9183538 +#> [700,] 0.7079312 0.7122314 0.7785440 +#> [701,] 1.2274216 1.3371795 1.3521911 +#> [702,] 0.6383516 0.6398683 0.6559131 +#> [703,] 0.8225748 0.8324250 0.8455299 +#> [704,] 0.5142809 0.5876460 0.6074908 +#> [705,] 0.5373668 0.5540923 0.5664522 +#> [706,] 0.5765510 0.5853877 0.6001879 +#> [707,] 0.6937080 0.7006146 0.7421462 +#> [708,] 0.6238756 0.6882597 0.6976726 +#> [709,] 0.8374410 0.9248079 0.9406653 +#> [710,] 0.7652198 0.8181279 0.8206264 +#> [711,] 0.5948992 0.5976967 0.6158515 +#> [712,] 0.9381280 0.9597892 0.9664203 +#> [713,] 1.0555835 1.0615033 1.0912192 +#> [714,] 0.8848121 0.9112138 0.9500410 +#> [715,] 1.0850121 1.0871142 1.0940913 +#> [716,] 0.7843407 0.8227103 0.8617159 +#> [717,] 0.4696288 0.4699835 0.4738325 +#> [718,] 0.5210939 0.5921035 0.6556833 +#> [719,] 1.0706496 1.0765221 1.1663353 +#> [720,] 0.8406039 0.8808923 0.8958698 +#> [721,] 0.8506859 0.8674290 0.8749529 +#> [722,] 1.1576949 1.1636063 1.1886189 +#> [723,] 0.7265791 0.7418144 0.7951605 +#> [724,] 0.7665664 0.8252081 0.8266375 +#> [725,] 0.6844479 0.6874830 0.7128726 +#> [726,] 0.9559077 1.1242090 1.1646467 +#> [727,] 0.7351015 0.7832581 0.8303848 +#> [728,] 0.9923359 1.0102435 1.0381903 +#> [729,] 1.4331600 1.4771536 1.4932030 +#> [730,] 0.6936138 0.7290462 0.7386993 +#> [731,] 0.5865848 0.5928012 0.5935847 +#> [732,] 0.8202470 0.8342600 0.8562082 +#> [733,] 0.6299515 0.7325734 0.7354251 +#> [734,] 2.0558621 2.0864969 2.1005553 +#> [735,] 0.6088727 0.6541174 0.6590748 +#> [736,] 1.2058457 1.2621229 1.2702962 +#> [737,] 1.1890757 1.2943075 1.2991210 +#> [738,] 0.5636207 0.5829550 0.6010974 +#> [739,] 1.2999900 1.3123376 1.3535130 +#> [740,] 0.5961094 0.6115983 0.6147504 +#> [741,] 1.1292652 1.1476807 1.1993746 +#> [742,] 0.6389123 0.6490562 0.6605395 +#> [743,] 0.5694909 0.6220328 0.6521282 +#> [744,] 0.6801958 0.6913184 0.7516634 +#> [745,] 0.7373071 0.7444701 0.7774382 +#> [746,] 0.5555416 0.5618440 0.5831496 +#> [747,] 1.3390375 1.3843793 1.4050764 +#> [748,] 0.7693490 0.7886514 0.8007839 +#> [749,] 1.2160325 1.2310418 1.2612687 +#> [750,] 0.7772398 0.7835136 0.7853064 +#> [751,] 0.6469686 0.6871881 0.6893706 +#> [752,] 1.0467176 1.0487921 1.0535688 +#> [753,] 0.7115954 0.7243692 0.7353212 +#> [754,] 1.0067329 1.1439401 1.1663841 +#> [755,] 0.7445498 0.8205199 0.8258803 +#> [756,] 0.5692131 0.5878257 0.6048488 +#> [757,] 1.5059130 1.5141408 1.5370495 +#> [758,] 0.7022300 0.7040191 0.7266179 +#> [759,] 2.0418621 2.1325963 2.2567825 +#> [760,] 0.5918048 0.5966892 0.6095008 +#> [761,] 0.5413950 0.5472648 0.5751142 +#> [762,] 0.5384481 0.5705695 0.5868046 +#> [763,] 0.8146452 0.8153426 0.8235265 +#> [764,] 0.6850527 0.6924807 0.7120703 +#> [765,] 1.1885719 1.2018503 1.2018910 +#> [766,] 1.3260409 1.3341675 1.3347847 +#> [767,] 0.6248858 0.6631087 0.6670681 +#> [768,] 0.9237803 0.9821653 1.0128317 +#> [769,] 0.8028055 0.8115816 0.8469829 +#> [770,] 1.3735787 1.4586779 1.4587643 +#> [771,] 1.0487313 1.1115367 1.1278697 +#> [772,] 0.6377302 0.7069487 0.7291969 +#> [773,] 0.9324725 0.9695837 1.0005072 +#> [774,] 0.7340137 0.7686250 0.7910865 +#> [775,] 0.7437403 0.7456979 0.7878617 +#> [776,] 0.6409072 0.6948549 0.7067249 +#> [777,] 0.7257133 0.7526207 0.8145793 +#> [778,] 0.5142809 0.5245879 0.5252494 +#> [779,] 0.6242367 0.6256287 0.6328125 +#> [780,] 0.6837660 0.6874830 0.7228639 +#> [781,] 0.8125125 0.8322545 0.8503036 +#> [782,] 0.5561102 0.5578403 0.5763036 +#> [783,] 0.5896110 0.5958654 0.6104892 +#> [784,] 0.7866859 0.7935557 0.7964822 +#> [785,] 0.6916844 0.7073426 0.7114112 +#> [786,] 0.6074524 0.6283285 0.6525452 +#> [787,] 0.8485117 0.8661158 0.8681542 +#> [788,] 0.7669146 0.7704553 0.7730321 +#> [789,] 0.7936552 0.8056731 0.8282903 +#> [790,] 0.7768582 0.7846403 0.7865361 +#> [791,] 0.9218025 0.9309645 0.9834923 +#> [792,] 0.7310340 0.7395698 0.7413927 +#> [793,] 0.5268015 0.5320507 0.5456090 +#> [794,] 0.6962281 0.7052326 0.7597433 +#> [795,] 0.7230854 0.7567452 0.7898359 +#> [796,] 1.4279747 1.4767257 1.4891995 +#> [797,] 0.5369971 0.6397847 0.6429611 +#> [798,] 0.7756439 0.7789369 0.7825231 +#> [799,] 1.3097582 1.4824465 1.5489940 +#> [800,] 0.7162136 0.7266179 0.7296064 +#> [801,] 0.4948228 0.5717583 0.5764634 +#> [802,] 0.9185322 0.9380741 0.9420223 +#> [803,] 0.6465218 0.6621150 0.6682168 +#> [804,] 0.6172237 0.6478300 0.6539777 +#> [805,] 1.8593783 1.8708800 1.9053267 +#> [806,] 0.7604921 0.7642574 0.7730720 +#> [807,] 0.7662884 0.7721703 0.7889959 +#> [808,] 0.5115099 0.5396213 0.5483203 +#> [809,] 1.0282668 1.0382065 1.0560935 +#> [810,] 0.5475677 0.5502896 0.5973181 +#> [811,] 1.1429708 1.1613028 1.2231753 +#> [812,] 0.6638050 0.6828485 0.6834797 +#> [813,] 0.9778346 1.0510234 1.1124590 +#> [814,] 1.2542723 1.3124297 1.3212982 +#> [815,] 0.7370478 0.7417071 0.7623365 +#> [816,] 0.8667483 0.8711702 0.8878093 +#> [817,] 0.6253059 0.6553342 0.6873358 +#> [818,] 0.8282982 0.8362473 0.8699008 +#> [819,] 0.9744774 0.9890063 1.0132698 +#> [820,] 0.6783649 0.7362620 0.7444487 +#> [821,] 0.6621150 0.6987482 0.7166870 +#> [822,] 0.9290550 1.0580104 1.0725154 +#> [823,] 1.0468094 1.0712767 1.0978278 +#> [824,] 0.6427528 0.6436058 0.6524632 +#> [825,] 0.6817233 0.7111853 0.7402580 +#> [826,] 0.5284033 0.5541392 0.5699656 +#> [827,] 1.3117818 1.3270609 1.3359374 +#> [828,] 0.6628685 0.6713565 0.6810039 +#> [829,] 0.8527906 0.8812073 0.9528009 +#> [830,] 0.6828485 0.6850597 0.6959316 +#> [831,] 0.8301323 0.8712192 0.9245956 +#> [832,] 0.6132856 0.6421572 0.6431027 +#> [833,] 0.7869176 0.8055432 0.8226591 +#> [834,] 1.2076362 1.2278662 1.2417090 +#> [835,] 0.7902266 0.8018794 0.8600771 +#> [836,] 0.7864186 0.7935557 0.8255778 +#> [837,] 0.7262376 0.7844301 0.7885148 +#> [838,] 1.4799255 1.5037535 1.5076612 +#> [839,] 0.5764634 0.6651793 0.6815833 +#> [840,] 0.6878447 0.6985192 0.7296800 +#> [841,] 0.4934944 0.5391688 0.5655502 +#> [842,] 1.0560959 1.0712848 1.0853428 +#> [843,] 0.7249468 0.7366193 0.7376150 +#> [844,] 0.7874349 0.7950016 0.8099821 +#> [845,] 0.7664371 0.7807477 0.8116326 +#> [846,] 0.6358013 0.6442066 0.6576187 +#> [847,] 0.4486907 0.4983990 0.4999563 +#> [848,] 0.9207612 0.9276422 0.9540991 +#> [849,] 0.4063097 0.4590751 0.4740971 +#> [850,] 0.6394717 0.6588610 0.6600258 +#> [851,] 0.8149142 0.8428174 0.8796094 +#> [852,] 0.6049990 0.6198042 0.6272176 +#> [853,] 0.9851428 1.0335664 1.0680627 +#> [854,] 1.0029237 1.0044478 1.0091058 +#> [855,] 0.9468798 0.9500668 0.9856499 +#> [856,] 1.1343272 1.1434065 1.1500361 +#> [857,] 0.9434459 0.9806701 0.9911303 +#> [858,] 0.6657246 0.6823429 0.6984509 +#> [859,] 0.9392122 1.0334538 1.0380556 +#> [860,] 0.6851883 0.6919852 0.7095744 +#> [861,] 0.7763655 0.7913457 0.8270811 +#> [862,] 0.6989579 0.7134870 0.7771113 +#> [863,] 0.7291426 0.7341086 0.7359247 +#> [864,] 1.2080432 1.2282035 1.3640246 +#> [865,] 0.7292787 0.7359247 0.8104232 +#> [866,] 0.8350270 0.8828692 0.8838416 +#> [867,] 1.6801516 1.7936242 1.8107486 +#> [868,] 0.7664883 0.7692705 0.7853750 +#> [869,] 0.6256287 0.6405012 0.6438171 +#> [870,] 0.9159168 0.9412794 0.9424081 +#> [871,] 0.4008547 0.4421991 0.4654775 +#> [872,] 1.5398663 1.5488528 1.5865198 +#> [873,] 0.5946995 0.5961094 0.5985655 +#> [874,] 1.2174384 1.2866041 1.3608177 +#> [875,] 0.6989579 0.7028270 0.7502891 +#> [876,] 1.1007515 1.1159988 1.1618348 +#> [877,] 0.9267543 0.9303417 0.9985390 +#> [878,] 0.7707595 0.7796337 0.8207193 +#> [879,] 0.7728698 0.8062140 0.8064596 +#> [880,] 0.9699226 1.0045098 1.0449545 +#> [881,] 0.8332732 0.8572955 0.8811205 +#> [882,] 0.7810091 0.7894367 0.8153426 +#> [883,] 0.7170823 0.7352067 0.7384600 +#> [884,] 0.5624806 0.5863302 0.6213767 +#> [885,] 0.9655477 0.9727644 1.0023837 +#> [886,] 0.6547229 0.6760726 0.6837625 +#> [887,] 0.6487290 0.6680559 0.7312532 +#> [888,] 0.8688664 0.9291881 0.9494333 +#> [889,] 1.3239755 1.3330514 1.3419673 +#> [890,] 0.4841708 0.5299665 0.5941560 +#> [891,] 1.1557669 1.2092693 1.2211860 +#> [892,] 0.5427501 0.5509455 0.5757741 +#> [893,] 1.0055165 1.1019036 1.1303557 +#> [894,] 1.5957744 1.6656746 1.6704343 +#> [895,] 0.5313030 0.5411869 0.5432273 +#> [896,] 1.3521568 1.3854506 1.3989331 +#> [897,] 1.2729158 1.2869336 1.3173147 +#> [898,] 1.3501308 1.4088867 1.4431790 +#> [899,] 0.5130520 0.5265425 0.5344388 +#> [900,] 0.6500784 0.6550834 0.6594093 +#> [901,] 0.9341268 0.9882515 1.0482291 +#> [902,] 0.6887986 0.6949705 0.7101375 +#> [903,] 1.8382930 1.8593577 1.8821169 +#> [904,] 0.8450184 0.8584939 0.8900323 +#> [905,] 1.3352933 1.3647099 1.3707155 +#> [906,] 0.6124874 0.6452023 0.6502552 +#> [907,] 0.6015697 0.6057881 0.6303679 +#> [908,] 0.7397444 0.7589513 0.7761755 +#> [909,] 1.1331706 1.1684123 1.2233917 +#> [910,] 0.7708239 0.8088441 0.8090672 +#> [911,] 0.7564921 0.8313556 0.8853633 +#> [912,] 0.5590943 0.5693136 0.6010974 +#> [913,] 0.5705695 0.5990171 0.5997920 +#> [914,] 0.6483972 0.6809148 0.6938318 +#> [915,] 1.1443646 1.1713342 1.1896728 +#> [916,] 0.7747097 0.7772398 0.8024150 +#> [917,] 0.7302801 0.7417071 0.7470587 +#> [918,] 0.6165999 0.6218071 0.6254043 +#> [919,] 0.6983487 0.8245811 0.8574372 +#> [920,] 1.0107482 1.0956074 1.1528632 +#> [921,] 0.5968568 0.6036069 0.6052295 +#> [922,] 0.7816347 0.8317877 0.8353579 +#> [923,] 0.7042752 0.7556773 0.7612428 +#> [924,] 0.8016984 0.8064596 0.8279561 +#> [925,] 0.6581170 0.6625708 0.6638874 +#> [926,] 0.7545084 0.7692472 0.8111023 +#> [927,] 0.8234208 0.8325586 0.8762490 +#> [928,] 0.8036009 0.8617396 0.9095927 +#> [929,] 0.9777802 0.9867707 0.9916101 +#> [930,] 0.6246838 0.6478300 0.6710057 +#> [931,] 0.7047821 0.7074970 0.7094189 +#> [932,] 0.5815310 0.5894735 0.5981939 +#> [933,] 0.8676934 0.8809400 0.9466005 +#> [934,] 1.0968149 1.1379194 1.1401699 +#> [935,] 0.5070188 0.5424867 0.5633698 +#> [936,] 0.9154519 0.9903226 0.9985880 +#> [937,] 1.0154784 1.0198059 1.0558893 +#> [938,] 0.7369948 0.7536130 0.8294904 +#> [939,] 1.4303140 1.4317122 1.4805684 +#> [940,] 0.8062438 0.8196058 0.8569086 +#> [941,] 1.1892653 1.2261792 1.2324396 +#> [942,] 0.5912759 0.5969571 0.6082670 +#> [943,] 0.5314154 0.5326785 0.5801175 +#> [944,] 0.8829630 0.8910723 0.8967822 +#> [945,] 1.3858221 1.4302707 1.4316539 +#> [946,] 0.6547229 0.6886322 0.6928918 +#> [947,] 0.6877890 0.7001301 0.7057125 +#> [948,] 1.8357152 1.9045001 1.9065517 +#> [949,] 1.3379349 1.4021315 1.4079049 +#> [950,] 1.0840138 1.1094766 1.1308136 +#> [951,] 0.7117756 0.7177677 0.7423945 +#> [952,] 0.7438321 0.7534034 0.7842284 +#> [953,] 0.7502960 0.7789664 0.7982690 +#> [954,] 0.6118587 0.6215960 0.6384658 +#> [955,] 1.2871531 1.3225393 1.3418591 +#> [956,] 0.7043195 0.7170823 0.7336379 +#> [957,] 1.0358191 1.0774448 1.1165770 +#> [958,] 0.6198076 0.6213680 0.6650598 +#> [959,] 1.1471868 1.2669688 1.2800816 +#> [960,] 0.8130187 0.8631036 0.8891762 +#> [961,] 0.4881796 0.5027134 0.5136565 +#> [962,] 0.5982893 0.6004924 0.6157327 +#> [963,] 0.7837144 0.7919935 0.7991795 +#> [964,] 0.6104453 0.6329952 0.6364906 +#> [965,] 0.7174851 0.7278104 0.7323772 +#> [966,] 0.6158712 0.6274870 0.6436952 +#> [967,] 0.9331233 0.9648185 0.9984615 +#> [968,] 0.9794996 0.9906871 1.0181875 +#> [969,] 0.9764982 0.9772140 0.9803378 +#> [970,] 0.8332279 0.8385844 0.8746902 +#> [971,] 0.8782562 0.9048243 0.9051167 +#> [972,] 0.7135772 0.7139887 0.7223360 +#> [973,] 0.6498677 0.6716114 0.7187771 +#> [974,] 0.7911595 0.7980903 0.8115715 +#> [975,] 1.0262068 1.0452156 1.0937855 +#> [976,] 0.7545626 0.7664371 0.7778610 +#> [977,] 0.9817203 1.0214761 1.0233572 +#> [978,] 0.7640607 0.7933599 0.8157303 +#> [979,] 0.5221971 0.5497720 0.6275281 +#> [980,] 0.8326922 0.8450174 0.8531720 +#> [981,] 0.6079576 0.6749162 0.6866674 +#> [982,] 0.5516274 0.5699598 0.6100876 +#> [983,] 0.6790706 0.7139076 0.7197768 +#> [984,] 1.2032878 1.2213903 1.2509943 +#> [985,] 0.6874485 0.7101610 0.7203690 +#> [986,] 1.0107862 1.0289652 1.0302160 +#> [987,] 0.5092006 0.5504881 0.5553071 +#> [988,] 0.6925121 0.7282713 0.7690535 +#> [989,] 0.7949332 0.8214750 0.8386153 +#> [990,] 1.1753586 1.2128637 1.2172076 +#> [991,] 0.7462066 0.8073125 0.8246749 +#> [992,] 0.6815427 0.7020037 0.7071514 +#> [993,] 0.8221489 0.8382346 0.8520056 +#> [994,] 0.5541392 0.5859897 0.5901825 +#> [995,] 0.6030581 0.6088673 0.6820477 +#> [996,] 1.0221451 1.0348177 1.0581953 +#> [997,] 1.1938107 1.2440572 1.2494514 +#> [998,] 0.7631284 0.8002346 0.8086030 +#> [999,] 0.6835776 0.6913845 0.6999382 +#> [1000,] 1.1236420 1.1943301 1.2301638 #> # Find the 10 approximate nearest neighbors @@ -3151,3010 +3151,3010 @@

    Examples) #> $neighbor_ids #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] -#> [1,] 992 435 985 238 66 939 919 181 825 995 -#> [2,] 153 154 787 895 289 432 114 784 379 774 -#> [3,] 945 35 553 884 360 7 956 357 86 562 -#> [4,] 556 616 499 964 211 688 89 761 155 805 -#> [5,] 86 553 637 377 7 624 242 751 283 3 -#> [6,] 830 941 940 569 678 256 323 356 199 851 -#> [7,] 956 624 553 86 714 3 945 360 132 232 -#> [8,] 822 604 76 493 740 229 547 514 222 578 -#> [9,] 619 378 531 527 732 975 585 728 549 589 -#> [10,] 266 575 291 863 469 900 268 376 838 638 -#> [11,] 972 153 154 570 157 245 895 121 746 529 -#> [12,] 600 235 911 331 617 468 367 615 134 23 -#> [13,] 464 284 978 495 117 89 706 211 189 702 -#> [14,] 325 555 879 622 812 799 433 672 123 176 -#> [15,] 690 152 420 907 701 492 35 981 657 86 -#> [16,] 140 64 162 164 143 552 230 366 243 167 -#> [17,] 386 502 643 891 413 551 120 567 876 257 -#> [18,] 341 909 533 598 995 340 487 351 724 66 -#> [19,] 488 909 92 340 322 341 18 855 486 183 -#> [20,] 372 856 507 943 72 315 309 22 136 655 -#> [21,] 280 90 689 680 307 778 189 436 702 359 -#> [22,] 72 118 943 309 507 796 856 824 20 470 -#> [23,] 373 327 843 844 676 134 936 12 699 617 -#> [24,] 29 742 122 826 334 847 756 178 382 955 -#> [25,] 921 169 916 344 866 225 991 500 930 700 -#> [26,] 380 93 196 758 452 911 158 78 77 891 -#> [27,] 285 312 69 292 36 223 362 237 661 180 -#> [28,] 440 62 52 226 908 241 677 71 217 878 -#> [29,] 742 122 24 382 331 816 955 847 826 334 -#> [30,] 68 855 461 974 952 735 183 472 614 345 -#> [31,] 893 589 787 531 52 62 595 975 850 908 -#> [32,] 205 835 375 477 79 315 953 151 136 713 -#> [33,] 463 221 179 653 361 479 95 871 938 529 -#> [34,] 971 539 1000 864 870 95 938 685 136 267 -#> [35,] 3 884 562 360 553 690 945 751 86 511 -#> [36,] 69 285 292 98 237 931 912 27 223 661 -#> [37,] 40 756 214 982 178 632 130 334 780 640 -#> [38,] 904 953 437 799 107 622 433 533 669 128 -#> [39,] 719 223 285 777 292 661 795 246 193 362 -#> [40,] 37 214 705 756 982 809 242 780 178 983 -#> [41,] 723 496 661 385 50 36 755 519 98 312 -#> [42,] 204 107 437 290 602 159 460 540 622 401 -#> [43,] 71 878 391 440 62 28 106 914 764 247 -#> [44,] 830 740 493 434 456 941 295 449 766 294 -#> [45,] 793 883 329 780 239 338 442 195 268 384 -#> [46,] 427 458 844 807 794 740 23 373 60 820 -#> [47,] 129 75 245 162 278 918 234 54 972 570 -#> [48,] 221 776 915 361 379 405 168 55 950 359 -#> [49,] 587 438 534 538 168 796 856 545 792 22 -#> [50,] 231 254 524 255 98 280 464 689 397 897 -#> [51,] 818 920 178 990 113 847 982 813 576 91 -#> [52,] 62 908 28 440 217 241 31 589 709 595 -#> [53,] 966 94 335 82 431 454 116 189 469 579 -#> [54,] 408 849 652 554 278 948 230 856 374 572 -#> [55,] 950 776 959 789 801 171 842 582 663 317 -#> [56,] 529 34 95 642 1000 938 354 539 630 245 -#> [57,] 914 347 245 570 642 47 234 56 864 971 -#> [58,] 949 937 601 220 870 462 829 267 862 630 -#> [59,] 434 119 456 528 708 505 949 684 267 277 -#> [60,] 794 218 699 515 373 516 514 763 294 508 -#> [61,] 875 451 579 963 116 865 915 958 335 860 -#> [62,] 52 440 28 908 31 241 217 589 878 709 -#> [63,] 788 797 836 655 546 372 194 518 477 984 -#> [64,] 366 140 552 16 143 243 126 490 85 164 -#> [65,] 628 902 520 763 518 514 836 880 910 186 -#> [66,] 425 181 825 598 533 472 726 182 18 390 -#> [67,] 578 278 938 95 849 141 836 984 880 76 -#> [68,] 952 345 461 735 974 183 156 370 30 567 -#> [69,] 36 237 27 285 170 912 292 180 342 443 -#> [70,] 676 711 820 806 775 78 93 750 843 465 -#> [71,] 391 43 28 677 878 534 587 440 62 792 -#> [72,] 22 309 943 631 20 824 372 507 856 135 -#> [73,] 699 314 877 277 294 917 665 119 508 327 -#> [74,] 519 292 719 755 362 661 557 39 618 285 -#> [75,] 374 641 572 47 948 999 54 960 422 230 -#> [76,] 880 578 141 980 836 858 902 67 822 984 -#> [77,] 932 939 610 452 196 506 891 26 935 158 -#> [78,] 93 617 158 321 777 465 373 26 989 750 -#> [79,] 390 32 954 151 477 833 181 953 643 835 -#> [80,] 498 679 19 488 486 81 416 402 92 712 -#> [81,] 435 561 322 486 612 710 1 238 995 939 -#> [82,] 94 365 933 694 963 431 53 743 363 900 -#> [83,] 263 798 819 796 300 647 526 834 22 206 -#> [84,] 517 281 525 248 378 687 233 549 355 673 -#> [85,] 366 162 64 164 16 490 918 143 302 980 -#> [86,] 553 5 7 714 956 3 35 624 232 360 -#> [87,] 829 566 483 339 862 523 88 182 630 220 -#> [88,] 802 957 924 871 194 463 339 568 630 829 -#> [89,] 443 170 947 211 482 4 706 556 499 603 -#> [90,] 778 280 689 359 21 436 524 399 950 931 -#> [91,] 813 197 818 586 990 535 983 51 674 468 -#> [92,] 488 19 30 855 542 909 183 857 351 487 -#> [93,] 78 617 158 26 321 634 196 380 911 465 -#> [94,] 82 365 53 694 933 963 431 363 61 900 -#> [95,] 938 67 121 245 653 871 278 971 33 129 -#> [96,] 326 680 861 970 922 791 21 654 853 307 -#> [97,] 610 733 841 901 103 932 304 77 723 452 -#> [98,] 254 223 285 36 661 292 931 69 39 795 -#> [99,] 627 618 768 444 109 550 930 237 823 916 -#> [100,] 370 754 183 935 977 345 729 952 68 156 -#> [101,] 284 117 640 453 231 397 611 334 706 332 -#> [102,] 299 450 738 809 945 571 536 831 790 3 -#> [103,] 901 97 733 841 610 913 898 355 723 304 -#> [104,] 287 848 228 236 804 336 960 573 313 374 -#> [105,] 722 190 876 407 257 107 120 602 437 146 -#> [106,] 497 247 471 986 289 532 633 635 874 489 -#> [107,] 437 42 602 290 460 38 204 105 707 190 -#> [108,] 139 714 522 330 212 771 232 377 860 215 -#> [109,] 823 478 627 99 490 973 143 768 444 393 -#> [110,] 946 259 367 12 854 235 191 753 600 844 -#> [111,] 834 693 782 349 633 532 538 819 746 83 -#> [112,] 353 419 629 811 536 563 639 313 667 175 -#> [113,] 920 847 658 178 332 51 146 982 310 762 -#> [114,] 432 436 778 969 774 359 895 784 90 379 -#> [115,] 857 351 176 487 662 879 669 14 485 855 -#> [116,] 875 61 579 189 335 702 451 727 964 668 -#> [117,] 284 640 544 101 445 704 249 978 837 335 -#> [118,] 593 545 22 306 943 961 438 470 72 796 -#> [119,] 456 294 708 434 277 218 699 917 59 73 -#> [120,] 551 190 658 17 876 386 502 105 643 257 -#> [121,] 938 871 129 95 245 856 278 234 534 49 -#> [122,] 29 24 178 334 847 742 756 816 586 813 -#> [123,] 404 672 325 42 205 929 894 540 799 107 -#> [124,] 163 523 506 181 192 829 786 541 954 872 -#> [125,] 369 734 512 674 983 265 40 720 817 632 -#> [126,] 973 143 64 366 552 288 140 671 243 490 -#> [127,] 667 336 520 745 252 573 852 552 515 313 -#> [128,] 559 833 473 217 182 405 350 381 803 38 -#> [129,] 278 162 245 121 938 95 583 67 849 164 -#> [130,] 632 615 756 262 334 753 122 367 596 265 -#> [131,] 500 137 169 659 858 832 736 618 944 296 -#> [132,] 146 297 826 624 227 409 956 7 441 876 -#> [133,] 770 906 594 186 721 508 264 308 457 737 -#> [134,] 844 373 967 617 344 308 264 955 235 331 -#> [135,] 309 631 943 470 72 581 507 810 22 824 -#> [136,] 194 477 655 984 315 20 871 462 797 802 -#> [137,] 131 500 786 659 736 937 944 858 541 169 -#> [138,] 675 821 614 348 645 602 783 574 415 437 -#> [139,] 108 798 216 714 420 152 771 681 701 537 -#> [140,] 552 143 243 64 16 745 520 490 366 252 -#> [141,] 880 980 76 578 490 788 836 910 67 628 -#> [142,] 759 544 730 146 722 297 624 958 956 105 -#> [143,] 140 243 552 973 64 16 823 126 478 109 -#> [144,] 415 411 485 662 138 614 979 675 204 783 -#> [145,] 693 269 647 188 798 253 526 83 263 208 -#> [146,] 722 132 544 142 759 297 876 737 658 826 -#> [147,] 407 737 906 457 696 789 594 713 388 887 -#> [148,] 282 406 491 283 637 705 869 982 576 242 -#> [149,] 503 358 370 381 260 355 951 281 524 324 -#> [150,] 346 762 453 332 977 754 413 371 742 621 -#> [151,] 721 835 713 457 810 594 257 79 205 32 -#> [152,] 681 657 300 216 907 690 15 798 981 389 -#> [153,] 154 11 895 2 379 529 972 33 850 221 -#> [154,] 153 11 895 157 2 391 972 289 570 529 -#> [155,] 616 603 588 4 392 596 185 556 428 396 -#> [156,] 726 345 567 472 935 183 413 598 68 260 -#> [157,] 746 11 972 154 570 224 391 633 934 153 -#> [158,] 321 196 465 93 78 891 251 634 26 380 -#> [159,] 401 792 204 677 42 538 460 290 923 241 -#> [160,] 859 967 994 716 344 867 866 700 428 398 -#> [161,] 692 405 796 460 538 819 168 602 545 105 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776 915 -#> [222,] 8 613 604 577 175 903 740 225 229 822 -#> [223,] 285 98 39 661 292 795 254 27 36 193 -#> [224,] 746 633 934 157 349 111 648 363 834 782 -#> [225,] 577 697 25 429 671 921 794 916 514 629 -#> [226,] 375 28 677 208 315 792 587 534 217 241 -#> [227,] 580 868 990 535 409 441 132 826 360 945 -#> [228,] 287 104 848 960 374 569 236 256 652 804 -#> [229,] 794 804 515 902 514 65 218 60 628 8 -#> [230,] 824 422 336 16 652 773 374 54 881 745 -#> [231,] 255 50 254 397 101 284 177 464 453 338 -#> [232,] 714 929 215 574 540 7 108 798 86 212 -#> [233,] 566 951 324 248 87 531 549 687 517 523 -#> [234,] 570 587 534 121 972 49 856 188 391 864 -#> [235,] 615 753 331 747 955 600 12 134 265 967 -#> [236,] 256 848 907 104 287 772 573 356 228 678 -#> [237,] 292 69 36 795 285 912 180 27 931 768 -#> [238,] 612 1 995 919 992 905 390 528 465 985 -#> [239,] 883 329 780 588 195 704 117 37 640 978 -#> [240,] 837 704 727 579 445 978 335 431 668 730 -#> [241,] 217 923 792 635 52 908 168 28 538 595 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402 -#> [342,] 393 439 867 428 556 69 840 170 237 180 -#> [343,] 731 362 496 750 814 519 775 312 715 74 -#> [344,] 866 991 700 246 921 916 777 930 160 867 -#> [345,] 156 68 567 726 952 183 935 370 413 551 -#> [346,] 150 453 332 611 219 762 371 977 742 847 -#> [347,] 685 864 608 971 34 914 1000 539 57 234 -#> [348,] 783 614 799 138 437 707 602 461 622 460 -#> [349,] 633 934 253 111 782 247 693 224 599 532 -#> [350,] 473 741 833 643 568 271 381 872 957 182 -#> [351,] 857 487 176 855 115 18 669 879 598 533 -#> [352,] 272 71 208 269 417 325 440 145 316 548 -#> [353,] 429 671 667 447 175 903 552 64 639 126 -#> [354,] 630 394 339 87 58 220 928 870 88 56 -#> [355,] 281 248 673 149 998 841 728 913 260 324 -#> [356,] 772 459 511 597 256 678 543 236 800 767 -#> [357,] 691 884 882 3 945 35 580 581 571 747 -#> [358,] 969 778 524 149 951 381 436 687 503 399 -#> [359,] 221 361 90 463 778 950 48 568 689 55 -#> [360,] 409 562 3 884 35 7 423 868 574 581 -#> [361,] 221 48 359 379 463 583 33 915 776 950 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345 190 783 -#> [462,] 870 194 829 984 949 937 477 267 136 58 -#> [463,] 924 221 957 33 359 568 361 698 88 179 -#> [464,] 284 13 702 189 706 280 482 443 117 50 -#> [465,] 158 321 528 78 196 944 93 786 710 954 -#> [466,] 395 861 670 424 727 430 644 654 853 584 -#> [467,] 307 897 779 21 403 970 286 524 90 689 -#> [468,] 327 12 91 586 535 990 227 674 955 23 -#> [469,] 454 900 575 291 694 838 827 268 863 484 -#> [470,] 507 135 796 22 407 118 545 309 147 943 -#> [471,] 986 635 532 923 187 497 874 589 774 900 -#> [472,] 156 726 598 345 825 66 567 260 183 935 -#> [473,] 350 741 833 643 128 797 271 776 568 954 -#> [474,] 649 476 261 927 660 333 956 591 210 418 -#> [475,] 752 411 701 421 420 215 368 597 751 511 -#> [476,] 320 261 927 213 474 252 418 649 717 455 -#> [477,] 655 984 136 32 194 462 63 797 79 954 -#> [478,] 823 109 164 143 583 490 788 99 768 140 -#> [479,] 279 529 33 179 653 642 95 938 245 311 -#> [480,] 644 504 521 773 889 206 387 438 972 746 -#> [481,] 446 626 846 942 330 869 283 522 431 212 -#> [482,] 443 702 791 170 706 189 912 556 89 211 -#> [483,] 709 862 87 620 566 803 531 425 595 220 -#> [484,] 808 310 735 827 503 370 952 268 249 821 -#> [485,] 662 411 979 316 144 325 14 415 555 799 -#> [486,] 322 710 251 416 402 712 843 911 612 909 -#> [487,] 669 351 533 341 18 598 176 909 724 857 -#> [488,] 19 92 855 909 351 487 18 30 341 857 -#> [489,] 289 694 784 900 471 774 303 106 895 986 -#> [490,] 980 141 823 109 880 143 478 140 627 164 -#> [491,] 442 282 406 283 869 645 785 148 481 446 -#> [492,] 15 501 152 337 657 962 389 690 419 420 -#> [493,] 941 740 830 547 760 456 434 8 554 708 -#> [494,] 276 838 165 614 348 491 291 986 874 144 -#> [495,] 13 329 464 513 284 89 239 231 338 255 -#> [496,] 362 723 519 661 312 41 74 304 27 719 -#> [497,] 106 874 471 893 986 635 31 289 589 187 -#> [498,] 679 91 197 80 402 651 468 486 813 818 -#> [499,] 4 688 805 791 922 211 89 556 964 947 -#> [500,] 169 131 858 137 944 25 820 921 244 659 -#> [501,] 492 337 430 389 657 641 962 599 15 152 -#> [502,] 17 413 695 386 567 382 120 876 441 643 -#> [503,] 149 370 484 808 358 273 381 454 551 863 -#> [504,] 521 387 167 644 606 773 451 206 306 584 -#> [505,] 685 59 608 760 988 949 267 34 862 864 -#> [506,] 124 872 523 163 560 192 932 77 825 260 -#> [507,] 470 20 22 290 796 72 943 309 835 315 -#> [508,] 186 518 917 810 763 594 721 133 218 457 -#> [509,] 479 853 279 918 529 2 153 207 179 33 -#> [510,] 641 572 188 999 962 145 948 569 269 960 -#> [511,] 562 772 459 356 597 690 884 35 907 360 -#> [512,] 632 37 262 130 588 40 590 125 983 756 -#> [513,] 13 495 211 89 978 922 947 464 189 680 -#> [514,] 902 628 921 65 763 794 218 880 858 910 -#> [515,] 804 516 229 313 127 60 794 667 767 218 -#> [516,] 691 515 767 60 319 313 678 357 563 804 -#> [517,] 248 84 687 281 358 525 969 913 233 951 -#> [518,] 508 186 943 309 520 372 628 65 836 749 -#> [519,] 362 74 661 719 292 496 39 285 312 755 -#> [520,] 65 518 628 763 961 127 388 660 943 745 -#> [521,] 504 387 167 644 584 606 451 288 773 480 -#> [522,] 330 771 692 860 212 108 626 730 481 161 -#> [523,] 124 163 506 260 324 381 350 825 872 182 -#> [524,] 399 778 358 90 897 689 436 50 280 254 -#> [525,] 687 376 969 549 517 84 784 774 378 248 -#> [526,] 894 656 929 274 208 540 631 83 72 290 -#> [527,] 585 619 9 732 975 589 974 176 638 378 -#> [528,] 277 321 465 989 390 119 612 59 79 890 -#> [529,] 479 245 33 938 95 153 56 642 279 121 -#> [530,] 202 311 58 937 870 949 354 630 220 137 -#> [531,] 9 31 566 378 549 787 893 589 233 975 -#> [532,] 986 471 187 635 782 923 111 401 933 159 -#> [533,] 598 669 904 18 724 341 487 909 66 340 -#> [534,] 587 391 49 538 168 792 677 234 226 241 -#> [535,] 990 227 409 873 868 91 360 200 51 818 -#> [536,] 691 357 563 102 811 419 981 35 299 313 -#> [537,] 771 648 301 782 625 365 522 108 139 828 -#> [538,] 792 168 49 587 534 923 796 159 161 677 -#> [539,] 1000 34 864 971 803 226 862 595 267 607 -#> [540,] 929 894 42 204 526 232 123 215 401 290 -#> [541,] 786 829 659 954 462 937 194 192 124 924 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960 -#> [642,] 245 479 529 938 95 311 56 279 278 302 -#> [643,] 386 17 257 473 713 663 350 954 502 120 -#> [644,] 480 521 504 451 889 387 773 206 167 61 -#> [645,] 675 785 821 138 212 574 920 658 576 283 -#> [646,] 623 262 219 611 392 312 275 101 371 231 -#> [647,] 693 798 145 83 420 215 540 263 701 526 -#> [648,] 537 625 301 771 834 365 782 363 963 828 -#> [649,] 660 745 591 961 881 455 474 476 210 261 -#> [650,] 765 295 383 44 416 612 905 843 449 81 -#> [651,] 873 679 410 535 91 200 990 734 751 498 -#> [652,] 749 408 849 54 554 372 824 943 72 309 -#> [653,] 179 95 33 311 924 938 630 463 870 88 -#> [654,] 288 395 521 326 504 791 167 387 688 973 -#> [655,] 984 477 836 136 63 372 194 462 20 788 -#> [656,] 526 894 631 208 72 290 507 20 274 205 -#> [657,] 389 426 681 152 981 216 591 300 976 881 -#> [658,] 920 120 821 876 113 146 190 502 310 722 -#> [659,] 541 786 444 137 829 910 858 192 141 937 -#> [660,] 649 961 745 455 881 591 474 476 520 606 -#> [661,] 223 285 519 98 39 292 362 74 36 27 -#> [662,] 485 979 144 614 411 783 316 799 14 669 -#> [663,] 926 842 789 906 737 407 317 271 643 386 -#> [664,] 44 295 997 941 414 493 830 6 383 940 -#> [665,] 423 868 877 917 314 800 810 73 270 151 -#> [666,] 769 925 424 705 195 790 831 301 704 837 -#> [667,] 127 852 252 429 313 552 811 243 336 745 -#> [668,] 725 964 418 717 958 210 445 213 875 451 -#> [669,] 487 799 533 724 904 176 598 783 38 351 -#> [670,] 584 964 395 668 717 688 116 211 521 725 -#> [671,] 429 398 252 667 552 126 243 973 225 994 -#> [672,] 123 404 325 400 979 744 996 592 14 724 -#> [673,] 728 281 355 619 998 542 472 248 609 260 -#> [674,] 586 983 813 734 265 91 632 636 122 615 -#> [675,] 645 138 574 415 576 821 200 409 920 658 -#> [676,] 70 806 843 78 93 820 23 373 617 710 -#> [677,] 792 208 587 159 226 534 538 28 290 894 -#> [678,] 767 270 800 356 236 459 516 581 877 104 -#> [679,] 498 402 651 91 410 535 468 990 712 818 -#> [680,] 861 326 791 189 21 96 922 482 702 947 -#> [681,] 216 976 300 564 426 152 389 881 657 206 -#> [682,] 729 258 100 977 754 888 935 183 998 219 -#> [683,] 757 338 255 454 808 827 863 231 503 484 -#> [684,] 851 400 199 205 996 896 749 32 656 548 -#> [685,] 347 608 505 864 971 34 760 547 414 554 -#> [686,] 448 368 410 651 886 936 449 679 744 328 -#> [687,] 549 969 525 774 358 951 517 248 784 566 -#> [688,] 556 791 439 499 4 973 717 964 213 288 -#> [689,] 931 280 90 21 912 399 524 359 950 702 -#> [690,] 15 511 35 907 562 772 420 152 86 701 -#> [691,] 357 516 767 882 319 884 747 313 563 536 -#> [692,] 522 161 330 212 626 460 105 860 707 933 -#> [693,] 647 145 798 111 83 263 269 188 834 962 -#> [694,] 900 82 94 365 933 489 469 471 291 774 -#> [695,] 502 724 441 151 904 533 658 567 382 17 -#> [696,] 887 333 842 906 926 317 737 927 663 147 -#> [697,] 225 629 398 577 994 429 160 671 25 967 -#> [698,] 399 560 568 957 924 172 463 403 192 339 -#> [699,] 73 294 218 60 119 373 877 277 508 314 -#> [700,] 991 866 930 916 344 892 867 444 921 910 -#> [701,] 215 420 798 377 232 647 108 139 86 690 -#> [702,] 189 482 280 464 171 706 443 116 284 689 -#> [703,] 761 239 185 588 329 603 965 396 155 616 -#> [704,] 837 445 240 195 117 978 640 730 727 705 -#> [705,] 195 769 780 40 790 214 704 148 282 242 -#> [706,] 443 284 180 170 482 702 69 464 254 98 -#> [707,] 602 190 460 437 821 107 348 559 105 120 -#> [708,] 119 456 218 890 294 434 989 514 902 766 -#> [709,] 483 908 803 217 52 620 862 595 425 62 -#> [710,] 612 843 486 251 322 528 158 465 93 995 -#> [711,] 70 815 465 676 820 612 736 775 304 137 -#> [712,] 402 314 744 592 400 486 665 277 996 449 -#> [713,] 257 457 835 721 594 151 643 407 810 147 -#> [714,] 232 7 108 86 216 860 212 564 956 215 -#> [715,] 166 814 840 343 428 312 731 392 362 27 -#> [716,] 994 160 859 293 885 596 967 753 616 428 -#> [717,] 213 210 418 455 725 476 606 668 320 649 -#> [718,] 210 418 725 639 591 717 811 584 396 426 -#> [719,] 39 777 750 519 74 246 362 452 292 661 -#> [720,] 839 734 203 265 3 945 102 357 536 35 -#> [721,] 457 594 713 151 810 835 257 917 133 508 -#> [722,] 737 146 876 105 407 142 605 759 544 789 -#> [723,] 41 496 733 661 519 177 385 304 362 932 -#> [724,] 695 592 533 669 341 904 151 487 799 909 -#> [725,] 418 210 668 717 213 476 591 474 649 964 -#> [726,] 156 567 345 551 472 260 17 182 825 381 -#> [727,] 978 240 837 335 966 116 579 189 211 964 -#> [728,] 324 260 281 951 673 732 248 472 825 619 -#> [729,] 977 682 754 100 219 346 332 935 453 177 -#> [730,] 759 142 297 624 544 212 785 146 283 958 -#> [731,] 312 392 623 246 600 859 362 750 331 27 -#> [732,] 585 589 728 974 9 527 549 433 425 619 -#> [733,] 97 103 723 610 304 932 496 901 841 898 -#> [734,] 674 839 720 983 91 586 265 813 535 873 -#> [735,] 461 68 974 952 821 310 484 345 190 348 -#> [736,] 304 137 557 131 296 74 192 519 901 500 -#> [737,] 906 876 722 147 696 663 826 407 594 887 -#> [738,] 450 102 299 396 185 536 596 811 369 831 -#> [739,] 198 328 548 383 115 14 316 352 272 879 -#> [740,] 766 493 434 456 8 708 794 218 44 229 -#> [741,] 350 473 381 582 568 872 801 899 551 271 -#> [742,] 29 24 346 122 847 382 611 380 758 150 -#> [743,] 365 942 431 446 846 828 82 481 626 845 -#> [744,] 410 672 543 328 996 712 402 979 404 400 -#> [745,] 649 660 243 455 961 606 591 252 881 552 -#> [746,] 157 224 972 111 11 570 363 834 633 188 -#> [747,] 571 955 235 319 264 753 580 308 265 615 -#> [748,] 550 832 627 342 126 109 973 393 99 814 -#> [749,] 631 309 652 943 372 270 72 518 20 135 -#> [750,] 777 246 719 362 758 39 78 731 93 519 -#> [751,] 873 35 360 3 562 553 5 637 839 86 -#> [752,] 475 368 751 411 701 637 421 174 690 420 -#> [753,] 615 235 293 265 747 130 571 596 367 331 -#> [754,] 977 935 150 100 183 413 453 346 567 729 -#> [755,] 74 36 292 618 519 237 931 912 69 661 -#> [756,] 178 214 122 37 982 334 24 847 130 113 -#> [757,] 683 338 255 454 863 231 50 827 808 575 -#> [758,] 380 623 611 600 26 742 331 452 371 346 -#> [759,] 544 142 730 146 958 249 722 297 624 605 -#> [760,] 547 554 408 493 941 984 608 849 864 685 -#> [761,] 4 616 185 603 396 703 155 964 211 805 -#> [762,] 150 453 346 621 413 382 742 371 899 386 -#> [763,] 388 628 65 186 902 520 508 991 910 514 -#> [764,] 440 878 62 52 975 28 43 607 709 908 -#> [765,] 905 612 238 650 919 59 528 710 995 843 -#> [766,] 740 434 708 858 989 456 514 493 8 76 -#> [767,] 678 516 563 691 313 800 270 236 581 884 -#> [768,] 99 618 627 172 444 237 931 912 109 271 -#> [769,] 790 705 925 831 5 553 86 242 283 195 -#> [770,] 264 133 826 308 955 594 906 457 737 441 -#> [771,] 860 522 537 819 834 579 108 963 865 648 -#> [772,] 511 356 459 597 907 256 562 236 690 581 -#> [773,] 206 438 306 504 593 118 387 545 167 976 -#> [774,] 784 273 289 787 687 893 549 969 31 471 -#> [775,] 70 304 750 343 806 820 711 519 676 736 -#> [776,] 55 789 48 950 407 801 663 959 473 797 -#> [777,] 246 719 750 39 866 344 78 991 452 617 -#> [778,] 436 90 524 358 359 969 399 568 280 689 -#> [779,] 467 307 286 897 21 970 403 524 436 96 -#> [780,] 195 883 705 239 37 704 40 282 148 640 -#> [781,] 928 233 339 913 394 517 248 354 403 87 -#> [782,] 537 111 532 349 771 828 648 365 834 633 -#> [783,] 348 614 799 138 622 669 433 437 461 176 -#> [784,] 774 289 893 687 489 525 114 969 787 31 -#> [785,] 283 645 297 212 242 730 869 624 146 920 -#> [786,] 541 829 659 937 954 124 601 192 137 944 -#> [787,] 850 595 31 566 774 379 531 217 549 273 -#> [788,] 63 836 546 910 880 797 141 655 628 518 -#> [789,] 407 776 663 55 926 842 801 147 722 887 -#> [790,] 831 769 445 925 624 553 705 956 809 102 -#> [791,] 326 482 922 680 688 211 499 443 189 947 -#> [792,] 159 538 677 241 168 587 290 534 923 796 -#> [793,] 45 268 883 384 338 827 942 683 575 329 -#> [794,] 60 229 514 218 515 902 763 921 516 740 -#> [795,] 842 237 180 292 223 285 39 926 317 930 -#> [796,] 507 22 168 49 290 545 470 538 161 118 -#> [797,] 63 194 315 788 833 473 136 477 776 546 -#> [798,] 420 647 83 263 215 701 139 693 232 714 -#> [799,] 622 669 38 783 904 433 42 348 437 107 -#> [800,] 270 459 423 877 917 581 678 665 810 631 -#> [801,] 605 317 55 899 959 789 663 741 551 842 -#> [802,] 88 194 871 803 957 833 924 136 473 797 -#> [803,] 217 595 802 850 833 709 128 267 182 315 -#> [804,] 515 313 104 229 127 563 336 667 516 573 -#> [805,] 499 4 885 89 428 688 616 556 603 761 -#> [806,] 676 70 911 93 26 561 78 12 617 710 -#> [807,] 458 46 886 448 259 516 629 319 203 327 -#> [808,] 484 370 863 503 310 268 683 827 575 735 -#> [809,] 214 945 299 624 956 242 571 102 297 756 -#> [810,] 917 457 721 835 508 270 713 151 594 205 -#> [811,] 852 299 667 261 319 313 127 536 252 571 -#> [812,] 953 879 14 375 592 620 622 325 433 205 -#> [813,] 586 197 91 983 674 818 51 178 636 122 -#> [814,] 840 428 867 342 859 27 312 166 393 362 -#> [815,] 711 70 820 676 244 775 736 612 137 765 -#> [816,] 334 122 742 29 332 412 847 636 911 219 -#> [817,] 734 498 983 125 674 91 197 854 651 586 -#> [818,] 51 197 990 91 813 920 200 535 178 576 -#> [819,] 834 545 206 860 83 865 771 438 161 976 -#> [820,] 169 70 500 676 244 25 944 711 775 78 -#> [821,] 190 461 658 138 707 735 645 310 120 602 -#> [822,] 578 8 604 76 547 554 67 311 760 880 -#> [823,] 478 109 143 164 490 99 788 444 627 910 -#> [824,] 72 22 300 309 943 230 881 118 573 135 -#> [825,] 260 181 66 182 163 726 472 523 425 324 -#> [826,] 770 580 737 24 132 441 876 906 146 955 -#> [827,] 454 484 249 335 284 808 683 310 966 605 -#> [828,] 968 365 743 782 537 625 648 845 82 301 -#> [829,] 541 786 462 194 937 58 924 954 124 601 -#> [830,] 941 493 294 456 6 851 678 218 119 44 -#> [831,] 790 769 553 86 956 102 981 925 624 945 -#> [832,] 550 627 131 618 99 748 755 768 916 74 -#> [833,] 473 128 315 350 802 803 797 643 182 194 -#> [834,] 819 111 771 83 889 49 438 538 206 161 -#> [835,] 713 32 205 810 721 457 151 507 257 315 -#> [836,] 655 880 788 63 984 628 141 518 902 372 -#> [837,] 240 704 445 727 978 668 579 117 335 730 -#> [838,] 291 900 986 575 471 469 638 974 626 863 -#> [839,] 720 734 3 751 945 873 35 553 357 203 -#> [840,] 428 814 342 166 27 867 69 312 170 392 -#> [841,] 610 97 901 163 992 355 77 103 932 825 -#> [842,] 926 663 317 795 696 271 899 582 887 789 -#> [843,] 710 612 676 23 416 486 989 78 528 806 -#> [844,] 134 373 967 23 60 617 344 921 25 319 -#> [845,] 165 743 986 968 900 532 828 291 365 782 -#> [846,] 481 942 446 431 626 869 330 743 522 283 -#> [847,] 332 113 334 122 742 346 178 150 920 24 -#> [848,] 287 236 104 228 256 960 374 336 569 824 -#> [849,] 408 54 554 278 652 67 372 856 578 836 -#> [850,] 595 787 803 379 566 217 802 241 88 871 -#> [851,] 199 684 896 987 941 548 996 608 400 414 -#> [852,] 811 667 261 127 252 319 313 320 515 429 -#> [853,] 432 114 307 861 96 21 680 94 509 359 -#> [854,] 946 367 636 12 191 412 110 586 936 674 -#> [855,] 351 598 472 857 487 30 18 176 533 909 -#> [856,] 20 22 372 72 507 943 796 315 49 656 -#> [857,] 115 351 176 487 855 669 879 662 783 585 -#> [858,] 76 500 514 880 980 944 659 921 141 169 -#> [859,] 160 716 867 428 731 344 994 967 814 246 -#> [860,] 865 771 522 819 564 216 958 714 579 142 -#> [861,] 680 189 326 791 116 21 96 702 482 211 -#> [862,] 267 620 220 483 87 949 58 709 919 803 -#> [863,] 575 808 266 503 268 10 683 484 469 370 -#> [864,] 971 34 347 685 1000 539 408 234 856 849 -#> [865,] 860 451 958 564 819 61 545 976 875 216 -#> [866,] 991 344 700 916 930 921 246 867 777 444 -#> [867,] 393 700 916 342 866 814 237 428 930 344 -#> [868,] 665 227 423 409 441 360 535 990 457 877 -#> [869,] 283 481 330 212 785 282 846 446 730 626 -#> [870,] 462 58 949 937 984 194 829 136 630 267 -#> [871,] 802 194 88 136 121 95 797 938 33 924 -#> [872,] 506 899 621 741 582 271 350 193 523 381 -#> [873,] 535 751 200 409 360 651 576 990 415 637 -#> [874,] 497 187 589 986 635 471 975 52 555 62 -#> [875,] 61 116 958 451 865 579 959 668 171 950 -#> [876,] 737 722 257 386 441 120 105 658 906 594 -#> [877,] 665 423 800 73 270 917 868 699 996 314 -#> [878,] 914 71 28 43 62 391 1000 440 52 764 -#> [879,] 176 812 433 14 622 351 669 799 953 908 -#> [880,] 141 836 980 76 578 628 788 910 902 65 -#> [881,] 591 649 573 976 660 336 745 564 824 681 -#> [882,] 580 581 357 264 884 474 571 308 691 945 -#> [883,] 239 780 329 195 640 704 45 442 793 978 -#> [884,] 581 35 562 357 3 360 423 882 511 907 -#> [885,] 994 398 428 716 616 805 160 639 859 4 -#> [886,] 327 448 699 458 23 73 877 516 936 60 -#> [887,] 696 333 317 927 842 737 926 906 147 789 -#> [888,] 258 682 561 610 97 209 998 939 729 733 -#> [889,] 438 963 363 915 644 834 49 545 306 819 -#> [890,] 708 186 546 989 944 508 321 954 477 628 -#> [891,] 17 196 386 380 158 634 413 643 321 502 -#> [892,] 320 439 393 700 696 213 476 180 887 930 -#> [893,] 31 784 774 531 589 497 975 289 549 787 -#> [894,] 526 656 208 540 929 290 677 123 42 205 -#> [895,] 153 379 363 432 289 154 11 889 915 221 -#> [896,] 987 199 851 548 417 274 684 597 996 608 -#> [897,] 403 399 524 931 689 755 467 698 98 36 -#> [898,] 103 913 733 385 723 41 355 403 97 998 -#> [899,] 621 872 582 801 317 397 842 271 741 663 -#> [900,] 694 291 469 838 471 986 82 489 365 933 -#> [901,] 97 841 103 610 736 733 163 992 304 932 -#> [902,] 628 65 514 763 880 836 518 218 910 520 -#> [903,] 613 175 604 173 366 64 804 229 353 997 -#> [904,] 38 953 799 533 669 437 724 107 695 433 -#> [905,] 919 238 985 620 765 995 1 862 220 601 -#> [906,] 737 133 594 663 696 147 926 770 876 457 -#> [907,] 236 772 573 581 981 884 690 511 300 152 -#> [908,] 52 217 433 62 709 589 622 440 28 241 -#> [909,] 341 18 340 598 533 487 322 724 995 251 -#> [910,] 546 444 788 628 930 880 991 141 763 902 -#> [911,] 26 380 93 816 758 12 617 251 29 600 -#> [912,] 931 237 36 69 768 443 170 689 482 292 -#> [913,] 403 517 248 355 781 281 358 103 841 233 -#> [914,] 878 347 1000 570 539 57 43 864 391 234 -#> [915,] 963 48 933 889 61 405 161 545 776 168 -#> [916,] 930 700 991 866 867 444 344 921 627 25 -#> [917,] 810 270 508 800 721 665 457 835 314 277 -#> [918,] 162 129 164 85 245 583 278 654 479 47 -#> [919,] 601 905 238 220 985 620 862 949 181 267 -#> [920,] 113 658 51 132 146 847 178 382 645 675 -#> [921,] 991 866 25 344 514 916 930 700 763 944 -#> [922,] 947 791 326 499 680 482 89 970 443 211 -#> [923,] 635 241 538 792 471 405 159 168 161 217 -#> [924,] 88 957 463 339 698 802 172 194 829 568 -#> [925,] 769 377 869 108 790 5 283 831 86 139 -#> [926,] 663 842 696 906 789 271 546 795 317 887 -#> [927,] 333 476 887 261 320 696 474 213 892 388 -#> [928,] 781 354 394 87 233 220 339 630 483 58 -#> [929,] 540 232 526 894 123 459 656 574 42 290 -#> [930,] 916 700 444 991 866 910 627 99 867 344 -#> [931,] 912 689 36 98 237 768 69 897 292 443 -#> [932,] 77 452 610 304 506 193 371 177 621 872 -#> [933,] 963 915 82 692 363 365 694 94 161 61 -#> [934,] 633 349 224 247 106 157 111 746 253 782 -#> [935,] 754 183 567 413 156 345 100 977 726 150 -#> [936,] 416 23 468 886 327 946 12 201 843 486 -#> [937,] 58 949 601 462 829 786 541 870 659 220 -#> [938,] 95 121 245 67 278 971 871 653 129 33 -#> [939,] 196 77 561 322 992 340 935 251 26 158 -#> [940,] 6 987 356 256 896 323 569 597 543 851 -#> [941,] 830 493 199 851 760 414 547 6 554 456 -#> 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599 693 647 798 139 701 145 152 15 -#> [963,] 933 915 61 889 363 82 771 579 94 834 -#> [964,] 668 211 213 4 717 556 725 688 418 670 -#> [965,] 185 603 596 588 155 761 616 703 293 396 -#> [966,] 53 335 189 727 978 454 827 464 702 116 -#> [967,] 134 319 160 308 844 344 264 747 235 955 -#> [968,] 828 845 743 365 165 782 625 537 446 82 -#> [969,] 687 358 436 778 525 549 273 774 517 114 -#> [970,] 947 912 286 326 443 931 680 170 922 482 -#> [971,] 864 34 938 95 67 1000 849 278 121 539 -#> [972,] 11 570 746 234 49 157 121 889 153 534 -#> [973,] 143 126 439 109 243 288 393 688 140 552 -#> [974,] 735 68 461 585 348 732 345 783 707 821 -#> [975,] 9 31 589 893 62 52 531 874 908 440 -#> [976,] 564 681 216 881 206 300 118 426 865 649 -#> [977,] 754 150 346 453 729 332 935 100 183 219 -#> [978,] 727 837 13 240 966 117 704 335 211 464 -#> [979,] 662 415 485 404 672 799 669 123 487 144 -#> [980,] 141 490 880 76 578 910 858 788 836 444 -#> [981,] 881 907 657 573 591 426 681 152 564 336 -#> [982,] 178 242 756 214 113 51 406 920 37 785 -#> [983,] 674 813 586 734 197 91 178 632 982 756 -#> [984,] 655 477 136 836 462 194 870 63 372 20 -#> [985,] 1 919 992 435 238 905 66 220 620 181 -#> [986,] 471 532 187 635 874 838 497 900 923 291 -#> [987,] 896 199 548 851 417 414 608 6 274 684 -#> [988,] 607 220 862 505 620 539 483 267 34 1000 -#> [989,] 944 890 708 373 528 78 321 119 921 218 -#> [990,] 535 227 51 409 818 91 868 441 920 873 -#> [991,] 866 700 930 344 916 921 444 910 763 546 -#> [992,] 1 939 163 238 825 181 66 985 435 77 -#> [993,] 769 637 148 705 5 751 839 174 40 925 -#> [994,] 716 885 160 398 859 697 428 629 671 429 -#> [995,] 390 18 592 341 238 909 612 322 251 340 -#> [996,] 400 543 800 449 877 684 665 672 459 270 -#> [997,] 613 173 323 175 903 228 804 104 960 287 -#> [998,] 355 673 841 281 682 542 258 610 100 97 -#> [999,] 572 641 188 263 300 206 773 374 83 824 -#> [1000,] 539 34 864 971 226 803 878 595 850 28 +#> [1,] 442 230 784 689 53 137 330 836 996 69 +#> [2,] 874 179 540 168 223 720 504 824 885 383 +#> [3,] 213 97 262 688 569 583 921 711 455 436 +#> [4,] 691 167 268 281 235 746 958 524 275 449 +#> [5,] 681 33 966 976 295 698 239 740 59 993 +#> [6,] 641 921 467 213 884 514 552 410 711 3 +#> [7,] 735 797 69 294 53 226 238 330 907 129 +#> [8,] 525 884 433 469 305 678 332 833 591 6 +#> [9,] 975 358 333 394 526 199 692 888 828 48 +#> [10,] 159 743 81 440 297 368 985 480 54 456 +#> [11,] 278 476 906 232 995 600 272 172 349 73 +#> [12,] 821 641 554 514 293 467 725 478 780 702 +#> [13,] 88 906 989 349 703 142 11 216 596 147 +#> [14,] 724 993 523 59 376 422 496 123 647 976 +#> [15,] 375 585 893 249 178 301 967 179 978 250 +#> [16,] 636 231 204 175 121 181 377 370 998 960 +#> [17,] 590 928 631 901 620 317 388 770 466 529 +#> [18,] 853 71 936 822 309 959 989 29 227 597 +#> [19,] 807 271 545 727 434 960 679 977 453 302 +#> [20,] 77 468 610 520 144 109 156 167 942 235 +#> [21,] 958 536 443 158 673 240 347 556 355 285 +#> [22,] 648 63 781 176 671 499 725 569 852 436 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778 459 148 344 296 127 +#> [44,] 522 899 892 419 705 437 653 576 146 513 +#> [45,] 93 913 299 598 110 409 294 626 408 334 +#> [46,] 402 465 86 976 978 124 350 681 261 848 +#> [47,] 290 722 707 206 753 533 328 512 750 198 +#> [48,] 59 594 394 374 647 248 724 518 526 5 +#> [49,] 156 109 601 116 77 383 587 484 942 504 +#> [50,] 794 158 443 240 558 744 155 748 879 536 +#> [51,] 494 377 693 365 644 745 712 55 972 121 +#> [52,] 318 965 201 137 182 143 907 602 568 37 +#> [53,] 512 69 330 154 7 784 143 707 441 797 +#> [54,] 138 440 203 783 612 67 368 115 10 78 +#> [55,] 181 204 721 365 609 104 377 51 448 121 +#> [56,] 984 842 827 897 564 713 312 415 96 190 +#> [57,] 310 437 419 930 742 705 895 932 44 899 +#> [58,] 694 668 551 918 75 89 751 409 778 652 +#> [59,] 724 976 48 33 523 14 993 5 394 647 +#> [60,] 26 830 989 983 868 141 633 404 386 902 +#> [61,] 275 64 758 825 840 554 471 487 800 27 +#> [62,] 459 778 30 110 102 72 335 817 203 385 +#> [63,] 499 648 852 731 436 22 947 569 129 584 +#> 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[825,] 471 554 641 61 852 569 758 688 194 800 +#> [826,] 280 841 987 535 606 292 746 674 994 171 +#> [827,] 897 56 984 412 414 300 244 338 96 564 +#> [828,] 342 526 618 356 333 779 394 186 698 845 +#> [829,] 209 429 630 357 343 868 221 655 560 251 +#> [830,] 983 404 786 221 26 479 785 812 141 364 +#> [831,] 802 160 877 639 267 615 675 125 169 876 +#> [832,] 314 150 98 306 423 463 619 810 612 961 +#> [833,] 184 270 141 785 525 8 26 678 425 469 +#> [834,] 603 929 399 904 262 988 196 614 22 569 +#> [835,] 263 732 119 224 350 482 568 182 992 135 +#> [836,] 837 137 189 120 996 330 118 354 784 474 +#> [837,] 836 189 354 120 137 543 118 113 474 330 +#> [838,] 909 741 542 799 36 357 697 343 490 184 +#> [839,] 90 492 313 815 615 851 971 801 730 935 +#> [840,] 806 758 275 999 97 789 520 61 954 410 +#> [841,] 826 535 987 606 280 674 171 756 746 626 +#> [842,] 564 56 312 95 984 415 713 190 96 328 +#> [843,] 390 777 205 341 202 642 926 111 953 323 +#> [844,] 647 956 217 372 277 376 406 248 123 48 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[865,] 285 556 883 347 302 956 151 536 863 21 +#> [866,] 529 320 348 254 38 857 694 792 693 559 +#> [867,] 192 498 402 360 257 627 165 465 261 835 +#> [868,] 221 983 346 630 26 829 429 211 560 404 +#> [869,] 347 761 217 372 180 964 295 779 186 698 +#> [870,] 258 578 385 81 444 817 944 656 962 308 +#> [871,] 250 847 41 361 849 555 470 890 513 760 +#> [872,] 307 771 571 253 103 511 575 123 933 991 +#> [873,] 366 632 681 105 887 85 738 361 740 943 +#> [874,] 540 2 179 168 223 720 978 528 15 951 +#> [875,] 291 76 550 804 912 801 582 862 900 406 +#> [876,] 802 475 637 831 160 496 457 997 877 267 +#> [877,] 831 169 548 639 267 802 24 457 160 796 +#> [878,] 581 477 813 924 79 579 748 509 980 920 +#> [879,] 748 215 532 500 534 233 558 50 79 924 +#> [880,] 163 329 957 200 750 722 710 290 996 408 +#> [881,] 960 326 453 463 998 488 718 306 807 727 +#> [882,] 427 567 521 115 170 592 183 23 860 763 +#> [883,] 151 556 382 865 347 991 148 956 273 677 +#> [884,] 433 469 305 525 467 8 514 6 173 921 +#> [885,] 614 249 432 139 179 399 353 218 375 809 +#> [886,] 908 667 519 111 938 174 685 946 446 323 +#> [887,] 943 366 738 873 105 717 858 632 675 555 +#> [888,] 819 818 771 372 549 510 594 48 779 575 +#> [889,] 565 683 920 35 509 337 477 307 980 379 +#> [890,] 485 41 871 649 250 978 178 555 375 849 +#> [891,] 811 236 659 194 986 384 695 546 114 149 +#> [892,] 522 44 634 387 942 468 899 653 793 386 +#> [893,] 15 112 375 585 969 178 967 617 405 259 +#> [894,] 805 187 791 473 193 843 360 737 362 202 +#> [895,] 274 612 517 98 78 932 314 423 150 420 +#> [896,] 819 647 510 724 277 199 527 549 888 395 +#> [897,] 827 56 96 244 300 564 984 412 338 414 +#> [898,] 134 184 537 8 833 525 542 428 332 108 +#> [899,] 44 522 419 146 437 761 964 180 610 892 +#> [900,] 727 912 153 212 664 807 992 434 550 582 +#> [901,] 393 716 388 17 371 990 631 570 317 916 +#> [902,] 387 386 776 633 973 919 658 892 914 246 +#> [903,] 450 563 370 381 282 16 363 82 636 289 +#> [904,] 256 225 139 583 506 616 658 399 973 633 +#> [905,] 505 920 581 424 715 924 878 452 337 672 +#> [906,] 995 476 11 173 216 786 232 13 413 349 +#> [907,] 143 37 318 562 797 474 531 762 925 982 +#> [908,] 886 519 938 667 685 946 111 691 411 820 +#> [909,] 741 838 36 697 357 567 654 343 427 80 +#> [910,] 640 816 599 798 927 325 177 861 311 354 +#> [911,] 769 579 676 509 980 477 79 337 188 104 +#> [912,] 153 632 664 582 219 105 900 423 150 738 +#> [913,] 409 45 93 299 252 598 562 762 75 264 +#> [914,] 265 981 591 733 999 552 97 410 892 386 +#> [915,] 823 968 166 765 352 728 604 662 508 25 +#> [916,] 570 458 441 176 512 707 226 129 750 680 +#> [917,] 407 100 309 822 661 125 71 851 815 839 +#> [918,] 58 551 778 89 694 668 780 478 459 62 +#> [919,] 387 504 942 653 963 902 246 892 973 699 +#> [920,] 581 565 878 813 337 477 509 980 905 683 +#> [921,] 552 455 410 97 344 266 6 591 3 514 +#> [922,] 378 92 775 403 461 657 862 406 677 863 +#> [923,] 222 931 926 197 355 488 673 553 323 679 +#> [924,] 878 672 581 813 806 748 789 715 879 532 +#> [925,] 797 808 294 531 474 907 373 37 327 319 +#> [926,] 323 673 777 953 923 519 843 111 222 744 +#> [927,] 599 589 177 856 910 426 120 640 656 189 +#> [928,] 631 620 279 17 662 739 590 32 621 946 +#> [929,] 603 262 569 641 213 773 834 6 471 711 +#> [930,] 677 57 310 151 314 437 76 895 804 419 +#> [931,] 355 331 923 488 673 197 718 255 222 208 +#> [932,] 437 705 274 895 423 643 105 742 98 522 +#> [933,] 481 229 818 991 382 714 260 774 273 771 +#> [934,] 859 216 749 703 13 454 308 476 906 88 +#> [935,] 782 480 297 517 219 479 801 90 615 418 +#> [936,] 29 227 853 172 18 675 272 796 71 548 +#> [937,] 816 768 414 861 107 244 359 68 910 412 +#> [938,] 588 519 908 886 371 685 777 323 111 926 +#> [939,] 530 682 242 744 486 794 790 240 430 380 +#> [940,] 170 730 998 231 115 370 586 121 719 313 +#> [941,] 587 692 484 728 49 949 604 109 401 156 +#> [942,] 653 504 919 892 77 387 246 20 419 468 +#> [943,] 555 740 717 887 366 649 681 871 849 361 +#> [944,] 126 308 551 753 416 385 668 870 918 767 +#> [945,] 511 422 577 379 657 862 35 42 376 864 +#> [946,] 519 240 621 691 958 323 32 886 667 908 +#> [947,] 129 767 780 725 499 731 63 803 436 626 +#> [948,] 505 715 608 905 766 787 83 672 194 806 +#> [949,] 501 622 941 692 823 604 765 405 613 726 +#> [950,] 593 695 126 336 669 478 243 12 944 293 +#> [951,] 795 432 528 485 890 649 464 369 451 624 +#> [952,] 772 315 953 186 362 277 642 539 510 647 +#> [953,] 673 642 926 952 323 843 341 285 536 958 +#> [954,] 520 43 128 468 733 524 148 97 273 275 +#> [955,] 589 177 670 445 927 157 856 354 426 599 +#> [956,] 372 217 844 347 991 869 865 285 883 151 +#> [957,] 163 880 200 996 589 189 329 120 836 837 +#> [958,] 21 691 536 667 162 443 4 347 946 240 +#> [959,] 822 71 309 100 407 917 18 788 661 851 +#> [960,] 881 392 998 727 302 807 463 19 745 453 +#> [961,] 150 423 340 801 420 517 612 76 297 78 +#> [962,] 684 660 979 502 495 702 573 335 210 706 +#> [963,] 246 919 942 653 387 902 419 481 310 504 +#> [964,] 295 180 761 146 899 698 869 98 105 610 +#> [965,] 52 483 283 696 132 318 91 137 201 143 +#> [966,] 239 740 366 681 105 5 295 267 873 964 +#> [967,] 708 15 292 969 764 994 178 397 280 893 +#> [968,] 716 662 393 166 915 701 823 388 84 279 +#> [969,] 112 284 617 238 967 259 708 893 261 327 +#> [970,] 69 689 133 298 710 339 112 238 441 7 +#> [971,] 851 815 839 313 90 730 398 106 661 550 +#> [972,] 644 684 704 783 578 440 203 979 694 67 +#> [973,] 633 699 902 591 658 387 776 225 386 506 +#> [974,] 627 850 135 446 318 397 257 131 201 360 +#> [975,] 9 358 394 333 526 828 48 199 888 845 +#> [976,] 59 33 5 848 350 993 523 465 845 86 +#> [977,] 367 721 561 19 545 377 392 355 42 55 +#> [978,] 890 375 485 178 617 871 555 41 250 46 +#> [979,] 210 706 684 495 660 335 962 502 233 247 +#> [980,] 260 382 911 477 683 878 769 337 151 991 +#> [981,] 410 552 733 999 678 914 265 921 72 954 +#> [982,] 762 651 37 174 264 162 562 205 497 913 +#> [983,] 830 404 705 364 812 786 868 274 386 221 +#> [984,] 56 312 842 827 713 415 435 190 289 897 +#> [985,] 207 311 10 81 258 159 381 23 282 418 +#> [986,] 117 799 833 236 134 678 659 811 8 425 +#> [987,] 606 826 535 841 634 280 674 218 574 144 +#> [988,] 645 603 711 855 431 596 803 305 616 142 +#> [989,] 703 597 13 216 147 172 60 88 29 272 +#> [990,] 140 570 901 198 388 533 916 17 458 611 +#> [991,] 382 883 956 556 307 151 229 933 260 185 +#> [992,] 664 212 153 119 732 900 244 912 632 482 +#> [993,] 14 59 5 724 976 496 376 123 239 48 +#> [994,] 292 708 280 535 356 618 764 826 847 342 +#> [995,] 906 476 173 232 11 786 600 195 30 404 +#> [996,] 836 189 957 722 837 880 137 47 329 290 +#> [997,] 438 160 831 125 876 815 877 407 917 802 +#> [998,] 730 940 370 170 960 881 313 418 745 115 +#> [999,] 733 981 410 840 806 914 552 97 273 954 +#> [1000,] 429 424 80 251 209 829 683 452 211 630 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] -#> [1,] 0.37440774 0.5196210 0.6317683 0.6323538 0.8849260 0.8892560 0.9006937 -#> [2,] 0.71849076 0.8712022 0.8970041 0.9249884 0.9328638 0.9559555 0.9912308 -#> [3,] 0.33816647 0.3531914 0.4294108 0.5196530 0.5297383 0.5577980 0.6443834 -#> [4,] 0.54020961 0.6153700 0.6328276 0.6360577 0.6708464 0.6854089 0.7296972 -#> [5,] 0.39339787 0.4755666 0.6066615 0.7330094 0.7611787 0.7646775 0.7696904 -#> [6,] 0.81778517 0.8393568 0.8864097 0.8899425 0.8930137 0.9004203 0.9592937 -#> [7,] 0.31336098 0.4734230 0.4790972 0.5125694 0.5484259 0.5577980 0.5839837 -#> [8,] 0.69429370 0.6999343 0.8040632 0.8374759 0.8581814 0.8620620 0.9037422 -#> [9,] 0.49870522 0.5159316 0.5161472 0.6504423 0.7226073 0.7458232 0.8078439 -#> [10,] 0.43286308 0.6523323 0.8222796 0.8646028 0.9897671 1.0654198 1.0962131 -#> [11,] 0.41437972 0.5560717 0.6561674 0.7609491 0.7669037 0.8590798 0.8752580 -#> [12,] 0.67937879 0.7172980 0.7480645 0.7544614 0.8050573 0.8083640 0.8481635 -#> [13,] 0.52070502 0.6684482 0.7688076 0.7823320 0.8670848 0.9316208 0.9584593 -#> [14,] 0.41137800 0.5708362 0.5865694 0.6090094 0.6456530 0.6700576 0.7213540 -#> [15,] 0.59022782 0.7694994 0.8918625 1.0814758 1.1290545 1.1519124 1.1534854 -#> [16,] 0.45092441 0.4958946 0.5104549 0.5122970 0.5530641 0.6073729 0.6617331 -#> [17,] 0.21549985 0.3255899 0.3544750 0.4664532 0.4715509 0.4860027 0.4879230 -#> [18,] 0.28419387 0.3691577 0.5267358 0.5449690 0.5967973 0.5985593 0.6283001 -#> [19,] 0.60423508 0.9040169 0.9165124 1.0119967 1.0878704 1.1063906 1.1405633 -#> [20,] 0.28229287 0.3660425 0.4033861 0.4280612 0.4487782 0.4534048 0.4866329 -#> [21,] 0.53505778 0.5400066 0.6122105 0.7306741 0.7555545 0.8497530 0.8610543 -#> [22,] 0.21870056 0.3441216 0.3792610 0.4279712 0.4352831 0.4734599 0.4760026 -#> [23,] 0.69971236 0.7776224 0.7876056 0.7994573 0.8586295 0.8605815 0.8819863 -#> [24,] 0.38762218 0.4285535 0.4381970 0.5334751 0.6623435 0.6643578 0.6768378 -#> [25,] 0.53268027 0.6801124 0.6909970 0.7254717 0.7283049 0.7764582 0.7892815 -#> [26,] 0.43329051 0.5178387 0.5360601 0.5642680 0.5666451 0.5944099 0.6307465 -#> [27,] 0.30426516 0.3859418 0.4251838 0.4719293 0.5115982 0.5455742 0.5471404 -#> [28,] 0.42629419 0.4520204 0.4723838 0.5216885 0.5984226 0.6227925 0.6259317 -#> [29,] 0.35403636 0.3840472 0.3876222 0.5544435 0.5753758 0.6441560 0.6696159 -#> [30,] 0.70399445 0.8185857 0.8728922 0.8887481 0.9390512 0.9504278 0.9534198 -#> [31,] 0.54620478 0.5678647 0.5794177 0.5859331 0.6016578 0.6631736 0.6973421 -#> [32,] 0.32203562 0.3870360 0.4510068 0.4565310 0.5181098 0.5219980 0.5803326 -#> [33,] 0.59842366 0.6252209 0.6259422 0.6469902 0.6731264 0.6884798 0.6987678 -#> [34,] 0.42295739 0.4799947 0.4822970 0.5100279 0.7521974 0.7531811 0.7617941 -#> [35,] 0.35319143 0.4240218 0.5113439 0.5796505 0.5815733 0.6434926 0.6531255 -#> [36,] 0.28798006 0.3680010 0.4005744 0.4271074 0.4391532 0.4795506 0.4806273 -#> [37,] 0.46960421 0.5704743 0.5713747 0.7433508 0.8263683 0.9032159 0.9188813 -#> [38,] 0.16379892 0.4343415 0.4634704 0.4812532 0.5262166 0.5456857 0.5900957 -#> [39,] 0.32942541 0.3488723 0.4544843 0.4706264 0.4737524 0.5055472 0.5182899 -#> [40,] 0.46960421 0.5094448 0.8000923 0.8405120 0.8940577 0.9118995 0.9520868 -#> [41,] 0.56070439 0.8039226 0.8458749 0.9259973 0.9706053 1.0019942 1.0221179 -#> [42,] 0.26218164 0.3335003 0.4401235 0.4973035 0.5241576 0.5242244 0.5406525 -#> [43,] 0.63571193 0.7382393 0.9638590 1.0666145 1.0770948 1.0874487 1.0945852 -#> [44,] 0.98042680 0.9936126 1.0111360 1.0134749 1.0909802 1.1755655 1.2292283 -#> [45,] 0.96471043 1.1360815 1.2717714 1.5203067 1.5482659 1.7086648 1.7576296 -#> [46,] 0.92897311 1.0798204 1.2356634 1.2538734 1.2887295 1.3175664 1.3497319 -#> [47,] 0.89238862 0.9257327 0.9620712 0.9739201 0.9943237 1.1340280 1.1505984 -#> [48,] 0.44108247 0.4464113 0.4928736 0.5033910 0.5245417 0.5959135 0.6397210 -#> [49,] 0.39469461 0.3966216 0.4374424 0.4706606 0.4847577 0.4929153 0.6373957 -#> [50,] 0.62219362 0.6576409 0.7388219 0.7729828 0.7943559 0.8368663 0.8794120 -#> [51,] 0.30483279 0.4510571 0.5991109 0.6021361 0.6431851 0.6820095 0.6822274 -#> [52,] 0.16342217 0.3812533 0.4723838 0.4917449 0.5672412 0.6012372 0.6016578 -#> [53,] 0.47146486 0.6398191 0.6982818 0.7187265 0.7796118 0.9114307 0.9247849 -#> [54,] 0.32811395 0.4597374 0.5698973 0.6545683 0.7488184 0.7636551 0.8007657 -#> [55,] 0.26351243 0.2898434 0.3279429 0.3904213 0.4334976 0.4823644 0.5486852 -#> [56,] 0.77615810 0.8922126 0.9410711 0.9435230 0.9454779 0.9498508 0.9649450 -#> [57,] 1.01660350 1.0576989 1.0578482 1.0948736 1.1998130 1.2010383 1.2067120 -#> [58,] 0.31354966 0.3359187 0.4454083 0.5013944 0.5523149 0.5649255 0.6207553 -#> [59,] 0.54093924 0.6735608 0.6915943 0.7171179 0.7925492 0.8258154 0.8404956 -#> [60,] 0.39442631 0.5581936 0.6069573 0.6558298 0.6593543 0.6951015 0.7095590 -#> [61,] 0.42184296 0.4722252 0.4810669 0.5213441 0.5457783 0.5978246 0.6324206 -#> [62,] 0.16342217 0.4269498 0.4520204 0.5193114 0.6631736 0.7057671 0.7128491 -#> [63,] 0.22126147 0.3532780 0.4414033 0.4531874 0.4647006 0.5107906 0.5602252 -#> [64,] 0.33416754 0.4265877 0.4359787 0.4958946 0.5433790 0.6881546 0.7001640 -#> [65,] 0.23679109 0.2815989 0.3487280 0.4295142 0.5025708 0.5573627 0.6129167 -#> [66,] 0.44637144 0.5003019 0.5529245 0.6283844 0.6367535 0.6652489 0.7487575 -#> [67,] 0.33537141 0.5092862 0.5898424 0.5930201 0.6001093 0.6728713 0.6872553 -#> [68,] 0.37747986 0.4014896 0.4185361 0.4482289 0.5682722 0.5703633 0.6131069 -#> [69,] 0.28798006 0.3927377 0.4251838 0.4288953 0.4558640 0.4906690 0.5028235 -#> [70,] 0.54611205 0.6926403 0.7215123 0.7658092 0.8311058 0.9560666 1.0400445 -#> [71,] 0.47206866 0.6357119 0.6360646 0.6926937 0.6989833 0.7644924 0.7893722 -#> [72,] 0.21870056 0.2927399 0.2999043 0.3591081 0.4487782 0.4585332 0.4617932 -#> [73,] 0.30035462 0.5001202 0.5593482 0.5631557 0.6212020 0.6350773 0.6398905 -#> [74,] 0.30645224 0.5000964 0.5348828 0.5447165 0.5564983 0.5910982 0.6571445 -#> [75,] 0.73137409 0.7876294 0.7927559 0.9257327 0.9287029 0.9333650 0.9570342 -#> [76,] 0.40150325 0.4310171 0.4984940 0.5758994 0.5810228 0.5926704 0.7203417 -#> [77,] 0.42677834 0.5655940 0.5780670 0.5869566 0.5955668 0.6814462 0.6902135 -#> [78,] 0.24603251 0.4255550 0.5281018 0.6320025 0.6766829 0.6816221 0.7057718 -#> [79,] 0.44042104 0.5181098 0.5632566 0.5853790 0.5921010 0.6301329 0.6573707 -#> [80,] 1.17838407 1.4621528 1.4891457 1.6146337 1.6568477 1.6983617 1.7513378 -#> [81,] 0.97605309 1.0511801 1.0518557 1.0868473 1.1142342 1.1686700 1.1819750 -#> [82,] 0.30897320 0.4068803 0.5589393 0.5719344 0.6460923 0.6503385 0.7187265 -#> [83,] 0.29770340 0.5268685 0.5810016 0.5937661 0.6260777 0.6646483 0.6767827 -#> [84,] 0.73278181 0.8382064 0.8712902 0.9346618 0.9439012 1.1061611 1.2219257 -#> [85,] 0.63396681 0.7252764 0.8155012 0.8245226 0.8380611 0.9923770 0.9993802 -#> [86,] 0.32458341 0.3933979 0.5125694 0.6410320 0.6680742 0.6691817 0.6752053 -#> [87,] 0.66478616 0.7107108 0.7159460 0.7237704 0.7282900 0.7332213 0.7434692 -#> [88,] 0.30996952 0.3251140 0.3526699 0.5128921 0.5429316 0.6395256 0.6586995 -#> [89,] 0.57281868 0.5758453 0.7006440 0.7207689 0.7258189 0.7296972 0.7965332 -#> [90,] 0.39852216 0.4400132 0.4526669 0.5161778 0.5400066 0.5461164 0.6188844 -#> [91,] 0.48933920 0.6051880 0.6382770 0.6502117 0.6693098 0.7108046 0.7692118 -#> [92,] 0.62260206 0.9165124 1.3085745 1.3806910 1.5870406 1.6285594 1.6554045 -#> [93,] 0.24603251 0.3878538 0.4591714 0.5178387 0.6585143 0.6621358 0.6810870 -#> [94,] 0.30897320 0.6290901 0.6398191 0.6496431 0.7823336 0.7843597 0.8580708 -#> [95,] 0.04497083 0.5930201 0.6119075 0.6270330 0.6429569 0.6600823 0.6656487 -#> [96,] 0.62583943 0.7505155 0.9832066 1.0235993 1.0636559 1.1201775 1.1666406 -#> [97,] 0.35144018 0.6382641 0.7079532 0.7095187 0.7659251 0.8091797 0.8513275 -#> [98,] 0.31801358 0.3401989 0.4208660 0.4271074 0.5054884 0.5429898 0.5665091 -#> [99,] 0.21601832 0.3354368 0.3484150 0.3856758 0.5485465 0.5505203 0.5557009 -#> [100,] 0.41366461 0.5126158 0.5594749 0.5739571 0.6100644 0.6774000 0.7450978 -#> [101,] 0.66441475 0.7549090 0.7557675 0.7765707 0.7936885 0.7990604 0.8064913 -#> [102,] 0.46871727 0.5296746 0.6837906 0.7008518 0.7655323 0.8326505 0.8770413 -#> [103,] 0.76556108 0.7659251 0.7682346 0.9744743 1.0745172 1.1330448 1.1399362 -#> [104,] 0.30557984 0.3708418 0.3794128 0.5570809 0.5645912 0.6157789 0.6886539 -#> [105,] 0.42014793 0.4929323 0.5075120 0.5230954 0.5328842 0.5333565 0.5456871 -#> [106,] 0.60144678 0.9305805 0.9306688 0.9501760 0.9659964 1.0453325 1.0542669 -#> [107,] 0.26191941 0.3335003 0.3864874 0.4739008 0.4901712 0.5262166 0.5277461 -#> [108,] 0.53526753 0.5824204 0.5919049 0.6908379 0.6932066 0.6949184 0.7062489 -#> [109,] 0.32366596 0.3816655 0.5062977 0.5485465 0.6035720 0.6073536 0.6347685 -#> [110,] 0.65787838 0.8523714 1.1802698 1.3560210 1.3630859 1.3759184 1.3968075 -#> [111,] 0.50862957 0.6866783 0.7279623 0.7948560 0.8165282 0.8374849 0.8375440 -#> [112,] 1.55852407 1.5673626 1.6632869 1.6701479 1.7246864 1.7747710 1.7934094 -#> [113,] 0.36644829 0.4296335 0.5638167 0.6098110 0.6319255 0.6431851 0.6599731 -#> [114,] 0.27515565 0.7833691 0.7984154 0.8626899 0.8700936 0.8907433 0.9139923 -#> [115,] 0.38214516 0.6842062 0.7184307 0.8236605 0.8753309 0.9271065 0.9900302 -#> [116,] 0.45027271 0.5457783 0.6602727 0.6741195 0.6863318 0.7091045 0.7172088 -#> [117,] 0.56160326 0.6429118 0.7370743 0.7549090 0.7710974 0.7860382 0.7889454 -#> [118,] 0.23376156 0.3276499 0.3441216 0.3508093 0.5015056 0.5053651 0.5176712 -#> [119,] 0.31583768 0.3737276 0.4558761 0.5437083 0.5458634 0.5974658 0.6560051 -#> [120,] 0.34765690 0.3987382 0.4601763 0.4879230 0.5018809 0.5067646 0.5416199 -#> [121,] 0.58701147 0.5952777 0.6049412 0.6119075 0.6268535 0.6821388 0.7100069 -#> [122,] 0.38404719 0.4381970 0.4623692 0.4922980 0.5168948 0.5305939 0.5628925 -#> [123,] 0.35215632 0.3951595 0.5682765 0.5992893 0.6292636 0.6469049 0.6536885 -#> [124,] 0.37061007 0.3711900 0.3871380 0.5807237 0.5986405 0.6415935 0.6531730 -#> [125,] 1.16275048 1.2245416 1.2926636 1.3602846 1.3698820 1.4084729 1.4638888 -#> [126,] 0.50990188 0.6099109 0.7001640 0.7341859 0.7767447 0.7813376 0.7953612 -#> [127,] 0.38172029 0.5355710 0.5596657 0.5633162 0.5823183 0.5929949 0.6202899 -#> [128,] 0.32678210 0.4734693 0.5506425 0.5621268 0.5665520 0.5690209 0.6215555 -#> [129,] 0.32240302 0.5219120 0.5762402 0.6049412 0.6920584 0.7313320 0.7474944 -#> [130,] 0.49883170 0.6053445 0.7167148 0.7190791 0.7574800 0.7613518 0.7938191 -#> [131,] 0.53564742 0.6130721 0.6764559 0.7958312 0.8108554 0.8286614 0.8786131 -#> [132,] 0.42579509 0.5151876 0.5341619 0.5671062 0.5755747 0.5903237 0.6075006 -#> [133,] 0.35147778 0.3636692 0.3647389 0.4423911 0.5608529 0.5691907 0.5779306 -#> [134,] 0.39681078 0.4251424 0.5178433 0.5361624 0.6805308 0.6867334 0.7135838 -#> [135,] 0.30942474 0.4231522 0.4267138 0.5000046 0.5050926 0.5430369 0.5739390 -#> [136,] 0.41118245 0.4267375 0.4318495 0.4339700 0.4768650 0.5465379 0.5586927 -#> [137,] 0.61307211 0.6772350 0.7043720 0.7118153 0.8149391 0.8453201 0.8556896 -#> [138,] 0.43229124 0.5113181 0.5293639 0.5827702 0.5852424 0.5925780 0.6027970 -#> [139,] 0.53526753 0.6705211 0.7233836 0.7638404 0.7897678 0.7912746 0.8126103 -#> [140,] 0.25761193 0.3248417 0.3820281 0.4265877 0.4509244 0.6123456 0.6371633 -#> [141,] 0.21551060 0.2478560 0.4984940 0.5043320 0.5153898 0.5377246 0.5380901 -#> [142,] 0.23215070 0.3977784 0.4000386 0.4393166 0.4936062 0.5116403 0.5226528 -#> [143,] 0.32484169 0.4430272 0.4500779 0.4606841 0.5433790 0.5530641 0.5891766 -#> [144,] 0.70360030 0.7115000 0.7115202 0.7263464 0.7909070 0.8401681 0.8523100 -#> [145,] 0.53097493 0.5426041 0.5660205 0.8089592 0.9680723 0.9860777 1.0241528 -#> [146,] 0.36684834 0.4257951 0.4369957 0.4393166 0.4547368 0.5026818 0.5330085 -#> [147,] 0.34840672 0.4549819 0.4564327 0.5024490 0.5335413 0.5341757 0.5377761 -#> [148,] 0.68439506 0.7249098 0.8863247 0.9094872 0.9198462 0.9435541 0.9756199 -#> [149,] 0.48461201 0.6281582 0.7102363 0.7320435 0.7869123 0.8343406 0.8597976 -#> [150,] 0.27767287 0.2814221 0.2841270 0.4878354 0.4954478 0.4975521 0.5313888 -#> [151,] 0.37046721 0.4650588 0.4816673 0.4861472 0.5433768 0.5727964 0.5752032 -#> [152,] 0.56256667 0.5770576 0.6789019 0.7157721 0.7351366 0.7589517 0.7694994 -#> [153,] 0.54053002 0.5560717 0.5999067 0.7184908 0.7479200 0.7761110 0.8660131 -#> [154,] 0.54053002 0.6561674 0.8307043 0.8541981 0.8712022 0.9810323 0.9903409 -#> [155,] 0.37489759 0.4336603 0.7790019 0.8323242 0.8554640 0.8577206 0.9099627 -#> [156,] 0.30569236 0.3262743 0.3579049 0.3630452 0.4926383 0.5405824 0.6052169 -#> [157,] 0.64667460 0.7669037 0.8257021 0.8541981 0.9243470 0.9655508 1.0201939 -#> [158,] 0.37740552 0.4020145 0.4190839 0.4591714 0.5281018 0.5550570 0.5788570 -#> [159,] 0.33547237 0.3463819 0.4977218 0.5180707 0.5242244 0.5635180 0.5817520 -#> [160,] 0.35829338 0.5718766 0.5747591 0.5779698 0.6522162 0.7144527 0.7876369 -#> [161,] 0.50669338 0.5422210 0.5631950 0.5689447 0.6092181 0.6320838 0.6627306 -#> [162,] 0.51045490 0.5219120 0.5858995 0.5937850 0.7252764 0.8000330 0.8418996 -#> [163,] 0.37061007 0.4042716 0.5416616 0.6091344 0.6365370 0.7418901 0.7538221 -#> [164,] 0.51229703 0.5239113 0.5903342 0.5937850 0.6369687 0.6395027 0.7197476 -#> [165,] 0.54908168 0.9813251 1.1372137 1.1695009 1.2031595 1.2249379 1.2830620 -#> [166,] 0.61272048 0.7611578 0.7883182 0.8482370 0.8973138 0.8973912 0.9098426 -#> [167,] 0.24308049 0.2554877 0.3695717 0.4638763 0.4841923 0.5672204 0.5678705 -#> [168,] 0.38041963 0.4770930 0.4780638 0.4847577 0.5092416 0.5473634 0.5519264 -#> [169,] 0.26265001 0.6764559 0.6801124 0.7182474 0.8034945 0.8243029 0.9143408 -#> [170,] 0.26578713 0.4558640 0.5643588 0.5758453 0.6031906 0.6038029 0.6317046 -#> [171,] 0.17370456 0.4308061 0.4805783 0.4823644 0.5499564 0.5587297 0.6249264 -#> [172,] 0.26941305 0.4017908 0.5415747 0.5470445 0.5757414 0.5872002 0.6124346 -#> [173,] 0.87618145 0.9526264 0.9565228 0.9713093 1.0146814 1.0413455 1.1054185 -#> [174,] 0.61152392 0.7089670 0.7760067 0.9366138 0.9987533 1.0345146 1.0764891 -#> [175,] 0.55117629 0.6955637 0.8672199 0.9175538 1.0857738 1.0870652 1.1226844 -#> [176,] 0.51603535 0.5265768 0.5388508 0.5712551 0.5801553 0.6707166 0.7064780 -#> [177,] 0.57667920 0.6744389 0.7106979 0.7830243 0.8084727 0.8349234 0.8471915 -#> [178,] 0.35086757 0.4558084 0.4623692 0.5991109 0.5998957 0.6098110 0.6568489 -#> [179,] 0.59394151 0.6259422 0.6412760 0.7072447 0.9181475 0.9528680 0.9805333 -#> [180,] 0.45973796 0.5236926 0.5413020 0.5590320 0.5625617 0.5949277 0.6129280 -#> [181,] 0.48466368 0.4966760 0.5003019 0.5597563 0.5807237 0.6365370 0.6573707 -#> [182,] 0.49667604 0.5257725 0.5446453 0.5665520 0.5922374 0.5939369 0.6009061 -#> [183,] 0.44863069 0.4890375 0.4972940 0.5129245 0.5339640 0.5405824 0.5594749 -#> [184,] 1.54969454 1.6058166 1.7903249 1.8486250 1.9415717 1.9861202 1.9921299 -#> [185,] 0.46840057 0.5643122 0.7830058 0.8577187 0.9074240 0.9099627 0.9585011 -#> [186,] 0.21503155 0.4423911 0.4427498 0.4470150 0.4800804 0.4852145 0.5140889 -#> [187,] 0.54488025 0.5484068 0.6032080 0.6201989 0.6812469 0.6997092 0.7071064 -#> [188,] 0.55211975 0.5650773 0.6230229 0.7717706 0.7985339 0.8089592 0.8240324 -#> [189,] 0.38410060 0.6421988 0.6741195 0.6748141 0.7148040 0.7160836 0.7192255 -#> [190,] 0.29678858 0.3855973 0.3987382 0.4023448 0.4417458 0.4929323 0.5541087 -#> [191,] 0.83653302 0.9011914 1.1703316 1.1948539 1.3102195 1.3296849 1.3600533 -#> [192,] 0.25389733 0.3532200 0.5986405 0.6218608 0.6532183 0.6823172 0.6910984 -#> [193,] 0.22947858 0.3398413 0.4375047 0.5370750 0.5849706 0.5936046 0.5978990 -#> [194,] 0.40798941 0.4111824 0.4547087 0.4821675 0.5014282 0.5111314 0.5392919 -#> [195,] 0.42611375 0.5618296 0.6964333 0.8322004 0.9590741 0.9636064 0.9893164 -#> [196,] 0.40201451 0.4776085 0.5035358 0.5344354 0.5360601 0.5955668 0.6228103 -#> [197,] 0.46147713 0.6051880 0.6178127 0.7489781 0.7982087 0.8318041 0.9819342 -#> [198,] 0.90254645 1.1476636 1.2435210 1.2555075 1.2713360 1.2953993 1.2964965 -#> [199,] 0.35295760 0.4725503 0.6273540 0.6553937 0.6742267 0.7099813 0.7272521 -#> [200,] 0.22003989 0.6339161 0.6577880 0.7028651 0.7089670 0.7099221 0.7364444 -#> [201,] 1.30424483 1.3801370 1.6572063 1.6867596 1.7135375 1.7356769 1.7962741 -#> [202,] 0.96348909 1.1220217 1.2021038 1.2176376 1.3685976 1.4039036 1.4407800 -#> [203,] 0.72611264 0.8111915 0.8342606 0.8372628 0.8585798 0.8709668 1.0290069 -#> [204,] 0.26218164 0.3876053 0.4977218 0.5012997 0.5277461 0.5384173 0.5414872 -#> [205,] 0.32203562 0.4016753 0.4300057 0.5546442 0.5858126 0.5875525 0.6082550 -#> [206,] 0.37711935 0.4536032 0.5418304 0.5571568 0.5603790 0.5993868 0.6085266 -#> [207,] 1.29870348 1.4759336 1.5193207 1.6065617 1.6335442 1.6481263 1.6744164 -#> [208,] 0.41828293 0.4505434 0.5149747 0.5504608 0.5652963 0.6662338 0.6896558 -#> [209,] 1.09602517 1.3455591 1.3968001 1.4076033 1.4311583 1.4532473 1.4562446 -#> [210,] 0.23259817 0.2557204 0.3945140 0.4285865 0.5073673 0.5450982 0.5976732 -#> [211,] 0.57035503 0.6708464 0.6737630 0.7192255 0.7207689 0.7496445 0.7887201 -#> [212,] 0.39122971 0.4981192 0.5229825 0.5269430 0.5292013 0.5795555 0.5996315 -#> [213,] 0.35637992 0.4327384 0.4632784 0.4996657 0.5418139 0.5668559 0.5799450 -#> [214,] 0.50944477 0.5110197 0.5355926 0.5386034 0.5713747 0.5859775 0.6705592 -#> [215,] 0.34791594 0.5351012 0.5943828 0.6316720 0.6639791 0.7338786 0.7368381 -#> [216,] 0.27087776 0.2839025 0.3817311 0.5293472 0.5908624 0.6269716 0.6411617 -#> [217,] 0.39744238 0.4157032 0.4207887 0.5008248 0.5621268 0.5672412 0.6119784 -#> [218,] 0.33093201 0.5025979 0.5581936 0.5705094 0.5770479 0.5935215 0.5974658 -#> [219,] 0.47130002 0.4807848 0.5290914 0.6324410 0.6944847 0.7013259 0.7344657 -#> [220,] 0.50139444 0.6117694 0.6213244 0.6379400 0.6655542 0.7627878 0.7797618 -#> [221,] 0.17049041 0.4410825 0.4612374 0.4953889 0.5724994 0.6252209 0.7118564 -#> [222,] 0.95693748 1.1132546 1.1244440 1.1303904 1.2668734 1.3012856 1.3358036 -#> [223,] 0.28983255 0.3401989 0.3488723 0.4446433 0.4599618 0.4971177 0.5089164 -#> [224,] 0.68358791 0.7398657 0.8153097 0.9655508 1.0165439 1.0306387 1.0955916 -#> [225,] 0.52787810 0.6537160 0.7764582 0.8385543 0.8581661 0.9558473 0.9584774 -#> [226,] 0.47700844 0.5216885 0.5275571 0.5504608 0.6699999 0.6742696 0.6864118 -#> [227,] 0.40229399 0.4330564 0.4653143 0.4910223 0.5192757 0.5500262 0.5755747 -#> [228,] 0.16765161 0.3794128 0.3972535 0.4516268 0.5227325 0.5984751 0.7192160 -#> [229,] 0.56725251 0.5680787 0.5804492 0.6965240 0.7132296 0.7339512 0.7397689 -#> [230,] 0.53481399 0.6018809 0.6580955 0.6617331 0.6902225 0.7393891 0.7903442 -#> [231,] 0.56754181 0.6221936 0.7386678 0.7643205 0.7936885 0.8099410 0.8349234 -#> [232,] 0.33706501 0.5086939 0.5351012 0.5672952 0.6229590 0.6575454 0.7062489 -#> [233,] 0.51643360 0.5335565 0.5571488 0.5878117 0.7780923 0.8003594 0.8174984 -#> [234,] 0.38920400 0.6969675 0.7000426 0.7243613 0.7523700 0.7669118 0.7962175 -#> [235,] 0.37712295 0.3991980 0.5946666 0.6124585 0.6698246 0.7125412 0.7172980 -#> [236,] 0.34385430 0.3561176 0.5080554 0.5570809 0.5827833 0.6707290 0.6944958 -#> [237,] 0.35853973 0.3927377 0.4391532 0.4562070 0.4633042 0.4707987 0.5236926 -#> [238,] 0.59462859 0.6323538 0.6356349 0.6532442 0.7442849 0.7569891 0.8079612 -#> [239,] 0.59284156 0.6264562 0.8839687 0.8899777 0.9636064 0.9918935 1.0633670 -#> [240,] 0.24728549 0.5359625 0.5978341 0.6676988 0.7735319 0.7764392 0.7795528 -#> [241,] 0.39744238 0.4338809 0.4520274 0.5868525 0.6012372 0.6065631 0.6152847 -#> [242,] 0.37716013 0.4836585 0.5110197 0.5421546 0.5715196 0.6801882 0.6905674 -#> [243,] 0.28394795 0.3820281 0.3876899 0.3974501 0.4430272 0.5167141 0.5183246 -#> [244,] 0.82430292 0.9597073 0.9680445 1.3547966 1.3548550 1.3607090 1.3921615 -#> [245,] 0.57624017 0.5876477 0.6268535 0.6270330 0.6747779 0.6848974 0.7931200 -#> [246,] 0.31299205 0.5034560 0.5043867 0.5419253 0.5535397 0.5575974 0.7085888 -#> [247,] 0.64262965 0.8921008 0.9305805 0.9339677 1.0069366 1.0193305 1.1414821 -#> [248,] 0.48782602 0.5189966 0.5340295 0.5627590 0.5878117 0.7211326 0.7482774 -#> [249,] 0.29951135 0.4259536 0.4972859 0.6099927 0.6159928 0.6249264 0.6333368 -#> [250,] 1.20210381 1.5519966 1.7618684 1.9120597 1.9633872 2.0200774 2.0272466 -#> [251,] 0.35031851 0.4776085 0.5788570 0.6083226 0.6146147 0.6818975 0.7005643 -#> [252,] 0.32958296 0.3974501 0.4641185 0.4644079 0.4666262 0.5781133 0.5823183 -#> [253,] 0.64262965 0.7900258 0.9860777 0.9909285 1.0919575 1.1173512 1.1255470 -#> [254,] 0.31801358 0.3946047 0.5089164 0.6378590 0.6576409 0.6856637 0.6870499 -#> [255,] 0.56754181 0.6402904 0.7009357 0.7173158 0.7729828 0.9631997 1.0913812 -#> [256,] 0.34385430 0.5192776 0.6300583 0.6377344 0.7117716 0.7386289 0.7809774 -#> [257,] 0.22952545 0.3700009 0.4237522 0.4269215 0.4446646 0.4706503 0.4720348 -#> [258,] 0.44150652 0.8000399 0.9923017 1.1193029 1.2113795 1.2947642 1.3275814 -#> [259,] 0.85237136 1.0701032 1.2366975 1.4143776 1.5372939 1.5401955 1.6497181 -#> [260,] 0.34800816 0.4439292 0.4792568 0.5645975 0.5678585 0.5823287 0.5922374 -#> [261,] 0.35396396 0.4361154 0.4656768 0.4666262 0.5121672 0.5249327 0.5773453 -#> [262,] 0.71907907 0.7288427 0.7508510 0.7767205 0.7890183 0.9310220 0.9435470 -#> [263,] 0.29770340 0.4141110 0.5676076 0.6408758 0.6567339 0.6658709 0.7265516 -#> [264,] 0.19402179 0.2678304 0.3317761 0.5779306 0.5793657 0.5922482 0.6011110 -#> [265,] 0.52654089 0.5694851 0.7143330 0.7409058 0.7444994 0.7695044 0.8202393 -#> [266,] 0.43286308 0.6539635 0.7156137 1.0338277 1.0880982 1.1014870 1.2061600 -#> [267,] 0.36675682 0.5501011 0.6080251 0.6144638 0.6671930 0.6740908 0.6979966 -#> [268,] 0.59586692 0.7958265 0.8402377 0.8576936 0.9461251 0.9771705 1.0383945 -#> [269,] 0.54260412 0.5650773 0.6896558 0.7013672 0.8024806 0.8243659 0.8619546 -#> [270,] 0.22500973 0.4578056 0.5132158 0.5347966 0.5573906 0.5618274 0.5656646 -#> [271,] 0.26941305 0.2701207 0.4742903 0.5017003 0.5199536 0.5307726 0.5483506 -#> [272,] 0.48405150 0.5934796 0.7977922 0.8339237 0.8528246 0.9053046 0.9145482 -#> [273,] 0.50643364 0.6628639 0.7460642 0.7497779 0.7905453 0.8047521 0.8092070 -#> [274,] 0.56440130 0.6220324 0.6527405 0.7055652 0.7417904 0.7430240 0.7638724 -#> [275,] 0.64111107 0.7362176 0.7411347 0.8071888 0.9011914 0.9139025 0.9205592 -#> [276,] 0.48583868 1.1128216 1.1280921 1.2256428 1.2970298 1.3186694 1.3719151 -#> [277,] 0.27111546 0.5458634 0.5522195 0.5631557 0.6328689 0.6480226 0.6719849 -#> [278,] 0.32240302 0.5092862 0.5285431 0.5858995 0.6302899 0.6656487 0.6848974 -#> [279,] 0.54272310 0.9510885 1.0333731 1.0378200 1.0842766 1.0886630 1.1258774 -#> [280,] 0.44001319 0.4482383 0.5350578 0.5680075 0.7093066 0.7229251 0.7477676 -#> [281,] 0.48782602 0.5382450 0.6441751 0.6856915 0.7746998 0.8234598 0.8382064 -#> [282,] 0.63216176 0.6843951 0.7033122 0.7206454 0.8457100 0.8556248 0.9346566 -#> [283,] 0.20119578 0.4330050 0.5292013 0.6444889 0.6596934 0.6674573 0.7008368 -#> [284,] 0.41791948 0.5500002 0.5616033 0.6644147 0.6684482 0.7115284 0.7714425 -#> [285,] 0.28122955 0.2898325 0.3042652 0.3680010 0.4208660 0.4288953 0.4544843 -#> [286,] 0.83117402 0.9536993 1.0759830 1.0794063 1.0797320 1.1290180 1.2017439 -#> [287,] 0.16765161 0.2524951 0.3055798 0.4410636 0.4944418 0.5827833 0.6699247 -#> [288,] 0.56499395 0.6104109 0.6494149 0.6533586 0.6589826 0.6912301 0.7058457 -#> [289,] 0.56128240 0.6060546 0.6891124 0.7835165 0.8987008 0.9107711 0.9328638 -#> [290,] 0.44254775 0.4739008 0.4938839 0.4973035 0.5154688 0.5364357 0.5900000 -#> [291,] 0.43114166 0.5037232 0.7502026 0.8022048 0.8222796 0.9218647 0.9456269 -#> [292,] 0.28122955 0.3585397 0.4005744 0.4599618 0.4719293 0.4737524 0.4941077 -#> [293,] 0.45982563 0.4627462 0.6790274 0.7352688 0.7444994 0.7550201 0.8146329 -#> [294,] 0.33093201 0.3737276 0.4075892 0.4965954 0.5459429 0.6212020 0.6413108 -#> [295,] 1.22922834 1.6228459 1.7320149 1.8213842 1.9268269 2.0307088 2.0696552 -#> [296,] 0.69835149 0.7256993 0.7417198 0.8211896 0.8380895 0.8780206 0.8932582 -#> [297,] 0.24047378 0.3771601 0.4302289 0.4714800 0.5026818 0.5079815 0.5116403 -#> [298,] 0.97717053 1.0341550 1.1530179 1.1615413 1.1964910 1.2109834 1.2236306 -#> [299,] 0.46871727 0.5940345 0.6718302 0.6963547 0.7474774 0.7541661 0.7727392 -#> [300,] 0.40337394 0.4141110 0.4921739 0.4987909 0.5418304 0.6260777 0.6305279 -#> [301,] 0.38832700 0.5591449 0.7470331 0.8280040 0.9345298 1.0007794 1.0097257 -#> [302,] 1.05544757 1.0864902 1.2096848 1.2278592 1.2559593 1.3363969 1.3384323 -#> [303,] 1.05931979 1.4083979 1.5475604 1.5779953 1.5884435 1.6557957 1.7377046 -#> [304,] 0.61747662 0.6944256 0.7058491 0.7141267 0.7232051 0.7310167 0.7342439 -#> [305,] 0.99875331 1.1196969 1.1629325 1.1712476 1.1934590 1.2020231 1.2147471 -#> [306,] 0.16044299 0.3508093 0.3586839 0.3947949 0.4432703 0.5215421 0.5766561 -#> [307,] 0.75555445 0.7985300 0.9673132 1.0086205 1.0165799 1.0217857 1.0825259 -#> [308,] 0.19402179 0.3555067 0.4337352 0.5723201 0.5901790 0.6238143 0.6588093 -#> [309,] 0.15739874 0.2927399 0.3094247 0.3420135 0.4140120 0.4279712 0.4617395 -#> [310,] 0.42526236 0.5833116 0.6245978 0.6534410 0.6637959 0.6728224 0.7151564 -#> [311,] 0.65948086 0.7622319 0.7696701 0.7931384 0.8242023 0.8477518 0.9194493 -#> [312,] 0.38594183 0.4376983 0.4930348 0.6038293 0.6202930 0.6476761 0.6973202 -#> [313,] 0.29619568 0.5594014 0.5688117 0.5864253 0.5870340 0.6421234 0.6713917 -#> [314,] 0.27111546 0.5001202 0.5686945 0.6167773 0.6353030 0.7064299 0.7158896 -#> [315,] 0.45340478 0.4768650 0.4864367 0.4908308 0.4909328 0.5154688 0.5219980 -#> [316,] 0.62039678 0.7758929 0.7894831 0.7977922 0.8005508 0.8373456 0.8664193 -#> [317,] 0.34725973 0.4317180 0.4657972 0.4728753 0.4784125 0.4947778 0.5146067 -#> [318,] 0.66774590 1.0664273 1.2667554 1.2998875 1.3524285 1.3923727 1.4089270 -#> [319,] 0.53683528 0.6458406 0.6819790 0.6968820 0.7221679 0.7954932 0.8013005 -#> [320,] 0.27692957 0.3295830 0.4315167 0.4361154 0.4632784 0.5194180 0.5731871 -#> [321,] 0.37740552 0.5298873 0.5674285 0.6048902 0.6227255 0.6320025 0.6411027 -#> [322,] 0.35031851 0.5399719 0.6108232 0.6269043 0.6609895 0.7051456 0.7055877 -#> [323,] 0.95372941 0.9592937 0.9818004 0.9823114 1.0534956 1.0553641 1.0968400 -#> [324,] 0.18607074 0.4439292 0.5175486 0.5340295 0.5501744 0.5571488 0.5711820 -#> [325,] 0.41137800 0.5682765 0.5934796 0.6069523 0.7628360 0.7758584 0.7772044 -#> [326,] 0.49029828 0.5084776 0.5933649 0.6258394 0.8065527 0.8477603 0.8576452 -#> [327,] 0.67399602 0.6808892 0.7301544 0.7776224 0.7818618 0.7955500 0.8297262 -#> [328,] 0.83159959 0.9046579 1.3424826 1.3942088 1.3985994 1.4262390 1.4263432 -#> [329,] 0.62645619 0.8139930 1.0637702 1.1379397 1.2330201 1.2445042 1.2717714 -#> [330,] 0.34718697 0.3912297 0.4194307 0.4439014 0.5228171 0.5727556 0.6406330 -#> [331,] 0.26035703 0.5753758 0.5946666 0.6105969 0.6638629 0.6697832 0.6705406 -#> [332,] 0.30855503 0.3710229 0.4878354 0.5026781 0.5290914 0.5319935 0.5828565 -#> [333,] 0.32922558 0.3842350 0.4455320 0.5087297 0.5693583 0.5817674 0.6002576 -#> [334,] 0.43580695 0.4922980 0.5026781 0.5831288 0.5879639 0.6053585 0.6735620 -#> [335,] 0.57875264 0.6839511 0.6853032 0.6863318 0.6982818 0.7301646 0.7401596 -#> [336,] 0.23416207 0.4904582 0.5355710 0.5864253 0.5963909 0.6157789 0.6580955 -#> [337,] 0.56117283 0.9724704 1.0097257 1.2080045 1.3264815 1.4421302 1.4433116 -#> [338,] 0.46558168 0.4841030 0.6402904 0.9160282 0.9315429 0.9379916 1.0990903 -#> [339,] 0.56536588 0.6463297 0.6586995 0.6940481 0.7130196 0.7237704 0.7773880 -#> [340,] 0.40700908 0.5399719 0.5425732 0.5478941 0.5985593 0.6146147 0.6517178 -#> [341,] 0.23253009 0.2841939 0.5425732 0.5526583 0.5707032 0.6098153 0.6189824 -#> [342,] 0.33335252 0.4853275 0.5666667 0.5674679 0.5784783 0.5826087 0.5944991 -#> [343,] 0.91191200 0.9235948 0.9993258 1.0262315 1.0356451 1.0573300 1.0660545 -#> [344,] 0.21286690 0.3670160 0.4329317 0.5043867 0.5509312 0.5822610 0.6361994 -#> [345,] 0.32627428 0.4014896 0.4233858 0.4694141 0.4925961 0.4972940 0.5446308 -#> [346,] 0.27767287 0.2783384 0.3710229 0.3929441 0.4713000 0.4934612 0.4958311 -#> [347,] 0.49907726 0.6222739 0.7091526 0.8132572 0.8963111 0.9075966 1.0341483 -#> [348,] 0.21792988 0.5361539 0.5793089 0.5827702 0.5841615 0.5937395 0.6079173 -#> [349,] 0.49561025 0.7515701 0.7900258 0.7948560 0.8526947 0.8921008 0.9249440 -#> [350,] 0.15272180 0.3451188 0.5312809 0.5427308 0.5455492 0.5483506 0.5682192 -#> [351,] 0.50111140 0.5290405 0.5388508 0.5638882 0.6842062 0.6856766 0.7081862 -#> [352,] 0.48405150 0.9997366 1.0063590 1.0186303 1.0258218 1.0370866 1.0842196 -#> [353,] 0.85506147 0.9796234 0.9997460 1.0331354 1.0857738 1.1596754 1.1766655 -#> [354,] 0.33886686 0.7598023 0.7773880 0.8075970 0.8530453 0.8797843 0.9533309 -#> [355,] 0.53824498 0.7482774 0.8041277 0.8343406 0.8527889 0.8808184 0.9057698 -#> [356,] 0.39460346 0.4735638 0.5339004 0.5406958 0.6300583 0.6759686 0.6760014 -#> [357,] 0.36786814 0.4943045 0.5858722 0.6555239 0.6883619 0.7011929 0.7140176 -#> [358,] 0.54467172 0.6008991 0.6133631 0.6281582 0.7045401 0.7285143 0.7305726 -#> [359,] 0.49538891 0.5077185 0.5161778 0.6036898 0.6470808 0.6509040 0.6822214 -#> [360,] 0.33360306 0.3951510 0.5297383 0.5445057 0.5796505 0.6006658 0.6055141 -#> [361,] 0.17049041 0.5033910 0.5077185 0.6048211 0.6167268 0.6264364 0.6731264 -#> [362,] 0.30195725 0.4376983 0.5229335 0.5471404 0.5548141 0.5564983 0.5812133 -#> [363,] 0.60005117 0.6010209 0.6738717 0.7179053 0.8279851 0.8281021 0.8510822 -#> [364,] 0.66774590 1.3163298 1.4704140 1.4948077 1.4982230 1.5405538 1.5528732 -#> [365,] 0.40688031 0.5753531 0.6290901 0.6897839 0.7227850 0.7229793 0.7529551 -#> [366,] 0.33416754 0.6339668 0.7072508 0.7096127 0.7341859 0.7492985 0.7689684 -#> [367,] 0.77150740 0.8281860 0.8365330 0.8415089 0.8481635 0.8571622 0.8613339 -#> [368,] 0.93525318 0.9501049 1.0214541 1.0440251 1.0497149 1.0786428 1.1421249 -#> [369,] 0.75502011 0.8643716 0.8916613 0.8925923 0.9750515 1.1627505 1.1896890 -#> [370,] 0.41366461 0.5810245 0.6492271 0.6670795 0.7102363 0.7141773 0.7190354 -#> [371,] 0.22947858 0.4005975 0.4329269 0.4377666 0.4958311 0.5414789 0.5570704 -#> [372,] 0.28229287 0.3060863 0.4140120 0.4617932 0.4860057 0.4901277 0.5107906 -#> [373,] 0.42234308 0.4251424 0.6440649 0.6532035 0.6593543 0.6997124 0.7057718 -#> [374,] 0.35705946 0.4944418 0.5227325 0.6337435 0.7313741 0.7448075 0.7467064 -#> [375,] 0.43000568 0.4510068 0.4537805 0.4770084 0.4908308 0.6091098 0.6605649 -#> [376,] 0.70550896 0.8658663 1.0162033 1.1059807 1.1066440 1.1351071 1.1641759 -#> [377,] 0.73231115 0.7330094 0.7646062 0.7711267 0.8413085 0.8651759 0.8653694 -#> [378,] 0.51593162 0.6416642 0.7694242 0.8225500 0.8955663 0.9084808 0.9439012 -#> [379,] 0.46123736 0.5245417 0.6048211 0.6248637 0.6372920 0.7045773 0.7268233 -#> [380,] 0.41684967 0.4332905 0.5386192 0.5518292 0.5708686 0.5978990 0.6011880 -#> [381,] 0.47919111 0.4792568 0.4871539 0.5175486 0.5446453 0.5682192 0.6344623 -#> [382,] 0.48667127 0.5048615 0.5272385 0.5503752 0.5544435 0.5757508 0.6004304 -#> [383,] 1.39237913 1.4389659 1.4775812 1.5511146 1.6401882 1.6666768 1.7071872 -#> [384,] 1.11081929 1.1862048 1.2363080 1.3240156 1.3580938 1.3619310 1.3934562 -#> [385,] 0.84719154 0.9257695 0.9259973 0.9631997 1.0528749 1.1507484 1.2607130 -#> [386,] 0.21549985 0.2927903 0.4528098 0.4554006 0.4847586 0.5067646 0.5147386 -#> [387,] 0.25548771 0.2627143 0.3012077 0.3369672 0.5852312 0.6533586 0.6665172 -#> [388,] 0.38313595 0.4852145 0.5667683 0.5705758 0.5723201 0.5816681 0.5817390 -#> [389,] 0.36776757 0.3820516 0.5654415 0.6478635 0.6581621 0.7580703 0.7614613 -#> [390,] 0.44042104 0.4462403 0.4861098 0.6844823 0.6898711 0.6920427 0.7149602 -#> [391,] 0.37755185 0.4720687 0.4748450 0.6676814 0.6830605 0.7232436 0.7517080 -#> [392,] 0.49303475 0.6282661 0.6352669 0.7280407 0.7822611 0.7883182 0.8423544 -#> [393,] 0.24156076 0.3333525 0.4894152 0.4907465 0.6046438 0.6096974 0.6198758 -#> [394,] 0.75980226 0.9182698 1.0389936 1.0786253 1.0842766 1.1125976 1.1204943 -#> [395,] 0.79131716 0.8450893 0.8701179 0.9600946 0.9620011 1.1613341 1.1650136 -#> [396,] 0.46840057 0.6900040 0.7541661 0.7634109 0.8923103 0.9210998 0.9261443 -#> [397,] 0.39460470 0.4769464 0.4993423 0.5605158 0.5964247 0.6108362 0.6189705 -#> [398,] 0.54175410 0.6101697 0.6214295 0.7347792 0.7857347 0.8037321 0.8482199 -#> [399,] 0.42443846 0.4946278 0.4988318 0.4991808 0.5728891 0.5872002 0.6422448 -#> [400,] 0.40589754 0.5994650 0.6405285 0.6597749 0.7341665 0.7384536 0.7440294 -#> [401,] 0.33547237 0.3876053 0.5448803 0.5742499 0.5754628 0.6805831 0.7048972 -#> [402,] 0.52774869 0.7536813 0.7796129 0.8243505 0.8382165 0.8643576 0.9220884 -#> [403,] 0.61366148 0.6602462 0.7427637 0.7735705 0.8246115 0.8442161 0.8613048 -#> [404,] 0.35215632 0.4564511 0.6322821 0.6395455 0.7042088 0.7094070 0.7266282 -#> [405,] 0.50924156 0.5422210 0.5544217 0.5690209 0.5959135 0.6045659 0.6229129 -#> [406,] 0.57640862 0.7099221 0.7166143 0.7249098 0.7888289 0.8557293 0.8600432 -#> [407,] 0.27916916 0.3484067 0.4446646 0.4531318 0.4875835 0.5116676 0.5139554 -#> [408,] 0.32811395 0.3350727 0.3440006 0.4969256 0.6380791 0.6789776 0.7619706 -#> [409,] 0.33360306 0.4789346 0.4905156 0.5192757 0.5432577 0.5903237 0.6025242 -#> [410,] 0.51734179 0.8135626 0.9046579 0.9546533 0.9682437 1.0339429 1.0568210 -#> [411,] 0.51230855 0.7115000 0.7758929 0.7828679 0.9054288 1.0238655 1.1780441 -#> [412,] 0.56052284 0.6773700 0.9139025 0.9216663 1.0707815 1.0758797 1.1157012 -#> [413,] 0.27429430 0.4174707 0.4715509 0.4769901 0.5313888 0.5681010 0.5757508 -#> [414,] 0.65431994 0.7272521 0.7962587 0.8683901 0.8706834 1.0330764 1.0452828 -#> [415,] 0.52130015 0.5345360 0.6173324 0.6322821 0.6335443 0.7028651 0.7036003 -#> [416,] 0.80280506 0.8307399 0.8473081 0.9751862 0.9957300 1.0787663 1.2395073 -#> [417,] 0.63880966 0.6527405 0.7013672 0.7099813 0.7667526 0.7864823 0.8593760 -#> [418,] 0.17091574 0.2557204 0.4154745 0.4707840 0.4996657 0.5305223 0.6249691 -#> [419,] 0.91355647 0.9407524 1.1901579 1.2092175 1.2215077 1.2843890 1.2951655 -#> [420,] 0.44077352 0.4423909 0.6316720 0.6967569 0.7555210 0.7897678 0.7911129 -#> [421,] 1.18129626 1.1820783 1.2192175 1.2663894 1.4015970 1.4078856 1.5088903 -#> [422,] 0.60188092 0.7306779 0.7614894 0.8201824 0.8507603 0.8563820 0.8624440 -#> [423,] 0.37105317 0.4343819 0.4415877 0.4975265 0.4978055 0.5155221 0.6055141 -#> [424,] 1.03306172 1.0416197 1.0467315 1.1375541 1.2039755 1.2894666 1.2903858 -#> [425,] 0.44637144 0.5257725 0.5597563 0.6685950 0.6792135 0.7737699 0.7910788 -#> [426,] 0.38205163 0.4427894 0.5293472 0.5416102 0.5452154 0.5486471 0.6135617 -#> [427,] 0.92897311 1.4607150 1.7194306 1.7638615 1.8088267 1.8113200 1.8456745 -#> [428,] 0.36893463 0.5189327 0.5674679 0.6249000 0.6716065 0.6980939 0.7404938 -#> [429,] 0.31315190 0.6597764 0.7347792 0.8168133 0.8300385 0.8385543 0.8550615 -#> [430,] 0.56117283 0.9280705 1.0769471 1.0922780 1.2842654 1.3766206 1.4703591 -#> [431,] 0.35991731 0.6071260 0.6503385 0.6590381 0.6897839 0.7125915 0.7392944 -#> [432,] 0.27515565 0.6780247 0.7875097 0.8063710 0.8114488 0.8716991 0.8797149 -#> [433,] 0.32404141 0.4805979 0.5681275 0.5712551 0.5734678 0.5900957 0.6468046 -#> [434,] 0.33579434 0.5409392 0.5437083 0.5716376 0.7179293 0.7368908 0.7704097 -#> [435,] 0.51962096 0.8417451 0.8673370 0.9462979 0.9760531 1.0758839 1.1064294 -#> [436,] 0.35551633 0.5461164 0.6081071 0.7107542 0.7305726 0.7630830 0.7731162 -#> [437,] 0.22473774 0.2619194 0.3630418 0.3737517 0.4401235 0.4417458 0.4634704 -#> [438,] 0.39662159 0.4432703 0.4436377 0.4566741 0.5134027 0.5176712 0.5475328 -#> [439,] 0.24156076 0.4339481 0.4853275 0.5150846 0.5453835 0.5731871 0.6445660 -#> [440,] 0.42629419 0.4269498 0.4917449 0.5967956 0.7033093 0.7925975 0.8220188 -#> [441,] 0.47387022 0.4973497 0.5272385 0.5500262 0.5524280 0.5579955 0.5641639 -#> [442,] 0.69234816 0.8231293 0.8600432 0.8741934 0.8783775 0.9141506 0.9162641 -#> [443,] 0.26578713 0.3455166 0.5271173 0.5728187 0.6090574 0.6193783 0.6266733 -#> [444,] 0.30916496 0.3856758 0.4017785 0.4945967 0.5464797 0.5544855 0.5725951 -#> [445,] 0.50248079 0.5973751 0.6198012 0.7106098 0.7710974 0.7735319 0.7790543 -#> [446,] 0.30696916 0.4064382 0.4439416 0.5047480 0.6406330 0.6714506 0.7422157 -#> [447,] 0.94100371 1.0331354 1.0410825 1.0532556 1.1293918 1.1827213 1.2545704 -#> [448,] 0.85654824 1.1190720 1.1199056 1.1775246 1.1937552 1.2525599 1.2540978 -#> [449,] 0.72588120 0.9358353 0.9492661 1.0811068 1.0912420 1.1134560 1.1234979 -#> [450,] 0.49268324 0.5296746 0.7474774 0.7683373 0.8557396 0.8643716 0.9074240 -#> [451,] 0.40730826 0.4722252 0.4829929 0.5956886 0.6168834 0.6456856 0.6482295 -#> [452,] 0.43750473 0.4411643 0.5386192 0.5570704 0.5666451 0.5785084 0.5869566 -#> [453,] 0.27833841 0.2841270 0.3683553 0.4005975 0.4819972 0.5281680 0.5319935 -#> [454,] 0.55997084 0.6393230 0.7498951 0.7731162 0.8337882 0.8384616 0.8678513 -#> [455,] 0.39223456 0.4003260 0.4009120 0.4256820 0.4335879 0.4368795 0.4841923 -#> [456,] 0.31583768 0.3357943 0.4075892 0.4683504 0.6180513 0.6915943 0.7676689 -#> [457,] 0.19718617 0.2507500 0.2952804 0.4110020 0.4269215 0.4505302 0.4861472 -#> [458,] 0.79403055 0.9079105 0.9744997 1.0245793 1.0396396 1.0684175 1.0798204 -#> [459,] 0.41279177 0.4735638 0.4978055 0.5009526 0.5194236 0.5210512 0.5656646 -#> [460,] 0.18365652 0.3565099 0.3737517 0.4901712 0.5012997 0.5406525 0.5541087 -#> [461,] 0.33777507 0.4185361 0.4500143 0.5213931 0.5732926 0.5733587 0.6311623 -#> [462,] 0.34000005 0.4547087 0.4803415 0.4979181 0.4981605 0.5139271 0.5324105 -#> [463,] 0.50958632 0.5724994 0.5762256 0.5984237 0.6036898 0.6167249 0.6167268 -#> [464,] 0.41791948 0.5207050 0.6310681 0.6748141 0.6753137 0.7997849 0.8257319 -#> [465,] 0.41908392 0.5298873 0.6748081 0.6816221 0.7071701 0.7271169 0.7385645 -#> [466,] 1.34494781 1.5233323 1.5635876 1.6090008 1.7593662 1.7745993 1.8503743 -#> [467,] 0.79853002 0.8112394 0.9556263 1.0278128 1.0292884 1.0529251 1.0797320 -#> [468,] 0.67399602 0.8083640 0.8330092 0.8502555 0.8880128 0.8915497 0.9213053 -#> [469,] 0.63932303 0.6410085 0.7169540 0.8022048 0.8101539 0.8853278 0.8998839 -#> [470,] 0.35517167 0.5000046 0.5068350 0.5085941 0.5143304 0.5251669 0.5482755 -#> [471,] 0.36491652 0.3965035 0.5752258 0.6001995 0.6997092 0.7815906 0.8311178 -#> [472,] 0.36304515 0.5643803 0.5915972 0.6311051 0.6366774 0.6652489 0.6658170 -#> [473,] 0.15272180 0.3878751 0.4335768 0.4719275 0.5506425 0.5845838 0.5928785 -#> [474,] 0.43303048 0.4391350 0.4656768 0.5288773 0.5328568 0.5817674 0.5961361 -#> [475,] 0.53830065 1.1780441 1.1802566 1.1812963 1.3166412 1.3192053 1.3597177 -#> [476,] 0.27692957 0.3539640 0.3934326 0.4327384 0.4391350 0.4641185 0.4707840 -#> [477,] 0.30784366 0.4136345 0.4267375 0.4565310 0.5014282 0.5324105 0.5731336 -#> [478,] 0.14874895 0.3816655 0.5239113 0.6164456 0.6900380 0.6975582 0.7122184 -#> [479,] 0.54272310 0.6060152 0.6884798 0.7072447 0.7911542 0.7989374 0.8566173 -#> [480,] 0.43637557 0.7652942 0.7878777 0.8075332 0.8276442 0.9581094 0.9721621 -#> [481,] 0.30696916 0.3199699 0.3438252 0.4263777 0.4439014 0.5274743 0.6674573 -#> [482,] 0.34551656 0.4346415 0.5532540 0.5643588 0.6144827 0.6421988 0.6648602 -#> [483,] 0.50575352 0.7093871 0.7159460 0.7826749 0.8296502 0.8301152 0.8694828 -#> [484,] 0.39196996 0.4252624 0.6739281 0.7272578 0.7433350 0.7891056 0.8303995 -#> [485,] 0.32267120 0.5123086 0.5447285 0.6203968 0.7115202 0.8486390 0.8656488 -#> [486,] 0.61082321 0.6598523 0.7005643 0.8473081 0.8643576 0.9271097 0.9627590 -#> [487,] 0.37131494 0.5290405 0.6032977 0.6206803 0.6283001 0.6460744 0.6707166 -#> [488,] 0.60423508 0.6226021 1.0455526 1.1612867 1.2450324 1.2661239 1.2846899 -#> [489,] 0.60605455 0.7563084 0.8980437 0.9233367 1.0435580 1.0566689 1.0593198 -#> [490,] 0.38529405 0.5153898 0.5944632 0.6035720 0.6498777 0.6666415 0.6975582 -#> [491,] 0.69234816 0.7206454 0.7888289 0.8132450 0.8274062 0.8489513 0.8564503 -#> [492,] 1.15191239 1.3899942 1.4500010 1.4885976 1.5228824 1.5852860 1.6965329 -#> [493,] 0.56315141 0.6642650 0.6965077 0.6999140 0.7645853 0.7683081 0.7897385 -#> [494,] 0.48583868 1.1109211 1.1695009 1.3133883 1.3257371 1.3295063 1.3357156 -#> [495,] 0.78233201 1.0637702 1.1451020 1.2800477 1.3045123 1.3432057 1.3498040 -#> [496,] 0.58121331 0.5842185 0.5934716 0.7158673 0.7288453 0.8039226 0.8781543 -#> [497,] 0.60144678 0.6523766 0.7815906 0.7855782 0.7883097 0.9350030 0.9439458 -#> [498,] 0.58608188 0.9931059 1.1399638 1.1783841 1.2097972 1.2181288 1.2365478 -#> [499,] 0.63282757 0.6682973 0.6743829 0.7578613 0.7671347 0.7887201 0.8602626 -#> [500,] 0.26265001 0.5356474 0.6421611 0.6772350 0.7457229 0.7951561 0.8453708 -#> [501,] 1.38999419 1.5992446 1.8790500 2.3113869 2.3168782 2.3637341 2.3922669 -#> [502,] 0.32558987 0.4174707 0.4371221 0.4554006 0.4772252 0.5048615 0.5416199 -#> [503,] 0.48461201 0.7215688 0.7433350 0.7617940 0.7680742 0.7905453 0.7961259 -#> [504,] 0.22486970 0.2627143 0.3695717 0.4947433 0.5551474 0.5586764 0.5956886 -#> [505,] 0.68271114 0.8258154 0.8675523 0.9214783 0.9267411 0.9584324 0.9606637 -#> [506,] 0.38713800 0.4160170 0.4167471 0.5416616 0.6201974 0.6218608 0.6513026 -#> [507,] 0.35517167 0.4033861 0.4352831 0.4425477 0.4630983 0.4665478 0.4729722 -#> [508,] 0.21503155 0.4364501 0.5066574 0.5068288 0.5470138 0.5629381 0.5653868 -#> [509,] 1.35540675 1.3651020 1.4498864 1.4697750 1.4866551 1.6000226 1.6029319 -#> [510,] 1.12729374 1.1422187 1.1849896 1.2738890 1.2847577 1.2913047 1.3467737 -#> [511,] 0.33004290 0.3838357 0.5194236 0.5339004 0.5774565 0.6389957 0.6516153 -#> [512,] 0.87751687 1.0572218 1.1211695 1.1232132 1.2340452 1.2617497 1.2899045 -#> [513,] 1.26501290 1.2800477 1.3927728 1.4072841 1.4812533 1.4894414 1.5189249 -#> [514,] 0.32349377 0.4353077 0.5567052 0.5573627 0.5938147 0.5940007 0.6354609 -#> [515,] 0.46681568 0.4981513 0.5804492 0.5870340 0.6382426 0.6558298 0.6613072 -#> [516,] 0.48176089 0.4981513 0.6124427 0.6951015 0.6968820 0.7107325 0.7603987 -#> [517,] 0.56275897 0.7327818 0.7415040 0.7746998 0.7948028 0.8078147 0.8275757 -#> [518,] 0.43645014 0.4427498 0.4582420 0.4617395 0.4696348 0.4860057 0.4869942 -#> [519,] 0.30195725 0.3064522 0.4834773 0.5319833 0.5844000 0.5934716 0.6602826 -#> [520,] 0.34872796 0.4696348 0.5094419 0.5328879 0.5508058 0.5596657 0.5667683 -#> [521,] 0.22486970 0.3012077 0.4638763 0.4827970 0.5581506 0.5857969 0.6168834 -#> [522,] 0.34718697 0.4734484 0.4759376 0.4821902 0.4981192 0.5919049 0.6765002 -#> [523,] 0.37119003 0.4042716 0.4167471 0.6327125 0.6424517 0.6451651 0.6544454 -#> [524,] 0.49883178 0.5564145 0.6133631 0.6188844 0.6548673 0.6671413 0.7107542 -#> [525,] 0.51487828 0.7055090 0.7121909 0.7625581 0.8078147 0.8712902 0.9196127 -#> [526,] 0.26233166 0.3301814 0.5477472 0.5644013 0.5652963 0.6095633 0.6419645 -#> [527,] 0.44657063 0.5469588 0.6504423 0.7273693 0.9348490 0.9960069 1.0127452 -#> [528,] 0.55221954 0.6227255 0.6748081 0.6885028 0.6898711 0.6936241 0.6971507 -#> [529,] 0.60601516 0.6747779 0.7274967 0.7710522 0.7746791 0.7761110 0.7761581 -#> [530,] 0.96348909 1.0005127 1.1531303 1.1819835 1.2705730 1.2881013 1.3097788 -#> [531,] 0.51614723 0.5859331 0.6091953 0.6416642 0.6484656 0.7493636 0.7608141 -#> [532,] 0.52734415 0.5752258 0.6201989 0.6509948 0.7475855 0.7848703 0.8374849 -#> [533,] 0.22768949 0.4755288 0.5001598 0.5267358 0.5417933 0.5707032 0.6032977 -#> [534,] 0.17308916 0.3775519 0.4374424 0.4842991 0.5473634 0.5522140 0.5544129 -#> [535,] 0.27366290 0.4910223 0.5432577 0.5697910 0.6618646 0.7108046 0.7424222 -#> [536,] 0.82986354 0.8525027 0.8694440 0.8770413 0.8785766 0.9407524 0.9698302 -#> [537,] 0.50957678 0.5401574 0.5591449 0.5853550 0.6122867 0.8077789 0.8343363 -#> [538,] 0.36353777 0.3804196 0.4706606 0.4781166 0.4842991 0.5207731 0.5435039 -#> [539,] 0.12432811 0.4799947 0.7927098 0.8045342 0.8104586 0.8335825 0.8565542 -#> [540,] 0.39267342 0.5104084 0.5539836 0.5571402 0.6095633 0.6229590 0.6637908 -#> [541,] 0.28212285 0.2891919 0.4649396 0.5648903 0.5664279 0.5829420 0.6284229 -#> [542,] 0.99208071 1.0640377 1.1408177 1.2567472 1.2610097 1.2973438 1.3444573 -#> [543,] 0.48996733 0.5606936 0.5728755 0.6760014 0.7544363 0.7715961 0.7818113 -#> [544,] 0.22723214 0.3977784 0.4259536 0.4369957 0.5030719 0.5087297 0.5148810 -#> [545,] 0.32764986 0.3947949 0.3989610 0.4436377 0.5049733 0.5212736 0.5482755 -#> [546,] 0.38903127 0.4299702 0.4647006 0.4800804 0.5080255 0.5473492 0.6005693 -#> [547,] 0.22075267 0.3000063 0.6380791 0.6999140 0.7695203 0.7772380 0.7839562 -#> [548,] 0.54444892 0.7026799 0.7865930 0.8482936 0.8593760 0.8771458 0.9025465 -#> [549,] 0.41975680 0.5425809 0.6484656 0.6700281 0.6740985 0.7462301 0.7625581 -#> [550,] 0.44410572 0.4786823 0.5505203 0.6208388 0.7245477 0.7528892 0.8241554 -#> [551,] 0.34765690 0.4860027 0.5374429 0.5498394 0.5611247 0.5674918 0.5913260 -#> [552,] 0.25761193 0.2839479 0.4359787 0.4500779 0.5492644 0.6073729 0.6101469 -#> [553,] 0.32458341 0.4294108 0.4755666 0.4790972 0.5430447 0.5497086 0.5698945 -#> [554,] 0.30000634 0.3440006 0.3895970 0.5069029 0.6480851 0.6545683 0.7117059 -#> [555,] 0.57083623 0.6741173 0.7033093 0.7443693 0.7894831 0.7964902 0.8458878 -#> [556,] 0.51508459 0.5199321 0.5402096 0.5418139 0.5784783 0.6096974 0.6549053 -#> [557,] 0.35322004 0.4095795 0.6571445 0.7186039 0.7256993 0.7310167 0.7931390 -#> [558,] 1.73915157 2.0895044 2.1799612 2.2052621 2.2889129 2.3213603 2.3241489 -#> [559,] 0.32678210 0.5064336 0.5544217 0.6344623 0.6354370 0.6742242 0.6952326 -#> [560,] 0.25389733 0.4095795 0.4867599 0.5728891 0.5757414 0.6128420 0.6201974 -#> [561,] 0.57920643 0.6609895 0.7130723 0.7608231 0.8280994 0.8915774 0.9119757 -#> [562,] 0.33004290 0.3951510 0.4803121 0.5113439 0.5922088 0.6058663 0.6105139 -#> [563,] 0.29619568 0.6414745 0.6818945 0.7300882 0.7494630 0.7524761 0.8068922 -#> [564,] 0.28390248 0.3352527 0.4368724 0.4797075 0.5452154 0.5546065 0.5663818 -#> [565,] 1.70387685 1.8756984 1.9238217 1.9351767 2.0168327 2.0358703 2.0389652 -#> [566,] 0.50368519 0.5164336 0.5711820 0.6091953 0.6135240 0.6179733 0.6527461 -#> [567,] 0.27429430 0.3579049 0.4233858 0.4472829 0.4561306 0.4772252 0.4890375 -#> [568,] 0.36176548 0.4923406 0.4991808 0.5343824 0.5415747 0.5455492 0.5481736 -#> [569,] 0.58372918 0.5984751 0.6508299 0.6699247 0.7298699 0.7735197 0.8480297 -#> [570,] 0.38920400 0.6251328 0.7517080 0.7609491 0.7827158 0.8434251 0.8563041 -#> [571,] 0.44136721 0.5160107 0.5731316 0.5940345 0.6567246 0.6588093 0.6591535 -#> [572,] 0.33373156 0.4814404 0.5521197 0.7448075 0.7741427 0.7927559 0.8362222 -#> [573,] 0.23416207 0.4356454 0.5688117 0.5912199 0.5929949 0.5965354 0.6701969 -#> [574,] 0.46386001 0.4905156 0.5672952 0.6115767 0.6319799 0.6335443 0.6418135 -#> [575,] 0.44102183 0.5958669 0.6523323 0.6539635 0.7169540 0.7502026 0.7989667 -#> [576,] 0.22003989 0.5764086 0.5800283 0.6960439 0.6992767 0.7734542 0.7760067 -#> [577,] 0.52787810 0.8656258 1.0886843 1.1303904 1.1652766 1.1992359 1.2040200 -#> [578,] 0.33537141 0.4310171 0.4855791 0.5043320 0.5506841 0.6286606 0.6449115 -#> [579,] 0.48106688 0.6400865 0.6602727 0.6610452 0.6643364 0.6676988 0.6725714 -#> [580,] 0.40229399 0.4831555 0.5020988 0.5160107 0.5657945 0.5793657 0.5832154 -#> [581,] 0.35648607 0.5155221 0.5430369 0.5512025 0.5672378 0.5931377 0.6105139 -#> [582,] 0.27012072 0.4017908 0.4559433 0.4892858 0.4946278 0.5128846 0.5350863 -#> [583,] 0.35868385 0.4425961 0.6264364 0.6395027 0.6446911 0.6611604 0.6894536 -#> [584,] 0.55815062 0.5852312 0.6701959 0.6814269 0.7058457 0.7367348 0.7549490 -#> [585,] 0.44657063 0.4577985 0.7049280 0.7064780 0.7427919 0.7677238 0.7866158 -#> [586,] 0.33284522 0.3968305 0.5929588 0.6385466 0.6502117 0.6619225 0.7842259 -#> [587,] 0.17308916 0.3946946 0.4523318 0.4748450 0.4781166 0.5063630 0.5519264 -#> [588,] 0.77900189 0.7890183 0.8378151 0.8851613 0.8899777 0.9409221 1.0251435 -#> [589,] 0.56786474 0.5713880 0.6050779 0.6742909 0.6922978 0.7159223 0.7236839 -#> [590,] 1.28990446 1.4368071 1.4372491 1.6684753 1.6883546 1.7540976 1.8215611 -#> [591,] 0.34060381 0.3857767 0.4285865 0.4323910 0.5243204 0.5416102 0.5619075 -#> [592,] 0.44624031 0.4964189 0.6007497 0.6597749 0.6793760 0.7046408 0.7064299 -#> [593,] 0.16044299 0.2337616 0.3989610 0.4425961 0.4827286 0.5134027 0.5305207 -#> [594,] 0.23433208 0.2507500 0.3647389 0.3863603 0.4060251 0.4638805 0.4706503 -#> [595,] 0.17597548 0.4429149 0.4458199 0.5008248 0.6135240 0.6417925 0.6768346 -#> [596,] 0.46274619 0.5643122 0.7634109 0.7683373 0.8060857 0.8275630 0.8577206 -#> [597,] 0.48996733 0.5210512 0.5406958 0.5489483 0.5774565 0.7836787 0.8296072 -#> [598,] 0.22768949 0.5449690 0.5478941 0.5777244 0.5915972 0.6030622 0.6073392 -#> [599,] 0.83371889 0.8694239 0.8819580 0.9786088 0.9887986 1.0007794 1.1333100 -#> [600,] 0.26035703 0.5574171 0.6283340 0.6793788 0.6816118 0.7125412 0.7249124 -#> [601,] 0.37701435 0.3873969 0.4454083 0.5852220 0.6213244 0.6464179 0.6551963 -#> [602,] 0.18365652 0.2247377 0.2577553 0.3864874 0.4023448 0.5241576 0.5414872 -#> [603,] 0.43366031 0.6522712 0.8378151 0.8617993 0.8700834 0.9045392 1.0198042 -#> [604,] 0.69993429 0.7284301 0.9402911 1.1137788 1.1158997 1.1169389 1.1244440 -#> [605,] 0.29951135 0.3365124 0.4805783 0.4852801 0.4947778 0.5059454 0.5593715 -#> [606,] 0.24308049 0.3369672 0.4299477 0.4368795 0.5183246 0.5551474 0.5619075 -#> [607,] 0.44525949 0.8932878 0.9821860 1.0011803 1.0103385 1.0981862 1.1486406 -#> [608,] 0.59405249 0.6543199 0.6553937 0.7091526 0.8328801 0.8486597 0.8609763 -#> [609,] 1.02977260 1.2619621 1.2667554 1.4606998 1.5334449 1.5560651 1.5957530 -#> [610,] 0.35144018 0.5686196 0.5780670 0.6654444 0.8031539 0.8919497 0.8937318 -#> [611,] 0.39294411 0.4329269 0.4807848 0.4819972 0.4849148 0.5227979 0.5640177 -#> [612,] 0.46886255 0.5946286 0.6971507 0.7394289 0.7854985 0.8576131 0.9304374 -#> [613,] 0.51490569 0.5511763 0.9546355 1.1132546 1.1169389 1.1330129 1.2553011 -#> [614,] 0.44559324 0.5293639 0.5361539 0.5733587 0.7285486 0.7346014 0.7350405 -#> [615,] 0.33760424 0.3771229 0.5694851 0.6053445 0.6638629 0.7151035 0.7352688 -#> [616,] 0.37489759 0.6153700 0.6522712 0.6900040 0.7691259 0.7830058 0.8490819 -#> [617,] 0.38785383 0.4255550 0.5361624 0.6440649 0.6580717 0.6705406 0.6816118 -#> [618,] 0.33543684 0.3541546 0.4724177 0.5725951 0.6208388 0.6248876 0.6351238 -#> [619,] 0.49870522 0.5469588 0.7579067 0.7694242 0.7866158 0.8033215 0.9437138 -#> [620,] 0.45415534 0.6671930 0.6792135 0.6862691 0.7155112 0.7678281 0.7826749 -#> [621,] 0.33984133 0.4188059 0.4377666 0.4584733 0.4993423 0.5206577 0.5281680 -#> [622,] 0.32404141 0.4348363 0.5456857 0.5670290 0.5897818 0.6090094 0.6204706 -#> [623,] 0.43463282 0.4849148 0.6283340 0.6380633 0.6411111 0.6772921 0.7280407 -#> [624,] 0.24047378 0.2949672 0.4734230 0.4836585 0.4855279 0.5226528 0.5671062 -#> [625,] 0.38832700 0.6122867 0.6906560 0.8819580 0.9070997 0.9280705 0.9724704 -#> [626,] 0.31996990 0.4064382 0.4194307 0.5095882 0.6187073 0.6682280 0.6765002 -#> [627,] 0.21601832 0.4441057 0.4724177 0.4945967 0.5062977 0.5134793 0.5488952 -#> [628,] 0.17402163 0.2367911 0.4004923 0.4353077 0.4847487 0.4869942 0.4950905 -#> [629,] 0.80130051 0.8168133 0.8360590 0.9012890 0.9719824 0.9892314 1.0326790 -#> [630,] 0.33886686 0.6463297 0.6651778 0.7060388 0.7318452 0.7538065 0.7692197 -#> [631,] 0.34201350 0.3591081 0.4149334 0.4231522 0.4760790 0.4992524 0.5132158 -#> [632,] 0.49883170 0.7197396 0.7218012 0.7508510 0.7842259 0.7848501 0.7930338 -#> [633,] 0.40473610 0.4956102 0.7398657 0.8165282 0.9339677 0.9609719 1.0370421 -#> [634,] 0.48667127 0.5674285 0.5851370 0.6052577 0.6083226 0.6202031 0.6234698 -#> [635,] 0.32803844 0.3965035 0.5484068 0.5868525 0.6253015 0.6509948 0.6716849 -#> [636,] 0.56052284 0.6385466 0.6993452 0.7218012 0.7321719 0.8379912 0.8791882 -#> [637,] 0.60666145 0.6115239 0.6960439 0.7364444 0.7907865 0.8651759 0.9106117 -#> [638,] 0.90824995 0.9976108 1.0257840 1.0490677 1.0991771 1.1008939 1.1014870 -#> [639,] 0.74307655 0.9445222 0.9865465 1.0185952 1.0381068 1.0908697 1.1027061 -#> [640,] 0.64291176 0.6669140 0.7011364 0.7486184 0.7557675 0.8058338 0.8103042 -#> [641,] 0.35605581 0.4814404 0.7876294 0.7985339 0.8573493 0.9576553 0.9659746 -#> [642,] 0.79311997 0.7989374 0.8668125 0.8767005 0.8896757 0.9194493 0.9435230 -#> [643,] 0.29279031 0.3544750 0.3700009 0.4719275 0.5045860 0.5201948 0.5427308 -#> [644,] 0.43637557 0.4827970 0.4947433 0.7024931 0.7082306 0.7102383 0.7503552 -#> [645,] 0.38434723 0.4441885 0.5685905 0.5852424 0.5996315 0.6418135 0.6847756 -#> [646,] 0.75806006 0.7767205 0.7881615 0.8075945 0.8449039 0.9816106 0.9991894 -#> [647,] 0.34015775 0.4648615 0.5660205 0.6646483 0.6967569 0.7338786 0.7546331 -#> [648,] 0.54015739 0.6906560 0.7470331 0.7691937 0.8596595 0.8851544 0.8855184 -#> [649,] 0.21401924 0.3291718 0.3406038 0.4113986 0.4169955 0.4256820 0.4330305 -#> [650,] 1.21571383 1.6228459 1.7779071 1.8151949 1.8291248 1.8450162 1.9125968 -#> [651,] 0.73862833 0.7855138 0.8135626 0.9202073 0.9247456 1.0629530 1.1103865 -#> [652,] 0.48695987 0.4969256 0.5372878 0.5698973 0.6480851 0.6495004 0.6571974 -#> [653,] 0.59394151 0.6429569 0.6469902 0.6594809 0.6669274 0.6801128 0.7318452 -#> [654,] 0.71059489 0.7913172 0.8570968 0.9106875 0.9401968 0.9611286 0.9756189 -#> [655,] 0.19780375 0.3078437 0.3579763 0.4318495 0.4531874 0.5124364 0.5570000 -#> [656,] 0.33018138 0.3567649 0.4992524 0.5149747 0.5353460 0.5900000 0.6121609 -#> [657,] 0.36776757 0.4427894 0.5663868 0.5770576 0.6664361 0.7138950 0.7603336 -#> [658,] 0.39142869 0.4601763 0.4863964 0.5127744 0.5638167 0.5713538 0.5737622 -#> [659,] 0.46493959 0.5561894 0.7030695 0.7118153 0.7135524 0.7341962 0.7462063 -#> [660,] 0.21401924 0.2281317 0.3397789 0.4009120 0.4742309 0.5243204 0.5328568 -#> [661,] 0.44464332 0.4574371 0.4834773 0.5054884 0.5055472 0.5161832 0.5229335 -#> [662,] 0.32267120 0.5252603 0.7263464 0.7772791 0.7828679 0.7894459 0.8005508 -#> [663,] 0.23163085 0.3125665 0.3891389 0.4232646 0.4854022 0.5116676 0.5146067 -#> [664,] 1.68507968 1.7320149 1.8154012 1.8993829 1.9175140 1.9931019 2.0122906 -#> [665,] 0.37105317 0.3822649 0.4080247 0.5637485 0.5686945 0.5851032 0.6203053 -#> [666,] 1.26596347 1.2987503 1.3163879 1.3481555 1.4350144 1.4667450 1.5794637 -#> [667,] 0.38172029 0.5221992 0.6277827 0.6597764 0.6713917 0.6715206 0.7095495 -#> [668,] 0.41705624 0.4903407 0.5305223 0.5740756 0.5962643 0.5976732 0.6198012 -#> [669,] 0.37131494 0.4369859 0.4755288 0.5471576 0.5577912 0.5801553 0.6030622 -#> [670,] 0.68142692 0.7803132 0.8450893 0.9206161 0.9284356 0.9300096 0.9300310 -#> [671,] 0.31315190 0.5417541 0.7687094 0.7741071 0.8012836 0.8145714 0.8155763 -#> [672,] 0.39515951 0.4564511 0.6069523 0.6405285 0.7031803 0.7632962 0.7708018 -#> [673,] 0.66039001 0.6856915 0.8041277 0.9437138 0.9860807 0.9920807 1.0049737 -#> [674,] 0.33284522 0.5010170 0.6251130 0.6716530 0.7409058 0.7854566 0.7930338 -#> [675,] 0.38434723 0.4322912 0.4638600 0.5213002 0.5800283 0.6553089 0.6577880 -#> [676,] 0.54611205 0.6339835 0.7537169 0.7589348 0.8409048 0.8528875 0.8586295 -#> [677,] 0.36503326 0.4182829 0.4523318 0.5180707 0.5275571 0.5544129 0.6218384 -#> [678,] 0.24648209 0.5618274 0.5625347 0.6759686 0.7198735 0.7292775 0.7603987 -#> [679,] 0.58608188 0.7796129 0.7855138 0.8454423 0.9682437 1.0820030 1.0851767 -#> [680,] 0.48666539 0.4902983 0.6471791 0.7160836 0.7306741 0.7505155 0.7845608 -#> [681,] 0.27087776 0.3374291 0.4033739 0.4368724 0.5486471 0.5625667 0.5654415 -#> [682,] 0.57372655 0.8000399 0.9717136 0.9758373 1.0200868 1.0596932 1.1610597 -#> [683,] 0.45429280 0.4655817 0.7009357 0.7498951 0.8086566 0.8632858 0.8719987 -#> [684,] 0.46307271 0.5994650 0.6273540 0.7221424 0.7638539 0.7792963 0.8033483 -#> [685,] 0.49907726 0.5940525 0.6827111 0.7004509 0.8277587 0.8280008 0.8723819 -#> [686,] 1.50596458 1.5657095 1.5754641 1.6338610 1.7295999 1.7931603 1.8113775 -#> [687,] 0.41975680 0.4354461 0.5148783 0.7302572 0.7369999 0.7374324 0.7415040 -#> [688,] 0.51993210 0.6507086 0.6613144 0.6682973 0.6854089 0.6890310 0.7054623 -#> [689,] 0.41315968 0.4482383 0.4526669 0.6122105 0.6367922 0.6531001 0.6671413 -#> [690,] 0.59022782 0.6389957 0.6434926 0.7062934 0.7356794 0.7406443 0.7555210 -#> [691,] 0.36786814 0.4817609 0.6667033 0.6692368 0.7221679 0.7474076 0.7715780 -#> [692,] 0.47593764 0.5066934 0.5228171 0.6133590 0.6682280 0.6710433 0.6933420 -#> [693,] 0.34015775 0.5309749 0.6754314 0.6866783 0.7454285 0.8355015 0.8627002 -#> [694,] 0.44287676 0.5719344 0.6496431 0.7229793 0.7558851 0.7563084 0.8101539 -#> [695,] 0.43712215 0.4553386 0.5641639 0.6392469 0.6439245 0.6870271 0.7060563 -#> [696,] 0.14706566 0.4455320 0.4473325 0.4511470 0.4564653 0.4728753 0.4769149 -#> [697,] 0.65371596 0.8360590 0.8482199 0.8656258 0.8673346 0.9124285 0.9252556 -#> [698,] 0.42443846 0.4867599 0.5343824 0.5512543 0.5709348 0.6297882 0.6391369 -#> [699,] 0.30035462 0.4965954 0.5705094 0.6069573 0.6560051 0.7111043 0.7248677 -#> [700,] 0.23566961 0.2962470 0.3037289 0.3804423 0.4329317 0.4957784 0.5218996 -#> [701,] 0.34791594 0.4423909 0.6339192 0.7711267 0.8043722 0.8176418 0.8193722 -#> [702,] 0.38410060 0.4346415 0.5680075 0.6310681 0.6459038 0.6606776 0.6892405 -#> [703,] 0.89461960 1.1036450 1.1944454 1.2430173 1.2445042 1.3393180 1.3762091 -#> [704,] 0.36525783 0.5024808 0.5359625 0.6964333 0.7860382 0.8233114 0.8387448 -#> [705,] 0.56182959 0.5877482 0.7154758 0.8000923 0.8829692 0.9316102 0.9406988 -#> [706,] 0.52711728 0.5500002 0.5625617 0.6038029 0.6144827 0.6606776 0.6733218 -#> [707,] 0.25775535 0.2967886 0.3565099 0.3630418 0.5500049 0.5858786 0.5937395 -#> [708,] 0.45587612 0.4683504 0.5025979 0.5197292 0.5459429 0.5716376 0.5886034 -#> [709,] 0.50575352 0.5658166 0.5931351 0.6119784 0.6813854 0.6862691 0.7420654 -#> [710,] 0.46886255 0.5376504 0.6598523 0.6818975 0.7356749 0.7961273 0.8070495 -#> [711,] 0.69264027 0.7333815 0.9616452 1.1140323 1.1178551 1.1265248 1.1347521 -#> [712,] 0.52774869 0.7639499 0.8649104 0.8873985 0.8907783 0.9271097 0.9462811 -#> [713,] 0.22952545 0.2952804 0.2981954 0.3316853 0.4060251 0.4816673 0.5045860 -#> [714,] 0.33706501 0.5484259 0.5824204 0.6410320 0.6418896 0.6458494 0.6616116 -#> [715,] 0.89731375 1.0537978 1.0872180 1.1281765 1.2874463 1.3430162 1.4573941 -#> [716,] 0.45879487 0.5779698 0.6209997 0.6790274 0.7966283 0.8060857 0.8867858 -#> [717,] 0.35637992 0.3945140 0.4154745 0.4335879 0.4409417 0.5278541 0.5740247 -#> [718,] 0.66195643 0.6783787 0.7290759 0.7430766 0.8105196 0.8730697 0.9213143 -#> [719,] 0.32942541 0.3510939 0.5217597 0.5319833 0.5348828 0.5535397 0.5548141 -#> [720,] 0.28226619 0.7007640 0.8111915 0.8873127 0.9021124 0.9370842 0.9768958 -#> [721,] 0.19718617 0.2343321 0.3316853 0.3704672 0.4115678 0.4458681 0.4720348 -#> [722,] 0.36214779 0.3668483 0.3811909 0.4201479 0.4531318 0.4936062 0.5059454 -#> [723,] 0.56070439 0.5842185 0.7960508 0.8029320 0.8727750 0.9239359 0.9257695 -#> [724,] 0.45533863 0.4964189 0.5417933 0.5471576 0.5526583 0.6159372 0.6252936 -#> [725,] 0.17091574 0.2325982 0.4170562 0.4409417 0.5799450 0.5900520 0.6431570 -#> [726,] 0.30569236 0.4472829 0.4694141 0.5611247 0.5643803 0.5823287 0.6055500 -#> [727,] 0.49841470 0.5978341 0.6058621 0.7401596 0.7509420 0.7776361 0.8039289 -#> [728,] 0.55017438 0.5645975 0.6441751 0.6565390 0.6603900 0.6950564 0.7211326 -#> [729,] 0.56164157 0.5737265 0.6881103 0.7450978 0.7895455 0.9096213 0.9724750 -#> [730,] 0.34414173 0.4000386 0.4302289 0.4855279 0.5030719 0.5269430 0.5926604 -#> [731,] 0.62029301 0.6352669 0.6380633 0.7147159 0.7249124 0.7291427 0.7433481 -#> [732,] 0.45779847 0.6922978 0.6950564 0.7096315 0.7226073 0.7273693 0.7833891 -#> [733,] 0.63826410 0.7682346 0.7960508 0.8937318 1.0084275 1.1145695 1.1332786 -#> [734,] 0.67165298 0.6951862 0.7007640 0.7114757 0.8941862 0.9199336 0.9738467 -#> [735,] 0.33777507 0.4482289 0.5056059 0.5320780 0.5597726 0.6534410 0.6739281 -#> [736,] 0.69442557 0.8149391 0.8167236 0.8786131 0.9513195 0.9557853 0.9811198 -#> [737,] 0.26835689 0.3334680 0.3621478 0.4549819 0.4769149 0.4854022 0.4865940 -#> [738,] 0.49268324 0.6837906 0.9234186 0.9441428 1.0225519 1.1003096 1.1280842 -#> [739,] 1.29539934 1.8722235 1.9284252 2.0010740 2.0750523 2.1162269 2.1169820 -#> [740,] 0.51734193 0.6642650 0.7704097 0.8122535 0.8581814 0.8711888 0.8950791 -#> [741,] 0.34511877 0.3878751 0.4791911 0.4892858 0.4923406 0.4952032 0.5455918 -#> [742,] 0.35403636 0.4285535 0.5277485 0.5305939 0.5498010 0.5503752 0.5640177 -#> [743,] 0.57535311 0.6563266 0.6590381 0.6714506 0.7532099 0.8052437 0.8501118 -#> [744,] 0.51734179 0.7632962 0.8085228 0.8315996 0.8591507 0.8649104 0.9220884 -#> [745,] 0.32917180 0.3397789 0.3876899 0.3922346 0.4001949 0.4299477 0.4323910 -#> [746,] 0.64667460 0.6835879 0.6979654 0.8804967 0.9374665 0.9778351 0.9948617 -#> [747,] 0.44136721 0.5503266 0.6124585 0.6458406 0.6666177 0.6790456 0.6853860 -#> [748,] 0.82415540 0.8868312 1.0763748 1.1582749 1.1680500 1.2156539 1.2208195 -#> [749,] 0.41493342 0.4700809 0.4869599 0.5400945 0.5436498 0.5573906 0.5594085 -#> [750,] 0.46799293 0.5034560 0.5217597 0.6771774 0.7306261 0.7400928 0.7437343 -#> [751,] 0.58434221 0.6646158 0.6969564 0.7237858 0.7355400 0.7586271 0.7755287 -#> [752,] 0.53830065 1.3152491 1.4967682 1.5136034 1.5573514 1.6377122 1.6517498 -#> [753,] 0.33760424 0.3991980 0.4598256 0.5265409 0.6790456 0.7613518 0.8217289 -#> [754,] 0.23008354 0.3640111 0.4975521 0.5126158 0.5339640 0.5841814 0.6017285 -#> [755,] 0.54471650 0.6192503 0.6313910 0.6351238 0.7059582 0.7568730 0.7921023 -#> [756,] 0.35086757 0.5386034 0.5628925 0.5704743 0.5749911 0.5831288 0.6768378 -#> [757,] 0.45429280 0.4841030 0.7173158 1.0579114 1.1550930 1.1706918 1.2055242 -#> [758,] 0.41684967 0.4346328 0.5227979 0.5574171 0.5642680 0.5748597 0.6105969 -#> [759,] 0.22723214 0.2321507 0.3441417 0.4547368 0.4606442 0.4972859 0.5295162 -#> [760,] 0.22075267 0.3895970 0.6789776 0.7645853 0.7901658 0.8013009 0.8486597 -#> [761,] 0.81532947 0.8531185 0.8577187 0.8617993 0.8923103 0.8946196 0.9825679 -#> [762,] 0.28142206 0.3683553 0.4934612 0.5206577 0.5777682 0.6004304 0.6115999 -#> [763,] 0.38313595 0.4004923 0.4295142 0.4470150 0.4973108 0.5328879 0.5470138 -#> [764,] 0.85570464 0.8876872 0.8948302 1.0199801 1.0230997 1.0844723 1.1143924 -#> [765,] 0.92943659 0.9304374 1.0307765 1.2157138 1.2584467 1.2722371 1.3115574 -#> [766,] 0.51734193 0.7179293 0.7347693 0.8160029 0.8686874 0.8732938 0.9418266 -#> [767,] 0.24648209 0.6124427 0.6414745 0.6667033 0.6792825 0.7024752 0.7242682 -#> [768,] 0.34841500 0.3541546 0.5134793 0.5470445 0.6012403 0.6016000 0.6136057 -#> [769,] 0.52454319 0.5877482 0.6497930 0.6978403 0.8646325 0.9219122 0.9685776 -#> [770,] 0.33177611 0.3514778 0.4325350 0.4337352 0.4599327 0.4638805 0.4926394 -#> [771,] 0.46283211 0.4734484 0.5095768 0.6294922 0.6707089 0.6725714 0.6949184 -#> [772,] 0.38383571 0.3946035 0.5009526 0.5489483 0.5762502 0.6377344 0.6440527 -#> [773,] 0.37711935 0.4566741 0.5215421 0.5586764 0.5766874 0.6164888 0.6767031 -#> [774,] 0.38993683 0.6628639 0.6891124 0.7015497 0.7302572 0.7341874 0.7989318 -#> [775,] 0.83110582 0.9107652 0.9549498 1.0660545 1.0960889 1.1519532 1.1663112 -#> [776,] 0.28984342 0.3565893 0.4464113 0.4694526 0.5826878 0.5859595 0.5972087 -#> [777,] 0.31299205 0.3510939 0.4679929 0.4706264 0.5949997 0.6361994 0.6766829 -#> [778,] 0.35551633 0.3985222 0.5564145 0.6008991 0.6470808 0.6550018 0.6857016 -#> [779,] 0.95562635 1.2624698 1.4753414 1.7136959 1.7248187 1.7823646 1.8136329 -#> [780,] 0.42611375 0.6624952 0.7154758 0.8839687 0.9812302 0.9990435 1.0114281 -#> [781,] 0.79814003 0.9909280 1.0555764 1.0578797 1.1204943 1.1938586 1.1983328 -#> [782,] 0.58535499 0.7279623 0.7475855 0.8526947 0.8637983 0.8680231 0.8855184 -#> [783,] 0.21792988 0.4455932 0.4849461 0.6027970 0.6261556 0.6593693 0.6599585 -#> [784,] 0.38993683 0.5612824 0.7160182 0.8346796 0.8980437 0.9196127 0.9264201 -#> [785,] 0.43300503 0.4441885 0.4714800 0.5229825 0.5421546 0.5926604 0.6107581 -#> [786,] 0.28212285 0.4773151 0.5561894 0.5812558 0.6418716 0.6531730 0.6551963 -#> [787,] 0.38701647 0.4429149 0.5794177 0.6179733 0.7015497 0.7045773 0.7493636 -#> [788,] 0.22126147 0.4290754 0.4299702 0.4498610 0.5164665 0.5368604 0.5377246 -#> [789,] 0.27916916 0.3565893 0.3891389 0.3904213 0.4800280 0.5232374 0.5258567 -#> [790,] 0.36237544 0.5245432 0.7106098 0.8368973 0.8374019 0.8462018 0.8829692 -#> [791,] 0.50847764 0.5532540 0.5913410 0.6471791 0.6507086 0.6737630 0.7578613 -#> [792,] 0.34638187 0.3635378 0.3650333 0.4520274 0.4780638 0.5063630 0.5364357 -#> [793,] 0.96471043 1.1238617 1.1670605 1.2363080 1.3438715 1.4656662 1.5202666 -#> [794,] 0.39442631 0.5672525 0.5940007 0.6562407 0.6613072 0.7771895 0.8676490 -#> [795,] 0.44218243 0.4562070 0.4597380 0.4941077 0.4971177 0.5149583 0.5182899 -#> [796,] 0.46309833 0.4734599 0.4770930 0.4929153 0.4938839 0.5049733 0.5068350 -#> [797,] 0.35327797 0.4821675 0.5343740 0.5368604 0.5838635 0.5845838 0.5888128 -#> [798,] 0.44077352 0.4648615 0.5268685 0.5676076 0.5943828 0.6339192 0.6705211 -#> [799,] 0.43483633 0.4369859 0.4812532 0.4849461 0.4924158 0.5734678 0.5745182 -#> [800,] 0.22500973 0.4127918 0.4415877 0.5176093 0.5338640 0.5512025 0.5625347 -#> [801,] 0.33651243 0.4317180 0.4334976 0.4602431 0.4944497 0.5258567 0.5421749 -#> [802,] 0.30996952 0.4079894 0.4082440 0.5007269 0.5075303 0.5519626 0.5956112 -#> [803,] 0.42078874 0.4458199 0.5007269 0.5594309 0.5710308 0.5931351 0.6654464 -#> [804,] 0.46681568 0.5594014 0.5645912 0.5680787 0.7353619 0.7524761 0.7629788 -#> [805,] 0.67438290 0.8576244 0.8710689 1.0787180 1.0813341 1.1490374 1.1719507 -#> [806,] 0.63398347 0.7658092 0.8245560 0.8568136 0.8746828 0.9278684 0.9469909 -#> [807,] 0.79403055 1.2538734 1.3409049 1.3886341 1.4143776 1.6039930 1.6385793 -#> [808,] 0.39196996 0.6492271 0.7006185 0.7617940 0.7707938 0.7958265 0.8086566 -#> [809,] 0.53559259 0.5987499 0.6718302 0.6761828 0.6896680 0.6905674 0.6947258 -#> [810,] 0.21910513 0.4110020 0.4115678 0.4192902 0.5068288 0.5347966 0.5386177 -#> [811,] 0.31452038 0.6963547 0.7095495 0.7133073 0.7954932 0.8097445 0.8705029 -#> [812,] 0.54548829 0.5467555 0.6456530 0.6605649 0.7046408 0.7678281 0.7826580 -#> [813,] 0.39683053 0.4614771 0.4893392 0.5376430 0.6251130 0.6571221 0.7005399 -#> [814,] 0.42103663 0.5189327 0.5921624 0.7932110 0.7953206 0.8013409 0.8744982 -#> [815,] 0.73338145 1.1091642 1.3315277 1.5215019 1.5316756 1.5636798 1.5704113 -#> [816,] 0.58796392 0.6011233 0.6173346 0.6441560 0.6747761 0.6773700 0.6797189 -#> [817,] 1.36065079 1.5137655 1.5193820 1.5240171 1.5498771 1.5958677 1.6121502 -#> [818,] 0.30483279 0.6178127 0.6380806 0.6382770 0.6571221 0.7243450 0.8326125 -#> [819,] 0.40273042 0.5212736 0.5603790 0.5628116 0.5810016 0.5887656 0.6294922 -#> [820,] 0.71824740 0.7215123 0.8453708 0.8528875 0.9597073 0.9738452 1.1058711 -#> [821,] 0.38559728 0.4500143 0.4863964 0.5113181 0.5500049 0.5597726 0.5685905 -#> [822,] 0.62866059 0.6942937 0.7284301 0.7386602 0.7695203 0.8087480 0.8161729 -#> [823,] 0.14874895 0.3236660 0.5891766 0.5903342 0.5944632 0.6515879 0.6564730 -#> [824,] 0.45853317 0.4877218 0.4921739 0.5230083 0.5250451 0.5348140 0.5552367 -#> [825,] 0.34800816 0.4846637 0.5529245 0.6009061 0.6091344 0.6167648 0.6366774 -#> [826,] 0.43253503 0.4831555 0.4865940 0.5334751 0.5341619 0.5524280 0.5628177 -#> [827,] 0.55997084 0.7272578 0.7378849 0.7621492 0.8044038 0.8407370 0.8632858 -#> [828,] 0.68204381 0.7227850 0.8052437 0.8680231 0.8800067 0.9070997 0.9344276 -#> [829,] 0.28919190 0.4773151 0.4803415 0.5392919 0.5550533 0.6207553 0.6225913 -#> [830,] 0.55774317 0.6965077 0.7545314 0.7733977 0.8177852 0.9057978 0.9184425 -#> [831,] 0.36237544 0.6978403 0.7546528 0.7967500 0.8791270 0.8803966 0.8834623 -#> [832,] 0.47868229 0.7783767 0.8286614 0.8366864 0.8746035 0.8868312 0.9465115 -#> [833,] 0.43357679 0.4734693 0.4909328 0.5312809 0.5519626 0.5710308 0.5838635 -#> [834,] 0.40273042 0.5086296 0.6707089 0.6912846 0.7190484 0.7190719 0.7273207 -#> [835,] 0.29819539 0.3870360 0.4016753 0.4192902 0.4458681 0.4505302 0.4650588 -#> [836,] 0.35797635 0.3874699 0.4290754 0.4414033 0.4415019 0.4950905 0.5380901 -#> [837,] 0.24728549 0.3652578 0.5973751 0.6058621 0.6714878 0.7875077 0.8330189 -#> [838,] 0.43114166 0.7145558 0.7729599 0.7989667 0.8771213 0.8853278 0.9082500 -#> [839,] 0.28226619 0.6951862 0.7774018 0.8110518 0.8782227 0.8891517 0.9318547 -#> [840,] 0.36893463 0.4210366 0.5944991 0.6127205 0.6323531 0.6557683 0.7007896 -#> [841,] 0.66544443 0.7079532 0.7227111 0.8612166 0.8741109 0.8808184 0.9161539 -#> [842,] 0.23240441 0.3125665 0.3472597 0.4421824 0.4473325 0.4742903 0.5013366 -#> [843,] 0.53765037 0.7394289 0.7537169 0.7876056 0.8028051 0.9627590 1.0137468 -#> [844,] 0.39681078 0.4223431 0.6354693 0.7994573 0.8189176 0.8444658 0.8649676 -#> [845,] 0.54908168 0.8796845 0.9251165 0.9912498 1.0151577 1.0183992 1.0188981 -#> [846,] 0.34382525 0.3694933 0.4439416 0.6071260 0.6187073 0.6506722 0.6954510 -#> [847,] 0.30855503 0.4296335 0.4358069 0.5168948 0.5498010 0.5910771 0.5998957 -#> [848,] 0.25249506 0.3561176 0.3708418 0.3972535 0.5192776 0.5754905 0.6337435 -#> [849,] 0.33507272 0.4597374 0.5069029 0.5285431 0.5372878 0.6001093 0.6130547 -#> [850,] 0.17597548 0.3870165 0.5594309 0.6248637 0.6527461 0.6582989 0.6609336 -#> [851,] 0.35295760 0.4630727 0.5233075 0.7415390 0.7522670 0.7865930 0.8056994 -#> [852,] 0.31452038 0.5221992 0.5249327 0.6202899 0.6636930 0.6819790 0.7412960 -#> [853,] 1.06356035 1.0736661 1.0825259 1.0962366 1.2298611 1.2869292 1.2916757 -#> [854,] 0.96573690 1.1756648 1.1910772 1.2641272 1.3102195 1.3528209 1.3630859 -#> [855,] 0.56388819 0.6959618 0.7599807 0.7856879 0.8119880 0.8185857 0.8418481 -#> [856,] 0.36604246 0.4760026 0.4901277 0.4980567 0.6008453 0.6148009 0.6341309 -#> [857,] 0.38214516 0.5011114 0.5160354 0.6922482 0.7856879 0.8467618 0.9077031 -#> [858,] 0.59267044 0.6421611 0.6810884 0.7180966 0.7294620 0.7447693 0.7462063 -#> [859,] 0.35829338 0.6209997 0.6765025 0.6980939 0.7291427 0.7454981 0.7605660 -#> [860,] 0.35107872 0.4628321 0.4821902 0.5628116 0.5663818 0.5908624 0.6366756 -#> [861,] 0.48666539 0.7148040 0.8065527 0.8443996 0.8621241 0.9785859 0.9832066 -#> [862,] 0.36675682 0.4541553 0.6117694 0.7093871 0.7282900 0.7284700 0.7376806 -#> [863,] 0.44102183 0.7006185 0.7156137 0.8526129 0.8576936 0.8646028 0.8719987 -#> [864,] 0.25530869 0.5100279 0.6222739 0.7004509 0.7518164 0.7927098 0.8247377 -#> [865,] 0.35107872 0.4073083 0.4727952 0.4797075 0.5887656 0.5978246 0.6011425 -#> [866,] 0.20711353 0.2128669 0.2962470 0.4271706 0.4383742 0.5015894 0.5419253 -#> [867,] 0.49074646 0.5218996 0.5402898 0.5666667 0.5851182 0.5921624 0.6165753 -#> [868,] 0.38226491 0.4330564 0.4343819 0.4789346 0.4973497 0.6238698 0.6618646 -#> [869,] 0.20119578 0.5274743 0.5727556 0.5795555 0.6107581 0.6321618 0.6506722 -#> [870,] 0.34000005 0.5523149 0.5835526 0.5923980 0.6091881 0.6279973 0.6840545 -#> [871,] 0.40824403 0.5111314 0.5128921 0.5586927 0.5952777 0.6600823 0.6684693 -#> [872,] 0.41601699 0.4297828 0.4584733 0.4952032 0.5350863 0.5674021 0.5813493 -#> [873,] 0.56979095 0.5843422 0.6339161 0.6882297 0.7151055 0.7386283 0.7734542 -#> [874,] 0.65237655 0.6812469 0.7236839 0.7454451 0.8110938 0.8311178 0.9366361 -#> [875,] 0.42184296 0.4502727 0.4504206 0.4829929 0.6192943 0.6400865 0.6467567 -#> [876,] 0.33346801 0.3811909 0.4237522 0.4528098 0.4738702 0.5018809 0.5075120 -#> [877,] 0.40802474 0.4975265 0.5176093 0.5593482 0.6650764 0.6870879 0.6948454 -#> [878,] 0.67952156 0.6989833 0.7085963 0.7382393 0.7438888 0.8188065 0.8575808 -#> [879,] 0.52657678 0.5467555 0.5681275 0.5865694 0.7248411 0.7300095 0.7356286 -#> [880,] 0.21551060 0.3874699 0.3986045 0.4015032 0.4855791 0.5016559 0.5164665 -#> [881,] 0.38577668 0.4169955 0.4356454 0.4523469 0.4742309 0.4904582 0.5262045 -#> [882,] 0.56579449 0.5672378 0.5858722 0.5922482 0.6274278 0.6352016 0.6591535 -#> [883,] 0.59284156 0.6624952 0.8139930 0.8322004 0.9945305 1.0395810 1.1360815 -#> [884,] 0.35648607 0.4240218 0.4803121 0.4943045 0.5196530 0.5445057 0.6082788 -#> [885,] 0.53011276 0.6101697 0.6716065 0.7966283 0.8490819 0.8710689 0.9369868 -#> [886,] 0.68088921 0.8565482 0.8652427 0.9079105 0.9379848 0.9565635 0.9650364 -#> [887,] 0.14706566 0.3842350 0.4784125 0.5095337 0.5187470 0.5218255 0.5586692 -#> [888,] 0.44150652 1.0596932 1.2575503 1.3015299 1.3514940 1.3968001 1.4491465 -#> [889,] 0.54753283 0.5853666 0.6010209 0.6263943 0.7082306 0.7190484 0.7254835 -#> [890,] 0.51972920 0.5291272 0.5473492 0.5569819 0.5688599 0.6242691 0.6411027 -#> [891,] 0.46645317 0.5035358 0.5147386 0.5518292 0.5550570 0.5851370 0.6037221 -#> [892,] 0.43151667 0.4339481 0.4894152 0.4957784 0.5787041 0.5879778 0.5921479 -#> [893,] 0.54620478 0.7160182 0.7341874 0.7608141 0.7634351 0.7855782 0.8240498 -#> [894,] 0.26233166 0.3567649 0.4505434 0.5104084 0.5520312 0.6066468 0.6306655 -#> [895,] 0.59990667 0.6372920 0.6738717 0.6780247 0.7835165 0.8307043 0.8752580 -#> [896,] 0.44276402 0.4725503 0.5233075 0.5444489 0.6388097 0.7417904 0.7792963 -#> [897,] 0.61366148 0.6422448 0.6548673 0.6628541 0.7373350 0.8047808 0.8112394 -#> [898,] 1.13993621 1.2238417 1.2944929 1.4138356 1.4223752 1.5587307 1.5715766 -#> [899,] 0.41880586 0.4297828 0.4559433 0.4602431 0.4657972 0.4769464 0.5013366 -#> [900,] 0.44287676 0.5037232 0.6410085 0.7145558 0.8573915 0.8691765 0.8887671 -#> [901,] 0.70951869 0.7227111 0.7655611 0.9049409 1.1103051 1.1436977 1.1671287 -#> [902,] 0.17402163 0.2815989 0.3234938 0.4973108 0.5245936 0.5454948 0.5846517 -#> [903,] 0.51490569 0.6955637 0.9402911 0.9713093 0.9992002 1.0926981 1.1465395 -#> [904,] 0.16379892 0.4782800 0.4924158 0.5001598 0.5577912 0.5829803 0.6159372 -#> [905,] 0.64480070 0.7569891 0.9153377 0.9287700 0.9294366 1.0306542 1.1035669 -#> [906,] 0.26835689 0.3636692 0.3863603 0.4232646 0.4511470 0.4564327 0.4722120 -#> [907,] 0.50805535 0.5762502 0.5965354 0.6315937 0.6394524 0.6589275 0.7062934 -#> [908,] 0.38125326 0.4157032 0.4805979 0.5193114 0.5658166 0.5713880 0.5897818 -#> [909,] 0.23253009 0.3691577 0.4070091 0.5777244 0.6250401 0.6715531 0.7055877 -#> [910,] 0.38903127 0.4017785 0.4498610 0.4847487 0.4936195 0.5238938 0.5465308 -#> [911,] 0.59440990 0.6989068 0.6995980 0.7242203 0.7455946 0.7480645 0.7795465 -#> [912,] 0.29872606 0.4707987 0.4806273 0.4906690 0.6176954 0.6193783 0.6317046 -#> [913,] 0.82461152 0.8591310 0.8731408 0.9164328 1.0578797 1.0834695 1.1278420 -#> [914,] 0.67952156 0.9075966 0.9496742 0.9526848 1.0082962 1.0166035 1.1136135 -#> [915,] 0.44171162 0.4928736 0.5012118 0.6263943 0.6324206 0.6440154 0.6997098 -#> [916,] 0.29134698 0.3804423 0.4118020 0.4271706 0.5402898 0.5544855 0.5822610 -#> [917,] 0.21910513 0.4578056 0.5066574 0.5338640 0.5411180 0.5637485 0.5884009 -#> [918,] 0.80003296 0.8520190 0.8595147 0.9993802 1.0713708 1.0777662 1.0786178 -#> [919,] 0.58522196 0.6448007 0.6532442 0.6655542 0.7092594 0.7155112 0.7459836 -#> [920,] 0.36644829 0.3914287 0.4510571 0.6330261 0.6349593 0.6370497 0.6568489 -#> [921,] 0.43640664 0.5015894 0.5326803 0.5509312 0.5567052 0.5991453 0.6388802 -#> [922,] 0.50561703 0.5913410 0.5933649 0.7671347 0.7845608 0.9111170 0.9142453 -#> [923,] 0.32803844 0.4338809 0.5207731 0.5649189 0.6001995 0.6045659 0.6381594 -#> [924,] 0.35266986 0.3835013 0.5095863 0.5653659 0.5709348 0.5956112 0.6124346 -#> [925,] 0.64979301 0.7646062 0.8164882 0.8276377 0.8368973 0.8421748 0.8771384 -#> [926,] 0.23163085 0.2324044 0.4564653 0.4722120 0.4800280 0.5017003 0.5080255 -#> [927,] 0.32922558 0.3934326 0.5095337 0.5121672 0.5194180 0.5259880 0.5288773 -#> [928,] 0.79814003 0.9533309 1.0389936 1.1709526 1.1749098 1.1781384 1.2001907 -#> [929,] 0.39267342 0.5086939 0.5477472 0.5520312 0.6469049 0.6839461 0.6940983 -#> [930,] 0.29134698 0.3037289 0.3091650 0.3305156 0.4383742 0.4936195 0.5488952 -#> [931,] 0.29872606 0.4131597 0.4795506 0.5665091 0.5997251 0.6136057 0.6261643 -#> [932,] 0.42677834 0.4411643 0.5686196 0.6174766 0.6513026 0.6731724 0.7777964 -#> [933,] 0.39206430 0.5012118 0.5589393 0.7086554 0.7179053 0.7529551 0.7558851 -#> [934,] 0.40473610 0.7515701 0.8153097 1.0193305 1.1129096 1.1454856 1.2042223 -#> [935,] 0.36401105 0.4486307 0.4561306 0.4769901 0.4926383 0.5446308 0.5739571 -#> [936,] 0.83073988 0.8819863 1.0641047 1.0782516 1.0969681 1.1447650 1.2193489 -#> [937,] 0.33591867 0.3466916 0.3873969 0.5139271 0.5550533 0.5812558 0.5829420 -#> [938,] 0.04497083 0.5870115 0.5876477 0.5898424 0.6302899 0.6714246 0.6754866 -#> [939,] 0.53443537 0.5655940 0.5792064 0.7051456 0.7086461 0.7153976 0.8056811 -#> [940,] 0.88640969 1.1744302 1.2819133 1.3042470 1.3137897 1.4205330 1.4232624 -#> [941,] 0.55774317 0.5631514 0.7511748 0.7522670 0.7901658 0.7962587 0.8298927 -#> [942,] 0.35991731 0.3694933 0.4263777 0.5047480 0.5095882 0.6563266 0.7308714 -#> [943,] 0.15739874 0.2999043 0.3060863 0.3792610 0.4267138 0.4280612 0.4582420 -#> [944,] 0.46932208 0.5688599 0.6562525 0.7224982 0.7271169 0.7447693 0.7451598 -#> [945,] 0.33816647 0.5002608 0.5497086 0.5832154 0.5839837 0.5987499 0.6531255 -#> [946,] 0.65787838 0.9657369 0.9883514 1.0701032 1.1374992 1.1447650 1.2451739 -#> [947,] 0.50561703 0.6266733 0.6672829 0.6746814 0.7006440 0.7663563 0.7955146 -#> [948,] 0.58372918 0.7636551 0.8544198 0.9077602 0.9096541 0.9287029 0.9601853 -#> [949,] 0.31354966 0.3466916 0.3770144 0.4981605 0.5835526 0.6080251 0.6379400 -#> [950,] 0.26351243 0.3076413 0.4308061 0.4694526 0.5942378 0.6482221 0.6509040 -#> [951,] 0.18607074 0.4871539 0.5036852 0.5189966 0.5335565 0.5425809 0.5678585 -#> [952,] 0.37747986 0.4925961 0.5129245 0.5213931 0.5320780 0.6816017 0.7071654 -#> [953,] 0.43434151 0.4537805 0.4782800 0.5454883 0.5546442 0.5803326 0.6204706 -#> [954,] 0.54966756 0.5632566 0.5648903 0.6048902 0.6158045 0.6161821 0.6182972 -#> [955,] 0.26783037 0.3555067 0.4599327 0.5020988 0.5503266 0.5731316 0.5883047 -#> [956,] 0.29496717 0.3133610 0.5002608 0.5079815 0.5430447 0.5891322 0.5961361 -#> [957,] 0.32511397 0.3617655 0.3835013 0.5075303 0.5512543 0.5762256 0.5950332 -#> [958,] 0.45042063 0.4606442 0.4727952 0.5148810 0.5263058 0.5962643 0.6366756 -#> [959,] 0.17370456 0.3076413 0.3279429 0.4852801 0.4944497 0.5166436 0.5732934 -#> [960,] 0.35705946 0.4410636 0.4516268 0.5754905 0.6508299 0.6886539 0.8302582 -#> [961,] 0.22813174 0.4001949 0.4003260 0.4113986 0.4827286 0.5053651 0.5508058 -#> [962,] 0.79111290 0.8337189 0.9096423 0.9653220 0.9693479 1.0392179 1.0942301 -#> [963,] 0.39206430 0.4417116 0.5213441 0.5853666 0.6000512 0.6460923 0.6988536 -#> [964,] 0.49034069 0.5703550 0.6110425 0.6360577 0.6465991 0.6549053 0.6917995 -#> [965,] 1.03453276 1.0574077 1.1989866 1.2401557 1.2488760 1.2885182 1.3480125 -#> [966,] 0.47146486 0.5787526 0.7349696 0.7509420 0.8018245 0.8678513 0.8806975 -#> [967,] 0.51784333 0.5368353 0.5718766 0.6238143 0.6354693 0.6705881 0.7379584 -#> [968,] 0.68204381 0.9912498 1.0265272 1.2226328 1.3495796 1.3968221 1.4309520 -#> [969,] 0.43544614 0.5446717 0.6081071 0.6550018 0.7121909 0.7462301 0.8047521 -#> [970,] 0.67468144 0.8115864 0.8311740 0.8576452 0.8817724 0.8970071 0.9092255 -#> [971,] 0.25530869 0.4229574 0.6714246 0.6844769 0.7469655 0.7584307 0.7610679 -#> [972,] 0.41437972 0.6251328 0.6979654 0.7523700 0.8096269 0.8257021 0.8363396 -#> [973,] 0.46068409 0.5099019 0.5453835 0.6073536 0.6115334 0.6494149 0.6606911 -#> [974,] 0.50560586 0.5682722 0.5732926 0.7049280 0.7061830 0.7096315 0.7706464 -#> [975,] 0.74582316 0.7609546 0.8046738 0.8240498 0.8241563 0.8281400 0.8356967 -#> [976,] 0.33525273 0.3374291 0.3817311 0.4523469 0.4536032 0.4987909 0.6120957 -#> [977,] 0.23008354 0.4954478 0.5046153 0.5614417 0.5616416 0.5828565 0.5882024 -#> [978,] 0.49841470 0.6714878 0.7688076 0.7764392 0.8018245 0.8105688 0.8233114 -#> [979,] 0.52526029 0.5345360 0.5447285 0.6395455 0.7031803 0.7376332 0.7483666 -#> [980,] 0.24785601 0.3852941 0.3986045 0.5758994 0.6449115 0.6810014 0.7294620 -#> [981,] 0.63397320 0.6394524 0.6664361 0.6701969 0.6802607 0.7022022 0.7301337 -#> [982,] 0.45580842 0.5715196 0.5749911 0.5859775 0.6681737 0.6822274 0.7166143 -#> [983,] 0.50101698 0.5376430 0.5929588 0.7114757 0.7489781 0.7692118 0.7773591 -#> [984,] 0.19780375 0.4136345 0.4339700 0.4415019 0.4979181 0.6075412 0.6091881 -#> [985,] 0.63176827 0.7092594 0.8033928 0.8673370 0.9140608 0.9153377 1.0441789 -#> [986,] 0.36491652 0.5273441 0.6032080 0.6253015 0.7454451 0.7729599 0.7883097 -#> [987,] 0.44276402 0.6742267 0.7026799 0.7415390 0.7864823 0.8683901 0.9982356 -#> [988,] 0.44525949 0.8690679 0.8875176 0.9267411 1.0588732 1.0614233 1.0699795 -#> [989,] 0.46932208 0.5569819 0.5886034 0.6532035 0.6885028 0.7242253 0.7372330 -#> [990,] 0.27366290 0.4653143 0.6021361 0.6025242 0.6380806 0.6693098 0.6744915 -#> [991,] 0.20711353 0.2356696 0.3305156 0.3670160 0.4118020 0.4364066 0.5464797 -#> [992,] 0.37440774 0.7086461 0.7418901 0.7442849 0.7787907 0.7836854 0.7855291 -#> [993,] 1.04193676 1.0992335 1.1503974 1.1741563 1.2870854 1.3807208 1.4045472 -#> [994,] 0.45879487 0.5301128 0.5747591 0.6214295 0.7605660 0.8673346 0.8709480 -#> [995,] 0.48610978 0.5967973 0.6007497 0.6189824 0.6356349 0.7741783 0.7854985 -#> [996,] 0.40589754 0.5606936 0.6975627 0.7258812 0.7362571 0.7638539 0.7670559 -#> [997,] 0.95463549 1.1054185 1.1069747 1.1304412 1.2374547 1.4004799 1.4737834 -#> [998,] 0.85278886 0.9860807 1.0845824 1.2091892 1.2122146 1.2567472 1.3275814 -#> [999,] 0.33373156 0.3560558 0.6230229 0.6408758 0.6742086 0.7076926 0.8252286 -#> [1000,] 0.12432811 0.4822970 0.7518164 0.7584307 0.8035997 0.8186917 0.8575808 +#> [1,] 0.75123324 0.7728336 0.8535100 0.9610233 0.9988456 1.0241624 1.0346625 +#> [2,] 0.72794121 0.8192607 1.0118221 1.0396370 1.0447104 1.0736597 1.1356458 +#> [3,] 0.31837803 0.4564968 0.4829120 0.5219559 0.5329834 0.6032451 0.6036069 +#> [4,] 0.31047166 0.3921811 0.4326476 0.4630233 0.5398038 0.5555416 0.6151065 +#> [5,] 0.46734788 0.4760593 0.5466231 0.5606464 0.5640889 0.5726195 0.5783608 +#> [6,] 0.51152215 0.5275568 0.5497865 0.5556941 0.5624806 0.6122315 0.6278000 +#> [7,] 0.29359887 0.5086648 0.5542773 0.5716974 0.5869704 0.5940340 0.5959630 +#> [8,] 0.26143033 0.4718084 0.5603310 0.5707832 0.6882329 0.7308097 0.7341715 +#> [9,] 0.24984831 0.5609735 0.8632232 0.9219492 1.0170726 1.0442705 1.0520745 +#> [10,] 0.28986430 0.4537154 0.5204006 0.5421002 0.5423238 0.5463735 0.5587194 +#> [11,] 0.46652531 0.5084248 0.5263341 0.5266163 0.5446352 0.5495054 0.5850168 +#> [12,] 0.37170190 0.4381048 0.5241100 0.5423993 0.5424052 0.5655038 0.6071722 +#> [13,] 0.57222011 0.6124874 0.6357935 0.6396935 0.6462147 0.6708131 0.7391421 +#> [14,] 0.34717912 0.5838188 0.5962627 0.6141551 0.6745910 0.8141714 0.8167852 +#> [15,] 0.58059369 0.6224053 0.6662234 0.6803393 0.7407235 0.8389967 0.8429282 +#> [16,] 0.63314368 0.8630664 0.8939270 1.0213772 1.0458141 1.0663115 1.1116785 +#> [17,] 0.54835213 0.7198504 0.7467861 0.8400073 0.8529885 0.9064278 0.9411035 +#> [18,] 0.66924590 0.6764030 0.8256855 0.8928497 0.9098371 0.9177645 0.9264857 +#> [19,] 0.47391219 0.5187272 0.6039699 0.6313239 0.7718839 0.8130187 0.8617744 +#> [20,] 0.15081454 0.3758961 0.3943047 0.4202105 0.4597621 0.5058134 0.5526436 +#> [21,] 0.26227964 0.3306440 0.4625359 0.4748135 0.5735866 0.5903636 0.6051760 +#> [22,] 0.43233503 0.5294653 0.5807848 0.6182965 0.6219620 0.6649769 0.6820446 +#> [23,] 0.51569242 0.5551573 0.6019943 0.6874485 0.7129012 0.7578704 0.7810091 +#> [24,] 0.26564505 0.8384672 0.9027206 0.9060224 0.9245897 0.9398366 0.9480771 +#> [25,] 0.58665844 0.8659828 0.8981276 0.9708535 1.0050467 1.0329852 1.0364884 +#> [26,] 0.19951012 0.3092625 0.3686001 0.4240887 0.6186630 0.6479357 0.7211515 +#> [27,] 0.36583627 0.4208790 0.4291136 0.5052316 0.5424372 0.6643204 0.6768602 +#> [28,] 0.63749376 0.6643837 0.7336474 0.8632298 0.9630081 1.0143946 1.0585654 +#> [29,] 0.22773632 0.2810413 0.3024392 0.6371490 0.7398366 0.7736565 0.7775577 +#> [30,] 0.41843597 0.4328745 0.4340449 0.5067279 0.5268015 0.5302476 0.5624027 +#> [31,] 0.44109626 0.5183360 0.6860934 0.9339130 0.9629930 1.0579500 1.0943981 +#> [32,] 0.51867312 0.5645729 0.6016788 0.6357531 0.6454604 0.6971910 0.7115832 +#> [33,] 0.47605929 0.5257607 0.5566021 0.5824304 0.6490195 0.7135536 0.7156801 +#> [34,] 0.97263621 1.1064219 1.1444155 1.1538665 1.1596231 1.2692750 1.3001680 +#> [35,] 0.98260936 1.1123529 1.1596231 1.1717316 1.1850187 1.2225809 1.2601779 +#> [36,] 0.49461149 0.5933300 0.6646068 0.6683092 0.8323921 0.8352585 0.8584428 +#> [37,] 0.33752748 0.3668717 0.3733893 0.3738390 0.4858712 0.5329507 0.5359664 +#> [38,] 0.63792553 0.6805158 0.8052105 0.8253294 0.9434459 0.9543999 0.9622925 +#> [39,] 0.79115486 0.9026125 1.0002906 1.0816304 1.1440999 1.2319974 1.2718061 +#> [40,] 0.43594864 0.7188591 0.7189500 0.8269726 1.0044478 1.0197690 1.0395219 +#> [41,] 0.29776484 0.3110705 0.3169707 0.3448560 0.3540405 0.3739873 0.4252863 +#> [42,] 0.56407060 0.8340099 0.9236600 0.9486578 0.9507656 0.9726362 0.9826094 +#> [43,] 0.35070927 0.4212043 0.4399540 0.5016230 0.5245879 0.5408275 0.5485840 +#> [44,] 0.13879613 0.2449583 0.3716588 0.3857744 0.4513943 0.4539900 0.4953717 +#> [45,] 0.27674962 0.3248874 0.3792540 0.4061885 0.4685934 0.5096766 0.5394649 +#> [46,] 0.70931322 0.7762396 0.7874432 0.8142466 0.8157303 0.8707487 0.8863619 +#> [47,] 0.34194145 0.6358041 0.6792359 0.6991985 0.7115954 0.7373865 0.8197065 +#> [48,] 0.54036550 0.6094105 0.6267325 0.6674362 0.7317710 0.7325700 0.7442621 +#> [49,] 0.53167438 0.6307679 0.7248237 0.7447617 0.7648825 0.7757223 0.7758032 +#> [50,] 0.34302240 0.4088468 0.4949305 0.6649502 0.6736528 0.6801958 0.6810368 +#> [51,] 0.44016974 0.5076383 0.5175767 0.7000366 0.7683283 0.7880880 0.8114595 +#> [52,] 0.34683737 0.4084745 0.5982558 0.5990630 0.6392620 0.6435954 0.6734625 +#> [53,] 0.43617718 0.4965451 0.5327786 0.5778905 0.5869704 0.5978329 0.6719906 +#> [54,] 0.30248329 0.3537647 0.3890210 0.5052213 0.5101134 0.5352680 0.5394586 +#> [55,] 0.52308478 0.5402685 0.5593313 0.5874356 0.7559557 0.7821695 0.7822892 +#> [56,] 0.69637689 0.7825293 0.8849706 0.9317456 0.9860083 1.0912192 1.1467084 +#> [57,] 0.16587260 0.2957155 0.3413110 0.4116207 0.4205946 0.5540923 0.6107552 +#> [58,] 0.21674498 0.2655099 0.3790429 0.3932341 0.4436496 0.5844425 0.6218011 +#> [59,] 0.40454188 0.4800348 0.5403655 0.5566021 0.5771780 0.6141551 0.6305519 +#> [60,] 0.75561149 0.7588936 0.7819681 0.7975871 0.8945171 0.9152283 0.9162163 +#> [61,] 0.30100470 0.4964479 0.5864232 0.5975842 0.6878447 0.6894267 0.6965180 +#> [62,] 0.16282452 0.3341115 0.4328745 0.4532430 0.4765493 0.5213406 0.5403077 +#> [63,] 0.27072483 0.3851949 0.4612409 0.5142720 0.5167208 0.5294653 0.6030569 +#> [64,] 0.49644789 0.5186731 0.5261169 0.5533974 0.5655843 0.6290988 0.6358013 +#> [65,] 0.35509133 0.5000259 0.5016800 0.5273160 0.5482631 0.6101186 0.6657547 +#> [66,] 0.51826095 0.5183360 0.6878115 0.9025787 0.9928195 1.0014826 1.0029237 +#> [67,] 0.20842890 0.3411604 0.3568485 0.4068925 0.4214355 0.4377764 0.4642517 +#> [68,] 0.47750098 0.6644961 0.8175037 0.8371260 0.8424111 0.8428174 0.8493960 +#> [69,] 0.45928747 0.4965451 0.5542773 0.5917991 0.6237566 0.6343305 0.6550373 +#> [70,] 1.00949093 1.1650926 1.2078393 1.2169444 1.2341341 1.2526122 1.3587940 +#> [71,] 0.22038706 0.3698291 0.5063805 0.6682706 0.6764030 0.6803568 0.7025122 +#> [72,] 0.41824000 0.4184360 0.4325937 0.4377125 0.4848735 0.5098943 0.5213406 +#> [73,] 0.36595529 0.4150283 0.4230495 0.4715471 0.4820203 0.4893759 0.5093840 +#> [74,] 0.48657110 0.5810022 0.7735514 0.8210664 0.8910608 0.8927631 0.9176643 +#> [75,] 0.40020124 0.4232959 0.4436496 0.4596097 0.5149030 0.5152366 0.5463399 +#> [76,] 0.48817965 0.4927598 0.5107277 0.5623835 0.5859066 0.5935301 0.5946564 +#> [77,] 0.15081454 0.4335340 0.4363955 0.5182849 0.5205824 0.5418517 0.5425802 +#> [78,] 0.30995948 0.3273770 0.4254990 0.4672448 0.5121810 0.5136565 0.5154224 +#> [79,] 0.47970446 0.4947340 0.6647554 0.7263034 0.7535873 0.7609947 0.7625186 +#> [80,] 0.79618626 0.8698408 0.8936291 0.8975484 0.9456737 0.9510369 0.9690743 +#> [81,] 0.47623620 0.4877438 0.4971451 0.5032684 0.5146468 0.5204006 0.5501698 +#> [82,] 0.93583477 1.0071221 1.0885044 1.1050823 1.1755327 1.1912636 1.2374069 +#> [83,] 0.70018889 0.8879985 0.9946745 1.0809744 1.1726914 1.2589149 1.3267449 +#> [84,] 0.85574145 0.9550328 0.9788586 0.9906871 1.0431189 1.0460363 1.0573611 +#> [85,] 0.32254407 0.4354345 0.4755552 0.4913438 0.5488552 0.5558120 0.6032168 +#> [86,] 0.58625836 0.7660761 0.7706474 0.7710468 0.7778610 0.7782860 0.7874432 +#> [87,] 0.39066227 0.4763041 0.5491078 0.6551946 0.6561217 0.6865512 0.7407140 +#> [88,] 0.40030939 0.5722201 0.6381720 0.6390878 0.6411925 0.6611606 0.6664912 +#> [89,] 0.46587098 0.5052316 0.5139742 0.5400055 0.5485160 0.5844425 0.5922445 +#> [90,] 0.19996637 0.2139283 0.3522130 0.3596801 0.5070188 0.5268149 0.5578403 +#> [91,] 0.34578357 0.3713855 0.5607744 0.5739698 0.5832266 0.5948363 0.6034775 +#> [92,] 0.13118949 0.4307515 0.5089294 0.6219634 0.7261603 0.7798434 0.8711605 +#> [93,] 0.15515797 0.2767496 0.3113777 0.3347892 0.4146456 0.4380970 0.4573444 +#> [94,] 0.40949008 0.7662003 0.8612254 0.8767726 0.8964731 1.1548959 1.1752675 +#> [95,] 0.81376542 0.8750154 0.8946274 0.8990536 0.9284389 0.9383646 0.9488876 +#> [96,] 0.36398921 0.7536512 0.7638342 0.8374410 0.8986590 0.9269765 0.9570827 +#> [97,] 0.40763037 0.4564968 0.4906205 0.4921939 0.4925928 0.5062591 0.5259640 +#> [98,] 0.27744439 0.3237234 0.4171864 0.4533024 0.4574603 0.5073590 0.5079862 +#> [99,] 0.44986007 0.5442689 0.6053123 0.6070115 0.6822141 0.6850527 0.8025247 +#> [100,] 0.36217182 0.4002402 0.5479470 0.6148331 0.6682706 0.6987118 0.7176815 +#> [101,] 1.16034211 1.1830457 1.4589260 1.4813706 1.5482614 1.6614958 1.7219734 +#> [102,] 0.20121330 0.2754540 0.3096327 0.3457012 0.3905825 0.3975459 0.4304232 +#> [103,] 1.20255514 1.2330142 1.2589149 1.3426098 1.3481100 1.5117781 1.6069093 +#> [104,] 0.44558674 0.5320156 0.5873528 0.5957944 0.6836550 0.6969275 0.7006954 +#> [105,] 0.28454807 0.4706502 0.4755460 0.5303708 0.5371465 0.5512903 0.5558524 +#> [106,] 0.47750098 0.4851378 0.4973154 0.5868762 0.7283071 0.8416151 0.8593591 +#> [107,] 0.98216529 1.0064208 1.1040333 1.2141759 1.2774960 1.2957623 1.3022217 +#> [108,] 0.47869309 0.8343755 0.9840538 1.0110834 1.0577510 1.0926077 1.1350025 +#> [109,] 0.49710326 0.5006748 0.5058134 0.5114418 0.5418517 0.5850060 0.5912959 +#> [110,] 0.19702653 0.3113777 0.3457012 0.4007505 0.4532430 0.4683849 0.4685934 +#> [111,] 0.36008810 0.4174495 0.4408895 0.4548463 0.4828149 0.5061000 0.5141196 +#> [112,] 0.57458749 0.6909275 0.7177089 0.7218150 0.7744658 0.8237228 0.8288619 +#> [113,] 0.57147892 0.6049098 0.6478061 0.6816491 0.7262376 0.7536512 0.8159034 +#> [114,] 0.53953399 0.6014050 0.6576091 0.6786399 0.6947844 0.7110033 0.7216347 +#> [115,] 0.48599759 0.5154224 0.5558413 0.5789364 0.6354666 0.6547929 0.6603546 +#> [116,] 0.33628768 0.4650064 0.5263157 0.7069239 0.7447617 0.7891283 0.8259153 +#> [117,] 0.69958193 0.9283156 1.0939061 1.1216403 1.1250323 1.1288179 1.1515432 +#> [118,] 0.36398921 0.5714789 0.6894734 0.7014631 0.7260693 0.7619641 0.8240498 +#> [119,] 0.29785763 0.3904106 0.5147426 0.5658674 0.5798745 0.5887206 0.5896853 +#> [120,] 0.35909924 0.4613995 0.4725982 0.5263326 0.5385190 0.5893542 0.6825840 +#> [121,] 0.35748401 0.4455867 0.5154027 0.6343369 0.6851376 0.7147886 0.7230207 +#> [122,] 0.26383426 0.2754540 0.2988481 0.2991627 0.3411604 0.4419143 0.4423288 +#> [123,] 0.62395483 0.6685404 0.7950016 0.8067952 0.8221489 0.8340085 0.8765175 +#> [124,] 0.82937244 0.8707487 0.9551027 1.0103346 1.0519770 1.0832535 1.2253165 +#> [125,] 0.55221468 0.5857830 0.6497453 0.6719993 0.6832759 0.6859977 0.7828531 +#> [126,] 0.45402224 0.6171313 0.6190679 0.7087893 0.7216347 0.7515560 0.7782790 +#> [127,] 0.27028389 0.3572052 0.3993061 0.4557764 0.4626339 0.4726576 0.4993475 +#> [128,] 0.28309416 0.4212043 0.4574603 0.4870235 0.5599778 0.5800733 0.5826591 +#> [129,] 0.25146438 0.4186988 0.5370477 0.5654464 0.5749203 0.6088727 0.6190746 +#> [130,] 0.49033543 0.5993018 0.6014718 0.6776866 0.7389885 0.7588429 0.7662003 +#> [131,] 0.35233048 0.4090712 0.5369264 0.6189582 0.6317574 0.6684878 0.6798811 +#> [132,] 0.48681494 0.5408715 0.6034775 0.6051837 0.6162894 0.6389791 0.6392017 +#> [133,] 0.20289628 0.5917991 0.7040529 0.8219994 0.8745047 0.9693476 0.9888539 +#> [134,] 0.86087471 0.8734834 0.9566091 1.0393017 1.0486299 1.1115831 1.1149204 +#> [135,] 0.27865399 0.2969847 0.3897075 0.4253274 0.4470561 0.4950304 0.5887206 +#> [136,] 0.73790913 0.7727693 0.7880136 0.8494167 0.9279067 1.0006806 1.0803707 +#> [137,] 0.45981635 0.4838382 0.5119779 0.5679398 0.5857583 0.5990630 0.6031911 +#> [138,] 0.30248329 0.3120646 0.3843879 0.3865320 0.4880859 0.4925018 0.5331744 +#> [139,] 0.36139975 0.3821192 0.3884705 0.4467783 0.6854213 0.6990176 0.7259507 +#> [140,] 0.79370421 1.1505867 1.1890757 1.1910424 1.2222432 1.3167299 1.3355435 +#> [141,] 0.19951012 0.2417104 0.2781614 0.4680106 0.6250949 0.6525452 0.6603980 +#> [142,] 0.37893352 0.4015229 0.4064958 0.4696581 0.4792169 0.4909026 0.5502156 +#> [143,] 0.31849457 0.3427354 0.3941537 0.4860365 0.5329507 0.5679398 0.5687805 +#> [144,] 0.36318027 0.4053421 0.4399584 0.4597621 0.5413375 0.5425802 0.5480974 +#> [145,] 0.38772521 0.4192009 0.4871528 0.5026201 0.5073320 0.6164275 0.6390106 +#> [146,] 0.26642362 0.3237234 0.4394007 0.4495239 0.4681588 0.4948503 0.5472648 +#> [147,] 0.61755802 0.6381720 0.6557861 0.7506527 0.7529128 0.7540028 0.7591031 +#> [148,] 0.18634156 0.5009155 0.5485840 0.5599778 0.5931087 0.5965084 0.6349871 +#> [149,] 0.12046345 0.5411184 0.6786399 0.6798061 0.6833299 0.7009866 0.7406080 +#> [150,] 0.17188245 0.3712625 0.3743934 0.3987000 0.4416277 0.5023142 0.5079862 +#> [151,] 0.37547110 0.4474790 0.4828926 0.5041523 0.6416367 0.6856432 0.7161804 +#> [152,] 0.79937319 0.8241613 0.8907011 1.0241436 1.1297508 1.1573210 1.1658958 +#> [153,] 0.19419128 0.2061225 0.3007250 0.4605015 0.5260537 0.5295352 0.5749598 +#> [154,] 0.30085979 0.3841699 0.5687805 0.5778905 0.6377990 0.6564521 0.6819878 +#> [155,] 0.19513800 0.4827132 0.5439209 0.5786936 0.5950256 0.6442066 0.6687645 +#> [156,] 0.46868378 0.5114418 0.5316744 0.5526436 0.5607080 0.6290993 0.6300735 +#> [157,] 0.59045114 0.9869900 1.1535666 1.1582809 1.1780062 1.2135801 1.2585351 +#> [158,] 0.24545491 0.3333003 0.3969425 0.4088468 0.4721369 0.4748135 0.5520819 +#> [159,] 0.28986430 0.3544125 0.3764880 0.3992212 0.5491586 0.5680241 0.5835483 +#> [160,] 0.43780840 0.4498638 0.7033036 0.7877849 0.8172419 0.8357316 0.8577205 +#> [161,] 0.40582631 0.5054017 0.7030924 0.7243780 0.7407140 0.7705307 0.7930608 +#> [162,] 0.35234103 0.4147042 0.4795022 0.4952720 0.4962613 0.4989207 0.5185135 +#> [163,] 0.54300594 0.5583139 0.6795859 0.8095291 0.8206264 0.9728522 1.1327863 +#> [164,] 0.52367020 0.5885379 0.8717503 0.8939719 0.9026125 0.9614319 1.0410679 +#> [165,] 0.96143186 1.0199945 1.0232013 1.0551349 1.0902843 1.1010699 1.1045643 +#> [166,] 0.61571818 0.7040191 0.7058041 0.7656191 0.7734775 0.7824822 0.8215516 +#> [167,] 0.35229033 0.3921811 0.5471192 0.5639015 0.5672174 0.5735262 0.5753703 +#> [168,] 0.96825642 1.0396370 1.1898986 1.2800371 1.2881848 1.3270543 1.3435666 +#> [169,] 0.66673361 0.6783771 0.6894816 0.7395991 0.7798434 0.8020988 0.8409993 +#> [170,] 0.28743920 0.4859976 0.6151464 0.6426209 0.6501716 0.6633979 0.7000754 +#> [171,] 0.31719653 0.4426525 0.4802058 0.5162458 0.5402894 0.5484489 0.5699656 +#> [172,] 0.08045996 0.2277363 0.4479699 0.6083513 0.7456291 0.7558175 0.7626539 +#> [173,] 0.26393459 0.3593245 0.3717283 0.3820295 0.4015229 0.4740713 0.5161408 +#> [174,] 0.30338965 0.3445281 0.3752012 0.3833929 0.4174495 0.4185740 0.5224324 +#> [175,] 0.60837870 0.6760452 0.7997734 0.8205199 0.8496036 0.8767713 0.8946601 +#> [176,] 0.61829653 0.6366517 0.6997558 0.7136063 0.7383797 0.7610103 0.8476542 +#> [177,] 0.29403821 0.3544237 0.5263326 0.5589544 0.6405376 0.6407043 0.6920594 +#> [178,] 0.30753941 0.4389056 0.4819657 0.5267271 0.5626776 0.5839383 0.6590841 +#> [179,] 0.61451410 0.8065479 0.8074916 0.8192607 0.8541983 0.8731405 0.9437775 +#> [180,] 0.26642362 0.3097676 0.3796937 0.3842518 0.5073590 0.5130520 0.5358143 +#> [181,] 0.47013970 0.5230848 0.5470321 0.8496036 0.8674290 0.9467661 0.9817323 +#> [182,] 0.36021158 0.4470561 0.4515219 0.4767623 0.5241343 0.5364844 0.5865542 +#> [183,] 0.53317440 0.6307292 0.6375269 0.6506767 0.6550839 0.6692890 0.6958393 +#> [184,] 0.59828158 0.7636470 0.8197293 0.8356096 0.8501639 0.8775427 0.9832344 +#> [185,] 0.51230390 0.5204844 0.5648963 0.7084205 0.7865361 0.7996885 0.8246749 +#> [186,] 0.38596645 0.4145507 0.5691145 0.5989056 0.6002112 0.6328652 0.6405012 +#> [187,] 0.96030647 1.0761704 1.0808862 1.0870889 1.1122201 1.1279417 1.1959206 +#> [188,] 0.49473402 0.6299515 0.6832576 0.6942725 0.6958393 0.7062105 0.7064561 +#> [189,] 0.35909924 0.4255819 0.5056986 0.5103887 0.6192480 0.7221886 0.7384616 +#> [190,] 0.48660327 0.7114507 0.7246401 0.7313637 0.8284993 1.0153069 1.0305508 +#> [191,] 0.65050082 0.6816491 0.6842797 0.7600037 0.7705307 0.7733659 0.8202470 +#> [192,] 0.81182782 0.8194373 0.8417935 1.1055276 1.1253849 1.1289979 1.1556449 +#> [193,] 0.66753911 0.7257133 0.7266951 0.7743539 0.8705561 0.9086395 0.9237531 +#> [194,] 0.62045966 0.7111853 0.7449372 0.8068628 0.8219124 0.8335637 0.8411046 +#> [195,] 0.32084307 0.3593245 0.3889879 0.4064958 0.4534542 0.5020450 0.5061745 +#> [196,] 0.59162905 0.6261190 0.6519473 0.6742100 0.7273983 0.7396244 0.7504098 +#> [197,] 0.25774459 0.3922203 0.4871528 0.5731440 0.5860091 0.6188489 0.6274038 +#> [198,] 0.55964953 0.6301278 0.7006146 0.7704925 0.8103618 0.8187889 0.8411695 +#> [199,] 0.56526701 0.5919769 0.7933394 0.8006608 0.8031460 0.8174900 0.9500898 +#> [200,] 0.63850522 0.6481022 0.7466410 0.8095291 0.8236840 0.8689609 0.8885308 +#> [201,] 0.27865399 0.2923046 0.5241343 0.5313052 0.5860547 0.5909128 0.5959543 +#> [202,] 0.42631331 0.5904569 0.5998869 0.7362744 0.7605896 0.8071532 0.8210664 +#> [203,] 0.20842890 0.2428933 0.2988481 0.3402142 0.3697308 0.3865320 0.3890210 +#> [204,] 0.47013970 0.5402685 0.6083787 0.7876943 0.8457159 0.8897081 0.8939270 +#> [205,] 0.34090425 0.4108805 0.4379580 0.5384481 0.5431463 0.5516274 0.5550702 +#> [206,] 0.52132482 0.5291135 0.5549221 0.6991985 0.7043297 0.7597526 0.7809424 +#> [207,] 0.21587317 0.5501698 0.5944061 0.6019943 0.6036912 0.6121399 0.6232368 +#> [208,] 0.19508862 0.3493520 0.4058071 0.5062397 0.5209490 0.5405174 0.5476367 +#> [209,] 0.34835901 0.3519596 0.4355776 0.7052760 0.7261054 0.7961863 0.7980408 +#> [210,] 0.15472873 0.2741477 0.3276143 0.3874743 0.4512306 0.4848735 0.5456915 +#> [211,] 0.30083629 0.5030022 0.5802170 0.6049625 0.6124386 0.6855360 0.7587768 +#> [212,] 0.50905999 0.5763424 0.6077715 0.6494028 0.6571812 0.7116446 0.7209059 +#> [213,] 0.31837803 0.3607279 0.5027575 0.5151727 0.5491726 0.5556941 0.6165135 +#> [214,] 0.58913260 0.8071999 0.9856087 0.9869900 1.0638696 1.1631375 1.1634441 +#> [215,] 0.31003120 0.3585382 0.3890705 0.3975790 0.4177120 0.5668593 0.5714364 +#> [216,] 0.40446900 0.4795265 0.5715888 0.5716924 0.6813370 0.7271226 0.7320339 +#> [217,] 0.41890859 0.4878582 0.5088305 0.5258547 0.5416196 0.5602363 0.5909218 +#> [218,] 0.40846562 0.4148849 0.4390372 0.4579331 0.4686838 0.5092006 0.5149004 +#> [219,] 0.31027223 0.3580854 0.4416277 0.4557797 0.4635054 0.4980985 0.5223489 +#> [220,] 0.67821809 0.7269518 0.7884421 0.8981276 0.9696410 1.0095566 1.0242894 +#> [221,] 0.29113310 0.4240887 0.4680106 0.5413932 0.5551246 0.6033871 0.6445696 +#> [222,] 0.31200656 0.3922203 0.5089802 0.6606336 0.6940500 0.7074970 0.7195269 +#> [223,] 0.80654788 0.8376506 0.9906952 1.0447104 1.0594308 1.1200518 1.1231975 +#> [224,] 0.29785763 0.3634108 0.3946379 0.4253274 0.5136025 0.5313052 0.5568903 +#> [225,] 0.51517267 0.6075172 0.6088218 0.6133793 0.6376472 0.6498677 0.6685887 +#> [226,] 0.41347323 0.4914823 0.5701759 0.5940340 0.6190746 0.6238119 0.6348328 +#> [227,] 0.30243922 0.4479699 0.4755517 0.5638175 0.6171614 0.6514300 0.7090876 +#> [228,] 0.90555460 0.9918885 1.1684544 1.1782701 1.2056914 1.2159078 1.2336533 +#> [229,] 0.51230390 0.6631301 0.6653664 0.7335557 0.7518248 0.8405305 0.9252294 +#> [230,] 0.77283362 0.8079880 0.8467161 0.8528566 0.8555573 0.9325761 0.9362719 +#> [231,] 0.59961280 0.6343369 0.7535997 0.7794280 0.8462224 0.8493717 0.8586241 +#> [232,] 0.26393459 0.2679328 0.2736122 0.3208431 0.3571812 0.4792169 0.4811862 +#> [233,] 0.17875079 0.3975790 0.4053346 0.4628703 0.5497720 0.5765510 0.6245413 +#> [234,] 0.67821809 0.6819256 0.6971910 0.7084205 0.7768582 0.8004708 0.8130695 +#> [235,] 0.35229033 0.4113023 0.4561031 0.4946848 0.4971033 0.5271013 0.5398038 +#> [236,] 0.62906531 0.7234641 0.7449372 0.7810325 0.8642700 0.8649101 0.8839147 +#> [237,] 0.20395837 0.3844797 0.4306182 0.5561102 0.5694909 0.5874698 0.5900798 +#> [238,] 0.57458749 0.5959630 0.6238756 0.6348328 0.6590748 0.6690772 0.7566615 +#> [239,] 0.24032959 0.5017250 0.5050407 0.5692543 0.5727376 0.5783608 0.6350611 +#> [240,] 0.41672649 0.4448618 0.4721369 0.5199025 0.5258675 0.5286509 0.5563468 +#> [241,] 0.98405382 1.1375119 1.1976571 1.2046299 1.2651680 1.3673162 1.3923991 +#> [242,] 0.57243631 0.9198506 1.0032346 1.1074054 1.1169257 1.1289227 1.1497500 +#> [243,] 0.55313581 0.6545472 0.7726849 0.8180935 0.8738729 0.8899513 0.9580921 +#> [244,] 0.57634239 0.6158662 0.6329802 0.6512504 0.6656699 0.6795726 0.7843879 +#> [245,] 0.40030939 0.4047201 0.6347717 0.6518593 0.6594240 0.6682516 0.6796197 +#> [246,] 0.20957563 0.5535769 0.5850788 0.5888863 0.5982422 0.6195995 0.6572079 +#> [247,] 0.32538741 0.3764248 0.4068925 0.4827132 0.5377097 0.5388781 0.5564219 +#> [248,] 0.07991648 0.3969090 0.4554043 0.5958814 0.6328125 0.6462949 0.6542727 +#> [249,] 0.40846562 0.5515295 0.6300735 0.6669118 0.6803393 0.7171484 0.7440268 +#> [250,] 0.24531337 0.2920796 0.3045802 0.3529613 0.3733389 0.3739873 0.4389056 +#> [251,] 0.30083629 0.3895963 0.5889364 0.6204556 0.6560988 0.6840995 0.7052760 +#> [252,] 0.35356658 0.4333924 0.4380970 0.4901973 0.5463399 0.5705713 0.5717453 +#> [253,] 0.71555951 0.8531945 0.9589535 1.0147257 1.0754897 1.0893189 1.0893488 +#> [254,] 0.32560846 0.3436373 0.5602555 0.6310238 0.7119669 0.7121295 0.8242852 +#> [255,] 0.19508862 0.3335111 0.3359325 0.4334802 0.4795022 0.5193626 0.5880298 +#> [256,] 0.33217500 0.5334556 0.5916505 0.7259507 0.7293199 0.7529128 0.7547055 +#> [257,] 0.34844429 0.3698551 0.4515219 0.6835393 0.7039375 0.7097591 0.7388971 +#> [258,] 0.48774381 0.5034437 0.6036912 0.6462597 0.6528830 0.6738408 0.7292561 +#> [259,] 0.50387183 0.5885379 0.8716588 0.9163334 0.9436734 0.9756567 0.9843683 +#> [260,] 0.31083119 0.5392876 0.5584831 0.6788038 0.6856432 0.6939049 0.7587768 +#> [261,] 0.50540167 0.6094466 0.6106162 0.7100869 0.7474043 0.7548418 0.7657958 +#> [262,] 0.36072787 0.4829120 0.6355625 0.6428138 0.6685887 0.7080839 0.7910339 +#> [263,] 0.39463790 0.3948480 0.5147426 0.5186912 0.5888827 0.6300291 0.6367832 +#> [264,] 0.39988625 0.4002021 0.4185740 0.4483313 0.4727237 0.4871645 0.4962613 +#> [265,] 0.23993803 0.5224575 0.5456090 0.5960820 0.6028731 0.6078198 0.6157228 +#> [266,] 0.37926665 0.4468730 0.4925928 0.4942371 0.5473633 0.5825448 0.6047881 +#> [267,] 0.46645854 0.5050407 0.6158712 0.7140490 0.7180839 0.7395991 0.7456979 +#> [268,] 0.38487972 0.4326476 0.4426525 0.4727237 0.4775865 0.4810028 0.5031708 +#> [269,] 1.06380439 1.1005776 1.1676122 1.1770550 1.2246484 1.2639915 1.3374345 +#> [270,] 0.35760660 0.5306228 0.6001879 0.6131775 0.6140907 0.6375269 0.6378469 +#> [271,] 0.37007701 0.3978038 0.4291071 0.5187272 0.5306961 0.5880864 0.6199867 +#> [272,] 0.08045996 0.2810413 0.4755517 0.5850168 0.7445571 0.7490401 0.7683102 +#> [273,] 0.48999353 0.5196985 0.5392876 0.6215960 0.6416367 0.6426553 0.6913845 +#> [274,] 0.24010441 0.3874158 0.4525542 0.4539625 0.4949872 0.5261021 0.5265085 +#> [275,] 0.30100470 0.5029816 0.5127194 0.5510996 0.5639015 0.5655843 0.6384658 +#> [276,] 0.54449025 1.0617308 1.1690400 1.2214387 1.2731221 1.2739128 1.4960939 +#> [277,] 0.29910942 0.5714456 0.5963457 0.6705824 0.6787581 0.7043262 0.7580649 +#> [278,] 0.35718116 0.4193687 0.4433985 0.4534542 0.4665253 0.5451624 0.5502156 +#> [279,] 0.41394639 0.6325459 0.6699654 0.7836029 0.7851510 0.8182959 0.8425422 +#> [280,] 0.22475057 0.3396683 0.4055814 0.4175321 0.4377923 0.4471095 0.4585823 +#> [281,] 0.29444451 0.4113023 0.4141784 0.4404892 0.4630233 0.5031708 0.5333971 +#> [282,] 0.31644694 0.5551573 0.5961214 0.6221072 0.6550789 0.7101610 0.7283993 +#> [283,] 0.34578357 0.3468282 0.3687117 0.4835465 0.5648596 0.6162894 0.6381854 +#> [284,] 0.50387183 0.5236702 0.7701056 0.7911549 0.8739263 0.8836861 0.9411378 +#> [285,] 0.41119252 0.5622579 0.6023753 0.6042996 0.6207918 0.6479959 0.6823808 +#> [286,] 0.85567071 1.0204548 1.0805443 1.1364532 1.3088280 1.3428671 1.3523249 +#> [287,] 0.52439153 0.7499065 0.9440280 0.9675250 1.0153069 1.2163210 1.2222432 +#> [288,] 0.54239075 0.6429094 0.8806943 1.3620115 1.4205182 1.4246796 1.4566494 +#> [289,] 0.94204478 0.9724045 0.9750135 1.0133426 1.1141167 1.1172216 1.1293275 +#> [290,] 0.34194145 0.4542825 0.5073444 0.5291135 0.7688035 0.7853064 0.8207269 +#> [291,] 0.26594121 0.4145992 0.5107277 0.5382576 0.5775870 0.6428034 0.6433268 +#> [292,] 0.33966834 0.3705808 0.4968724 0.5614452 0.5912959 0.6030132 0.6323524 +#> [293,] 0.54240523 0.5685634 0.6545472 0.7480650 0.7893958 0.7894660 0.8047075 +#> [294,] 0.26927678 0.4298212 0.4530965 0.4804884 0.5015095 0.5394649 0.5716974 +#> [295,] 0.26343145 0.2979331 0.4463409 0.5502562 0.5519391 0.5640889 0.5888744 +#> [296,] 0.28309416 0.3568485 0.4377125 0.4419143 0.4674355 0.4870335 0.5484208 +#> [297,] 0.23681564 0.3086668 0.4232261 0.4378483 0.4828435 0.5027134 0.5113881 +#> [298,] 0.44123648 0.4935362 0.5216645 0.5916291 0.6151879 0.7155653 0.7452075 +#> [299,] 0.15515797 0.1970265 0.3792540 0.3921772 0.4304232 0.4333924 0.4406006 +#> [300,] 0.35029758 0.3859824 0.6158662 0.6386173 0.6545420 0.6789222 0.6912502 +#> [301,] 0.17672129 0.2920796 0.3487168 0.4119493 0.4148849 0.4666492 0.5515295 +#> [302,] 0.53302955 0.5382576 0.5505231 0.6596260 0.6688616 0.6746336 0.6863087 +#> [303,] 1.26360735 1.2688591 1.4489720 1.5091963 1.5111890 1.6452195 1.7570417 +#> [304,] 0.32254407 0.3594234 0.4605015 0.4645395 0.5425355 0.5434271 0.5556632 +#> [305,] 0.24866835 0.3789335 0.3792134 0.4561757 0.5161408 0.5266475 0.5673960 +#> [306,] 0.26740534 0.3208248 0.4667387 0.4988816 0.5023142 0.5210939 0.5911413 +#> [307,] 0.68948956 0.8591377 0.8632621 0.8758765 0.9509291 0.9731328 1.0001691 +#> [308,] 0.51152798 0.7055470 0.7202057 0.7660243 0.7819818 0.7912739 0.8258615 +#> [309,] 0.50638053 0.5179364 0.5468375 0.6046618 0.6985545 0.7881233 0.8091576 +#> [310,] 0.16587260 0.3356574 0.3871199 0.4471608 0.4613834 0.5982422 0.6546082 +#> [311,] 0.43717069 0.4465631 0.5035108 0.6308780 0.6550789 0.6917758 0.7570848 +#> [312,] 0.35346968 0.4866033 0.4911228 0.9278780 0.9597156 0.9675250 0.9823241 +#> [313,] 0.46884574 0.4715928 0.4735928 0.5765781 0.5976214 0.6209378 0.6795702 +#> [314,] 0.18842874 0.2774444 0.3743934 0.4975694 0.5050988 0.5495635 0.5568096 +#> [315,] 0.48747026 0.5115595 0.5776815 0.5810022 0.5833092 0.5961116 0.6199867 +#> [316,] 0.71882339 0.7298102 0.7980408 0.8027938 0.8137303 0.8317879 0.8445150 +#> [317,] 0.42087897 0.4312480 0.5634303 0.6422605 0.6566718 0.7048257 0.7623149 +#> [318,] 0.34683737 0.4090368 0.4860365 0.5359664 0.5383853 0.6108532 0.6336967 +#> [319,] 0.37138546 0.4117716 0.4785496 0.4868149 0.5997890 0.6381854 0.6638874 +#> [320,] 0.46517599 0.5368974 0.6422321 0.6776026 0.6805158 0.7277149 0.7588429 +#> [321,] 0.09697109 0.3934919 0.3992212 0.4013733 0.4841724 0.5219711 0.6044576 +#> [322,] 0.61354902 0.7331312 0.8358797 0.8843709 0.9381280 0.9839040 0.9903867 +#> [323,] 0.37300207 0.3944722 0.4408895 0.5156462 0.6216648 0.6234677 0.6260663 +#> [324,] 0.30975111 0.5447037 0.5730397 0.5768426 0.6028126 0.6794550 0.6901274 +#> [325,] 0.49046188 0.5118977 0.5345808 0.5487884 0.6182018 0.6263767 0.6269757 +#> [326,] 0.48914700 0.5917555 0.6135490 0.8236793 0.8388834 0.8661062 0.8718881 +#> [327,] 0.47466248 0.6094466 0.6433676 0.6557585 0.6625708 0.6628817 0.6690772 +#> [328,] 0.73136370 0.8106643 0.8197065 0.9443775 1.0060133 1.0388432 1.0488026 +#> [329,] 0.56057729 0.5963301 0.6683562 0.6795859 0.7329504 0.8120086 0.8243287 +#> [330,] 0.27949333 0.4054250 0.4598163 0.4782392 0.5327786 0.6019534 0.6318225 +#> [331,] 0.33351112 0.3356392 0.3441288 0.4058071 0.4617185 0.4678582 0.5969929 +#> [332,] 0.50641307 0.5115280 0.5247714 0.5806314 0.6723671 0.7161356 0.7278448 +#> [333,] 0.52374859 0.5609209 0.5919769 0.7004560 0.7470308 0.7624804 0.7892986 +#> [334,] 0.42795110 0.4530965 0.4612184 0.4931298 0.5000259 0.5240484 0.5290541 +#> [335,] 0.32761430 0.3475464 0.3843879 0.3949626 0.4205965 0.4415721 0.4535849 +#> [336,] 0.50451422 0.8163552 1.0484713 1.0540369 1.1106780 1.1204779 1.1334821 +#> [337,] 0.56262031 0.7564921 0.8078945 0.8326922 0.8352566 0.8653057 0.8771891 +#> [338,] 0.35029758 0.6987542 0.7735514 0.8113368 0.8188095 0.8228033 0.8238807 +#> [339,] 0.49353624 0.6602226 0.7272707 0.7396244 0.7642973 0.8857255 0.9054262 +#> [340,] 0.26385543 0.4138580 0.4591137 0.4841722 0.4971807 0.5357776 0.5859066 +#> [341,] 0.25334228 0.4108805 0.4143892 0.4465461 0.5185135 0.5862135 0.6011670 +#> [342,] 0.23535424 0.3844714 0.3873494 0.4871865 0.5377632 0.5901825 0.6002112 +#> [343,] 0.49333622 0.5099957 0.5933300 0.6932422 0.7805514 0.7853750 0.9455148 +#> [344,] 0.42777114 0.4942371 0.4996934 0.5374261 0.5378361 0.5491287 0.5572177 +#> [345,] 0.43543452 0.4731234 0.5097656 0.5154537 0.5501798 0.5601731 0.5648554 +#> [346,] 0.27816143 0.2911331 0.3092625 0.4558091 0.6182421 0.6550839 0.6832576 +#> [347,] 0.31133296 0.5416196 0.5924440 0.5951297 0.6042996 0.6043597 0.6051760 +#> [348,] 0.32560846 0.4348900 0.5081217 0.5956861 0.7277149 0.8389705 0.8713819 +#> [349,] 0.27110632 0.2736122 0.3717283 0.4193687 0.4696581 0.5124989 0.5188817 +#> [350,] 0.46804512 0.5862584 0.6132736 0.6300291 0.6931258 0.7347957 0.8070378 +#> [351,] 0.59930178 0.6040936 0.6243774 0.7537008 0.8512212 0.8618755 0.8767726 +#> [352,] 0.72695182 0.8925216 1.0102435 1.0211662 1.0279754 1.0329852 1.2980993 +#> [353,] 0.31170307 0.6374938 0.7231658 0.8318084 0.9315260 0.9454788 1.0235594 +#> [354,] 0.47014037 0.5311183 0.5807093 0.5893542 0.6192480 0.6478061 0.6894734 +#> [355,] 0.31605501 0.5687865 0.6450369 0.6562205 0.6602688 0.6698618 0.6733962 +#> [356,] 0.21566331 0.3844714 0.4404892 0.4961319 0.5078930 0.5369264 0.5770353 +#> [357,] 0.44777486 0.5099957 0.5576428 0.6046208 0.6613447 0.6683092 0.6720861 +#> [358,] 0.44599817 0.5609735 0.7096210 0.8861194 0.8862931 0.8908547 0.9431895 +#> [359,] 0.59854713 0.7551560 0.7975922 0.8340979 0.9488876 0.9678762 1.0104143 +#> [360,] 0.69343238 0.8115715 0.8518886 0.8595648 0.8660315 0.9372885 1.0002448 +#> [361,] 0.18945147 0.3203283 0.3361130 0.3448560 0.3493174 0.3777877 0.4662042 +#> [362,] 0.48657110 0.6705820 0.7001198 0.7362744 0.8745113 0.9834923 0.9946699 +#> [363,] 0.31644694 0.5156924 0.6910260 0.7869916 0.8203473 0.8254108 0.8631331 +#> [364,] 0.57077554 0.6313865 0.6719993 0.6959316 0.7819634 0.8589755 0.8680502 +#> [365,] 0.58735280 0.5874356 0.6407729 0.6911030 0.7000366 0.7373071 0.7389704 +#> [366,] 0.28454807 0.3498404 0.3726540 0.3987329 0.4167784 0.4767417 0.4883674 +#> [367,] 0.24909537 0.6217602 0.8683734 0.9142125 0.9621563 0.9622823 0.9941056 +#> [368,] 0.20121330 0.2632861 0.2991627 0.3697308 0.4134017 0.4259888 0.5201796 +#> [369,] 0.24136134 0.6594240 0.6920199 0.7117756 0.7880136 0.8037376 0.8159284 +#> [370,] 0.49568273 0.6052306 0.6221072 0.6234920 0.6700591 0.7000754 0.7869916 +#> [371,] 0.47399983 0.6625547 0.7281672 0.7694525 0.7743539 0.8051282 0.8220837 +#> [372,] 0.36567019 0.4189086 0.5234874 0.5771078 0.5944776 0.5951297 0.7530624 +#> [373,] 0.18460255 0.2060190 0.3782162 0.3791834 0.4426923 0.4612184 0.4794289 +#> [374,] 0.46500636 0.5511406 0.5911722 0.5958814 0.6242367 0.6417307 0.6562524 +#> [375,] 0.30753941 0.5299665 0.5805937 0.5926426 0.6214401 0.6687676 0.7171484 +#> [376,] 0.58633486 0.6239548 0.6599681 0.6648714 0.6745910 0.7043262 0.7398995 +#> [377,] 0.50763829 0.5663951 0.6228037 0.7147886 0.7307823 0.7430718 0.7822892 +#> [378,] 0.30361897 0.3430907 0.5306958 0.5863829 0.6143961 0.6843397 0.7215748 +#> [379,] 0.62529285 0.9373032 0.9589361 0.9839291 1.0569027 1.1136162 1.1334750 +#> [380,] 0.31004980 0.6990266 0.9175304 1.0122607 1.1262807 1.2133086 1.2242808 +#> [381,] 0.49568273 0.5961214 0.6232368 0.6734077 0.8202901 0.8360364 0.8509438 +#> [382,] 0.31083119 0.4828926 0.4899935 0.5448074 0.5461205 0.5871392 0.7828199 +#> [383,] 0.33628768 0.5511406 0.5918048 0.6436058 0.6527433 0.6718151 0.7163910 +#> [384,] 0.44077776 0.5411184 0.6801610 0.7706525 0.7962910 0.8294079 0.8335637 +#> [385,] 0.15604009 0.4762362 0.5680078 0.5814557 0.6462597 0.6486402 0.6494302 +#> [386,] 0.38532828 0.4359686 0.5209098 0.5689635 0.5757741 0.5904509 0.5991853 +#> [387,] 0.33470661 0.3647560 0.5047202 0.5630189 0.5689635 0.5829721 0.5891104 +#> [388,] 0.65667184 0.6750756 0.6775991 0.7616078 0.7726849 0.9088996 0.9411035 +#> [389,] 0.31575585 0.5786936 0.6070519 0.6149352 0.7121295 0.7450731 0.7560740 +#> [390,] 0.40636897 0.4379580 0.4465461 0.5390755 0.5909128 0.5998869 0.6395035 +#> [391,] 0.71637391 0.9092624 0.9917192 1.0071221 1.0974428 1.1297508 1.1602162 +#> [392,] 0.51834711 0.5505231 0.5846558 0.5926610 0.7513978 0.7774382 0.7794280 +#> [393,] 0.14605421 0.6075121 0.6775991 0.7048257 0.7444487 0.7576389 0.8104532 +#> [394,] 0.46638959 0.5294214 0.5480739 0.6234146 0.6267325 0.6959634 0.7060311 +#> [395,] 1.04920444 1.1822441 1.2197848 1.3257101 1.3857820 1.3957540 1.3989331 +#> [396,] 0.50074816 0.9368811 1.0471965 1.0540369 1.0659909 1.0825885 1.0936585 +#> [397,] 0.35233048 0.3636660 0.4969829 0.5377632 0.5908810 0.6138392 0.7085501 +#> [398,] 0.33018724 0.5976214 0.6068353 0.6865432 0.7154680 0.7209059 0.7564411 +#> [399,] 0.32685154 0.3821192 0.5760403 0.6261190 0.6692866 0.7112562 0.7409202 +#> [400,] 0.99219177 1.0147922 1.1050029 1.1439902 1.2163210 1.2301288 1.2718061 +#> [401,] 0.50067478 0.5271013 0.5735262 0.6820610 0.6876870 0.7728214 0.7903175 +#> [402,] 0.61061623 0.6870153 0.7093132 0.7347957 0.8417935 0.9099727 0.9207612 +#> [403,] 0.13118949 0.5519855 0.5731555 0.6801153 0.8020988 0.8270089 0.9160206 +#> [404,] 0.35945348 0.3929751 0.4128428 0.4949872 0.5282306 0.6033871 0.6078198 +#> [405,] 0.47216259 0.6429094 0.8313961 0.9559077 1.1019036 1.1677363 1.2491498 +#> [406,] 0.52840771 0.5306958 0.5440199 0.5557236 0.5803419 0.5863349 0.5922188 +#> [407,] 0.18051066 0.4002402 0.5468375 0.5576100 0.5873958 0.6803568 0.7861184 +#> [408,] 0.42982120 0.4931298 0.5219711 0.5226653 0.5355817 0.5618288 0.6011145 +#> [409,] 0.23307038 0.5096766 0.5400055 0.5411882 0.5479479 0.5729807 0.5868046 +#> [410,] 0.22690537 0.3102786 0.4061678 0.4906205 0.5086148 0.5583424 0.6063374 +#> [411,] 0.59354400 0.6070115 0.6145340 0.6739434 0.6935096 0.6939493 0.7555520 +#> [412,] 0.45311652 0.4835465 0.6051837 0.6545420 0.6795726 0.6883190 0.7681392 +#> [413,] 0.52874860 0.5485678 0.5557123 0.5716924 0.6452023 0.7073426 0.7129012 +#> [414,] 0.59854713 0.7433678 0.7931495 1.1149152 1.1309300 1.2472189 1.2511145 +#> [415,] 1.00915273 1.0397144 1.0531656 1.0897849 1.1299244 1.2105582 1.2705682 +#> [416,] 0.44685385 0.4802813 0.4841724 0.5951493 0.6011145 0.6486402 0.6837553 +#> [417,] 0.39066227 0.4058263 0.5686761 0.5893919 0.7694337 0.7836191 0.8194804 +#> [418,] 0.30577483 0.4465631 0.5118977 0.5633698 0.5763036 0.6474377 0.6518129 +#> [419,] 0.33565738 0.3413110 0.3634738 0.3796179 0.3857744 0.4868420 0.5030333 +#> [420,] 0.26385543 0.3050787 0.3099595 0.4493096 0.5142780 0.5426927 0.5432273 +#> [421,] 0.72981016 0.8191095 1.0391618 1.0635243 1.1439234 1.1497564 1.1579309 +#> [422,] 0.77901084 0.8141714 0.8880066 0.9686581 0.9776988 0.9981969 1.0372120 +#> [423,] 0.17188245 0.3102722 0.4068494 0.4948228 0.5050988 0.5092787 0.5166929 +#> [424,] 0.58021695 0.6560988 0.8278941 0.8310321 0.8902744 0.9046550 0.9497534 +#> [425,] 0.50074816 0.7472879 0.7886271 0.8055432 0.8072300 0.8322109 0.8544600 +#> [426,] 0.65841128 0.7551560 0.8793542 0.8990536 0.9343049 0.9647801 1.0091527 +#> [427,] 0.40360403 0.4477749 0.4521097 0.5402954 0.5465505 0.6198224 0.6646068 +#> [428,] 0.47869309 0.9571049 1.1259877 1.1363119 1.2431542 1.2535680 1.2883587 +#> [429,] 0.34027172 0.3519596 0.3895963 0.5030022 0.5741917 0.5803987 0.7337276 +#> [430,] 0.21863794 0.5792501 0.5799310 0.6055756 0.6098908 0.6619151 0.7052326 +#> [431,] 0.42262902 0.5161931 0.5211340 0.5809020 0.6295294 0.6742100 0.7002335 +#> [432,] 0.57267127 0.5730397 0.6557861 0.6854213 0.6911311 0.6957793 0.7067249 +#> [433,] 0.24866835 0.2996319 0.4200978 0.4258462 0.4310830 0.5603310 0.5667096 +#> [434,] 0.39780376 0.4386172 0.4797447 0.4874703 0.4933392 0.5682878 0.6303169 +#> [435,] 0.49112284 0.6390106 0.6759421 0.7114507 0.7823791 0.7902742 0.7998991 +#> [436,] 0.36738405 0.4135458 0.5167208 0.5334669 0.5473633 0.5482934 0.5957012 +#> [437,] 0.29571554 0.3634738 0.3637507 0.3814911 0.3871199 0.3876728 0.4539900 +#> [438,] 0.49119061 0.7828531 0.8027938 0.8172419 0.9485036 1.0184482 1.0185377 +#> [439,] 0.83437551 0.9571049 1.0803707 1.1735669 1.1976571 1.2111689 1.3843671 +#> [440,] 0.24289334 0.3120646 0.3537647 0.3949626 0.4134017 0.4214355 0.4319502 +#> [441,] 0.20289628 0.6237566 0.6844604 0.7369896 0.8385844 0.8803578 0.8962578 +#> [442,] 0.75123324 0.9219082 1.0496610 1.1107187 1.1215952 1.1425865 1.1637278 +#> [443,] 0.24545491 0.4625359 0.4660731 0.4949305 0.5009155 0.5330862 0.5507820 +#> [444,] 0.67768660 0.7038044 0.7892187 0.8172974 0.8343469 0.8612254 0.8907011 +#> [445,] 1.22623347 1.2420842 1.3854303 1.3953893 1.4621698 1.5357017 1.5569942 +#> [446,] 0.47289305 0.6145340 0.6684878 0.6760726 0.6819878 0.6879263 0.6969249 +#> [447,] 1.45228667 1.6043613 1.6490182 1.8686264 1.8838568 1.8952957 1.9006795 +#> [448,] 0.57243631 0.7067615 0.8611921 1.0199524 1.0782641 1.1621953 1.1898859 +#> [449,] 0.24863800 0.3598209 0.3796937 0.4948503 0.5142888 0.5173598 0.5306004 +#> [450,] 0.44366791 0.7192560 1.2657779 1.2739660 1.3403521 1.3811454 1.5902023 +#> [451,] 0.50167997 0.5237082 0.5300692 0.5317664 0.5451624 0.5490896 0.5717995 +#> [452,] 0.64144872 0.8653057 1.1236420 1.2908359 1.3073813 1.3171347 1.3352933 +#> [453,] 0.40963634 0.4797447 0.5306961 0.6045138 0.6223325 0.6235444 0.6778300 +#> [454,] 0.57965193 0.7163739 0.7299407 0.7894505 0.9358348 1.0104482 1.0190099 +#> [455,] 0.33731955 0.3678879 0.3792666 0.4171944 0.5020450 0.5062591 0.5226281 +#> [456,] 0.09697109 0.3223750 0.3544125 0.4270109 0.4468538 0.5355817 0.5816812 +#> [457,] 0.26564505 0.7653629 0.8236488 0.9174340 0.9267543 0.9272176 0.9374222 +#> [458,] 0.48470066 0.5052092 0.5596495 0.6027456 0.6594966 0.6607136 0.6997558 +#> [459,] 0.16282452 0.2891480 0.4182400 0.4340449 0.4996934 0.5051723 0.5113368 +#> [460,] 0.44060061 0.4449831 0.4557764 0.4573444 0.4681588 0.5077163 0.5246272 +#> [461,] 0.27996485 0.5602452 0.6143961 0.6219634 0.6801153 0.7140490 0.7973198 +#> [462,] 0.70380443 0.8241613 1.1755510 1.1950636 1.2795708 1.3081447 1.3256072 +#> [463,] 0.26740534 0.3117459 0.4585147 0.4678582 0.5209490 0.5859090 0.5880765 +#> [464,] 0.52166448 0.5273160 0.6314860 0.6347717 0.6591848 0.6602226 0.6829279 +#> [465,] 0.46804512 0.6870153 0.7545626 0.7706474 0.7762396 0.8118278 0.9270370 +#> [466,] 0.31004980 0.7137102 0.8195100 0.8730117 0.9593647 1.0354425 1.1030984 +#> [467,] 0.29292073 0.3314803 0.4310830 0.4425622 0.4561757 0.5497865 0.5655038 +#> [468,] 0.27981317 0.3631803 0.3758961 0.4363955 0.4510979 0.4921939 0.5126823 +#> [469,] 0.34390429 0.3668414 0.4200978 0.5247714 0.5707832 0.5720249 0.6123558 +#> [470,] 0.21164504 0.2930111 0.3169707 0.3361130 0.3613120 0.3687756 0.3733389 +#> [471,] 0.20417561 0.4786287 0.5084755 0.5140385 0.5410413 0.5428946 0.6918404 +#> [472,] 0.83587974 0.8890259 0.9255049 0.9664203 1.0821634 1.0852990 1.1843941 +#> [473,] 0.76058958 0.9090353 0.9256838 0.9697771 1.0202062 1.0394324 1.0615033 +#> [474,] 0.40542497 0.4449027 0.4514099 0.5119779 0.5300852 0.5545604 0.5947904 +#> [475,] 0.87266945 1.0586150 1.0839485 1.0860132 1.1040333 1.1843674 1.3202047 +#> [476,] 0.39915058 0.4044690 0.4656943 0.5084248 0.5287486 0.5500657 0.6266317 +#> [477,] 0.51479463 0.5694424 0.6092113 0.6469373 0.6859729 0.6948786 0.6948917 +#> [478,] 0.33332358 0.4540222 0.6165999 0.6311446 0.6354071 0.6525144 0.6576091 +#> [479,] 0.32348784 0.4167936 0.4622053 0.4756200 0.4828435 0.4856521 0.5458660 +#> [480,] 0.20774666 0.2368156 0.3148796 0.3318808 0.4167936 0.5171208 0.5223489 +#> [481,] 0.64265530 0.6615948 0.6939049 0.7691026 0.7837144 0.8313751 0.8374920 +#> [482,] 0.31191358 0.5115595 0.5186912 0.5568903 0.5658674 0.5805367 0.6526331 +#> [483,] 0.36871174 0.4752569 0.4946232 0.5832266 0.6778089 0.6828681 0.7059491 +#> [484,] 0.61434841 0.6643837 0.7710334 0.8202840 0.8318084 1.0382065 1.1255773 +#> [485,] 0.32282065 0.4160175 0.4522604 0.4870973 0.5622388 0.5837745 0.6021089 +#> [486,] 0.69902660 0.9255049 0.9593647 0.9603065 1.0848989 1.1964572 1.2806124 +#> [487,] 0.36583627 0.4312480 0.5921655 0.6179367 0.6290988 0.6688491 0.6798061 +#> [488,] 0.48914700 0.5809788 0.5860091 0.6228037 0.6670446 0.7151664 0.7331312 +#> [489,] 0.54426886 0.5818935 0.6092135 0.6802616 0.7296064 0.7703605 0.7863003 +#> [490,] 0.64144872 1.1515432 1.2574863 1.3068810 1.3621837 1.4400532 1.5037535 +#> [491,] 0.68609343 0.6878115 1.0190392 1.1752675 1.2500793 1.4017630 1.4175909 +#> [492,] 0.19996637 0.2641698 0.4474902 0.4658086 0.5312450 0.5522147 0.5635916 +#> [493,] 0.55034352 0.6327033 0.7231658 0.9630081 1.1152965 1.1166193 1.2305216 +#> [494,] 0.44016974 0.7307823 0.8092806 0.8182231 0.8593062 0.8993036 0.9097510 +#> [495,] 0.27017635 0.3699957 0.4096830 0.4512306 0.4612165 0.4628703 0.4941352 +#> [496,] 0.73232324 0.7710468 0.8167852 0.8925772 0.9230566 0.9272176 0.9380741 +#> [497,] 0.35942336 0.4049898 0.4334802 0.4913438 0.5031696 0.5197681 0.5405174 +#> [498,] 1.03943243 1.0721897 1.0905577 1.1089035 1.1314227 1.1912541 1.2253874 +#> [499,] 0.27072483 0.3865848 0.4292685 0.5141644 0.5370477 0.5482934 0.5624287 +#> [500,] 0.37642480 0.3978315 0.3998387 0.5439209 0.5668593 0.6262270 0.6268746 +#> [501,] 0.47216259 0.5423907 0.7833552 0.9727131 1.1413017 1.1811073 1.2372009 +#> [502,] 0.34754640 0.3576066 0.3862040 0.3874743 0.4431694 0.4952917 0.5221971 +#> [503,] 1.25593536 1.3176629 1.3585019 1.3851334 1.3869811 1.4232511 1.4684821 +#> [504,] 0.41890633 0.4389962 0.4798121 0.6254812 0.6718151 0.6731796 0.7411969 +#> [505,] 0.78545275 0.8847553 1.0815974 1.1519782 1.3383411 1.4112341 1.5026801 +#> [506,] 0.37644458 0.5334556 0.5487723 0.7187771 0.7560823 0.7745370 0.9103913 +#> [507,] 0.70927902 0.7398905 0.8395667 0.8904261 0.8994460 0.9188252 0.9655937 +#> [508,] 0.51459128 0.6009983 0.6357531 0.6474427 0.6819256 0.7059153 0.7111395 +#> [509,] 0.43697204 0.4958141 0.5626203 0.5694424 0.5959871 0.6326815 0.7707595 +#> [510,] 0.40476741 0.5652670 0.5761168 0.6490482 0.6917122 0.7084974 0.7534034 +#> [511,] 0.82162781 0.9675018 0.9981969 1.0832153 1.2422757 1.2847455 1.2862323 +#> [512,] 0.30085979 0.4361772 0.5251689 0.5338096 0.5823034 0.6650514 0.7381164 +#> [513,] 0.29301114 0.3731118 0.4421991 0.4486907 0.4543662 0.4699835 0.4710620 +#> [514,] 0.29292073 0.4098502 0.4798583 0.5378361 0.5423993 0.5514421 0.5667096 +#> [515,] 0.26793281 0.2711063 0.3820295 0.3889879 0.4171944 0.4261615 0.4433985 +#> [516,] 0.27028389 0.3415438 0.3734846 0.3840070 0.4426923 0.4609179 0.4715471 +#> [517,] 0.30866684 0.3234878 0.3318808 0.4254990 0.4446930 0.4488586 0.4648980 +#> [518,] 0.07991648 0.4038128 0.4762512 0.5911722 0.6350611 0.6912811 0.6981930 +#> [519,] 0.37025403 0.3944722 0.4546167 0.5061000 0.5270976 0.5509830 0.5810267 +#> [520,] 0.27981317 0.4053421 0.4202105 0.4315446 0.5017654 0.5127194 0.5205824 +#> [521,] 0.34512046 0.4042546 0.4521097 0.5453375 0.5888239 0.6046208 0.6074047 +#> [522,] 0.13879613 0.3419732 0.3665608 0.4178758 0.4448724 0.4893759 0.5030333 +#> [523,] 0.54363676 0.5771780 0.5962627 0.7278883 0.8893138 0.9051578 0.9270370 +#> [524,] 0.33184739 0.3507093 0.3598209 0.5017654 0.5346923 0.5475948 0.5485160 +#> [525,] 0.26143033 0.3439043 0.3928074 0.4258462 0.5064131 0.5837491 0.7095298 +#> [526,] 0.28883648 0.4871865 0.5237486 0.5480739 0.6635628 0.6831837 0.6882478 +#> [527,] 0.77901084 1.1327007 1.2185269 1.2789434 1.2862323 1.3177358 1.4660522 +#> [528,] 0.38801771 0.3950953 0.4755505 0.5452861 0.5768426 0.6079400 0.6224556 +#> [529,] 0.27912903 0.3436373 0.4348900 0.6630490 0.6776026 0.7560740 0.8175933 +#> [530,] 0.45669225 0.5905829 0.6098908 0.8362491 0.8714916 0.8802147 0.9061382 +#> [531,] 0.36687170 0.4117716 0.4755552 0.5197681 0.5227071 0.5425355 0.5607744 +#> [532,] 0.33962879 0.3890705 0.4053346 0.5261169 0.6142398 0.6576187 0.6897535 +#> [533,] 0.73738653 0.8103618 0.8189857 0.8284993 0.8510004 0.8795685 0.8951109 +#> [534,] 0.17875079 0.3396288 0.3585382 0.5533974 0.5907733 0.6179367 0.6273748 +#> [535,] 0.32500550 0.3266515 0.3547841 0.3792344 0.4175321 0.4422283 0.4517481 +#> [536,] 0.33064403 0.3969425 0.5079901 0.5199025 0.5263975 0.5330862 0.5579990 +#> [537,] 0.86476367 0.9477399 0.9832344 1.0586136 1.2534981 1.3615438 1.3869811 +#> [538,] 1.51838574 1.6109467 1.7708302 1.8085940 1.9709012 1.9897454 2.1130770 +#> [539,] 0.31191358 0.4145507 0.4342199 0.5136025 0.5556632 0.5776815 0.5888827 +#> [540,] 0.48520405 0.6145141 0.9431510 0.9827255 1.0118221 1.0709103 1.1359482 +#> [541,] 0.40472014 0.4864877 0.6390878 0.6591848 0.7155856 0.7660701 0.7727693 +#> [542,] 0.76364702 0.7853750 0.8647637 1.0036165 1.0303411 1.0931007 1.0985984 +#> [543,] 0.58070930 0.6279492 0.6657246 0.6752513 0.6872129 0.7148179 0.7352546 +#> [544,] 0.61975077 0.9142557 0.9437180 1.0027772 1.0378565 1.1022966 1.2146529 +#> [545,] 0.60396993 0.8556707 0.8925672 0.9236600 0.9622823 0.9663578 0.9743262 +#> [546,] 0.12046345 0.4407778 0.6910868 0.7058085 0.7183874 0.7288252 0.7548969 +#> [547,] 1.17888285 1.2565012 1.3092816 1.3485371 1.3592511 1.3868103 1.3965006 +#> [548,] 0.55249129 0.5894451 0.6794550 0.6825160 0.7403855 0.7499781 0.7685453 +#> [549,] 0.40476741 0.5235680 0.5698808 0.6055756 0.6478767 0.7933394 0.8048888 +#> [550,] 0.59574307 0.6550834 0.7081091 0.8176899 0.8303848 0.8308750 0.8356224 +#> [551,] 0.36883720 0.3790429 0.4196418 0.4813874 0.6354071 0.7087893 0.7210488 +#> [552,] 0.22690537 0.3318503 0.3474324 0.3919076 0.5672823 0.5812286 0.5816418 +#> [553,] 0.25774459 0.3120066 0.5073320 0.6759421 0.6994948 0.7042752 0.7059491 +#> [554,] 0.48465152 0.5241100 0.5428946 0.5505425 0.5685634 0.5802675 0.6282014 +#> [555,] 0.28688126 0.3224021 0.4224186 0.4696288 0.4751730 0.4799773 0.4841708 +#> [556,] 0.42682207 0.4577456 0.5079901 0.5520819 0.6207918 0.6501106 0.6740800 +#> [557,] 0.51817417 0.5442637 0.5868762 0.6314365 0.6477322 0.6781473 0.6986159 +#> [558,] 0.19513800 0.3253874 0.3978315 0.5390598 0.5714364 0.5780151 0.6273748 +#> [559,] 0.60147181 0.6422321 0.6654582 0.7315526 0.7750984 0.8343469 0.8389705 +#> [560,] 0.58039868 0.6049625 0.6840995 0.7140941 0.7261054 0.7437403 0.7692705 +#> [561,] 0.56407060 0.5846558 0.8028055 0.8462224 0.8719151 0.8746693 0.8830011 +#> [562,] 0.34273538 0.3841699 0.4105118 0.4383739 0.4858712 0.5065422 0.5251689 +#> [563,] 0.44366791 0.7238057 1.1969462 1.3358588 1.3543267 1.3886413 1.5250261 +#> [564,] 0.63184141 0.7544464 0.7638342 0.8534673 0.9591824 0.9823241 0.9860083 +#> [565,] 0.53892468 0.6336688 0.8073962 0.8078945 0.9509165 0.9673514 1.0400164 +#> [566,] 1.40517342 1.4799383 1.5337433 1.6619145 1.6645287 1.6742392 1.6770461 +#> [567,] 0.40425462 0.5054819 0.5465505 0.6699691 0.6847809 0.7834679 0.7970432 +#> [568,] 0.29230462 0.2969847 0.3602116 0.3634108 0.3904106 0.5878294 0.6034387 +#> [569,] 0.41354584 0.4359867 0.4786287 0.5156658 0.5329834 0.5480443 0.5491726 +#> [570,] 0.40415075 0.6027456 0.6301278 0.6499022 0.6650514 0.7953267 0.8599447 +#> [571,] 1.01472574 1.1123529 1.1923692 1.3073333 1.3876238 1.5133920 1.5342320 +#> [572,] 1.41605561 1.5498688 1.7465202 1.7507080 1.8019820 1.8112516 1.8627146 +#> [573,] 0.59337182 0.6013404 0.6559592 0.6794917 0.7073037 0.7472879 0.7559327 +#> [574,] 0.20601898 0.2138112 0.2836252 0.3734846 0.3993061 0.4641697 0.5026104 +#> [575,] 0.56988075 0.5799310 0.6264804 0.6338821 0.7155595 0.7585791 0.8802147 +#> [576,] 0.24010441 0.3451244 0.3776825 0.4448724 0.5067279 0.5093840 0.5153017 +#> [577,] 0.44522840 0.4637061 0.5803419 0.6279280 0.7888915 0.8112898 0.8401171 +#> [578,] 0.46177516 0.5032684 0.5560926 0.6384115 0.6558610 0.6738408 0.6795696 +#> [579,] 0.25183425 0.4369720 0.4405283 0.4724317 0.6092113 0.6395914 0.6836550 +#> [580,] 0.55034352 0.8932676 0.9048609 0.9122109 0.9204655 1.0020373 1.0966724 +#> [581,] 0.27957885 0.4029978 0.5986835 0.6859729 0.7006915 0.8743741 0.9495803 +#> [582,] 0.46009541 0.5295352 0.5377479 0.5922188 0.5985136 0.6594093 0.6648714 +#> [583,] 0.16973124 0.3884705 0.4261615 0.4953801 0.5302664 0.6032451 0.6088218 +#> [584,] 0.36096082 0.4292685 0.4315726 0.5161931 0.5861869 0.6151879 0.6519473 +#> [585,] 0.62240532 0.8313961 0.8539695 1.0598134 1.0899057 1.1185382 1.1891441 +#> [586,] 0.53355768 0.6586069 0.7373932 0.8106935 0.8137303 0.8560083 0.8586241 +#> [587,] 0.61434841 0.6742504 0.7758032 0.8632298 0.9866009 0.9946745 1.0171249 +#> [588,] 0.50994700 0.7137102 0.7281672 0.8292282 0.8709165 0.9152130 0.9175304 +#> [589,] 0.35442371 0.4167385 0.4613995 0.4975055 0.5103887 0.7466410 0.7591619 +#> [590,] 0.54835213 0.7573177 0.7731458 0.7745561 0.7955375 0.8407062 0.8494256 +#> [591,] 0.48960202 0.5124989 0.5754902 0.5816418 0.5968568 0.6040564 0.6065801 +#> [592,] 0.40807386 0.5852049 0.5944061 0.6151464 0.6305765 0.6354666 0.6405491 +#> [593,] 0.50451422 0.5435610 0.8748761 0.9055023 1.0413266 1.0492853 1.1193411 +#> [594,] 0.60941048 0.8286666 0.8362473 0.8475980 0.8652686 0.9248949 0.9401446 +#> [595,] 1.02764981 1.0675792 1.1726157 1.1737706 1.2117283 1.2122693 1.2136554 +#> [596,] 0.48648770 0.6071182 0.6077914 0.6451965 0.6664912 0.6796197 0.6990176 +#> [597,] 0.31537054 0.4401888 0.7049388 0.7611104 0.7926868 0.8169729 0.8347674 +#> [598,] 0.33478924 0.3535666 0.3921772 0.3934919 0.4061885 0.4270109 0.4683849 +#> [599,] 0.29403821 0.4011936 0.4167385 0.4725982 0.5336573 0.5487884 0.5762095 +#> [600,] 0.34154377 0.3776825 0.4150283 0.4241536 0.4626339 0.4811862 0.5238636 +#> [601,] 0.29062669 0.3969090 0.4038128 0.6562524 0.6739732 0.6791804 0.7069239 +#> [602,] 0.36985508 0.4767623 0.6505008 0.6659052 0.6908772 0.6949283 0.7074873 +#> [603,] 0.46510675 0.5777555 0.6102530 0.6860259 0.6954669 0.8290772 0.8351042 +#> [604,] 0.58189352 0.6589000 0.6686780 0.6893716 0.8203466 0.8461130 0.8599066 +#> [605,] 0.28459575 0.3376415 0.4192009 0.5731440 0.6105425 0.6149352 0.6214294 +#> [606,] 0.11608719 0.3638735 0.3792344 0.3799525 0.3879428 0.4390372 0.4585823 +#> [607,] 0.80719987 0.8293724 1.0997222 1.2354086 1.3177878 1.3927904 1.4089550 +#> [608,] 0.70018889 1.2503321 1.2780366 1.3383411 1.4643526 1.5313353 1.5387897 +#> [609,] 0.54703206 0.6326815 0.7559557 0.8459306 0.8506859 0.8917204 0.9307966 +#> [610,] 0.27361090 0.3943047 0.4335340 0.5142888 0.5265425 0.5371743 0.5416264 +#> [611,] 0.82697259 0.8951109 0.9285000 0.9448010 1.0888297 1.2039308 1.2067116 +#> [612,] 0.30507871 0.3273770 0.4344419 0.4591137 0.4751239 0.4870335 0.5101134 +#> [613,] 0.54105950 0.7512295 0.8366602 0.9077201 1.0226334 1.0241518 1.1789045 +#> [614,] 0.32685154 0.4467783 0.6437903 0.6957793 0.7111202 0.7504098 0.7703679 +#> [615,] 0.21392826 0.2641698 0.4380816 0.4557797 0.5332089 0.5424867 0.5470235 +#> [616,] 0.16973124 0.3613998 0.4432428 0.4559979 0.5624052 0.5760403 0.6043345 +#> [617,] 0.54910784 0.5686761 0.6911297 0.6945931 0.7177089 0.7198688 0.7597752 +#> [618,] 0.21566331 0.3873494 0.4141784 0.4209737 0.4946848 0.5121898 0.5441918 +#> [619,] 0.31174585 0.3208248 0.3302222 0.3356392 0.3359325 0.3493520 0.4049898 +#> [620,] 0.48416998 0.6688491 0.6699654 0.7330502 0.7655117 0.7961905 0.8147810 +#> [621,] 0.54243723 0.5634303 0.5645729 0.5921655 0.5974787 0.6009983 0.6831728 +#> [622,] 0.44986007 0.6802616 0.6893716 0.8791343 0.8939719 0.9821576 0.9837626 +#> [623,] 0.58665844 0.8052953 0.8582436 0.9003027 0.9672961 0.9803843 1.0155728 +#> [624,] 0.24136134 0.5790625 0.6168309 0.6571811 0.6682516 0.7423945 0.7494334 +#> [625,] 0.52631572 0.6946062 0.8203172 0.8286666 0.8862931 1.0503166 1.0956282 +#> [626,] 0.28362515 0.3572052 0.3782162 0.4301535 0.4609179 0.4736896 0.4951976 +#> [627,] 0.34844429 0.4708596 0.4950304 0.5364844 0.5959543 0.6394717 0.6509156 +#> [628,] 0.70116622 0.8738729 0.9232706 0.9257615 0.9500668 1.0094909 1.0283225 +#> [629,] 0.41787351 0.5240484 0.6101186 0.6395927 0.6518593 0.6559082 0.6670681 +#> [630,] 0.34027172 0.3483590 0.5775658 0.5876252 0.6204556 0.6855360 0.7085435 +#> [631,] 0.48183801 0.7126242 0.7467861 0.7836029 0.8784556 0.8980112 0.8987570 +#> [632,] 0.19419128 0.2839768 0.3802278 0.4279197 0.4645395 0.4706502 0.4918564 +#> [633,] 0.23167235 0.4764203 0.4957492 0.5487723 0.5527083 0.5540839 0.6585077 +#> [634,] 0.37995251 0.4259055 0.4356021 0.4399584 0.4510979 0.5149004 0.6219822 +#> [635,] 0.39363928 0.4841722 0.5142780 0.5335577 0.5623835 0.5888239 0.6495236 +#> [636,] 0.63314368 1.0503016 1.1153360 1.1235266 1.1364532 1.1380188 1.1802990 +#> [637,] 0.85546966 0.8595941 0.8765175 0.8949732 0.9058662 0.9230566 0.9535782 +#> [638,] 0.54449025 1.2893829 1.4274326 1.4313215 1.4664327 1.6344595 1.6742548 +#> [639,] 0.46645854 0.4802830 0.6528180 0.6667336 0.6717098 0.6732536 0.6761374 +#> [640,] 0.35965794 0.4701404 0.5471553 0.5762095 0.6405376 0.6763833 0.6825840 +#> [641,] 0.43598666 0.4381048 0.5115222 0.5120130 0.5410413 0.5505425 0.5846763 +#> [642,] 0.25334228 0.5324512 0.5390755 0.5392326 0.5592968 0.5691145 0.5746819 +#> [643,] 0.36595529 0.3844797 0.4811879 0.5233490 0.5263663 0.5361204 0.5486290 +#> [644,] 0.34235251 0.5245471 0.5290194 0.5560926 0.5857627 0.6764634 0.6833624 +#> [645,] 0.36347243 0.4590754 0.6295294 0.6487658 0.6860259 0.7038720 0.7096475 +#> [646,] 0.20395837 0.4657923 0.4935208 0.5361204 0.5476945 0.5932247 0.6263767 +#> [647,] 0.29910942 0.4783190 0.5909218 0.6377302 0.6388986 0.7084974 0.7317710 +#> [648,] 0.38519494 0.4323350 0.5596796 0.6020497 0.6978972 0.7114342 0.7876461 +#> [649,] 0.35376145 0.4160175 0.4224186 0.4543662 0.4552867 0.4556376 0.4574192 +#> [650,] 1.26516795 1.2810324 1.5369662 1.5829576 1.5865118 1.6985075 1.7013157 +#> [651,] 0.30338965 0.3409042 0.3426225 0.3523410 0.3687144 0.4049237 0.4143892 +#> [652,] 0.38772521 0.4232959 0.5335816 0.5549221 0.5569106 0.6105425 0.6243774 +#> [653,] 0.37150479 0.4389962 0.4868420 0.4953717 0.5373714 0.5376232 0.5427501 +#> [654,] 0.78346792 0.7997734 0.8752845 0.9425318 0.9635824 0.9930162 0.9941969 +#> [655,] 0.49333622 0.4946115 0.5402954 0.5576428 0.6074047 0.7881233 0.8216602 +#> [656,] 0.81634704 0.8325586 0.9138814 0.9159168 0.9335997 0.9660048 1.0039897 +#> [657,] 0.30361897 0.5440199 0.5652880 0.6127630 0.6279280 0.6685404 0.7063750 +#> [658,] 0.37644458 0.4764203 0.5026760 0.5916505 0.6111725 0.6610760 0.7162576 +#> [659,] 0.46121647 0.4937272 0.6014050 0.6290653 0.6328639 0.7201901 0.7545655 +#> [660,] 0.27017635 0.4135228 0.4148534 0.4937272 0.5395340 0.5418916 0.5552143 +#> [661,] 0.58739582 0.6725229 0.7993370 0.9048243 0.9329313 0.9839407 1.0221971 +#> [662,] 0.41394639 0.6016788 0.6157182 0.6422605 0.6474427 0.6831728 0.7126242 +#> [663,] 0.18460255 0.2138112 0.3822591 0.3840070 0.3862574 0.4230495 0.4736896 +#> [664,] 0.30072497 0.3464240 0.3802278 0.3995926 0.6015810 0.6372212 0.6519142 +#> [665,] 0.30975111 0.3880177 0.4556376 0.5139233 0.5483299 0.5894451 0.6325165 +#> [666,] 0.43594864 0.5407081 0.8323526 0.9285000 0.9896595 1.0405765 1.0770914 +#> [667,] 0.34452809 0.3600881 0.3892187 0.4049237 0.4952720 0.5409081 0.5506858 +#> [668,] 0.25073339 0.2655099 0.3688372 0.4002012 0.5335816 0.5430112 0.5562233 +#> [669,] 0.55313581 0.8070900 0.8268404 0.9176893 0.9838827 1.0484415 1.0492853 +#> [670,] 0.77684925 0.7780350 0.8705382 0.8738428 0.9277626 0.9402255 0.9558444 +#> [671,] 0.62196198 0.6366517 0.6978972 0.8322545 0.8497819 0.9580972 1.0241518 +#> [672,] 0.30084255 0.5388117 0.5648963 0.5803478 0.7518248 0.7807733 0.8681542 +#> [673,] 0.43749453 0.5505227 0.5672890 0.5687865 0.5735866 0.5894259 0.5947715 +#> [674,] 0.24863800 0.3318474 0.4301535 0.4310850 0.4422283 0.4810028 0.4870022 +#> [675,] 0.53320894 0.6171614 0.6680559 0.6984509 0.7053176 0.7166298 0.7309727 +#> [676,] 0.39983873 0.4405283 0.4797045 0.5156670 0.5388781 0.5728042 0.6942725 +#> [677,] 0.35368533 0.4474790 0.4496334 0.4678426 0.6593369 0.7176423 0.7300486 +#> [678,] 0.39190757 0.4899965 0.5306228 0.5583424 0.6397865 0.6539415 0.6601470 +#> [679,] 0.42631331 0.4291071 0.5682878 0.6235444 0.6862337 0.7036078 0.7612428 +#> [680,] 0.40020213 0.4387530 0.4914823 0.5162458 0.5411882 0.5538449 0.5839871 +#> [681,] 0.27824019 0.3971149 0.4167784 0.4673479 0.4958828 0.5184400 0.5692543 +#> [682,] 0.44486184 0.5100506 0.5699179 0.5792501 0.5905829 0.6962281 0.6983024 +#> [683,] 0.63366877 0.7138283 0.8833182 0.9951903 0.9986345 1.0016824 1.0486325 +#> [684,] 0.32908268 0.3829368 0.4572627 0.4617752 0.5499103 0.5675280 0.5688572 +#> [685,] 0.47289305 0.4739998 0.6328005 0.6521120 0.6720328 0.6739434 0.7266951 +#> [686,] 0.35509133 0.4265314 0.6051751 0.6168309 0.6865512 0.7016635 0.7183189 +#> [687,] 0.71885913 0.8323526 0.9055023 0.9176893 0.9448010 1.1334821 1.1536165 +#> [688,] 0.51403851 0.5219559 0.5413375 0.5480443 0.6355625 0.6555136 0.6644830 +#> [689,] 0.61265766 0.6874574 0.7786590 0.8613076 0.9503610 0.9610233 1.0182848 +#> [690,] 0.42653138 0.4625601 0.4763041 0.5893919 0.6858187 0.6941150 0.6977133 +#> [691,] 0.29444451 0.3104717 0.4775865 0.5029744 0.5991155 0.6026409 0.6093745 +#> [692,] 0.69054547 0.8296649 0.8948670 0.9504654 1.0171249 1.0282789 1.0300253 +#> [693,] 0.51757671 0.5368974 0.7315526 0.7389885 0.8092806 0.8729183 0.8828692 +#> [694,] 0.21674498 0.2507334 0.4196418 0.4596097 0.5467713 0.5569106 0.6194824 +#> [695,] 0.69062254 0.8392936 1.1106780 1.1193411 1.1366575 1.1374524 1.1464533 +#> [696,] 0.34682822 0.3859824 0.4531165 0.4946232 0.5408715 0.5927076 0.5948363 +#> [697,] 0.95620290 1.1272844 1.1292652 1.3601725 1.4219230 1.4411477 1.4704535 +#> [698,] 0.26343145 0.4435184 0.5219604 0.5413950 0.5726195 0.6162140 0.6336426 +#> [699,] 0.42701073 0.5527083 0.8069478 0.8241123 0.8512284 0.8574372 0.8688199 +#> [700,] 0.36336045 0.4141241 0.4178735 0.4315726 0.5211340 0.6684754 0.6770869 +#> [701,] 0.54105950 0.9021926 0.9023072 1.1646467 1.1690400 1.1740738 1.2017255 +#> [702,] 0.33332358 0.4941352 0.5491287 0.5514421 0.5823703 0.6171313 0.6328639 +#> [703,] 0.31537054 0.4176534 0.6411925 0.6462147 0.7403294 0.7454633 0.7899675 +#> [704,] 0.26328614 0.2638343 0.3096327 0.3402142 0.4377764 0.4409665 0.5062397 +#> [705,] 0.38149111 0.3966306 0.4026726 0.4178758 0.4513943 0.4525542 0.5153017 +#> [706,] 0.15472873 0.3533348 0.3699957 0.3862040 0.4205965 0.4325937 0.5418916 +#> [707,] 0.43000173 0.5073444 0.5338096 0.5381579 0.6499022 0.6594966 0.6792359 +#> [708,] 0.33323683 0.4416356 0.4746625 0.4969829 0.5978585 0.6048488 0.6156391 +#> [709,] 0.60490980 0.6197508 0.6389791 0.6671744 0.6842797 0.8041659 0.8240498 +#> [710,] 0.44123648 0.5963301 0.6770869 0.6782984 0.7436061 0.7591996 0.7596100 +#> [711,] 0.36738405 0.4432428 0.4953801 0.5027575 0.5266475 0.5653288 0.5719470 +#> [712,] 0.44109626 0.5182610 0.8114595 0.8182231 0.8704664 0.8718881 0.8750923 +#> [713,] 0.35346968 0.7246401 0.7499065 0.7823791 0.8305809 0.9931664 1.0404170 +#> [714,] 0.58893642 0.6124386 0.6788038 0.7691026 0.7834769 0.8118543 0.8310321 +#> [715,] 0.66340973 0.8016984 0.8089124 0.8649101 0.8843676 0.9499100 1.0330265 +#> [716,] 0.14605421 0.6347718 0.6725681 0.7590693 0.7616078 0.7722215 0.7821952 +#> [717,] 0.25735799 0.3537615 0.3695420 0.3777877 0.4214002 0.4252863 0.4281232 +#> [718,] 0.33022224 0.3441288 0.4096363 0.4386172 0.4585147 0.5031696 0.5193626 +#> [719,] 0.65860686 0.8196058 0.8914782 0.9086368 0.9839407 1.0014516 1.0230460 +#> [720,] 0.39509527 0.5139233 0.5447037 0.5524913 0.8080131 0.8155186 0.8215988 +#> [721,] 0.55933129 0.6217602 0.6965583 0.7067615 0.7703424 0.8276266 0.8368484 +#> [722,] 0.63580413 0.7688035 0.8106643 0.9246408 0.9380414 0.9439554 1.0680439 +#> [723,] 0.46256011 0.4801519 0.5622388 0.6051751 0.6279492 0.6551946 0.6571811 +#> [724,] 0.34717912 0.4045419 0.5436368 0.6388986 0.7033968 0.7442621 0.7580649 +#> [725,] 0.51181402 0.5664642 0.6071722 0.6465218 0.6607136 0.6631087 0.6820446 +#> [726,] 0.75122951 0.7833552 0.8497819 0.8599066 0.8806943 0.9128641 0.9215414 +#> [727,] 0.22735807 0.5369339 0.5880864 0.6313239 0.6365637 0.6494028 0.6978221 +#> [728,] 0.69858196 0.8308600 0.8988011 0.9483479 0.9529792 0.9557612 0.9866009 +#> [729,] 0.58913260 0.5904511 1.0997222 1.2397709 1.3674834 1.3693199 1.3854303 +#> [730,] 0.47359279 0.5291050 0.5563820 0.6234920 0.6501716 0.6651793 0.6910260 +#> [731,] 0.38658483 0.4641697 0.4951976 0.5142720 0.5381997 0.5654464 0.5733830 +#> [732,] 0.53774224 0.6493739 0.6512504 0.6671744 0.7118943 0.7749027 0.7782724 +#> [733,] 0.40440349 0.4300163 0.5196985 0.5397259 0.6101514 0.6157228 0.6182368 +#> [734,] 1.35351303 1.6109467 1.7051863 1.7236095 1.8262364 1.9428414 2.0175106 +#> [735,] 0.29359887 0.4279511 0.4804884 0.5369971 0.5482631 0.5865848 0.5953025 +#> [736,] 0.90613818 0.9500898 0.9713147 1.0095566 1.0338680 1.1879064 1.2013942 +#> [737,] 0.52439153 0.6675391 0.8305809 1.0501612 1.0567316 1.1273738 1.1840653 +#> [738,] 0.42791970 0.4785496 0.5145920 0.5260537 0.5268112 0.5434271 0.5488552 +#> [739,] 0.77256093 0.8926279 1.0544813 1.1382764 1.1418979 1.1830457 1.2970655 +#> [740,] 0.27824019 0.3109608 0.3987329 0.4258963 0.5017250 0.5105818 0.5888744 +#> [741,] 0.27773200 0.8213555 0.8559930 0.9525178 0.9838940 1.0723668 1.1206739 +#> [742,] 0.38767281 0.4026726 0.4205946 0.4471608 0.4802830 0.5507127 0.5815310 +#> [743,] 0.32237498 0.3764880 0.4013733 0.4537154 0.4802813 0.4904619 0.5476945 +#> [744,] 0.49446897 0.5258675 0.5579990 0.5600066 0.5672890 0.5699179 0.6562205 +#> [745,] 0.35748401 0.5123131 0.5290194 0.5663951 0.5957944 0.6198647 0.6897391 +#> [746,] 0.31719653 0.3848797 0.4561031 0.4979489 0.5333971 0.5371761 0.5391688 +#> [747,] 0.67604518 0.9275340 1.0278988 1.0327197 1.2267464 1.2375629 1.3199692 +#> [748,] 0.12222005 0.4177120 0.6268746 0.7180089 0.7371032 0.7431611 0.7436420 +#> [749,] 0.74546332 0.7926868 0.8942326 1.0656954 1.1363119 1.1613837 1.1821319 +#> [750,] 0.43000173 0.5052092 0.5965068 0.6683562 0.7105456 0.7366041 0.7704925 +#> [751,] 0.28459575 0.3694457 0.5026201 0.6070519 0.6188489 0.6194824 0.6218011 +#> [752,] 0.70730375 0.7993732 0.8869694 0.9236479 0.9618237 1.0278988 1.0328210 +#> [753,] 0.45428253 0.5213248 0.5381579 0.5430112 0.6351318 0.6863909 0.6964509 +#> [754,] 0.73790913 0.9146707 0.9268745 0.9441675 0.9728522 0.9886703 1.0041557 +#> [755,] 0.51540272 0.5320156 0.5852049 0.6104892 0.6833624 0.6897391 0.6911030 +#> [756,] 0.41347323 0.4387530 0.4678809 0.4871645 0.4934944 0.5402894 0.5618440 +#> [757,] 1.17827006 1.2534981 1.2886819 1.3553739 1.3561492 1.4709502 1.4736376 +#> [758,] 0.48695844 0.5510996 0.5864232 0.6654570 0.6918404 0.6931311 0.6985820 +#> [759,] 1.60098152 1.6043613 1.6645287 1.8161045 1.8887888 1.9565765 2.0145514 +#> [760,] 0.17672129 0.2947138 0.3361387 0.3529613 0.4579331 0.4654775 0.5653037 +#> [761,] 0.27361090 0.3842518 0.3946160 0.4463409 0.4822301 0.4970682 0.5173598 +#> [762,] 0.09831649 0.3687144 0.3738390 0.3752012 0.4383739 0.4483313 0.4989207 +#> [763,] 0.40807386 0.6692890 0.7407087 0.7445498 0.7617625 0.7812317 0.8029511 +#> [764,] 0.33323683 0.3636660 0.4090712 0.4678809 0.5384396 0.5770353 0.6342586 +#> [765,] 0.74149780 0.7884421 0.8924413 0.8925216 1.0852243 1.1068269 1.1279884 +#> [766,] 0.88475525 1.0307034 1.1031474 1.1288179 1.1767783 1.2611665 1.3072379 +#> [767,] 0.39276221 0.4141241 0.4186988 0.5460700 0.5804832 0.5861869 0.5965068 +#> [768,] 0.60683531 0.6239664 0.6329802 0.6571812 0.7014294 0.7681392 0.9043524 +#> [769,] 0.25183425 0.3287616 0.4958141 0.5156670 0.6469373 0.7240607 0.7927622 +#> [770,] 0.81951004 1.0033135 1.0122607 1.1505867 1.1603421 1.1781983 1.1982659 +#> [771,] 0.63388207 0.7708441 0.8405305 0.8498749 0.9045846 0.9466005 0.9803991 +#> [772,] 0.38596645 0.4342199 0.4684409 0.5602363 0.5714456 0.5805367 0.5833092 +#> [773,] 0.63764715 0.7092790 0.8200161 0.8512293 0.8517123 0.9066578 0.9243237 +#> [774,] 0.29062669 0.4554043 0.4762512 0.6821908 0.6890077 0.7307151 0.7315235 +#> [775,] 0.27996485 0.5089294 0.5409551 0.5731555 0.6783771 0.7118041 0.7215748 +#> [776,] 0.38532828 0.4681975 0.5026760 0.5540839 0.5630189 0.6028126 0.6175580 +#> [777,] 0.51411964 0.5156462 0.5420390 0.5666462 0.6606336 0.6987629 0.7096344 +#> [778,] 0.28914798 0.3341115 0.3905825 0.4007505 0.4423288 0.4925744 0.5092038 +#> [779,] 0.44351835 0.5441918 0.5751142 0.5771078 0.5850060 0.6032333 0.6109984 +#> [780,] 0.42911358 0.4658710 0.4704423 0.5636624 0.5749203 0.6282014 0.6493615 +#> [781,] 0.58078479 0.6287389 0.6727315 0.7011662 0.7128726 0.7296091 0.7383797 +#> [782,] 0.17515967 0.3148796 0.4622053 0.4935208 0.5181742 0.5225545 0.5405678 +#> [783,] 0.34235251 0.4319502 0.4692709 0.4880859 0.5052213 0.5123131 0.5347047 +#> [784,] 0.27949333 0.5545604 0.5978329 0.6031911 0.6126577 0.6343305 0.7437618 +#> [785,] 0.24171039 0.3686001 0.4558091 0.5551246 0.6074524 0.6198224 0.6720896 +#> [786,] 0.39297509 0.3943971 0.4756200 0.5476847 0.5500657 0.5557123 0.5801731 +#> [787,] 0.62045966 0.6634097 0.6801610 0.7152110 0.7696425 0.8052953 0.8215516 +#> [788,] 0.54426365 0.5638175 0.6749506 0.6987118 0.7195750 0.7425097 0.7472007 +#> [789,] 0.30084255 0.5204844 0.5390357 0.6601272 0.6631301 0.7861197 0.7917998 +#> [790,] 0.21863794 0.4566923 0.5100506 0.6264804 0.6478767 0.7233856 0.7629658 +#> [791,] 0.69343238 0.7916039 0.8174900 0.8209361 0.8597213 0.8956150 0.9191062 +#> [792,] 0.31575585 0.3376415 0.3694457 0.6310238 0.6630490 0.6687645 0.6753017 +#> [793,] 0.33731955 0.3451244 0.4241536 0.4726576 0.4765648 0.4850090 0.5104808 +#> [794,] 0.33330030 0.3430224 0.4944690 0.5286509 0.5507820 0.5691711 0.6740800 +#> [795,] 0.48015193 0.4870973 0.5434333 0.5681859 0.5790625 0.6224556 0.6920199 +#> [796,] 0.91545188 0.9851428 0.9985390 1.2401699 1.2706049 1.4116211 1.4267028 +#> [797,] 0.26927678 0.3941537 0.4221518 0.4449027 0.4632372 0.4782392 0.5086648 +#> [798,] 0.30577483 0.4371707 0.5345808 0.6105168 0.6122402 0.6865432 0.7719839 +#> [799,] 0.85802249 0.9283156 1.2431804 1.2509933 1.2636073 1.2909028 1.3068810 +#> [800,] 0.60499899 0.6353063 0.6588731 0.6686780 0.6783649 0.7031266 0.7059153 +#> [801,] 0.35808541 0.3596801 0.4380816 0.4387298 0.4474902 0.4715928 0.4920686 +#> [802,] 0.42497290 0.4498638 0.7653629 0.8186734 0.8834345 0.8955751 0.9027206 +#> [803,] 0.36096082 0.3633604 0.4226290 0.5460700 0.5624287 0.6155368 0.6287389 +#> [804,] 0.41459923 0.4496334 0.4880470 0.4927598 0.4971807 0.5330295 0.5530242 +#> [805,] 0.87209495 1.2580143 1.5146902 1.5192367 1.6207223 1.7483121 1.7926297 +#> [806,] 0.32580181 0.5388117 0.5390357 0.6096010 0.6983498 0.7022300 0.7152110 +#> [807,] 0.22735807 0.3700770 0.4739122 0.4933392 0.6045138 0.6468782 0.7561484 +#> [808,] 0.36954200 0.3791834 0.3862574 0.4063097 0.4711711 0.4919237 0.4926647 +#> [809,] 0.31170307 0.6327033 0.7336474 0.9043976 0.9048609 0.9850208 1.0023837 +#> [810,] 0.42322607 0.4259888 0.4446164 0.4449831 0.4667387 0.4983489 0.5171208 +#> [811,] 0.47742160 0.7927973 0.8392936 0.9368811 0.9712860 1.0107862 1.0526832 +#> [812,] 0.57426260 0.5857830 0.6128149 0.6252175 0.6313865 0.6448859 0.6608414 +#> [813,] 0.40299783 0.5424989 0.6948786 0.7302712 0.8294974 0.9501195 0.9531711 +#> [814,] 0.72727075 0.8829206 0.9873766 1.0244392 1.0398980 1.2170482 1.2480076 +#> [815,] 0.24949429 0.4851378 0.4852129 0.5202777 0.6531028 0.7033036 0.7176815 +#> [816,] 0.51507672 0.5471553 0.5615487 0.6850255 0.7931495 0.7975922 0.8424111 +#> [817,] 0.15604009 0.4431694 0.5146468 0.5624673 0.5727703 0.6123558 0.6131775 +#> [818,] 0.65828602 0.6739732 0.6864811 0.6890077 0.7181508 0.7231773 0.7530624 +#> [819,] 0.51564803 0.5235680 0.5761168 0.7585791 0.8006608 0.8498749 0.9355411 +#> [820,] 0.51459128 0.5371761 0.5935440 0.6053123 0.6092135 0.6686627 0.6725681 +#> [821,] 0.33148026 0.3717019 0.4098502 0.5120130 0.5334669 0.5664642 0.6158515 +#> [822,] 0.22038706 0.2836823 0.5179364 0.5479470 0.5576100 0.6203619 0.8928497 +#> [823,] 0.65890004 0.6900357 0.7414978 0.8366602 0.9023072 0.9483216 0.9959242 +#> [824,] 0.29471381 0.3487168 0.4474640 0.4799773 0.4854754 0.4999563 0.5302813 +#> [825,] 0.20417561 0.4846515 0.5846763 0.5975842 0.6272176 0.6513768 0.6654570 +#> [826,] 0.22475057 0.2403118 0.3006863 0.3547841 0.3638735 0.4968724 0.4979489 +#> [827,] 0.33702873 0.8849706 1.0227250 1.2455886 1.2472189 1.2625984 1.2728276 +#> [828,] 0.23535424 0.2888365 0.4209737 0.5078930 0.5609209 0.6032333 0.6234146 +#> [829,] 0.43557756 0.5741917 0.5876252 0.6720861 0.6932422 0.7299574 0.8358283 +#> [830,] 0.34242776 0.3594535 0.3943971 0.6445696 0.6479357 0.6531994 0.6720896 +#> [831,] 0.42497290 0.4378084 0.6613781 0.6717098 0.7180839 0.8036945 0.8090444 +#> [832,] 0.18842874 0.3712625 0.4171864 0.4988816 0.5338943 0.5859090 0.6002829 +#> [833,] 0.59828158 0.6140907 0.6609668 0.6916844 0.7095298 0.7533940 0.7615608 +#> [834,] 0.85086686 0.8693708 1.0402638 1.0913809 1.1162744 1.1580728 1.1882179 +#> [835,] 0.39484804 0.5377422 0.5896853 0.5998148 0.6132736 0.7345074 0.7400159 +#> [836,] 0.28200459 0.4838382 0.5056986 0.7097586 0.7118269 0.7154967 0.7260693 +#> [837,] 0.28200459 0.4255819 0.5311183 0.5385190 0.5857583 0.6752513 0.7014631 +#> [838,] 0.72854123 0.8213555 1.2479845 1.2509933 1.3578865 1.3883013 1.4704535 +#> [839,] 0.35221305 0.4658086 0.4688457 0.4852129 0.5470235 0.5481457 0.5696647 +#> [840,] 0.32580181 0.4869584 0.5029816 0.5929361 0.6505765 0.6601272 0.6815772 +#> [841,] 0.24031182 0.3266515 0.3289196 0.3879428 0.4055814 0.4310850 0.4802058 +#> [842,] 0.63184141 0.7825293 0.9278780 0.9812174 0.9830406 1.0397144 1.0555835 +#> [843,] 0.40636897 0.5420390 0.5550702 0.5862135 0.5904569 0.6071488 0.6836963 +#> [844,] 0.47831904 0.4992977 0.5258547 0.5944776 0.5963457 0.6599681 0.7640140 +#> [845,] 0.46638959 0.5044446 0.5824304 0.6530868 0.6635628 0.6810039 0.7583725 +#> [846,] 0.18634156 0.4660731 0.5016230 0.5377097 0.5390598 0.5907733 0.6081748 +#> [847,] 0.29038507 0.3045802 0.3361387 0.3493174 0.3687756 0.3766028 0.4119493 +#> [848,] 0.50444460 0.5294214 0.6075915 0.7424908 0.8023538 0.8194373 0.9051578 +#> [849,] 0.18945147 0.2116450 0.2573580 0.3110705 0.3223047 0.3731118 0.3766028 +#> [850,] 0.38970748 0.5717071 0.5865542 0.5878294 0.5908949 0.6282007 0.6367832 +#> [851,] 0.24949429 0.4835535 0.4973154 0.5481457 0.6148331 0.7302801 0.7861184 +#> [852,] 0.46124088 0.5084755 0.5156658 0.5596796 0.5802675 0.6023530 0.6047881 +#> [853,] 0.66924590 0.7049388 0.8096001 0.8637338 0.8647272 0.8857966 0.9629770 +#> [854,] 0.40949008 0.4903354 0.6040936 0.6654582 0.7892187 0.9190275 0.9911303 +#> [855,] 0.45907545 0.5777555 0.5780089 0.7002335 0.7745468 0.8503036 0.8679628 +#> [856,] 0.60555636 0.7423913 0.7591619 0.7960978 0.9138814 0.9284428 1.1086736 +#> [857,] 0.54070810 0.5956861 0.7189500 0.7750984 0.7991863 0.8518376 0.8815974 +#> [858,] 0.43061825 0.4657923 0.4811879 0.5268112 0.5396213 0.5876804 0.5909889 +#> [859,] 0.47952646 0.5485678 0.5796519 0.7068683 0.7246722 0.7839269 0.8756713 +#> [860,] 0.34512046 0.3936393 0.5426927 0.5617833 0.5775658 0.6252175 0.6699691 +#> [861,] 0.33018724 0.6105168 0.6239664 0.6644961 0.6850255 0.7567525 0.7708239 +#> [862,] 0.44522840 0.4481359 0.5284077 0.5530242 0.5652880 0.5863829 0.6606663 +#> [863,] 0.44813593 0.4637061 0.4678426 0.4880470 0.5557236 0.6843397 0.7063750 +#> [864,] 0.62529285 0.8191095 1.0646795 1.0720833 1.0783879 1.1276875 1.1329767 +#> [865,] 0.41119252 0.4577456 0.5452479 0.6297758 0.6929260 0.7024502 0.7180998 +#> [866,] 0.27912903 0.4651760 0.5081217 0.5602555 0.6379255 0.7991863 0.8322765 +#> [867,] 1.15661048 1.3462438 1.4471441 1.5645124 1.5808967 1.6063300 1.6320500 +#> [868,] 0.54139322 0.6773988 0.7105605 0.7149278 0.7211515 0.7299574 0.7337276 +#> [869,] 0.31133296 0.4822301 0.5088305 0.5234874 0.5358143 0.5401556 0.5946605 +#> [870,] 0.50344372 0.6795696 0.7893724 0.8130988 0.8172974 0.8690556 0.8829630 +#> [871,] 0.24531337 0.2903851 0.2977648 0.3203283 0.3223047 0.3224021 0.3613120 +#> [872,] 1.09268104 1.1382408 1.1923692 1.2465492 1.3481100 1.4001927 1.4189594 +#> [873,] 0.37265397 0.4918564 0.4958828 0.5303708 0.5321874 0.5558120 0.5829550 +#> [874,] 0.48520405 0.7279412 0.8074916 0.9682564 1.1254497 1.1566882 1.1824327 +#> [875,] 0.26594121 0.5953002 0.5957431 0.6172237 0.6716399 0.6896890 0.6984653 +#> [876,] 0.81867341 0.8726695 0.9058662 0.9245956 0.9318064 1.0447502 1.0583478 +#> [877,] 0.66137812 0.6894816 0.8342098 0.8775902 0.9044680 0.9185322 0.9245897 +#> [878,] 0.27957885 0.5147946 0.5424989 0.5528173 0.7625186 0.7667228 0.7693490 +#> [879,] 0.12222005 0.3100312 0.6142398 0.6262270 0.6349211 0.6857133 0.7149717 +#> [880,] 0.55831393 0.5605773 0.6047171 0.6481022 0.9314802 0.9439554 0.9520623 +#> [881,] 0.49736419 0.5917555 0.6223325 0.6957458 0.7322672 0.7716297 0.7745400 +#> [882,] 0.40360403 0.5054819 0.5453375 0.5558413 0.6633979 0.7233839 0.7326161 +#> [883,] 0.37547110 0.4268221 0.5448074 0.5452479 0.6043597 0.6078415 0.6495392 +#> [884,] 0.29963193 0.3668414 0.3792134 0.3928074 0.4425622 0.4718084 0.4798583 +#> [885,] 0.64379029 0.7468705 0.8206195 0.8262232 0.8541983 0.8743703 0.9315260 +#> [886,] 0.30096399 0.3892187 0.4546167 0.4548463 0.5983123 0.6060355 0.6328005 +#> [887,] 0.46703537 0.4767417 0.5145920 0.5321874 0.5712901 0.5777134 0.5909889 +#> [888,] 0.51564803 0.6582860 0.7708441 0.7788900 0.8357769 0.8599289 0.8652686 +#> [889,] 0.53892468 1.0486325 1.1644871 1.1717316 1.2046336 1.2400584 1.2450309 +#> [890,] 0.32282065 0.3540405 0.4008547 0.4574192 0.4597129 0.4749997 0.4819657 +#> [891,] 0.47742160 0.7234641 0.8592318 1.0554974 1.0620862 1.1109065 1.1366575 +#> [892,] 0.34197322 0.3716588 0.4356021 0.5047202 0.5166129 0.5323596 0.5344388 +#> [893,] 0.66622344 0.6909275 0.8534496 0.8539695 0.9764982 0.9863673 0.9984615 +#> [894,] 0.87209495 1.0808862 1.3254115 1.3404295 1.4891477 1.5381128 1.5951257 +#> [895,] 0.38741583 0.4344419 0.4488586 0.4533024 0.4672448 0.4848637 0.4975694 +#> [896,] 1.03245278 1.1925389 1.2063668 1.2165550 1.2345554 1.2772759 1.3177358 +#> [897,] 0.33702873 0.9317456 1.1587109 1.2196853 1.2244513 1.2387571 1.2509943 +#> [898,] 0.86087471 0.8775427 1.0586136 1.2316273 1.2486592 1.3250910 1.3474000 +#> [899,] 0.24495826 0.3665608 0.3796179 0.4394007 0.4699607 0.4970682 0.4973593 +#> [900,] 0.53693393 0.5509196 0.5758092 0.6077715 0.6372212 0.6468782 0.6495503 +#> [901,] 0.60751210 0.6347718 0.6750756 0.8400073 0.8639459 0.8938192 0.8987570 +#> [902,] 0.36475602 0.4359686 0.4681975 0.4957492 0.5509920 0.6065373 0.6610760 +#> [903,] 0.71925597 0.7238057 1.4242708 1.4808959 1.6356863 1.7811930 1.8049915 +#> [904,] 0.33217500 0.6075172 0.7308439 0.7573067 0.7745370 0.8331341 0.8373647 +#> [905,] 1.08159736 1.0956074 1.1598090 1.2341912 1.2433489 1.2582241 1.2708966 +#> [906,] 0.29083287 0.3991506 0.5263341 0.5313566 0.5715888 0.5801731 0.5866417 +#> [907,] 0.31849457 0.3375275 0.4090368 0.4105118 0.4221518 0.4514099 0.5643394 +#> [908,] 0.30096399 0.5509830 0.5541190 0.6393489 0.6521120 0.6928918 0.7301782 +#> [909,] 0.27773200 0.7285412 0.9501224 0.9562029 0.9740056 1.0353836 1.1208545 +#> [910,] 0.35965794 0.5150767 0.5336573 0.6122402 0.6531913 0.6848237 0.6920594 +#> [911,] 0.32876163 0.4724317 0.5728042 0.5959871 0.6017204 0.7424823 0.7535873 +#> [912,] 0.20612255 0.2839768 0.3995926 0.4600954 0.4980985 0.5371465 0.5509196 +#> [913,] 0.23307038 0.3248874 0.4146456 0.4839062 0.4901973 0.5347778 0.5501868 +#> [914,] 0.23993803 0.5254370 0.6040564 0.6101514 0.6314974 0.6320861 0.6400916 +#> [915,] 0.69003570 0.8739171 0.8805306 0.8924413 1.0279754 1.0449738 1.1265626 +#> [916,] 0.40415075 0.4847007 0.6844604 0.7136063 0.7381164 0.7421462 0.7458784 +#> [917,] 0.18051066 0.3621718 0.6046618 0.6203619 0.6725229 0.6859977 0.7025122 +#> [918,] 0.39323414 0.4813874 0.5092038 0.5139742 0.5467713 0.5562233 0.5636624 +#> [919,] 0.33470661 0.4189063 0.4808973 0.5373714 0.5594251 0.6065373 0.6195995 +#> [920,] 0.70069148 0.8073962 0.8207193 0.8294974 0.8352566 0.8714288 0.9656345 +#> [921,] 0.34743241 0.3678879 0.4061678 0.4076304 0.4277711 0.4468730 0.5275568 +#> [922,] 0.34309068 0.4307515 0.5409551 0.5519855 0.5602452 0.6127630 0.7134870 +#> [923,] 0.50898016 0.5401785 0.6252212 0.6393644 0.6450369 0.6670446 0.6746208 +#> [924,] 0.55281735 0.5803478 0.5986835 0.7302712 0.7642574 0.7886514 0.7917998 +#> [925,] 0.46323720 0.4711711 0.5015095 0.5227071 0.5300852 0.6057881 0.6281770 +#> [926,] 0.37300207 0.5505227 0.5666462 0.5839332 0.6252212 0.6690963 0.6836963 +#> [927,] 0.40119359 0.4975055 0.5589544 0.6055564 0.6531913 0.6584113 0.7604565 +#> [928,] 0.48183801 0.4841700 0.6325459 0.7198504 0.7318016 0.7725609 0.7955375 +#> [929,] 0.61025299 0.7080839 0.8064294 0.8291200 0.8344719 0.8517123 0.8693708 +#> [930,] 0.35368533 0.4116207 0.4613834 0.5041523 0.5568096 0.5683251 0.6135434 +#> [931,] 0.31605501 0.4617185 0.5401785 0.5809788 0.5947715 0.6274038 0.6835825 +#> [932,] 0.36375067 0.3966306 0.4539625 0.4848637 0.5092787 0.5233490 0.5512903 +#> [933,] 0.66159483 0.6653664 0.7231773 0.7462066 0.7828199 0.7834769 0.8125567 +#> [934,] 0.72467222 0.8892709 0.8942326 0.9342293 1.0019782 1.0190099 1.0817150 +#> [935,] 0.17515967 0.2077467 0.4378483 0.4446930 0.4635054 0.4856521 0.4920686 +#> [936,] 0.63714896 0.6514300 0.8096001 0.8222615 0.8256855 0.8450948 0.8645342 +#> [937,] 0.56154873 0.7014294 0.7433678 0.8551267 1.0064208 1.0101535 1.0104143 +#> [938,] 0.50994700 0.5270976 0.5541190 0.5983123 0.6625547 0.6720328 0.6987629 +#> [939,] 1.13746210 1.1554039 1.1709452 1.2012700 1.3270357 1.3400856 1.4142670 +#> [940,] 0.28743920 0.5563820 0.5856901 0.5996128 0.6547929 0.6700591 0.7373932 +#> [941,] 0.67425040 0.6905455 0.7710334 0.9557612 0.9796315 1.0197606 1.1882176 +#> [942,] 0.37150479 0.4798121 0.4808973 0.5166129 0.5182849 0.5829721 0.5888863 +#> [943,] 0.28688126 0.4258963 0.4281232 0.4670354 0.4883674 0.5060454 0.5184400 +#> [944,] 0.77827903 0.7819818 0.8017048 0.8162861 0.8181842 0.8197966 0.8473799 +#> [945,] 0.82162781 1.0372120 1.1002160 1.1334750 1.2121846 1.2270154 1.2946210 +#> [946,] 0.37025403 0.4167265 0.5974787 0.6093745 0.6213680 0.6260663 0.6454604 +#> [947,] 0.25146438 0.3927622 0.4704423 0.5118140 0.5141644 0.5981667 0.6030569 +#> [948,] 0.78545275 1.4532707 1.5313353 1.5427409 1.6086276 1.7504291 1.7885411 +#> [949,] 0.97271309 0.9821576 1.0197606 1.0300253 1.0468094 1.0555918 1.2018503 +#> [950,] 0.54356099 0.6906225 0.7515560 0.8163552 0.8268404 1.0246197 1.0835439 +#> [951,] 0.54343326 0.5726713 0.6079400 0.6362282 0.6411489 0.6882258 0.7099575 +#> [952,] 0.46844091 0.5961116 0.6552774 0.6681492 0.6705820 0.6705824 0.6773838 +#> [953,] 0.43749453 0.5324512 0.5839332 0.6552774 0.6928143 0.7366193 0.7452899 +#> [954,] 0.43154461 0.4399540 0.4870235 0.5126823 0.5397259 0.5775316 0.5931087 +#> [955,] 1.02021825 1.1881607 1.1982660 1.2262335 1.2503180 1.2585351 1.2634868 +#> [956,] 0.36567019 0.4878582 0.4992977 0.5924440 0.6344151 0.6589955 0.7024502 +#> [957,] 0.54300594 0.6047171 0.6385052 0.8357751 0.8688679 0.8910615 0.9210669 +#> [958,] 0.26227964 0.5029744 0.5263975 0.5506858 0.5875720 0.5980812 0.6151065 +#> [959,] 0.28368227 0.3698291 0.6985545 0.7824819 0.8126316 0.8950014 0.9177645 +#> [960,] 0.49736419 0.5183471 0.6872977 0.7351015 0.7555208 0.7721703 0.8106311 +#> [961,] 0.39870005 0.4068494 0.4138580 0.4387298 0.4493096 0.4648980 0.4751239 +#> [962,] 0.32908268 0.4148534 0.4663373 0.4952917 0.5148448 0.5823703 0.5933718 +#> [963,] 0.20957563 0.5594251 0.6219183 0.6602812 0.7078393 0.7360118 0.7703560 +#> [964,] 0.29793315 0.3097676 0.3946160 0.4495239 0.4973593 0.5219604 0.5401556 +#> [965,] 0.40847446 0.4752569 0.5648596 0.5927076 0.6392017 0.6626822 0.6950428 +#> [966,] 0.24032959 0.3109608 0.3498404 0.3971149 0.4755460 0.5466231 0.5519391 +#> [967,] 0.83996762 0.8429282 0.8430529 0.8506495 0.8805370 0.9030809 0.9297668 +#> [968,] 0.75906932 0.7979691 0.8549952 0.8591549 0.8739171 0.9021926 0.9483216 +#> [969,] 0.72181504 0.7701056 0.8012313 0.8261697 0.8506495 0.9436734 0.9631076 +#> [970,] 0.45928747 0.6874574 0.7040529 0.7155653 0.7596100 0.7642973 0.8288619 +#> [971,] 0.48355347 0.5202777 0.5696647 0.6209378 0.8020739 0.8293285 0.8423353 +#> [972,] 0.52454708 0.5688572 0.5876460 0.5896110 0.6384115 0.6425840 0.6848581 +#> [973,] 0.23167235 0.4270107 0.5509920 0.6065801 0.6111725 0.6277603 0.6409072 +#> [974,] 0.47085961 0.6588610 0.6596290 0.6879263 0.7065132 0.7088534 0.7781482 +#> [975,] 0.24984831 0.4459982 0.7060311 0.8481894 0.8836024 0.9899705 0.9995362 +#> [976,] 0.48003483 0.5257607 0.5606464 0.6075915 0.6931258 0.7270252 0.7278883 +#> [977,] 0.24909537 0.6965583 0.8719151 0.8899695 0.8925672 0.9524394 0.9771197 +#> [978,] 0.47499973 0.5926426 0.6021089 0.6590841 0.6911297 0.7376685 0.7610334 +#> [979,] 0.27414771 0.3533348 0.3829368 0.4096830 0.4135228 0.4415721 0.4663373 +#> [980,] 0.55848306 0.5871392 0.6017204 0.6948917 0.7138283 0.7796337 0.8115816 +#> [981,] 0.31027863 0.3318503 0.4300163 0.4788421 0.4899965 0.5254370 0.5960820 +#> [982,] 0.09831649 0.3426225 0.3733893 0.3833929 0.3998862 0.4147042 0.5065422 +#> [983,] 0.34242776 0.4128428 0.5373668 0.5707755 0.5742626 0.6556826 0.6773988 +#> [984,] 0.69637689 0.9597156 0.9830406 1.0227250 1.0404170 1.0897849 1.1604625 +#> [985,] 0.21587317 0.5035108 0.5587194 0.6152778 0.6528830 0.6680267 0.6734077 +#> [986,] 0.69958193 0.8580225 0.8754899 0.9156674 0.9566091 0.9670229 0.9763510 +#> [987,] 0.11608719 0.3006863 0.3250055 0.3289196 0.4259055 0.4377923 0.4870022 +#> [988,] 0.36347243 0.4651067 0.5653288 0.5780089 0.5809020 0.6451965 0.6682168 +#> [989,] 0.41765340 0.4401888 0.6357935 0.6813370 0.7506527 0.7766402 0.7819681 +#> [990,] 0.79370421 0.8864215 0.8938192 0.9058586 1.0523116 1.1082587 1.1725493 +#> [991,] 0.54612053 0.6078415 0.6344151 0.6866979 0.6894896 0.7161804 0.7335557 +#> [992,] 0.34642398 0.5090600 0.5749598 0.5798745 0.6493739 0.6495503 0.6656699 +#> [993,] 0.58381883 0.6305519 0.6584598 0.7033968 0.7270252 0.7323232 0.7672032 +#> [994,] 0.37058080 0.4416356 0.4471095 0.4517481 0.4961319 0.5121898 0.5384396 +#> [995,] 0.29083287 0.4656943 0.4740713 0.5162538 0.5446352 0.5476847 0.5501051 +#> [996,] 0.71182690 0.8015365 0.8357751 0.9246408 0.9344621 1.0045098 1.0209222 +#> [997,] 0.49119061 0.8981545 1.0922723 1.0947023 1.1007515 1.1540649 1.1801942 +#> [998,] 0.52910495 0.5856901 0.6052306 0.6426209 0.6872977 0.7322672 0.7591426 +#> [999,] 0.40440349 0.4788421 0.5086148 0.5929361 0.6096010 0.6314974 0.6784615 +#> [1000,] 0.95906486 0.9779007 0.9891183 0.9967242 1.0371942 1.0655567 1.1123559 #> [,8] [,9] [,10] -#> [1,] 1.0220903 1.0506990 1.0647866 -#> [2,] 1.0159804 1.0628464 1.0808911 -#> [3,] 0.6555239 0.6691817 0.6939634 -#> [4,] 0.8153295 0.8323242 0.8576244 -#> [5,] 0.7755287 0.8105826 0.8419308 -#> [6,] 0.9605567 0.9751774 0.9853637 -#> [7,] 0.6006658 0.6082166 0.6575454 -#> [8,] 0.9434125 0.9569375 0.9573127 -#> [9,] 0.8578887 0.8690163 0.9022197 -#> [10,] 1.1059807 1.1271065 1.2717197 -#> [11,] 0.9275638 0.9374665 0.9629951 -#> [12,] 0.8683344 0.8833672 0.8889993 -#> [13,] 0.9806743 0.9860205 1.0262920 -#> [14,] 0.8045830 0.8120747 0.8122041 -#> [15,] 1.1613124 1.1683864 1.1694879 -#> [16,] 0.7096127 0.7521758 0.7795502 -#> [17,] 0.5288156 0.5698295 0.5967761 -#> [18,] 0.6856766 0.7122816 0.7510326 -#> [19,] 1.1743609 1.2663257 1.2730675 -#> [20,] 0.5036007 0.5465379 0.5710962 -#> [21,] 0.8614368 0.8647220 0.9100972 -#> [22,] 0.4877218 0.5036007 0.5085941 -#> [23,] 0.8889993 0.9078457 0.9205416 -#> [24,] 0.6778038 0.6823220 0.6916917 -#> [25,] 0.7951561 0.9031122 0.9116083 -#> [26,] 0.7183941 0.7444101 0.7563863 -#> [27,] 0.5881498 0.6069905 0.6132131 -#> [28,] 0.6360646 0.6735662 0.7085963 -#> [29,] 0.6846481 0.6876163 0.7291948 -#> [30,] 1.0284256 1.0312844 1.0480669 -#> [31,] 0.7609546 0.7702795 0.8113416 -#> [32,] 0.5915577 0.6097883 0.6270160 -#> [33,] 0.7015258 0.7143286 0.7274967 -#> [34,] 0.8280008 0.8410305 0.8838150 -#> [35,] 0.6646158 0.6752053 0.6759780 -#> [36,] 0.5115982 0.5658013 0.6014117 -#> [37,] 0.9767749 0.9812302 0.9813567 -#> [38,] 0.6544361 0.6599681 0.6723698 -#> [39,] 0.5575974 0.5998258 0.6160434 -#> [40,] 1.0114281 1.0269782 1.0689315 -#> [41,] 1.0221192 1.0405471 1.0802871 -#> [42,] 0.5539836 0.5670290 0.5742499 -#> [43,] 1.1136135 1.1143924 1.1565844 -#> [44,] 1.2811282 1.3083940 1.3553939 -#> [45,] 1.7795953 1.7843617 1.8282038 -#> [46,] 1.3636859 1.3862933 1.4477444 -#> [47,] 1.1656563 1.1768936 1.2009966 -#> [48,] 0.6516211 0.6789018 0.6822214 -#> [49,] 0.6571013 0.6922736 0.7112233 -#> [50,] 0.9127270 0.9160051 0.9327613 -#> [51,] 0.7005399 0.7818339 0.7852307 -#> [52,] 0.6050779 0.6813854 0.7484952 -#> [53,] 0.9623091 0.9637074 0.9640488 -#> [54,] 0.9004985 0.9043681 0.9113352 -#> [55,] 0.5764813 0.5861439 0.6036196 -#> [56,] 0.9721654 0.9896678 1.0213423 -#> [57,] 1.2183686 1.2607060 1.2665570 -#> [58,] 0.6979966 0.7376806 0.7692197 -#> [59,] 0.9177779 0.9393185 0.9394270 -#> [60,] 0.7310089 0.7778650 0.8020409 -#> [61,] 0.6713180 0.7570872 0.7576333 -#> [62,] 0.7238832 0.7438888 0.7741449 -#> [63,] 0.5693800 0.5731336 0.6171722 -#> [64,] 0.8134684 0.8155012 0.8486851 -#> [65,] 0.6314431 0.6620528 0.6695307 -#> [66,] 0.7487702 0.7510326 0.7665815 -#> [67,] 0.7037280 0.7139507 0.7341633 -#> [68,] 0.6670795 0.7039944 0.7103590 -#> [69,] 0.5590320 0.5826087 0.6090574 -#> [70,] 1.0705625 1.0839160 1.1077849 -#> [71,] 0.7925975 0.8267848 0.8600750 -#> [72,] 0.4665478 0.4980567 0.5050926 -#> [73,] 0.6832122 0.7127399 0.7301544 -#> [74,] 0.6706489 0.7036290 0.7122281 -#> [75,] 0.9944253 1.0938387 1.1284889 -#> [76,] 0.7341633 0.7386602 0.7543114 -#> [77,] 0.7444101 0.7878845 0.8116886 -#> [78,] 0.7183941 0.7242253 0.7437343 -#> [79,] 0.6669422 0.6838606 0.7015809 -#> [80,] 1.7852066 1.9035128 1.9593996 -#> [81,] 1.2074716 1.2937300 1.3053031 -#> [82,] 0.8501118 0.8510822 0.8887671 -#> [83,] 0.6912846 0.7224415 0.7439148 -#> [84,] 1.2312541 1.2640422 1.2865493 -#> [85,] 1.0518943 1.0554476 1.0708872 -#> [86,] 0.6896308 0.7412201 0.7649269 -#> [87,] 0.7517473 0.7538065 0.7627878 -#> [88,] 0.6607499 0.6651778 0.6755015 -#> [89,] 0.8049486 0.8602626 0.9045392 -#> [90,] 0.7128878 0.7728106 0.8510530 -#> [91,] 0.7852307 0.7854566 0.8330092 -#> [92,] 1.7256766 1.7263170 1.7344674 -#> [93,] 0.6820320 0.6995980 0.7385645 -#> [94,] 0.9136147 1.0265890 1.0361744 -#> [95,] 0.6844769 0.6987678 0.7313320 -#> [96,] 1.1858038 1.2298611 1.2433319 -#> [97,] 0.9149957 1.1553283 1.1715203 -#> [98,] 0.6350549 0.6468977 0.6569167 -#> [99,] 0.6095392 0.6515879 0.7125169 -#> [100,] 0.7548809 0.7640128 0.7852657 -#> [101,] 0.8339963 0.8746430 0.8802706 -#> [102,] 0.8803966 0.9150893 0.9655447 -#> [103,] 1.3828986 1.3844780 1.4077336 -#> [104,] 0.6973268 0.7098145 0.7467064 -#> [105,] 0.5697776 0.6048633 0.6226070 -#> [106,] 1.0555791 1.0815560 1.0834790 -#> [107,] 0.5333565 0.5858786 0.5920383 -#> [108,] 0.7323111 0.7431155 0.7739945 -#> [109,] 0.6553638 0.7204231 0.7339759 -#> [110,] 1.4211427 1.5612817 1.5691502 -#> [111,] 0.8804201 0.8804967 0.9133936 -#> [112,] 1.8300943 1.8462969 1.8554780 -#> [113,] 0.6681737 0.7201891 0.7234314 -#> [114,] 0.9264201 0.9446354 0.9898591 -#> [115,] 1.0305274 1.0873706 1.1108897 -#> [116,] 0.7776361 0.8285550 0.8445627 -#> [117,] 0.8105688 0.8334105 0.8344281 -#> [118,] 0.5251669 0.5258499 0.5761898 -#> [119,] 0.6680991 0.6735608 0.6832122 -#> [120,] 0.5456871 0.5893277 0.5902274 -#> [121,] 0.7243613 0.7391918 0.7430023 -#> [122,] 0.6011233 0.6619225 0.7385597 -#> [123,] 0.6637908 0.6920283 0.7221301 -#> [124,] 0.6622352 0.6793424 0.6797925 -#> [125,] 1.4978961 1.5240171 1.5591992 -#> [126,] 0.8145714 0.8294044 0.8933134 -#> [127,] 0.6213495 0.6382426 0.6421234 -#> [128,] 0.6497090 0.6654464 0.6723698 -#> [129,] 0.7529188 0.7531056 0.8286505 -#> [130,] 0.8415089 0.8776250 0.8833272 -#> [131,] 0.9066364 0.9705027 0.9867259 -#> [132,] 0.6082166 0.6088717 0.6183626 -#> [133,] 0.5901790 0.5906260 0.6049659 -#> [134,] 0.7160267 0.7525095 0.7664416 -#> [135,] 0.5871855 0.5888006 0.5958864 -#> [136,] 0.5596206 0.5888128 0.5969488 -#> [137,] 0.8632859 0.8702170 0.9241328 -#> [138,] 0.6115767 0.6173324 0.6180640 -#> [139,] 0.8219311 0.8441528 0.8611271 -#> [140,] 0.7012192 0.7072508 0.7099819 -#> [141,] 0.5743078 0.6728713 0.6747774 -#> [142,] 0.5263058 0.5891322 0.6317845 -#> [143,] 0.6099109 0.6164456 0.6347685 -#> [144,] 0.8930163 0.9479502 0.9620478 -#> [145,] 1.0256061 1.0524403 1.0587543 -#> [146,] 0.5566784 0.5713538 0.5788591 -#> [147,] 0.5543984 0.5705758 0.5721490 -#> [148,] 0.9795626 1.0067947 1.0458673 -#> [149,] 0.8637182 0.9008321 0.9043360 -#> [150,] 0.5414789 0.5788055 0.5862132 -#> [151,] 0.5853790 0.5875525 0.5915577 -#> [152,] 0.7714320 0.7798431 0.7872667 -#> [153,] 0.9014666 0.9705633 0.9777831 -#> [154,] 1.0157654 1.0779330 1.0913184 -#> [155,] 0.9316542 0.9658182 0.9670866 -#> [156,] 0.6073392 0.6131069 0.6592461 -#> [157,] 1.1114114 1.1454856 1.1555352 -#> [158,] 0.6052577 0.6307465 0.7290489 -#> [159,] 0.6285511 0.6381594 0.6443042 -#> [160,] 0.7880060 0.8260032 0.8534824 -#> [161,] 0.6633763 0.6657410 0.6797596 -#> [162,] 0.8476128 0.8624028 0.8771263 -#> [163,] 0.7845580 0.8266930 0.8318053 -#> [164,] 0.7392637 0.7512895 0.8111425 -#> [165,] 1.2922349 1.3063313 1.3084353 -#> [166,] 0.9128304 0.9454870 1.0354152 -#> [167,] 0.6104109 0.6456856 0.6493743 -#> [168,] 0.6152847 0.6210218 0.6397210 -#> [169,] 0.9241328 0.9419218 1.1478319 -#> [170,] 0.6543718 0.6672829 0.7638711 -#> [171,] 0.6459038 0.6709559 0.6813143 -#> [172,] 0.6297882 0.6436494 0.6455486 -#> [173,] 1.1226844 1.1330129 1.2040133 -#> [174,] 1.0910784 1.1286312 1.1740047 -#> [175,] 1.1304412 1.1901069 1.1943255 -#> [176,] 0.7143797 0.7184307 0.7481196 -#> [177,] 0.8513361 0.8603842 0.8674875 -#> [178,] 0.6705592 0.6735620 0.6778038 -#> [179,] 1.0194385 1.0333731 1.0398635 -#> [180,] 0.6132131 0.6198758 0.6648093 -#> [181,] 0.6900043 0.7318831 0.7641128 -#> [182,] 0.6079576 0.6144660 0.6170095 -#> [183,] 0.5681010 0.5703633 0.6560972 -#> [184,] 2.0221354 2.0532234 2.0724232 -#> [185,] 1.0225519 1.0345328 1.0356459 -#> [186,] 0.5291272 0.5727759 0.5803860 -#> [187,] 0.8187429 0.8696613 0.8934775 -#> [188,] 0.8677932 0.8900682 0.9412017 -#> [189,] 0.7229251 0.7349696 0.7951147 -#> [190,] 0.5737622 0.5920383 0.6537043 -#> [191,] 1.3769688 1.3968075 1.4065190 -#> [192,] 0.6929701 0.6971881 0.6983515 -#> [193,] 0.5998258 0.6034515 0.6136679 -#> [194,] 0.5429316 0.5570000 0.5602252 -#> [195,] 1.0188878 1.0793454 1.0858714 -#> [196,] 0.6269043 0.6810870 0.6868403 -#> [197,] 1.0075614 1.0431346 1.0821745 -#> [198,] 1.3594604 1.4146364 1.4263432 -#> [199,] 0.7430240 0.7511748 0.8771458 -#> [200,] 0.7863389 0.8066169 0.8143462 -#> [201,] 1.8213842 1.8912685 1.9962653 -#> [202,] 1.4548162 1.4980519 1.4988507 -#> [203,] 1.0292585 1.0564403 1.0715925 -#> [204,] 0.5571402 0.6248315 0.6380658 -#> [205,] 0.6196099 0.6290472 0.6292636 -#> [206,] 0.6378416 0.6567339 0.6771992 -#> [207,] 1.6953200 1.7081563 1.7206302 -#> [208,] 0.7497781 0.7546189 0.7557900 -#> [209,] 1.5233936 1.5288099 1.5962986 -#> [210,] 0.6239921 0.6247975 0.6265817 -#> [211,] 0.7915838 0.8227246 0.8271605 -#> [212,] 0.6133590 0.6334791 0.6504994 -#> [213,] 0.5879778 0.5889366 0.6110425 -#> [214,] 0.7508885 0.8324732 0.8941450 -#> [215,] 0.7468176 0.7739945 0.8493845 -#> [216,] 0.6418896 0.6420441 0.6478635 -#> [217,] 0.6582989 0.6735662 0.7013804 -#> [218,] 0.6180513 0.6354609 0.6562407 -#> [219,] 0.7390813 0.7445928 0.7881615 -#> [220,] 0.8158097 0.8690679 0.8797843 -#> [221,] 0.7647663 0.7783410 0.7837751 -#> [222,] 1.3820083 1.3966081 1.4695749 -#> [223,] 0.5455742 0.5658013 0.6034515 -#> [224,] 1.1583985 1.2959913 1.3045000 -#> [225,] 0.9930158 1.0073708 1.0326790 -#> [226,] 0.7177860 0.7245656 0.7300216 -#> [227,] 0.6152128 0.6768162 0.7500130 -#> [228,] 0.8081437 0.8210160 0.8341311 -#> [229,] 0.8173428 0.8390229 0.8620620 -#> [230,] 0.8007657 0.8012049 0.8112083 -#> [231,] 0.9177369 0.9279086 0.9315429 -#> [232,] 0.7094825 0.7412201 0.7487212 -#> [233,] 0.8801390 0.8919684 0.9048960 -#> [234,] 0.8240324 0.8333244 0.8385498 -#> [235,] 0.7525095 0.7695044 0.7708732 -#> [236,] 0.7133467 0.7192160 0.7198735 -#> [237,] 0.5881498 0.5997251 0.6016000 -#> [238,] 0.8105593 0.9042581 0.9140608 -#> [239,] 1.0796850 1.0905094 1.0981334 -#> [240,] 0.8508098 0.8635917 0.8980599 -#> [241,] 0.6227925 0.6280889 0.6417925 -#> [242,] 0.7067708 0.7184563 0.7377581 -#> [243,] 0.6115334 0.6430333 0.6456477 -#> [244,] 1.4331381 1.4607150 1.5316756 -#> [245,] 0.8501881 0.8563041 0.8590798 -#> [246,] 0.7118094 0.7147159 0.7300108 -#> [247,] 1.1565844 1.1967436 1.2031595 -#> [248,] 0.7812252 0.7929947 0.8109086 -#> [249,] 0.6637959 0.6669140 0.6801039 -#> [250,] 2.0397549 2.0483508 2.0556776 -#> [251,] 0.7355141 0.7445453 0.7960024 -#> [252,] 0.6012655 0.6101469 0.6277827 -#> [253,] 1.1820783 1.1844585 1.1852437 -#> [254,] 0.7059918 0.7131183 0.7172770 -#> [255,] 1.1761711 1.1954909 1.2139322 -#> [256,] 0.8081437 0.8302582 0.8359470 -#> [257,] 0.4847586 0.4983993 0.5328842 -#> [258,] 1.3630821 1.3645951 1.4483778 -#> [259,] 1.6683581 1.6846586 1.6916542 -#> [260,] 0.6327125 0.6592461 0.6797594 -#> [261,] 0.5816681 0.6651170 0.6719349 -#> [262,] 1.0199230 1.0306691 1.0465887 -#> [263,] 0.7328701 0.7366519 0.7488794 -#> [264,] 0.6567246 0.6632021 0.6666177 -#> [265,] 0.8342606 0.8833272 0.8873127 -#> [266,] 1.2187290 1.2362250 1.3522488 -#> [267,] 0.7023748 0.7102512 0.7225791 -#> [268,] 1.0880982 1.0941461 1.0960332 -#> [269,] 0.8627002 0.8743276 0.8751708 -#> [270,] 0.6198851 0.6314126 0.6475378 -#> [271,] 0.5674021 0.5693480 0.5928785 -#> [272,] 0.9341928 0.9833019 0.9973478 -#> [273,] 0.8096329 0.8292255 0.8555027 -#> [274,] 0.7891404 0.8243659 0.8278745 -#> [275,] 0.9317763 0.9409960 0.9466711 -#> [276,] 1.3790276 1.3802968 1.4076604 -#> [277,] 0.6844823 0.7143535 0.7407388 -#> [278,] 0.7100069 0.7488184 0.7600098 -#> [279,] 1.1506401 1.1908013 1.2663376 -#> [280,] 0.7630830 0.7659436 0.7761110 -#> [281,] 0.8431615 0.8637182 0.9226522 -#> [282,] 0.9590741 1.0251275 1.0283219 -#> [283,] 0.7033122 0.7052669 0.7377581 -#> [284,] 0.8044038 0.8099410 0.8531779 -#> [285,] 0.4574371 0.4633042 0.5149583 -#> [286,] 1.2142199 1.2325390 1.2486723 -#> [287,] 0.7117716 0.7845475 0.8023944 -#> [288,] 0.7105949 0.7146610 0.7506698 -#> [289,] 0.9633728 0.9659964 1.0147951 -#> [290,] 0.6066468 0.6082550 0.6091098 -#> [291,] 1.0277905 1.0338277 1.0357108 -#> [292,] 0.5000964 0.5028235 0.5161832 -#> [293,] 0.8557396 0.8838206 0.9363732 -#> [294,] 0.7162112 0.7261345 0.7368908 -#> [295,] 2.1098008 2.1263382 2.1662556 -#> [296,] 0.9174849 0.9391519 0.9513195 -#> [297,] 0.5151876 0.5552198 0.6246004 -#> [298,] 1.2236423 1.2574848 1.2584744 -#> [299,] 0.8009335 0.8341694 0.8629700 -#> [300,] 0.6411617 0.6742086 0.6789019 -#> [301,] 1.0222484 1.0651726 1.0824499 -#> [302,] 1.4780679 1.4931978 1.4998941 -#> [303,] 1.7793757 1.8106507 1.8345273 -#> [304,] 0.8031539 0.8513275 0.8791171 -#> [305,] 1.2292597 1.2357294 1.2826214 -#> [306,] 0.6505470 0.6771992 0.6788451 -#> [307,] 1.1818239 1.1945705 1.2083771 -#> [308,] 0.6593763 0.6723912 0.6867334 -#> [309,] 0.4700809 0.4816955 0.4866329 -#> [310,] 0.7201891 0.7371411 0.7627039 -#> [311,] 0.9326344 1.0005127 1.0038540 -#> [312,] 0.7288453 0.7482867 0.7527413 -#> [313,] 0.6792825 0.7098145 0.7107325 -#> [314,] 0.7188043 0.7305566 0.7341665 -#> [315,] 0.5343740 0.5371986 0.6196099 -#> [316,] 1.0993493 1.1180550 1.1213501 -#> [317,] 0.5166436 0.5418754 0.5499564 -#> [318,] 1.4359835 1.4469369 1.4834774 -#> [319,] 0.8046689 0.8057893 0.8264896 -#> [320,] 0.5838682 0.5972898 0.6249691 -#> [321,] 0.6478044 0.6480226 0.6585143 -#> [322,] 0.7356749 0.7797624 0.8027854 -#> [323,] 1.0977021 1.1011990 1.1069747 -#> [324,] 0.6170095 0.6424517 0.6700281 -#> [325,] 0.7829631 0.7942367 0.7964902 -#> [326,] 0.8682310 0.9106875 1.0473610 -#> [327,] 0.8372628 0.8838178 0.9024265 -#> [328,] 1.4275131 1.5188548 1.5214910 -#> [329,] 1.2825670 1.3081612 1.3743889 -#> [330,] 0.6444889 0.6908379 0.6954510 -#> [331,] 0.6772921 0.7085888 0.7115589 -#> [332,] 0.6055590 0.6319255 0.6535943 -#> [333,] 0.6089178 0.6171698 0.6420984 -#> [334,] 0.7013259 0.7197396 0.7201731 -#> [335,] 0.7554972 0.7570872 0.7621492 -#> [336,] 0.6903874 0.7099542 0.7263274 -#> [337,] 1.4731754 1.4770385 1.4885976 -#> [338,] 1.1171090 1.1343383 1.1866288 -#> [339,] 0.7904959 0.8211896 0.8407411 -#> [340,] 0.6571768 0.7153976 0.7344173 -#> [341,] 0.6206803 0.7202563 0.7536813 -#> [342,] 0.6031906 0.6348537 0.6648093 -#> [343,] 1.0684635 1.1281765 1.2701446 -#> [344,] 0.6379043 0.6522162 0.6533374 -#> [345,] 0.5810245 0.5867504 0.5913260 -#> [346,] 0.5046153 0.5277485 0.5910771 -#> [347,] 1.0505311 1.0576989 1.0992925 -#> [348,] 0.6311623 0.6560215 0.6822626 -#> [349,] 1.0165439 1.1333100 1.1340170 -#> [350,] 0.5813493 0.5950332 0.6079576 -#> [351,] 0.7300095 0.7602339 0.7629245 -#> [352,] 1.1053641 1.1180550 1.1312491 -#> [353,] 1.1837643 1.1988522 1.2302067 -#> [354,] 0.9533387 0.9564164 0.9649450 -#> [355,] 0.9164328 0.9176535 1.0082143 -#> [356,] 0.7133467 0.7360781 0.7797316 -#> [357,] 0.7177315 0.7849657 0.7935783 -#> [358,] 0.7369999 0.7680742 0.7718746 -#> [359,] 0.6829276 0.7062740 0.7828293 -#> [360,] 0.6238698 0.6319799 0.6712264 -#> [361,] 0.8012627 0.8229591 0.8455997 -#> [362,] 0.6160434 0.6178926 0.6240917 -#> [363,] 0.9001376 0.9136147 0.9147746 -#> [364,] 1.5932991 1.6157023 1.6168954 -#> [365,] 0.8077789 0.8595672 0.8851544 -#> [366,] 0.8105764 0.9519390 0.9627244 -#> [367,] 0.8791882 0.8820581 0.9409960 -#> [368,] 1.1542080 1.2095336 1.2174031 -#> [369,] 1.2001517 1.2151301 1.2367852 -#> [370,] 0.7215688 0.7504660 0.7612260 -#> [371,] 0.5766792 0.6133128 0.6378261 -#> [372,] 0.5124364 0.5342045 0.5436498 -#> [373,] 0.7111043 0.7964800 0.8074443 -#> [374,] 0.7735197 0.7903442 0.8573493 -#> [375,] 0.6717405 0.6987919 0.7056988 -#> [376,] 1.1960716 1.2061600 1.2549521 -#> [377,] 0.9120929 0.9420186 1.0101190 -#> [378,] 0.9728332 0.9873418 0.9990933 -#> [379,] 0.7385341 0.7460797 0.7479200 -#> [380,] 0.6228103 0.6252471 0.6378261 -#> [381,] 0.6451651 0.6466886 0.6497090 -#> [382,] 0.6164454 0.6313534 0.6431640 -#> [383,] 1.7215563 1.7413460 1.7505421 -#> [384,] 1.4146987 1.4767661 1.5423798 -#> [385,] 1.2790766 1.3318983 1.3342449 -#> [386,] 0.5253829 0.5374429 0.5478135 -#> [387,] 0.6767031 0.6980473 0.7102383 -#> [388,] 0.6058627 0.6468854 0.6582067 -#> [389,] 0.7614894 0.7666488 0.7872667 -#> [390,] 0.7636327 0.7665815 0.7671899 -#> [391,] 0.7542396 0.7742806 0.8188065 -#> [392,] 0.8449039 0.8477306 0.8554640 -#> [393,] 0.6606911 0.6652344 0.7106932 -#> [394,] 1.1621956 1.1644541 1.1986978 -#> [395,] 1.1677274 1.2597720 1.2879303 -#> [396,] 0.9441428 0.9562852 0.9670866 -#> [397,] 0.6461569 0.6517059 0.6642091 -#> [398,] 0.8534824 0.8593124 0.9181514 -#> [399,] 0.6507469 0.6531001 0.6857016 -#> [400,] 0.7627499 0.8010999 0.8162060 -#> [401,] 0.7167615 0.7590209 0.7663757 -#> [402,] 0.9385822 0.9546533 0.9735454 -#> [403,] 0.9174849 0.9597572 0.9760358 -#> [404,] 0.7314190 0.7399096 0.7420626 -#> [405,] 0.6440154 0.6769841 0.6813038 -#> [406,] 0.9042870 0.9199815 0.9366138 -#> [407,] 0.5143304 0.5230954 0.5826878 -#> [408,] 0.7751121 0.8150130 0.8160969 -#> [409,] 0.6680155 0.6764425 0.6882297 -#> [410,] 1.0683585 1.0714703 1.1421249 -#> [411,] 1.2147471 1.2663894 1.2727458 -#> [412,] 1.1390125 1.1560421 1.1667422 -#> [413,] 0.5777682 0.5841814 0.5867504 -#> [414,] 1.0770330 1.1461043 1.1826790 -#> [415,] 0.7190763 0.7826114 0.8346353 -#> [416,] 1.2734965 1.3180883 1.3209671 -#> [417,] 0.8609763 0.8951846 0.9389049 -#> [418,] 0.6299388 0.6377190 0.6472083 -#> [419,] 1.3202222 1.3234820 1.3480002 -#> [420,] 0.7911226 0.8918625 0.9113162 -#> [421,] 1.5099517 1.5879799 1.6275022 -#> [422,] 0.8648793 0.8801506 0.8962246 -#> [423,] 0.6058663 0.6082788 0.6314126 -#> [424,] 1.3163879 1.3328708 1.3662731 -#> [425,] 0.7955620 0.7975097 0.8114964 -#> [426,] 0.6418109 0.6934614 0.7022022 -#> [427,] 1.8529715 1.9275538 1.9412448 -#> [428,] 0.8037321 0.8180881 0.8260032 -#> [429,] 0.8748160 0.9124285 0.9167675 -#> [430,] 1.4781769 1.5195083 1.5757886 -#> [431,] 0.7765806 0.7796118 0.7852608 -#> [432,] 0.9012644 0.9333909 0.9559555 -#> [433,] 0.6599585 0.6928193 0.6981878 -#> [434,] 0.7897385 0.8571447 0.8957822 -#> [435,] 1.1936272 1.2030167 1.2407876 -#> [436,] 0.7833691 0.8614368 0.8945441 -#> [437,] 0.5384173 0.5829803 0.5841615 -#> [438,] 0.5571568 0.6174222 0.6316854 -#> [439,] 0.6609872 0.6613144 0.7115270 -#> [440,] 0.8451830 0.8557046 0.8650737 -#> [441,] 0.5728784 0.5745183 0.5839120 -#> [442,] 1.0467757 1.0550746 1.0574801 -#> [443,] 0.6892405 0.7332718 0.7333728 -#> [444,] 0.5805887 0.6012403 0.6268490 -#> [445,] 0.7912182 0.8435330 0.8641570 -#> [446,] 0.7852608 0.8762724 0.9075501 -#> [447,] 1.3676645 1.3747721 1.4162996 -#> [448,] 1.2725432 1.2803105 1.2833201 -#> [449,] 1.1295200 1.2154808 1.2187803 -#> [450,] 0.9210998 0.9405099 1.0160491 -#> [451,] 0.6643364 0.6665172 0.6964813 -#> [452,] 0.6314404 0.6404495 0.6684119 -#> [453,] 0.5370750 0.5614417 0.6017285 -#> [454,] 0.9114307 0.9131999 0.9218992 -#> [455,] 0.5167141 0.5323207 0.5668559 -#> [456,] 0.7683081 0.7733977 0.8122535 -#> [457,] 0.5024490 0.5412091 0.5569929 -#> [458,] 1.0857416 1.1236817 1.1830759 -#> [459,] 0.5728755 0.5922088 0.5931377 -#> [460,] 0.5689447 0.5754628 0.5817520 -#> [461,] 0.6513352 0.7065268 0.7117218 -#> [462,] 0.5501011 0.5596206 0.5649255 -#> [463,] 0.6391369 0.6395256 0.6412760 -#> [464,] 0.8306194 0.8751047 0.8794120 -#> [465,] 0.7393981 0.8423412 0.8437964 -#> [466,] 1.8836910 1.9095367 1.9135119 -#> [467,] 1.1950243 1.2333716 1.2435436 -#> [468,] 0.9667808 0.9857054 0.9916732 -#> [469,] 0.9461251 0.9463880 0.9540307 -#> [470,] 0.5763679 0.5815961 0.5851288 -#> [471,] 0.8321809 0.8519922 0.8573915 -#> [472,] 0.6797594 0.6974174 0.7011072 -#> [473,] 0.6045314 0.6086948 0.6161821 -#> [474,] 0.6178535 0.6265817 0.6299388 -#> [475,] 1.4563943 1.4668533 1.4956531 -#> [476,] 0.4759897 0.5278541 0.5323207 -#> [477,] 0.5904797 0.5921010 0.6182972 -#> [478,] 0.7475949 0.7595601 0.7750158 -#> [479,] 0.8676342 0.9958579 1.0082914 -#> [480,] 0.9988466 1.0379648 1.0873266 -#> [481,] 0.6811404 0.7125915 0.7438543 -#> [482,] 0.7233252 0.7258189 0.7496445 -#> [483,] 0.8908971 0.9220001 0.9393187 -#> [484,] 0.8402377 0.8782716 0.8924074 -#> [485,] 0.8752551 0.9683506 0.9777102 -#> [486,] 0.9860885 1.0097891 1.0138331 -#> [487,] 0.6715531 0.6862316 0.6922482 -#> [488,] 1.2949801 1.3199758 1.3314084 -#> [489,] 1.0834790 1.1111410 1.1434177 -#> [490,] 0.7012192 0.7035310 0.7197476 -#> [491,] 0.8863247 0.9151983 0.9861853 -#> [492,] 1.7159364 1.7248158 1.7670427 -#> [493,] 0.8374759 0.9264355 0.9493804 -#> [494,] 1.3369391 1.3572066 1.3602157 -#> [495,] 1.3663135 1.3747328 1.3915746 -#> [496,] 0.9388876 0.9787855 0.9818573 -#> [497,] 0.9633728 0.9716273 1.0105809 -#> [498,] 1.2406684 1.2583409 1.3507659 -#> [499,] 0.8820550 1.0146170 1.0150288 -#> [500,] 0.9055686 0.9680445 0.9941518 -#> [501,] 2.4145756 2.4420108 2.4464436 -#> [502,] 0.5520771 0.5745183 0.5826584 -#> [503,] 0.8337882 0.8505912 0.8526129 -#> [504,] 0.6882318 0.6964999 0.7367348 -#> [505,] 0.9813650 0.9874835 1.0472607 -#> [506,] 0.6814462 0.7060224 0.7076732 -#> [507,] 0.4816955 0.4847935 0.4864367 -#> [508,] 0.5691907 0.5770479 0.5780491 -#> [509,] 1.6335442 1.6385179 1.6535956 -#> [510,] 1.3528960 1.3563794 1.3611355 -#> [511,] 0.6759780 0.7094116 0.7247117 -#> [512,] 1.2926636 1.3023587 1.3749284 -#> [513,] 1.5299030 1.5399956 1.6611706 -#> [514,] 0.6710257 0.6810884 0.6950817 -#> [515,] 0.7584446 0.8062355 0.8130625 -#> [516,] 0.8178128 0.8178424 0.8205612 -#> [517,] 0.8591310 0.8919684 0.9240162 -#> [518,] 0.5025708 0.5418103 0.5693054 -#> [519,] 0.6895236 0.6973202 0.7059582 -#> [520,] 0.5773875 0.5787767 0.6029737 -#> [521,] 0.7146610 0.7643505 0.7878777 -#> [522,] 0.6803957 0.6811404 0.7170140 -#> [523,] 0.6578455 0.6659730 0.6749460 -#> [524,] 0.7388219 0.7477676 0.7486448 -#> [525,] 0.9893545 1.0141225 1.0483922 -#> [526,] 0.6767827 0.7060023 0.7315015 -#> [527,] 1.0208764 1.0257840 1.0330603 -#> [528,] 0.7171179 0.7334094 0.7448059 -#> [529,] 0.8668125 0.9510885 0.9550453 -#> [530,] 1.3518969 1.3648092 1.3849989 -#> [531,] 0.7892308 0.8003594 0.8356967 -#> [532,] 0.8611332 0.8613864 0.9164915 -#> [533,] 0.6250401 0.6367535 0.6517178 -#> [534,] 0.7000426 0.7177860 0.7378278 -#> [535,] 0.7863389 0.8349013 0.8376880 -#> [536,] 0.9794036 1.0058872 1.0216113 -#> [537,] 0.8364121 0.8611271 0.8800067 -#> [538,] 0.5635180 0.6092181 0.6218384 -#> [539,] 0.8904440 0.8921551 0.8932878 -#> [540,] 0.6639791 0.7167615 0.7429688 -#> [541,] 0.6532183 0.6622352 0.6784005 -#> [542,] 1.3483684 1.3507839 1.3894962 -#> [543,] 0.8085228 0.8488790 0.8680777 -#> [544,] 0.5340517 0.5593715 0.6246004 -#> [545,] 0.5689344 0.6011425 0.6378416 -#> [546,] 0.6056311 0.6089128 0.6109068 -#> [547,] 0.7942207 0.8298927 0.8602639 -#> [548,] 0.9145482 0.9155776 0.9157779 -#> [549,] 0.7833891 0.7929947 0.7977212 -#> [550,] 0.8923763 0.8936716 0.8943902 -#> [551,] 0.6252989 0.6302890 0.6325989 -#> [552,] 0.6213495 0.6715206 0.6738913 -#> [553,] 0.5815733 0.6928156 0.7067708 -#> [554,] 0.7766909 0.7895843 0.8063112 -#> [555,] 0.8607480 0.8663636 0.8850935 -#> [556,] 0.6819663 0.7233252 0.7428187 -#> [557,] 0.8135751 0.8167236 0.8241794 -#> [558,] 2.3432253 2.3690277 2.3950798 -#> [559,] 0.7093489 0.7243632 0.7538692 -#> [560,] 0.6790146 0.6813491 0.6987252 -#> [561,] 0.9278684 0.9670014 0.9923017 -#> [562,] 0.6440527 0.6939634 0.6946025 -#> [563,] 0.8178424 0.8214247 0.8249273 -#> [564,] 0.5854172 0.6030661 0.6703721 -#> [565,] 2.2018303 2.2170551 2.2715903 -#> [566,] 0.6740985 0.6961154 0.7107108 -#> [567,] 0.5288156 0.6289047 0.6432439 -#> [568,] 0.6086948 0.6167249 0.6182271 -#> [569,] 0.8899425 0.8907170 0.9248265 -#> [570,] 0.8781574 0.9243470 0.9436126 -#> [571,] 0.6611107 0.6947258 0.7779487 -#> [572,] 0.8544198 0.8607628 0.9113352 -#> [573,] 0.6818945 0.6944958 0.6973268 -#> [574,] 0.6450755 0.7042088 0.7045850 -#> [575,] 0.8500746 0.9282655 1.0189408 -#> [576,] 0.7818339 0.7826114 0.8231869 -#> [577,] 1.2586485 1.2649063 1.4049734 -#> [578,] 0.6757237 0.6791727 0.7117059 -#> [579,] 0.6788451 0.6853032 0.7137189 -#> [580,] 0.6333447 0.6695882 0.6853860 -#> [581,] 0.6198851 0.6315937 0.6712264 -#> [582,] 0.5481736 0.5764813 0.6090256 -#> [583,] 0.6900380 0.6917397 0.7118564 -#> [584,] 0.8031552 0.8159384 0.8325545 -#> [585,] 0.8078439 0.8962927 0.9024955 -#> [586,] 0.7863477 0.8318041 0.8382579 -#> [587,] 0.6263667 0.6662338 0.6693745 -#> [588,] 1.0664086 1.0867883 1.1595318 -#> [589,] 0.7238832 0.7291073 0.7634351 -#> [590,] 1.8472453 1.9284869 1.9455458 -#> [591,] 0.6030661 0.6150027 0.6178535 -#> [592,] 0.7067891 0.7202563 0.7385543 -#> [593,] 0.5766874 0.6071901 0.6722267 -#> [594,] 0.5037597 0.5140889 0.5278857 -#> [595,] 0.6973421 0.7460797 0.7484519 -#> [596,] 0.8587832 0.8776250 0.8925923 -#> [597,] 0.8990254 0.8998505 0.9043764 -#> [598,] 0.6098153 0.6283844 0.6289047 -#> [599,] 1.1384414 1.1628123 1.1670567 -#> [600,] 0.7396622 0.7411347 0.7429837 -#> [601,] 0.6981834 0.7385372 0.7571183 -#> [602,] 0.5697776 0.5925780 0.6079173 -#> [603,] 1.0356459 1.0574077 1.0919283 -#> [604,] 1.1836077 1.2198348 1.2271238 -#> [605,] 0.6046276 0.6299998 0.6461569 -#> [606,] 0.5649939 0.5740247 0.5826055 -#> [607,] 1.1519656 1.1540375 1.1572590 -#> [608,] 0.8675523 0.8853132 0.9111472 -#> [609,] 1.6049527 1.6168954 1.6674373 -#> [610,] 0.9049409 0.9094752 1.0089479 -#> [611,] 0.5936046 0.6057138 0.6595870 -#> [612,] 0.9681270 0.9843482 1.0041864 -#> [613,] 1.2830695 1.3338333 1.3386482 -#> [614,] 0.7772791 0.8072681 0.8344979 -#> [615,] 0.7472657 0.7642566 0.7715074 -#> [616,] 0.8531185 0.8587832 0.8961581 -#> [617,] 0.7300108 0.7336099 0.7580219 -#> [618,] 0.6519833 0.7036290 0.7411941 -#> [619,] 0.9758230 1.0551752 1.0664273 -#> [620,] 0.8801867 0.8923898 0.9039039 -#> [621,] 0.5862132 0.6693059 0.6782872 -#> [622,] 0.6219328 0.6248315 0.6261556 -#> [623,] 0.7478602 0.7580601 0.7706583 -#> [624,] 0.5698945 0.6299090 0.6403390 -#> [625,] 1.0167930 1.0453051 1.1333771 -#> [626,] 0.7392944 0.7548943 0.7563227 -#> [627,] 0.6061947 0.6288226 0.6932874 -#> [628,] 0.5016559 0.5094419 0.5727759 -#> [629,] 1.0443008 1.0609972 1.0629319 -#> [630,] 0.7998817 0.8195488 0.8678657 -#> [631,] 0.5547715 0.6030755 0.6154857 -#> [632,] 0.8144859 0.8281860 0.8697099 -#> [633,] 1.0451144 1.0542669 1.1114114 -#> [634,] 0.6419922 0.6432989 0.6447714 -#> [635,] 0.6742909 0.7286320 0.7541595 -#> [636,] 0.9607071 0.9640626 0.9819342 -#> [637,] 0.9198462 0.9676332 0.9754811 -#> [638,] 1.1200604 1.2439004 1.2668609 -#> [639,] 1.1293918 1.1544637 1.1925736 -#> [640,] 0.8213356 0.8387448 0.8433437 -#> [641,] 0.9677572 0.9888431 1.0394407 -#> [642,] 1.0378200 1.0572936 1.0864902 -#> [643,] 0.5496676 0.5826584 0.5893277 -#> [644,] 0.8202058 0.8503474 0.8628839 -#> [645,] 0.6876549 0.6992767 0.7008368 -#> [646,] 1.0006761 1.0891220 1.1000991 -#> [647,] 0.7692970 0.8176418 0.8722259 -#> [648,] 0.9001376 0.9306692 0.9344276 -#> [649,] 0.4759897 0.5073673 0.5773453 -#> [650,] 1.9382943 1.9416419 2.0260886 -#> [651,] 1.1290313 1.2173869 1.2181288 -#> [652,] 0.6658469 0.6770162 0.6778106 -#> [653,] 0.7356093 0.7722727 0.7758287 -#> [654,] 1.0047432 1.0131991 1.0317723 -#> [655,] 0.5662782 0.5710962 0.5811230 -#> [656,] 0.6184569 0.6220324 0.6290472 -#> [657,] 0.7677005 0.7693379 0.7703628 -#> [658,] 0.5899925 0.6245978 0.6298449 -#> [659,] 0.7469655 0.7567931 0.7701006 -#> [660,] 0.5644498 0.5773875 0.6264687 -#> [661,] 0.5910982 0.6014117 0.6069905 -#> [662,] 0.8325475 0.8360327 0.8718518 -#> [663,] 0.5199536 0.5201948 0.5253829 -#> [664,] 2.0411730 2.0795131 2.1723752 -#> [665,] 0.6398905 0.7198262 0.7426566 -#> [666,] 1.6206125 1.6538794 1.6952439 -#> [667,] 0.7155574 0.7265504 0.7561248 -#> [668,] 0.6236368 0.6674390 0.7730710 -#> [669,] 0.6593693 0.6599681 0.7081862 -#> [670,] 0.9392153 0.9818740 0.9936594 -#> [671,] 0.8417960 0.8581661 0.9167841 -#> [672,] 0.8022114 0.8045830 0.8129684 -#> [673,] 1.0238475 1.0297726 1.0514701 -#> [674,] 0.8379912 0.8492909 0.8665540 -#> [675,] 0.6764425 0.7128808 0.7414976 -#> [676,] 0.9237796 0.9572670 0.9769117 -#> [677,] 0.6259317 0.6287526 0.6306655 -#> [678,] 0.7745990 0.7968260 0.8289013 -#> [679,] 1.1319135 1.1391750 1.1587812 -#> [680,] 0.7995431 0.8797833 0.9001406 -#> [681,] 0.5658163 0.5663868 0.6085266 -#> [682,] 1.1814759 1.2122146 1.2527892 -#> [683,] 0.9574515 0.9917246 1.0476483 -#> [684,] 0.8202589 0.8405467 0.8482936 -#> [685,] 1.0408116 1.0770330 1.0790551 -#> [686,] 1.8179036 1.8340617 1.9116460 -#> [687,] 0.8240114 0.8346796 0.8540282 -#> [688,] 0.7111981 0.7168102 0.7506698 -#> [689,] 0.7062740 0.7083143 0.7332891 -#> [690,] 0.7589517 0.7814489 0.8743718 -#> [691,] 0.7887940 0.8068922 0.8298635 -#> [692,] 0.7042882 0.7073357 0.7086554 -#> [693,] 0.8677932 0.9081009 0.9096423 -#> [694,] 0.9426249 0.9456269 0.9996745 -#> [695,] 0.7261435 0.7369679 0.7431326 -#> [696,] 0.5259880 0.5325244 0.5335413 -#> [697,] 0.9357843 1.0164852 1.1235415 -#> [698,] 0.6602462 0.6971881 0.7130196 -#> [699,] 0.7407388 0.7441478 0.7587615 -#> [700,] 0.5805887 0.6411091 0.6493704 -#> [701,] 0.8441528 0.8526117 0.8743718 -#> [702,] 0.7091045 0.7115284 0.7332891 -#> [703,] 1.4329723 1.4998115 1.5321308 -#> [704,] 0.8716388 0.9324892 0.9406988 -#> [705,] 0.9435541 1.0315119 1.0640330 -#> [706,] 0.6753137 0.7172770 0.7210186 -#> [707,] 0.6354370 0.6485246 0.6687828 -#> [708,] 0.7013456 0.7131056 0.7347693 -#> [709,] 0.7484519 0.7737699 0.7741449 -#> [710,] 0.8423412 0.8555198 0.8749579 -#> [711,] 1.1663112 1.2098689 1.2143511 -#> [712,] 0.9481154 0.9482776 0.9492661 -#> [713,] 0.5139554 0.5386177 0.5543984 -#> [714,] 0.6937074 0.7245582 0.7468176 -#> [715,] 1.4709076 1.4824762 1.4912133 -#> [716,] 0.8951693 0.8961581 0.9878208 -#> [717,] 0.5740756 0.5972898 0.6142427 -#> [718,] 0.9250145 0.9261443 0.9285668 -#> [719,] 0.5785084 0.6069989 0.6116374 -#> [720,] 0.9843897 1.0550667 1.0561660 -#> [721,] 0.5411180 0.5608529 0.5653868 -#> [722,] 0.5295162 0.5340517 0.5555209 -#> [723,] 0.9688074 1.0018221 1.0154645 -#> [724,] 0.6862316 0.7080334 0.7092927 -#> [725,] 0.6652081 0.6720352 0.6917995 -#> [726,] 0.6144660 0.6167648 0.6466886 -#> [727,] 0.8975746 0.9026949 0.9269203 -#> [728,] 0.7297279 0.7441841 0.7579067 -#> [729,] 0.9753392 0.9793578 0.9902628 -#> [730,] 0.6466303 0.6596934 0.6713759 -#> [731,] 0.7510750 0.8031048 0.8497506 -#> [732,] 0.7844187 0.7910788 0.8033215 -#> [733,] 1.1436977 1.2593077 1.2944929 -#> [734,] 1.0233959 1.0988194 1.1192609 -#> [735,] 0.6888786 0.7709872 0.8026905 -#> [736,] 1.1020634 1.1103051 1.1116794 -#> [737,] 0.4875835 0.5037597 0.5218255 -#> [738,] 1.2087302 1.2367852 1.2630820 -#> [739,] 2.1226628 2.1489737 2.1847682 -#> [740,] 0.9699873 0.9936126 0.9981753 -#> [741,] 0.5541666 0.5674918 0.5693480 -#> [742,] 0.5708686 0.5748597 0.5788055 -#> [743,] 0.8565868 0.8744701 0.8796845 -#> [744,] 0.9290540 0.9548992 0.9762425 -#> [745,] 0.4644079 0.5262045 0.5492644 -#> [746,] 1.0158253 1.0370421 1.0656568 -#> [747,] 0.7036279 0.7143330 0.7151035 -#> [748,] 1.2295782 1.2701114 1.3213964 -#> [749,] 0.5693054 0.6197711 0.6784878 -#> [750,] 0.7510750 0.7788458 0.7880611 -#> [751,] 0.7907865 0.8110518 0.8268999 -#> [752,] 1.6528810 1.6852090 1.7012686 -#> [753,] 0.8275630 0.8613339 0.8736702 -#> [754,] 0.6022206 0.6873235 0.6881103 -#> [755,] 0.7941230 0.7957952 0.7978296 -#> [756,] 0.7081964 0.7167148 0.7362297 -#> [757,] 1.2384498 1.2410470 1.3512977 -#> [758,] 0.6404495 0.7010819 0.7160734 -#> [759,] 0.5552198 0.6299090 0.6299998 -#> [760,] 0.8529619 0.8699727 0.8723819 -#> [761,] 1.1095331 1.1378732 1.1933431 -#> [762,] 0.6133128 0.6134790 0.6152627 -#> [763,] 0.5848662 0.5881791 0.5938147 -#> [764,] 1.2015164 1.2033767 1.2357261 -#> [765,] 1.3644598 1.3664599 1.3791541 -#> [766,] 0.9682310 0.9753498 0.9834251 -#> [767,] 0.7501720 0.7553391 0.7784621 -#> [768,] 0.6176954 0.6553638 0.7037984 -#> [769,] 0.9799616 0.9971581 1.0188878 -#> [770,] 0.6067608 0.6166483 0.6215483 -#> [771,] 0.6988536 0.7143979 0.7691937 -#> [772,] 0.6707290 0.7406443 0.7925577 -#> [773,] 0.6920620 0.7192525 0.7378939 -#> [774,] 0.8061181 0.8335395 0.8519922 -#> [775,] 1.2012728 1.2145928 1.2264780 -#> [776,] 0.6036117 0.6045314 0.6074887 -#> [777,] 0.7134724 0.7330610 0.7336099 -#> [778,] 0.7357161 0.7659436 0.7765781 -#> [779,] 1.9690753 1.9715492 1.9898267 -#> [780,] 1.0283219 1.0732000 1.1302308 -#> [781,] 1.2927882 1.2994474 1.3193296 -#> [782,] 0.8892492 0.9349566 0.9609719 -#> [783,] 0.7018778 0.7117218 0.7143797 -#> [784,] 0.9308265 0.9329245 0.9864876 -#> [785,] 0.6700494 0.6884271 0.7247373 -#> [786,] 0.6910984 0.7043720 0.7224982 -#> [787,] 0.8368802 0.8438580 0.8626579 -#> [788,] 0.5811230 0.6050018 0.6342684 -#> [789,] 0.5341757 0.5555209 0.5730101 -#> [790,] 0.8837296 0.8878474 0.9150893 -#> [791,] 0.7885298 0.7951147 0.7955146 -#> [792,] 0.5522140 0.5649189 0.5940605 -#> [793,] 1.5337373 1.5347884 1.5472385 -#> [794,] 0.8744181 0.8762210 0.8950791 -#> [795,] 0.5354529 0.6159843 0.6497799 -#> [796,] 0.5435039 0.5631950 0.5761898 -#> [797,] 0.5904797 0.6074887 0.6143992 -#> [798,] 0.6754314 0.7094825 0.7628576 -#> [799,] 0.5793089 0.5914720 0.6272817 -#> [800,] 0.5851032 0.6079851 0.6545558 -#> [801,] 0.5455918 0.5498394 0.5578871 -#> [802,] 0.5969488 0.6414602 0.6594972 -#> [803,] 0.7023748 0.7029518 0.7169213 -#> [804,] 0.8185188 0.8205612 0.8326591 -#> [805,] 1.1750752 1.1903984 1.1933431 -#> [806,] 1.0302284 1.0537445 1.0748683 -#> [807,] 1.6709016 1.7005440 1.7394570 -#> [808,] 0.8407370 0.8500746 0.8637478 -#> [809,] 0.7008518 0.7150811 0.7800171 -#> [810,] 0.5433768 0.5853286 0.5858126 -#> [811,] 0.8785766 0.8927469 0.9212768 -#> [812,] 0.7942367 0.8017567 0.8151381 -#> [813,] 0.7228474 0.7321719 0.7385597 -#> [814,] 0.9128304 0.9264948 0.9299598 -#> [815,] 1.5803471 1.5953455 1.6277466 -#> [816,] 0.6993452 0.7242203 0.7344657 -#> [817,] 1.6124134 1.6636003 1.6760532 -#> [818,] 0.8376880 0.8426910 0.8453352 -#> [819,] 0.6316854 0.6320838 0.6329795 -#> [820,] 1.1178551 1.1519532 1.1632556 -#> [821,] 0.5833116 0.6029864 0.6135050 -#> [822,] 0.9326344 0.9529245 0.9951438 -#> [823,] 0.6772256 0.6932874 0.7187178 -#> [824,] 0.5820719 0.5912199 0.5958864 -#> [825,] 0.6578455 0.6685950 0.6729128 -#> [826,] 0.5650308 0.5788591 0.5883047 -#> [827,] 0.8792121 0.8806975 0.8816077 -#> [828,] 1.0188981 1.0945970 1.1559622 -#> [829,] 0.6354521 0.6415935 0.6464179 -#> [830,] 0.9306057 0.9731270 0.9804268 -#> [831,] 0.8952237 0.9297569 0.9763403 -#> [832,] 1.0843053 1.0897857 1.1173875 -#> [833,] 0.5930187 0.5939369 0.6069079 -#> [834,] 0.7465824 0.7697630 0.7820416 -#> [835,] 0.4847935 0.4983993 0.5371986 -#> [836,] 0.5418103 0.5454948 0.5471028 -#> [837,] 0.8334105 0.8618845 0.9283201 -#> [838,] 0.9496958 1.0138397 1.0735338 -#> [839,] 0.9777460 1.0175556 1.0292585 -#> [840,] 0.7527413 0.7735569 0.7822611 -#> [841,] 0.9744743 1.0102749 1.0125469 -#> [842,] 0.5128846 0.5187470 0.5232374 -#> [843,] 1.0283224 1.0680810 1.0750131 -#> [844,] 0.9416318 0.9482930 0.9629695 -#> [845,] 1.0277905 1.0347361 1.1459346 -#> [846,] 0.7532099 0.8083029 0.8129840 -#> [847,] 0.6244790 0.6370497 0.6643578 -#> [848,] 0.7263274 0.7298699 0.7523639 -#> [849,] 0.6670763 0.6757237 0.7488167 -#> [850,] 0.7400483 0.7407983 0.7678197 -#> [851,] 0.8328801 0.8593535 0.8706834 -#> [852,] 0.8042118 0.8181203 0.8300385 -#> [853,] 1.3269187 1.3651020 1.3995892 -#> [854,] 1.4041430 1.4212342 1.4334688 -#> [855,] 0.8489212 0.8509315 0.9226693 -#> [856,] 0.6371847 0.6373957 0.6489260 -#> [857,] 0.9479864 1.0184980 1.0239902 -#> [858,] 0.7565456 0.7837856 0.8034945 -#> [859,] 0.7948078 0.7953206 0.8008431 -#> [860,] 0.6458494 0.6610452 0.6853779 -#> [861,] 0.9921289 1.0741951 1.0819476 -#> [862,] 0.7420654 0.7459836 0.7656647 -#> [863,] 0.9134277 0.9463880 0.9774547 -#> [864,] 0.8385498 0.8457286 0.8462907 -#> [865,] 0.6143098 0.6192943 0.6269716 -#> [866,] 0.5851182 0.5949997 0.6690769 -#> [867,] 0.6249000 0.6323681 0.6533374 -#> [868,] 0.6744915 0.6873026 0.6948454 -#> [869,] 0.7422157 0.7559815 0.7563227 -#> [870,] 0.6857204 0.7060388 0.7225791 -#> [871,] 0.6754866 0.7015258 0.7165442 -#> [872,] 0.6624938 0.6659730 0.6665198 -#> [873,] 0.7918862 0.8899351 0.9106117 -#> [874,] 0.9897825 0.9967091 0.9998667 -#> [875,] 0.6674390 0.6709559 0.6722478 -#> [876,] 0.5127744 0.5200857 0.5278857 -#> [877,] 0.7248677 0.7362571 0.7574972 -#> [878,] 0.8650737 0.8673128 0.8876872 -#> [879,] 0.8082818 0.8188058 0.8420042 -#> [880,] 0.5238938 0.5245936 0.6314431 -#> [881,] 0.5546065 0.5552367 0.5658163 -#> [882,] 0.6593763 0.6692368 0.6819575 -#> [883,] 1.1362660 1.1670605 1.1814908 -#> [884,] 0.6274278 0.6516153 0.6589275 -#> [885,] 0.9445222 0.9758457 0.9935428 -#> [886,] 1.0196759 1.0782516 1.1500725 -#> [887,] 0.5611785 0.5721490 0.5730101 -#> [888,] 1.4525418 1.5335511 1.5650556 -#> [889,] 0.7290624 0.7626622 0.7701266 -#> [890,] 0.6575866 0.6631294 0.6709890 -#> [891,] 0.6369631 0.6478044 0.6552745 -#> [892,] 0.5949277 0.6268538 0.6411613 -#> [893,] 0.8987008 0.8996465 0.9122463 -#> [894,] 0.6536885 0.7169877 0.7448199 -#> [895,] 0.8754574 0.8961878 0.9137037 -#> [896,] 0.8990254 0.9600782 0.9845505 -#> [897,] 0.8324243 0.8734084 0.8839719 -#> [898,] 1.6232812 1.6329324 1.6843737 -#> [899,] 0.5307726 0.5541666 0.5542382 -#> [900,] 0.9233367 0.9341166 0.9619989 -#> [901,] 1.1674370 1.2091835 1.2888790 -#> [902,] 0.5935215 0.6133391 0.6145858 -#> [903,] 1.1552582 1.1596754 1.2374547 -#> [904,] 0.6360963 0.6439245 0.6468046 -#> [905,] 1.1296944 1.1306698 1.1445650 -#> [906,] 0.4926394 0.5200857 0.5569929 -#> [907,] 0.7094116 0.7272964 0.7351366 -#> [908,] 0.5967956 0.5984226 0.6065631 -#> [909,] 0.7092927 0.7741783 0.8215954 -#> [910,] 0.5743078 0.5881791 0.6133391 -#> [911,] 0.7960024 0.8147715 0.8201958 -#> [912,] 0.6367922 0.6648602 0.7074269 -#> [913,] 1.1330448 1.1393728 1.2083914 -#> [914,] 1.1261638 1.1363303 1.1381319 -#> [915,] 0.7265642 0.7372435 0.7600814 -#> [916,] 0.5991453 0.6288226 0.6909970 -#> [917,] 0.5936259 0.6167773 0.6328689 -#> [918,] 1.0889671 1.1266647 1.1340280 -#> [919,] 0.8134718 0.8773818 0.8794205 -#> [920,] 0.6634449 0.6847756 0.7128808 -#> [921,] 0.6411091 0.6529900 0.6562525 -#> [922,] 0.9428644 0.9479833 0.9766401 -#> [923,] 0.6546695 0.7623191 0.7831897 -#> [924,] 0.6176913 0.6225913 0.6360304 -#> [925,] 0.8952237 0.8954024 0.9522548 -#> [926,] 0.5354529 0.5418754 0.5586692 -#> [927,] 0.5889366 0.6452530 0.6895276 -#> [928,] 1.2360886 1.2645463 1.4156511 -#> [929,] 0.7045850 0.7102461 0.7254271 -#> [930,] 0.5557009 0.6323681 0.6379043 -#> [931,] 0.6628541 0.7176973 0.7332718 -#> [932,] 0.7830243 0.7958926 0.8087172 -#> [933,] 0.7823336 0.7935952 0.8060961 -#> [934,] 1.2170469 1.3016234 1.3293415 -#> [935,] 0.5882024 0.6785070 0.6851937 -#> [936,] 1.3042448 1.3067202 1.3290534 -#> [937,] 0.5923980 0.7701006 0.7797618 -#> [938,] 0.6801128 0.6920584 0.7143286 -#> [939,] 0.8133878 0.8762922 0.8792833 -#> [940,] 1.4887429 1.5084606 1.5273402 -#> [941,] 0.8393568 0.9422893 0.9868203 -#> [942,] 0.8230795 0.8939918 0.9083788 -#> [943,] 0.4729722 0.4760790 0.5015056 -#> [944,] 0.7457229 0.7612536 0.8136419 -#> [945,] 0.6536390 0.6611107 0.6819575 -#> [946,] 1.2655733 1.3786466 1.4154325 -#> [947,] 0.8477603 0.9001406 0.9536993 -#> [948,] 0.9881205 1.0120315 1.0276484 -#> [949,] 0.7025475 0.7284700 0.8108278 -#> [950,] 0.6722478 0.6789018 0.6876039 -#> [951,] 0.6565390 0.6916976 0.7045401 -#> [952,] 0.7141773 0.7169669 0.7371411 -#> [953,] 0.6559141 0.6639398 0.6669422 -#> [954,] 0.6337316 0.6354521 0.6418716 -#> [955,] 0.6696159 0.6698246 0.6916917 -#> [956,] 0.6075006 0.6443834 0.6680742 -#> [957,] 0.6603293 0.6623050 0.6940481 -#> [958,] 0.6436329 0.6482295 0.6713180 -#> [959,] 0.6036117 0.6117387 0.6139310 -#> [960,] 0.8303042 0.8761815 0.9232052 -#> [961,] 0.5678705 0.5766561 0.5903886 -#> [962,] 1.1215965 1.1605183 1.2426790 -#> [963,] 0.7745578 0.7843597 0.8036236 -#> [964,] 0.7111981 0.7140241 0.7803132 -#> [965,] 1.3762091 1.4262877 1.4529862 -#> [966,] 0.9081192 0.9342811 0.9352012 -#> [967,] 0.7595652 0.7708732 0.7737753 -#> [968,] 1.4909861 1.5724724 1.5742275 -#> [969,] 0.8061181 0.8275757 0.8626899 -#> [970,] 0.9417043 0.9428644 0.9652038 -#> [971,] 0.7672127 0.7708118 0.8045342 -#> [972,] 0.8512705 0.8660131 0.8675326 -#> [973,] 0.6890310 0.7102459 0.7356957 -#> [974,] 0.8293702 0.8737719 0.8771729 -#> [975,] 0.9366361 0.9594017 1.0164096 -#> [976,] 0.6135617 0.6143098 0.6145980 -#> [977,] 0.6100644 0.6560972 0.6944847 -#> [978,] 0.8658190 0.9287271 0.9562004 -#> [979,] 0.8026439 0.8061461 0.8523100 -#> [980,] 0.7544792 0.7700905 0.7944546 -#> [981,] 0.7798431 0.7893466 0.7930837 -#> [982,] 0.7172235 0.7433508 0.7481295 -#> [983,] 0.8697099 0.8845862 0.8919917 -#> [984,] 0.6171722 0.6253003 0.6681443 -#> [985,] 1.0713757 1.0957445 1.1245306 -#> [986,] 0.8691765 0.8912487 0.9218647 -#> [987,] 1.0751697 1.0866799 1.0975572 -#> [988,] 1.1608268 1.1729700 1.1819343 -#> [989,] 0.7698354 0.7771256 0.8079784 -#> [990,] 0.6881899 0.7428363 0.7918862 -#> [991,] 0.5465308 0.5848662 0.6901507 -#> [992,] 0.8033928 0.8417451 0.8513532 -#> [993,] 1.4140997 1.4184929 1.4390561 -#> [994,] 0.9012890 0.9167841 0.9214295 -#> [995,] 0.8027854 0.8349415 0.8488035 -#> [996,] 0.7708018 0.7850795 0.8009255 -#> [997,] 1.5251204 1.5339383 1.5403993 -#> [998,] 1.3328246 1.3609737 1.3649186 -#> [999,] 0.8731058 0.8916354 0.9132031 -#> [1000,] 0.8604964 0.8725707 0.9429566 +#> [1,] 1.1277723 1.1816461 1.1990002 +#> [2,] 1.2216679 1.2352019 1.2608456 +#> [3,] 0.6189972 0.6357971 0.6409345 +#> [4,] 0.6563752 0.6593882 0.6812084 +#> [5,] 0.6451487 0.6575858 0.6584598 +#> [6,] 0.6787011 0.7148674 0.7266524 +#> [7,] 0.6318225 0.6663029 0.6779481 +#> [8,] 0.7533940 0.7807431 0.7992978 +#> [9,] 1.0873969 1.0961940 1.1964236 +#> [10,] 0.5822407 0.5883231 0.5917679 +#> [11,] 0.6083513 0.6091392 0.6192702 +#> [12,] 0.7107465 0.7228639 0.7490380 +#> [13,] 0.7498239 0.7559947 0.7591031 +#> [14,] 0.8340085 0.8789927 0.8985272 +#> [15,] 0.8731405 0.8742264 0.9712348 +#> [16,] 1.1797889 1.1845552 1.2039234 +#> [17,] 1.0033135 1.0354425 1.0443826 +#> [18,] 0.9330399 0.9576537 0.9757561 +#> [19,] 0.8899695 0.9015966 0.9232722 +#> [20,] 0.5672174 0.5912759 0.6029007 +#> [21,] 0.6501106 0.6602688 0.6823808 +#> [22,] 0.7445577 0.7545314 0.7877731 +#> [23,] 0.7894505 0.8406640 0.8509438 +#> [24,] 0.9551027 0.9654747 1.0030288 +#> [25,] 1.0730757 1.0733707 1.0883246 +#> [26,] 0.7280532 0.7556115 0.7615608 +#> [27,] 0.7040471 0.7180793 0.7558149 +#> [28,] 1.1124655 1.1726914 1.2164293 +#> [29,] 0.7843067 0.7848351 0.8214750 +#> [30,] 0.5764293 0.6088673 0.6187806 +#> [31,] 1.1153360 1.1448984 1.1843941 +#> [32,] 0.7166056 0.7172308 0.7263161 +#> [33,] 0.7250852 0.7477290 0.7512148 +#> [34,] 1.3742790 1.4484635 1.4505247 +#> [35,] 1.2946210 1.2946745 1.3529488 +#> [36,] 0.9501224 0.9525178 0.9781398 +#> [37,] 0.5908949 0.6201243 0.6238170 +#> [38,] 0.9860467 1.0113623 1.0792367 +#> [39,] 1.3823259 1.4800667 1.4810435 +#> [40,] 1.0405158 1.0413266 1.0484415 +#> [41,] 0.4522604 0.4552867 0.4983990 +#> [42,] 1.0056548 1.0083335 1.0214761 +#> [43,] 0.5572177 0.5785664 0.6033729 +#> [44,] 0.5246025 0.5514685 0.5659403 +#> [45,] 0.5520987 0.5618288 0.5626486 +#> [46,] 0.9168416 0.9505614 0.9540991 +#> [47,] 0.9491927 0.9748667 1.0007042 +#> [48,] 0.7468701 0.7627787 0.7740428 +#> [49,] 0.8202840 0.8203177 0.8369207 +#> [50,] 0.7371032 0.7728698 0.7842856 +#> [51,] 0.8279323 0.8695652 0.8741601 +#> [52,] 0.6908772 0.7031286 0.7333217 +#> [53,] 0.6937080 0.7369896 0.7374129 +#> [54,] 0.5789364 0.5883231 0.5903267 +#> [55,] 0.8279323 0.8611921 0.8671132 +#> [56,] 1.2105582 1.3609712 1.4209254 +#> [57,] 0.6228348 0.6295301 0.6422188 +#> [58,] 0.6590690 0.6728996 0.6740843 +#> [59,] 0.6575858 0.6959634 0.7385297 +#> [60,] 0.9164001 0.9352856 0.9474459 +#> [61,] 0.6986781 0.7031266 0.7040471 +#> [62,] 0.5624673 0.5656930 0.5680078 +#> [63,] 0.6356963 0.6507316 0.6567545 +#> [64,] 0.6932837 0.7119049 0.7338189 +#> [65,] 0.6698017 0.6710056 0.6977133 +#> [66,] 1.0568114 1.0584024 1.0628807 +#> [67,] 0.4925018 0.5051723 0.5192192 +#> [68,] 0.8972152 0.9264264 1.0154784 +#> [69,] 0.7329504 0.7436061 0.7528723 +#> [70,] 1.3642570 1.4449321 1.4667406 +#> [71,] 0.8690166 0.9903226 1.0250856 +#> [72,] 0.5239980 0.5479711 0.5800733 +#> [73,] 0.5115099 0.5237082 0.5635509 +#> [74,] 0.9470885 0.9779822 1.0129587 +#> [75,] 0.5990171 0.6098017 0.6144892 +#> [76,] 0.5953002 0.6128149 0.6135434 +#> [77,] 0.5607080 0.6527787 0.6877265 +#> [78,] 0.5357776 0.5617833 0.5903267 +#> [79,] 0.7854694 0.8062140 0.8121136 +#> [80,] 0.9891183 0.9941969 0.9993140 +#> [81,] 0.5521847 0.5686514 0.5835483 +#> [82,] 1.2409497 1.2821540 1.3338516 +#> [83,] 1.3557836 1.3559887 1.3816546 +#> [84,] 1.1189773 1.1394716 1.2007114 +#> [85,] 0.6464739 0.6472974 0.6520667 +#> [86,] 0.8236488 0.8243628 0.8298920 +#> [87,] 0.7494334 0.7898359 0.8762251 +#> [88,] 0.7067036 0.7517859 0.7558175 +#> [89,] 0.6324472 0.6384841 0.6393787 +#> [90,] 0.5765781 0.6253036 0.6467292 +#> [91,] 0.6217369 0.6738939 0.6883190 +#> [92,] 0.9049822 0.9530349 1.0050902 +#> [93,] 0.5503597 0.5701566 0.5904144 +#> [94,] 1.1755510 1.2042861 1.2316526 +#> [95,] 0.9724045 0.9812174 0.9884258 +#> [96,] 0.9592701 0.9728378 1.0266564 +#> [97,] 0.5480974 0.5812286 0.6118587 +#> [98,] 0.5246272 0.5265085 0.5475677 +#> [99,] 0.8461130 0.8477330 0.8717503 +#> [100,] 0.7628803 0.7824819 0.7993370 +#> [101,] 1.7962282 1.8124344 1.8204522 +#> [102,] 0.4446164 0.4626767 0.4765493 +#> [103,] 1.6171545 1.6189861 1.6381932 +#> [104,] 0.7240607 0.7821695 0.8235358 +#> [105,] 0.5712901 0.5985136 0.6067016 +#> [106,] 0.8766191 0.8782562 0.8872883 +#> [107,] 1.3221504 1.3258106 1.3595579 +#> [108,] 1.2497242 1.2797769 1.2847999 +#> [109,] 0.6307679 0.6548610 0.6825140 +#> [110,] 0.5077163 0.5152366 0.5201796 +#> [111,] 0.5897513 0.6775438 0.7249468 +#> [112,] 0.8716588 0.9054262 0.9855289 +#> [113,] 0.8448758 0.8714976 0.9142557 +#> [114,] 0.7288252 0.8052105 0.8151615 +#> [115,] 0.6718406 0.6722277 0.6948328 +#> [116,] 0.8939690 0.9073273 0.9459113 +#> [117,] 1.1866277 1.2049009 1.2131600 +#> [118,] 0.9652541 0.9756351 0.9942206 +#> [119,] 0.5998649 0.6091943 0.6123165 +#> [120,] 0.6872129 0.7097586 0.7301151 +#> [121,] 0.7390389 0.7604702 0.7630935 +#> [122,] 0.4938862 0.4983489 0.5466885 +#> [123,] 0.9209192 0.9381286 0.9578028 +#> [124,] 1.2576783 1.3457942 1.3543061 +#> [125,] 0.8000203 0.8167809 0.8301323 +#> [126,] 0.8340372 0.8514435 0.8515045 +#> [127,] 0.5103176 0.5475948 0.5882427 +#> [128,] 0.5827694 0.5828899 0.5951991 +#> [129,] 0.6340862 0.6482273 0.6507316 +#> [130,] 0.7738277 0.8869694 0.8988091 +#> [131,] 0.6822141 0.6939493 0.6992247 +#> [132,] 0.6574931 0.7422702 0.7461058 +#> [133,] 0.9973896 1.0184821 1.0237132 +#> [134,] 1.1486590 1.1613028 1.1837634 +#> [135,] 0.6238170 0.6395035 0.6520195 +#> [136,] 1.0973044 1.1709569 1.2332039 +#> [137,] 0.6108532 0.6429611 0.6630001 +#> [138,] 0.5521847 0.5843494 0.6012981 +#> [139,] 0.7308439 0.7434359 0.7581942 +#> [140,] 1.3928817 1.4068608 1.4439310 +#> [141,] 0.6609668 0.6850597 0.7299244 +#> [142,] 0.5915916 0.6050565 0.6077914 +#> [143,] 0.5823034 0.5947904 0.6019534 +#> [144,] 0.5553071 0.5645280 0.6446181 +#> [145,] 0.6940500 0.7282091 0.7310340 +#> [146,] 0.5495635 0.5514685 0.5826591 +#> [147,] 0.7611104 0.8040045 0.8043343 +#> [148,] 0.6495392 0.6776919 0.6854624 +#> [149,] 0.7480650 0.8191044 0.8823637 +#> [150,] 0.5411869 0.5502896 0.5583409 +#> [151,] 0.7180998 0.7336379 0.7341086 +#> [152,] 1.1912636 1.2095629 1.2352239 +#> [153,] 0.5758092 0.6116671 0.6165203 +#> [154,] 0.7524017 0.7819264 0.7953267 +#> [155,] 0.6733962 0.6810368 0.6899465 +#> [156,] 0.6521463 0.6840823 0.7100448 +#> [157,] 1.3620943 1.3688559 1.3708892 +#> [158,] 0.5600066 0.6737385 0.6776919 +#> [159,] 0.5901161 0.5951493 0.6357927 +#> [160,] 0.8658120 0.8981545 0.9256894 +#> [161,] 0.7964822 0.7980622 0.8108124 +#> [162,] 0.5224324 0.5541192 0.5592968 +#> [163,] 1.1540350 1.1687959 1.1734590 +#> [164,] 1.0675792 1.1022426 1.1054776 +#> [165,] 1.1229310 1.1440999 1.1454136 +#> [166,] 0.8308600 0.8457055 0.8557414 +#> [167,] 0.5969554 0.5991155 0.6028723 +#> [168,] 1.3838868 1.3842514 1.3956961 +#> [169,] 0.8712192 0.9154238 0.9272356 +#> [170,] 0.7390389 0.7535997 0.7970432 +#> [171,] 0.6023530 0.6238119 0.6355553 +#> [172,] 0.7766402 0.7777657 0.7899675 +#> [173,] 0.5313566 0.5754902 0.5769897 +#> [174,] 0.5431463 0.5839871 0.6055436 +#> [175,] 0.9994798 1.0213772 1.0685104 +#> [176,] 0.8997026 0.9091246 0.9232706 +#> [177,] 0.7352546 0.7384616 0.7423913 +#> [178,] 0.6937898 0.6945931 0.7143432 +#> [179,] 1.0741032 1.0937999 1.0980578 +#> [180,] 0.5416264 0.5501798 0.5502562 +#> [181,] 0.9843336 1.0336066 1.0590858 +#> [182,] 0.6091943 0.6363898 0.6373057 +#> [183,] 0.7003897 0.7075189 0.7136140 +#> [184,] 0.9884055 1.0074228 1.0174219 +#> [185,] 0.8916240 0.8993724 0.9282640 +#> [186,] 0.6490195 0.6628685 0.6681492 +#> [187,] 1.2156100 1.2544759 1.2724148 +#> [188,] 0.7837702 0.8218098 0.8225141 +#> [189,] 0.7619641 0.8015365 0.8407601 +#> [190,] 1.0560959 1.1273738 1.1434620 +#> [191,] 0.8261643 0.8521884 0.9099727 +#> [192,] 1.1566105 1.1855532 1.1886661 +#> [193,] 0.9440280 0.9661984 0.9921918 +#> [194,] 0.8645134 0.8670063 0.9061379 +#> [195,] 0.5188817 0.5238636 0.5719127 +#> [196,] 0.8252084 0.8782798 0.8829206 +#> [197,] 0.6393644 0.7216211 0.7395698 +#> [198,] 0.9058586 0.9194339 0.9787950 +#> [199,] 0.9836911 1.0023737 1.0260539 +#> [200,] 0.8919486 0.9284428 0.9431832 +#> [201,] 0.5982558 0.6336967 0.6477662 +#> [202,] 0.8238807 0.8295729 0.8532570 +#> [203,] 0.3975459 0.4535849 0.4674355 +#> [204,] 0.8965174 0.8982999 0.8993036 +#> [205,] 0.5746819 0.5860547 0.5897513 +#> [206,] 0.7902742 0.8046735 0.8447648 +#> [207,] 0.6917758 0.7283993 0.7666928 +#> [208,] 0.5911413 0.5921035 0.5962793 +#> [209,] 0.8112749 0.8424908 0.8805676 +#> [210,] 0.5499103 0.5552143 0.5724341 +#> [211,] 0.7664883 0.7766420 0.8158615 +#> [212,] 0.7401987 0.7662884 0.7797529 +#> [213,] 0.6575542 0.6580014 0.6768431 +#> [214,] 1.2166165 1.2253165 1.2261671 +#> [215,] 0.6186600 0.6671991 0.6732922 +#> [216,] 0.7398366 0.7403294 0.7425097 +#> [217,] 0.5989056 0.6023753 0.6490482 +#> [218,] 0.5521809 0.5585820 0.6238076 +#> [219,] 0.5268149 0.5624969 0.5636207 +#> [220,] 1.1284222 1.1794205 1.1963549 +#> [221,] 0.6613447 0.6957399 0.7197768 +#> [222,] 0.7376104 0.7534260 0.7571064 +#> [223,] 1.1254497 1.1310411 1.1359482 +#> [224,] 0.5717071 0.5998148 0.6373057 +#> [225,] 0.7136713 0.7231501 0.7289041 +#> [226,] 0.6541174 0.7063086 0.7362559 +#> [227,] 0.7185699 0.7271226 0.7680247 +#> [228,] 1.2394236 1.2587553 1.3192218 +#> [229,] 0.9253467 0.9658028 0.9916496 +#> [230,] 0.9420330 0.9539283 0.9658795 +#> [231,] 0.8630664 0.8804020 0.8901090 +#> [232,] 0.4850090 0.5162538 0.5226281 +#> [233,] 0.6355172 0.6559131 0.6857133 +#> [234,] 0.8232688 0.8362491 0.8615428 +#> [235,] 0.5794818 0.5823488 0.5878504 +#> [236,] 0.8986594 0.9156674 0.9355886 +#> [237,] 0.6070168 0.6146959 0.6385568 +#> [238,] 0.7800092 0.7919962 0.8261697 +#> [239,] 0.6446008 0.6517779 0.6528180 +#> [240,] 0.5903636 0.6649502 0.6650598 +#> [241,] 1.5181657 1.5191653 1.6806529 +#> [242,] 1.1709452 1.2108411 1.2212352 +#> [243,] 0.9794785 1.0148312 1.0588020 +#> [244,] 0.8228033 0.9406653 0.9412112 +#> [245,] 0.7355959 0.7427091 0.8169518 +#> [246,] 0.6605395 0.7010432 0.7101375 +#> [247,] 0.5724341 0.5965084 0.6013598 +#> [248,] 0.6581380 0.6864811 0.7007856 +#> [249,] 0.7468705 0.7483195 0.7560462 +#> [250,] 0.4474640 0.4590751 0.4597129 +#> [251,] 0.8388607 0.9121455 0.9395272 +#> [252,] 0.6237672 0.6324957 0.6378056 +#> [253,] 1.1115367 1.1695811 1.2465492 +#> [254,] 0.8507018 0.8815974 0.9280725 +#> [255,] 0.5880765 0.6234843 0.6359189 +#> [256,] 0.7837060 0.7840352 0.8021130 +#> [257,] 0.7432229 0.7474043 0.7781482 +#> [258,] 0.8157246 0.8163470 0.8207238 +#> [259,] 1.0071526 1.0514318 1.0816304 +#> [260,] 0.7723188 0.8073125 0.8125567 +#> [261,] 0.7733659 0.7765029 0.7781799 +#> [262,] 0.8045442 0.8443363 0.8512293 +#> [263,] 0.6691929 0.6805346 0.7250852 +#> [264,] 0.5409081 0.5479479 0.5533832 +#> [265,] 0.6240639 0.6376946 0.6383766 +#> [266,] 0.6140820 0.6224795 0.6236054 +#> [267,] 0.7967967 0.8110177 0.8293811 +#> [268,] 0.5538449 0.5592717 0.5692131 +#> [269,] 1.3894109 1.3983734 1.4073322 +#> [270,] 0.6532931 0.6535509 0.6603980 +#> [271,] 0.6954934 0.8071532 0.8544645 +#> [272,] 0.7997930 0.8001390 0.8008286 +#> [273,] 0.7323858 0.7352067 0.7820764 +#> [274,] 0.5457597 0.5458660 0.5603155 +#> [275,] 0.6593882 0.6684101 0.6983498 +#> [276,] 1.5282590 1.5432321 1.5518024 +#> [277,] 0.7831023 0.8821525 0.8906814 +#> [278,] 0.5610538 0.5769897 0.6043345 +#> [279,] 0.8457055 0.8582436 0.9107608 +#> [280,] 0.5585820 0.5978585 0.6342586 +#> [281,] 0.5753703 0.6876870 0.7038946 +#> [282,] 0.7290462 0.8648736 0.8648975 +#> [283,] 0.6778300 0.6789222 0.7318722 +#> [284,] 0.9411683 0.9855289 1.0050375 +#> [285,] 0.7043195 0.7432437 0.7502960 +#> [286,] 1.3651561 1.3892015 1.3943802 +#> [287,] 1.2771909 1.2880192 1.3741400 +#> [288,] 1.5075022 1.5866471 1.6241959 +#> [289,] 1.1508820 1.2054700 1.2060725 +#> [290,] 0.8510004 0.8512387 0.8942729 +#> [291,] 0.6606663 0.6876140 0.7072640 +#> [292,] 0.6391744 0.6506912 0.6697097 +#> [293,] 0.8054279 0.8070900 0.8415829 +#> [294,] 0.6004037 0.6064343 0.6525837 +#> [295,] 0.5946605 0.6109984 0.6217659 +#> [296,] 0.5564219 0.5682669 0.5785664 +#> [297,] 0.5121810 0.5405678 0.5407712 +#> [298,] 0.8108245 0.8151554 0.8167585 +#> [299,] 0.4839062 0.4925744 0.5149030 +#> [300,] 0.7401987 0.7627323 0.7641001 +#> [301,] 0.5626776 0.6190075 0.6316696 +#> [302,] 0.6929260 0.7291426 0.7316196 +#> [303,] 1.9051118 1.9100229 1.9334947 +#> [304,] 0.5817847 0.5997890 0.6207748 +#> [305,] 0.5720249 0.5837491 0.5898708 +#> [306,] 0.5965174 0.6005517 0.6137788 +#> [307,] 1.0034240 1.0314873 1.0320467 +#> [308,] 0.8265704 0.8445410 0.8636050 +#> [309,] 0.8943133 0.9098371 0.9781398 +#> [310,] 0.6571992 0.6586063 0.6722925 +#> [311,] 0.8053866 0.8088441 0.8141323 +#> [312,] 0.9904840 1.0488026 1.0567316 +#> [313,] 0.6881557 0.7582715 0.7591426 +#> [314,] 0.5568581 0.5827694 0.6204041 +#> [315,] 0.6862337 0.7001198 0.7561484 +#> [316,] 0.8492696 0.8711605 0.8899702 +#> [317,] 0.7655117 0.7722215 0.7753759 +#> [318,] 0.6363898 0.6404232 0.6509156 +#> [319,] 0.6670985 0.6672640 0.6962625 +#> [320,] 0.8233239 0.8242852 0.8484660 +#> [321,] 0.6495988 0.6508068 0.6542615 +#> [322,] 1.0180832 1.0250659 1.0266634 +#> [323,] 0.6837625 0.6928143 0.6936870 +#> [324,] 0.7342534 0.7540028 0.7679974 +#> [325,] 0.6308780 0.6407043 0.6508068 +#> [326,] 0.9350917 0.9420448 0.9533092 +#> [327,] 0.6695906 0.6926156 0.7533284 +#> [328,] 1.0668279 1.0853428 1.2035339 +#> [329,] 0.8267822 0.8279529 0.8500662 +#> [330,] 0.6328065 0.6525837 0.6550373 +#> [331,] 0.6137788 0.6349687 0.6698618 +#> [332,] 0.7303193 0.7322579 0.7341715 +#> [333,] 0.8000697 0.8059705 0.8269345 +#> [334,] 0.5379070 0.5381997 0.5402068 +#> [335,] 0.4572627 0.4642517 0.4692709 +#> [336,] 1.1938257 1.2105804 1.2646468 +#> [337,] 0.8833182 0.8913455 0.9097912 +#> [338,] 0.8483925 0.8485289 0.8739361 +#> [339,] 0.9268745 0.9301437 0.9494924 +#> [340,] 0.5926610 0.6433268 0.6688616 +#> [341,] 0.6328652 0.6697167 0.6775438 +#> [342,] 0.6189582 0.6336426 0.6530868 +#> [343,] 0.9829755 0.9838940 1.0288515 +#> [344,] 0.5624027 0.5672823 0.5751450 +#> [345,] 0.5785100 0.5880912 0.6032128 +#> [346,] 0.7105605 0.7254459 0.7285652 +#> [347,] 0.6198076 0.6297758 0.6598254 +#> [348,] 0.8729183 0.8793803 0.9190275 +#> [349,] 0.5209098 0.5302664 0.5624052 +#> [350,] 0.8863619 0.9420280 0.9459780 +#> [351,] 0.8880786 0.9228884 0.9664693 +#> [352,] 1.3276280 1.3284202 1.3549191 +#> [353,] 1.0438588 1.0442103 1.0701039 +#> [354,] 0.7461058 0.7690134 0.7864186 +#> [355,] 0.7151664 0.7450731 0.7689244 +#> [356,] 0.5878504 0.5908810 0.6090971 +#> [357,] 0.6847809 0.8065705 0.8067604 +#> [358,] 1.0136404 1.0154122 1.0282789 +#> [359,] 1.0531656 1.0646987 1.1138957 +#> [360,] 1.0397803 1.0461697 1.0537380 +#> [361,] 0.4926647 0.5154537 0.5237127 +#> [362,] 0.9955914 1.0392933 1.0474345 +#> [363,] 0.8972945 0.8996744 0.9027318 +#> [364,] 0.9159105 0.9435594 0.9511380 +#> [365,] 0.7430718 0.7604702 0.7980560 +#> [366,] 0.5674692 0.5727376 0.6627259 +#> [367,] 1.0199524 1.0204548 1.0471335 +#> [368,] 0.5353742 0.5394586 0.5463735 +#> [369,] 0.8494756 0.8770611 0.8840677 +#> [370,] 0.9045793 0.9141658 0.9328294 +#> [371,] 0.8484185 0.8639459 0.9237728 +#> [372,] 0.7616295 0.7788900 0.8071625 +#> [373,] 0.5097656 0.5103176 0.5450248 +#> [374,] 0.6674362 0.6946062 0.8056381 +#> [375,] 0.7198688 0.7292259 0.7533332 +#> [376,] 0.7404467 0.7665664 0.7672032 +#> [377,] 0.8388834 0.8573182 0.8683734 +#> [378,] 0.7261603 0.7888915 0.8065603 +#> [379,] 1.1768365 1.2174035 1.2453905 +#> [380,] 1.2598092 1.3246854 1.3305341 +#> [381,] 0.8631331 0.8777154 0.8879424 +#> [382,] 0.8118543 0.8243492 0.8374920 +#> [383,] 0.7757223 0.7796205 0.8203172 +#> [384,] 0.8415829 0.8425422 0.8427918 +#> [385,] 0.6887134 0.6924916 0.6935413 +#> [386,] 0.6301571 0.6443765 0.6467538 +#> [387,] 0.6254812 0.6277603 0.6707994 +#> [388,] 0.9504140 0.9787950 0.9788155 +#> [389,] 0.7573177 0.7574468 0.7613383 +#> [390,] 0.6926296 0.7004906 0.7667403 +#> [391,] 1.1676943 1.2179838 1.2325915 +#> [392,] 0.8188247 0.8241835 0.8339341 +#> [393,] 0.8329418 0.8386294 0.8460252 +#> [394,] 0.7096210 0.7292739 0.7584514 +#> [395,] 1.4799383 1.5064008 1.5235157 +#> [396,] 1.1022143 1.1110085 1.1184343 +#> [397,] 0.7088534 0.7127603 0.7214720 +#> [398,] 0.7625693 0.7998142 0.8176899 +#> [399,] 0.7830381 0.8207924 0.8252420 +#> [400,] 1.2736809 1.3238208 1.3259648 +#> [401,] 0.7904562 0.8157185 0.8233814 +#> [402,] 0.9497575 0.9665054 0.9835361 +#> [403,] 0.9377258 1.0256769 1.0350779 +#> [404,] 0.6182421 0.6186630 0.6250949 +#> [405,] 1.3379349 1.3868488 1.4096641 +#> [406,] 0.6810388 0.7326497 0.7436179 +#> [407,] 0.8000203 0.8126316 0.8261912 +#> [408,] 0.6668383 0.6772600 0.7011845 +#> [409,] 0.5904144 0.6055436 0.6144892 +#> [410,] 0.6182368 0.6483972 0.6787011 +#> [411,] 0.7589513 0.7703605 0.7758075 +#> [412,] 0.7763655 0.7924663 0.8046182 +#> [413,] 0.7299407 0.7307987 0.7472007 +#> [414,] 1.2705682 1.3039116 1.3173147 +#> [415,] 1.3978243 1.4099353 1.4368023 +#> [416,] 0.7055470 0.7556795 0.7571401 +#> [417,] 0.8557944 0.9016194 0.9258674 +#> [418,] 0.6795702 0.6936138 0.7203690 +#> [419,] 0.5507127 0.5616786 0.5664522 +#> [420,] 0.5946564 0.6271226 0.6589521 +#> [421,] 1.2660488 1.2935256 1.3210175 +#> [422,] 1.0777358 1.1552969 1.2204435 +#> [423,] 0.5313030 0.5338943 0.5407712 +#> [424,] 0.9779007 1.0178157 1.0591399 +#> [425,] 0.8950220 0.9454731 0.9560139 +#> [426,] 1.0097005 1.0485973 1.1099787 +#> [427,] 0.6957399 0.7095744 0.7805514 +#> [428,] 1.3067943 1.3501308 1.3673162 +#> [429,] 0.8278941 0.8936291 0.9025688 +#> [430,] 0.7547116 0.7996885 0.8211158 +#> [431,] 0.7154578 0.7296091 0.7660701 +#> [432,] 0.7421226 0.7538467 0.8155186 +#> [433,] 0.5806314 0.5915916 0.6548079 +#> [434,] 0.6349687 0.6365637 0.6500784 +#> [435,] 0.8473934 0.8574420 0.9175749 +#> [436,] 0.6155368 0.6198042 0.6409345 +#> [437,] 0.4699607 0.5248791 0.5486645 +#> [438,] 1.0221971 1.0283199 1.0576939 +#> [439,] 1.4077905 1.4078477 1.4427959 +#> [440,] 0.4409665 0.4626767 0.4938862 +#> [441,] 0.9048603 0.9087658 0.9091246 +#> [442,] 1.1669433 1.1732456 1.1790484 +#> [443,] 0.5563468 0.5780151 0.5950256 +#> [444,] 0.9618237 0.9843987 1.0340714 +#> [445,] 1.6385503 1.6417239 1.6554494 +#> [446,] 0.7878675 0.7962567 0.7979834 +#> [447,] 2.0097095 2.0525425 2.0587405 +#> [448,] 1.1924949 1.2178915 1.2492241 +#> [449,] 0.5403095 0.5631900 0.5737795 +#> [450,] 1.6583900 1.7281786 1.7370262 +#> [451,] 0.6314860 0.6396309 0.6518001 +#> [452,] 1.3654383 1.3918685 1.4056749 +#> [453,] 0.6978221 0.7011060 0.7079638 +#> [454,] 1.0720183 1.1228689 1.1266475 +#> [455,] 0.5374261 0.5787567 0.5882427 +#> [456,] 0.5917679 0.6269757 0.6475247 +#> [457,] 1.0103346 1.0583478 1.0754355 +#> [458,] 0.7366074 0.7696810 0.8113648 +#> [459,] 0.5130750 0.5307737 0.5408275 +#> [460,] 0.5350491 0.5520000 0.5657748 +#> [461,] 0.8358193 0.8409993 0.8453590 +#> [462,] 1.3948239 1.4562316 1.4921572 +#> [463,] 0.5976927 0.6159258 0.6270187 +#> [464,] 0.7016635 0.7099575 0.7111202 +#> [465,] 0.9276422 0.9977400 1.0455170 +#> [466,] 1.1230235 1.1773242 1.1921464 +#> [467,] 0.5719470 0.6084708 0.6584985 +#> [468,] 0.5323596 0.5371743 0.6082670 +#> [469,] 0.6305895 0.6475206 0.6699390 +#> [470,] 0.3822591 0.4214002 0.4794289 +#> [471,] 0.6965180 0.7162136 0.7932884 +#> [472,] 1.2037354 1.2412579 1.2781834 +#> [473,] 1.1528347 1.1575442 1.1691273 +#> [474,] 0.6404232 0.6628817 0.6633204 +#> [475,] 1.3680536 1.3806925 1.4502315 +#> [476,] 0.7013151 0.7044657 0.7068683 +#> [477,] 0.7424823 0.8617070 0.8710939 +#> [478,] 0.6833299 0.7013946 0.7057739 +#> [479,] 0.5670320 0.5874698 0.6266317 +#> [480,] 0.5411971 0.5717583 0.5764336 +#> [481,] 0.8700075 0.8761098 0.8879985 +#> [482,] 0.6665749 0.6856635 0.6897393 +#> [483,] 0.7095349 0.7123296 0.7411007 +#> [484,] 1.2568529 1.3388399 1.3498595 +#> [485,] 0.6030901 0.6106986 0.6362282 +#> [486,] 1.3270357 1.3446570 1.3753723 +#> [487,] 0.6944722 0.6986781 0.7058085 +#> [488,] 0.7442717 0.7716297 0.8258617 +#> [489,] 0.8303598 0.8338579 0.8443118 +#> [490,] 1.5111890 1.5445189 1.5564387 +#> [491,] 1.4287770 1.4338115 1.4534610 +#> [492,] 0.6028583 0.6085813 0.6364568 +#> [493,] 1.2444039 1.2724466 1.2782419 +#> [494,] 0.9186961 0.9288731 0.9339130 +#> [495,] 0.5148448 0.5639078 0.6331913 +#> [496,] 0.9704159 0.9718047 0.9869484 +#> [497,] 0.5541192 0.5699598 0.5739698 +#> [498,] 1.2294258 1.2405399 1.2632070 +#> [499,] 0.5804832 0.6020497 0.6649769 +#> [500,] 0.6395914 0.6407729 0.6647554 +#> [501,] 1.2427244 1.2891885 1.3755788 +#> [502,] 0.5479711 0.5639078 0.5675280 +#> [503,] 1.5388021 1.6372228 1.6697375 +#> [504,] 0.7529697 0.7724098 0.7991795 +#> [505,] 1.5642651 1.5753041 1.5896113 +#> [506,] 0.9122109 0.9163889 0.9183538 +#> [507,] 0.9718225 0.9727489 0.9946086 +#> [508,] 0.7348817 0.7541810 0.7824822 +#> [509,] 0.8368484 0.8441140 0.8712227 +#> [510,] 0.7831023 0.8224706 0.8269345 +#> [511,] 1.2972052 1.2995585 1.3610271 +#> [512,] 0.7433959 0.7674406 0.8095525 +#> [513,] 0.4820203 0.4914494 0.5091375 +#> [514,] 0.5673960 0.6052295 0.6122315 +#> [515,] 0.4559979 0.4896020 0.4909026 +#> [516,] 0.4765648 0.5061745 0.5290541 +#> [517,] 0.5166929 0.5225545 0.5312450 +#> [518,] 0.7012764 0.7025821 0.7181508 +#> [519,] 0.6690963 0.6722143 0.7096344 +#> [520,] 0.5259640 0.5403095 0.5471192 +#> [521,] 0.7425617 0.7567662 0.7822832 +#> [522,] 0.5248791 0.5320507 0.5325069 +#> [523,] 0.9687019 0.9696667 1.0605354 +#> [524,] 0.5592717 0.5775316 0.6013149 +#> [525,] 0.7134244 0.7180454 0.7746114 +#> [526,] 0.6894268 0.6917122 0.7388943 +#> [527,] 1.5320355 1.5359497 1.5481314 +#> [528,] 0.6239519 0.6647352 0.6698996 +#> [529,] 0.8253294 0.8426068 0.8512405 +#> [530,] 0.9175670 0.9586984 0.9696410 +#> [531,] 0.5643394 0.6282007 0.6373988 +#> [532,] 0.7024892 0.7180089 0.7402136 +#> [533,] 0.9412042 0.9443775 0.9501321 +#> [534,] 0.6331913 0.6349211 0.6712309 +#> [535,] 0.4731234 0.5026104 0.5306004 +#> [536,] 0.5622579 0.5691711 0.5894259 +#> [537,] 1.3938269 1.4201260 1.4538800 +#> [538,] 2.1562114 2.1712210 2.1791550 +#> [539,] 0.5998649 0.6303169 0.6841752 +#> [540,] 1.2021338 1.2431104 1.2434704 +#> [541,] 0.8252084 0.8266875 0.8453320 +#> [542,] 1.1119488 1.1136882 1.1416424 +#> [543,] 0.7668405 0.7668689 0.7780350 +#> [544,] 1.2257260 1.2418458 1.3271819 +#> [545,] 0.9903867 1.0414513 1.0425481 +#> [546,] 0.7894660 0.7979414 0.8440595 +#> [547,] 1.4748032 1.5288034 1.6200355 +#> [548,] 0.8342098 0.8576379 0.8589755 +#> [549,] 0.8357769 0.8472275 0.8531945 +#> [550,] 0.8899300 0.9027397 0.9051167 +#> [551,] 0.7655653 0.7879630 0.8017048 +#> [552,] 0.6278000 0.6282079 0.6320861 +#> [553,] 0.7442717 0.7569825 0.7942681 +#> [554,] 0.6768602 0.6894267 0.7283503 +#> [555,] 0.4914494 0.5066574 0.5105818 +#> [556,] 0.6791070 0.6866979 0.7237962 +#> [557,] 0.7090876 0.7166298 0.7232070 +#> [558,] 0.6349871 0.6720933 0.6736528 +#> [559,] 0.8838416 0.8964731 0.9543999 +#> [560,] 0.7819956 0.8213843 0.8433024 +#> [561,] 0.8987376 0.9224726 0.9437928 +#> [562,] 0.5383853 0.5501868 0.5729807 +#> [563,] 1.6153224 1.6803922 1.7070157 +#> [564,] 0.9931664 1.0353998 1.0435389 +#> [565,] 1.1575457 1.2387429 1.2800923 +#> [566,] 1.7779091 1.7815292 1.8143845 +#> [567,] 0.8014217 0.8219595 0.8323921 +#> [568,] 0.6558496 0.6684397 0.6691929 +#> [569,] 0.5976967 0.6356963 0.6428138 +#> [570,] 0.8780852 0.8807945 0.8864215 +#> [571,] 1.5694029 1.6226836 1.6559844 +#> [572,] 1.8867064 1.8946499 1.8951040 +#> [573,] 0.7954502 0.8146452 0.8159977 +#> [574,] 0.5106542 0.5251450 0.5402068 +#> [575,] 0.9253467 0.9494333 0.9523070 +#> [576,] 0.5224575 0.5246025 0.5282306 +#> [577,] 0.8574534 0.8758765 0.9156594 +#> [578,] 0.6972932 0.7002176 0.7072410 +#> [579,] 0.7263034 0.7667228 0.7703424 +#> [580,] 1.1658215 1.2123779 1.2285181 +#> [581,] 0.9509007 0.9569566 0.9901120 +#> [582,] 0.6984653 0.7123291 0.7231011 +#> [583,] 0.6165135 0.6549497 0.6617059 +#> [584,] 0.6559082 0.6567545 0.6708039 +#> [585,] 1.2372009 1.2431104 1.3414206 +#> [586,] 0.8837551 0.8870166 0.8986792 +#> [587,] 1.1300497 1.1378241 1.1534381 +#> [588,] 0.9341930 0.9854642 0.9955070 +#> [589,] 0.7817602 0.7950334 0.8359405 +#> [590,] 0.8507018 0.8532815 0.8706052 +#> [591,] 0.6070573 0.6123936 0.6133793 +#> [592,] 0.6506767 0.6857493 0.6886219 +#> [593,] 1.1799360 1.2041982 1.2203544 +#> [594,] 0.9431895 1.0075567 1.0447846 +#> [595,] 1.2599315 1.2657402 1.2736123 +#> [596,] 0.7112562 0.7154578 0.7300252 +#> [597,] 0.9327264 0.9697106 0.9757561 +#> [598,] 0.5226653 0.5347778 0.6004037 +#> [599,] 0.7221886 0.7933000 0.7960978 +#> [600,] 0.5261021 0.5263663 0.5302476 +#> [601,] 0.7163910 0.7248237 0.7679930 +#> [602,] 0.7257560 0.7548418 0.7964255 +#> [603,] 0.8378586 0.8414736 0.8446556 +#> [604,] 0.9077201 0.9923359 1.0106972 +#> [605,] 0.6994948 0.7045753 0.7119669 +#> [606,] 0.5645280 0.5647955 0.5910950 +#> [607,] 1.4204439 1.4740305 1.4971383 +#> [608,] 1.5498688 1.6069093 1.7384498 +#> [609,] 0.9435022 0.9635824 0.9656355 +#> [610,] 0.5616786 0.5708692 0.6117274 +#> [611,] 1.2172076 1.2886264 1.3355435 +#> [612,] 0.5113881 0.5568581 0.5676932 +#> [613,] 1.1811073 1.2101745 1.2565012 +#> [614,] 0.8288751 0.8812426 0.9096677 +#> [615,] 0.5957393 0.6231754 0.6320876 +#> [616,] 0.6050565 0.6071182 0.6480847 +#> [617,] 0.7711614 0.7980622 0.8012313 +#> [618,] 0.5614452 0.6487696 0.6725718 +#> [619,] 0.5817847 0.6002829 0.6276241 +#> [620,] 0.8364844 0.8494256 0.8512405 +#> [621,] 0.7005655 0.7149314 0.7576389 +#> [622,] 0.9959242 1.0071526 1.0788781 +#> [623,] 1.0235715 1.0478058 1.0535272 +#> [624,] 0.7768493 0.7793272 0.8205517 +#> [625,] 1.1115848 1.1248822 1.1475012 +#> [626,] 0.5346923 0.5503597 0.5520000 +#> [627,] 0.6558496 0.6659052 0.7765029 +#> [628,] 1.0708415 1.1446938 1.2266482 +#> [629,] 0.6829279 0.7051407 0.7155856 +#> [630,] 0.7140941 0.7149278 0.7381929 +#> [631,] 0.9039229 0.9152130 0.9270557 +#> [632,] 0.5377479 0.5674692 0.6032168 +#> [633,] 0.6707994 0.6877516 0.7379386 +#> [634,] 0.6339956 0.6650379 0.6674789 +#> [635,] 0.6655739 0.6722275 0.7188234 +#> [636,] 1.2039409 1.2538158 1.3004335 +#> [637,] 0.9567400 0.9805564 1.0181206 +#> [638,] 1.7680531 1.7727131 1.8282199 +#> [639,] 0.7118041 0.7131544 0.7225123 +#> [640,] 0.7567525 0.7756439 0.7817602 +#> [641,] 0.5957012 0.6084708 0.6490912 +#> [642,] 0.6071488 0.6660500 0.6773838 +#> [643,] 0.5670320 0.5772271 0.6008826 +#> [644,] 0.6868794 0.6977937 0.7150017 +#> [645,] 0.7461080 0.7604850 0.7808844 +#> [646,] 0.6266361 0.6314365 0.6341940 +#> [647,] 0.7385297 0.7404467 0.7519838 +#> [648,] 0.8369020 0.8398624 0.8476542 +#> [649,] 0.4740971 0.4755505 0.5060454 +#> [650,] 1.7220624 1.7519612 1.7616198 +#> [651,] 0.4828149 0.5392326 0.5533832 +#> [652,] 0.6740843 0.6871881 0.7243692 +#> [653,] 0.5535769 0.5727801 0.5891104 +#> [654,] 1.0375054 1.0590858 1.0765221 +#> [655,] 0.8332197 0.8422317 0.8527906 +#> [656,] 1.0206574 1.0517808 1.0649530 +#> [657,] 0.7398995 0.8453590 0.8595941 +#> [658,] 0.8261406 0.8307582 0.8373647 +#> [659,] 0.7806629 0.7927973 0.7979414 +#> [660,] 0.5940258 0.6013404 0.6245413 +#> [661,] 1.0329767 1.0371129 1.0452618 +#> [662,] 0.7318016 0.7900017 0.7961905 +#> [663,] 0.4782290 0.4993475 0.5221172 +#> [664,] 0.6718837 0.6856635 0.7067344 +#> [665,] 0.6518001 0.6655306 0.6815493 +#> [666,] 1.1155896 1.1403134 1.1439860 +#> [667,] 0.5810267 0.5871655 0.6011670 +#> [668,] 0.7238108 0.7438533 0.7550931 +#> [669,] 1.0504722 1.0550247 1.0723984 +#> [670,] 0.9731496 0.9735136 0.9877512 +#> [671,] 1.0529481 1.0845242 1.1297322 +#> [672,] 0.8843676 0.8887155 0.9268679 +#> [673,] 0.6216648 0.6479959 0.6746208 +#> [674,] 0.5251450 0.5284033 0.5484489 +#> [675,] 0.7403855 0.7486979 0.7514374 +#> [676,] 0.6969275 0.7127841 0.7689411 +#> [677,] 0.7326497 0.7373563 0.7384600 +#> [678,] 0.6684066 0.6699390 0.6867790 +#> [679,] 0.7660027 0.7884803 0.7889959 +#> [680,] 0.6324472 0.6806701 0.6925097 +#> [681,] 0.6062568 0.6252554 0.6853151 +#> [682,] 0.7331707 0.7955418 0.8179501 +#> [683,] 1.0878071 1.1123559 1.1526806 +#> [684,] 0.5940258 0.6229879 0.6271196 +#> [685,] 0.8574649 0.8976148 0.9387797 +#> [686,] 0.7263605 0.7401842 0.7438612 +#> [687,] 1.3116721 1.3278361 1.3876646 +#> [688,] 0.6817233 0.6931311 0.7211141 +#> [689,] 1.0200061 1.0265487 1.0418415 +#> [690,] 0.7148179 0.7231723 0.7243780 +#> [691,] 0.6315268 0.6431302 0.6574496 +#> [692,] 1.0381903 1.0520745 1.0933699 +#> [693,] 0.9025787 0.9038915 0.9198327 +#> [694,] 0.6214294 0.6753017 0.7139887 +#> [695,] 1.1763199 1.1795062 1.2006747 +#> [696,] 0.6034387 0.6123165 0.6987542 +#> [697,] 1.4813297 1.6121141 1.7260634 +#> [698,] 0.6417307 0.6438171 0.6462949 +#> [699,] 0.8838049 0.9152433 0.9183538 +#> [700,] 0.7079312 0.7122314 0.7785440 +#> [701,] 1.2274216 1.3371795 1.3521911 +#> [702,] 0.6383516 0.6398683 0.6559131 +#> [703,] 0.8225748 0.8324250 0.8455299 +#> [704,] 0.5142809 0.5876460 0.6074908 +#> [705,] 0.5373668 0.5540923 0.5664522 +#> [706,] 0.5765510 0.5853877 0.6001879 +#> [707,] 0.6937080 0.7006146 0.7421462 +#> [708,] 0.6238756 0.6882597 0.6976726 +#> [709,] 0.8374410 0.9248079 0.9406653 +#> [710,] 0.7652198 0.8181279 0.8206264 +#> [711,] 0.5948992 0.5976967 0.6158515 +#> [712,] 0.9381280 0.9597892 0.9664203 +#> [713,] 1.0555835 1.0615033 1.0912192 +#> [714,] 0.8848121 0.9112138 0.9500410 +#> [715,] 1.0850121 1.0871142 1.0940913 +#> [716,] 0.7843407 0.8227103 0.8617159 +#> [717,] 0.4696288 0.4699835 0.4738325 +#> [718,] 0.5210939 0.5921035 0.6556833 +#> [719,] 1.0706496 1.0765221 1.1663353 +#> [720,] 0.8406039 0.8808923 0.8958698 +#> [721,] 0.8506859 0.8674290 0.8749529 +#> [722,] 1.1576949 1.1636063 1.1886189 +#> [723,] 0.7265791 0.7418144 0.7951605 +#> [724,] 0.7665664 0.8252081 0.8266375 +#> [725,] 0.6844479 0.6874830 0.7128726 +#> [726,] 0.9559077 1.1242090 1.1646467 +#> [727,] 0.7351015 0.7832581 0.8303848 +#> [728,] 0.9923359 1.0102435 1.0381903 +#> [729,] 1.4331600 1.4771536 1.4932030 +#> [730,] 0.6936138 0.7290462 0.7386993 +#> [731,] 0.5865848 0.5928012 0.5935847 +#> [732,] 0.8202470 0.8342600 0.8562082 +#> [733,] 0.6299515 0.7325734 0.7354251 +#> [734,] 2.0558621 2.0864969 2.1005553 +#> [735,] 0.6088727 0.6541174 0.6590748 +#> [736,] 1.2058457 1.2621229 1.2702962 +#> [737,] 1.1890757 1.2943075 1.2991210 +#> [738,] 0.5636207 0.5829550 0.6010974 +#> [739,] 1.2999900 1.3123376 1.3535130 +#> [740,] 0.5961094 0.6115983 0.6147504 +#> [741,] 1.1292652 1.1476807 1.1993746 +#> [742,] 0.6389123 0.6490562 0.6605395 +#> [743,] 0.5694909 0.6220328 0.6521282 +#> [744,] 0.6801958 0.6913184 0.7516634 +#> [745,] 0.7373071 0.7444701 0.7774382 +#> [746,] 0.5555416 0.5618440 0.5831496 +#> [747,] 1.3390375 1.3843793 1.4050764 +#> [748,] 0.7693490 0.7886514 0.8007839 +#> [749,] 1.2160325 1.2310418 1.2612687 +#> [750,] 0.7772398 0.7835136 0.7853064 +#> [751,] 0.6469686 0.6871881 0.6893706 +#> [752,] 1.0467176 1.0487921 1.0535688 +#> [753,] 0.7115954 0.7243692 0.7353212 +#> [754,] 1.0067329 1.1439401 1.1663841 +#> [755,] 0.7445498 0.8205199 0.8258803 +#> [756,] 0.5692131 0.5878257 0.6048488 +#> [757,] 1.5059130 1.5141408 1.5370495 +#> [758,] 0.7022300 0.7040191 0.7266179 +#> [759,] 2.0418621 2.1325963 2.2567825 +#> [760,] 0.5918048 0.5966892 0.6095008 +#> [761,] 0.5413950 0.5472648 0.5751142 +#> [762,] 0.5384481 0.5705695 0.5868046 +#> [763,] 0.8146452 0.8153426 0.8235265 +#> [764,] 0.6850527 0.6924807 0.7120703 +#> [765,] 1.1885719 1.2018503 1.2018910 +#> [766,] 1.3260409 1.3341675 1.3347847 +#> [767,] 0.6248858 0.6631087 0.6670681 +#> [768,] 0.9237803 0.9821653 1.0128317 +#> [769,] 0.8028055 0.8115816 0.8469829 +#> [770,] 1.3735787 1.4586779 1.4587643 +#> [771,] 1.0487313 1.1115367 1.1278697 +#> [772,] 0.6377302 0.7069487 0.7291969 +#> [773,] 0.9324725 0.9695837 1.0005072 +#> [774,] 0.7340137 0.7686250 0.7910865 +#> [775,] 0.7437403 0.7456979 0.7878617 +#> [776,] 0.6409072 0.6948549 0.7067249 +#> [777,] 0.7257133 0.7526207 0.8145793 +#> [778,] 0.5142809 0.5245879 0.5252494 +#> [779,] 0.6242367 0.6256287 0.6328125 +#> [780,] 0.6837660 0.6874830 0.7228639 +#> [781,] 0.8125125 0.8322545 0.8503036 +#> [782,] 0.5561102 0.5578403 0.5763036 +#> [783,] 0.5896110 0.5958654 0.6104892 +#> [784,] 0.7866859 0.7935557 0.7964822 +#> [785,] 0.6916844 0.7073426 0.7114112 +#> [786,] 0.6074524 0.6283285 0.6525452 +#> [787,] 0.8485117 0.8661158 0.8681542 +#> [788,] 0.7669146 0.7704553 0.7730321 +#> [789,] 0.7936552 0.8056731 0.8282903 +#> [790,] 0.7768582 0.7846403 0.7865361 +#> [791,] 0.9218025 0.9309645 0.9834923 +#> [792,] 0.7310340 0.7395698 0.7413927 +#> [793,] 0.5268015 0.5320507 0.5456090 +#> [794,] 0.6962281 0.7052326 0.7597433 +#> [795,] 0.7230854 0.7567452 0.7898359 +#> [796,] 1.4279747 1.4767257 1.4891995 +#> [797,] 0.5369971 0.6397847 0.6429611 +#> [798,] 0.7756439 0.7789369 0.7825231 +#> [799,] 1.3097582 1.4824465 1.5489940 +#> [800,] 0.7162136 0.7266179 0.7296064 +#> [801,] 0.4948228 0.5717583 0.5764634 +#> [802,] 0.9185322 0.9380741 0.9420223 +#> [803,] 0.6465218 0.6621150 0.6682168 +#> [804,] 0.6172237 0.6478300 0.6539777 +#> [805,] 1.8593783 1.8708800 1.9053267 +#> [806,] 0.7604921 0.7642574 0.7730720 +#> [807,] 0.7662884 0.7721703 0.7889959 +#> [808,] 0.5115099 0.5396213 0.5483203 +#> [809,] 1.0282668 1.0382065 1.0560935 +#> [810,] 0.5475677 0.5502896 0.5973181 +#> [811,] 1.1429708 1.1613028 1.2231753 +#> [812,] 0.6638050 0.6828485 0.6834797 +#> [813,] 0.9778346 1.0510234 1.1124590 +#> [814,] 1.2542723 1.3124297 1.3212982 +#> [815,] 0.7370478 0.7417071 0.7623365 +#> [816,] 0.8667483 0.8711702 0.8878093 +#> [817,] 0.6253059 0.6553342 0.6873358 +#> [818,] 0.8282982 0.8362473 0.8699008 +#> [819,] 0.9744774 0.9890063 1.0132698 +#> [820,] 0.6783649 0.7362620 0.7444487 +#> [821,] 0.6621150 0.6987482 0.7166870 +#> [822,] 0.9290550 1.0580104 1.0725154 +#> [823,] 1.0468094 1.0712767 1.0978278 +#> [824,] 0.6427528 0.6436058 0.6524632 +#> [825,] 0.6817233 0.7111853 0.7402580 +#> [826,] 0.5284033 0.5541392 0.5699656 +#> [827,] 1.3117818 1.3270609 1.3359374 +#> [828,] 0.6628685 0.6713565 0.6810039 +#> [829,] 0.8527906 0.8812073 0.9528009 +#> [830,] 0.6828485 0.6850597 0.6959316 +#> [831,] 0.8301323 0.8712192 0.9245956 +#> [832,] 0.6132856 0.6421572 0.6431027 +#> [833,] 0.7869176 0.8055432 0.8226591 +#> [834,] 1.2076362 1.2278662 1.2417090 +#> [835,] 0.7902266 0.8018794 0.8600771 +#> [836,] 0.7864186 0.7935557 0.8255778 +#> [837,] 0.7262376 0.7844301 0.7885148 +#> [838,] 1.4799255 1.5037535 1.5076612 +#> [839,] 0.5764634 0.6651793 0.6815833 +#> [840,] 0.6878447 0.6985192 0.7296800 +#> [841,] 0.4934944 0.5391688 0.5655502 +#> [842,] 1.0560959 1.0712848 1.0853428 +#> [843,] 0.7249468 0.7366193 0.7376150 +#> [844,] 0.7874349 0.7950016 0.8099821 +#> [845,] 0.7664371 0.7807477 0.8116326 +#> [846,] 0.6358013 0.6442066 0.6576187 +#> [847,] 0.4486907 0.4983990 0.4999563 +#> [848,] 0.9207612 0.9276422 0.9540991 +#> [849,] 0.4063097 0.4590751 0.4740971 +#> [850,] 0.6394717 0.6588610 0.6600258 +#> [851,] 0.8149142 0.8428174 0.8796094 +#> [852,] 0.6049990 0.6198042 0.6272176 +#> [853,] 0.9851428 1.0335664 1.0680627 +#> [854,] 1.0029237 1.0044478 1.0091058 +#> [855,] 0.9468798 0.9500668 0.9856499 +#> [856,] 1.1343272 1.1434065 1.1500361 +#> [857,] 0.9434459 0.9806701 0.9911303 +#> [858,] 0.6657246 0.6823429 0.6984509 +#> [859,] 0.9392122 1.0334538 1.0380556 +#> [860,] 0.6851883 0.6919852 0.7095744 +#> [861,] 0.7763655 0.7913457 0.8270811 +#> [862,] 0.6989579 0.7134870 0.7771113 +#> [863,] 0.7291426 0.7341086 0.7359247 +#> [864,] 1.2080432 1.2282035 1.3640246 +#> [865,] 0.7292787 0.7359247 0.8104232 +#> [866,] 0.8350270 0.8828692 0.8838416 +#> [867,] 1.6801516 1.7936242 1.8107486 +#> [868,] 0.7664883 0.7692705 0.7853750 +#> [869,] 0.6256287 0.6405012 0.6438171 +#> [870,] 0.9159168 0.9412794 0.9424081 +#> [871,] 0.4008547 0.4421991 0.4654775 +#> [872,] 1.5398663 1.5488528 1.5865198 +#> [873,] 0.5946995 0.5961094 0.5985655 +#> [874,] 1.2174384 1.2866041 1.3608177 +#> [875,] 0.6989579 0.7028270 0.7502891 +#> [876,] 1.1007515 1.1159988 1.1618348 +#> [877,] 0.9267543 0.9303417 0.9985390 +#> [878,] 0.7707595 0.7796337 0.8207193 +#> [879,] 0.7728698 0.8062140 0.8064596 +#> [880,] 0.9699226 1.0045098 1.0449545 +#> [881,] 0.8332732 0.8572955 0.8811205 +#> [882,] 0.7810091 0.7894367 0.8153426 +#> [883,] 0.7170823 0.7352067 0.7384600 +#> [884,] 0.5624806 0.5863302 0.6213767 +#> [885,] 0.9655477 0.9727644 1.0023837 +#> [886,] 0.6547229 0.6760726 0.6837625 +#> [887,] 0.6487290 0.6680559 0.7312532 +#> [888,] 0.8688664 0.9291881 0.9494333 +#> [889,] 1.3239755 1.3330514 1.3419673 +#> [890,] 0.4841708 0.5299665 0.5941560 +#> [891,] 1.1557669 1.2092693 1.2211860 +#> [892,] 0.5427501 0.5509455 0.5757741 +#> [893,] 1.0055165 1.1019036 1.1303557 +#> [894,] 1.5957744 1.6656746 1.6704343 +#> [895,] 0.5313030 0.5411869 0.5432273 +#> [896,] 1.3521568 1.3854506 1.3989331 +#> [897,] 1.2729158 1.2869336 1.3173147 +#> [898,] 1.3501308 1.4088867 1.4431790 +#> [899,] 0.5130520 0.5265425 0.5344388 +#> [900,] 0.6500784 0.6550834 0.6594093 +#> [901,] 0.9341268 0.9882515 1.0482291 +#> [902,] 0.6887986 0.6949705 0.7101375 +#> [903,] 1.8382930 1.8593577 1.8821169 +#> [904,] 0.8450184 0.8584939 0.8900323 +#> [905,] 1.3352933 1.3647099 1.3707155 +#> [906,] 0.6124874 0.6452023 0.6502552 +#> [907,] 0.6015697 0.6057881 0.6303679 +#> [908,] 0.7397444 0.7589513 0.7761755 +#> [909,] 1.1331706 1.1684123 1.2233917 +#> [910,] 0.7708239 0.8088441 0.8090672 +#> [911,] 0.7564921 0.8313556 0.8853633 +#> [912,] 0.5590943 0.5693136 0.6010974 +#> [913,] 0.5705695 0.5990171 0.5997920 +#> [914,] 0.6483972 0.6809148 0.6938318 +#> [915,] 1.1443646 1.1713342 1.1896728 +#> [916,] 0.7747097 0.7772398 0.8024150 +#> [917,] 0.7302801 0.7417071 0.7470587 +#> [918,] 0.6165999 0.6218071 0.6254043 +#> [919,] 0.6983487 0.8245811 0.8574372 +#> [920,] 1.0107482 1.0956074 1.1528632 +#> [921,] 0.5968568 0.6036069 0.6052295 +#> [922,] 0.7816347 0.8317877 0.8353579 +#> [923,] 0.7042752 0.7556773 0.7612428 +#> [924,] 0.8016984 0.8064596 0.8279561 +#> [925,] 0.6581170 0.6625708 0.6638874 +#> [926,] 0.7545084 0.7692472 0.8111023 +#> [927,] 0.8234208 0.8325586 0.8762490 +#> [928,] 0.8036009 0.8617396 0.9095927 +#> [929,] 0.9777802 0.9867707 0.9916101 +#> [930,] 0.6246838 0.6478300 0.6710057 +#> [931,] 0.7047821 0.7074970 0.7094189 +#> [932,] 0.5815310 0.5894735 0.5981939 +#> [933,] 0.8676934 0.8809400 0.9466005 +#> [934,] 1.0968149 1.1379194 1.1401699 +#> [935,] 0.5070188 0.5424867 0.5633698 +#> [936,] 0.9154519 0.9903226 0.9985880 +#> [937,] 1.0154784 1.0198059 1.0558893 +#> [938,] 0.7369948 0.7536130 0.8294904 +#> [939,] 1.4303140 1.4317122 1.4805684 +#> [940,] 0.8062438 0.8196058 0.8569086 +#> [941,] 1.1892653 1.2261792 1.2324396 +#> [942,] 0.5912759 0.5969571 0.6082670 +#> [943,] 0.5314154 0.5326785 0.5801175 +#> [944,] 0.8829630 0.8910723 0.8967822 +#> [945,] 1.3858221 1.4302707 1.4316539 +#> [946,] 0.6547229 0.6886322 0.6928918 +#> [947,] 0.6877890 0.7001301 0.7057125 +#> [948,] 1.8357152 1.9045001 1.9065517 +#> [949,] 1.3379349 1.4021315 1.4079049 +#> [950,] 1.0840138 1.1094766 1.1308136 +#> [951,] 0.7117756 0.7177677 0.7423945 +#> [952,] 0.7438321 0.7534034 0.7842284 +#> [953,] 0.7502960 0.7789664 0.7982690 +#> [954,] 0.6118587 0.6215960 0.6384658 +#> [955,] 1.2871531 1.3225393 1.3418591 +#> [956,] 0.7043195 0.7170823 0.7336379 +#> [957,] 1.0358191 1.0774448 1.1165770 +#> [958,] 0.6198076 0.6213680 0.6650598 +#> [959,] 1.1471868 1.2669688 1.2800816 +#> [960,] 0.8130187 0.8631036 0.8891762 +#> [961,] 0.4881796 0.5027134 0.5136565 +#> [962,] 0.5982893 0.6004924 0.6157327 +#> [963,] 0.7837144 0.7919935 0.7991795 +#> [964,] 0.6104453 0.6329952 0.6364906 +#> [965,] 0.7174851 0.7278104 0.7323772 +#> [966,] 0.6158712 0.6274870 0.6436952 +#> [967,] 0.9331233 0.9648185 0.9984615 +#> [968,] 0.9794996 0.9906871 1.0181875 +#> [969,] 0.9764982 0.9772140 0.9803378 +#> [970,] 0.8332279 0.8385844 0.8746902 +#> [971,] 0.8782562 0.9048243 0.9051167 +#> [972,] 0.7135772 0.7139887 0.7223360 +#> [973,] 0.6498677 0.6716114 0.7187771 +#> [974,] 0.7911595 0.7980903 0.8115715 +#> [975,] 1.0262068 1.0452156 1.0937855 +#> [976,] 0.7545626 0.7664371 0.7778610 +#> [977,] 0.9817203 1.0214761 1.0233572 +#> [978,] 0.7640607 0.7933599 0.8157303 +#> [979,] 0.5221971 0.5497720 0.6275281 +#> [980,] 0.8326922 0.8450174 0.8531720 +#> [981,] 0.6079576 0.6749162 0.6866674 +#> [982,] 0.5516274 0.5699598 0.6100876 +#> [983,] 0.6790706 0.7139076 0.7197768 +#> [984,] 1.2032878 1.2213903 1.2509943 +#> [985,] 0.6874485 0.7101610 0.7203690 +#> [986,] 1.0107862 1.0289652 1.0302160 +#> [987,] 0.5092006 0.5504881 0.5553071 +#> [988,] 0.6925121 0.7282713 0.7690535 +#> [989,] 0.7949332 0.8214750 0.8386153 +#> [990,] 1.1753586 1.2128637 1.2172076 +#> [991,] 0.7462066 0.8073125 0.8246749 +#> [992,] 0.6815427 0.7020037 0.7071514 +#> [993,] 0.8221489 0.8382346 0.8520056 +#> [994,] 0.5541392 0.5859897 0.5901825 +#> [995,] 0.6030581 0.6088673 0.6820477 +#> [996,] 1.0221451 1.0348177 1.0581953 +#> [997,] 1.1938107 1.2440572 1.2494514 +#> [998,] 0.7631284 0.8002346 0.8086030 +#> [999,] 0.6835776 0.6913845 0.6999382 +#> [1000,] 1.1236420 1.1943301 1.2301638 #> diff --git a/reference/tof_get_model_penalty.html b/reference/tof_get_model_penalty.html index 9b22833..78fa3b3 100644 --- a/reference/tof_get_model_penalty.html +++ b/reference/tof_get_model_penalty.html @@ -119,7 +119,7 @@

    Examples ) tof_get_model_penalty(regression_model) -#> [1] 0.003162278 +#> [1] 1 diff --git a/reference/tof_get_model_training_data.html b/reference/tof_get_model_training_data.html index 7e33f7c..c72d86a 100644 --- a/reference/tof_get_model_training_data.html +++ b/reference/tof_get_model_training_data.html @@ -120,18 +120,18 @@

    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 16 0.00502 0.101 0.832 0.852 class2 class1 0 8.29 -#> 2 67 0.0231 0.704 0.515 3.33 class2 class3 1 13.1 -#> 3 56 0.0133 0.0196 0.197 -0.208 class2 class2 1 13.7 -#> 4 21 0.247 0.111 0.172 1.92 class2 class1 0 10.0 -#> 5 6 0.0996 0.337 0.679 1.65 class2 class1 0 11.3 -#> 6 37 0.953 0.381 0.484 7.73 class1 class2 1 12.9 -#> 7 43 0.853 0.423 0.128 2.52 class2 class2 1 12.0 -#> 8 76 0.592 0.126 0.640 1.87 class2 class3 1 9.05 -#> 9 10 0.616 0.478 0.199 1.71 class2 class1 0 11.3 -#> 10 73 0.712 0.215 0.367 2.07 class2 class3 1 10.5 +#> sample cd45 pstat5 cd34 outcome class multiclass event time_to_event +#> <chr> <dbl> <dbl> <dbl> <dbl> <fct> <fct> <dbl> <dbl> +#> 1 85 0.788 0.914 0.440 6.14 class1 class3 1 6.43 +#> 2 31 0.806 0.240 0.782 3.61 class2 class2 1 9.90 +#> 3 48 0.375 0.825 0.0230 4.55 class1 class2 1 12.2 +#> 4 61 0.0185 0.946 0.314 3.68 class2 class3 1 8.11 +#> 5 22 0.196 0.691 0.347 2.99 class2 class1 0 8.27 +#> 6 79 0.535 0.249 0.523 2.49 class2 class3 1 10.2 +#> 7 74 0.689 0.856 0.907 6.98 class1 class3 1 10.9 +#> 8 9 0.239 0.408 0.344 3.82 class1 class1 0 10.9 +#> 9 53 0.134 0.906 0.919 5.34 class1 class2 1 8.89 +#> 10 81 0.169 0.947 0.178 4.31 class1 class3 1 6.35 #> # ℹ 90 more rows diff --git a/reference/tof_get_model_x.html b/reference/tof_get_model_x.html index 727c5ee..d0982a3 100644 --- a/reference/tof_get_model_x.html +++ b/reference/tof_get_model_x.html @@ -119,107 +119,107 @@

    Examples ) tof_get_model_x(regression_model) -#> cd45 pstat5 cd34 -#> [1,] 0.557496857 0.55254884 -1.38428152 -#> [2,] 0.143171407 -0.77158277 -0.96941346 -#> [3,] -1.502965899 1.57749290 0.43071065 -#> [4,] -1.487689698 -1.05402386 0.26670370 -#> [5,] 0.301986654 -1.41109437 0.62034936 -#> [6,] -1.056499901 0.16076292 1.21403260 -#> [7,] 1.182601035 1.16135604 -1.25154531 -#> [8,] 0.175161417 0.26428063 1.46442058 -#> [9,] -0.176386967 1.20998237 -0.73032910 -#> [10,] 1.361071950 -1.39595395 -0.06137393 -#> [11,] -0.705411677 -0.57942208 0.24049595 -#> [12,] -1.115293290 -0.76255463 -1.04310500 -#> [13,] 1.175452272 -1.22436156 -1.38640227 -#> [14,] 1.163323464 0.93595248 -0.35468719 -#> [15,] 0.740115180 -0.67577632 0.01731844 -#> [16,] 1.601440012 -1.49938411 0.92018807 -#> [17,] 0.240059379 -0.23790043 -0.96933282 -#> [18,] -0.916705090 0.94753440 1.36072730 -#> [19,] 1.618689241 -0.90362707 -0.04163005 -#> [20,] 0.562887422 -0.80338410 -1.07272806 -#> [21,] 0.020818643 0.07529508 0.56738816 -#> [22,] -1.593153944 -0.36883048 -0.23429092 -#> [23,] 1.406199371 -0.02143408 1.26338516 -#> [24,] -1.429128536 0.46864660 -1.58328718 -#> [25,] -0.934904314 -0.63212679 0.49228289 -#> [26,] 1.433305360 0.42283996 1.39473103 -#> [27,] 0.019767484 -1.28450984 -1.55476772 -#> [28,] 0.508009412 0.38076053 1.20257860 -#> [29,] -1.004761203 -0.13291545 1.32921921 -#> [30,] 1.399130241 -1.31633977 0.26874764 -#> [31,] 0.482602659 -1.02751941 -0.50095342 -#> [32,] -0.543356713 1.28087863 1.23478788 -#> [33,] -0.507422484 -1.35009733 1.22676601 -#> [34,] 0.368655653 -0.52200992 -0.15475605 -#> [35,] -0.081804366 -0.31056493 -0.85835736 -#> [36,] 0.037561521 0.60339003 0.28293992 -#> [37,] -1.491887476 1.21099572 -1.12477401 -#> [38,] 0.005129352 -0.33981920 1.34288859 -#> [39,] -1.302914735 -1.67374465 1.06058374 -#> [40,] 1.305653373 0.61042250 -1.49525241 -#> [41,] 0.482143429 0.06664269 0.45823394 -#> [42,] -1.492688729 1.50206999 0.76974567 -#> [43,] 0.617022851 0.40265098 1.36800527 -#> [44,] -0.617457478 0.95260935 0.66416594 -#> [45,] -1.606625323 -1.44734923 -0.88052376 -#> [46,] -0.638064343 -1.31229252 -0.57006223 -#> [47,] 1.090721278 1.66480604 -1.44087462 -#> [48,] 1.486976621 1.31668060 -0.21521681 -#> [49,] -1.468830013 0.77331120 -1.56317205 -#> [50,] 1.455674529 1.44060148 1.11282839 -#> [51,] -0.290803815 -0.05969706 -1.45511364 -#> [52,] 0.733134872 -1.52929026 -0.97373036 -#> [53,] 0.344436164 -1.32132534 -1.09050376 -#> [54,] 0.025103475 -0.87335631 -0.37249570 -#> [55,] 0.005565627 0.47061529 -0.90081899 -#> [56,] 0.088159041 1.36984081 0.30931130 -#> [57,] 1.479750148 1.66158548 0.57718636 -#> [58,] 0.043417799 -1.24565767 -0.64769623 -#> [59,] 0.822253646 0.76005166 -0.69140429 -#> [60,] 0.329282537 -0.65838399 -1.01716064 -#> [61,] 0.071329812 1.25781906 1.31319492 -#> [62,] -0.855197996 0.70530147 -1.31843757 -#> [63,] 0.658248655 0.73338389 1.49260712 -#> [64,] 1.452178644 -0.85450389 0.61899811 -#> [65,] -0.835366335 0.75340630 -0.44970310 -#> [66,] 1.326492491 -1.51463056 1.40166921 -#> [67,] 0.036750337 -0.50928817 1.52348777 -#> [68,] -1.504910611 1.42806769 0.14878131 -#> [69,] -0.821539840 -0.49322190 -0.22241568 -#> [70,] -0.589478093 0.76056918 0.06020416 -#> [71,] -1.500470526 0.84789696 1.18340728 -#> [72,] -1.287270835 0.47255714 -0.38925795 -#> [73,] 0.930305547 1.75773170 1.23534137 -#> [74,] 0.884229017 -0.61870832 0.58878309 -#> [75,] -0.671317579 -1.51476542 0.93921205 -#> [76,] -1.457536947 -0.44484394 1.11904540 -#> [77,] -1.217264634 -0.23667642 -1.27240224 -#> [78,] -1.547997709 0.88847735 -0.47816578 -#> [79,] 0.373987416 -1.68670147 -1.40146429 -#> [80,] 1.594535000 -0.38942638 1.34122937 -#> [81,] 0.707546080 0.76234299 1.44107612 -#> [82,] -0.937221893 -1.28057014 -0.79493906 -#> [83,] 0.359529230 1.48544158 0.54443627 -#> [84,] 1.408014465 0.64311365 -1.48655835 -#> [85,] 0.271926331 0.18442901 0.13738623 -#> [86,] 0.581231249 -0.93864001 -0.66737277 -#> [87,] -0.757940402 -0.38858262 -1.18463333 -#> [88,] 0.642676960 -1.46812212 -0.99615908 -#> [89,] -1.289230186 0.50206737 0.67252825 -#> [90,] 0.361422796 0.33176833 0.49695858 -#> [91,] -0.124556298 1.45847359 -0.42208603 -#> [92,] -1.643635969 1.12659784 0.99615075 -#> [93,] -0.401240219 -0.81190439 -1.56386233 -#> [94,] 1.325629029 -1.58461564 -0.67454196 -#> [95,] -1.609741631 -0.45822804 0.86180774 -#> [96,] -0.299746586 0.51120513 0.79680852 -#> [97,] -0.495243445 0.63245996 -1.05074541 -#> [98,] 1.606864285 0.67175963 0.46781733 -#> [99,] 0.812649785 1.18037103 1.46240172 -#> [100,] -0.407814777 0.59991283 -1.34126926 +#> cd45 pstat5 cd34 +#> [1,] -1.78317206 -1.0787379605 1.333560457 +#> [2,] 1.10354848 1.0628629638 -0.007971152 +#> [3,] 0.28101225 -0.9863854712 0.943167367 +#> [4,] 0.15853500 0.9648649256 1.478340692 +#> [5,] -0.19131391 -0.5968674218 -1.511126613 +#> [6,] 0.46303565 0.9286830086 -0.295172477 +#> [7,] -1.70231141 1.5557823494 1.379459045 +#> [8,] 1.49148700 -1.4755548574 1.159147834 +#> [9,] 1.72386496 0.5710371564 0.330228937 +#> [10,] -0.39782305 0.2049274774 0.357080999 +#> [11,] 0.44302282 1.3419463533 0.167546255 +#> [12,] -1.76651518 0.8491942626 0.976864801 +#> [13,] -0.63356464 -1.4433745349 0.332350031 +#> [14,] -0.77306937 -1.1757549265 -0.945803345 +#> [15,] -0.63717571 -0.8130631050 -1.004776766 +#> [16,] -1.45990328 -1.6729066815 -1.581385700 +#> [17,] 0.10951455 0.0323062376 0.230590132 +#> [18,] 0.70539364 0.6626321482 0.559850122 +#> [19,] 0.33545021 -0.0490838722 -0.260766820 +#> [20,] -0.76876632 -1.3719608221 0.685592057 +#> [21,] 1.18076987 0.9757881406 0.409331098 +#> [22,] 0.29693186 -1.5711703850 -0.593965239 +#> [23,] 1.06160014 1.9560886989 -0.359778415 +#> [24,] 0.83531440 0.2409114010 -1.332756134 +#> [25,] 0.66249487 -1.2375144652 -1.575283965 +#> [26,] -0.98960673 -0.6669653896 -1.340737940 +#> [27,] 1.41488135 -0.9242453876 0.725438340 +#> [28,] -0.59096372 1.0250240427 -1.545310478 +#> [29,] 1.75503156 -0.2734310127 0.267503896 +#> [30,] -0.44195731 -0.0417721597 0.552188957 +#> [31,] 0.63496759 -1.3558765673 -0.836229703 +#> [32,] -1.40325154 -0.4987362393 1.478943176 +#> [33,] -0.41326441 -0.7409474669 1.386029524 +#> [34,] -0.11448838 -1.0214624007 -1.602191748 +#> [35,] -0.16017563 -0.8513593263 1.137506946 +#> [36,] 0.58728012 1.5438732777 -0.832493326 +#> [37,] -1.08957913 -0.5714635307 -0.817002991 +#> [38,] 0.56855878 1.6475160444 1.245318999 +#> [39,] -0.17115678 0.5475570558 -0.644908178 +#> [40,] 0.02686691 0.9672587787 -1.026616390 +#> [41,] 0.33192934 0.0145352092 -0.173320975 +#> [42,] -1.05596792 -1.0748526758 -0.191256247 +#> [43,] 0.25905544 -0.7499622282 -0.087657555 +#> [44,] 0.15440636 1.3524495620 0.168353586 +#> [45,] -1.16808454 -0.7985231102 -0.803837572 +#> [46,] -1.45180107 -1.3360255127 -1.439330467 +#> [47,] -1.69432680 1.6889785690 1.329175952 +#> [48,] 0.28276938 -0.0993963932 0.013122238 +#> [49,] 0.12893504 -0.4431576358 -0.229954891 +#> [50,] 0.98692593 1.0557804701 1.190741254 +#> [51,] 0.92667438 0.1577735525 -1.669680934 +#> [52,] -1.27372723 0.5730161197 0.053478552 +#> [53,] 1.06871379 -1.2095639195 -0.451003047 +#> [54,] 0.77182004 0.3530487708 -1.059214355 +#> [55,] 1.29626841 0.8187685274 -0.478643532 +#> [56,] 1.74791062 0.9897908331 -0.425375490 +#> [57,] -0.16235393 -0.0910664797 1.705414173 +#> [58,] -0.15738729 1.8711936081 -0.126729294 +#> [59,] -0.29535792 -0.1358952865 1.397848103 +#> [60,] 1.79191607 0.6458904947 -1.638537173 +#> [61,] -0.66798290 -1.5840545159 -0.419631398 +#> [62,] -0.32974182 1.1886707737 -0.173524097 +#> [63,] -0.83681468 0.6331643597 0.082403326 +#> [64,] -0.73922866 -0.2322845260 0.101457935 +#> [65,] -0.57623004 -1.3937666103 1.443584669 +#> [66,] -0.42359729 1.5135844751 0.072547217 +#> [67,] -0.32929042 -0.0006291011 -1.692261786 +#> [68,] -0.24088988 1.8261944212 0.224511135 +#> [69,] -0.95149649 -1.1081043364 -0.378855087 +#> [70,] 1.91723194 -0.3502579310 -1.844824658 +#> [71,] 1.25176008 1.2581136351 1.233394505 +#> [72,] 0.07080782 -0.8866318099 0.638288680 +#> [73,] -1.54302889 1.6857596143 -0.287625849 +#> [74,] 1.18695821 -0.5113548744 0.394427408 +#> [75,] -0.82486995 -1.6204020019 -0.588203677 +#> [76,] -1.79645181 -0.6864040144 -1.311847684 +#> [77,] -1.55872438 -1.0220035939 1.789038093 +#> [78,] -0.77897578 0.3692559643 0.428812512 +#> [79,] 1.57042159 -1.4346590275 0.833725974 +#> [80,] -0.06382824 0.2857970510 1.481791590 +#> [81,] -1.61397628 -0.8465167432 -1.648507113 +#> [82,] 0.20313852 0.0542426137 -0.118507950 +#> [83,] 0.62050888 0.3346826055 1.023535635 +#> [84,] -1.14703558 -1.5574576712 1.355170017 +#> [85,] 0.42846179 -0.1753167314 1.112048926 +#> [86,] 0.41997232 0.4549634910 -0.711286973 +#> [87,] -0.19044873 0.8933333770 -0.474693737 +#> [88,] -0.81555060 1.0777003059 -0.877067329 +#> [89,] 1.17350911 -0.4341643860 0.698854727 +#> [90,] 1.71932353 1.0695474903 0.804058565 +#> [91,] -0.20881194 0.9738611349 1.699522445 +#> [92,] 0.81303834 -0.4329034118 -1.108390019 +#> [93,] 0.81484298 -0.3213290673 -0.321048497 +#> [94,] 1.65329116 0.6674428429 -0.108223498 +#> [95,] 1.26883535 0.1571099065 -1.479695708 +#> [96,] -1.37947623 0.1786102124 0.793738394 +#> [97,] 0.03251312 0.3384958529 1.722195881 +#> [98,] -1.52463054 -1.2938448569 0.866870876 +#> [99,] 0.72690694 1.0860309387 -0.340135386 +#> [100,] -0.17026210 -0.3968786695 -1.021002674 diff --git a/reference/tof_get_model_y.html b/reference/tof_get_model_y.html index c463624..03641b5 100644 --- a/reference/tof_get_model_y.html +++ b/reference/tof_get_model_y.html @@ -119,23 +119,23 @@

    Examples ) tof_get_model_y(regression_model) -#> [1] 5.07911944 1.05678953 2.23934946 4.90880410 3.94374665 6.53658525 -#> [7] 2.57238069 2.92755504 1.93907094 3.77123417 3.82940916 3.26356318 -#> [13] 3.84825760 2.40057374 4.40478254 6.02535041 5.44352997 2.95309313 -#> [19] 3.00570147 5.14461239 4.39227241 2.98797491 6.23613147 2.86382843 -#> [25] 1.65266879 -0.44598386 1.02615166 2.74603837 3.73981858 6.94316675 -#> [31] 2.93417951 3.35549657 2.73999449 1.10539803 1.70830250 3.58635098 -#> [37] 1.28806087 2.81915418 3.13872282 6.20266732 4.37399516 4.97421467 -#> [43] 3.70563582 5.22433880 1.34701982 0.51430319 2.67376327 3.59549867 -#> [49] 5.04825709 3.26804763 2.83442489 2.81225459 3.24932848 4.93538219 -#> [55] 4.16472611 2.76181792 3.88390283 3.39162937 8.17669511 3.63533456 -#> [61] 3.06576604 4.44669588 4.64586848 6.06715629 2.05589047 2.77448895 -#> [67] 3.56069761 3.18679927 1.49435688 4.63091489 2.80400707 -0.08882069 -#> [73] 1.69272079 3.83614544 3.20063962 2.38496150 4.77629459 2.06289074 -#> [79] 3.67755742 4.86142919 -0.03458803 5.14039305 5.25721799 -0.11626374 -#> [85] 0.38867651 4.09989691 3.99624665 2.11558569 2.90413662 4.74220971 -#> [91] 2.43009103 2.66588818 2.77099636 6.15168702 2.77969727 2.99476337 -#> [97] 4.04757098 2.87846362 2.71481505 4.03923739 +#> [1] 3.52157326 4.18706346 4.99297032 4.17405690 6.40545747 2.50865483 +#> [7] 2.33612014 5.33430165 2.56476097 3.01796819 2.89283573 3.66406721 +#> [13] 5.22714046 4.35298919 2.58000727 2.15550594 3.60309971 4.89536032 +#> [19] 3.81554420 6.30328847 3.53770881 1.86969723 4.09551566 7.24970111 +#> [25] 2.86504606 3.55623919 3.95237545 4.11631444 5.47602150 0.28915723 +#> [31] 3.07282122 5.27719683 3.72704122 6.58541327 5.98730966 3.61953499 +#> [37] 3.87629426 2.33057302 5.04979749 3.18911819 3.49611601 3.47001035 +#> [43] 7.14360147 3.63460106 4.13321827 5.18586447 2.17984783 3.86569248 +#> [49] 5.01891594 0.53959254 6.47362294 4.17886077 4.79635337 3.38241659 +#> [55] 3.88628723 1.25895231 3.16017574 4.52602869 1.12086352 4.14951568 +#> [61] 3.76323375 4.80787027 7.01629099 3.03324953 3.10142062 4.20861853 +#> [67] 2.47186593 4.27612420 4.45194221 7.21234030 3.62721676 4.33038331 +#> [73] 3.34296498 3.24989602 3.46735076 0.29506689 0.03436764 1.68920782 +#> [79] 5.35307405 5.71910412 2.83330794 0.24626349 2.92575949 0.42468688 +#> [85] 1.52052028 3.57405227 8.28915490 7.86521083 4.00969324 4.79126650 +#> [91] 1.25838678 2.55059481 2.77286430 1.54709918 1.48236052 0.40922086 +#> [97] 3.08609250 3.17320626 3.97903992 2.75743110 diff --git a/reference/tof_metacluster.html b/reference/tof_metacluster.html index bec286c..070d228 100644 --- a/reference/tof_metacluster.html +++ b/reference/tof_metacluster.html @@ -169,18 +169,18 @@

    Examples method = "flowsom" ) #> # A tibble: 1,000 × 6 -#> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster -#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> -#> 1 -0.252 -0.602 -0.501 0.324 h 1 -#> 2 -0.752 -0.0650 0.604 -0.268 e 5 -#> 3 -0.0158 0.846 1.19 -1.40 i 2 -#> 4 -0.618 0.0271 0.255 0.415 o 2 -#> 5 -1.18 0.184 0.734 0.456 h 1 -#> 6 0.264 1.36 -0.490 0.274 o 2 -#> 7 -1.55 0.855 2.99 0.997 e 5 -#> 8 0.204 1.64 0.0465 0.939 n 1 -#> 9 0.644 0.201 -0.208 0.853 u 1 -#> 10 -0.218 1.30 0.435 -1.42 n 1 +#> cd45 cd38 cd34 cd19 cluster_id .flowsom_metacluster +#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> +#> 1 1.85 -1.32 -1.84 -0.719 y 2 +#> 2 1.32 -0.265 0.545 -1.95 i 4 +#> 3 1.12 1.42 0.995 0.844 h 2 +#> 4 -1.07 -0.107 0.620 0.0522 r 4 +#> 5 0.474 -0.470 0.874 -0.0629 b 2 +#> 6 0.985 0.559 0.408 -0.162 w 2 +#> 7 -0.321 0.307 1.96 1.64 p 2 +#> 8 0.145 -1.28 -0.451 -1.04 s 2 +#> 9 -0.378 -0.390 0.0341 -0.130 a 1 +#> 10 0.387 -0.367 0.873 -0.678 m 2 #> # ℹ 990 more rows tof_metacluster( @@ -189,18 +189,18 @@

    Examples method = "phenograph" ) #> # A tibble: 1,000 × 6 -#> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster -#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> -#> 1 -0.252 -0.602 -0.501 0.324 h 2 -#> 2 -0.752 -0.0650 0.604 -0.268 e 1 -#> 3 -0.0158 0.846 1.19 -1.40 i 2 -#> 4 -0.618 0.0271 0.255 0.415 o 2 -#> 5 -1.18 0.184 0.734 0.456 h 2 -#> 6 0.264 1.36 -0.490 0.274 o 2 -#> 7 -1.55 0.855 2.99 0.997 e 1 -#> 8 0.204 1.64 0.0465 0.939 n 3 -#> 9 0.644 0.201 -0.208 0.853 u 1 -#> 10 -0.218 1.30 0.435 -1.42 n 3 +#> cd45 cd38 cd34 cd19 cluster_id .phenograph_metacluster +#> <dbl> <dbl> <dbl> <dbl> <chr> <chr> +#> 1 1.85 -1.32 -1.84 -0.719 y 1 +#> 2 1.32 -0.265 0.545 -1.95 i 4 +#> 3 1.12 1.42 0.995 0.844 h 2 +#> 4 -1.07 -0.107 0.620 0.0522 r 4 +#> 5 0.474 -0.470 0.874 -0.0629 b 3 +#> 6 0.985 0.559 0.408 -0.162 w 3 +#> 7 -0.321 0.307 1.96 1.64 p 1 +#> 8 0.145 -1.28 -0.451 -1.04 s 2 +#> 9 -0.378 -0.390 0.0341 -0.130 a 2 +#> 10 0.387 -0.367 0.873 -0.678 m 2 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_consensus.html b/reference/tof_metacluster_consensus.html index 851db93..b5ff945 100644 --- a/reference/tof_metacluster_consensus.html +++ b/reference/tof_metacluster_consensus.html @@ -204,16 +204,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .consensus_metacluster #> <chr> -#> 1 9 +#> 1 8 #> 2 7 -#> 3 10 -#> 4 5 -#> 5 7 -#> 6 5 -#> 7 5 -#> 8 5 -#> 9 2 -#> 10 7 +#> 3 8 +#> 4 1 +#> 5 1 +#> 6 6 +#> 7 1 +#> 8 6 +#> 9 8 +#> 10 3 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_flowsom.html b/reference/tof_metacluster_flowsom.html index ab75db1..d3a5351 100644 --- a/reference/tof_metacluster_flowsom.html +++ b/reference/tof_metacluster_flowsom.html @@ -191,16 +191,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> <chr> -#> 1 5 -#> 2 1 -#> 3 2 +#> 1 2 +#> 2 3 +#> 3 4 #> 4 4 -#> 5 1 -#> 6 1 -#> 7 2 -#> 8 2 -#> 9 7 -#> 10 1 +#> 5 3 +#> 6 5 +#> 7 1 +#> 8 3 +#> 9 4 +#> 10 4 #> # ℹ 990 more rows tof_metacluster_flowsom( @@ -211,14 +211,14 @@

    Examples#> # A tibble: 1,000 × 1 #> .flowsom_metacluster #> <chr> -#> 1 2 -#> 2 3 -#> 3 1 -#> 4 2 -#> 5 3 -#> 6 3 +#> 1 1 +#> 2 2 +#> 3 3 +#> 4 3 +#> 5 1 +#> 6 4 #> 7 1 -#> 8 1 +#> 8 3 #> 9 3 #> 10 3 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_hierarchical.html b/reference/tof_metacluster_hierarchical.html index 902b4ad..4bcbe58 100644 --- a/reference/tof_metacluster_hierarchical.html +++ b/reference/tof_metacluster_hierarchical.html @@ -174,16 +174,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .hierarchical_metacluster #> <chr> -#> 1 6 -#> 2 4 -#> 3 4 +#> 1 1 +#> 2 2 +#> 3 9 #> 4 5 -#> 5 10 -#> 6 4 -#> 7 6 -#> 8 6 -#> 9 4 -#> 10 3 +#> 5 2 +#> 6 1 +#> 7 7 +#> 8 3 +#> 9 3 +#> 10 8 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_kmeans.html b/reference/tof_metacluster_kmeans.html index 4f1e5cf..ebfc504 100644 --- a/reference/tof_metacluster_kmeans.html +++ b/reference/tof_metacluster_kmeans.html @@ -158,16 +158,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .kmeans_metacluster #> <chr> -#> 1 8 -#> 2 2 -#> 3 8 -#> 4 7 -#> 5 7 -#> 6 5 -#> 7 4 -#> 8 1 -#> 9 5 -#> 10 8 +#> 1 6 +#> 2 3 +#> 3 4 +#> 4 6 +#> 5 9 +#> 6 6 +#> 7 6 +#> 8 4 +#> 9 9 +#> 10 9 #> # ℹ 990 more rows diff --git a/reference/tof_metacluster_phenograph.html b/reference/tof_metacluster_phenograph.html index a9e4139..6456c91 100644 --- a/reference/tof_metacluster_phenograph.html +++ b/reference/tof_metacluster_phenograph.html @@ -164,15 +164,15 @@

    Examples#> .phenograph_metacluster #> <chr> #> 1 1 -#> 2 4 -#> 3 1 -#> 4 3 +#> 2 3 +#> 3 3 +#> 4 2 #> 5 3 -#> 6 2 -#> 7 4 -#> 8 4 -#> 9 3 -#> 10 1 +#> 6 3 +#> 7 5 +#> 8 1 +#> 9 1 +#> 10 2 #> # ℹ 990 more rows diff --git a/reference/tof_predict.html b/reference/tof_predict.html index b4fa33e..e793278 100644 --- a/reference/tof_predict.html +++ b/reference/tof_predict.html @@ -173,26 +173,26 @@

    Examples#> # A tibble: 20 × 1 #> .pred #> <dbl> -#> 1 0.597 -#> 2 2.21 -#> 3 6.20 -#> 4 2.57 -#> 5 0.414 -#> 6 1.23 -#> 7 4.79 -#> 8 3.31 -#> 9 1.82 -#> 10 3.87 -#> 11 3.24 -#> 12 2.99 -#> 13 3.13 -#> 14 4.74 -#> 15 2.12 -#> 16 1.71 -#> 17 3.21 -#> 18 0.993 -#> 19 4.12 -#> 20 5.84 +#> 1 4.06 +#> 2 1.01 +#> 3 5.13 +#> 4 4.07 +#> 5 4.76 +#> 6 4.54 +#> 7 2.65 +#> 8 2.13 +#> 9 2.95 +#> 10 5.19 +#> 11 4.63 +#> 12 1.27 +#> 13 3.74 +#> 14 3.48 +#> 15 2.03 +#> 16 3.08 +#> 17 1.20 +#> 18 5.99 +#> 19 0.665 +#> 20 0.118 diff --git a/reference/tof_reduce_dimensions.html b/reference/tof_reduce_dimensions.html index aea2eb0..b039a67 100644 --- a/reference/tof_reduce_dimensions.html +++ b/reference/tof_reduce_dimensions.html @@ -138,35 +138,35 @@

    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 2.13 0.705 -1.16 -1.37 -2.52 1.08 1.42 -0.377 -#> 2 -2.16 0.719 0.784 -1.65 1.22 1.68 -0.397 1.87 -#> 3 1.30 0.463 0.284 0.665 -0.973 -0.110 -0.212 -1.36 -#> 4 0.266 -0.303 -0.681 -1.05 -0.475 0.600 1.34 0.277 -#> 5 0.986 1.27 0.810 0.459 -0.971 0.420 -1.03 -1.05 -#> 6 -0.0816 2.04 -0.00146 -0.362 -1.17 0.675 -1.04 0.700 -#> 7 -0.0698 0.612 1.75 -0.132 0.510 1.16 -1.02 -0.737 -#> 8 -0.390 1.50 -0.156 0.269 -0.693 -0.0874 -0.915 0.577 -#> 9 -1.32 0.986 0.0330 -0.910 0.206 0.815 -0.341 1.44 -#> 10 0.117 0.275 -1.00 -0.487 -0.819 0.0165 0.819 0.519 +#> cd45 cd38 cd34 cd19 .pc1 .pc2 .pc3 .pc4 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1.39 -0.189 -0.202 -0.956 -0.752 0.370 -1.34 -0.395 +#> 2 1.39 -1.25 -2.00 -0.963 0.656 -0.729 -2.61 -0.274 +#> 3 -0.688 -0.896 0.0756 0.610 0.743 -0.189 0.720 -0.455 +#> 4 -1.18 -0.0477 -1.78 -0.249 1.17 -0.839 -0.456 1.45 +#> 5 0.925 0.198 -0.208 -0.0243 -0.801 -0.288 -0.504 -0.217 +#> 6 0.388 -0.689 0.252 1.34 -0.118 -0.791 0.672 -1.17 +#> 7 -0.207 -1.14 1.05 0.931 0.259 0.189 1.18 -1.39 +#> 8 0.947 2.46 0.174 1.87 -2.63 -1.68 1.15 0.439 +#> 9 -0.258 -2.54 -0.769 -1.52 2.07 0.916 -1.49 -0.655 +#> 10 1.77 0.659 0.726 -0.134 -1.95 0.245 -0.390 -0.724 #> # 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 2.13 0.705 -1.16 -1.37 4.34 1.87 -#> 2 -2.16 0.719 0.784 -1.65 -0.889 -4.03 -#> 3 1.30 0.463 0.284 0.665 0.463 3.67 -#> 4 0.266 -0.303 -0.681 -1.05 2.77 1.60 -#> 5 0.986 1.27 0.810 0.459 0.562 4.25 -#> 6 -0.0816 2.04 -0.00146 -0.362 2.41 -3.16 -#> 7 -0.0698 0.612 1.75 -0.132 -3.58 -2.75 -#> 8 -0.390 1.50 -0.156 0.269 2.36 -2.21 -#> 9 -1.32 0.986 0.0330 -0.910 -0.201 -3.36 -#> 10 0.117 0.275 -1.00 -0.487 2.87 0.441 +#> cd45 cd38 cd34 cd19 .tsne1 .tsne2 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1.39 -0.189 -0.202 -0.956 1.44 -0.343 +#> 2 1.39 -1.25 -2.00 -0.963 -2.51 2.47 +#> 3 -0.688 -0.896 0.0756 0.610 -2.30 -1.68 +#> 4 -1.18 -0.0477 -1.78 -0.249 -2.58 0.667 +#> 5 0.925 0.198 -0.208 -0.0243 1.65 1.49 +#> 6 0.388 -0.689 0.252 1.34 -2.63 -3.18 +#> 7 -0.207 -1.14 1.05 0.931 -1.81 -3.71 +#> 8 0.947 2.46 0.174 1.87 2.89 4.60 +#> 9 -0.258 -2.54 -0.769 -1.52 0.474 -3.26 +#> 10 1.77 0.659 0.726 -0.134 3.42 2.05 #> # ℹ 90 more rows # calculate umap @@ -176,18 +176,18 @@

    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 2.13 0.705 -1.16 -1.37 -2.29 -0.706 -#> 2 -2.16 0.719 0.784 -1.65 -0.666 3.13 -#> 3 1.30 0.463 0.284 0.665 0.858 -3.05 -#> 4 0.266 -0.303 -0.681 -1.05 -1.79 -2.01 -#> 5 0.986 1.27 0.810 0.459 1.45 -3.23 -#> 6 -0.0816 2.04 -0.00146 -0.362 -1.91 2.35 -#> 7 -0.0698 0.612 1.75 -0.132 2.53 -0.873 -#> 8 -0.390 1.50 -0.156 0.269 -1.56 2.07 -#> 9 -1.32 0.986 0.0330 -0.910 -0.667 2.86 -#> 10 0.117 0.275 -1.00 -0.487 -2.09 0.537 +#> cd45 cd38 cd34 cd19 .umap1 .umap2 +#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> +#> 1 1.39 -0.189 -0.202 -0.956 -3.80 -2.20 +#> 2 1.39 -1.25 -2.00 -0.963 1.54 -1.89 +#> 3 -0.688 -0.896 0.0756 0.610 1.52 0.938 +#> 4 -1.18 -0.0477 -1.78 -0.249 0.418 -0.149 +#> 5 0.925 0.198 -0.208 -0.0243 -3.80 -1.13 +#> 6 0.388 -0.689 0.252 1.34 3.91 -2.45 +#> 7 -0.207 -1.14 1.05 0.931 4.12 -3.12 +#> 8 0.947 2.46 0.174 1.87 -2.15 1.50 +#> 9 -0.258 -2.54 -0.769 -1.52 -1.83 -2.71 +#> 10 1.77 0.659 0.726 -0.134 -4.28 0.887 #> # ℹ 90 more rows diff --git a/reference/tof_reduce_pca.html b/reference/tof_reduce_pca.html index dfc43a1..9bf7218 100644 --- a/reference/tof_reduce_pca.html +++ b/reference/tof_reduce_pca.html @@ -160,18 +160,18 @@

    Examples# calculate pca tof_reduce_pca(tof_tibble = sim_data, num_comp = 2) #> # A tibble: 200 × 2 -#> .pc1 .pc2 -#> <dbl> <dbl> -#> 1 -1.18 -0.437 -#> 2 0.230 -0.548 -#> 3 2.73 -0.128 -#> 4 0.772 0.965 -#> 5 0.122 1.24 -#> 6 -0.878 1.50 -#> 7 0.104 -0.543 -#> 8 0.295 2.14 -#> 9 0.712 -0.784 -#> 10 0.723 0.0671 +#> .pc1 .pc2 +#> <dbl> <dbl> +#> 1 -0.324 -2.56 +#> 2 -1.03 0.829 +#> 3 0.657 -0.668 +#> 4 0.511 -0.681 +#> 5 0.250 0.361 +#> 6 -0.448 -0.371 +#> 7 -1.13 1.54 +#> 8 -0.259 -1.95 +#> 9 -0.0261 0.277 +#> 10 0.0256 0.445 #> # ℹ 190 more rows # return recipe instead of embeddings @@ -180,18 +180,18 @@

    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 -0.735 1.24 1.48 -0.128 -#> 2 -1.05 -1.17 0.753 0.296 -#> 3 1.32 -1.42 1.41 -1.47 -#> 4 -0.454 -0.144 -0.479 -0.0116 -#> 5 1.91 -0.175 1.27 -0.345 -#> 6 1.01 -0.480 0.184 0.309 -#> 7 -0.134 -0.107 0.0852 0.968 -#> 8 -0.203 0.748 -0.966 0.658 -#> 9 -1.95 -1.78 -1.25 -0.0726 -#> 10 1.14 0.0262 -0.00742 -0.297 +#> PC1 PC2 PC3 PC4 +#> <dbl> <dbl> <dbl> <dbl> +#> 1 1.06 1.68 -0.145 0.324 +#> 2 -0.295 0.281 -0.912 1.73 +#> 3 0.459 -0.686 -2.27 0.921 +#> 4 2.57 -0.607 -0.349 -0.0860 +#> 5 -0.637 -1.86 1.13 0.370 +#> 6 -0.265 -0.281 0.562 -0.349 +#> 7 -0.806 -0.395 -0.558 1.36 +#> 8 -0.421 1.33 -0.410 -1.35 +#> 9 0.667 -0.798 1.59 -0.0229 +#> 10 -0.329 -0.494 0.0244 -0.315 #> # ℹ 40 more rows diff --git a/reference/tof_reduce_tsne.html b/reference/tof_reduce_tsne.html index 02e9bdb..35e76c6 100644 --- a/reference/tof_reduce_tsne.html +++ b/reference/tof_reduce_tsne.html @@ -158,16 +158,16 @@

    Examples#> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 8.08 -7.66 -#> 2 2.37 -4.12 -#> 3 6.81 -4.01 -#> 4 -3.07 4.23 -#> 5 3.07 3.35 -#> 6 -6.88 -6.09 -#> 7 10.3 1.02 -#> 8 2.62 4.12 -#> 9 -0.307 -2.68 -#> 10 0.692 -6.27 +#> 1 5.71 -1.94 +#> 2 6.79 -3.89 +#> 3 4.59 1.47 +#> 4 -5.97 3.60 +#> 5 1.44 -6.66 +#> 6 6.40 -4.35 +#> 7 0.858 6.37 +#> 8 3.04 0.757 +#> 9 -4.07 -1.17 +#> 10 2.57 -7.17 #> # ℹ 190 more rows # calculate tsne with only 2 columns @@ -175,16 +175,16 @@

    Examples#> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 -6.38 -3.76 -#> 2 1.55 0.251 -#> 3 -6.33 2.92 -#> 4 -0.516 -0.728 -#> 5 0.278 5.36 -#> 6 5.36 2.85 -#> 7 -8.58 -2.40 -#> 8 -1.77 4.14 -#> 9 3.73 -1.01 -#> 10 5.09 1.20 +#> 1 -6.78 4.69 +#> 2 -3.11 6.73 +#> 3 -1.96 4.62 +#> 4 8.84 -2.51 +#> 5 6.52 2.62 +#> 6 -3.57 2.33 +#> 7 8.31 1.91 +#> 8 1.10 6.10 +#> 9 -0.347 -5.74 +#> 10 4.42 6.23 #> # ℹ 190 more rows diff --git a/reference/tof_reduce_umap.html b/reference/tof_reduce_umap.html index 5e4afb6..7104b79 100644 --- a/reference/tof_reduce_umap.html +++ b/reference/tof_reduce_umap.html @@ -184,18 +184,18 @@

    Examples#> Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics' #> Also defined by ‘spam’ #> # A tibble: 200 × 2 -#> .umap1 .umap2 -#> <dbl> <dbl> -#> 1 -2.96 1.60 -#> 2 -0.206 -2.37 -#> 3 -0.635 -0.560 -#> 4 -3.37 1.09 -#> 5 4.26 0.397 -#> 6 3.30 1.62 -#> 7 -3.49 0.106 -#> 8 -0.0529 1.23 -#> 9 -3.13 1.31 -#> 10 4.73 -0.264 +#> .umap1 .umap2 +#> <dbl> <dbl> +#> 1 -0.107 -2.62 +#> 2 1.28 0.775 +#> 3 -3.33 1.56 +#> 4 1.09 0.991 +#> 5 -2.92 -3.27 +#> 6 -3.02 -3.33 +#> 7 1.37 2.20 +#> 8 0.917 2.54 +#> 9 -0.459 -1.25 +#> 10 2.54 2.57 #> # ℹ 190 more rows # calculate umap with only 2 columns @@ -203,16 +203,16 @@

    Examples#> # A tibble: 200 × 2 #> .tsne1 .tsne2 #> <dbl> <dbl> -#> 1 2.75 6.43 -#> 2 -2.40 -6.28 -#> 3 5.86 -2.11 -#> 4 4.77 5.55 -#> 5 -6.98 -1.11 -#> 6 -7.44 -2.29 -#> 7 2.70 -7.05 -#> 8 5.13 -6.46 -#> 9 2.85 6.16 -#> 10 -9.17 0.0725 +#> 1 1.89 -6.18 +#> 2 -7.23 -0.302 +#> 3 2.95 1.72 +#> 4 -7.82 -0.0960 +#> 5 -0.989 -5.83 +#> 6 -0.605 -5.28 +#> 7 -3.50 3.27 +#> 8 -4.11 4.16 +#> 9 6.96 -3.82 +#> 10 -4.94 8.10 #> # ℹ 190 more rows # return recipe @@ -225,18 +225,18 @@

    Examples# apply recipe to new data recipes::bake(umap_recipe, new_data = new_data) #> # A tibble: 50 × 2 -#> UMAP1 UMAP2 -#> <dbl> <dbl> -#> 1 0.184 -2.79 -#> 2 1.51 1.19 -#> 3 -0.476 -0.746 -#> 4 1.90 -2.91 -#> 5 -0.0406 2.33 -#> 6 1.92 -2.74 -#> 7 -0.471 2.95 -#> 8 1.71 2.39 -#> 9 -3.73 0.00121 -#> 10 -2.18 -2.45 +#> UMAP1 UMAP2 +#> <dbl> <dbl> +#> 1 -0.411 1.91 +#> 2 -0.689 -1.06 +#> 3 -5.01 -0.0412 +#> 4 5.71 -0.340 +#> 5 -4.68 0.0799 +#> 6 -4.96 -0.0157 +#> 7 4.94 3.30 +#> 8 -5.05 -0.222 +#> 9 4.13 0.650 +#> 10 3.40 2.58 #> # ℹ 40 more rows diff --git a/reference/tof_spade_density.html b/reference/tof_spade_density.html index b22fb67..183899c 100644 --- a/reference/tof_spade_density.html +++ b/reference/tof_spade_density.html @@ -199,16 +199,16 @@

    Examples#> # A tibble: 1,000 × 1 #> .spade_density #> <dbl> -#> 1 0.5 -#> 2 0 -#> 3 0.167 +#> 1 0.375 +#> 2 0.25 +#> 3 0.25 #> 4 0 -#> 5 0.167 -#> 6 0.333 -#> 7 0.333 -#> 8 0 -#> 9 0.333 -#> 10 0.5 +#> 5 0.625 +#> 6 0.125 +#> 7 0.375 +#> 8 0.125 +#> 9 0.25 +#> 10 0.375 #> # ℹ 990 more rows # perform the density estimation with a smaller search radius around @@ -222,14 +222,14 @@

    Examples#> <dbl> #> 1 1 #> 2 1 -#> 3 1 -#> 4 0.5 -#> 5 1 -#> 6 0.3 +#> 3 0.7 +#> 4 0.1 +#> 5 0.3 +#> 6 0.9 #> 7 1 -#> 8 1 +#> 8 0.5 #> 9 1 -#> 10 0.5 +#> 10 0.1 #> # ℹ 990 more rows diff --git a/reference/tof_split_data.html b/reference/tof_split_data.html index 08ab7f8..90ec905 100644 --- a/reference/tof_split_data.html +++ b/reference/tof_split_data.html @@ -206,16 +206,16 @@

    Examples#> # A tibble: 10 × 2 #> splits id #> <list> <chr> -#> 1 <split [100/35]> Bootstrap01 -#> 2 <split [100/35]> Bootstrap02 -#> 3 <split [100/33]> Bootstrap03 -#> 4 <split [100/41]> Bootstrap04 -#> 5 <split [100/40]> Bootstrap05 -#> 6 <split [100/40]> Bootstrap06 -#> 7 <split [100/36]> Bootstrap07 -#> 8 <split [100/34]> Bootstrap08 -#> 9 <split [100/41]> Bootstrap09 -#> 10 <split [100/34]> Bootstrap10 +#> 1 <split [100/37]> Bootstrap01 +#> 2 <split [100/41]> Bootstrap02 +#> 3 <split [100/37]> Bootstrap03 +#> 4 <split [100/34]> Bootstrap04 +#> 5 <split [100/37]> Bootstrap05 +#> 6 <split [100/33]> Bootstrap06 +#> 7 <split [100/34]> Bootstrap07 +#> 8 <split [100/37]> Bootstrap08 +#> 9 <split [100/34]> Bootstrap09 +#> 10 <split [100/38]> Bootstrap10 # split the dataset into a single training/test set # stratified by the "class" column diff --git a/reference/tof_train_model.html b/reference/tof_train_model.html index c2db922..5191000 100644 --- a/reference/tof_train_model.html +++ b/reference/tof_train_model.html @@ -299,14 +299,14 @@

    Examples response_col = outcome, model_type = "linear" ) -#> A linear `tof_model` with a mixture parameter (alpha) of 0 and a penalty parameter (lambda) of 1e-10 +#> A linear `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 3.162e-08 #> # A tibble: 4 × 2 #> feature coefficient #> <chr> <dbl> -#> 1 (Intercept) 3.35 -#> 2 pstat5 1.09 -#> 3 cd45 0.710 -#> 4 cd34 -0.0956 +#> 1 (Intercept) 3.44 +#> 2 pstat5 1.18 +#> 3 cd45 0.958 +#> 4 cd34 0.133 # train a logistic regression classifier tof_train_model( @@ -315,12 +315,14 @@

    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 -0.0588 -#> 2 (Intercept) 0.00000102 +#> 1 pstat5 -1.53 +#> 2 cd45 -1.21 +#> 3 cd34 -0.233 +#> 4 (Intercept) -0.0361 # train a cox regression survival model tof_train_model( @@ -330,9 +332,13 @@

    Examples event_col = event, model_type = "survival" ) -#> A survival `tof_model` with a mixture parameter (alpha) of 1 and a penalty parameter (lambda) of 1e+00 -#> # A tibble: 0 × 2 -#> # ℹ 2 variables: feature <chr>, coefficient <dbl> +#> 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 +#> <chr> <dbl> +#> 1 cd45 -0.175 +#> 2 pstat5 0.0684 +#> 3 cd34 -0.0448 diff --git a/reference/tof_upsample.html b/reference/tof_upsample.html index 08343d9..f9943b9 100644 --- a/reference/tof_upsample.html +++ b/reference/tof_upsample.html @@ -178,18 +178,18 @@

    Examples method = "distance" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .upsample_cluster -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 0.391 0.908 -0.584 -0.281 b -#> 2 0.805 -1.23 0.799 -0.472 b -#> 3 0.943 0.371 -0.575 0.928 b -#> 4 -0.631 -1.17 -0.0679 -0.0230 a -#> 5 -0.189 -0.638 2.32 -0.734 b -#> 6 -0.701 0.263 -0.179 0.972 a -#> 7 -0.422 0.707 1.03 -2.52 a -#> 8 -0.453 0.614 0.806 2.03 a -#> 9 -1.67 1.09 -0.390 -0.473 a -#> 10 -1.17 -1.38 0.599 0.635 b +#> cd45 cd38 cd34 cd19 .upsample_cluster +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -1.24 0.373 -0.937 -0.704 b +#> 2 0.269 1.52 1.81 0.506 a +#> 3 -0.736 -0.0917 -0.830 0.821 b +#> 4 0.638 0.608 0.564 -0.428 a +#> 5 1.29 -0.926 -0.153 0.324 b +#> 6 -0.634 -0.00913 -0.996 0.145 b +#> 7 -0.481 -0.782 -0.704 -0.812 b +#> 8 0.924 1.21 -1.44 -1.48 a +#> 9 -1.24 0.128 0.107 -0.509 a +#> 10 0.255 -0.885 1.41 1.78 b #> # ℹ 990 more rows # upsample using distance to nearest neighbor @@ -200,18 +200,18 @@

    Examples method = "neighbor" ) #> # A tibble: 1,000 × 5 -#> cd45 cd38 cd34 cd19 .upsample_cluster -#> <dbl> <dbl> <dbl> <dbl> <chr> -#> 1 0.391 0.908 -0.584 -0.281 a -#> 2 0.805 -1.23 0.799 -0.472 a -#> 3 0.943 0.371 -0.575 0.928 a -#> 4 -0.631 -1.17 -0.0679 -0.0230 a -#> 5 -0.189 -0.638 2.32 -0.734 b -#> 6 -0.701 0.263 -0.179 0.972 b -#> 7 -0.422 0.707 1.03 -2.52 a -#> 8 -0.453 0.614 0.806 2.03 b -#> 9 -1.67 1.09 -0.390 -0.473 a -#> 10 -1.17 -1.38 0.599 0.635 a +#> cd45 cd38 cd34 cd19 .upsample_cluster +#> <dbl> <dbl> <dbl> <dbl> <chr> +#> 1 -1.24 0.373 -0.937 -0.704 b +#> 2 0.269 1.52 1.81 0.506 a +#> 3 -0.736 -0.0917 -0.830 0.821 a +#> 4 0.638 0.608 0.564 -0.428 a +#> 5 1.29 -0.926 -0.153 0.324 a +#> 6 -0.634 -0.00913 -0.996 0.145 a +#> 7 -0.481 -0.782 -0.704 -0.812 a +#> 8 0.924 1.21 -1.44 -1.48 b +#> 9 -1.24 0.128 0.107 -0.509 a +#> 10 0.255 -0.885 1.41 1.78 b #> # ℹ 990 more rows diff --git a/reference/tof_upsample_distance.html b/reference/tof_upsample_distance.html index e8c3c24..503f557 100644 --- a/reference/tof_upsample_distance.html +++ b/reference/tof_upsample_distance.html @@ -201,15 +201,15 @@

    Examples#> .upsample_cluster #> <chr> #> 1 a -#> 2 b -#> 3 a -#> 4 b -#> 5 a +#> 2 a +#> 3 b +#> 4 a +#> 5 b #> 6 a -#> 7 a -#> 8 a -#> 9 a -#> 10 a +#> 7 b +#> 8 b +#> 9 b +#> 10 b #> # ℹ 990 more rows # upsample using cosine distance @@ -222,15 +222,15 @@

    Examples#> # A tibble: 1,000 × 1 #> .upsample_cluster #> <chr> -#> 1 b +#> 1 a #> 2 b -#> 3 b -#> 4 b +#> 3 a +#> 4 a #> 5 a #> 6 a -#> 7 b -#> 8 a -#> 9 a +#> 7 a +#> 8 b +#> 9 b #> 10 a #> # ℹ 990 more rows diff --git a/reference/tof_upsample_neighbor.html b/reference/tof_upsample_neighbor.html index 803f905..bae3945 100644 --- a/reference/tof_upsample_neighbor.html +++ b/reference/tof_upsample_neighbor.html @@ -176,15 +176,15 @@

    Examples#> .upsample_cluster #> <chr> #> 1 a -#> 2 b +#> 2 a #> 3 b -#> 4 a -#> 5 b -#> 6 a +#> 4 b +#> 5 a +#> 6 b #> 7 a #> 8 b #> 9 b -#> 10 b +#> 10 a #> # ℹ 990 more rows # upsample using cosine distance @@ -197,14 +197,14 @@

    Examples#> # A tibble: 1,000 × 1 #> .upsample_cluster #> <chr> -#> 1 b +#> 1 a #> 2 a #> 3 a #> 4 a -#> 5 b +#> 5 a #> 6 a #> 7 a -#> 8 a +#> 8 b #> 9 b #> 10 b #> # ℹ 990 more rows diff --git a/search.json b/search.json index 470b164..5746c46 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_0.99.0 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.0 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_0.6.34 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.0 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.8 #> [19] tools_4.3.3 igraph_2.0.2 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_2.14.0 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_1.2.7 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.1 #> [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_0.6.1 #> [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_0.6.34 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_0.4.8 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_1.2.7 bslib_0.6.1 #> [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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [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_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] labeling_0.4.3 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 highr_0.10 #> [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] data.table_1.15.2 hms_1.1.3 #> [73] tidygraph_1.3.1 utf8_1.2.4 #> [75] XVector_0.42.0 ggrepel_0.9.5 #> [77] BiocVersion_3.18.1 foreach_1.5.2 #> [79] pillar_1.9.0 RcppHNSW_0.6.0 #> [81] later_1.3.2 splines_4.3.3 #> [83] tweenr_2.0.3 lattice_0.22-5 #> [85] survival_3.5-8 bit_4.0.5 #> [87] RProtoBufLib_2.14.0 tidyselect_1.2.0 #> [89] Biostrings_2.70.2 knitr_1.45 #> [91] gridExtra_2.3 xfun_0.42 #> [93] graphlayouts_1.1.1 hardhat_1.3.1 #> [95] timeDate_4032.109 stringi_1.8.3 #> [97] yaml_2.3.8 evaluate_0.23 #> [99] codetools_0.2-19 ggraph_2.2.1 #> [101] tibble_3.2.1 BiocManager_1.30.22 #> [103] cli_3.6.2 rpart_4.1.23 #> [105] xtable_1.8-4 systemfonts_1.0.6 #> [107] munsell_0.5.0 jquerylib_0.1.4 #> [109] Rcpp_1.0.12 globals_0.16.3 #> [111] png_0.1-8 parallel_4.3.3 #> [113] ellipsis_0.3.2 pkgdown_2.0.7 #> [115] gower_1.0.1 readr_2.1.5 #> [117] blob_1.2.4 listenv_0.9.1 #> [119] glmnet_4.1-8 viridisLite_0.4.2 #> [121] ipred_0.9-14 scales_1.3.0 #> [123] prodlim_2023.08.28 purrr_1.0.2 #> [125] crayon_1.5.2 rlang_1.1.3 #> [127] 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 1.10 17.2 #> 2 -4.99 14.2 #> 3 -28.9 16.0 #> 4 -15.2 12.0 #> 5 -1.45 19.8 #> 6 -13.3 21.6 phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .tsne1 .tsne2 #> #> 1 8.65 13.2 #> 2 1.61 16.2 #> 3 -9.86 38.4 #> 4 -8.74 17.2 #> 5 6.72 15.9 #> 6 -4.57 24.6 #> 7 0.840 17.9 #> 8 -0.335 30.1 #> 9 -3.15 24.4 #> 10 4.96 8.83 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"umap\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .umap1 .umap2 #> #> 1 9.72 2.09 #> 2 8.54 2.11 #> 3 6.34 0.991 #> 4 4.00 -2.02 #> 5 9.69 1.81 #> 6 1.18 -3.16 #> 7 9.50 1.33 #> 8 4.95 -1.21 #> 9 4.90 1.34 #> 10 9.49 4.33 #> # 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_0.99.0 #> #> 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] png_0.1-8 systemfonts_1.0.6 vctrs_0.6.5 #> [10] stringr_1.5.1 pkgconfig_2.0.3 shape_1.4.6.1 #> [13] fastmap_1.1.1 ellipsis_0.3.2 labeling_0.4.3 #> [16] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [19] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.2.7 #> [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 keras_2.13.0 tweenr_2.0.3 #> [31] irlba_2.3.5.1 tensorflow_2.15.0 parallel_4.3.3 #> [34] R6_2.5.1 bslib_0.6.1 stringi_1.8.3 #> [37] reticulate_1.35.0 parallelly_1.37.1 rpart_4.1.23 #> [40] lubridate_1.9.3 jquerylib_0.1.4 Rcpp_1.0.12 #> [43] iterators_1.0.14 knitr_1.45 future.apply_1.11.1 #> [46] base64enc_0.1-3 readr_2.1.5 flowCore_2.14.1 #> [49] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [52] igraph_2.0.2 timechange_0.3.0 tidyselect_1.2.0 #> [55] yaml_2.3.8 viridis_0.6.5 timeDate_4032.109 #> [58] doParallel_1.0.17 codetools_0.2-19 listenv_0.9.1 #> [61] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [64] withr_3.0.0 evaluate_0.23 Rtsne_0.17 #> [67] future_1.33.1 desc_1.4.3 survival_3.5-8 #> [70] polyclip_1.10-6 embed_1.1.3 pillar_1.9.0 #> [73] whisker_0.4.1 foreach_1.5.2 stats4_4.3.3 #> [76] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [79] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [82] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [85] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [88] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [91] grid_4.3.3 yardstick_1.3.0 tidyr_1.3.1 #> [94] RProtoBufLib_2.14.0 ipred_0.9-14 colorspace_2.1-0 #> [97] ggforce_0.4.2 cli_3.6.2 tfruns_1.5.2 #> [100] textshaping_0.3.7 fansi_1.0.6 cytolib_2.14.1 #> [103] viridisLite_0.4.2 lava_1.8.0 uwot_0.1.16 #> [106] gtable_0.3.4 zeallot_0.1.0 sass_0.4.8 #> [109] digest_0.6.34 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [112] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [115] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [118] 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_0.99.0 #> #> 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_1.2.7 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_0.6.1 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_2.14.1 #> [40] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [43] igraph_2.0.2 timechange_0.3.0 tidyselect_1.2.0 #> [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_1.3.0 tidyr_1.3.1 #> [82] RProtoBufLib_2.14.0 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_0.4.8 #> [94] digest_0.6.34 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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [15] BiocGenerics_0.48.1 stringr_1.5.1 #> [17] dplyr_1.1.4 tidytof_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] philentropy_0.8.0 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 ggforce_0.4.2 #> [53] MASS_7.3-60.0.1 lava_1.8.0 #> [55] rappdirs_0.3.3 DelayedArray_0.28.0 #> [57] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [59] httpuv_1.6.14 future.apply_1.11.1 #> [61] nnet_7.3-19 glue_1.7.0 #> [63] promises_1.2.1 grid_4.3.3 #> [65] generics_0.1.3 recipes_1.0.10 #> [67] gtable_0.3.4 tzdb_0.4.0 #> [69] class_7.3-22 tidyr_1.3.1 #> [71] data.table_1.15.2 hms_1.1.3 #> [73] tidygraph_1.3.1 utf8_1.2.4 #> [75] XVector_0.42.0 ggrepel_0.9.5 #> [77] BiocVersion_3.18.1 foreach_1.5.2 #> [79] pillar_1.9.0 RcppHNSW_0.6.0 #> [81] later_1.3.2 splines_4.3.3 #> [83] tweenr_2.0.3 lattice_0.22-5 #> [85] survival_3.5-8 bit_4.0.5 #> [87] emdist_0.3-3 RProtoBufLib_2.14.0 #> [89] tidyselect_1.2.0 Biostrings_2.70.2 #> [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] ggplot2_3.5.0 readr_2.1.5 #> [119] blob_1.2.4 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/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_0.99.0 #> #> 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_1.2.7 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_1.2.0 bslib_0.6.1 #> [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_2.14.1 Matrix_1.6-5 #> [46] splines_4.3.3 nnet_7.3-19 igraph_2.0.2 #> [49] timechange_0.3.0 tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 RProtoBufLib_2.14.0 #> [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_0.4.8 #> [103] digest_0.6.34 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. 2.81 1.17 #> 2 229. -0.800 10.6 #> 3 292. 4.68 1.42 #> 4 431. -0.406 13.2 #> # 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_0.99.0 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.0 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_0.6.34 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.0 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.8 #> [19] tools_4.3.3 igraph_2.0.2 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_2.14.0 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_1.2.7 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.1 #> [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_0.6.1 #> [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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] ggplot2_3.5.0 dplyr_1.1.4 #> [19] tidytof_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] labeling_0.4.3 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 highr_0.10 #> [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] stringr_1.5.1 RcppHNSW_0.6.0 #> [83] later_1.3.2 splines_4.3.3 #> [85] tweenr_2.0.3 lattice_0.22-5 #> [87] survival_3.5-8 bit_4.0.5 #> [89] RProtoBufLib_2.14.0 tidyselect_1.2.0 #> [91] Biostrings_2.70.2 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 readr_2.1.5 #> [119] blob_1.2.4 listenv_0.9.1 #> [121] glmnet_4.1-8 viridisLite_0.4.2 #> [123] ipred_0.9-14 ggridges_0.5.6 #> [125] scales_1.3.0 prodlim_2023.08.28 #> [127] purrr_1.0.2 crayon_1.5.2 #> [129] 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_0.99.0 #> #> 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_1.2.7 #> [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_0.6.1 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_2.14.1 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.2 timechange_0.3.0 #> [46] tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.0 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_0.4.8 #> [97] digest_0.6.34 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_0.99.0 #> #> 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_1.2.7 #> [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_0.6.1 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_2.14.1 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.2 timechange_0.3.0 #> [46] tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.0 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_0.4.8 digest_0.6.34 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 (2024). tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles. R package version 0.99.0, https://keyes-timothy.github.io/tidytof/, https://keyes-timothy.github.io/tidytof.","code":"@Manual{, title = {tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles}, author = {Timothy Keyes}, year = {2024}, note = {R package version 0.99.0, https://keyes-timothy.github.io/tidytof/}, url = {https://keyes-timothy.github.io/tidytof}, }"},{"path":"https://keyes-timothy.github.io/tidytof/index.html","id":"tidytof-a-user-friendly-framework-for-interactive-and-highly-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… 3 cluster1 -0.0336 2.46 0.608 #> 2 H1_PhenoGraph_c… 7 cluster1 0.324 0.856 -0.116 #> 3 H1_PhenoGraph_c… 3 cluster1 0.532 2.67 0.909 #> 4 H1_PhenoGraph_c… 2 cluster1 0.0163 2.97 0.0725 #> 5 H1_PhenoGraph_c… 4 cluster1 0.144 2.98 0.128 #> 6 H1_PhenoGraph_c… 2 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: 24 × 3 #> phenograph_cluster .flowsom_metacluster n #> #> 1 cluster2 13 483 #> 2 cluster3 18 418 #> 3 cluster3 11 300 #> 4 cluster2 20 215 #> 5 cluster1 3 213 #> 6 cluster3 12 182 #> 7 cluster1 4 177 #> 8 cluster1 1 167 #> 9 cluster1 2 165 #> 10 cluster2 19 124 #> # ℹ 14 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 11 #> 2 7 #> 3 11 #> 4 16 #> 5 4 #> 6 16"},{"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 #> .tsne_1 .tsne_2 #> #> 1 7.44 -5.16 #> 2 5.64 -9.25 #> 3 -10.9 -25.6 #> 4 0.781 -17.2 #> 5 3.50 -7.82 #> 6 2.82 -24.9 # plot the tsne embeddings using color to distinguish between clusters phenograph_tsne |> ggplot(aes(x = .tsne_1, y = .tsne_2, 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 = .tsne_1, y = .tsne_2, 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 = 1:length(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-Rx 0.0395 0.618 0.0634 0.572 2.93 0.944 #> 2 UPN2 0.139 0.0662 0.0221 0.0825 2.25 0.454 #> 3 UPN3 0.633 0.0234 0.0165 0.0327 2.25 0.226 #> 4 UPN7 0.474 0.966 0.124 1.24 2.59 0.243 #> 5 UPN8 0.951 0.958 0.161 0.556 3.18 0.556 #> 6 UPN9 15.6 0.446 0.0445 0.163 2.86 0.434 #> # 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 UPN1 3.06 0.583 0.00449 0.164 1.94 0.416 #> 2 UPN6 5.62 0.550 0.00374 0.622 2.86 0.342 #> 3 UPN10 0.00374 0.761 0.000696 0.829 3.19 0.886 #> 4 UPN13 0.0634 0.0300 0.0219 0.109 2.34 0.314 #> 5 UPN22 3.29 1.63 0.128 0.525 3.38 0.688 #> 6 UPN22-Rx 0.0643 1.68 0.0804 1.56 3.06 0.529 #> # 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-10 #> # 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 directory","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the extension for a file — get_extension","text":"file extension `filename`","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"L2 normalize an input vector x to a length of 1 — l2_normalize","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"L2 normalize input vector x length 1","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L2 normalize an input vector x to a length of 1 — 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/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 6 #> 2 7 #> 3 15 #> 4 2 #> 5 20 #> 6 10 #> 7 3 #> 8 19 #> 9 20 #> 10 16 #> # ℹ 990 more rows tof_cluster_kmeans(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .kmeans_cluster #> #> 1 12 #> 2 15 #> 3 18 #> 4 8 #> 5 13 #> 6 19 #> 7 15 #> 8 15 #> 9 2 #> 10 8 #> # 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 2 #> 2 10 #> 3 10 #> 4 4 #> 5 3 #> 6 9 #> 7 1 #> 8 8 #> 9 11 #> 10 4 #> # 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 2 #> 3 7 #> 4 12 #> 5 10 #> 6 5 #> 7 7 #> 8 1 #> 9 13 #> 10 5 #> # 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 0.124 -0.0156 -2.05 0.0134 g #> 2 1.65 -1.39 -0.180 -0.710 c #> 3 0.636 -0.170 0.778 -1.97 a #> 4 -1.44 0.340 0.241 1.63 n #> 5 -0.876 -1.04 0.109 0.403 i #> 6 -2.78 -0.871 -0.725 0.717 d #> 7 0.809 -0.349 -0.243 -0.735 w #> 8 -1.10 -0.263 -2.10 -1.32 j #> 9 1.95 -0.584 -0.993 -0.651 p #> 10 1.99 -0.220 -1.03 -2.58 a #> # 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 0.797 0.755 1.77 0.876 r #> 2 -0.570 0.751 -1.00 -0.810 n #> 3 -0.450 -0.998 -0.986 1.78 w #> 4 -0.311 1.98 -0.719 -1.06 z #> 5 -0.669 -0.263 0.830 1.43 f #> 6 0.661 -0.696 -0.728 -0.639 l #> 7 -0.165 0.176 -0.203 0.0940 f #> 8 -0.770 0.292 0.309 0.132 t #> 9 0.490 1.07 -0.408 -0.541 a #> 10 0.874 0.445 1.02 -0.806 u #> # 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: 101 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 2.02 -0.234 1.02 -0.727 i #> 2 1.52 -0.652 2.83 -0.0350 q #> 3 0.281 -0.616 -0.00816 -1.07 k #> 4 1.79 -0.490 0.426 1.61 y #> 5 1.99 -0.220 -1.03 -2.58 a #> 6 1.43 1.35 0.391 -0.886 x #> 7 -0.335 1.80 -0.470 -0.747 r #> 8 -1.03 -0.145 -1.36 -1.08 c #> 9 0.630 -0.491 -0.990 -1.11 z #> 10 -0.110 0.501 0.903 -1.36 n #> # ℹ 91 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 -0.0579 -1.72 -1.42 0.289 x #> 2 2.73 0.172 0.177 1.18 h #> 3 0.538 -2.43 0.701 -0.222 n #> 4 -1.16 -0.397 1.38 -0.903 a #> 5 -0.210 0.404 -1.36 0.656 m #> 6 1.08 -0.877 0.165 -2.33 t #> 7 0.216 -1.20 0.776 -1.04 b #> 8 0.670 2.26 -0.317 0.340 s #> 9 0.468 0.819 0.0918 0.853 s #> 10 2.01 0.284 -0.721 -1.49 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 -1.16 -0.397 1.38 -0.903 a #> 2 -0.135 -0.177 -0.800 0.947 h #> 3 0.0699 -1.46 1.82 -0.283 g #> 4 0.840 1.47 0.305 -0.474 n #> 5 1.08 -0.877 0.165 -2.33 t #> 6 1.80 1.07 1.60 0.549 q #> 7 2.01 0.284 -0.721 -1.49 y #> 8 0.0317 -0.919 -0.970 0.168 t #> 9 -0.884 -0.334 0.0589 0.366 l #> 10 -1.09 -0.656 -1.48 -1.57 v #> # 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: 514 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -1.76 -0.130 0.750 0.569 #> 2 -0.325 0.995 1.42 -1.90 #> 3 0.139 -0.0432 -0.569 1.27 #> 4 -0.221 -1.13 1.43 0.595 #> 5 1.82 0.0462 0.937 -0.0783 #> 6 1.47 0.909 -1.03 -1.34 #> 7 -0.287 -1.11 0.295 -0.0843 #> 8 -0.467 -0.421 -0.487 -1.22 #> 9 0.311 -0.00684 0.877 -0.194 #> 10 1.41 0.107 -0.0705 0.292 #> # ℹ 504 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: 201 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -0.325 0.995 1.42 -1.90 #> 2 -1.48 -1.45 0.0642 -0.158 #> 3 1.82 0.0462 0.937 -0.0783 #> 4 0.311 -0.00684 0.877 -0.194 #> 5 0.118 0.233 -1.21 -0.548 #> 6 0.648 -1.34 -0.0295 -0.718 #> 7 -0.403 -1.06 0.418 -0.470 #> 8 1.14 -1.03 -0.990 0.589 #> 9 -0.105 -0.0490 0.514 -0.102 #> 10 0.0821 0.844 1.40 -0.0105 #> # ℹ 191 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: 226 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 0.113 -2.21 0.328 0.912 #> 2 -0.221 -1.13 1.43 0.595 #> 3 1.28 2.66 1.32 0.302 #> 4 0.648 -1.34 -0.0295 -0.718 #> 5 -0.508 0.647 0.783 0.965 #> 6 -0.382 -0.949 0.423 0.316 #> 7 1.29 -0.837 -0.445 -0.362 #> 8 0.118 -0.781 0.707 -1.72 #> 9 0.368 0.897 -2.19 0.497 #> 10 1.37 0.563 -0.343 0.968 #> # ℹ 216 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 -2.68 -0.366 0.00593 -0.150 x #> 2 -0.0284 0.659 0.234 0.469 g #> 3 -1.97 -0.272 -1.01 -0.245 h #> 4 -0.139 0.326 0.497 -1.61 q #> 5 -0.830 -0.881 -0.198 0.854 u #> 6 -0.971 1.23 -0.347 -0.173 m #> 7 1.53 0.522 0.843 2.24 t #> 8 1.43 1.73 -0.129 0.411 d #> 9 1.79 -1.61 0.990 1.02 g #> 10 -0.716 0.200 0.856 -0.306 e #> # 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 2.00 0.505 1.01 1.17 a #> 2 -0.239 -0.817 0.556 -0.381 a #> 3 -0.148 2.25 0.850 1.25 a #> 4 0.658 -2.70 -0.985 -1.30 a #> 5 -1.62 -0.0323 -1.14 1.48 b #> 6 0.300 -1.15 -1.44 1.39 b #> 7 1.22 -1.28 0.340 -1.08 b #> 8 0.128 -0.844 1.38 0.218 b #> 9 0.561 0.619 0.395 -0.0378 b #> 10 0.0478 1.47 -0.618 -1.26 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 -0.406 -0.0145 -1.41 -0.484 1 #> 2 -0.0872 0.00235 -0.843 -0.796 1 #> 3 0.901 -0.100 -1.44 -1.02 1 #> 4 -0.793 -2.06 0.599 1.23 1 #> 5 1.60 -1.71 -0.553 0.595 1 #> 6 -0.579 0.516 -0.501 -0.0455 1 #> 7 0.0192 -0.216 0.352 -0.482 1 #> 8 -0.00891 1.54 -0.209 -0.767 1 #> 9 0.936 1.95 -0.224 -0.195 1 #> 10 1.23 0.389 -0.0372 0.565 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 -0.406 -0.0145 -1.41 -0.484 1 #> 2 -0.0872 0.00235 -0.843 -0.796 1 #> 3 0.901 -0.100 -1.44 -1.02 1 #> 4 -0.793 -2.06 0.599 1.23 0 #> 5 1.60 -1.71 -0.553 0.595 0.4 #> 6 -0.579 0.516 -0.501 -0.0455 1 #> 7 0.0192 -0.216 0.352 -0.482 1 #> 8 -0.00891 1.54 -0.209 -0.767 1 #> 9 0.936 1.95 -0.224 -0.195 1 #> 10 1.23 0.389 -0.0372 0.565 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 -0.0756 -0.0766 0.725 0.0444 -0.172 #> 2 mario 0.144 -0.0654 -0.389 -0.189 0.265 #> # 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 -0.0756 #> 2 kirby a cd38 -0.0766 #> 3 kirby a cd34 0.725 #> 4 kirby a cd19 0.0444 #> 5 kirby b cd45 -0.172 #> 6 kirby b cd38 0.238 #> 7 kirby b cd34 -0.0607 #> 8 kirby b cd19 0.0159 #> 9 kirby c cd45 0.247 #> 10 kirby c cd38 -0.143 #> # 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@t_emd` `stim_cd38@t_emd` `stim_cd34@t_emd` #> #> 1 kirby 9.33 9.77 6.04 #> 2 mario NA NA NA #> # ℹ 101 more variables: `stim_cd19@t_emd` , `stim_cd45@d_emd` , #> # `stim_cd38@d_emd` , `stim_cd34@d_emd` , `stim_cd19@d_emd` , #> # `stim_cd45@s_emd` , `stim_cd38@s_emd` , `stim_cd34@s_emd` , #> # `stim_cd19@s_emd` , `stim_cd45@i_emd` , `stim_cd38@i_emd` , #> # `stim_cd34@i_emd` , `stim_cd19@i_emd` , `stim_cd45@l_emd` , #> # `stim_cd38@l_emd` , `stim_cd34@l_emd` , `stim_cd19@l_emd` , #> # `stim_cd45@j_emd` , `stim_cd38@j_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 t cd45 stim 9.18 #> 2 t cd38 stim 3.88 #> 3 t cd34 stim 7.49 #> 4 t cd19 stim 10.4 #> 5 d cd45 stim 7.16 #> 6 d cd38 stim 4.41 #> 7 d cd34 stim 4.79 #> 8 d cd19 stim 5.43 #> 9 s cd45 stim 14.1 #> 10 s cd38 stim 5.16 #> # 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 0.0234 0.0331 0.0507 0.0273 0.0409 0.0409 0.0331 #> 2 mario 0.0370 0.0390 0.0349 0.0513 0.0370 0.0370 0.0287 #> # 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 0.0234 0.0331 0.0507 0.0273 0.0409 0.0409 0.0331 #> 2 mario 0.0370 0.0390 0.0349 0.0513 0.0370 0.0370 0.0287 #> # 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@y_jsd` `stim_cd38@y_jsd` `stim_cd34@y_jsd` #> #> 1 kirby NA NA NA #> 2 mario NA NA NA #> # ℹ 101 more variables: `stim_cd19@y_jsd` , `stim_cd45@a_jsd` , #> # `stim_cd38@a_jsd` , `stim_cd34@a_jsd` , `stim_cd19@a_jsd` , #> # `stim_cd45@r_jsd` , `stim_cd38@r_jsd` , `stim_cd34@r_jsd` , #> # `stim_cd19@r_jsd` , `stim_cd45@t_jsd` , `stim_cd38@t_jsd` , #> # `stim_cd34@t_jsd` , `stim_cd19@t_jsd` , `stim_cd45@g_jsd` , #> # `stim_cd38@g_jsd` , `stim_cd34@g_jsd` , `stim_cd19@g_jsd` , #> # `stim_cd45@m_jsd` , `stim_cd38@m_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 y cd45 stim 0.788 #> 2 y cd38 stim 0.937 #> 3 y cd34 stim 0.812 #> 4 y cd19 stim 0.915 #> 5 a cd45 stim 0.813 #> 6 a cd38 stim 0.690 #> 7 a cd34 stim 0.923 #> 8 a cd19 stim 0.741 #> 9 r cd45 stim 0.737 #> 10 r cd38 stim 0.769 #> # 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 0.0281 0.0281 0.0561 0.0421 0.0341 0.0261 0.0561 #> 2 mario 0.0419 0.0379 0.0479 0.0379 0.0419 0.0220 0.0559 #> # 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 0.0281 #> 2 kirby b 0.0281 #> 3 kirby c 0.0561 #> 4 kirby d 0.0421 #> 5 kirby e 0.0341 #> 6 kirby f 0.0261 #> 7 kirby g 0.0561 #> 8 kirby h 0.0301 #> 9 kirby i 0.0321 #> 10 kirby j 0.0401 #> # 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 0.0769 0.0769 #> 2 mario 0 0.0667 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 #> 2 kirby a cd38 0.0769 #> 3 kirby a cd34 0.0769 #> 4 kirby a cd19 0.0769 #> 5 kirby b cd45 0.0769 #> 6 kirby b cd38 0 #> 7 kirby b cd34 0.0385 #> 8 kirby b cd19 0.115 #> 9 kirby c cd45 0 #> 10 kirby c cd38 0.0833 #> # 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,] 992 435 985 238 66 939 919 181 825 995 #> [2,] 153 154 787 895 289 432 114 784 379 774 #> [3,] 945 35 553 884 360 7 956 357 86 562 #> [4,] 556 616 499 964 211 688 89 761 155 805 #> [5,] 86 553 637 377 7 624 242 751 283 3 #> [6,] 830 941 940 569 678 256 323 356 199 851 #> [7,] 956 624 553 86 714 3 945 360 132 232 #> [8,] 822 604 76 493 740 229 547 514 222 578 #> [9,] 619 378 531 527 732 975 585 728 549 589 #> [10,] 266 575 291 863 469 900 268 376 838 638 #> [11,] 972 153 154 570 157 245 895 121 746 529 #> [12,] 600 235 911 331 617 468 367 615 134 23 #> [13,] 464 284 978 495 117 89 706 211 189 702 #> [14,] 325 555 879 622 812 799 433 672 123 176 #> [15,] 690 152 420 907 701 492 35 981 657 86 #> [16,] 140 64 162 164 143 552 230 366 243 167 #> [17,] 386 502 643 891 413 551 120 567 876 257 #> [18,] 341 909 533 598 995 340 487 351 724 66 #> [19,] 488 909 92 340 322 341 18 855 486 183 #> [20,] 372 856 507 943 72 315 309 22 136 655 #> [21,] 280 90 689 680 307 778 189 436 702 359 #> [22,] 72 118 943 309 507 796 856 824 20 470 #> [23,] 373 327 843 844 676 134 936 12 699 617 #> [24,] 29 742 122 826 334 847 756 178 382 955 #> [25,] 921 169 916 344 866 225 991 500 930 700 #> [26,] 380 93 196 758 452 911 158 78 77 891 #> [27,] 285 312 69 292 36 223 362 237 661 180 #> [28,] 440 62 52 226 908 241 677 71 217 878 #> [29,] 742 122 24 382 331 816 955 847 826 334 #> [30,] 68 855 461 974 952 735 183 472 614 345 #> [31,] 893 589 787 531 52 62 595 975 850 908 #> [32,] 205 835 375 477 79 315 953 151 136 713 #> [33,] 463 221 179 653 361 479 95 871 938 529 #> [34,] 971 539 1000 864 870 95 938 685 136 267 #> [35,] 3 884 562 360 553 690 945 751 86 511 #> [36,] 69 285 292 98 237 931 912 27 223 661 #> [37,] 40 756 214 982 178 632 130 334 780 640 #> [38,] 904 953 437 799 107 622 433 533 669 128 #> [39,] 719 223 285 777 292 661 795 246 193 362 #> [40,] 37 214 705 756 982 809 242 780 178 983 #> [41,] 723 496 661 385 50 36 755 519 98 312 #> [42,] 204 107 437 290 602 159 460 540 622 401 #> [43,] 71 878 391 440 62 28 106 914 764 247 #> [44,] 830 740 493 434 456 941 295 449 766 294 #> [45,] 793 883 329 780 239 338 442 195 268 384 #> [46,] 427 458 844 807 794 740 23 373 60 820 #> [47,] 129 75 245 162 278 918 234 54 972 570 #> [48,] 221 776 915 361 379 405 168 55 950 359 #> [49,] 587 438 534 538 168 796 856 545 792 22 #> [50,] 231 254 524 255 98 280 464 689 397 897 #> [51,] 818 920 178 990 113 847 982 813 576 91 #> [52,] 62 908 28 440 217 241 31 589 709 595 #> [53,] 966 94 335 82 431 454 116 189 469 579 #> [54,] 408 849 652 554 278 948 230 856 374 572 #> [55,] 950 776 959 789 801 171 842 582 663 317 #> [56,] 529 34 95 642 1000 938 354 539 630 245 #> [57,] 914 347 245 570 642 47 234 56 864 971 #> [58,] 949 937 601 220 870 462 829 267 862 630 #> [59,] 434 119 456 528 708 505 949 684 267 277 #> [60,] 794 218 699 515 373 516 514 763 294 508 #> [61,] 875 451 579 963 116 865 915 958 335 860 #> [62,] 52 440 28 908 31 241 217 589 878 709 #> [63,] 788 797 836 655 546 372 194 518 477 984 #> [64,] 366 140 552 16 143 243 126 490 85 164 #> [65,] 628 902 520 763 518 514 836 880 910 186 #> [66,] 425 181 825 598 533 472 726 182 18 390 #> [67,] 578 278 938 95 849 141 836 984 880 76 #> [68,] 952 345 461 735 974 183 156 370 30 567 #> [69,] 36 237 27 285 170 912 292 180 342 443 #> [70,] 676 711 820 806 775 78 93 750 843 465 #> [71,] 391 43 28 677 878 534 587 440 62 792 #> [72,] 22 309 943 631 20 824 372 507 856 135 #> [73,] 699 314 877 277 294 917 665 119 508 327 #> [74,] 519 292 719 755 362 661 557 39 618 285 #> [75,] 374 641 572 47 948 999 54 960 422 230 #> [76,] 880 578 141 980 836 858 902 67 822 984 #> [77,] 932 939 610 452 196 506 891 26 935 158 #> [78,] 93 617 158 321 777 465 373 26 989 750 #> [79,] 390 32 954 151 477 833 181 953 643 835 #> [80,] 498 679 19 488 486 81 416 402 92 712 #> [81,] 435 561 322 486 612 710 1 238 995 939 #> [82,] 94 365 933 694 963 431 53 743 363 900 #> [83,] 263 798 819 796 300 647 526 834 22 206 #> [84,] 517 281 525 248 378 687 233 549 355 673 #> [85,] 366 162 64 164 16 490 918 143 302 980 #> [86,] 553 5 7 714 956 3 35 624 232 360 #> [87,] 829 566 483 339 862 523 88 182 630 220 #> [88,] 802 957 924 871 194 463 339 568 630 829 #> [89,] 443 170 947 211 482 4 706 556 499 603 #> [90,] 778 280 689 359 21 436 524 399 950 931 #> [91,] 813 197 818 586 990 535 983 51 674 468 #> [92,] 488 19 30 855 542 909 183 857 351 487 #> [93,] 78 617 158 26 321 634 196 380 911 465 #> [94,] 82 365 53 694 933 963 431 363 61 900 #> [95,] 938 67 121 245 653 871 278 971 33 129 #> [96,] 326 680 861 970 922 791 21 654 853 307 #> [97,] 610 733 841 901 103 932 304 77 723 452 #> [98,] 254 223 285 36 661 292 931 69 39 795 #> [99,] 627 618 768 444 109 550 930 237 823 916 #> [100,] 370 754 183 935 977 345 729 952 68 156 #> [101,] 284 117 640 453 231 397 611 334 706 332 #> [102,] 299 450 738 809 945 571 536 831 790 3 #> [103,] 901 97 733 841 610 913 898 355 723 304 #> [104,] 287 848 228 236 804 336 960 573 313 374 #> [105,] 722 190 876 407 257 107 120 602 437 146 #> [106,] 497 247 471 986 289 532 633 635 874 489 #> [107,] 437 42 602 290 460 38 204 105 707 190 #> [108,] 139 714 522 330 212 771 232 377 860 215 #> [109,] 823 478 627 99 490 973 143 768 444 393 #> [110,] 946 259 367 12 854 235 191 753 600 844 #> [111,] 834 693 782 349 633 532 538 819 746 83 #> [112,] 353 419 629 811 536 563 639 313 667 175 #> [113,] 920 847 658 178 332 51 146 982 310 762 #> [114,] 432 436 778 969 774 359 895 784 90 379 #> [115,] 857 351 176 487 662 879 669 14 485 855 #> [116,] 875 61 579 189 335 702 451 727 964 668 #> [117,] 284 640 544 101 445 704 249 978 837 335 #> [118,] 593 545 22 306 943 961 438 470 72 796 #> [119,] 456 294 708 434 277 218 699 917 59 73 #> [120,] 551 190 658 17 876 386 502 105 643 257 #> [121,] 938 871 129 95 245 856 278 234 534 49 #> [122,] 29 24 178 334 847 742 756 816 586 813 #> [123,] 404 672 325 42 205 929 894 540 799 107 #> [124,] 163 523 506 181 192 829 786 541 954 872 #> [125,] 369 734 512 674 983 265 40 720 817 632 #> [126,] 973 143 64 366 552 288 140 671 243 490 #> [127,] 667 336 520 745 252 573 852 552 515 313 #> [128,] 559 833 473 217 182 405 350 381 803 38 #> [129,] 278 162 245 121 938 95 583 67 849 164 #> [130,] 632 615 756 262 334 753 122 367 596 265 #> [131,] 500 137 169 659 858 832 736 618 944 296 #> [132,] 146 297 826 624 227 409 956 7 441 876 #> [133,] 770 906 594 186 721 508 264 308 457 737 #> [134,] 844 373 967 617 344 308 264 955 235 331 #> [135,] 309 631 943 470 72 581 507 810 22 824 #> [136,] 194 477 655 984 315 20 871 462 797 802 #> [137,] 131 500 786 659 736 937 944 858 541 169 #> [138,] 675 821 614 348 645 602 783 574 415 437 #> [139,] 108 798 216 714 420 152 771 681 701 537 #> [140,] 552 143 243 64 16 745 520 490 366 252 #> [141,] 880 980 76 578 490 788 836 910 67 628 #> [142,] 759 544 730 146 722 297 624 958 956 105 #> [143,] 140 243 552 973 64 16 823 126 478 109 #> [144,] 415 411 485 662 138 614 979 675 204 783 #> [145,] 693 269 647 188 798 253 526 83 263 208 #> [146,] 722 132 544 142 759 297 876 737 658 826 #> [147,] 407 737 906 457 696 789 594 713 388 887 #> [148,] 282 406 491 283 637 705 869 982 576 242 #> [149,] 503 358 370 381 260 355 951 281 524 324 #> [150,] 346 762 453 332 977 754 413 371 742 621 #> [151,] 721 835 713 457 810 594 257 79 205 32 #> [152,] 681 657 300 216 907 690 15 798 981 389 #> [153,] 154 11 895 2 379 529 972 33 850 221 #> [154,] 153 11 895 157 2 391 972 289 570 529 #> [155,] 616 603 588 4 392 596 185 556 428 396 #> [156,] 726 345 567 472 935 183 413 598 68 260 #> [157,] 746 11 972 154 570 224 391 633 934 153 #> [158,] 321 196 465 93 78 891 251 634 26 380 #> [159,] 401 792 204 677 42 538 460 290 923 241 #> [160,] 859 967 994 716 344 867 866 700 428 398 #> [161,] 692 405 796 460 538 819 168 602 545 105 #> [162,] 16 129 278 164 85 918 583 849 230 64 #> [163,] 124 523 506 825 181 992 260 87 192 829 #> [164,] 16 478 823 162 143 583 490 109 140 141 #> [165,] 845 986 532 494 247 187 291 782 838 743 #> [166,] 840 312 392 27 715 170 428 814 69 89 #> [167,] 606 387 504 521 455 745 961 288 451 243 #> [168,] 538 796 792 49 405 534 587 241 315 48 #> [169,] 500 131 25 820 858 244 944 137 921 916 #> [170,] 443 69 482 89 342 706 912 36 947 556 #> [171,] 959 950 605 55 317 801 249 702 875 958 #> [172,] 271 582 568 768 560 399 924 698 926 463 #> [173,] 960 374 228 903 287 104 997 175 613 804 #> [174,] 637 200 576 406 305 873 144 148 415 751 #> [175,] 613 903 804 229 353 515 173 997 667 429 #> [176,] 857 879 351 433 669 487 585 783 115 799 #> [177,] 371 193 621 932 453 231 385 254 661 452 #> [178,] 756 982 122 51 847 113 920 214 334 24 #> [179,] 653 33 463 479 924 361 221 95 279 938 #> [180,] 795 237 285 69 706 892 223 27 393 342 #> [181,] 825 182 66 425 124 163 79 523 260 726 #> [182,] 181 425 381 128 260 833 825 350 726 324 #> [183,] 935 567 345 952 754 156 100 413 68 977 #> [184,] 368 940 419 752 15 356 448 690 475 511 #> [185,] 396 596 616 761 450 155 293 738 965 603 #> [186,] 508 133 518 763 546 388 594 890 628 721 #> [187,] 401 635 986 532 874 471 159 923 204 460 #> [188,] 572 269 999 263 641 145 234 693 83 274 #> [189,] 702 482 116 464 861 680 211 280 966 791 #> [190,] 707 821 120 602 437 105 460 658 107 551 #> [191,] 367 275 412 636 854 632 600 816 110 262 #> [192,] 560 557 124 506 541 172 786 271 698 296 #> [193,] 371 621 452 453 899 611 380 39 223 150 #> [194,] 802 136 462 797 477 871 829 88 655 63 #> [195,] 780 705 704 883 282 239 837 769 240 445 #> [196,] 158 251 891 939 26 77 380 322 93 634 #> [197,] 813 91 818 983 51 586 636 674 178 990 #> [198,] 548 272 316 352 987 739 325 896 672 328 #> [199,] 851 896 684 608 987 417 414 274 941 548 #> [200,] 576 873 675 415 174 406 637 535 990 409 #> [201,] 936 416 46 886 23 843 807 295 458 946 #> [202,] 530 137 250 131 500 296 858 311 169 659 #> [203,] 747 720 265 327 357 691 571 839 319 536 #> [204,] 42 401 159 460 107 437 602 540 622 799 #> [205,] 32 835 375 953 810 151 290 315 656 123 #> [206,] 773 976 300 438 819 118 681 545 263 306 #> [207,] 96 179 302 970 509 467 550 279 326 479 #> [208,] 677 894 656 226 526 587 269 792 290 375 #> [209,] 806 275 888 412 191 775 561 911 258 70 #> [210,] 725 418 717 591 649 476 668 213 455 474 #> [211,] 964 4 791 189 89 482 499 702 556 688 #> [212,] 330 522 785 730 283 869 645 692 142 297 #> [213,] 717 476 320 418 556 455 725 892 927 964 #> [214,] 40 242 809 756 37 982 178 297 624 640 #> [215,] 701 232 798 420 540 647 929 714 108 86 #> [216,] 681 564 976 426 860 865 300 714 819 389 #> [217,] 241 908 803 595 128 52 709 850 28 833 #> [218,] 294 708 60 699 508 902 119 456 514 794 #> [219,] 346 611 332 453 977 334 816 150 371 646 #> [220,] 58 862 601 949 919 87 937 267 988 354 #> [221,] 361 48 379 359 463 33 583 871 776 915 #> [222,] 8 613 604 577 175 903 740 225 229 822 #> [223,] 285 98 39 661 292 795 254 27 36 193 #> [224,] 746 633 934 157 349 111 648 363 834 782 #> [225,] 577 697 25 429 671 921 794 916 514 629 #> [226,] 375 28 677 208 315 792 587 534 217 241 #> [227,] 580 868 990 535 409 441 132 826 360 945 #> [228,] 287 104 848 960 374 569 236 256 652 804 #> [229,] 794 804 515 902 514 65 218 60 628 8 #> [230,] 824 422 336 16 652 773 374 54 881 745 #> [231,] 255 50 254 397 101 284 177 464 453 338 #> [232,] 714 929 215 574 540 7 108 798 86 212 #> [233,] 566 951 324 248 87 531 549 687 517 523 #> [234,] 570 587 534 121 972 49 856 188 391 864 #> [235,] 615 753 331 747 955 600 12 134 265 967 #> [236,] 256 848 907 104 287 772 573 356 228 678 #> [237,] 292 69 36 795 285 912 180 27 931 768 #> [238,] 612 1 995 919 992 905 390 528 465 985 #> [239,] 883 329 780 588 195 704 117 37 640 978 #> [240,] 837 704 727 579 445 978 335 431 668 730 #> [241,] 217 923 792 635 52 908 168 28 538 595 #> [242,] 297 624 214 785 982 132 809 553 956 283 #> [243,] 552 140 745 252 143 455 606 973 320 127 #> [244,] 169 820 500 25 131 766 858 137 427 815 #> [245,] 129 938 121 95 529 278 642 234 570 11 #> [246,] 777 750 344 866 719 39 331 991 731 617 #> [247,] 253 349 106 633 187 934 532 43 71 165 #> [248,] 281 951 324 517 233 728 355 358 549 260 #> [249,] 605 544 759 722 146 171 801 310 640 959 #> [250,] 202 832 131 550 296 748 137 530 500 169 #> [251,] 322 196 158 634 340 710 486 321 93 911 #> [252,] 320 243 476 745 261 455 127 649 552 667 #> [253,] 247 349 145 693 647 633 187 421 401 111 #> [254,] 98 397 223 899 50 285 621 582 317 706 #> [255,] 231 338 683 757 50 385 177 464 254 827 #> [256,] 236 848 356 772 287 907 104 228 960 274 #> [257,] 713 643 876 457 407 594 721 386 835 105 #> [258,] 888 682 561 939 610 729 998 77 97 19 #> [259,] 110 946 458 807 844 629 753 967 46 319 #> [260,] 825 324 381 728 951 726 182 523 156 472 #> [261,] 476 320 474 252 927 852 649 388 660 745 #> [262,] 130 334 632 646 588 219 101 756 623 611 #> [263,] 83 300 798 999 206 681 819 526 22 824 #> [264,] 308 955 770 133 580 882 826 571 388 747 #> [265,] 753 615 747 674 293 235 571 203 130 720 #> [266,] 10 575 863 291 268 638 376 838 469 808 #> [267,] 862 462 949 136 620 477 58 803 194 870 #> [268,] 575 808 484 863 469 298 827 266 838 491 #> [269,] 145 188 208 417 526 274 677 693 894 647 #> [270,] 800 917 631 810 749 678 459 581 423 135 #> [271,] 172 582 842 926 663 899 350 872 741 473 #> [272,] 352 325 316 14 208 894 548 555 677 540 #> [273,] 559 774 405 128 503 969 381 549 741 923 #> [274,] 526 656 417 631 896 199 894 72 269 208 #> [275,] 623 758 600 816 191 412 611 331 367 911 #> [276,] 494 614 144 491 783 348 735 662 461 138 #> [277,] 314 119 528 73 917 321 151 390 721 699 #> [278,] 129 67 849 162 938 95 245 121 54 578 #> [279,] 479 529 179 642 394 653 33 56 311 630 #> [280,] 90 689 21 702 171 189 524 436 778 950 #> [281,] 248 355 728 673 517 324 84 951 149 260 #> [282,] 869 148 283 491 846 481 446 195 785 780 #> [283,] 869 785 212 330 730 481 645 282 297 242 #> [284,] 464 706 117 101 13 702 171 827 231 397 #> [285,] 292 223 27 36 98 69 39 661 237 795 #> [286,] 970 947 50 21 467 443 897 680 170 41 #> [287,] 228 848 104 960 374 236 569 256 336 652 #> [288,] 606 167 973 387 143 243 584 654 521 688 #> [289,] 784 489 774 895 893 471 2 497 106 31 #> [290,] 507 107 796 42 315 792 656 894 205 375 #> [291,] 838 900 575 469 10 986 694 845 266 471 #> [292,] 285 237 36 223 27 39 795 74 69 661 #> [293,] 753 596 716 615 265 369 235 450 130 747 #> [294,] 218 119 456 699 708 73 917 508 270 434 #> [295,] 44 650 664 201 383 449 740 830 46 434 #> [296,] 192 557 560 339 698 659 618 403 924 736 #> [297,] 624 242 730 785 146 956 142 132 759 544 #> [298,] 268 808 735 442 863 952 484 575 68 30 #> [299,] 102 571 809 811 450 396 261 945 852 418 #> [300,] 681 263 824 976 206 83 881 216 999 152 #> [301,] 625 537 648 771 139 599 337 579 925 108 #> [302,] 85 642 479 918 311 279 179 653 164 162 #> [303,] 489 694 289 94 900 784 82 365 114 853 #> [304,] 932 736 719 519 74 557 452 610 97 750 #> [305,] 174 200 415 979 144 662 411 651 485 410 #> [306,] 593 118 583 545 438 773 961 22 206 167 #> [307,] 21 467 90 778 436 114 853 359 432 280 #> [308,] 264 955 770 388 133 967 571 882 826 134 #> [309,] 943 72 135 631 372 22 518 749 507 20 #> [310,] 484 821 658 735 249 190 120 113 952 605 #> [311,] 653 67 95 938 870 578 642 822 530 76 #> [312,] 27 362 392 285 731 661 519 496 223 840 #> [313,] 563 804 573 336 515 127 667 767 104 516 #> [314,] 277 73 665 917 151 592 119 721 810 400 #> [315,] 20 136 507 375 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[642,] 245 479 529 938 95 311 56 279 278 302 #> [643,] 386 17 257 473 713 663 350 954 502 120 #> [644,] 480 521 504 451 889 387 773 206 167 61 #> [645,] 675 785 821 138 212 574 920 658 576 283 #> [646,] 623 262 219 611 392 312 275 101 371 231 #> [647,] 693 798 145 83 420 215 540 263 701 526 #> [648,] 537 625 301 771 834 365 782 363 963 828 #> [649,] 660 745 591 961 881 455 474 476 210 261 #> [650,] 765 295 383 44 416 612 905 843 449 81 #> [651,] 873 679 410 535 91 200 990 734 751 498 #> [652,] 749 408 849 54 554 372 824 943 72 309 #> [653,] 179 95 33 311 924 938 630 463 870 88 #> [654,] 288 395 521 326 504 791 167 387 688 973 #> [655,] 984 477 836 136 63 372 194 462 20 788 #> [656,] 526 894 631 208 72 290 507 20 274 205 #> [657,] 389 426 681 152 981 216 591 300 976 881 #> [658,] 920 120 821 876 113 146 190 502 310 722 #> [659,] 541 786 444 137 829 910 858 192 141 937 #> [660,] 649 961 745 455 881 591 474 476 520 606 #> [661,] 223 285 519 98 39 292 362 74 36 27 #> [662,] 485 979 144 614 411 783 316 799 14 669 #> [663,] 926 842 789 906 737 407 317 271 643 386 #> [664,] 44 295 997 941 414 493 830 6 383 940 #> [665,] 423 868 877 917 314 800 810 73 270 151 #> [666,] 769 925 424 705 195 790 831 301 704 837 #> [667,] 127 852 252 429 313 552 811 243 336 745 #> [668,] 725 964 418 717 958 210 445 213 875 451 #> [669,] 487 799 533 724 904 176 598 783 38 351 #> [670,] 584 964 395 668 717 688 116 211 521 725 #> [671,] 429 398 252 667 552 126 243 973 225 994 #> [672,] 123 404 325 400 979 744 996 592 14 724 #> [673,] 728 281 355 619 998 542 472 248 609 260 #> [674,] 586 983 813 734 265 91 632 636 122 615 #> [675,] 645 138 574 415 576 821 200 409 920 658 #> [676,] 70 806 843 78 93 820 23 373 617 710 #> [677,] 792 208 587 159 226 534 538 28 290 894 #> [678,] 767 270 800 356 236 459 516 581 877 104 #> [679,] 498 402 651 91 410 535 468 990 712 818 #> [680,] 861 326 791 189 21 96 922 482 702 947 #> [681,] 216 976 300 564 426 152 389 881 657 206 #> [682,] 729 258 100 977 754 888 935 183 998 219 #> [683,] 757 338 255 454 808 827 863 231 503 484 #> [684,] 851 400 199 205 996 896 749 32 656 548 #> [685,] 347 608 505 864 971 34 760 547 414 554 #> [686,] 448 368 410 651 886 936 449 679 744 328 #> [687,] 549 969 525 774 358 951 517 248 784 566 #> [688,] 556 791 439 499 4 973 717 964 213 288 #> [689,] 931 280 90 21 912 399 524 359 950 702 #> [690,] 15 511 35 907 562 772 420 152 86 701 #> [691,] 357 516 767 882 319 884 747 313 563 536 #> [692,] 522 161 330 212 626 460 105 860 707 933 #> [693,] 647 145 798 111 83 263 269 188 834 962 #> [694,] 900 82 94 365 933 489 469 471 291 774 #> [695,] 502 724 441 151 904 533 658 567 382 17 #> [696,] 887 333 842 906 926 317 737 927 663 147 #> [697,] 225 629 398 577 994 429 160 671 25 967 #> [698,] 399 560 568 957 924 172 463 403 192 339 #> [699,] 73 294 218 60 119 373 877 277 508 314 #> [700,] 991 866 930 916 344 892 867 444 921 910 #> [701,] 215 420 798 377 232 647 108 139 86 690 #> [702,] 189 482 280 464 171 706 443 116 284 689 #> [703,] 761 239 185 588 329 603 965 396 155 616 #> [704,] 837 445 240 195 117 978 640 730 727 705 #> [705,] 195 769 780 40 790 214 704 148 282 242 #> [706,] 443 284 180 170 482 702 69 464 254 98 #> [707,] 602 190 460 437 821 107 348 559 105 120 #> [708,] 119 456 218 890 294 434 989 514 902 766 #> [709,] 483 908 803 217 52 620 862 595 425 62 #> [710,] 612 843 486 251 322 528 158 465 93 995 #> [711,] 70 815 465 676 820 612 736 775 304 137 #> [712,] 402 314 744 592 400 486 665 277 996 449 #> [713,] 257 457 835 721 594 151 643 407 810 147 #> [714,] 232 7 108 86 216 860 212 564 956 215 #> [715,] 166 814 840 343 428 312 731 392 362 27 #> [716,] 994 160 859 293 885 596 967 753 616 428 #> [717,] 213 210 418 455 725 476 606 668 320 649 #> [718,] 210 418 725 639 591 717 811 584 396 426 #> [719,] 39 777 750 519 74 246 362 452 292 661 #> [720,] 839 734 203 265 3 945 102 357 536 35 #> [721,] 457 594 713 151 810 835 257 917 133 508 #> [722,] 737 146 876 105 407 142 605 759 544 789 #> [723,] 41 496 733 661 519 177 385 304 362 932 #> [724,] 695 592 533 669 341 904 151 487 799 909 #> [725,] 418 210 668 717 213 476 591 474 649 964 #> [726,] 156 567 345 551 472 260 17 182 825 381 #> [727,] 978 240 837 335 966 116 579 189 211 964 #> [728,] 324 260 281 951 673 732 248 472 825 619 #> [729,] 977 682 754 100 219 346 332 935 453 177 #> [730,] 759 142 297 624 544 212 785 146 283 958 #> [731,] 312 392 623 246 600 859 362 750 331 27 #> [732,] 585 589 728 974 9 527 549 433 425 619 #> [733,] 97 103 723 610 304 932 496 901 841 898 #> [734,] 674 839 720 983 91 586 265 813 535 873 #> [735,] 461 68 974 952 821 310 484 345 190 348 #> [736,] 304 137 557 131 296 74 192 519 901 500 #> [737,] 906 876 722 147 696 663 826 407 594 887 #> [738,] 450 102 299 396 185 536 596 811 369 831 #> [739,] 198 328 548 383 115 14 316 352 272 879 #> [740,] 766 493 434 456 8 708 794 218 44 229 #> [741,] 350 473 381 582 568 872 801 899 551 271 #> [742,] 29 24 346 122 847 382 611 380 758 150 #> [743,] 365 942 431 446 846 828 82 481 626 845 #> [744,] 410 672 543 328 996 712 402 979 404 400 #> [745,] 649 660 243 455 961 606 591 252 881 552 #> [746,] 157 224 972 111 11 570 363 834 633 188 #> [747,] 571 955 235 319 264 753 580 308 265 615 #> [748,] 550 832 627 342 126 109 973 393 99 814 #> [749,] 631 309 652 943 372 270 72 518 20 135 #> [750,] 777 246 719 362 758 39 78 731 93 519 #> [751,] 873 35 360 3 562 553 5 637 839 86 #> [752,] 475 368 751 411 701 637 421 174 690 420 #> [753,] 615 235 293 265 747 130 571 596 367 331 #> [754,] 977 935 150 100 183 413 453 346 567 729 #> [755,] 74 36 292 618 519 237 931 912 69 661 #> [756,] 178 214 122 37 982 334 24 847 130 113 #> [757,] 683 338 255 454 863 231 50 827 808 575 #> [758,] 380 623 611 600 26 742 331 452 371 346 #> [759,] 544 142 730 146 958 249 722 297 624 605 #> [760,] 547 554 408 493 941 984 608 849 864 685 #> [761,] 4 616 185 603 396 703 155 964 211 805 #> [762,] 150 453 346 621 413 382 742 371 899 386 #> [763,] 388 628 65 186 902 520 508 991 910 514 #> [764,] 440 878 62 52 975 28 43 607 709 908 #> [765,] 905 612 238 650 919 59 528 710 995 843 #> [766,] 740 434 708 858 989 456 514 493 8 76 #> [767,] 678 516 563 691 313 800 270 236 581 884 #> [768,] 99 618 627 172 444 237 931 912 109 271 #> [769,] 790 705 925 831 5 553 86 242 283 195 #> [770,] 264 133 826 308 955 594 906 457 737 441 #> [771,] 860 522 537 819 834 579 108 963 865 648 #> [772,] 511 356 459 597 907 256 562 236 690 581 #> [773,] 206 438 306 504 593 118 387 545 167 976 #> [774,] 784 273 289 787 687 893 549 969 31 471 #> [775,] 70 304 750 343 806 820 711 519 676 736 #> [776,] 55 789 48 950 407 801 663 959 473 797 #> [777,] 246 719 750 39 866 344 78 991 452 617 #> [778,] 436 90 524 358 359 969 399 568 280 689 #> [779,] 467 307 286 897 21 970 403 524 436 96 #> [780,] 195 883 705 239 37 704 40 282 148 640 #> [781,] 928 233 339 913 394 517 248 354 403 87 #> [782,] 537 111 532 349 771 828 648 365 834 633 #> [783,] 348 614 799 138 622 669 433 437 461 176 #> [784,] 774 289 893 687 489 525 114 969 787 31 #> [785,] 283 645 297 212 242 730 869 624 146 920 #> [786,] 541 829 659 937 954 124 601 192 137 944 #> [787,] 850 595 31 566 774 379 531 217 549 273 #> [788,] 63 836 546 910 880 797 141 655 628 518 #> [789,] 407 776 663 55 926 842 801 147 722 887 #> [790,] 831 769 445 925 624 553 705 956 809 102 #> [791,] 326 482 922 680 688 211 499 443 189 947 #> [792,] 159 538 677 241 168 587 290 534 923 796 #> [793,] 45 268 883 384 338 827 942 683 575 329 #> [794,] 60 229 514 218 515 902 763 921 516 740 #> [795,] 842 237 180 292 223 285 39 926 317 930 #> [796,] 507 22 168 49 290 545 470 538 161 118 #> [797,] 63 194 315 788 833 473 136 477 776 546 #> [798,] 420 647 83 263 215 701 139 693 232 714 #> [799,] 622 669 38 783 904 433 42 348 437 107 #> [800,] 270 459 423 877 917 581 678 665 810 631 #> [801,] 605 317 55 899 959 789 663 741 551 842 #> [802,] 88 194 871 803 957 833 924 136 473 797 #> [803,] 217 595 802 850 833 709 128 267 182 315 #> [804,] 515 313 104 229 127 563 336 667 516 573 #> [805,] 499 4 885 89 428 688 616 556 603 761 #> [806,] 676 70 911 93 26 561 78 12 617 710 #> [807,] 458 46 886 448 259 516 629 319 203 327 #> [808,] 484 370 863 503 310 268 683 827 575 735 #> [809,] 214 945 299 624 956 242 571 102 297 756 #> [810,] 917 457 721 835 508 270 713 151 594 205 #> [811,] 852 299 667 261 319 313 127 536 252 571 #> [812,] 953 879 14 375 592 620 622 325 433 205 #> [813,] 586 197 91 983 674 818 51 178 636 122 #> [814,] 840 428 867 342 859 27 312 166 393 362 #> [815,] 711 70 820 676 244 775 736 612 137 765 #> [816,] 334 122 742 29 332 412 847 636 911 219 #> [817,] 734 498 983 125 674 91 197 854 651 586 #> [818,] 51 197 990 91 813 920 200 535 178 576 #> [819,] 834 545 206 860 83 865 771 438 161 976 #> [820,] 169 70 500 676 244 25 944 711 775 78 #> [821,] 190 461 658 138 707 735 645 310 120 602 #> [822,] 578 8 604 76 547 554 67 311 760 880 #> [823,] 478 109 143 164 490 99 788 444 627 910 #> [824,] 72 22 300 309 943 230 881 118 573 135 #> [825,] 260 181 66 182 163 726 472 523 425 324 #> [826,] 770 580 737 24 132 441 876 906 146 955 #> [827,] 454 484 249 335 284 808 683 310 966 605 #> [828,] 968 365 743 782 537 625 648 845 82 301 #> [829,] 541 786 462 194 937 58 924 954 124 601 #> [830,] 941 493 294 456 6 851 678 218 119 44 #> [831,] 790 769 553 86 956 102 981 925 624 945 #> [832,] 550 627 131 618 99 748 755 768 916 74 #> [833,] 473 128 315 350 802 803 797 643 182 194 #> [834,] 819 111 771 83 889 49 438 538 206 161 #> [835,] 713 32 205 810 721 457 151 507 257 315 #> [836,] 655 880 788 63 984 628 141 518 902 372 #> [837,] 240 704 445 727 978 668 579 117 335 730 #> [838,] 291 900 986 575 471 469 638 974 626 863 #> [839,] 720 734 3 751 945 873 35 553 357 203 #> [840,] 428 814 342 166 27 867 69 312 170 392 #> [841,] 610 97 901 163 992 355 77 103 932 825 #> [842,] 926 663 317 795 696 271 899 582 887 789 #> [843,] 710 612 676 23 416 486 989 78 528 806 #> [844,] 134 373 967 23 60 617 344 921 25 319 #> [845,] 165 743 986 968 900 532 828 291 365 782 #> [846,] 481 942 446 431 626 869 330 743 522 283 #> [847,] 332 113 334 122 742 346 178 150 920 24 #> [848,] 287 236 104 228 256 960 374 336 569 824 #> [849,] 408 54 554 278 652 67 372 856 578 836 #> [850,] 595 787 803 379 566 217 802 241 88 871 #> [851,] 199 684 896 987 941 548 996 608 400 414 #> [852,] 811 667 261 127 252 319 313 320 515 429 #> [853,] 432 114 307 861 96 21 680 94 509 359 #> [854,] 946 367 636 12 191 412 110 586 936 674 #> [855,] 351 598 472 857 487 30 18 176 533 909 #> [856,] 20 22 372 72 507 943 796 315 49 656 #> [857,] 115 351 176 487 855 669 879 662 783 585 #> [858,] 76 500 514 880 980 944 659 921 141 169 #> [859,] 160 716 867 428 731 344 994 967 814 246 #> [860,] 865 771 522 819 564 216 958 714 579 142 #> [861,] 680 189 326 791 116 21 96 702 482 211 #> [862,] 267 620 220 483 87 949 58 709 919 803 #> [863,] 575 808 266 503 268 10 683 484 469 370 #> [864,] 971 34 347 685 1000 539 408 234 856 849 #> [865,] 860 451 958 564 819 61 545 976 875 216 #> [866,] 991 344 700 916 930 921 246 867 777 444 #> [867,] 393 700 916 342 866 814 237 428 930 344 #> [868,] 665 227 423 409 441 360 535 990 457 877 #> [869,] 283 481 330 212 785 282 846 446 730 626 #> [870,] 462 58 949 937 984 194 829 136 630 267 #> [871,] 802 194 88 136 121 95 797 938 33 924 #> [872,] 506 899 621 741 582 271 350 193 523 381 #> [873,] 535 751 200 409 360 651 576 990 415 637 #> [874,] 497 187 589 986 635 471 975 52 555 62 #> [875,] 61 116 958 451 865 579 959 668 171 950 #> [876,] 737 722 257 386 441 120 105 658 906 594 #> [877,] 665 423 800 73 270 917 868 699 996 314 #> [878,] 914 71 28 43 62 391 1000 440 52 764 #> [879,] 176 812 433 14 622 351 669 799 953 908 #> [880,] 141 836 980 76 578 628 788 910 902 65 #> [881,] 591 649 573 976 660 336 745 564 824 681 #> [882,] 580 581 357 264 884 474 571 308 691 945 #> [883,] 239 780 329 195 640 704 45 442 793 978 #> [884,] 581 35 562 357 3 360 423 882 511 907 #> [885,] 994 398 428 716 616 805 160 639 859 4 #> [886,] 327 448 699 458 23 73 877 516 936 60 #> [887,] 696 333 317 927 842 737 926 906 147 789 #> [888,] 258 682 561 610 97 209 998 939 729 733 #> [889,] 438 963 363 915 644 834 49 545 306 819 #> [890,] 708 186 546 989 944 508 321 954 477 628 #> [891,] 17 196 386 380 158 634 413 643 321 502 #> [892,] 320 439 393 700 696 213 476 180 887 930 #> [893,] 31 784 774 531 589 497 975 289 549 787 #> [894,] 526 656 208 540 929 290 677 123 42 205 #> [895,] 153 379 363 432 289 154 11 889 915 221 #> [896,] 987 199 851 548 417 274 684 597 996 608 #> [897,] 403 399 524 931 689 755 467 698 98 36 #> [898,] 103 913 733 385 723 41 355 403 97 998 #> [899,] 621 872 582 801 317 397 842 271 741 663 #> [900,] 694 291 469 838 471 986 82 489 365 933 #> [901,] 97 841 103 610 736 733 163 992 304 932 #> [902,] 628 65 514 763 880 836 518 218 910 520 #> [903,] 613 175 604 173 366 64 804 229 353 997 #> [904,] 38 953 799 533 669 437 724 107 695 433 #> [905,] 919 238 985 620 765 995 1 862 220 601 #> [906,] 737 133 594 663 696 147 926 770 876 457 #> [907,] 236 772 573 581 981 884 690 511 300 152 #> [908,] 52 217 433 62 709 589 622 440 28 241 #> [909,] 341 18 340 598 533 487 322 724 995 251 #> [910,] 546 444 788 628 930 880 991 141 763 902 #> [911,] 26 380 93 816 758 12 617 251 29 600 #> [912,] 931 237 36 69 768 443 170 689 482 292 #> [913,] 403 517 248 355 781 281 358 103 841 233 #> [914,] 878 347 1000 570 539 57 43 864 391 234 #> [915,] 963 48 933 889 61 405 161 545 776 168 #> [916,] 930 700 991 866 867 444 344 921 627 25 #> [917,] 810 270 508 800 721 665 457 835 314 277 #> [918,] 162 129 164 85 245 583 278 654 479 47 #> [919,] 601 905 238 220 985 620 862 949 181 267 #> [920,] 113 658 51 132 146 847 178 382 645 675 #> [921,] 991 866 25 344 514 916 930 700 763 944 #> [922,] 947 791 326 499 680 482 89 970 443 211 #> [923,] 635 241 538 792 471 405 159 168 161 217 #> [924,] 88 957 463 339 698 802 172 194 829 568 #> [925,] 769 377 869 108 790 5 283 831 86 139 #> [926,] 663 842 696 906 789 271 546 795 317 887 #> [927,] 333 476 887 261 320 696 474 213 892 388 #> [928,] 781 354 394 87 233 220 339 630 483 58 #> [929,] 540 232 526 894 123 459 656 574 42 290 #> [930,] 916 700 444 991 866 910 627 99 867 344 #> [931,] 912 689 36 98 237 768 69 897 292 443 #> [932,] 77 452 610 304 506 193 371 177 621 872 #> [933,] 963 915 82 692 363 365 694 94 161 61 #> [934,] 633 349 224 247 106 157 111 746 253 782 #> [935,] 754 183 567 413 156 345 100 977 726 150 #> [936,] 416 23 468 886 327 946 12 201 843 486 #> [937,] 58 949 601 462 829 786 541 870 659 220 #> [938,] 95 121 245 67 278 971 871 653 129 33 #> [939,] 196 77 561 322 992 340 935 251 26 158 #> [940,] 6 987 356 256 896 323 569 597 543 851 #> [941,] 830 493 199 851 760 414 547 6 554 456 #> [942,] 431 846 481 446 626 743 330 522 869 365 #> [943,] 309 72 372 22 135 20 518 507 631 118 #> [944,] 989 890 921 786 465 858 78 500 321 659 #> [945,] 3 956 553 580 7 809 35 624 571 882 #> [946,] 110 854 12 259 367 936 235 23 753 844 #> [947,] 922 443 170 970 89 482 791 326 680 286 #> [948,] 569 54 572 408 274 75 374 417 228 960 #> [949,] 58 937 601 462 870 267 220 829 862 541 #> [950,] 55 959 171 776 789 801 359 875 48 842 #> [951,] 324 381 566 248 233 549 260 728 182 358 #> [952,] 68 345 183 461 735 567 821 370 413 310 #> [953,] 38 375 904 812 205 32 622 799 833 79 #> [954,] 643 79 541 321 350 473 477 546 829 786 #> [955,] 264 308 770 580 747 571 826 29 235 24 #> [956,] 624 7 945 297 553 142 474 132 3 86 #> [957,] 88 568 924 802 698 463 350 473 172 339 #> [958,] 875 759 865 544 142 668 860 959 451 61 #> [959,] 171 950 55 605 801 317 789 776 842 887 #> [960,] 374 287 228 848 569 104 256 236 173 572 #> [961,] 660 745 455 649 593 118 520 167 306 881 #> [962,] 420 599 693 647 798 139 701 145 152 15 #> [963,] 933 915 61 889 363 82 771 579 94 834 #> [964,] 668 211 213 4 717 556 725 688 418 670 #> [965,] 185 603 596 588 155 761 616 703 293 396 #> [966,] 53 335 189 727 978 454 827 464 702 116 #> [967,] 134 319 160 308 844 344 264 747 235 955 #> [968,] 828 845 743 365 165 782 625 537 446 82 #> [969,] 687 358 436 778 525 549 273 774 517 114 #> [970,] 947 912 286 326 443 931 680 170 922 482 #> [971,] 864 34 938 95 67 1000 849 278 121 539 #> [972,] 11 570 746 234 49 157 121 889 153 534 #> [973,] 143 126 439 109 243 288 393 688 140 552 #> [974,] 735 68 461 585 348 732 345 783 707 821 #> [975,] 9 31 589 893 62 52 531 874 908 440 #> [976,] 564 681 216 881 206 300 118 426 865 649 #> [977,] 754 150 346 453 729 332 935 100 183 219 #> [978,] 727 837 13 240 966 117 704 335 211 464 #> [979,] 662 415 485 404 672 799 669 123 487 144 #> [980,] 141 490 880 76 578 910 858 788 836 444 #> [981,] 881 907 657 573 591 426 681 152 564 336 #> [982,] 178 242 756 214 113 51 406 920 37 785 #> [983,] 674 813 586 734 197 91 178 632 982 756 #> [984,] 655 477 136 836 462 194 870 63 372 20 #> [985,] 1 919 992 435 238 905 66 220 620 181 #> [986,] 471 532 187 635 874 838 497 900 923 291 #> [987,] 896 199 548 851 417 414 608 6 274 684 #> [988,] 607 220 862 505 620 539 483 267 34 1000 #> [989,] 944 890 708 373 528 78 321 119 921 218 #> [990,] 535 227 51 409 818 91 868 441 920 873 #> [991,] 866 700 930 344 916 921 444 910 763 546 #> [992,] 1 939 163 238 825 181 66 985 435 77 #> [993,] 769 637 148 705 5 751 839 174 40 925 #> [994,] 716 885 160 398 859 697 428 629 671 429 #> [995,] 390 18 592 341 238 909 612 322 251 340 #> [996,] 400 543 800 449 877 684 665 672 459 270 #> [997,] 613 173 323 175 903 228 804 104 960 287 #> [998,] 355 673 841 281 682 542 258 610 100 97 #> [999,] 572 641 188 263 300 206 773 374 83 824 #> [1000,] 539 34 864 971 226 803 878 595 850 28 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.37440774 0.5196210 0.6317683 0.6323538 0.8849260 0.8892560 0.9006937 #> [2,] 0.71849076 0.8712022 0.8970041 0.9249884 0.9328638 0.9559555 0.9912308 #> [3,] 0.33816647 0.3531914 0.4294108 0.5196530 0.5297383 0.5577980 0.6443834 #> [4,] 0.54020961 0.6153700 0.6328276 0.6360577 0.6708464 0.6854089 0.7296972 #> [5,] 0.39339787 0.4755666 0.6066615 0.7330094 0.7611787 0.7646775 0.7696904 #> [6,] 0.81778517 0.8393568 0.8864097 0.8899425 0.8930137 0.9004203 0.9592937 #> [7,] 0.31336098 0.4734230 0.4790972 0.5125694 0.5484259 0.5577980 0.5839837 #> [8,] 0.69429370 0.6999343 0.8040632 0.8374759 0.8581814 0.8620620 0.9037422 #> [9,] 0.49870522 0.5159316 0.5161472 0.6504423 0.7226073 0.7458232 0.8078439 #> [10,] 0.43286308 0.6523323 0.8222796 0.8646028 0.9897671 1.0654198 1.0962131 #> [11,] 0.41437972 0.5560717 0.6561674 0.7609491 0.7669037 0.8590798 0.8752580 #> [12,] 0.67937879 0.7172980 0.7480645 0.7544614 0.8050573 0.8083640 0.8481635 #> [13,] 0.52070502 0.6684482 0.7688076 0.7823320 0.8670848 0.9316208 0.9584593 #> [14,] 0.41137800 0.5708362 0.5865694 0.6090094 0.6456530 0.6700576 0.7213540 #> [15,] 0.59022782 0.7694994 0.8918625 1.0814758 1.1290545 1.1519124 1.1534854 #> [16,] 0.45092441 0.4958946 0.5104549 0.5122970 0.5530641 0.6073729 0.6617331 #> [17,] 0.21549985 0.3255899 0.3544750 0.4664532 0.4715509 0.4860027 0.4879230 #> [18,] 0.28419387 0.3691577 0.5267358 0.5449690 0.5967973 0.5985593 0.6283001 #> [19,] 0.60423508 0.9040169 0.9165124 1.0119967 1.0878704 1.1063906 1.1405633 #> [20,] 0.28229287 0.3660425 0.4033861 0.4280612 0.4487782 0.4534048 0.4866329 #> [21,] 0.53505778 0.5400066 0.6122105 0.7306741 0.7555545 0.8497530 0.8610543 #> [22,] 0.21870056 0.3441216 0.3792610 0.4279712 0.4352831 0.4734599 0.4760026 #> [23,] 0.69971236 0.7776224 0.7876056 0.7994573 0.8586295 0.8605815 0.8819863 #> [24,] 0.38762218 0.4285535 0.4381970 0.5334751 0.6623435 0.6643578 0.6768378 #> [25,] 0.53268027 0.6801124 0.6909970 0.7254717 0.7283049 0.7764582 0.7892815 #> [26,] 0.43329051 0.5178387 0.5360601 0.5642680 0.5666451 0.5944099 0.6307465 #> [27,] 0.30426516 0.3859418 0.4251838 0.4719293 0.5115982 0.5455742 0.5471404 #> [28,] 0.42629419 0.4520204 0.4723838 0.5216885 0.5984226 0.6227925 0.6259317 #> [29,] 0.35403636 0.3840472 0.3876222 0.5544435 0.5753758 0.6441560 0.6696159 #> [30,] 0.70399445 0.8185857 0.8728922 0.8887481 0.9390512 0.9504278 0.9534198 #> [31,] 0.54620478 0.5678647 0.5794177 0.5859331 0.6016578 0.6631736 0.6973421 #> [32,] 0.32203562 0.3870360 0.4510068 0.4565310 0.5181098 0.5219980 0.5803326 #> [33,] 0.59842366 0.6252209 0.6259422 0.6469902 0.6731264 0.6884798 0.6987678 #> [34,] 0.42295739 0.4799947 0.4822970 0.5100279 0.7521974 0.7531811 0.7617941 #> [35,] 0.35319143 0.4240218 0.5113439 0.5796505 0.5815733 0.6434926 0.6531255 #> [36,] 0.28798006 0.3680010 0.4005744 0.4271074 0.4391532 0.4795506 0.4806273 #> [37,] 0.46960421 0.5704743 0.5713747 0.7433508 0.8263683 0.9032159 0.9188813 #> [38,] 0.16379892 0.4343415 0.4634704 0.4812532 0.5262166 0.5456857 0.5900957 #> [39,] 0.32942541 0.3488723 0.4544843 0.4706264 0.4737524 0.5055472 0.5182899 #> [40,] 0.46960421 0.5094448 0.8000923 0.8405120 0.8940577 0.9118995 0.9520868 #> [41,] 0.56070439 0.8039226 0.8458749 0.9259973 0.9706053 1.0019942 1.0221179 #> [42,] 0.26218164 0.3335003 0.4401235 0.4973035 0.5241576 0.5242244 0.5406525 #> [43,] 0.63571193 0.7382393 0.9638590 1.0666145 1.0770948 1.0874487 1.0945852 #> [44,] 0.98042680 0.9936126 1.0111360 1.0134749 1.0909802 1.1755655 1.2292283 #> [45,] 0.96471043 1.1360815 1.2717714 1.5203067 1.5482659 1.7086648 1.7576296 #> [46,] 0.92897311 1.0798204 1.2356634 1.2538734 1.2887295 1.3175664 1.3497319 #> [47,] 0.89238862 0.9257327 0.9620712 0.9739201 0.9943237 1.1340280 1.1505984 #> [48,] 0.44108247 0.4464113 0.4928736 0.5033910 0.5245417 0.5959135 0.6397210 #> [49,] 0.39469461 0.3966216 0.4374424 0.4706606 0.4847577 0.4929153 0.6373957 #> [50,] 0.62219362 0.6576409 0.7388219 0.7729828 0.7943559 0.8368663 0.8794120 #> [51,] 0.30483279 0.4510571 0.5991109 0.6021361 0.6431851 0.6820095 0.6822274 #> [52,] 0.16342217 0.3812533 0.4723838 0.4917449 0.5672412 0.6012372 0.6016578 #> [53,] 0.47146486 0.6398191 0.6982818 0.7187265 0.7796118 0.9114307 0.9247849 #> [54,] 0.32811395 0.4597374 0.5698973 0.6545683 0.7488184 0.7636551 0.8007657 #> [55,] 0.26351243 0.2898434 0.3279429 0.3904213 0.4334976 0.4823644 0.5486852 #> [56,] 0.77615810 0.8922126 0.9410711 0.9435230 0.9454779 0.9498508 0.9649450 #> [57,] 1.01660350 1.0576989 1.0578482 1.0948736 1.1998130 1.2010383 1.2067120 #> [58,] 0.31354966 0.3359187 0.4454083 0.5013944 0.5523149 0.5649255 0.6207553 #> [59,] 0.54093924 0.6735608 0.6915943 0.7171179 0.7925492 0.8258154 0.8404956 #> [60,] 0.39442631 0.5581936 0.6069573 0.6558298 0.6593543 0.6951015 0.7095590 #> [61,] 0.42184296 0.4722252 0.4810669 0.5213441 0.5457783 0.5978246 0.6324206 #> [62,] 0.16342217 0.4269498 0.4520204 0.5193114 0.6631736 0.7057671 0.7128491 #> [63,] 0.22126147 0.3532780 0.4414033 0.4531874 0.4647006 0.5107906 0.5602252 #> [64,] 0.33416754 0.4265877 0.4359787 0.4958946 0.5433790 0.6881546 0.7001640 #> [65,] 0.23679109 0.2815989 0.3487280 0.4295142 0.5025708 0.5573627 0.6129167 #> [66,] 0.44637144 0.5003019 0.5529245 0.6283844 0.6367535 0.6652489 0.7487575 #> [67,] 0.33537141 0.5092862 0.5898424 0.5930201 0.6001093 0.6728713 0.6872553 #> [68,] 0.37747986 0.4014896 0.4185361 0.4482289 0.5682722 0.5703633 0.6131069 #> [69,] 0.28798006 0.3927377 0.4251838 0.4288953 0.4558640 0.4906690 0.5028235 #> [70,] 0.54611205 0.6926403 0.7215123 0.7658092 0.8311058 0.9560666 1.0400445 #> [71,] 0.47206866 0.6357119 0.6360646 0.6926937 0.6989833 0.7644924 0.7893722 #> [72,] 0.21870056 0.2927399 0.2999043 0.3591081 0.4487782 0.4585332 0.4617932 #> [73,] 0.30035462 0.5001202 0.5593482 0.5631557 0.6212020 0.6350773 0.6398905 #> [74,] 0.30645224 0.5000964 0.5348828 0.5447165 0.5564983 0.5910982 0.6571445 #> [75,] 0.73137409 0.7876294 0.7927559 0.9257327 0.9287029 0.9333650 0.9570342 #> [76,] 0.40150325 0.4310171 0.4984940 0.5758994 0.5810228 0.5926704 0.7203417 #> [77,] 0.42677834 0.5655940 0.5780670 0.5869566 0.5955668 0.6814462 0.6902135 #> [78,] 0.24603251 0.4255550 0.5281018 0.6320025 0.6766829 0.6816221 0.7057718 #> [79,] 0.44042104 0.5181098 0.5632566 0.5853790 0.5921010 0.6301329 0.6573707 #> [80,] 1.17838407 1.4621528 1.4891457 1.6146337 1.6568477 1.6983617 1.7513378 #> [81,] 0.97605309 1.0511801 1.0518557 1.0868473 1.1142342 1.1686700 1.1819750 #> [82,] 0.30897320 0.4068803 0.5589393 0.5719344 0.6460923 0.6503385 0.7187265 #> [83,] 0.29770340 0.5268685 0.5810016 0.5937661 0.6260777 0.6646483 0.6767827 #> [84,] 0.73278181 0.8382064 0.8712902 0.9346618 0.9439012 1.1061611 1.2219257 #> [85,] 0.63396681 0.7252764 0.8155012 0.8245226 0.8380611 0.9923770 0.9993802 #> [86,] 0.32458341 0.3933979 0.5125694 0.6410320 0.6680742 0.6691817 0.6752053 #> [87,] 0.66478616 0.7107108 0.7159460 0.7237704 0.7282900 0.7332213 0.7434692 #> [88,] 0.30996952 0.3251140 0.3526699 0.5128921 0.5429316 0.6395256 0.6586995 #> [89,] 0.57281868 0.5758453 0.7006440 0.7207689 0.7258189 0.7296972 0.7965332 #> [90,] 0.39852216 0.4400132 0.4526669 0.5161778 0.5400066 0.5461164 0.6188844 #> [91,] 0.48933920 0.6051880 0.6382770 0.6502117 0.6693098 0.7108046 0.7692118 #> [92,] 0.62260206 0.9165124 1.3085745 1.3806910 1.5870406 1.6285594 1.6554045 #> [93,] 0.24603251 0.3878538 0.4591714 0.5178387 0.6585143 0.6621358 0.6810870 #> [94,] 0.30897320 0.6290901 0.6398191 0.6496431 0.7823336 0.7843597 0.8580708 #> [95,] 0.04497083 0.5930201 0.6119075 0.6270330 0.6429569 0.6600823 0.6656487 #> [96,] 0.62583943 0.7505155 0.9832066 1.0235993 1.0636559 1.1201775 1.1666406 #> [97,] 0.35144018 0.6382641 0.7079532 0.7095187 0.7659251 0.8091797 0.8513275 #> [98,] 0.31801358 0.3401989 0.4208660 0.4271074 0.5054884 0.5429898 0.5665091 #> [99,] 0.21601832 0.3354368 0.3484150 0.3856758 0.5485465 0.5505203 0.5557009 #> [100,] 0.41366461 0.5126158 0.5594749 0.5739571 0.6100644 0.6774000 0.7450978 #> [101,] 0.66441475 0.7549090 0.7557675 0.7765707 0.7936885 0.7990604 0.8064913 #> [102,] 0.46871727 0.5296746 0.6837906 0.7008518 0.7655323 0.8326505 0.8770413 #> [103,] 0.76556108 0.7659251 0.7682346 0.9744743 1.0745172 1.1330448 1.1399362 #> [104,] 0.30557984 0.3708418 0.3794128 0.5570809 0.5645912 0.6157789 0.6886539 #> [105,] 0.42014793 0.4929323 0.5075120 0.5230954 0.5328842 0.5333565 0.5456871 #> [106,] 0.60144678 0.9305805 0.9306688 0.9501760 0.9659964 1.0453325 1.0542669 #> [107,] 0.26191941 0.3335003 0.3864874 0.4739008 0.4901712 0.5262166 0.5277461 #> [108,] 0.53526753 0.5824204 0.5919049 0.6908379 0.6932066 0.6949184 0.7062489 #> [109,] 0.32366596 0.3816655 0.5062977 0.5485465 0.6035720 0.6073536 0.6347685 #> [110,] 0.65787838 0.8523714 1.1802698 1.3560210 1.3630859 1.3759184 1.3968075 #> [111,] 0.50862957 0.6866783 0.7279623 0.7948560 0.8165282 0.8374849 0.8375440 #> [112,] 1.55852407 1.5673626 1.6632869 1.6701479 1.7246864 1.7747710 1.7934094 #> [113,] 0.36644829 0.4296335 0.5638167 0.6098110 0.6319255 0.6431851 0.6599731 #> [114,] 0.27515565 0.7833691 0.7984154 0.8626899 0.8700936 0.8907433 0.9139923 #> [115,] 0.38214516 0.6842062 0.7184307 0.8236605 0.8753309 0.9271065 0.9900302 #> [116,] 0.45027271 0.5457783 0.6602727 0.6741195 0.6863318 0.7091045 0.7172088 #> [117,] 0.56160326 0.6429118 0.7370743 0.7549090 0.7710974 0.7860382 0.7889454 #> [118,] 0.23376156 0.3276499 0.3441216 0.3508093 0.5015056 0.5053651 0.5176712 #> [119,] 0.31583768 0.3737276 0.4558761 0.5437083 0.5458634 0.5974658 0.6560051 #> [120,] 0.34765690 0.3987382 0.4601763 0.4879230 0.5018809 0.5067646 0.5416199 #> [121,] 0.58701147 0.5952777 0.6049412 0.6119075 0.6268535 0.6821388 0.7100069 #> [122,] 0.38404719 0.4381970 0.4623692 0.4922980 0.5168948 0.5305939 0.5628925 #> [123,] 0.35215632 0.3951595 0.5682765 0.5992893 0.6292636 0.6469049 0.6536885 #> [124,] 0.37061007 0.3711900 0.3871380 0.5807237 0.5986405 0.6415935 0.6531730 #> [125,] 1.16275048 1.2245416 1.2926636 1.3602846 1.3698820 1.4084729 1.4638888 #> [126,] 0.50990188 0.6099109 0.7001640 0.7341859 0.7767447 0.7813376 0.7953612 #> [127,] 0.38172029 0.5355710 0.5596657 0.5633162 0.5823183 0.5929949 0.6202899 #> [128,] 0.32678210 0.4734693 0.5506425 0.5621268 0.5665520 0.5690209 0.6215555 #> [129,] 0.32240302 0.5219120 0.5762402 0.6049412 0.6920584 0.7313320 0.7474944 #> [130,] 0.49883170 0.6053445 0.7167148 0.7190791 0.7574800 0.7613518 0.7938191 #> [131,] 0.53564742 0.6130721 0.6764559 0.7958312 0.8108554 0.8286614 0.8786131 #> [132,] 0.42579509 0.5151876 0.5341619 0.5671062 0.5755747 0.5903237 0.6075006 #> [133,] 0.35147778 0.3636692 0.3647389 0.4423911 0.5608529 0.5691907 0.5779306 #> [134,] 0.39681078 0.4251424 0.5178433 0.5361624 0.6805308 0.6867334 0.7135838 #> [135,] 0.30942474 0.4231522 0.4267138 0.5000046 0.5050926 0.5430369 0.5739390 #> [136,] 0.41118245 0.4267375 0.4318495 0.4339700 0.4768650 0.5465379 0.5586927 #> [137,] 0.61307211 0.6772350 0.7043720 0.7118153 0.8149391 0.8453201 0.8556896 #> [138,] 0.43229124 0.5113181 0.5293639 0.5827702 0.5852424 0.5925780 0.6027970 #> [139,] 0.53526753 0.6705211 0.7233836 0.7638404 0.7897678 0.7912746 0.8126103 #> [140,] 0.25761193 0.3248417 0.3820281 0.4265877 0.4509244 0.6123456 0.6371633 #> [141,] 0.21551060 0.2478560 0.4984940 0.5043320 0.5153898 0.5377246 0.5380901 #> [142,] 0.23215070 0.3977784 0.4000386 0.4393166 0.4936062 0.5116403 0.5226528 #> [143,] 0.32484169 0.4430272 0.4500779 0.4606841 0.5433790 0.5530641 0.5891766 #> [144,] 0.70360030 0.7115000 0.7115202 0.7263464 0.7909070 0.8401681 0.8523100 #> [145,] 0.53097493 0.5426041 0.5660205 0.8089592 0.9680723 0.9860777 1.0241528 #> [146,] 0.36684834 0.4257951 0.4369957 0.4393166 0.4547368 0.5026818 0.5330085 #> [147,] 0.34840672 0.4549819 0.4564327 0.5024490 0.5335413 0.5341757 0.5377761 #> [148,] 0.68439506 0.7249098 0.8863247 0.9094872 0.9198462 0.9435541 0.9756199 #> [149,] 0.48461201 0.6281582 0.7102363 0.7320435 0.7869123 0.8343406 0.8597976 #> [150,] 0.27767287 0.2814221 0.2841270 0.4878354 0.4954478 0.4975521 0.5313888 #> [151,] 0.37046721 0.4650588 0.4816673 0.4861472 0.5433768 0.5727964 0.5752032 #> [152,] 0.56256667 0.5770576 0.6789019 0.7157721 0.7351366 0.7589517 0.7694994 #> [153,] 0.54053002 0.5560717 0.5999067 0.7184908 0.7479200 0.7761110 0.8660131 #> [154,] 0.54053002 0.6561674 0.8307043 0.8541981 0.8712022 0.9810323 0.9903409 #> [155,] 0.37489759 0.4336603 0.7790019 0.8323242 0.8554640 0.8577206 0.9099627 #> [156,] 0.30569236 0.3262743 0.3579049 0.3630452 0.4926383 0.5405824 0.6052169 #> [157,] 0.64667460 0.7669037 0.8257021 0.8541981 0.9243470 0.9655508 1.0201939 #> [158,] 0.37740552 0.4020145 0.4190839 0.4591714 0.5281018 0.5550570 0.5788570 #> [159,] 0.33547237 0.3463819 0.4977218 0.5180707 0.5242244 0.5635180 0.5817520 #> [160,] 0.35829338 0.5718766 0.5747591 0.5779698 0.6522162 0.7144527 0.7876369 #> [161,] 0.50669338 0.5422210 0.5631950 0.5689447 0.6092181 0.6320838 0.6627306 #> [162,] 0.51045490 0.5219120 0.5858995 0.5937850 0.7252764 0.8000330 0.8418996 #> [163,] 0.37061007 0.4042716 0.5416616 0.6091344 0.6365370 0.7418901 0.7538221 #> [164,] 0.51229703 0.5239113 0.5903342 0.5937850 0.6369687 0.6395027 0.7197476 #> [165,] 0.54908168 0.9813251 1.1372137 1.1695009 1.2031595 1.2249379 1.2830620 #> [166,] 0.61272048 0.7611578 0.7883182 0.8482370 0.8973138 0.8973912 0.9098426 #> [167,] 0.24308049 0.2554877 0.3695717 0.4638763 0.4841923 0.5672204 0.5678705 #> [168,] 0.38041963 0.4770930 0.4780638 0.4847577 0.5092416 0.5473634 0.5519264 #> [169,] 0.26265001 0.6764559 0.6801124 0.7182474 0.8034945 0.8243029 0.9143408 #> [170,] 0.26578713 0.4558640 0.5643588 0.5758453 0.6031906 0.6038029 0.6317046 #> [171,] 0.17370456 0.4308061 0.4805783 0.4823644 0.5499564 0.5587297 0.6249264 #> [172,] 0.26941305 0.4017908 0.5415747 0.5470445 0.5757414 0.5872002 0.6124346 #> [173,] 0.87618145 0.9526264 0.9565228 0.9713093 1.0146814 1.0413455 1.1054185 #> [174,] 0.61152392 0.7089670 0.7760067 0.9366138 0.9987533 1.0345146 1.0764891 #> [175,] 0.55117629 0.6955637 0.8672199 0.9175538 1.0857738 1.0870652 1.1226844 #> [176,] 0.51603535 0.5265768 0.5388508 0.5712551 0.5801553 0.6707166 0.7064780 #> [177,] 0.57667920 0.6744389 0.7106979 0.7830243 0.8084727 0.8349234 0.8471915 #> [178,] 0.35086757 0.4558084 0.4623692 0.5991109 0.5998957 0.6098110 0.6568489 #> [179,] 0.59394151 0.6259422 0.6412760 0.7072447 0.9181475 0.9528680 0.9805333 #> [180,] 0.45973796 0.5236926 0.5413020 0.5590320 0.5625617 0.5949277 0.6129280 #> [181,] 0.48466368 0.4966760 0.5003019 0.5597563 0.5807237 0.6365370 0.6573707 #> [182,] 0.49667604 0.5257725 0.5446453 0.5665520 0.5922374 0.5939369 0.6009061 #> [183,] 0.44863069 0.4890375 0.4972940 0.5129245 0.5339640 0.5405824 0.5594749 #> [184,] 1.54969454 1.6058166 1.7903249 1.8486250 1.9415717 1.9861202 1.9921299 #> [185,] 0.46840057 0.5643122 0.7830058 0.8577187 0.9074240 0.9099627 0.9585011 #> [186,] 0.21503155 0.4423911 0.4427498 0.4470150 0.4800804 0.4852145 0.5140889 #> [187,] 0.54488025 0.5484068 0.6032080 0.6201989 0.6812469 0.6997092 0.7071064 #> [188,] 0.55211975 0.5650773 0.6230229 0.7717706 0.7985339 0.8089592 0.8240324 #> [189,] 0.38410060 0.6421988 0.6741195 0.6748141 0.7148040 0.7160836 0.7192255 #> [190,] 0.29678858 0.3855973 0.3987382 0.4023448 0.4417458 0.4929323 0.5541087 #> [191,] 0.83653302 0.9011914 1.1703316 1.1948539 1.3102195 1.3296849 1.3600533 #> [192,] 0.25389733 0.3532200 0.5986405 0.6218608 0.6532183 0.6823172 0.6910984 #> [193,] 0.22947858 0.3398413 0.4375047 0.5370750 0.5849706 0.5936046 0.5978990 #> [194,] 0.40798941 0.4111824 0.4547087 0.4821675 0.5014282 0.5111314 0.5392919 #> [195,] 0.42611375 0.5618296 0.6964333 0.8322004 0.9590741 0.9636064 0.9893164 #> [196,] 0.40201451 0.4776085 0.5035358 0.5344354 0.5360601 0.5955668 0.6228103 #> [197,] 0.46147713 0.6051880 0.6178127 0.7489781 0.7982087 0.8318041 0.9819342 #> [198,] 0.90254645 1.1476636 1.2435210 1.2555075 1.2713360 1.2953993 1.2964965 #> [199,] 0.35295760 0.4725503 0.6273540 0.6553937 0.6742267 0.7099813 0.7272521 #> [200,] 0.22003989 0.6339161 0.6577880 0.7028651 0.7089670 0.7099221 0.7364444 #> [201,] 1.30424483 1.3801370 1.6572063 1.6867596 1.7135375 1.7356769 1.7962741 #> [202,] 0.96348909 1.1220217 1.2021038 1.2176376 1.3685976 1.4039036 1.4407800 #> [203,] 0.72611264 0.8111915 0.8342606 0.8372628 0.8585798 0.8709668 1.0290069 #> [204,] 0.26218164 0.3876053 0.4977218 0.5012997 0.5277461 0.5384173 0.5414872 #> [205,] 0.32203562 0.4016753 0.4300057 0.5546442 0.5858126 0.5875525 0.6082550 #> [206,] 0.37711935 0.4536032 0.5418304 0.5571568 0.5603790 0.5993868 0.6085266 #> [207,] 1.29870348 1.4759336 1.5193207 1.6065617 1.6335442 1.6481263 1.6744164 #> [208,] 0.41828293 0.4505434 0.5149747 0.5504608 0.5652963 0.6662338 0.6896558 #> [209,] 1.09602517 1.3455591 1.3968001 1.4076033 1.4311583 1.4532473 1.4562446 #> [210,] 0.23259817 0.2557204 0.3945140 0.4285865 0.5073673 0.5450982 0.5976732 #> [211,] 0.57035503 0.6708464 0.6737630 0.7192255 0.7207689 0.7496445 0.7887201 #> [212,] 0.39122971 0.4981192 0.5229825 0.5269430 0.5292013 0.5795555 0.5996315 #> [213,] 0.35637992 0.4327384 0.4632784 0.4996657 0.5418139 0.5668559 0.5799450 #> [214,] 0.50944477 0.5110197 0.5355926 0.5386034 0.5713747 0.5859775 0.6705592 #> [215,] 0.34791594 0.5351012 0.5943828 0.6316720 0.6639791 0.7338786 0.7368381 #> [216,] 0.27087776 0.2839025 0.3817311 0.5293472 0.5908624 0.6269716 0.6411617 #> [217,] 0.39744238 0.4157032 0.4207887 0.5008248 0.5621268 0.5672412 0.6119784 #> [218,] 0.33093201 0.5025979 0.5581936 0.5705094 0.5770479 0.5935215 0.5974658 #> [219,] 0.47130002 0.4807848 0.5290914 0.6324410 0.6944847 0.7013259 0.7344657 #> [220,] 0.50139444 0.6117694 0.6213244 0.6379400 0.6655542 0.7627878 0.7797618 #> [221,] 0.17049041 0.4410825 0.4612374 0.4953889 0.5724994 0.6252209 0.7118564 #> [222,] 0.95693748 1.1132546 1.1244440 1.1303904 1.2668734 1.3012856 1.3358036 #> [223,] 0.28983255 0.3401989 0.3488723 0.4446433 0.4599618 0.4971177 0.5089164 #> [224,] 0.68358791 0.7398657 0.8153097 0.9655508 1.0165439 1.0306387 1.0955916 #> [225,] 0.52787810 0.6537160 0.7764582 0.8385543 0.8581661 0.9558473 0.9584774 #> [226,] 0.47700844 0.5216885 0.5275571 0.5504608 0.6699999 0.6742696 0.6864118 #> [227,] 0.40229399 0.4330564 0.4653143 0.4910223 0.5192757 0.5500262 0.5755747 #> [228,] 0.16765161 0.3794128 0.3972535 0.4516268 0.5227325 0.5984751 0.7192160 #> [229,] 0.56725251 0.5680787 0.5804492 0.6965240 0.7132296 0.7339512 0.7397689 #> [230,] 0.53481399 0.6018809 0.6580955 0.6617331 0.6902225 0.7393891 0.7903442 #> [231,] 0.56754181 0.6221936 0.7386678 0.7643205 0.7936885 0.8099410 0.8349234 #> [232,] 0.33706501 0.5086939 0.5351012 0.5672952 0.6229590 0.6575454 0.7062489 #> [233,] 0.51643360 0.5335565 0.5571488 0.5878117 0.7780923 0.8003594 0.8174984 #> [234,] 0.38920400 0.6969675 0.7000426 0.7243613 0.7523700 0.7669118 0.7962175 #> [235,] 0.37712295 0.3991980 0.5946666 0.6124585 0.6698246 0.7125412 0.7172980 #> [236,] 0.34385430 0.3561176 0.5080554 0.5570809 0.5827833 0.6707290 0.6944958 #> [237,] 0.35853973 0.3927377 0.4391532 0.4562070 0.4633042 0.4707987 0.5236926 #> [238,] 0.59462859 0.6323538 0.6356349 0.6532442 0.7442849 0.7569891 0.8079612 #> [239,] 0.59284156 0.6264562 0.8839687 0.8899777 0.9636064 0.9918935 1.0633670 #> [240,] 0.24728549 0.5359625 0.5978341 0.6676988 0.7735319 0.7764392 0.7795528 #> [241,] 0.39744238 0.4338809 0.4520274 0.5868525 0.6012372 0.6065631 0.6152847 #> [242,] 0.37716013 0.4836585 0.5110197 0.5421546 0.5715196 0.6801882 0.6905674 #> [243,] 0.28394795 0.3820281 0.3876899 0.3974501 0.4430272 0.5167141 0.5183246 #> [244,] 0.82430292 0.9597073 0.9680445 1.3547966 1.3548550 1.3607090 1.3921615 #> [245,] 0.57624017 0.5876477 0.6268535 0.6270330 0.6747779 0.6848974 0.7931200 #> [246,] 0.31299205 0.5034560 0.5043867 0.5419253 0.5535397 0.5575974 0.7085888 #> [247,] 0.64262965 0.8921008 0.9305805 0.9339677 1.0069366 1.0193305 1.1414821 #> [248,] 0.48782602 0.5189966 0.5340295 0.5627590 0.5878117 0.7211326 0.7482774 #> [249,] 0.29951135 0.4259536 0.4972859 0.6099927 0.6159928 0.6249264 0.6333368 #> [250,] 1.20210381 1.5519966 1.7618684 1.9120597 1.9633872 2.0200774 2.0272466 #> [251,] 0.35031851 0.4776085 0.5788570 0.6083226 0.6146147 0.6818975 0.7005643 #> [252,] 0.32958296 0.3974501 0.4641185 0.4644079 0.4666262 0.5781133 0.5823183 #> [253,] 0.64262965 0.7900258 0.9860777 0.9909285 1.0919575 1.1173512 1.1255470 #> [254,] 0.31801358 0.3946047 0.5089164 0.6378590 0.6576409 0.6856637 0.6870499 #> [255,] 0.56754181 0.6402904 0.7009357 0.7173158 0.7729828 0.9631997 1.0913812 #> [256,] 0.34385430 0.5192776 0.6300583 0.6377344 0.7117716 0.7386289 0.7809774 #> [257,] 0.22952545 0.3700009 0.4237522 0.4269215 0.4446646 0.4706503 0.4720348 #> [258,] 0.44150652 0.8000399 0.9923017 1.1193029 1.2113795 1.2947642 1.3275814 #> [259,] 0.85237136 1.0701032 1.2366975 1.4143776 1.5372939 1.5401955 1.6497181 #> [260,] 0.34800816 0.4439292 0.4792568 0.5645975 0.5678585 0.5823287 0.5922374 #> [261,] 0.35396396 0.4361154 0.4656768 0.4666262 0.5121672 0.5249327 0.5773453 #> [262,] 0.71907907 0.7288427 0.7508510 0.7767205 0.7890183 0.9310220 0.9435470 #> [263,] 0.29770340 0.4141110 0.5676076 0.6408758 0.6567339 0.6658709 0.7265516 #> [264,] 0.19402179 0.2678304 0.3317761 0.5779306 0.5793657 0.5922482 0.6011110 #> [265,] 0.52654089 0.5694851 0.7143330 0.7409058 0.7444994 0.7695044 0.8202393 #> [266,] 0.43286308 0.6539635 0.7156137 1.0338277 1.0880982 1.1014870 1.2061600 #> [267,] 0.36675682 0.5501011 0.6080251 0.6144638 0.6671930 0.6740908 0.6979966 #> [268,] 0.59586692 0.7958265 0.8402377 0.8576936 0.9461251 0.9771705 1.0383945 #> [269,] 0.54260412 0.5650773 0.6896558 0.7013672 0.8024806 0.8243659 0.8619546 #> [270,] 0.22500973 0.4578056 0.5132158 0.5347966 0.5573906 0.5618274 0.5656646 #> [271,] 0.26941305 0.2701207 0.4742903 0.5017003 0.5199536 0.5307726 0.5483506 #> [272,] 0.48405150 0.5934796 0.7977922 0.8339237 0.8528246 0.9053046 0.9145482 #> [273,] 0.50643364 0.6628639 0.7460642 0.7497779 0.7905453 0.8047521 0.8092070 #> [274,] 0.56440130 0.6220324 0.6527405 0.7055652 0.7417904 0.7430240 0.7638724 #> [275,] 0.64111107 0.7362176 0.7411347 0.8071888 0.9011914 0.9139025 0.9205592 #> [276,] 0.48583868 1.1128216 1.1280921 1.2256428 1.2970298 1.3186694 1.3719151 #> [277,] 0.27111546 0.5458634 0.5522195 0.5631557 0.6328689 0.6480226 0.6719849 #> [278,] 0.32240302 0.5092862 0.5285431 0.5858995 0.6302899 0.6656487 0.6848974 #> [279,] 0.54272310 0.9510885 1.0333731 1.0378200 1.0842766 1.0886630 1.1258774 #> [280,] 0.44001319 0.4482383 0.5350578 0.5680075 0.7093066 0.7229251 0.7477676 #> [281,] 0.48782602 0.5382450 0.6441751 0.6856915 0.7746998 0.8234598 0.8382064 #> [282,] 0.63216176 0.6843951 0.7033122 0.7206454 0.8457100 0.8556248 0.9346566 #> [283,] 0.20119578 0.4330050 0.5292013 0.6444889 0.6596934 0.6674573 0.7008368 #> [284,] 0.41791948 0.5500002 0.5616033 0.6644147 0.6684482 0.7115284 0.7714425 #> [285,] 0.28122955 0.2898325 0.3042652 0.3680010 0.4208660 0.4288953 0.4544843 #> [286,] 0.83117402 0.9536993 1.0759830 1.0794063 1.0797320 1.1290180 1.2017439 #> [287,] 0.16765161 0.2524951 0.3055798 0.4410636 0.4944418 0.5827833 0.6699247 #> [288,] 0.56499395 0.6104109 0.6494149 0.6533586 0.6589826 0.6912301 0.7058457 #> [289,] 0.56128240 0.6060546 0.6891124 0.7835165 0.8987008 0.9107711 0.9328638 #> [290,] 0.44254775 0.4739008 0.4938839 0.4973035 0.5154688 0.5364357 0.5900000 #> [291,] 0.43114166 0.5037232 0.7502026 0.8022048 0.8222796 0.9218647 0.9456269 #> [292,] 0.28122955 0.3585397 0.4005744 0.4599618 0.4719293 0.4737524 0.4941077 #> [293,] 0.45982563 0.4627462 0.6790274 0.7352688 0.7444994 0.7550201 0.8146329 #> [294,] 0.33093201 0.3737276 0.4075892 0.4965954 0.5459429 0.6212020 0.6413108 #> [295,] 1.22922834 1.6228459 1.7320149 1.8213842 1.9268269 2.0307088 2.0696552 #> [296,] 0.69835149 0.7256993 0.7417198 0.8211896 0.8380895 0.8780206 0.8932582 #> [297,] 0.24047378 0.3771601 0.4302289 0.4714800 0.5026818 0.5079815 0.5116403 #> [298,] 0.97717053 1.0341550 1.1530179 1.1615413 1.1964910 1.2109834 1.2236306 #> [299,] 0.46871727 0.5940345 0.6718302 0.6963547 0.7474774 0.7541661 0.7727392 #> [300,] 0.40337394 0.4141110 0.4921739 0.4987909 0.5418304 0.6260777 0.6305279 #> [301,] 0.38832700 0.5591449 0.7470331 0.8280040 0.9345298 1.0007794 1.0097257 #> [302,] 1.05544757 1.0864902 1.2096848 1.2278592 1.2559593 1.3363969 1.3384323 #> [303,] 1.05931979 1.4083979 1.5475604 1.5779953 1.5884435 1.6557957 1.7377046 #> [304,] 0.61747662 0.6944256 0.7058491 0.7141267 0.7232051 0.7310167 0.7342439 #> [305,] 0.99875331 1.1196969 1.1629325 1.1712476 1.1934590 1.2020231 1.2147471 #> [306,] 0.16044299 0.3508093 0.3586839 0.3947949 0.4432703 0.5215421 0.5766561 #> [307,] 0.75555445 0.7985300 0.9673132 1.0086205 1.0165799 1.0217857 1.0825259 #> [308,] 0.19402179 0.3555067 0.4337352 0.5723201 0.5901790 0.6238143 0.6588093 #> [309,] 0.15739874 0.2927399 0.3094247 0.3420135 0.4140120 0.4279712 0.4617395 #> [310,] 0.42526236 0.5833116 0.6245978 0.6534410 0.6637959 0.6728224 0.7151564 #> [311,] 0.65948086 0.7622319 0.7696701 0.7931384 0.8242023 0.8477518 0.9194493 #> [312,] 0.38594183 0.4376983 0.4930348 0.6038293 0.6202930 0.6476761 0.6973202 #> [313,] 0.29619568 0.5594014 0.5688117 0.5864253 0.5870340 0.6421234 0.6713917 #> [314,] 0.27111546 0.5001202 0.5686945 0.6167773 0.6353030 0.7064299 0.7158896 #> [315,] 0.45340478 0.4768650 0.4864367 0.4908308 0.4909328 0.5154688 0.5219980 #> [316,] 0.62039678 0.7758929 0.7894831 0.7977922 0.8005508 0.8373456 0.8664193 #> [317,] 0.34725973 0.4317180 0.4657972 0.4728753 0.4784125 0.4947778 0.5146067 #> [318,] 0.66774590 1.0664273 1.2667554 1.2998875 1.3524285 1.3923727 1.4089270 #> [319,] 0.53683528 0.6458406 0.6819790 0.6968820 0.7221679 0.7954932 0.8013005 #> [320,] 0.27692957 0.3295830 0.4315167 0.4361154 0.4632784 0.5194180 0.5731871 #> [321,] 0.37740552 0.5298873 0.5674285 0.6048902 0.6227255 0.6320025 0.6411027 #> [322,] 0.35031851 0.5399719 0.6108232 0.6269043 0.6609895 0.7051456 0.7055877 #> [323,] 0.95372941 0.9592937 0.9818004 0.9823114 1.0534956 1.0553641 1.0968400 #> [324,] 0.18607074 0.4439292 0.5175486 0.5340295 0.5501744 0.5571488 0.5711820 #> [325,] 0.41137800 0.5682765 0.5934796 0.6069523 0.7628360 0.7758584 0.7772044 #> [326,] 0.49029828 0.5084776 0.5933649 0.6258394 0.8065527 0.8477603 0.8576452 #> [327,] 0.67399602 0.6808892 0.7301544 0.7776224 0.7818618 0.7955500 0.8297262 #> [328,] 0.83159959 0.9046579 1.3424826 1.3942088 1.3985994 1.4262390 1.4263432 #> [329,] 0.62645619 0.8139930 1.0637702 1.1379397 1.2330201 1.2445042 1.2717714 #> [330,] 0.34718697 0.3912297 0.4194307 0.4439014 0.5228171 0.5727556 0.6406330 #> [331,] 0.26035703 0.5753758 0.5946666 0.6105969 0.6638629 0.6697832 0.6705406 #> [332,] 0.30855503 0.3710229 0.4878354 0.5026781 0.5290914 0.5319935 0.5828565 #> [333,] 0.32922558 0.3842350 0.4455320 0.5087297 0.5693583 0.5817674 0.6002576 #> [334,] 0.43580695 0.4922980 0.5026781 0.5831288 0.5879639 0.6053585 0.6735620 #> [335,] 0.57875264 0.6839511 0.6853032 0.6863318 0.6982818 0.7301646 0.7401596 #> [336,] 0.23416207 0.4904582 0.5355710 0.5864253 0.5963909 0.6157789 0.6580955 #> [337,] 0.56117283 0.9724704 1.0097257 1.2080045 1.3264815 1.4421302 1.4433116 #> [338,] 0.46558168 0.4841030 0.6402904 0.9160282 0.9315429 0.9379916 1.0990903 #> [339,] 0.56536588 0.6463297 0.6586995 0.6940481 0.7130196 0.7237704 0.7773880 #> [340,] 0.40700908 0.5399719 0.5425732 0.5478941 0.5985593 0.6146147 0.6517178 #> [341,] 0.23253009 0.2841939 0.5425732 0.5526583 0.5707032 0.6098153 0.6189824 #> [342,] 0.33335252 0.4853275 0.5666667 0.5674679 0.5784783 0.5826087 0.5944991 #> [343,] 0.91191200 0.9235948 0.9993258 1.0262315 1.0356451 1.0573300 1.0660545 #> [344,] 0.21286690 0.3670160 0.4329317 0.5043867 0.5509312 0.5822610 0.6361994 #> [345,] 0.32627428 0.4014896 0.4233858 0.4694141 0.4925961 0.4972940 0.5446308 #> [346,] 0.27767287 0.2783384 0.3710229 0.3929441 0.4713000 0.4934612 0.4958311 #> [347,] 0.49907726 0.6222739 0.7091526 0.8132572 0.8963111 0.9075966 1.0341483 #> [348,] 0.21792988 0.5361539 0.5793089 0.5827702 0.5841615 0.5937395 0.6079173 #> [349,] 0.49561025 0.7515701 0.7900258 0.7948560 0.8526947 0.8921008 0.9249440 #> [350,] 0.15272180 0.3451188 0.5312809 0.5427308 0.5455492 0.5483506 0.5682192 #> [351,] 0.50111140 0.5290405 0.5388508 0.5638882 0.6842062 0.6856766 0.7081862 #> [352,] 0.48405150 0.9997366 1.0063590 1.0186303 1.0258218 1.0370866 1.0842196 #> [353,] 0.85506147 0.9796234 0.9997460 1.0331354 1.0857738 1.1596754 1.1766655 #> [354,] 0.33886686 0.7598023 0.7773880 0.8075970 0.8530453 0.8797843 0.9533309 #> [355,] 0.53824498 0.7482774 0.8041277 0.8343406 0.8527889 0.8808184 0.9057698 #> [356,] 0.39460346 0.4735638 0.5339004 0.5406958 0.6300583 0.6759686 0.6760014 #> [357,] 0.36786814 0.4943045 0.5858722 0.6555239 0.6883619 0.7011929 0.7140176 #> [358,] 0.54467172 0.6008991 0.6133631 0.6281582 0.7045401 0.7285143 0.7305726 #> [359,] 0.49538891 0.5077185 0.5161778 0.6036898 0.6470808 0.6509040 0.6822214 #> [360,] 0.33360306 0.3951510 0.5297383 0.5445057 0.5796505 0.6006658 0.6055141 #> [361,] 0.17049041 0.5033910 0.5077185 0.6048211 0.6167268 0.6264364 0.6731264 #> [362,] 0.30195725 0.4376983 0.5229335 0.5471404 0.5548141 0.5564983 0.5812133 #> [363,] 0.60005117 0.6010209 0.6738717 0.7179053 0.8279851 0.8281021 0.8510822 #> [364,] 0.66774590 1.3163298 1.4704140 1.4948077 1.4982230 1.5405538 1.5528732 #> [365,] 0.40688031 0.5753531 0.6290901 0.6897839 0.7227850 0.7229793 0.7529551 #> [366,] 0.33416754 0.6339668 0.7072508 0.7096127 0.7341859 0.7492985 0.7689684 #> [367,] 0.77150740 0.8281860 0.8365330 0.8415089 0.8481635 0.8571622 0.8613339 #> [368,] 0.93525318 0.9501049 1.0214541 1.0440251 1.0497149 1.0786428 1.1421249 #> [369,] 0.75502011 0.8643716 0.8916613 0.8925923 0.9750515 1.1627505 1.1896890 #> [370,] 0.41366461 0.5810245 0.6492271 0.6670795 0.7102363 0.7141773 0.7190354 #> [371,] 0.22947858 0.4005975 0.4329269 0.4377666 0.4958311 0.5414789 0.5570704 #> [372,] 0.28229287 0.3060863 0.4140120 0.4617932 0.4860057 0.4901277 0.5107906 #> [373,] 0.42234308 0.4251424 0.6440649 0.6532035 0.6593543 0.6997124 0.7057718 #> [374,] 0.35705946 0.4944418 0.5227325 0.6337435 0.7313741 0.7448075 0.7467064 #> [375,] 0.43000568 0.4510068 0.4537805 0.4770084 0.4908308 0.6091098 0.6605649 #> [376,] 0.70550896 0.8658663 1.0162033 1.1059807 1.1066440 1.1351071 1.1641759 #> [377,] 0.73231115 0.7330094 0.7646062 0.7711267 0.8413085 0.8651759 0.8653694 #> [378,] 0.51593162 0.6416642 0.7694242 0.8225500 0.8955663 0.9084808 0.9439012 #> [379,] 0.46123736 0.5245417 0.6048211 0.6248637 0.6372920 0.7045773 0.7268233 #> [380,] 0.41684967 0.4332905 0.5386192 0.5518292 0.5708686 0.5978990 0.6011880 #> [381,] 0.47919111 0.4792568 0.4871539 0.5175486 0.5446453 0.5682192 0.6344623 #> [382,] 0.48667127 0.5048615 0.5272385 0.5503752 0.5544435 0.5757508 0.6004304 #> [383,] 1.39237913 1.4389659 1.4775812 1.5511146 1.6401882 1.6666768 1.7071872 #> [384,] 1.11081929 1.1862048 1.2363080 1.3240156 1.3580938 1.3619310 1.3934562 #> [385,] 0.84719154 0.9257695 0.9259973 0.9631997 1.0528749 1.1507484 1.2607130 #> [386,] 0.21549985 0.2927903 0.4528098 0.4554006 0.4847586 0.5067646 0.5147386 #> [387,] 0.25548771 0.2627143 0.3012077 0.3369672 0.5852312 0.6533586 0.6665172 #> [388,] 0.38313595 0.4852145 0.5667683 0.5705758 0.5723201 0.5816681 0.5817390 #> [389,] 0.36776757 0.3820516 0.5654415 0.6478635 0.6581621 0.7580703 0.7614613 #> [390,] 0.44042104 0.4462403 0.4861098 0.6844823 0.6898711 0.6920427 0.7149602 #> [391,] 0.37755185 0.4720687 0.4748450 0.6676814 0.6830605 0.7232436 0.7517080 #> [392,] 0.49303475 0.6282661 0.6352669 0.7280407 0.7822611 0.7883182 0.8423544 #> [393,] 0.24156076 0.3333525 0.4894152 0.4907465 0.6046438 0.6096974 0.6198758 #> [394,] 0.75980226 0.9182698 1.0389936 1.0786253 1.0842766 1.1125976 1.1204943 #> [395,] 0.79131716 0.8450893 0.8701179 0.9600946 0.9620011 1.1613341 1.1650136 #> [396,] 0.46840057 0.6900040 0.7541661 0.7634109 0.8923103 0.9210998 0.9261443 #> [397,] 0.39460470 0.4769464 0.4993423 0.5605158 0.5964247 0.6108362 0.6189705 #> [398,] 0.54175410 0.6101697 0.6214295 0.7347792 0.7857347 0.8037321 0.8482199 #> [399,] 0.42443846 0.4946278 0.4988318 0.4991808 0.5728891 0.5872002 0.6422448 #> [400,] 0.40589754 0.5994650 0.6405285 0.6597749 0.7341665 0.7384536 0.7440294 #> [401,] 0.33547237 0.3876053 0.5448803 0.5742499 0.5754628 0.6805831 0.7048972 #> [402,] 0.52774869 0.7536813 0.7796129 0.8243505 0.8382165 0.8643576 0.9220884 #> [403,] 0.61366148 0.6602462 0.7427637 0.7735705 0.8246115 0.8442161 0.8613048 #> [404,] 0.35215632 0.4564511 0.6322821 0.6395455 0.7042088 0.7094070 0.7266282 #> [405,] 0.50924156 0.5422210 0.5544217 0.5690209 0.5959135 0.6045659 0.6229129 #> [406,] 0.57640862 0.7099221 0.7166143 0.7249098 0.7888289 0.8557293 0.8600432 #> [407,] 0.27916916 0.3484067 0.4446646 0.4531318 0.4875835 0.5116676 0.5139554 #> [408,] 0.32811395 0.3350727 0.3440006 0.4969256 0.6380791 0.6789776 0.7619706 #> [409,] 0.33360306 0.4789346 0.4905156 0.5192757 0.5432577 0.5903237 0.6025242 #> [410,] 0.51734179 0.8135626 0.9046579 0.9546533 0.9682437 1.0339429 1.0568210 #> [411,] 0.51230855 0.7115000 0.7758929 0.7828679 0.9054288 1.0238655 1.1780441 #> [412,] 0.56052284 0.6773700 0.9139025 0.9216663 1.0707815 1.0758797 1.1157012 #> [413,] 0.27429430 0.4174707 0.4715509 0.4769901 0.5313888 0.5681010 0.5757508 #> [414,] 0.65431994 0.7272521 0.7962587 0.8683901 0.8706834 1.0330764 1.0452828 #> [415,] 0.52130015 0.5345360 0.6173324 0.6322821 0.6335443 0.7028651 0.7036003 #> [416,] 0.80280506 0.8307399 0.8473081 0.9751862 0.9957300 1.0787663 1.2395073 #> [417,] 0.63880966 0.6527405 0.7013672 0.7099813 0.7667526 0.7864823 0.8593760 #> [418,] 0.17091574 0.2557204 0.4154745 0.4707840 0.4996657 0.5305223 0.6249691 #> [419,] 0.91355647 0.9407524 1.1901579 1.2092175 1.2215077 1.2843890 1.2951655 #> [420,] 0.44077352 0.4423909 0.6316720 0.6967569 0.7555210 0.7897678 0.7911129 #> [421,] 1.18129626 1.1820783 1.2192175 1.2663894 1.4015970 1.4078856 1.5088903 #> [422,] 0.60188092 0.7306779 0.7614894 0.8201824 0.8507603 0.8563820 0.8624440 #> [423,] 0.37105317 0.4343819 0.4415877 0.4975265 0.4978055 0.5155221 0.6055141 #> [424,] 1.03306172 1.0416197 1.0467315 1.1375541 1.2039755 1.2894666 1.2903858 #> [425,] 0.44637144 0.5257725 0.5597563 0.6685950 0.6792135 0.7737699 0.7910788 #> [426,] 0.38205163 0.4427894 0.5293472 0.5416102 0.5452154 0.5486471 0.6135617 #> [427,] 0.92897311 1.4607150 1.7194306 1.7638615 1.8088267 1.8113200 1.8456745 #> [428,] 0.36893463 0.5189327 0.5674679 0.6249000 0.6716065 0.6980939 0.7404938 #> [429,] 0.31315190 0.6597764 0.7347792 0.8168133 0.8300385 0.8385543 0.8550615 #> [430,] 0.56117283 0.9280705 1.0769471 1.0922780 1.2842654 1.3766206 1.4703591 #> [431,] 0.35991731 0.6071260 0.6503385 0.6590381 0.6897839 0.7125915 0.7392944 #> [432,] 0.27515565 0.6780247 0.7875097 0.8063710 0.8114488 0.8716991 0.8797149 #> [433,] 0.32404141 0.4805979 0.5681275 0.5712551 0.5734678 0.5900957 0.6468046 #> [434,] 0.33579434 0.5409392 0.5437083 0.5716376 0.7179293 0.7368908 0.7704097 #> [435,] 0.51962096 0.8417451 0.8673370 0.9462979 0.9760531 1.0758839 1.1064294 #> [436,] 0.35551633 0.5461164 0.6081071 0.7107542 0.7305726 0.7630830 0.7731162 #> [437,] 0.22473774 0.2619194 0.3630418 0.3737517 0.4401235 0.4417458 0.4634704 #> [438,] 0.39662159 0.4432703 0.4436377 0.4566741 0.5134027 0.5176712 0.5475328 #> [439,] 0.24156076 0.4339481 0.4853275 0.5150846 0.5453835 0.5731871 0.6445660 #> [440,] 0.42629419 0.4269498 0.4917449 0.5967956 0.7033093 0.7925975 0.8220188 #> [441,] 0.47387022 0.4973497 0.5272385 0.5500262 0.5524280 0.5579955 0.5641639 #> [442,] 0.69234816 0.8231293 0.8600432 0.8741934 0.8783775 0.9141506 0.9162641 #> [443,] 0.26578713 0.3455166 0.5271173 0.5728187 0.6090574 0.6193783 0.6266733 #> [444,] 0.30916496 0.3856758 0.4017785 0.4945967 0.5464797 0.5544855 0.5725951 #> [445,] 0.50248079 0.5973751 0.6198012 0.7106098 0.7710974 0.7735319 0.7790543 #> [446,] 0.30696916 0.4064382 0.4439416 0.5047480 0.6406330 0.6714506 0.7422157 #> [447,] 0.94100371 1.0331354 1.0410825 1.0532556 1.1293918 1.1827213 1.2545704 #> [448,] 0.85654824 1.1190720 1.1199056 1.1775246 1.1937552 1.2525599 1.2540978 #> [449,] 0.72588120 0.9358353 0.9492661 1.0811068 1.0912420 1.1134560 1.1234979 #> [450,] 0.49268324 0.5296746 0.7474774 0.7683373 0.8557396 0.8643716 0.9074240 #> [451,] 0.40730826 0.4722252 0.4829929 0.5956886 0.6168834 0.6456856 0.6482295 #> [452,] 0.43750473 0.4411643 0.5386192 0.5570704 0.5666451 0.5785084 0.5869566 #> [453,] 0.27833841 0.2841270 0.3683553 0.4005975 0.4819972 0.5281680 0.5319935 #> [454,] 0.55997084 0.6393230 0.7498951 0.7731162 0.8337882 0.8384616 0.8678513 #> [455,] 0.39223456 0.4003260 0.4009120 0.4256820 0.4335879 0.4368795 0.4841923 #> [456,] 0.31583768 0.3357943 0.4075892 0.4683504 0.6180513 0.6915943 0.7676689 #> [457,] 0.19718617 0.2507500 0.2952804 0.4110020 0.4269215 0.4505302 0.4861472 #> [458,] 0.79403055 0.9079105 0.9744997 1.0245793 1.0396396 1.0684175 1.0798204 #> [459,] 0.41279177 0.4735638 0.4978055 0.5009526 0.5194236 0.5210512 0.5656646 #> [460,] 0.18365652 0.3565099 0.3737517 0.4901712 0.5012997 0.5406525 0.5541087 #> [461,] 0.33777507 0.4185361 0.4500143 0.5213931 0.5732926 0.5733587 0.6311623 #> [462,] 0.34000005 0.4547087 0.4803415 0.4979181 0.4981605 0.5139271 0.5324105 #> [463,] 0.50958632 0.5724994 0.5762256 0.5984237 0.6036898 0.6167249 0.6167268 #> [464,] 0.41791948 0.5207050 0.6310681 0.6748141 0.6753137 0.7997849 0.8257319 #> [465,] 0.41908392 0.5298873 0.6748081 0.6816221 0.7071701 0.7271169 0.7385645 #> [466,] 1.34494781 1.5233323 1.5635876 1.6090008 1.7593662 1.7745993 1.8503743 #> [467,] 0.79853002 0.8112394 0.9556263 1.0278128 1.0292884 1.0529251 1.0797320 #> [468,] 0.67399602 0.8083640 0.8330092 0.8502555 0.8880128 0.8915497 0.9213053 #> [469,] 0.63932303 0.6410085 0.7169540 0.8022048 0.8101539 0.8853278 0.8998839 #> [470,] 0.35517167 0.5000046 0.5068350 0.5085941 0.5143304 0.5251669 0.5482755 #> [471,] 0.36491652 0.3965035 0.5752258 0.6001995 0.6997092 0.7815906 0.8311178 #> [472,] 0.36304515 0.5643803 0.5915972 0.6311051 0.6366774 0.6652489 0.6658170 #> [473,] 0.15272180 0.3878751 0.4335768 0.4719275 0.5506425 0.5845838 0.5928785 #> [474,] 0.43303048 0.4391350 0.4656768 0.5288773 0.5328568 0.5817674 0.5961361 #> [475,] 0.53830065 1.1780441 1.1802566 1.1812963 1.3166412 1.3192053 1.3597177 #> [476,] 0.27692957 0.3539640 0.3934326 0.4327384 0.4391350 0.4641185 0.4707840 #> [477,] 0.30784366 0.4136345 0.4267375 0.4565310 0.5014282 0.5324105 0.5731336 #> [478,] 0.14874895 0.3816655 0.5239113 0.6164456 0.6900380 0.6975582 0.7122184 #> [479,] 0.54272310 0.6060152 0.6884798 0.7072447 0.7911542 0.7989374 0.8566173 #> [480,] 0.43637557 0.7652942 0.7878777 0.8075332 0.8276442 0.9581094 0.9721621 #> [481,] 0.30696916 0.3199699 0.3438252 0.4263777 0.4439014 0.5274743 0.6674573 #> [482,] 0.34551656 0.4346415 0.5532540 0.5643588 0.6144827 0.6421988 0.6648602 #> [483,] 0.50575352 0.7093871 0.7159460 0.7826749 0.8296502 0.8301152 0.8694828 #> [484,] 0.39196996 0.4252624 0.6739281 0.7272578 0.7433350 0.7891056 0.8303995 #> [485,] 0.32267120 0.5123086 0.5447285 0.6203968 0.7115202 0.8486390 0.8656488 #> [486,] 0.61082321 0.6598523 0.7005643 0.8473081 0.8643576 0.9271097 0.9627590 #> [487,] 0.37131494 0.5290405 0.6032977 0.6206803 0.6283001 0.6460744 0.6707166 #> [488,] 0.60423508 0.6226021 1.0455526 1.1612867 1.2450324 1.2661239 1.2846899 #> [489,] 0.60605455 0.7563084 0.8980437 0.9233367 1.0435580 1.0566689 1.0593198 #> [490,] 0.38529405 0.5153898 0.5944632 0.6035720 0.6498777 0.6666415 0.6975582 #> [491,] 0.69234816 0.7206454 0.7888289 0.8132450 0.8274062 0.8489513 0.8564503 #> [492,] 1.15191239 1.3899942 1.4500010 1.4885976 1.5228824 1.5852860 1.6965329 #> [493,] 0.56315141 0.6642650 0.6965077 0.6999140 0.7645853 0.7683081 0.7897385 #> [494,] 0.48583868 1.1109211 1.1695009 1.3133883 1.3257371 1.3295063 1.3357156 #> [495,] 0.78233201 1.0637702 1.1451020 1.2800477 1.3045123 1.3432057 1.3498040 #> [496,] 0.58121331 0.5842185 0.5934716 0.7158673 0.7288453 0.8039226 0.8781543 #> [497,] 0.60144678 0.6523766 0.7815906 0.7855782 0.7883097 0.9350030 0.9439458 #> [498,] 0.58608188 0.9931059 1.1399638 1.1783841 1.2097972 1.2181288 1.2365478 #> [499,] 0.63282757 0.6682973 0.6743829 0.7578613 0.7671347 0.7887201 0.8602626 #> [500,] 0.26265001 0.5356474 0.6421611 0.6772350 0.7457229 0.7951561 0.8453708 #> [501,] 1.38999419 1.5992446 1.8790500 2.3113869 2.3168782 2.3637341 2.3922669 #> [502,] 0.32558987 0.4174707 0.4371221 0.4554006 0.4772252 0.5048615 0.5416199 #> [503,] 0.48461201 0.7215688 0.7433350 0.7617940 0.7680742 0.7905453 0.7961259 #> [504,] 0.22486970 0.2627143 0.3695717 0.4947433 0.5551474 0.5586764 0.5956886 #> [505,] 0.68271114 0.8258154 0.8675523 0.9214783 0.9267411 0.9584324 0.9606637 #> [506,] 0.38713800 0.4160170 0.4167471 0.5416616 0.6201974 0.6218608 0.6513026 #> [507,] 0.35517167 0.4033861 0.4352831 0.4425477 0.4630983 0.4665478 0.4729722 #> [508,] 0.21503155 0.4364501 0.5066574 0.5068288 0.5470138 0.5629381 0.5653868 #> [509,] 1.35540675 1.3651020 1.4498864 1.4697750 1.4866551 1.6000226 1.6029319 #> [510,] 1.12729374 1.1422187 1.1849896 1.2738890 1.2847577 1.2913047 1.3467737 #> [511,] 0.33004290 0.3838357 0.5194236 0.5339004 0.5774565 0.6389957 0.6516153 #> [512,] 0.87751687 1.0572218 1.1211695 1.1232132 1.2340452 1.2617497 1.2899045 #> [513,] 1.26501290 1.2800477 1.3927728 1.4072841 1.4812533 1.4894414 1.5189249 #> [514,] 0.32349377 0.4353077 0.5567052 0.5573627 0.5938147 0.5940007 0.6354609 #> [515,] 0.46681568 0.4981513 0.5804492 0.5870340 0.6382426 0.6558298 0.6613072 #> [516,] 0.48176089 0.4981513 0.6124427 0.6951015 0.6968820 0.7107325 0.7603987 #> [517,] 0.56275897 0.7327818 0.7415040 0.7746998 0.7948028 0.8078147 0.8275757 #> [518,] 0.43645014 0.4427498 0.4582420 0.4617395 0.4696348 0.4860057 0.4869942 #> [519,] 0.30195725 0.3064522 0.4834773 0.5319833 0.5844000 0.5934716 0.6602826 #> [520,] 0.34872796 0.4696348 0.5094419 0.5328879 0.5508058 0.5596657 0.5667683 #> [521,] 0.22486970 0.3012077 0.4638763 0.4827970 0.5581506 0.5857969 0.6168834 #> [522,] 0.34718697 0.4734484 0.4759376 0.4821902 0.4981192 0.5919049 0.6765002 #> [523,] 0.37119003 0.4042716 0.4167471 0.6327125 0.6424517 0.6451651 0.6544454 #> [524,] 0.49883178 0.5564145 0.6133631 0.6188844 0.6548673 0.6671413 0.7107542 #> [525,] 0.51487828 0.7055090 0.7121909 0.7625581 0.8078147 0.8712902 0.9196127 #> [526,] 0.26233166 0.3301814 0.5477472 0.5644013 0.5652963 0.6095633 0.6419645 #> [527,] 0.44657063 0.5469588 0.6504423 0.7273693 0.9348490 0.9960069 1.0127452 #> [528,] 0.55221954 0.6227255 0.6748081 0.6885028 0.6898711 0.6936241 0.6971507 #> [529,] 0.60601516 0.6747779 0.7274967 0.7710522 0.7746791 0.7761110 0.7761581 #> [530,] 0.96348909 1.0005127 1.1531303 1.1819835 1.2705730 1.2881013 1.3097788 #> [531,] 0.51614723 0.5859331 0.6091953 0.6416642 0.6484656 0.7493636 0.7608141 #> [532,] 0.52734415 0.5752258 0.6201989 0.6509948 0.7475855 0.7848703 0.8374849 #> [533,] 0.22768949 0.4755288 0.5001598 0.5267358 0.5417933 0.5707032 0.6032977 #> [534,] 0.17308916 0.3775519 0.4374424 0.4842991 0.5473634 0.5522140 0.5544129 #> [535,] 0.27366290 0.4910223 0.5432577 0.5697910 0.6618646 0.7108046 0.7424222 #> [536,] 0.82986354 0.8525027 0.8694440 0.8770413 0.8785766 0.9407524 0.9698302 #> [537,] 0.50957678 0.5401574 0.5591449 0.5853550 0.6122867 0.8077789 0.8343363 #> [538,] 0.36353777 0.3804196 0.4706606 0.4781166 0.4842991 0.5207731 0.5435039 #> [539,] 0.12432811 0.4799947 0.7927098 0.8045342 0.8104586 0.8335825 0.8565542 #> [540,] 0.39267342 0.5104084 0.5539836 0.5571402 0.6095633 0.6229590 0.6637908 #> [541,] 0.28212285 0.2891919 0.4649396 0.5648903 0.5664279 0.5829420 0.6284229 #> [542,] 0.99208071 1.0640377 1.1408177 1.2567472 1.2610097 1.2973438 1.3444573 #> [543,] 0.48996733 0.5606936 0.5728755 0.6760014 0.7544363 0.7715961 0.7818113 #> [544,] 0.22723214 0.3977784 0.4259536 0.4369957 0.5030719 0.5087297 0.5148810 #> [545,] 0.32764986 0.3947949 0.3989610 0.4436377 0.5049733 0.5212736 0.5482755 #> [546,] 0.38903127 0.4299702 0.4647006 0.4800804 0.5080255 0.5473492 0.6005693 #> [547,] 0.22075267 0.3000063 0.6380791 0.6999140 0.7695203 0.7772380 0.7839562 #> [548,] 0.54444892 0.7026799 0.7865930 0.8482936 0.8593760 0.8771458 0.9025465 #> [549,] 0.41975680 0.5425809 0.6484656 0.6700281 0.6740985 0.7462301 0.7625581 #> [550,] 0.44410572 0.4786823 0.5505203 0.6208388 0.7245477 0.7528892 0.8241554 #> [551,] 0.34765690 0.4860027 0.5374429 0.5498394 0.5611247 0.5674918 0.5913260 #> [552,] 0.25761193 0.2839479 0.4359787 0.4500779 0.5492644 0.6073729 0.6101469 #> [553,] 0.32458341 0.4294108 0.4755666 0.4790972 0.5430447 0.5497086 0.5698945 #> [554,] 0.30000634 0.3440006 0.3895970 0.5069029 0.6480851 0.6545683 0.7117059 #> [555,] 0.57083623 0.6741173 0.7033093 0.7443693 0.7894831 0.7964902 0.8458878 #> [556,] 0.51508459 0.5199321 0.5402096 0.5418139 0.5784783 0.6096974 0.6549053 #> [557,] 0.35322004 0.4095795 0.6571445 0.7186039 0.7256993 0.7310167 0.7931390 #> [558,] 1.73915157 2.0895044 2.1799612 2.2052621 2.2889129 2.3213603 2.3241489 #> [559,] 0.32678210 0.5064336 0.5544217 0.6344623 0.6354370 0.6742242 0.6952326 #> [560,] 0.25389733 0.4095795 0.4867599 0.5728891 0.5757414 0.6128420 0.6201974 #> [561,] 0.57920643 0.6609895 0.7130723 0.7608231 0.8280994 0.8915774 0.9119757 #> [562,] 0.33004290 0.3951510 0.4803121 0.5113439 0.5922088 0.6058663 0.6105139 #> [563,] 0.29619568 0.6414745 0.6818945 0.7300882 0.7494630 0.7524761 0.8068922 #> [564,] 0.28390248 0.3352527 0.4368724 0.4797075 0.5452154 0.5546065 0.5663818 #> [565,] 1.70387685 1.8756984 1.9238217 1.9351767 2.0168327 2.0358703 2.0389652 #> [566,] 0.50368519 0.5164336 0.5711820 0.6091953 0.6135240 0.6179733 0.6527461 #> [567,] 0.27429430 0.3579049 0.4233858 0.4472829 0.4561306 0.4772252 0.4890375 #> [568,] 0.36176548 0.4923406 0.4991808 0.5343824 0.5415747 0.5455492 0.5481736 #> [569,] 0.58372918 0.5984751 0.6508299 0.6699247 0.7298699 0.7735197 0.8480297 #> [570,] 0.38920400 0.6251328 0.7517080 0.7609491 0.7827158 0.8434251 0.8563041 #> [571,] 0.44136721 0.5160107 0.5731316 0.5940345 0.6567246 0.6588093 0.6591535 #> [572,] 0.33373156 0.4814404 0.5521197 0.7448075 0.7741427 0.7927559 0.8362222 #> [573,] 0.23416207 0.4356454 0.5688117 0.5912199 0.5929949 0.5965354 0.6701969 #> [574,] 0.46386001 0.4905156 0.5672952 0.6115767 0.6319799 0.6335443 0.6418135 #> [575,] 0.44102183 0.5958669 0.6523323 0.6539635 0.7169540 0.7502026 0.7989667 #> [576,] 0.22003989 0.5764086 0.5800283 0.6960439 0.6992767 0.7734542 0.7760067 #> [577,] 0.52787810 0.8656258 1.0886843 1.1303904 1.1652766 1.1992359 1.2040200 #> [578,] 0.33537141 0.4310171 0.4855791 0.5043320 0.5506841 0.6286606 0.6449115 #> [579,] 0.48106688 0.6400865 0.6602727 0.6610452 0.6643364 0.6676988 0.6725714 #> [580,] 0.40229399 0.4831555 0.5020988 0.5160107 0.5657945 0.5793657 0.5832154 #> [581,] 0.35648607 0.5155221 0.5430369 0.5512025 0.5672378 0.5931377 0.6105139 #> [582,] 0.27012072 0.4017908 0.4559433 0.4892858 0.4946278 0.5128846 0.5350863 #> [583,] 0.35868385 0.4425961 0.6264364 0.6395027 0.6446911 0.6611604 0.6894536 #> [584,] 0.55815062 0.5852312 0.6701959 0.6814269 0.7058457 0.7367348 0.7549490 #> [585,] 0.44657063 0.4577985 0.7049280 0.7064780 0.7427919 0.7677238 0.7866158 #> [586,] 0.33284522 0.3968305 0.5929588 0.6385466 0.6502117 0.6619225 0.7842259 #> [587,] 0.17308916 0.3946946 0.4523318 0.4748450 0.4781166 0.5063630 0.5519264 #> [588,] 0.77900189 0.7890183 0.8378151 0.8851613 0.8899777 0.9409221 1.0251435 #> [589,] 0.56786474 0.5713880 0.6050779 0.6742909 0.6922978 0.7159223 0.7236839 #> [590,] 1.28990446 1.4368071 1.4372491 1.6684753 1.6883546 1.7540976 1.8215611 #> [591,] 0.34060381 0.3857767 0.4285865 0.4323910 0.5243204 0.5416102 0.5619075 #> [592,] 0.44624031 0.4964189 0.6007497 0.6597749 0.6793760 0.7046408 0.7064299 #> [593,] 0.16044299 0.2337616 0.3989610 0.4425961 0.4827286 0.5134027 0.5305207 #> [594,] 0.23433208 0.2507500 0.3647389 0.3863603 0.4060251 0.4638805 0.4706503 #> [595,] 0.17597548 0.4429149 0.4458199 0.5008248 0.6135240 0.6417925 0.6768346 #> [596,] 0.46274619 0.5643122 0.7634109 0.7683373 0.8060857 0.8275630 0.8577206 #> [597,] 0.48996733 0.5210512 0.5406958 0.5489483 0.5774565 0.7836787 0.8296072 #> [598,] 0.22768949 0.5449690 0.5478941 0.5777244 0.5915972 0.6030622 0.6073392 #> [599,] 0.83371889 0.8694239 0.8819580 0.9786088 0.9887986 1.0007794 1.1333100 #> [600,] 0.26035703 0.5574171 0.6283340 0.6793788 0.6816118 0.7125412 0.7249124 #> [601,] 0.37701435 0.3873969 0.4454083 0.5852220 0.6213244 0.6464179 0.6551963 #> [602,] 0.18365652 0.2247377 0.2577553 0.3864874 0.4023448 0.5241576 0.5414872 #> [603,] 0.43366031 0.6522712 0.8378151 0.8617993 0.8700834 0.9045392 1.0198042 #> [604,] 0.69993429 0.7284301 0.9402911 1.1137788 1.1158997 1.1169389 1.1244440 #> [605,] 0.29951135 0.3365124 0.4805783 0.4852801 0.4947778 0.5059454 0.5593715 #> [606,] 0.24308049 0.3369672 0.4299477 0.4368795 0.5183246 0.5551474 0.5619075 #> [607,] 0.44525949 0.8932878 0.9821860 1.0011803 1.0103385 1.0981862 1.1486406 #> [608,] 0.59405249 0.6543199 0.6553937 0.7091526 0.8328801 0.8486597 0.8609763 #> [609,] 1.02977260 1.2619621 1.2667554 1.4606998 1.5334449 1.5560651 1.5957530 #> [610,] 0.35144018 0.5686196 0.5780670 0.6654444 0.8031539 0.8919497 0.8937318 #> [611,] 0.39294411 0.4329269 0.4807848 0.4819972 0.4849148 0.5227979 0.5640177 #> [612,] 0.46886255 0.5946286 0.6971507 0.7394289 0.7854985 0.8576131 0.9304374 #> [613,] 0.51490569 0.5511763 0.9546355 1.1132546 1.1169389 1.1330129 1.2553011 #> [614,] 0.44559324 0.5293639 0.5361539 0.5733587 0.7285486 0.7346014 0.7350405 #> [615,] 0.33760424 0.3771229 0.5694851 0.6053445 0.6638629 0.7151035 0.7352688 #> [616,] 0.37489759 0.6153700 0.6522712 0.6900040 0.7691259 0.7830058 0.8490819 #> [617,] 0.38785383 0.4255550 0.5361624 0.6440649 0.6580717 0.6705406 0.6816118 #> [618,] 0.33543684 0.3541546 0.4724177 0.5725951 0.6208388 0.6248876 0.6351238 #> [619,] 0.49870522 0.5469588 0.7579067 0.7694242 0.7866158 0.8033215 0.9437138 #> [620,] 0.45415534 0.6671930 0.6792135 0.6862691 0.7155112 0.7678281 0.7826749 #> [621,] 0.33984133 0.4188059 0.4377666 0.4584733 0.4993423 0.5206577 0.5281680 #> [622,] 0.32404141 0.4348363 0.5456857 0.5670290 0.5897818 0.6090094 0.6204706 #> [623,] 0.43463282 0.4849148 0.6283340 0.6380633 0.6411111 0.6772921 0.7280407 #> [624,] 0.24047378 0.2949672 0.4734230 0.4836585 0.4855279 0.5226528 0.5671062 #> [625,] 0.38832700 0.6122867 0.6906560 0.8819580 0.9070997 0.9280705 0.9724704 #> [626,] 0.31996990 0.4064382 0.4194307 0.5095882 0.6187073 0.6682280 0.6765002 #> [627,] 0.21601832 0.4441057 0.4724177 0.4945967 0.5062977 0.5134793 0.5488952 #> [628,] 0.17402163 0.2367911 0.4004923 0.4353077 0.4847487 0.4869942 0.4950905 #> [629,] 0.80130051 0.8168133 0.8360590 0.9012890 0.9719824 0.9892314 1.0326790 #> [630,] 0.33886686 0.6463297 0.6651778 0.7060388 0.7318452 0.7538065 0.7692197 #> [631,] 0.34201350 0.3591081 0.4149334 0.4231522 0.4760790 0.4992524 0.5132158 #> [632,] 0.49883170 0.7197396 0.7218012 0.7508510 0.7842259 0.7848501 0.7930338 #> [633,] 0.40473610 0.4956102 0.7398657 0.8165282 0.9339677 0.9609719 1.0370421 #> [634,] 0.48667127 0.5674285 0.5851370 0.6052577 0.6083226 0.6202031 0.6234698 #> [635,] 0.32803844 0.3965035 0.5484068 0.5868525 0.6253015 0.6509948 0.6716849 #> [636,] 0.56052284 0.6385466 0.6993452 0.7218012 0.7321719 0.8379912 0.8791882 #> [637,] 0.60666145 0.6115239 0.6960439 0.7364444 0.7907865 0.8651759 0.9106117 #> [638,] 0.90824995 0.9976108 1.0257840 1.0490677 1.0991771 1.1008939 1.1014870 #> [639,] 0.74307655 0.9445222 0.9865465 1.0185952 1.0381068 1.0908697 1.1027061 #> [640,] 0.64291176 0.6669140 0.7011364 0.7486184 0.7557675 0.8058338 0.8103042 #> [641,] 0.35605581 0.4814404 0.7876294 0.7985339 0.8573493 0.9576553 0.9659746 #> [642,] 0.79311997 0.7989374 0.8668125 0.8767005 0.8896757 0.9194493 0.9435230 #> [643,] 0.29279031 0.3544750 0.3700009 0.4719275 0.5045860 0.5201948 0.5427308 #> [644,] 0.43637557 0.4827970 0.4947433 0.7024931 0.7082306 0.7102383 0.7503552 #> [645,] 0.38434723 0.4441885 0.5685905 0.5852424 0.5996315 0.6418135 0.6847756 #> [646,] 0.75806006 0.7767205 0.7881615 0.8075945 0.8449039 0.9816106 0.9991894 #> [647,] 0.34015775 0.4648615 0.5660205 0.6646483 0.6967569 0.7338786 0.7546331 #> [648,] 0.54015739 0.6906560 0.7470331 0.7691937 0.8596595 0.8851544 0.8855184 #> [649,] 0.21401924 0.3291718 0.3406038 0.4113986 0.4169955 0.4256820 0.4330305 #> [650,] 1.21571383 1.6228459 1.7779071 1.8151949 1.8291248 1.8450162 1.9125968 #> [651,] 0.73862833 0.7855138 0.8135626 0.9202073 0.9247456 1.0629530 1.1103865 #> [652,] 0.48695987 0.4969256 0.5372878 0.5698973 0.6480851 0.6495004 0.6571974 #> [653,] 0.59394151 0.6429569 0.6469902 0.6594809 0.6669274 0.6801128 0.7318452 #> [654,] 0.71059489 0.7913172 0.8570968 0.9106875 0.9401968 0.9611286 0.9756189 #> [655,] 0.19780375 0.3078437 0.3579763 0.4318495 0.4531874 0.5124364 0.5570000 #> [656,] 0.33018138 0.3567649 0.4992524 0.5149747 0.5353460 0.5900000 0.6121609 #> [657,] 0.36776757 0.4427894 0.5663868 0.5770576 0.6664361 0.7138950 0.7603336 #> [658,] 0.39142869 0.4601763 0.4863964 0.5127744 0.5638167 0.5713538 0.5737622 #> [659,] 0.46493959 0.5561894 0.7030695 0.7118153 0.7135524 0.7341962 0.7462063 #> [660,] 0.21401924 0.2281317 0.3397789 0.4009120 0.4742309 0.5243204 0.5328568 #> [661,] 0.44464332 0.4574371 0.4834773 0.5054884 0.5055472 0.5161832 0.5229335 #> [662,] 0.32267120 0.5252603 0.7263464 0.7772791 0.7828679 0.7894459 0.8005508 #> [663,] 0.23163085 0.3125665 0.3891389 0.4232646 0.4854022 0.5116676 0.5146067 #> [664,] 1.68507968 1.7320149 1.8154012 1.8993829 1.9175140 1.9931019 2.0122906 #> [665,] 0.37105317 0.3822649 0.4080247 0.5637485 0.5686945 0.5851032 0.6203053 #> [666,] 1.26596347 1.2987503 1.3163879 1.3481555 1.4350144 1.4667450 1.5794637 #> [667,] 0.38172029 0.5221992 0.6277827 0.6597764 0.6713917 0.6715206 0.7095495 #> [668,] 0.41705624 0.4903407 0.5305223 0.5740756 0.5962643 0.5976732 0.6198012 #> [669,] 0.37131494 0.4369859 0.4755288 0.5471576 0.5577912 0.5801553 0.6030622 #> [670,] 0.68142692 0.7803132 0.8450893 0.9206161 0.9284356 0.9300096 0.9300310 #> [671,] 0.31315190 0.5417541 0.7687094 0.7741071 0.8012836 0.8145714 0.8155763 #> [672,] 0.39515951 0.4564511 0.6069523 0.6405285 0.7031803 0.7632962 0.7708018 #> [673,] 0.66039001 0.6856915 0.8041277 0.9437138 0.9860807 0.9920807 1.0049737 #> [674,] 0.33284522 0.5010170 0.6251130 0.6716530 0.7409058 0.7854566 0.7930338 #> [675,] 0.38434723 0.4322912 0.4638600 0.5213002 0.5800283 0.6553089 0.6577880 #> [676,] 0.54611205 0.6339835 0.7537169 0.7589348 0.8409048 0.8528875 0.8586295 #> [677,] 0.36503326 0.4182829 0.4523318 0.5180707 0.5275571 0.5544129 0.6218384 #> [678,] 0.24648209 0.5618274 0.5625347 0.6759686 0.7198735 0.7292775 0.7603987 #> [679,] 0.58608188 0.7796129 0.7855138 0.8454423 0.9682437 1.0820030 1.0851767 #> [680,] 0.48666539 0.4902983 0.6471791 0.7160836 0.7306741 0.7505155 0.7845608 #> [681,] 0.27087776 0.3374291 0.4033739 0.4368724 0.5486471 0.5625667 0.5654415 #> [682,] 0.57372655 0.8000399 0.9717136 0.9758373 1.0200868 1.0596932 1.1610597 #> [683,] 0.45429280 0.4655817 0.7009357 0.7498951 0.8086566 0.8632858 0.8719987 #> [684,] 0.46307271 0.5994650 0.6273540 0.7221424 0.7638539 0.7792963 0.8033483 #> [685,] 0.49907726 0.5940525 0.6827111 0.7004509 0.8277587 0.8280008 0.8723819 #> [686,] 1.50596458 1.5657095 1.5754641 1.6338610 1.7295999 1.7931603 1.8113775 #> [687,] 0.41975680 0.4354461 0.5148783 0.7302572 0.7369999 0.7374324 0.7415040 #> [688,] 0.51993210 0.6507086 0.6613144 0.6682973 0.6854089 0.6890310 0.7054623 #> [689,] 0.41315968 0.4482383 0.4526669 0.6122105 0.6367922 0.6531001 0.6671413 #> [690,] 0.59022782 0.6389957 0.6434926 0.7062934 0.7356794 0.7406443 0.7555210 #> [691,] 0.36786814 0.4817609 0.6667033 0.6692368 0.7221679 0.7474076 0.7715780 #> [692,] 0.47593764 0.5066934 0.5228171 0.6133590 0.6682280 0.6710433 0.6933420 #> [693,] 0.34015775 0.5309749 0.6754314 0.6866783 0.7454285 0.8355015 0.8627002 #> [694,] 0.44287676 0.5719344 0.6496431 0.7229793 0.7558851 0.7563084 0.8101539 #> [695,] 0.43712215 0.4553386 0.5641639 0.6392469 0.6439245 0.6870271 0.7060563 #> [696,] 0.14706566 0.4455320 0.4473325 0.4511470 0.4564653 0.4728753 0.4769149 #> [697,] 0.65371596 0.8360590 0.8482199 0.8656258 0.8673346 0.9124285 0.9252556 #> [698,] 0.42443846 0.4867599 0.5343824 0.5512543 0.5709348 0.6297882 0.6391369 #> [699,] 0.30035462 0.4965954 0.5705094 0.6069573 0.6560051 0.7111043 0.7248677 #> [700,] 0.23566961 0.2962470 0.3037289 0.3804423 0.4329317 0.4957784 0.5218996 #> [701,] 0.34791594 0.4423909 0.6339192 0.7711267 0.8043722 0.8176418 0.8193722 #> [702,] 0.38410060 0.4346415 0.5680075 0.6310681 0.6459038 0.6606776 0.6892405 #> [703,] 0.89461960 1.1036450 1.1944454 1.2430173 1.2445042 1.3393180 1.3762091 #> [704,] 0.36525783 0.5024808 0.5359625 0.6964333 0.7860382 0.8233114 0.8387448 #> [705,] 0.56182959 0.5877482 0.7154758 0.8000923 0.8829692 0.9316102 0.9406988 #> [706,] 0.52711728 0.5500002 0.5625617 0.6038029 0.6144827 0.6606776 0.6733218 #> [707,] 0.25775535 0.2967886 0.3565099 0.3630418 0.5500049 0.5858786 0.5937395 #> [708,] 0.45587612 0.4683504 0.5025979 0.5197292 0.5459429 0.5716376 0.5886034 #> [709,] 0.50575352 0.5658166 0.5931351 0.6119784 0.6813854 0.6862691 0.7420654 #> [710,] 0.46886255 0.5376504 0.6598523 0.6818975 0.7356749 0.7961273 0.8070495 #> [711,] 0.69264027 0.7333815 0.9616452 1.1140323 1.1178551 1.1265248 1.1347521 #> [712,] 0.52774869 0.7639499 0.8649104 0.8873985 0.8907783 0.9271097 0.9462811 #> [713,] 0.22952545 0.2952804 0.2981954 0.3316853 0.4060251 0.4816673 0.5045860 #> [714,] 0.33706501 0.5484259 0.5824204 0.6410320 0.6418896 0.6458494 0.6616116 #> [715,] 0.89731375 1.0537978 1.0872180 1.1281765 1.2874463 1.3430162 1.4573941 #> [716,] 0.45879487 0.5779698 0.6209997 0.6790274 0.7966283 0.8060857 0.8867858 #> [717,] 0.35637992 0.3945140 0.4154745 0.4335879 0.4409417 0.5278541 0.5740247 #> [718,] 0.66195643 0.6783787 0.7290759 0.7430766 0.8105196 0.8730697 0.9213143 #> [719,] 0.32942541 0.3510939 0.5217597 0.5319833 0.5348828 0.5535397 0.5548141 #> [720,] 0.28226619 0.7007640 0.8111915 0.8873127 0.9021124 0.9370842 0.9768958 #> [721,] 0.19718617 0.2343321 0.3316853 0.3704672 0.4115678 0.4458681 0.4720348 #> [722,] 0.36214779 0.3668483 0.3811909 0.4201479 0.4531318 0.4936062 0.5059454 #> [723,] 0.56070439 0.5842185 0.7960508 0.8029320 0.8727750 0.9239359 0.9257695 #> [724,] 0.45533863 0.4964189 0.5417933 0.5471576 0.5526583 0.6159372 0.6252936 #> [725,] 0.17091574 0.2325982 0.4170562 0.4409417 0.5799450 0.5900520 0.6431570 #> [726,] 0.30569236 0.4472829 0.4694141 0.5611247 0.5643803 0.5823287 0.6055500 #> [727,] 0.49841470 0.5978341 0.6058621 0.7401596 0.7509420 0.7776361 0.8039289 #> [728,] 0.55017438 0.5645975 0.6441751 0.6565390 0.6603900 0.6950564 0.7211326 #> [729,] 0.56164157 0.5737265 0.6881103 0.7450978 0.7895455 0.9096213 0.9724750 #> [730,] 0.34414173 0.4000386 0.4302289 0.4855279 0.5030719 0.5269430 0.5926604 #> [731,] 0.62029301 0.6352669 0.6380633 0.7147159 0.7249124 0.7291427 0.7433481 #> [732,] 0.45779847 0.6922978 0.6950564 0.7096315 0.7226073 0.7273693 0.7833891 #> [733,] 0.63826410 0.7682346 0.7960508 0.8937318 1.0084275 1.1145695 1.1332786 #> [734,] 0.67165298 0.6951862 0.7007640 0.7114757 0.8941862 0.9199336 0.9738467 #> [735,] 0.33777507 0.4482289 0.5056059 0.5320780 0.5597726 0.6534410 0.6739281 #> [736,] 0.69442557 0.8149391 0.8167236 0.8786131 0.9513195 0.9557853 0.9811198 #> [737,] 0.26835689 0.3334680 0.3621478 0.4549819 0.4769149 0.4854022 0.4865940 #> [738,] 0.49268324 0.6837906 0.9234186 0.9441428 1.0225519 1.1003096 1.1280842 #> [739,] 1.29539934 1.8722235 1.9284252 2.0010740 2.0750523 2.1162269 2.1169820 #> [740,] 0.51734193 0.6642650 0.7704097 0.8122535 0.8581814 0.8711888 0.8950791 #> [741,] 0.34511877 0.3878751 0.4791911 0.4892858 0.4923406 0.4952032 0.5455918 #> [742,] 0.35403636 0.4285535 0.5277485 0.5305939 0.5498010 0.5503752 0.5640177 #> [743,] 0.57535311 0.6563266 0.6590381 0.6714506 0.7532099 0.8052437 0.8501118 #> [744,] 0.51734179 0.7632962 0.8085228 0.8315996 0.8591507 0.8649104 0.9220884 #> [745,] 0.32917180 0.3397789 0.3876899 0.3922346 0.4001949 0.4299477 0.4323910 #> [746,] 0.64667460 0.6835879 0.6979654 0.8804967 0.9374665 0.9778351 0.9948617 #> [747,] 0.44136721 0.5503266 0.6124585 0.6458406 0.6666177 0.6790456 0.6853860 #> [748,] 0.82415540 0.8868312 1.0763748 1.1582749 1.1680500 1.2156539 1.2208195 #> [749,] 0.41493342 0.4700809 0.4869599 0.5400945 0.5436498 0.5573906 0.5594085 #> [750,] 0.46799293 0.5034560 0.5217597 0.6771774 0.7306261 0.7400928 0.7437343 #> [751,] 0.58434221 0.6646158 0.6969564 0.7237858 0.7355400 0.7586271 0.7755287 #> [752,] 0.53830065 1.3152491 1.4967682 1.5136034 1.5573514 1.6377122 1.6517498 #> [753,] 0.33760424 0.3991980 0.4598256 0.5265409 0.6790456 0.7613518 0.8217289 #> [754,] 0.23008354 0.3640111 0.4975521 0.5126158 0.5339640 0.5841814 0.6017285 #> [755,] 0.54471650 0.6192503 0.6313910 0.6351238 0.7059582 0.7568730 0.7921023 #> [756,] 0.35086757 0.5386034 0.5628925 0.5704743 0.5749911 0.5831288 0.6768378 #> [757,] 0.45429280 0.4841030 0.7173158 1.0579114 1.1550930 1.1706918 1.2055242 #> [758,] 0.41684967 0.4346328 0.5227979 0.5574171 0.5642680 0.5748597 0.6105969 #> [759,] 0.22723214 0.2321507 0.3441417 0.4547368 0.4606442 0.4972859 0.5295162 #> [760,] 0.22075267 0.3895970 0.6789776 0.7645853 0.7901658 0.8013009 0.8486597 #> [761,] 0.81532947 0.8531185 0.8577187 0.8617993 0.8923103 0.8946196 0.9825679 #> [762,] 0.28142206 0.3683553 0.4934612 0.5206577 0.5777682 0.6004304 0.6115999 #> [763,] 0.38313595 0.4004923 0.4295142 0.4470150 0.4973108 0.5328879 0.5470138 #> [764,] 0.85570464 0.8876872 0.8948302 1.0199801 1.0230997 1.0844723 1.1143924 #> [765,] 0.92943659 0.9304374 1.0307765 1.2157138 1.2584467 1.2722371 1.3115574 #> [766,] 0.51734193 0.7179293 0.7347693 0.8160029 0.8686874 0.8732938 0.9418266 #> [767,] 0.24648209 0.6124427 0.6414745 0.6667033 0.6792825 0.7024752 0.7242682 #> [768,] 0.34841500 0.3541546 0.5134793 0.5470445 0.6012403 0.6016000 0.6136057 #> [769,] 0.52454319 0.5877482 0.6497930 0.6978403 0.8646325 0.9219122 0.9685776 #> [770,] 0.33177611 0.3514778 0.4325350 0.4337352 0.4599327 0.4638805 0.4926394 #> [771,] 0.46283211 0.4734484 0.5095768 0.6294922 0.6707089 0.6725714 0.6949184 #> [772,] 0.38383571 0.3946035 0.5009526 0.5489483 0.5762502 0.6377344 0.6440527 #> [773,] 0.37711935 0.4566741 0.5215421 0.5586764 0.5766874 0.6164888 0.6767031 #> [774,] 0.38993683 0.6628639 0.6891124 0.7015497 0.7302572 0.7341874 0.7989318 #> [775,] 0.83110582 0.9107652 0.9549498 1.0660545 1.0960889 1.1519532 1.1663112 #> [776,] 0.28984342 0.3565893 0.4464113 0.4694526 0.5826878 0.5859595 0.5972087 #> [777,] 0.31299205 0.3510939 0.4679929 0.4706264 0.5949997 0.6361994 0.6766829 #> [778,] 0.35551633 0.3985222 0.5564145 0.6008991 0.6470808 0.6550018 0.6857016 #> [779,] 0.95562635 1.2624698 1.4753414 1.7136959 1.7248187 1.7823646 1.8136329 #> [780,] 0.42611375 0.6624952 0.7154758 0.8839687 0.9812302 0.9990435 1.0114281 #> [781,] 0.79814003 0.9909280 1.0555764 1.0578797 1.1204943 1.1938586 1.1983328 #> [782,] 0.58535499 0.7279623 0.7475855 0.8526947 0.8637983 0.8680231 0.8855184 #> [783,] 0.21792988 0.4455932 0.4849461 0.6027970 0.6261556 0.6593693 0.6599585 #> [784,] 0.38993683 0.5612824 0.7160182 0.8346796 0.8980437 0.9196127 0.9264201 #> [785,] 0.43300503 0.4441885 0.4714800 0.5229825 0.5421546 0.5926604 0.6107581 #> [786,] 0.28212285 0.4773151 0.5561894 0.5812558 0.6418716 0.6531730 0.6551963 #> [787,] 0.38701647 0.4429149 0.5794177 0.6179733 0.7015497 0.7045773 0.7493636 #> [788,] 0.22126147 0.4290754 0.4299702 0.4498610 0.5164665 0.5368604 0.5377246 #> [789,] 0.27916916 0.3565893 0.3891389 0.3904213 0.4800280 0.5232374 0.5258567 #> [790,] 0.36237544 0.5245432 0.7106098 0.8368973 0.8374019 0.8462018 0.8829692 #> [791,] 0.50847764 0.5532540 0.5913410 0.6471791 0.6507086 0.6737630 0.7578613 #> [792,] 0.34638187 0.3635378 0.3650333 0.4520274 0.4780638 0.5063630 0.5364357 #> [793,] 0.96471043 1.1238617 1.1670605 1.2363080 1.3438715 1.4656662 1.5202666 #> [794,] 0.39442631 0.5672525 0.5940007 0.6562407 0.6613072 0.7771895 0.8676490 #> [795,] 0.44218243 0.4562070 0.4597380 0.4941077 0.4971177 0.5149583 0.5182899 #> [796,] 0.46309833 0.4734599 0.4770930 0.4929153 0.4938839 0.5049733 0.5068350 #> [797,] 0.35327797 0.4821675 0.5343740 0.5368604 0.5838635 0.5845838 0.5888128 #> [798,] 0.44077352 0.4648615 0.5268685 0.5676076 0.5943828 0.6339192 0.6705211 #> [799,] 0.43483633 0.4369859 0.4812532 0.4849461 0.4924158 0.5734678 0.5745182 #> [800,] 0.22500973 0.4127918 0.4415877 0.5176093 0.5338640 0.5512025 0.5625347 #> [801,] 0.33651243 0.4317180 0.4334976 0.4602431 0.4944497 0.5258567 0.5421749 #> [802,] 0.30996952 0.4079894 0.4082440 0.5007269 0.5075303 0.5519626 0.5956112 #> [803,] 0.42078874 0.4458199 0.5007269 0.5594309 0.5710308 0.5931351 0.6654464 #> [804,] 0.46681568 0.5594014 0.5645912 0.5680787 0.7353619 0.7524761 0.7629788 #> [805,] 0.67438290 0.8576244 0.8710689 1.0787180 1.0813341 1.1490374 1.1719507 #> [806,] 0.63398347 0.7658092 0.8245560 0.8568136 0.8746828 0.9278684 0.9469909 #> [807,] 0.79403055 1.2538734 1.3409049 1.3886341 1.4143776 1.6039930 1.6385793 #> [808,] 0.39196996 0.6492271 0.7006185 0.7617940 0.7707938 0.7958265 0.8086566 #> [809,] 0.53559259 0.5987499 0.6718302 0.6761828 0.6896680 0.6905674 0.6947258 #> [810,] 0.21910513 0.4110020 0.4115678 0.4192902 0.5068288 0.5347966 0.5386177 #> [811,] 0.31452038 0.6963547 0.7095495 0.7133073 0.7954932 0.8097445 0.8705029 #> [812,] 0.54548829 0.5467555 0.6456530 0.6605649 0.7046408 0.7678281 0.7826580 #> [813,] 0.39683053 0.4614771 0.4893392 0.5376430 0.6251130 0.6571221 0.7005399 #> [814,] 0.42103663 0.5189327 0.5921624 0.7932110 0.7953206 0.8013409 0.8744982 #> [815,] 0.73338145 1.1091642 1.3315277 1.5215019 1.5316756 1.5636798 1.5704113 #> [816,] 0.58796392 0.6011233 0.6173346 0.6441560 0.6747761 0.6773700 0.6797189 #> [817,] 1.36065079 1.5137655 1.5193820 1.5240171 1.5498771 1.5958677 1.6121502 #> [818,] 0.30483279 0.6178127 0.6380806 0.6382770 0.6571221 0.7243450 0.8326125 #> [819,] 0.40273042 0.5212736 0.5603790 0.5628116 0.5810016 0.5887656 0.6294922 #> [820,] 0.71824740 0.7215123 0.8453708 0.8528875 0.9597073 0.9738452 1.1058711 #> [821,] 0.38559728 0.4500143 0.4863964 0.5113181 0.5500049 0.5597726 0.5685905 #> [822,] 0.62866059 0.6942937 0.7284301 0.7386602 0.7695203 0.8087480 0.8161729 #> [823,] 0.14874895 0.3236660 0.5891766 0.5903342 0.5944632 0.6515879 0.6564730 #> [824,] 0.45853317 0.4877218 0.4921739 0.5230083 0.5250451 0.5348140 0.5552367 #> [825,] 0.34800816 0.4846637 0.5529245 0.6009061 0.6091344 0.6167648 0.6366774 #> [826,] 0.43253503 0.4831555 0.4865940 0.5334751 0.5341619 0.5524280 0.5628177 #> [827,] 0.55997084 0.7272578 0.7378849 0.7621492 0.8044038 0.8407370 0.8632858 #> [828,] 0.68204381 0.7227850 0.8052437 0.8680231 0.8800067 0.9070997 0.9344276 #> [829,] 0.28919190 0.4773151 0.4803415 0.5392919 0.5550533 0.6207553 0.6225913 #> [830,] 0.55774317 0.6965077 0.7545314 0.7733977 0.8177852 0.9057978 0.9184425 #> [831,] 0.36237544 0.6978403 0.7546528 0.7967500 0.8791270 0.8803966 0.8834623 #> [832,] 0.47868229 0.7783767 0.8286614 0.8366864 0.8746035 0.8868312 0.9465115 #> [833,] 0.43357679 0.4734693 0.4909328 0.5312809 0.5519626 0.5710308 0.5838635 #> [834,] 0.40273042 0.5086296 0.6707089 0.6912846 0.7190484 0.7190719 0.7273207 #> [835,] 0.29819539 0.3870360 0.4016753 0.4192902 0.4458681 0.4505302 0.4650588 #> [836,] 0.35797635 0.3874699 0.4290754 0.4414033 0.4415019 0.4950905 0.5380901 #> [837,] 0.24728549 0.3652578 0.5973751 0.6058621 0.6714878 0.7875077 0.8330189 #> [838,] 0.43114166 0.7145558 0.7729599 0.7989667 0.8771213 0.8853278 0.9082500 #> [839,] 0.28226619 0.6951862 0.7774018 0.8110518 0.8782227 0.8891517 0.9318547 #> [840,] 0.36893463 0.4210366 0.5944991 0.6127205 0.6323531 0.6557683 0.7007896 #> [841,] 0.66544443 0.7079532 0.7227111 0.8612166 0.8741109 0.8808184 0.9161539 #> [842,] 0.23240441 0.3125665 0.3472597 0.4421824 0.4473325 0.4742903 0.5013366 #> [843,] 0.53765037 0.7394289 0.7537169 0.7876056 0.8028051 0.9627590 1.0137468 #> [844,] 0.39681078 0.4223431 0.6354693 0.7994573 0.8189176 0.8444658 0.8649676 #> [845,] 0.54908168 0.8796845 0.9251165 0.9912498 1.0151577 1.0183992 1.0188981 #> [846,] 0.34382525 0.3694933 0.4439416 0.6071260 0.6187073 0.6506722 0.6954510 #> [847,] 0.30855503 0.4296335 0.4358069 0.5168948 0.5498010 0.5910771 0.5998957 #> [848,] 0.25249506 0.3561176 0.3708418 0.3972535 0.5192776 0.5754905 0.6337435 #> [849,] 0.33507272 0.4597374 0.5069029 0.5285431 0.5372878 0.6001093 0.6130547 #> [850,] 0.17597548 0.3870165 0.5594309 0.6248637 0.6527461 0.6582989 0.6609336 #> [851,] 0.35295760 0.4630727 0.5233075 0.7415390 0.7522670 0.7865930 0.8056994 #> [852,] 0.31452038 0.5221992 0.5249327 0.6202899 0.6636930 0.6819790 0.7412960 #> [853,] 1.06356035 1.0736661 1.0825259 1.0962366 1.2298611 1.2869292 1.2916757 #> [854,] 0.96573690 1.1756648 1.1910772 1.2641272 1.3102195 1.3528209 1.3630859 #> [855,] 0.56388819 0.6959618 0.7599807 0.7856879 0.8119880 0.8185857 0.8418481 #> [856,] 0.36604246 0.4760026 0.4901277 0.4980567 0.6008453 0.6148009 0.6341309 #> [857,] 0.38214516 0.5011114 0.5160354 0.6922482 0.7856879 0.8467618 0.9077031 #> [858,] 0.59267044 0.6421611 0.6810884 0.7180966 0.7294620 0.7447693 0.7462063 #> [859,] 0.35829338 0.6209997 0.6765025 0.6980939 0.7291427 0.7454981 0.7605660 #> [860,] 0.35107872 0.4628321 0.4821902 0.5628116 0.5663818 0.5908624 0.6366756 #> [861,] 0.48666539 0.7148040 0.8065527 0.8443996 0.8621241 0.9785859 0.9832066 #> [862,] 0.36675682 0.4541553 0.6117694 0.7093871 0.7282900 0.7284700 0.7376806 #> [863,] 0.44102183 0.7006185 0.7156137 0.8526129 0.8576936 0.8646028 0.8719987 #> [864,] 0.25530869 0.5100279 0.6222739 0.7004509 0.7518164 0.7927098 0.8247377 #> [865,] 0.35107872 0.4073083 0.4727952 0.4797075 0.5887656 0.5978246 0.6011425 #> [866,] 0.20711353 0.2128669 0.2962470 0.4271706 0.4383742 0.5015894 0.5419253 #> [867,] 0.49074646 0.5218996 0.5402898 0.5666667 0.5851182 0.5921624 0.6165753 #> [868,] 0.38226491 0.4330564 0.4343819 0.4789346 0.4973497 0.6238698 0.6618646 #> [869,] 0.20119578 0.5274743 0.5727556 0.5795555 0.6107581 0.6321618 0.6506722 #> [870,] 0.34000005 0.5523149 0.5835526 0.5923980 0.6091881 0.6279973 0.6840545 #> [871,] 0.40824403 0.5111314 0.5128921 0.5586927 0.5952777 0.6600823 0.6684693 #> [872,] 0.41601699 0.4297828 0.4584733 0.4952032 0.5350863 0.5674021 0.5813493 #> [873,] 0.56979095 0.5843422 0.6339161 0.6882297 0.7151055 0.7386283 0.7734542 #> [874,] 0.65237655 0.6812469 0.7236839 0.7454451 0.8110938 0.8311178 0.9366361 #> [875,] 0.42184296 0.4502727 0.4504206 0.4829929 0.6192943 0.6400865 0.6467567 #> [876,] 0.33346801 0.3811909 0.4237522 0.4528098 0.4738702 0.5018809 0.5075120 #> [877,] 0.40802474 0.4975265 0.5176093 0.5593482 0.6650764 0.6870879 0.6948454 #> [878,] 0.67952156 0.6989833 0.7085963 0.7382393 0.7438888 0.8188065 0.8575808 #> [879,] 0.52657678 0.5467555 0.5681275 0.5865694 0.7248411 0.7300095 0.7356286 #> [880,] 0.21551060 0.3874699 0.3986045 0.4015032 0.4855791 0.5016559 0.5164665 #> [881,] 0.38577668 0.4169955 0.4356454 0.4523469 0.4742309 0.4904582 0.5262045 #> [882,] 0.56579449 0.5672378 0.5858722 0.5922482 0.6274278 0.6352016 0.6591535 #> [883,] 0.59284156 0.6624952 0.8139930 0.8322004 0.9945305 1.0395810 1.1360815 #> [884,] 0.35648607 0.4240218 0.4803121 0.4943045 0.5196530 0.5445057 0.6082788 #> [885,] 0.53011276 0.6101697 0.6716065 0.7966283 0.8490819 0.8710689 0.9369868 #> [886,] 0.68088921 0.8565482 0.8652427 0.9079105 0.9379848 0.9565635 0.9650364 #> [887,] 0.14706566 0.3842350 0.4784125 0.5095337 0.5187470 0.5218255 0.5586692 #> [888,] 0.44150652 1.0596932 1.2575503 1.3015299 1.3514940 1.3968001 1.4491465 #> [889,] 0.54753283 0.5853666 0.6010209 0.6263943 0.7082306 0.7190484 0.7254835 #> [890,] 0.51972920 0.5291272 0.5473492 0.5569819 0.5688599 0.6242691 0.6411027 #> [891,] 0.46645317 0.5035358 0.5147386 0.5518292 0.5550570 0.5851370 0.6037221 #> [892,] 0.43151667 0.4339481 0.4894152 0.4957784 0.5787041 0.5879778 0.5921479 #> [893,] 0.54620478 0.7160182 0.7341874 0.7608141 0.7634351 0.7855782 0.8240498 #> [894,] 0.26233166 0.3567649 0.4505434 0.5104084 0.5520312 0.6066468 0.6306655 #> [895,] 0.59990667 0.6372920 0.6738717 0.6780247 0.7835165 0.8307043 0.8752580 #> [896,] 0.44276402 0.4725503 0.5233075 0.5444489 0.6388097 0.7417904 0.7792963 #> [897,] 0.61366148 0.6422448 0.6548673 0.6628541 0.7373350 0.8047808 0.8112394 #> [898,] 1.13993621 1.2238417 1.2944929 1.4138356 1.4223752 1.5587307 1.5715766 #> [899,] 0.41880586 0.4297828 0.4559433 0.4602431 0.4657972 0.4769464 0.5013366 #> [900,] 0.44287676 0.5037232 0.6410085 0.7145558 0.8573915 0.8691765 0.8887671 #> [901,] 0.70951869 0.7227111 0.7655611 0.9049409 1.1103051 1.1436977 1.1671287 #> [902,] 0.17402163 0.2815989 0.3234938 0.4973108 0.5245936 0.5454948 0.5846517 #> [903,] 0.51490569 0.6955637 0.9402911 0.9713093 0.9992002 1.0926981 1.1465395 #> [904,] 0.16379892 0.4782800 0.4924158 0.5001598 0.5577912 0.5829803 0.6159372 #> [905,] 0.64480070 0.7569891 0.9153377 0.9287700 0.9294366 1.0306542 1.1035669 #> [906,] 0.26835689 0.3636692 0.3863603 0.4232646 0.4511470 0.4564327 0.4722120 #> [907,] 0.50805535 0.5762502 0.5965354 0.6315937 0.6394524 0.6589275 0.7062934 #> [908,] 0.38125326 0.4157032 0.4805979 0.5193114 0.5658166 0.5713880 0.5897818 #> [909,] 0.23253009 0.3691577 0.4070091 0.5777244 0.6250401 0.6715531 0.7055877 #> [910,] 0.38903127 0.4017785 0.4498610 0.4847487 0.4936195 0.5238938 0.5465308 #> [911,] 0.59440990 0.6989068 0.6995980 0.7242203 0.7455946 0.7480645 0.7795465 #> [912,] 0.29872606 0.4707987 0.4806273 0.4906690 0.6176954 0.6193783 0.6317046 #> [913,] 0.82461152 0.8591310 0.8731408 0.9164328 1.0578797 1.0834695 1.1278420 #> [914,] 0.67952156 0.9075966 0.9496742 0.9526848 1.0082962 1.0166035 1.1136135 #> [915,] 0.44171162 0.4928736 0.5012118 0.6263943 0.6324206 0.6440154 0.6997098 #> [916,] 0.29134698 0.3804423 0.4118020 0.4271706 0.5402898 0.5544855 0.5822610 #> [917,] 0.21910513 0.4578056 0.5066574 0.5338640 0.5411180 0.5637485 0.5884009 #> [918,] 0.80003296 0.8520190 0.8595147 0.9993802 1.0713708 1.0777662 1.0786178 #> [919,] 0.58522196 0.6448007 0.6532442 0.6655542 0.7092594 0.7155112 0.7459836 #> [920,] 0.36644829 0.3914287 0.4510571 0.6330261 0.6349593 0.6370497 0.6568489 #> [921,] 0.43640664 0.5015894 0.5326803 0.5509312 0.5567052 0.5991453 0.6388802 #> [922,] 0.50561703 0.5913410 0.5933649 0.7671347 0.7845608 0.9111170 0.9142453 #> [923,] 0.32803844 0.4338809 0.5207731 0.5649189 0.6001995 0.6045659 0.6381594 #> [924,] 0.35266986 0.3835013 0.5095863 0.5653659 0.5709348 0.5956112 0.6124346 #> [925,] 0.64979301 0.7646062 0.8164882 0.8276377 0.8368973 0.8421748 0.8771384 #> [926,] 0.23163085 0.2324044 0.4564653 0.4722120 0.4800280 0.5017003 0.5080255 #> [927,] 0.32922558 0.3934326 0.5095337 0.5121672 0.5194180 0.5259880 0.5288773 #> [928,] 0.79814003 0.9533309 1.0389936 1.1709526 1.1749098 1.1781384 1.2001907 #> [929,] 0.39267342 0.5086939 0.5477472 0.5520312 0.6469049 0.6839461 0.6940983 #> [930,] 0.29134698 0.3037289 0.3091650 0.3305156 0.4383742 0.4936195 0.5488952 #> [931,] 0.29872606 0.4131597 0.4795506 0.5665091 0.5997251 0.6136057 0.6261643 #> [932,] 0.42677834 0.4411643 0.5686196 0.6174766 0.6513026 0.6731724 0.7777964 #> [933,] 0.39206430 0.5012118 0.5589393 0.7086554 0.7179053 0.7529551 0.7558851 #> [934,] 0.40473610 0.7515701 0.8153097 1.0193305 1.1129096 1.1454856 1.2042223 #> [935,] 0.36401105 0.4486307 0.4561306 0.4769901 0.4926383 0.5446308 0.5739571 #> [936,] 0.83073988 0.8819863 1.0641047 1.0782516 1.0969681 1.1447650 1.2193489 #> [937,] 0.33591867 0.3466916 0.3873969 0.5139271 0.5550533 0.5812558 0.5829420 #> [938,] 0.04497083 0.5870115 0.5876477 0.5898424 0.6302899 0.6714246 0.6754866 #> [939,] 0.53443537 0.5655940 0.5792064 0.7051456 0.7086461 0.7153976 0.8056811 #> [940,] 0.88640969 1.1744302 1.2819133 1.3042470 1.3137897 1.4205330 1.4232624 #> [941,] 0.55774317 0.5631514 0.7511748 0.7522670 0.7901658 0.7962587 0.8298927 #> [942,] 0.35991731 0.3694933 0.4263777 0.5047480 0.5095882 0.6563266 0.7308714 #> [943,] 0.15739874 0.2999043 0.3060863 0.3792610 0.4267138 0.4280612 0.4582420 #> [944,] 0.46932208 0.5688599 0.6562525 0.7224982 0.7271169 0.7447693 0.7451598 #> [945,] 0.33816647 0.5002608 0.5497086 0.5832154 0.5839837 0.5987499 0.6531255 #> [946,] 0.65787838 0.9657369 0.9883514 1.0701032 1.1374992 1.1447650 1.2451739 #> [947,] 0.50561703 0.6266733 0.6672829 0.6746814 0.7006440 0.7663563 0.7955146 #> [948,] 0.58372918 0.7636551 0.8544198 0.9077602 0.9096541 0.9287029 0.9601853 #> [949,] 0.31354966 0.3466916 0.3770144 0.4981605 0.5835526 0.6080251 0.6379400 #> [950,] 0.26351243 0.3076413 0.4308061 0.4694526 0.5942378 0.6482221 0.6509040 #> [951,] 0.18607074 0.4871539 0.5036852 0.5189966 0.5335565 0.5425809 0.5678585 #> [952,] 0.37747986 0.4925961 0.5129245 0.5213931 0.5320780 0.6816017 0.7071654 #> [953,] 0.43434151 0.4537805 0.4782800 0.5454883 0.5546442 0.5803326 0.6204706 #> [954,] 0.54966756 0.5632566 0.5648903 0.6048902 0.6158045 0.6161821 0.6182972 #> [955,] 0.26783037 0.3555067 0.4599327 0.5020988 0.5503266 0.5731316 0.5883047 #> [956,] 0.29496717 0.3133610 0.5002608 0.5079815 0.5430447 0.5891322 0.5961361 #> [957,] 0.32511397 0.3617655 0.3835013 0.5075303 0.5512543 0.5762256 0.5950332 #> [958,] 0.45042063 0.4606442 0.4727952 0.5148810 0.5263058 0.5962643 0.6366756 #> [959,] 0.17370456 0.3076413 0.3279429 0.4852801 0.4944497 0.5166436 0.5732934 #> [960,] 0.35705946 0.4410636 0.4516268 0.5754905 0.6508299 0.6886539 0.8302582 #> [961,] 0.22813174 0.4001949 0.4003260 0.4113986 0.4827286 0.5053651 0.5508058 #> [962,] 0.79111290 0.8337189 0.9096423 0.9653220 0.9693479 1.0392179 1.0942301 #> [963,] 0.39206430 0.4417116 0.5213441 0.5853666 0.6000512 0.6460923 0.6988536 #> [964,] 0.49034069 0.5703550 0.6110425 0.6360577 0.6465991 0.6549053 0.6917995 #> [965,] 1.03453276 1.0574077 1.1989866 1.2401557 1.2488760 1.2885182 1.3480125 #> [966,] 0.47146486 0.5787526 0.7349696 0.7509420 0.8018245 0.8678513 0.8806975 #> [967,] 0.51784333 0.5368353 0.5718766 0.6238143 0.6354693 0.6705881 0.7379584 #> [968,] 0.68204381 0.9912498 1.0265272 1.2226328 1.3495796 1.3968221 1.4309520 #> [969,] 0.43544614 0.5446717 0.6081071 0.6550018 0.7121909 0.7462301 0.8047521 #> [970,] 0.67468144 0.8115864 0.8311740 0.8576452 0.8817724 0.8970071 0.9092255 #> [971,] 0.25530869 0.4229574 0.6714246 0.6844769 0.7469655 0.7584307 0.7610679 #> [972,] 0.41437972 0.6251328 0.6979654 0.7523700 0.8096269 0.8257021 0.8363396 #> [973,] 0.46068409 0.5099019 0.5453835 0.6073536 0.6115334 0.6494149 0.6606911 #> [974,] 0.50560586 0.5682722 0.5732926 0.7049280 0.7061830 0.7096315 0.7706464 #> [975,] 0.74582316 0.7609546 0.8046738 0.8240498 0.8241563 0.8281400 0.8356967 #> [976,] 0.33525273 0.3374291 0.3817311 0.4523469 0.4536032 0.4987909 0.6120957 #> [977,] 0.23008354 0.4954478 0.5046153 0.5614417 0.5616416 0.5828565 0.5882024 #> [978,] 0.49841470 0.6714878 0.7688076 0.7764392 0.8018245 0.8105688 0.8233114 #> [979,] 0.52526029 0.5345360 0.5447285 0.6395455 0.7031803 0.7376332 0.7483666 #> [980,] 0.24785601 0.3852941 0.3986045 0.5758994 0.6449115 0.6810014 0.7294620 #> [981,] 0.63397320 0.6394524 0.6664361 0.6701969 0.6802607 0.7022022 0.7301337 #> [982,] 0.45580842 0.5715196 0.5749911 0.5859775 0.6681737 0.6822274 0.7166143 #> [983,] 0.50101698 0.5376430 0.5929588 0.7114757 0.7489781 0.7692118 0.7773591 #> [984,] 0.19780375 0.4136345 0.4339700 0.4415019 0.4979181 0.6075412 0.6091881 #> [985,] 0.63176827 0.7092594 0.8033928 0.8673370 0.9140608 0.9153377 1.0441789 #> [986,] 0.36491652 0.5273441 0.6032080 0.6253015 0.7454451 0.7729599 0.7883097 #> [987,] 0.44276402 0.6742267 0.7026799 0.7415390 0.7864823 0.8683901 0.9982356 #> [988,] 0.44525949 0.8690679 0.8875176 0.9267411 1.0588732 1.0614233 1.0699795 #> [989,] 0.46932208 0.5569819 0.5886034 0.6532035 0.6885028 0.7242253 0.7372330 #> [990,] 0.27366290 0.4653143 0.6021361 0.6025242 0.6380806 0.6693098 0.6744915 #> [991,] 0.20711353 0.2356696 0.3305156 0.3670160 0.4118020 0.4364066 0.5464797 #> [992,] 0.37440774 0.7086461 0.7418901 0.7442849 0.7787907 0.7836854 0.7855291 #> [993,] 1.04193676 1.0992335 1.1503974 1.1741563 1.2870854 1.3807208 1.4045472 #> [994,] 0.45879487 0.5301128 0.5747591 0.6214295 0.7605660 0.8673346 0.8709480 #> [995,] 0.48610978 0.5967973 0.6007497 0.6189824 0.6356349 0.7741783 0.7854985 #> [996,] 0.40589754 0.5606936 0.6975627 0.7258812 0.7362571 0.7638539 0.7670559 #> [997,] 0.95463549 1.1054185 1.1069747 1.1304412 1.2374547 1.4004799 1.4737834 #> [998,] 0.85278886 0.9860807 1.0845824 1.2091892 1.2122146 1.2567472 1.3275814 #> [999,] 0.33373156 0.3560558 0.6230229 0.6408758 0.6742086 0.7076926 0.8252286 #> [1000,] 0.12432811 0.4822970 0.7518164 0.7584307 0.8035997 0.8186917 0.8575808 #> [,8] [,9] [,10] #> [1,] 1.0220903 1.0506990 1.0647866 #> [2,] 1.0159804 1.0628464 1.0808911 #> [3,] 0.6555239 0.6691817 0.6939634 #> [4,] 0.8153295 0.8323242 0.8576244 #> [5,] 0.7755287 0.8105826 0.8419308 #> [6,] 0.9605567 0.9751774 0.9853637 #> [7,] 0.6006658 0.6082166 0.6575454 #> [8,] 0.9434125 0.9569375 0.9573127 #> [9,] 0.8578887 0.8690163 0.9022197 #> [10,] 1.1059807 1.1271065 1.2717197 #> [11,] 0.9275638 0.9374665 0.9629951 #> [12,] 0.8683344 0.8833672 0.8889993 #> [13,] 0.9806743 0.9860205 1.0262920 #> [14,] 0.8045830 0.8120747 0.8122041 #> [15,] 1.1613124 1.1683864 1.1694879 #> [16,] 0.7096127 0.7521758 0.7795502 #> [17,] 0.5288156 0.5698295 0.5967761 #> [18,] 0.6856766 0.7122816 0.7510326 #> [19,] 1.1743609 1.2663257 1.2730675 #> [20,] 0.5036007 0.5465379 0.5710962 #> [21,] 0.8614368 0.8647220 0.9100972 #> [22,] 0.4877218 0.5036007 0.5085941 #> [23,] 0.8889993 0.9078457 0.9205416 #> [24,] 0.6778038 0.6823220 0.6916917 #> [25,] 0.7951561 0.9031122 0.9116083 #> [26,] 0.7183941 0.7444101 0.7563863 #> [27,] 0.5881498 0.6069905 0.6132131 #> [28,] 0.6360646 0.6735662 0.7085963 #> [29,] 0.6846481 0.6876163 0.7291948 #> [30,] 1.0284256 1.0312844 1.0480669 #> [31,] 0.7609546 0.7702795 0.8113416 #> [32,] 0.5915577 0.6097883 0.6270160 #> [33,] 0.7015258 0.7143286 0.7274967 #> [34,] 0.8280008 0.8410305 0.8838150 #> [35,] 0.6646158 0.6752053 0.6759780 #> [36,] 0.5115982 0.5658013 0.6014117 #> [37,] 0.9767749 0.9812302 0.9813567 #> [38,] 0.6544361 0.6599681 0.6723698 #> [39,] 0.5575974 0.5998258 0.6160434 #> [40,] 1.0114281 1.0269782 1.0689315 #> [41,] 1.0221192 1.0405471 1.0802871 #> [42,] 0.5539836 0.5670290 0.5742499 #> [43,] 1.1136135 1.1143924 1.1565844 #> [44,] 1.2811282 1.3083940 1.3553939 #> [45,] 1.7795953 1.7843617 1.8282038 #> [46,] 1.3636859 1.3862933 1.4477444 #> [47,] 1.1656563 1.1768936 1.2009966 #> [48,] 0.6516211 0.6789018 0.6822214 #> [49,] 0.6571013 0.6922736 0.7112233 #> [50,] 0.9127270 0.9160051 0.9327613 #> [51,] 0.7005399 0.7818339 0.7852307 #> [52,] 0.6050779 0.6813854 0.7484952 #> [53,] 0.9623091 0.9637074 0.9640488 #> [54,] 0.9004985 0.9043681 0.9113352 #> [55,] 0.5764813 0.5861439 0.6036196 #> [56,] 0.9721654 0.9896678 1.0213423 #> [57,] 1.2183686 1.2607060 1.2665570 #> [58,] 0.6979966 0.7376806 0.7692197 #> [59,] 0.9177779 0.9393185 0.9394270 #> [60,] 0.7310089 0.7778650 0.8020409 #> [61,] 0.6713180 0.7570872 0.7576333 #> [62,] 0.7238832 0.7438888 0.7741449 #> [63,] 0.5693800 0.5731336 0.6171722 #> [64,] 0.8134684 0.8155012 0.8486851 #> [65,] 0.6314431 0.6620528 0.6695307 #> [66,] 0.7487702 0.7510326 0.7665815 #> [67,] 0.7037280 0.7139507 0.7341633 #> [68,] 0.6670795 0.7039944 0.7103590 #> [69,] 0.5590320 0.5826087 0.6090574 #> [70,] 1.0705625 1.0839160 1.1077849 #> [71,] 0.7925975 0.8267848 0.8600750 #> [72,] 0.4665478 0.4980567 0.5050926 #> [73,] 0.6832122 0.7127399 0.7301544 #> [74,] 0.6706489 0.7036290 0.7122281 #> [75,] 0.9944253 1.0938387 1.1284889 #> [76,] 0.7341633 0.7386602 0.7543114 #> [77,] 0.7444101 0.7878845 0.8116886 #> [78,] 0.7183941 0.7242253 0.7437343 #> [79,] 0.6669422 0.6838606 0.7015809 #> [80,] 1.7852066 1.9035128 1.9593996 #> [81,] 1.2074716 1.2937300 1.3053031 #> [82,] 0.8501118 0.8510822 0.8887671 #> [83,] 0.6912846 0.7224415 0.7439148 #> [84,] 1.2312541 1.2640422 1.2865493 #> [85,] 1.0518943 1.0554476 1.0708872 #> [86,] 0.6896308 0.7412201 0.7649269 #> [87,] 0.7517473 0.7538065 0.7627878 #> [88,] 0.6607499 0.6651778 0.6755015 #> [89,] 0.8049486 0.8602626 0.9045392 #> [90,] 0.7128878 0.7728106 0.8510530 #> [91,] 0.7852307 0.7854566 0.8330092 #> [92,] 1.7256766 1.7263170 1.7344674 #> [93,] 0.6820320 0.6995980 0.7385645 #> [94,] 0.9136147 1.0265890 1.0361744 #> [95,] 0.6844769 0.6987678 0.7313320 #> [96,] 1.1858038 1.2298611 1.2433319 #> [97,] 0.9149957 1.1553283 1.1715203 #> [98,] 0.6350549 0.6468977 0.6569167 #> [99,] 0.6095392 0.6515879 0.7125169 #> [100,] 0.7548809 0.7640128 0.7852657 #> [101,] 0.8339963 0.8746430 0.8802706 #> [102,] 0.8803966 0.9150893 0.9655447 #> [103,] 1.3828986 1.3844780 1.4077336 #> [104,] 0.6973268 0.7098145 0.7467064 #> [105,] 0.5697776 0.6048633 0.6226070 #> [106,] 1.0555791 1.0815560 1.0834790 #> [107,] 0.5333565 0.5858786 0.5920383 #> [108,] 0.7323111 0.7431155 0.7739945 #> [109,] 0.6553638 0.7204231 0.7339759 #> [110,] 1.4211427 1.5612817 1.5691502 #> [111,] 0.8804201 0.8804967 0.9133936 #> [112,] 1.8300943 1.8462969 1.8554780 #> [113,] 0.6681737 0.7201891 0.7234314 #> [114,] 0.9264201 0.9446354 0.9898591 #> [115,] 1.0305274 1.0873706 1.1108897 #> [116,] 0.7776361 0.8285550 0.8445627 #> [117,] 0.8105688 0.8334105 0.8344281 #> [118,] 0.5251669 0.5258499 0.5761898 #> [119,] 0.6680991 0.6735608 0.6832122 #> [120,] 0.5456871 0.5893277 0.5902274 #> [121,] 0.7243613 0.7391918 0.7430023 #> [122,] 0.6011233 0.6619225 0.7385597 #> [123,] 0.6637908 0.6920283 0.7221301 #> [124,] 0.6622352 0.6793424 0.6797925 #> [125,] 1.4978961 1.5240171 1.5591992 #> [126,] 0.8145714 0.8294044 0.8933134 #> [127,] 0.6213495 0.6382426 0.6421234 #> [128,] 0.6497090 0.6654464 0.6723698 #> [129,] 0.7529188 0.7531056 0.8286505 #> [130,] 0.8415089 0.8776250 0.8833272 #> [131,] 0.9066364 0.9705027 0.9867259 #> [132,] 0.6082166 0.6088717 0.6183626 #> [133,] 0.5901790 0.5906260 0.6049659 #> [134,] 0.7160267 0.7525095 0.7664416 #> [135,] 0.5871855 0.5888006 0.5958864 #> [136,] 0.5596206 0.5888128 0.5969488 #> [137,] 0.8632859 0.8702170 0.9241328 #> [138,] 0.6115767 0.6173324 0.6180640 #> [139,] 0.8219311 0.8441528 0.8611271 #> [140,] 0.7012192 0.7072508 0.7099819 #> [141,] 0.5743078 0.6728713 0.6747774 #> [142,] 0.5263058 0.5891322 0.6317845 #> [143,] 0.6099109 0.6164456 0.6347685 #> [144,] 0.8930163 0.9479502 0.9620478 #> [145,] 1.0256061 1.0524403 1.0587543 #> [146,] 0.5566784 0.5713538 0.5788591 #> [147,] 0.5543984 0.5705758 0.5721490 #> [148,] 0.9795626 1.0067947 1.0458673 #> [149,] 0.8637182 0.9008321 0.9043360 #> [150,] 0.5414789 0.5788055 0.5862132 #> [151,] 0.5853790 0.5875525 0.5915577 #> [152,] 0.7714320 0.7798431 0.7872667 #> [153,] 0.9014666 0.9705633 0.9777831 #> [154,] 1.0157654 1.0779330 1.0913184 #> [155,] 0.9316542 0.9658182 0.9670866 #> [156,] 0.6073392 0.6131069 0.6592461 #> [157,] 1.1114114 1.1454856 1.1555352 #> [158,] 0.6052577 0.6307465 0.7290489 #> [159,] 0.6285511 0.6381594 0.6443042 #> [160,] 0.7880060 0.8260032 0.8534824 #> [161,] 0.6633763 0.6657410 0.6797596 #> [162,] 0.8476128 0.8624028 0.8771263 #> [163,] 0.7845580 0.8266930 0.8318053 #> [164,] 0.7392637 0.7512895 0.8111425 #> [165,] 1.2922349 1.3063313 1.3084353 #> [166,] 0.9128304 0.9454870 1.0354152 #> [167,] 0.6104109 0.6456856 0.6493743 #> [168,] 0.6152847 0.6210218 0.6397210 #> [169,] 0.9241328 0.9419218 1.1478319 #> [170,] 0.6543718 0.6672829 0.7638711 #> [171,] 0.6459038 0.6709559 0.6813143 #> [172,] 0.6297882 0.6436494 0.6455486 #> [173,] 1.1226844 1.1330129 1.2040133 #> [174,] 1.0910784 1.1286312 1.1740047 #> [175,] 1.1304412 1.1901069 1.1943255 #> [176,] 0.7143797 0.7184307 0.7481196 #> [177,] 0.8513361 0.8603842 0.8674875 #> [178,] 0.6705592 0.6735620 0.6778038 #> [179,] 1.0194385 1.0333731 1.0398635 #> [180,] 0.6132131 0.6198758 0.6648093 #> [181,] 0.6900043 0.7318831 0.7641128 #> [182,] 0.6079576 0.6144660 0.6170095 #> [183,] 0.5681010 0.5703633 0.6560972 #> [184,] 2.0221354 2.0532234 2.0724232 #> [185,] 1.0225519 1.0345328 1.0356459 #> [186,] 0.5291272 0.5727759 0.5803860 #> [187,] 0.8187429 0.8696613 0.8934775 #> [188,] 0.8677932 0.8900682 0.9412017 #> [189,] 0.7229251 0.7349696 0.7951147 #> [190,] 0.5737622 0.5920383 0.6537043 #> [191,] 1.3769688 1.3968075 1.4065190 #> [192,] 0.6929701 0.6971881 0.6983515 #> [193,] 0.5998258 0.6034515 0.6136679 #> [194,] 0.5429316 0.5570000 0.5602252 #> [195,] 1.0188878 1.0793454 1.0858714 #> [196,] 0.6269043 0.6810870 0.6868403 #> [197,] 1.0075614 1.0431346 1.0821745 #> [198,] 1.3594604 1.4146364 1.4263432 #> [199,] 0.7430240 0.7511748 0.8771458 #> [200,] 0.7863389 0.8066169 0.8143462 #> [201,] 1.8213842 1.8912685 1.9962653 #> [202,] 1.4548162 1.4980519 1.4988507 #> [203,] 1.0292585 1.0564403 1.0715925 #> [204,] 0.5571402 0.6248315 0.6380658 #> [205,] 0.6196099 0.6290472 0.6292636 #> [206,] 0.6378416 0.6567339 0.6771992 #> [207,] 1.6953200 1.7081563 1.7206302 #> [208,] 0.7497781 0.7546189 0.7557900 #> [209,] 1.5233936 1.5288099 1.5962986 #> [210,] 0.6239921 0.6247975 0.6265817 #> [211,] 0.7915838 0.8227246 0.8271605 #> [212,] 0.6133590 0.6334791 0.6504994 #> [213,] 0.5879778 0.5889366 0.6110425 #> [214,] 0.7508885 0.8324732 0.8941450 #> [215,] 0.7468176 0.7739945 0.8493845 #> [216,] 0.6418896 0.6420441 0.6478635 #> [217,] 0.6582989 0.6735662 0.7013804 #> [218,] 0.6180513 0.6354609 0.6562407 #> [219,] 0.7390813 0.7445928 0.7881615 #> [220,] 0.8158097 0.8690679 0.8797843 #> [221,] 0.7647663 0.7783410 0.7837751 #> [222,] 1.3820083 1.3966081 1.4695749 #> [223,] 0.5455742 0.5658013 0.6034515 #> [224,] 1.1583985 1.2959913 1.3045000 #> [225,] 0.9930158 1.0073708 1.0326790 #> [226,] 0.7177860 0.7245656 0.7300216 #> [227,] 0.6152128 0.6768162 0.7500130 #> [228,] 0.8081437 0.8210160 0.8341311 #> [229,] 0.8173428 0.8390229 0.8620620 #> [230,] 0.8007657 0.8012049 0.8112083 #> [231,] 0.9177369 0.9279086 0.9315429 #> [232,] 0.7094825 0.7412201 0.7487212 #> [233,] 0.8801390 0.8919684 0.9048960 #> [234,] 0.8240324 0.8333244 0.8385498 #> [235,] 0.7525095 0.7695044 0.7708732 #> [236,] 0.7133467 0.7192160 0.7198735 #> [237,] 0.5881498 0.5997251 0.6016000 #> [238,] 0.8105593 0.9042581 0.9140608 #> [239,] 1.0796850 1.0905094 1.0981334 #> [240,] 0.8508098 0.8635917 0.8980599 #> [241,] 0.6227925 0.6280889 0.6417925 #> [242,] 0.7067708 0.7184563 0.7377581 #> [243,] 0.6115334 0.6430333 0.6456477 #> [244,] 1.4331381 1.4607150 1.5316756 #> [245,] 0.8501881 0.8563041 0.8590798 #> [246,] 0.7118094 0.7147159 0.7300108 #> [247,] 1.1565844 1.1967436 1.2031595 #> [248,] 0.7812252 0.7929947 0.8109086 #> [249,] 0.6637959 0.6669140 0.6801039 #> [250,] 2.0397549 2.0483508 2.0556776 #> [251,] 0.7355141 0.7445453 0.7960024 #> [252,] 0.6012655 0.6101469 0.6277827 #> [253,] 1.1820783 1.1844585 1.1852437 #> [254,] 0.7059918 0.7131183 0.7172770 #> [255,] 1.1761711 1.1954909 1.2139322 #> [256,] 0.8081437 0.8302582 0.8359470 #> [257,] 0.4847586 0.4983993 0.5328842 #> [258,] 1.3630821 1.3645951 1.4483778 #> [259,] 1.6683581 1.6846586 1.6916542 #> [260,] 0.6327125 0.6592461 0.6797594 #> [261,] 0.5816681 0.6651170 0.6719349 #> [262,] 1.0199230 1.0306691 1.0465887 #> [263,] 0.7328701 0.7366519 0.7488794 #> [264,] 0.6567246 0.6632021 0.6666177 #> [265,] 0.8342606 0.8833272 0.8873127 #> [266,] 1.2187290 1.2362250 1.3522488 #> [267,] 0.7023748 0.7102512 0.7225791 #> [268,] 1.0880982 1.0941461 1.0960332 #> [269,] 0.8627002 0.8743276 0.8751708 #> [270,] 0.6198851 0.6314126 0.6475378 #> [271,] 0.5674021 0.5693480 0.5928785 #> [272,] 0.9341928 0.9833019 0.9973478 #> [273,] 0.8096329 0.8292255 0.8555027 #> [274,] 0.7891404 0.8243659 0.8278745 #> [275,] 0.9317763 0.9409960 0.9466711 #> [276,] 1.3790276 1.3802968 1.4076604 #> [277,] 0.6844823 0.7143535 0.7407388 #> [278,] 0.7100069 0.7488184 0.7600098 #> [279,] 1.1506401 1.1908013 1.2663376 #> [280,] 0.7630830 0.7659436 0.7761110 #> [281,] 0.8431615 0.8637182 0.9226522 #> [282,] 0.9590741 1.0251275 1.0283219 #> [283,] 0.7033122 0.7052669 0.7377581 #> [284,] 0.8044038 0.8099410 0.8531779 #> [285,] 0.4574371 0.4633042 0.5149583 #> [286,] 1.2142199 1.2325390 1.2486723 #> [287,] 0.7117716 0.7845475 0.8023944 #> [288,] 0.7105949 0.7146610 0.7506698 #> [289,] 0.9633728 0.9659964 1.0147951 #> [290,] 0.6066468 0.6082550 0.6091098 #> [291,] 1.0277905 1.0338277 1.0357108 #> [292,] 0.5000964 0.5028235 0.5161832 #> [293,] 0.8557396 0.8838206 0.9363732 #> [294,] 0.7162112 0.7261345 0.7368908 #> [295,] 2.1098008 2.1263382 2.1662556 #> [296,] 0.9174849 0.9391519 0.9513195 #> [297,] 0.5151876 0.5552198 0.6246004 #> [298,] 1.2236423 1.2574848 1.2584744 #> [299,] 0.8009335 0.8341694 0.8629700 #> [300,] 0.6411617 0.6742086 0.6789019 #> [301,] 1.0222484 1.0651726 1.0824499 #> [302,] 1.4780679 1.4931978 1.4998941 #> [303,] 1.7793757 1.8106507 1.8345273 #> [304,] 0.8031539 0.8513275 0.8791171 #> [305,] 1.2292597 1.2357294 1.2826214 #> [306,] 0.6505470 0.6771992 0.6788451 #> [307,] 1.1818239 1.1945705 1.2083771 #> [308,] 0.6593763 0.6723912 0.6867334 #> [309,] 0.4700809 0.4816955 0.4866329 #> [310,] 0.7201891 0.7371411 0.7627039 #> [311,] 0.9326344 1.0005127 1.0038540 #> [312,] 0.7288453 0.7482867 0.7527413 #> [313,] 0.6792825 0.7098145 0.7107325 #> [314,] 0.7188043 0.7305566 0.7341665 #> [315,] 0.5343740 0.5371986 0.6196099 #> [316,] 1.0993493 1.1180550 1.1213501 #> [317,] 0.5166436 0.5418754 0.5499564 #> [318,] 1.4359835 1.4469369 1.4834774 #> [319,] 0.8046689 0.8057893 0.8264896 #> [320,] 0.5838682 0.5972898 0.6249691 #> [321,] 0.6478044 0.6480226 0.6585143 #> [322,] 0.7356749 0.7797624 0.8027854 #> [323,] 1.0977021 1.1011990 1.1069747 #> [324,] 0.6170095 0.6424517 0.6700281 #> [325,] 0.7829631 0.7942367 0.7964902 #> [326,] 0.8682310 0.9106875 1.0473610 #> [327,] 0.8372628 0.8838178 0.9024265 #> [328,] 1.4275131 1.5188548 1.5214910 #> [329,] 1.2825670 1.3081612 1.3743889 #> [330,] 0.6444889 0.6908379 0.6954510 #> [331,] 0.6772921 0.7085888 0.7115589 #> [332,] 0.6055590 0.6319255 0.6535943 #> [333,] 0.6089178 0.6171698 0.6420984 #> [334,] 0.7013259 0.7197396 0.7201731 #> [335,] 0.7554972 0.7570872 0.7621492 #> [336,] 0.6903874 0.7099542 0.7263274 #> [337,] 1.4731754 1.4770385 1.4885976 #> [338,] 1.1171090 1.1343383 1.1866288 #> [339,] 0.7904959 0.8211896 0.8407411 #> [340,] 0.6571768 0.7153976 0.7344173 #> [341,] 0.6206803 0.7202563 0.7536813 #> [342,] 0.6031906 0.6348537 0.6648093 #> [343,] 1.0684635 1.1281765 1.2701446 #> [344,] 0.6379043 0.6522162 0.6533374 #> [345,] 0.5810245 0.5867504 0.5913260 #> [346,] 0.5046153 0.5277485 0.5910771 #> [347,] 1.0505311 1.0576989 1.0992925 #> [348,] 0.6311623 0.6560215 0.6822626 #> [349,] 1.0165439 1.1333100 1.1340170 #> [350,] 0.5813493 0.5950332 0.6079576 #> [351,] 0.7300095 0.7602339 0.7629245 #> [352,] 1.1053641 1.1180550 1.1312491 #> [353,] 1.1837643 1.1988522 1.2302067 #> [354,] 0.9533387 0.9564164 0.9649450 #> [355,] 0.9164328 0.9176535 1.0082143 #> [356,] 0.7133467 0.7360781 0.7797316 #> [357,] 0.7177315 0.7849657 0.7935783 #> [358,] 0.7369999 0.7680742 0.7718746 #> [359,] 0.6829276 0.7062740 0.7828293 #> [360,] 0.6238698 0.6319799 0.6712264 #> [361,] 0.8012627 0.8229591 0.8455997 #> [362,] 0.6160434 0.6178926 0.6240917 #> [363,] 0.9001376 0.9136147 0.9147746 #> [364,] 1.5932991 1.6157023 1.6168954 #> [365,] 0.8077789 0.8595672 0.8851544 #> [366,] 0.8105764 0.9519390 0.9627244 #> [367,] 0.8791882 0.8820581 0.9409960 #> [368,] 1.1542080 1.2095336 1.2174031 #> [369,] 1.2001517 1.2151301 1.2367852 #> [370,] 0.7215688 0.7504660 0.7612260 #> [371,] 0.5766792 0.6133128 0.6378261 #> [372,] 0.5124364 0.5342045 0.5436498 #> [373,] 0.7111043 0.7964800 0.8074443 #> [374,] 0.7735197 0.7903442 0.8573493 #> [375,] 0.6717405 0.6987919 0.7056988 #> [376,] 1.1960716 1.2061600 1.2549521 #> [377,] 0.9120929 0.9420186 1.0101190 #> [378,] 0.9728332 0.9873418 0.9990933 #> [379,] 0.7385341 0.7460797 0.7479200 #> [380,] 0.6228103 0.6252471 0.6378261 #> [381,] 0.6451651 0.6466886 0.6497090 #> [382,] 0.6164454 0.6313534 0.6431640 #> [383,] 1.7215563 1.7413460 1.7505421 #> [384,] 1.4146987 1.4767661 1.5423798 #> [385,] 1.2790766 1.3318983 1.3342449 #> [386,] 0.5253829 0.5374429 0.5478135 #> [387,] 0.6767031 0.6980473 0.7102383 #> [388,] 0.6058627 0.6468854 0.6582067 #> [389,] 0.7614894 0.7666488 0.7872667 #> [390,] 0.7636327 0.7665815 0.7671899 #> [391,] 0.7542396 0.7742806 0.8188065 #> [392,] 0.8449039 0.8477306 0.8554640 #> [393,] 0.6606911 0.6652344 0.7106932 #> [394,] 1.1621956 1.1644541 1.1986978 #> [395,] 1.1677274 1.2597720 1.2879303 #> [396,] 0.9441428 0.9562852 0.9670866 #> [397,] 0.6461569 0.6517059 0.6642091 #> [398,] 0.8534824 0.8593124 0.9181514 #> [399,] 0.6507469 0.6531001 0.6857016 #> [400,] 0.7627499 0.8010999 0.8162060 #> [401,] 0.7167615 0.7590209 0.7663757 #> [402,] 0.9385822 0.9546533 0.9735454 #> [403,] 0.9174849 0.9597572 0.9760358 #> [404,] 0.7314190 0.7399096 0.7420626 #> [405,] 0.6440154 0.6769841 0.6813038 #> [406,] 0.9042870 0.9199815 0.9366138 #> [407,] 0.5143304 0.5230954 0.5826878 #> [408,] 0.7751121 0.8150130 0.8160969 #> [409,] 0.6680155 0.6764425 0.6882297 #> [410,] 1.0683585 1.0714703 1.1421249 #> [411,] 1.2147471 1.2663894 1.2727458 #> [412,] 1.1390125 1.1560421 1.1667422 #> [413,] 0.5777682 0.5841814 0.5867504 #> [414,] 1.0770330 1.1461043 1.1826790 #> [415,] 0.7190763 0.7826114 0.8346353 #> [416,] 1.2734965 1.3180883 1.3209671 #> [417,] 0.8609763 0.8951846 0.9389049 #> [418,] 0.6299388 0.6377190 0.6472083 #> [419,] 1.3202222 1.3234820 1.3480002 #> [420,] 0.7911226 0.8918625 0.9113162 #> [421,] 1.5099517 1.5879799 1.6275022 #> [422,] 0.8648793 0.8801506 0.8962246 #> [423,] 0.6058663 0.6082788 0.6314126 #> [424,] 1.3163879 1.3328708 1.3662731 #> [425,] 0.7955620 0.7975097 0.8114964 #> [426,] 0.6418109 0.6934614 0.7022022 #> [427,] 1.8529715 1.9275538 1.9412448 #> [428,] 0.8037321 0.8180881 0.8260032 #> [429,] 0.8748160 0.9124285 0.9167675 #> [430,] 1.4781769 1.5195083 1.5757886 #> [431,] 0.7765806 0.7796118 0.7852608 #> [432,] 0.9012644 0.9333909 0.9559555 #> [433,] 0.6599585 0.6928193 0.6981878 #> [434,] 0.7897385 0.8571447 0.8957822 #> [435,] 1.1936272 1.2030167 1.2407876 #> [436,] 0.7833691 0.8614368 0.8945441 #> [437,] 0.5384173 0.5829803 0.5841615 #> [438,] 0.5571568 0.6174222 0.6316854 #> [439,] 0.6609872 0.6613144 0.7115270 #> [440,] 0.8451830 0.8557046 0.8650737 #> [441,] 0.5728784 0.5745183 0.5839120 #> [442,] 1.0467757 1.0550746 1.0574801 #> [443,] 0.6892405 0.7332718 0.7333728 #> [444,] 0.5805887 0.6012403 0.6268490 #> [445,] 0.7912182 0.8435330 0.8641570 #> [446,] 0.7852608 0.8762724 0.9075501 #> [447,] 1.3676645 1.3747721 1.4162996 #> [448,] 1.2725432 1.2803105 1.2833201 #> [449,] 1.1295200 1.2154808 1.2187803 #> [450,] 0.9210998 0.9405099 1.0160491 #> [451,] 0.6643364 0.6665172 0.6964813 #> [452,] 0.6314404 0.6404495 0.6684119 #> [453,] 0.5370750 0.5614417 0.6017285 #> [454,] 0.9114307 0.9131999 0.9218992 #> [455,] 0.5167141 0.5323207 0.5668559 #> [456,] 0.7683081 0.7733977 0.8122535 #> [457,] 0.5024490 0.5412091 0.5569929 #> [458,] 1.0857416 1.1236817 1.1830759 #> [459,] 0.5728755 0.5922088 0.5931377 #> [460,] 0.5689447 0.5754628 0.5817520 #> [461,] 0.6513352 0.7065268 0.7117218 #> [462,] 0.5501011 0.5596206 0.5649255 #> [463,] 0.6391369 0.6395256 0.6412760 #> [464,] 0.8306194 0.8751047 0.8794120 #> [465,] 0.7393981 0.8423412 0.8437964 #> [466,] 1.8836910 1.9095367 1.9135119 #> [467,] 1.1950243 1.2333716 1.2435436 #> [468,] 0.9667808 0.9857054 0.9916732 #> [469,] 0.9461251 0.9463880 0.9540307 #> [470,] 0.5763679 0.5815961 0.5851288 #> [471,] 0.8321809 0.8519922 0.8573915 #> [472,] 0.6797594 0.6974174 0.7011072 #> [473,] 0.6045314 0.6086948 0.6161821 #> [474,] 0.6178535 0.6265817 0.6299388 #> [475,] 1.4563943 1.4668533 1.4956531 #> [476,] 0.4759897 0.5278541 0.5323207 #> [477,] 0.5904797 0.5921010 0.6182972 #> [478,] 0.7475949 0.7595601 0.7750158 #> [479,] 0.8676342 0.9958579 1.0082914 #> [480,] 0.9988466 1.0379648 1.0873266 #> [481,] 0.6811404 0.7125915 0.7438543 #> [482,] 0.7233252 0.7258189 0.7496445 #> [483,] 0.8908971 0.9220001 0.9393187 #> [484,] 0.8402377 0.8782716 0.8924074 #> [485,] 0.8752551 0.9683506 0.9777102 #> [486,] 0.9860885 1.0097891 1.0138331 #> [487,] 0.6715531 0.6862316 0.6922482 #> [488,] 1.2949801 1.3199758 1.3314084 #> [489,] 1.0834790 1.1111410 1.1434177 #> [490,] 0.7012192 0.7035310 0.7197476 #> [491,] 0.8863247 0.9151983 0.9861853 #> [492,] 1.7159364 1.7248158 1.7670427 #> [493,] 0.8374759 0.9264355 0.9493804 #> [494,] 1.3369391 1.3572066 1.3602157 #> [495,] 1.3663135 1.3747328 1.3915746 #> [496,] 0.9388876 0.9787855 0.9818573 #> [497,] 0.9633728 0.9716273 1.0105809 #> [498,] 1.2406684 1.2583409 1.3507659 #> [499,] 0.8820550 1.0146170 1.0150288 #> [500,] 0.9055686 0.9680445 0.9941518 #> [501,] 2.4145756 2.4420108 2.4464436 #> [502,] 0.5520771 0.5745183 0.5826584 #> [503,] 0.8337882 0.8505912 0.8526129 #> [504,] 0.6882318 0.6964999 0.7367348 #> [505,] 0.9813650 0.9874835 1.0472607 #> [506,] 0.6814462 0.7060224 0.7076732 #> [507,] 0.4816955 0.4847935 0.4864367 #> [508,] 0.5691907 0.5770479 0.5780491 #> [509,] 1.6335442 1.6385179 1.6535956 #> [510,] 1.3528960 1.3563794 1.3611355 #> [511,] 0.6759780 0.7094116 0.7247117 #> [512,] 1.2926636 1.3023587 1.3749284 #> [513,] 1.5299030 1.5399956 1.6611706 #> [514,] 0.6710257 0.6810884 0.6950817 #> [515,] 0.7584446 0.8062355 0.8130625 #> [516,] 0.8178128 0.8178424 0.8205612 #> [517,] 0.8591310 0.8919684 0.9240162 #> [518,] 0.5025708 0.5418103 0.5693054 #> [519,] 0.6895236 0.6973202 0.7059582 #> [520,] 0.5773875 0.5787767 0.6029737 #> [521,] 0.7146610 0.7643505 0.7878777 #> [522,] 0.6803957 0.6811404 0.7170140 #> [523,] 0.6578455 0.6659730 0.6749460 #> [524,] 0.7388219 0.7477676 0.7486448 #> [525,] 0.9893545 1.0141225 1.0483922 #> [526,] 0.6767827 0.7060023 0.7315015 #> [527,] 1.0208764 1.0257840 1.0330603 #> [528,] 0.7171179 0.7334094 0.7448059 #> [529,] 0.8668125 0.9510885 0.9550453 #> [530,] 1.3518969 1.3648092 1.3849989 #> [531,] 0.7892308 0.8003594 0.8356967 #> [532,] 0.8611332 0.8613864 0.9164915 #> [533,] 0.6250401 0.6367535 0.6517178 #> [534,] 0.7000426 0.7177860 0.7378278 #> [535,] 0.7863389 0.8349013 0.8376880 #> [536,] 0.9794036 1.0058872 1.0216113 #> [537,] 0.8364121 0.8611271 0.8800067 #> [538,] 0.5635180 0.6092181 0.6218384 #> [539,] 0.8904440 0.8921551 0.8932878 #> [540,] 0.6639791 0.7167615 0.7429688 #> [541,] 0.6532183 0.6622352 0.6784005 #> [542,] 1.3483684 1.3507839 1.3894962 #> [543,] 0.8085228 0.8488790 0.8680777 #> [544,] 0.5340517 0.5593715 0.6246004 #> [545,] 0.5689344 0.6011425 0.6378416 #> [546,] 0.6056311 0.6089128 0.6109068 #> [547,] 0.7942207 0.8298927 0.8602639 #> [548,] 0.9145482 0.9155776 0.9157779 #> [549,] 0.7833891 0.7929947 0.7977212 #> [550,] 0.8923763 0.8936716 0.8943902 #> [551,] 0.6252989 0.6302890 0.6325989 #> [552,] 0.6213495 0.6715206 0.6738913 #> [553,] 0.5815733 0.6928156 0.7067708 #> [554,] 0.7766909 0.7895843 0.8063112 #> [555,] 0.8607480 0.8663636 0.8850935 #> [556,] 0.6819663 0.7233252 0.7428187 #> [557,] 0.8135751 0.8167236 0.8241794 #> [558,] 2.3432253 2.3690277 2.3950798 #> [559,] 0.7093489 0.7243632 0.7538692 #> [560,] 0.6790146 0.6813491 0.6987252 #> [561,] 0.9278684 0.9670014 0.9923017 #> [562,] 0.6440527 0.6939634 0.6946025 #> [563,] 0.8178424 0.8214247 0.8249273 #> [564,] 0.5854172 0.6030661 0.6703721 #> [565,] 2.2018303 2.2170551 2.2715903 #> [566,] 0.6740985 0.6961154 0.7107108 #> [567,] 0.5288156 0.6289047 0.6432439 #> [568,] 0.6086948 0.6167249 0.6182271 #> [569,] 0.8899425 0.8907170 0.9248265 #> [570,] 0.8781574 0.9243470 0.9436126 #> [571,] 0.6611107 0.6947258 0.7779487 #> [572,] 0.8544198 0.8607628 0.9113352 #> [573,] 0.6818945 0.6944958 0.6973268 #> [574,] 0.6450755 0.7042088 0.7045850 #> [575,] 0.8500746 0.9282655 1.0189408 #> [576,] 0.7818339 0.7826114 0.8231869 #> [577,] 1.2586485 1.2649063 1.4049734 #> [578,] 0.6757237 0.6791727 0.7117059 #> [579,] 0.6788451 0.6853032 0.7137189 #> [580,] 0.6333447 0.6695882 0.6853860 #> [581,] 0.6198851 0.6315937 0.6712264 #> [582,] 0.5481736 0.5764813 0.6090256 #> [583,] 0.6900380 0.6917397 0.7118564 #> [584,] 0.8031552 0.8159384 0.8325545 #> [585,] 0.8078439 0.8962927 0.9024955 #> [586,] 0.7863477 0.8318041 0.8382579 #> [587,] 0.6263667 0.6662338 0.6693745 #> [588,] 1.0664086 1.0867883 1.1595318 #> [589,] 0.7238832 0.7291073 0.7634351 #> [590,] 1.8472453 1.9284869 1.9455458 #> [591,] 0.6030661 0.6150027 0.6178535 #> [592,] 0.7067891 0.7202563 0.7385543 #> [593,] 0.5766874 0.6071901 0.6722267 #> [594,] 0.5037597 0.5140889 0.5278857 #> [595,] 0.6973421 0.7460797 0.7484519 #> [596,] 0.8587832 0.8776250 0.8925923 #> [597,] 0.8990254 0.8998505 0.9043764 #> [598,] 0.6098153 0.6283844 0.6289047 #> [599,] 1.1384414 1.1628123 1.1670567 #> [600,] 0.7396622 0.7411347 0.7429837 #> [601,] 0.6981834 0.7385372 0.7571183 #> [602,] 0.5697776 0.5925780 0.6079173 #> [603,] 1.0356459 1.0574077 1.0919283 #> [604,] 1.1836077 1.2198348 1.2271238 #> [605,] 0.6046276 0.6299998 0.6461569 #> [606,] 0.5649939 0.5740247 0.5826055 #> [607,] 1.1519656 1.1540375 1.1572590 #> [608,] 0.8675523 0.8853132 0.9111472 #> [609,] 1.6049527 1.6168954 1.6674373 #> [610,] 0.9049409 0.9094752 1.0089479 #> [611,] 0.5936046 0.6057138 0.6595870 #> [612,] 0.9681270 0.9843482 1.0041864 #> [613,] 1.2830695 1.3338333 1.3386482 #> [614,] 0.7772791 0.8072681 0.8344979 #> [615,] 0.7472657 0.7642566 0.7715074 #> [616,] 0.8531185 0.8587832 0.8961581 #> [617,] 0.7300108 0.7336099 0.7580219 #> [618,] 0.6519833 0.7036290 0.7411941 #> [619,] 0.9758230 1.0551752 1.0664273 #> [620,] 0.8801867 0.8923898 0.9039039 #> [621,] 0.5862132 0.6693059 0.6782872 #> [622,] 0.6219328 0.6248315 0.6261556 #> [623,] 0.7478602 0.7580601 0.7706583 #> [624,] 0.5698945 0.6299090 0.6403390 #> [625,] 1.0167930 1.0453051 1.1333771 #> [626,] 0.7392944 0.7548943 0.7563227 #> [627,] 0.6061947 0.6288226 0.6932874 #> [628,] 0.5016559 0.5094419 0.5727759 #> [629,] 1.0443008 1.0609972 1.0629319 #> [630,] 0.7998817 0.8195488 0.8678657 #> [631,] 0.5547715 0.6030755 0.6154857 #> [632,] 0.8144859 0.8281860 0.8697099 #> [633,] 1.0451144 1.0542669 1.1114114 #> [634,] 0.6419922 0.6432989 0.6447714 #> [635,] 0.6742909 0.7286320 0.7541595 #> [636,] 0.9607071 0.9640626 0.9819342 #> [637,] 0.9198462 0.9676332 0.9754811 #> [638,] 1.1200604 1.2439004 1.2668609 #> [639,] 1.1293918 1.1544637 1.1925736 #> [640,] 0.8213356 0.8387448 0.8433437 #> [641,] 0.9677572 0.9888431 1.0394407 #> [642,] 1.0378200 1.0572936 1.0864902 #> [643,] 0.5496676 0.5826584 0.5893277 #> [644,] 0.8202058 0.8503474 0.8628839 #> [645,] 0.6876549 0.6992767 0.7008368 #> [646,] 1.0006761 1.0891220 1.1000991 #> [647,] 0.7692970 0.8176418 0.8722259 #> [648,] 0.9001376 0.9306692 0.9344276 #> [649,] 0.4759897 0.5073673 0.5773453 #> [650,] 1.9382943 1.9416419 2.0260886 #> [651,] 1.1290313 1.2173869 1.2181288 #> [652,] 0.6658469 0.6770162 0.6778106 #> [653,] 0.7356093 0.7722727 0.7758287 #> [654,] 1.0047432 1.0131991 1.0317723 #> [655,] 0.5662782 0.5710962 0.5811230 #> [656,] 0.6184569 0.6220324 0.6290472 #> [657,] 0.7677005 0.7693379 0.7703628 #> [658,] 0.5899925 0.6245978 0.6298449 #> [659,] 0.7469655 0.7567931 0.7701006 #> [660,] 0.5644498 0.5773875 0.6264687 #> [661,] 0.5910982 0.6014117 0.6069905 #> [662,] 0.8325475 0.8360327 0.8718518 #> [663,] 0.5199536 0.5201948 0.5253829 #> [664,] 2.0411730 2.0795131 2.1723752 #> [665,] 0.6398905 0.7198262 0.7426566 #> [666,] 1.6206125 1.6538794 1.6952439 #> [667,] 0.7155574 0.7265504 0.7561248 #> [668,] 0.6236368 0.6674390 0.7730710 #> [669,] 0.6593693 0.6599681 0.7081862 #> [670,] 0.9392153 0.9818740 0.9936594 #> [671,] 0.8417960 0.8581661 0.9167841 #> [672,] 0.8022114 0.8045830 0.8129684 #> [673,] 1.0238475 1.0297726 1.0514701 #> [674,] 0.8379912 0.8492909 0.8665540 #> [675,] 0.6764425 0.7128808 0.7414976 #> [676,] 0.9237796 0.9572670 0.9769117 #> [677,] 0.6259317 0.6287526 0.6306655 #> [678,] 0.7745990 0.7968260 0.8289013 #> [679,] 1.1319135 1.1391750 1.1587812 #> [680,] 0.7995431 0.8797833 0.9001406 #> [681,] 0.5658163 0.5663868 0.6085266 #> [682,] 1.1814759 1.2122146 1.2527892 #> [683,] 0.9574515 0.9917246 1.0476483 #> [684,] 0.8202589 0.8405467 0.8482936 #> [685,] 1.0408116 1.0770330 1.0790551 #> [686,] 1.8179036 1.8340617 1.9116460 #> [687,] 0.8240114 0.8346796 0.8540282 #> [688,] 0.7111981 0.7168102 0.7506698 #> [689,] 0.7062740 0.7083143 0.7332891 #> [690,] 0.7589517 0.7814489 0.8743718 #> [691,] 0.7887940 0.8068922 0.8298635 #> [692,] 0.7042882 0.7073357 0.7086554 #> [693,] 0.8677932 0.9081009 0.9096423 #> [694,] 0.9426249 0.9456269 0.9996745 #> [695,] 0.7261435 0.7369679 0.7431326 #> [696,] 0.5259880 0.5325244 0.5335413 #> [697,] 0.9357843 1.0164852 1.1235415 #> [698,] 0.6602462 0.6971881 0.7130196 #> [699,] 0.7407388 0.7441478 0.7587615 #> [700,] 0.5805887 0.6411091 0.6493704 #> [701,] 0.8441528 0.8526117 0.8743718 #> [702,] 0.7091045 0.7115284 0.7332891 #> [703,] 1.4329723 1.4998115 1.5321308 #> [704,] 0.8716388 0.9324892 0.9406988 #> [705,] 0.9435541 1.0315119 1.0640330 #> [706,] 0.6753137 0.7172770 0.7210186 #> [707,] 0.6354370 0.6485246 0.6687828 #> [708,] 0.7013456 0.7131056 0.7347693 #> [709,] 0.7484519 0.7737699 0.7741449 #> [710,] 0.8423412 0.8555198 0.8749579 #> [711,] 1.1663112 1.2098689 1.2143511 #> [712,] 0.9481154 0.9482776 0.9492661 #> [713,] 0.5139554 0.5386177 0.5543984 #> [714,] 0.6937074 0.7245582 0.7468176 #> [715,] 1.4709076 1.4824762 1.4912133 #> [716,] 0.8951693 0.8961581 0.9878208 #> [717,] 0.5740756 0.5972898 0.6142427 #> [718,] 0.9250145 0.9261443 0.9285668 #> [719,] 0.5785084 0.6069989 0.6116374 #> [720,] 0.9843897 1.0550667 1.0561660 #> [721,] 0.5411180 0.5608529 0.5653868 #> [722,] 0.5295162 0.5340517 0.5555209 #> [723,] 0.9688074 1.0018221 1.0154645 #> [724,] 0.6862316 0.7080334 0.7092927 #> [725,] 0.6652081 0.6720352 0.6917995 #> [726,] 0.6144660 0.6167648 0.6466886 #> [727,] 0.8975746 0.9026949 0.9269203 #> [728,] 0.7297279 0.7441841 0.7579067 #> [729,] 0.9753392 0.9793578 0.9902628 #> [730,] 0.6466303 0.6596934 0.6713759 #> [731,] 0.7510750 0.8031048 0.8497506 #> [732,] 0.7844187 0.7910788 0.8033215 #> [733,] 1.1436977 1.2593077 1.2944929 #> [734,] 1.0233959 1.0988194 1.1192609 #> [735,] 0.6888786 0.7709872 0.8026905 #> [736,] 1.1020634 1.1103051 1.1116794 #> [737,] 0.4875835 0.5037597 0.5218255 #> [738,] 1.2087302 1.2367852 1.2630820 #> [739,] 2.1226628 2.1489737 2.1847682 #> [740,] 0.9699873 0.9936126 0.9981753 #> [741,] 0.5541666 0.5674918 0.5693480 #> [742,] 0.5708686 0.5748597 0.5788055 #> [743,] 0.8565868 0.8744701 0.8796845 #> [744,] 0.9290540 0.9548992 0.9762425 #> [745,] 0.4644079 0.5262045 0.5492644 #> [746,] 1.0158253 1.0370421 1.0656568 #> [747,] 0.7036279 0.7143330 0.7151035 #> [748,] 1.2295782 1.2701114 1.3213964 #> [749,] 0.5693054 0.6197711 0.6784878 #> [750,] 0.7510750 0.7788458 0.7880611 #> [751,] 0.7907865 0.8110518 0.8268999 #> [752,] 1.6528810 1.6852090 1.7012686 #> [753,] 0.8275630 0.8613339 0.8736702 #> [754,] 0.6022206 0.6873235 0.6881103 #> [755,] 0.7941230 0.7957952 0.7978296 #> [756,] 0.7081964 0.7167148 0.7362297 #> [757,] 1.2384498 1.2410470 1.3512977 #> [758,] 0.6404495 0.7010819 0.7160734 #> [759,] 0.5552198 0.6299090 0.6299998 #> [760,] 0.8529619 0.8699727 0.8723819 #> [761,] 1.1095331 1.1378732 1.1933431 #> [762,] 0.6133128 0.6134790 0.6152627 #> [763,] 0.5848662 0.5881791 0.5938147 #> [764,] 1.2015164 1.2033767 1.2357261 #> [765,] 1.3644598 1.3664599 1.3791541 #> [766,] 0.9682310 0.9753498 0.9834251 #> [767,] 0.7501720 0.7553391 0.7784621 #> [768,] 0.6176954 0.6553638 0.7037984 #> [769,] 0.9799616 0.9971581 1.0188878 #> [770,] 0.6067608 0.6166483 0.6215483 #> [771,] 0.6988536 0.7143979 0.7691937 #> [772,] 0.6707290 0.7406443 0.7925577 #> [773,] 0.6920620 0.7192525 0.7378939 #> [774,] 0.8061181 0.8335395 0.8519922 #> [775,] 1.2012728 1.2145928 1.2264780 #> [776,] 0.6036117 0.6045314 0.6074887 #> [777,] 0.7134724 0.7330610 0.7336099 #> [778,] 0.7357161 0.7659436 0.7765781 #> [779,] 1.9690753 1.9715492 1.9898267 #> [780,] 1.0283219 1.0732000 1.1302308 #> [781,] 1.2927882 1.2994474 1.3193296 #> [782,] 0.8892492 0.9349566 0.9609719 #> [783,] 0.7018778 0.7117218 0.7143797 #> [784,] 0.9308265 0.9329245 0.9864876 #> [785,] 0.6700494 0.6884271 0.7247373 #> [786,] 0.6910984 0.7043720 0.7224982 #> [787,] 0.8368802 0.8438580 0.8626579 #> [788,] 0.5811230 0.6050018 0.6342684 #> [789,] 0.5341757 0.5555209 0.5730101 #> [790,] 0.8837296 0.8878474 0.9150893 #> [791,] 0.7885298 0.7951147 0.7955146 #> [792,] 0.5522140 0.5649189 0.5940605 #> [793,] 1.5337373 1.5347884 1.5472385 #> [794,] 0.8744181 0.8762210 0.8950791 #> [795,] 0.5354529 0.6159843 0.6497799 #> [796,] 0.5435039 0.5631950 0.5761898 #> [797,] 0.5904797 0.6074887 0.6143992 #> [798,] 0.6754314 0.7094825 0.7628576 #> [799,] 0.5793089 0.5914720 0.6272817 #> [800,] 0.5851032 0.6079851 0.6545558 #> [801,] 0.5455918 0.5498394 0.5578871 #> [802,] 0.5969488 0.6414602 0.6594972 #> [803,] 0.7023748 0.7029518 0.7169213 #> [804,] 0.8185188 0.8205612 0.8326591 #> [805,] 1.1750752 1.1903984 1.1933431 #> [806,] 1.0302284 1.0537445 1.0748683 #> [807,] 1.6709016 1.7005440 1.7394570 #> [808,] 0.8407370 0.8500746 0.8637478 #> [809,] 0.7008518 0.7150811 0.7800171 #> [810,] 0.5433768 0.5853286 0.5858126 #> [811,] 0.8785766 0.8927469 0.9212768 #> [812,] 0.7942367 0.8017567 0.8151381 #> [813,] 0.7228474 0.7321719 0.7385597 #> [814,] 0.9128304 0.9264948 0.9299598 #> [815,] 1.5803471 1.5953455 1.6277466 #> [816,] 0.6993452 0.7242203 0.7344657 #> [817,] 1.6124134 1.6636003 1.6760532 #> [818,] 0.8376880 0.8426910 0.8453352 #> [819,] 0.6316854 0.6320838 0.6329795 #> [820,] 1.1178551 1.1519532 1.1632556 #> [821,] 0.5833116 0.6029864 0.6135050 #> [822,] 0.9326344 0.9529245 0.9951438 #> [823,] 0.6772256 0.6932874 0.7187178 #> [824,] 0.5820719 0.5912199 0.5958864 #> [825,] 0.6578455 0.6685950 0.6729128 #> [826,] 0.5650308 0.5788591 0.5883047 #> [827,] 0.8792121 0.8806975 0.8816077 #> [828,] 1.0188981 1.0945970 1.1559622 #> [829,] 0.6354521 0.6415935 0.6464179 #> [830,] 0.9306057 0.9731270 0.9804268 #> [831,] 0.8952237 0.9297569 0.9763403 #> [832,] 1.0843053 1.0897857 1.1173875 #> [833,] 0.5930187 0.5939369 0.6069079 #> [834,] 0.7465824 0.7697630 0.7820416 #> [835,] 0.4847935 0.4983993 0.5371986 #> [836,] 0.5418103 0.5454948 0.5471028 #> [837,] 0.8334105 0.8618845 0.9283201 #> [838,] 0.9496958 1.0138397 1.0735338 #> [839,] 0.9777460 1.0175556 1.0292585 #> [840,] 0.7527413 0.7735569 0.7822611 #> [841,] 0.9744743 1.0102749 1.0125469 #> [842,] 0.5128846 0.5187470 0.5232374 #> [843,] 1.0283224 1.0680810 1.0750131 #> [844,] 0.9416318 0.9482930 0.9629695 #> [845,] 1.0277905 1.0347361 1.1459346 #> [846,] 0.7532099 0.8083029 0.8129840 #> [847,] 0.6244790 0.6370497 0.6643578 #> [848,] 0.7263274 0.7298699 0.7523639 #> [849,] 0.6670763 0.6757237 0.7488167 #> [850,] 0.7400483 0.7407983 0.7678197 #> [851,] 0.8328801 0.8593535 0.8706834 #> [852,] 0.8042118 0.8181203 0.8300385 #> [853,] 1.3269187 1.3651020 1.3995892 #> [854,] 1.4041430 1.4212342 1.4334688 #> [855,] 0.8489212 0.8509315 0.9226693 #> [856,] 0.6371847 0.6373957 0.6489260 #> [857,] 0.9479864 1.0184980 1.0239902 #> [858,] 0.7565456 0.7837856 0.8034945 #> [859,] 0.7948078 0.7953206 0.8008431 #> [860,] 0.6458494 0.6610452 0.6853779 #> [861,] 0.9921289 1.0741951 1.0819476 #> [862,] 0.7420654 0.7459836 0.7656647 #> [863,] 0.9134277 0.9463880 0.9774547 #> [864,] 0.8385498 0.8457286 0.8462907 #> [865,] 0.6143098 0.6192943 0.6269716 #> [866,] 0.5851182 0.5949997 0.6690769 #> [867,] 0.6249000 0.6323681 0.6533374 #> [868,] 0.6744915 0.6873026 0.6948454 #> [869,] 0.7422157 0.7559815 0.7563227 #> [870,] 0.6857204 0.7060388 0.7225791 #> [871,] 0.6754866 0.7015258 0.7165442 #> [872,] 0.6624938 0.6659730 0.6665198 #> [873,] 0.7918862 0.8899351 0.9106117 #> [874,] 0.9897825 0.9967091 0.9998667 #> [875,] 0.6674390 0.6709559 0.6722478 #> [876,] 0.5127744 0.5200857 0.5278857 #> [877,] 0.7248677 0.7362571 0.7574972 #> [878,] 0.8650737 0.8673128 0.8876872 #> [879,] 0.8082818 0.8188058 0.8420042 #> [880,] 0.5238938 0.5245936 0.6314431 #> [881,] 0.5546065 0.5552367 0.5658163 #> [882,] 0.6593763 0.6692368 0.6819575 #> [883,] 1.1362660 1.1670605 1.1814908 #> [884,] 0.6274278 0.6516153 0.6589275 #> [885,] 0.9445222 0.9758457 0.9935428 #> [886,] 1.0196759 1.0782516 1.1500725 #> [887,] 0.5611785 0.5721490 0.5730101 #> [888,] 1.4525418 1.5335511 1.5650556 #> [889,] 0.7290624 0.7626622 0.7701266 #> [890,] 0.6575866 0.6631294 0.6709890 #> [891,] 0.6369631 0.6478044 0.6552745 #> [892,] 0.5949277 0.6268538 0.6411613 #> [893,] 0.8987008 0.8996465 0.9122463 #> [894,] 0.6536885 0.7169877 0.7448199 #> [895,] 0.8754574 0.8961878 0.9137037 #> [896,] 0.8990254 0.9600782 0.9845505 #> [897,] 0.8324243 0.8734084 0.8839719 #> [898,] 1.6232812 1.6329324 1.6843737 #> [899,] 0.5307726 0.5541666 0.5542382 #> [900,] 0.9233367 0.9341166 0.9619989 #> [901,] 1.1674370 1.2091835 1.2888790 #> [902,] 0.5935215 0.6133391 0.6145858 #> [903,] 1.1552582 1.1596754 1.2374547 #> [904,] 0.6360963 0.6439245 0.6468046 #> [905,] 1.1296944 1.1306698 1.1445650 #> [906,] 0.4926394 0.5200857 0.5569929 #> [907,] 0.7094116 0.7272964 0.7351366 #> [908,] 0.5967956 0.5984226 0.6065631 #> [909,] 0.7092927 0.7741783 0.8215954 #> [910,] 0.5743078 0.5881791 0.6133391 #> [911,] 0.7960024 0.8147715 0.8201958 #> [912,] 0.6367922 0.6648602 0.7074269 #> [913,] 1.1330448 1.1393728 1.2083914 #> [914,] 1.1261638 1.1363303 1.1381319 #> [915,] 0.7265642 0.7372435 0.7600814 #> [916,] 0.5991453 0.6288226 0.6909970 #> [917,] 0.5936259 0.6167773 0.6328689 #> [918,] 1.0889671 1.1266647 1.1340280 #> [919,] 0.8134718 0.8773818 0.8794205 #> [920,] 0.6634449 0.6847756 0.7128808 #> [921,] 0.6411091 0.6529900 0.6562525 #> [922,] 0.9428644 0.9479833 0.9766401 #> [923,] 0.6546695 0.7623191 0.7831897 #> [924,] 0.6176913 0.6225913 0.6360304 #> [925,] 0.8952237 0.8954024 0.9522548 #> [926,] 0.5354529 0.5418754 0.5586692 #> [927,] 0.5889366 0.6452530 0.6895276 #> [928,] 1.2360886 1.2645463 1.4156511 #> [929,] 0.7045850 0.7102461 0.7254271 #> [930,] 0.5557009 0.6323681 0.6379043 #> [931,] 0.6628541 0.7176973 0.7332718 #> [932,] 0.7830243 0.7958926 0.8087172 #> [933,] 0.7823336 0.7935952 0.8060961 #> [934,] 1.2170469 1.3016234 1.3293415 #> [935,] 0.5882024 0.6785070 0.6851937 #> [936,] 1.3042448 1.3067202 1.3290534 #> [937,] 0.5923980 0.7701006 0.7797618 #> [938,] 0.6801128 0.6920584 0.7143286 #> [939,] 0.8133878 0.8762922 0.8792833 #> [940,] 1.4887429 1.5084606 1.5273402 #> [941,] 0.8393568 0.9422893 0.9868203 #> [942,] 0.8230795 0.8939918 0.9083788 #> [943,] 0.4729722 0.4760790 0.5015056 #> [944,] 0.7457229 0.7612536 0.8136419 #> [945,] 0.6536390 0.6611107 0.6819575 #> [946,] 1.2655733 1.3786466 1.4154325 #> [947,] 0.8477603 0.9001406 0.9536993 #> [948,] 0.9881205 1.0120315 1.0276484 #> [949,] 0.7025475 0.7284700 0.8108278 #> [950,] 0.6722478 0.6789018 0.6876039 #> [951,] 0.6565390 0.6916976 0.7045401 #> [952,] 0.7141773 0.7169669 0.7371411 #> [953,] 0.6559141 0.6639398 0.6669422 #> [954,] 0.6337316 0.6354521 0.6418716 #> [955,] 0.6696159 0.6698246 0.6916917 #> [956,] 0.6075006 0.6443834 0.6680742 #> [957,] 0.6603293 0.6623050 0.6940481 #> [958,] 0.6436329 0.6482295 0.6713180 #> [959,] 0.6036117 0.6117387 0.6139310 #> [960,] 0.8303042 0.8761815 0.9232052 #> [961,] 0.5678705 0.5766561 0.5903886 #> [962,] 1.1215965 1.1605183 1.2426790 #> [963,] 0.7745578 0.7843597 0.8036236 #> [964,] 0.7111981 0.7140241 0.7803132 #> [965,] 1.3762091 1.4262877 1.4529862 #> [966,] 0.9081192 0.9342811 0.9352012 #> [967,] 0.7595652 0.7708732 0.7737753 #> [968,] 1.4909861 1.5724724 1.5742275 #> [969,] 0.8061181 0.8275757 0.8626899 #> [970,] 0.9417043 0.9428644 0.9652038 #> [971,] 0.7672127 0.7708118 0.8045342 #> [972,] 0.8512705 0.8660131 0.8675326 #> [973,] 0.6890310 0.7102459 0.7356957 #> [974,] 0.8293702 0.8737719 0.8771729 #> [975,] 0.9366361 0.9594017 1.0164096 #> [976,] 0.6135617 0.6143098 0.6145980 #> [977,] 0.6100644 0.6560972 0.6944847 #> [978,] 0.8658190 0.9287271 0.9562004 #> [979,] 0.8026439 0.8061461 0.8523100 #> [980,] 0.7544792 0.7700905 0.7944546 #> [981,] 0.7798431 0.7893466 0.7930837 #> [982,] 0.7172235 0.7433508 0.7481295 #> [983,] 0.8697099 0.8845862 0.8919917 #> [984,] 0.6171722 0.6253003 0.6681443 #> [985,] 1.0713757 1.0957445 1.1245306 #> [986,] 0.8691765 0.8912487 0.9218647 #> [987,] 1.0751697 1.0866799 1.0975572 #> [988,] 1.1608268 1.1729700 1.1819343 #> [989,] 0.7698354 0.7771256 0.8079784 #> [990,] 0.6881899 0.7428363 0.7918862 #> [991,] 0.5465308 0.5848662 0.6901507 #> [992,] 0.8033928 0.8417451 0.8513532 #> [993,] 1.4140997 1.4184929 1.4390561 #> [994,] 0.9012890 0.9167841 0.9214295 #> [995,] 0.8027854 0.8349415 0.8488035 #> [996,] 0.7708018 0.7850795 0.8009255 #> [997,] 1.5251204 1.5339383 1.5403993 #> [998,] 1.3328246 1.3609737 1.3649186 #> [999,] 0.8731058 0.8916354 0.9132031 #> [1000,] 0.8604964 0.8725707 0.9429566 #> # 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,] 992 435 985 238 66 939 919 181 825 995 #> [2,] 153 154 787 895 289 432 114 784 379 774 #> [3,] 945 35 553 884 360 7 956 357 86 562 #> [4,] 556 616 499 964 211 688 89 761 155 805 #> [5,] 86 553 637 377 7 624 242 751 283 3 #> [6,] 830 941 940 569 678 256 323 356 199 851 #> [7,] 956 624 553 86 714 3 945 360 132 232 #> [8,] 822 604 76 493 740 229 547 514 222 578 #> [9,] 619 378 531 527 732 975 585 728 549 589 #> [10,] 266 575 291 863 469 900 268 376 838 638 #> [11,] 972 153 154 570 157 245 895 121 746 529 #> [12,] 600 235 911 331 617 468 367 615 134 23 #> [13,] 464 284 978 495 117 89 706 211 189 702 #> [14,] 325 555 879 622 812 799 433 672 123 176 #> [15,] 690 152 420 907 701 492 35 981 657 86 #> [16,] 140 64 162 164 143 552 230 366 243 167 #> [17,] 386 502 643 891 413 551 120 567 876 257 #> [18,] 341 909 533 598 995 340 487 351 724 66 #> [19,] 488 909 92 340 322 341 18 855 486 183 #> [20,] 372 856 507 943 72 315 309 22 136 655 #> [21,] 280 90 689 680 307 778 189 436 702 359 #> [22,] 72 118 943 309 507 796 856 824 20 470 #> [23,] 373 327 843 844 676 134 936 12 699 617 #> [24,] 29 742 122 826 334 847 756 178 382 955 #> [25,] 921 169 916 344 866 225 991 500 930 700 #> [26,] 380 93 196 758 452 911 158 78 77 891 #> [27,] 285 312 69 292 36 223 362 237 661 180 #> [28,] 440 62 52 226 908 241 677 71 217 878 #> [29,] 742 122 24 382 331 816 955 847 826 334 #> [30,] 68 855 461 974 952 735 183 472 614 345 #> [31,] 893 589 787 531 52 62 595 975 850 908 #> [32,] 205 835 375 477 79 315 953 151 136 713 #> [33,] 463 221 179 653 361 479 95 871 938 529 #> [34,] 971 539 1000 864 870 95 938 685 136 267 #> [35,] 3 884 562 360 553 690 945 751 86 511 #> [36,] 69 285 292 98 237 931 912 27 223 661 #> [37,] 40 756 214 982 178 632 130 334 780 640 #> [38,] 904 953 437 799 107 622 433 533 669 128 #> [39,] 719 223 285 777 292 661 795 246 193 362 #> [40,] 37 214 705 756 982 809 242 780 178 983 #> [41,] 723 496 661 385 50 36 755 519 98 312 #> [42,] 204 107 437 290 602 159 460 540 622 401 #> [43,] 71 878 391 440 62 28 106 914 764 247 #> [44,] 830 740 493 434 456 941 295 449 766 294 #> [45,] 793 883 329 780 239 338 442 195 268 384 #> [46,] 427 458 844 807 794 740 23 373 60 820 #> [47,] 129 75 245 162 278 918 234 54 972 570 #> [48,] 221 776 915 361 379 405 168 55 950 359 #> [49,] 587 438 534 538 168 796 856 545 792 22 #> [50,] 231 254 524 255 98 280 464 689 397 897 #> [51,] 818 920 178 990 113 847 982 813 576 91 #> [52,] 62 908 28 440 217 241 31 589 709 595 #> [53,] 966 94 335 82 431 454 116 189 469 579 #> [54,] 408 849 652 554 278 948 230 856 374 572 #> [55,] 950 776 959 789 801 171 842 582 663 317 #> [56,] 529 34 95 642 1000 938 354 539 630 245 #> [57,] 914 347 245 570 642 47 234 56 864 971 #> [58,] 949 937 601 220 870 462 829 267 862 630 #> [59,] 434 119 456 528 708 505 949 684 267 277 #> [60,] 794 218 699 515 373 516 514 763 294 508 #> [61,] 875 451 579 963 116 865 915 958 335 860 #> [62,] 52 440 28 908 31 241 217 589 878 709 #> [63,] 788 797 836 655 546 372 194 518 477 984 #> [64,] 366 140 552 16 143 243 126 490 85 164 #> [65,] 628 902 520 763 518 514 836 880 910 186 #> [66,] 425 181 825 598 533 472 726 182 18 390 #> [67,] 578 278 938 95 849 141 836 984 880 76 #> [68,] 952 345 461 735 974 183 156 370 30 567 #> [69,] 36 237 27 285 170 912 292 180 342 443 #> [70,] 676 711 820 806 775 78 93 750 843 465 #> [71,] 391 43 28 677 878 534 587 440 62 792 #> [72,] 22 309 943 631 20 824 372 507 856 135 #> [73,] 699 314 877 277 294 917 665 119 508 327 #> [74,] 519 292 719 755 362 661 557 39 618 285 #> [75,] 374 641 572 47 948 999 54 960 422 230 #> [76,] 880 578 141 980 836 858 902 67 822 984 #> [77,] 932 939 610 452 196 506 891 26 935 158 #> [78,] 93 617 158 321 777 465 373 26 989 750 #> [79,] 390 32 954 151 477 833 181 953 643 835 #> [80,] 498 679 19 488 486 81 416 402 92 712 #> [81,] 435 561 322 486 612 710 1 238 995 939 #> [82,] 94 365 933 694 963 431 53 743 363 900 #> [83,] 263 798 819 796 300 647 526 834 22 206 #> [84,] 517 281 525 248 378 687 233 549 355 673 #> [85,] 366 162 64 164 16 490 918 143 302 980 #> [86,] 553 5 7 714 956 3 35 624 232 360 #> [87,] 829 566 483 339 862 523 88 182 630 220 #> [88,] 802 957 924 871 194 463 339 568 630 829 #> [89,] 443 170 947 211 482 4 706 556 499 603 #> [90,] 778 280 689 359 21 436 524 399 950 931 #> [91,] 813 197 818 586 990 535 983 51 674 468 #> [92,] 488 19 30 855 542 909 183 857 351 487 #> [93,] 78 617 158 26 321 634 196 380 911 465 #> [94,] 82 365 53 694 933 963 431 363 61 900 #> [95,] 938 67 121 245 653 871 278 971 33 129 #> [96,] 326 680 861 970 922 791 21 654 853 307 #> [97,] 610 733 841 901 103 932 304 77 723 452 #> [98,] 254 223 285 36 661 292 931 69 39 795 #> [99,] 627 618 768 444 109 550 930 237 823 916 #> [100,] 370 754 183 935 977 345 729 952 68 156 #> [101,] 284 117 640 453 231 397 611 334 706 332 #> [102,] 299 450 738 809 945 571 536 831 790 3 #> [103,] 901 97 733 841 610 913 898 355 723 304 #> [104,] 287 848 228 236 804 336 960 573 313 374 #> [105,] 722 190 876 407 257 107 120 602 437 146 #> [106,] 497 247 471 986 289 532 633 635 874 489 #> [107,] 437 42 602 290 460 38 204 105 707 190 #> [108,] 139 714 522 330 212 771 232 377 860 215 #> [109,] 823 478 627 99 490 973 143 768 444 393 #> [110,] 946 259 367 12 854 235 191 753 600 844 #> [111,] 834 693 782 349 633 532 538 819 746 83 #> [112,] 353 419 629 811 536 563 639 313 667 175 #> [113,] 920 847 658 178 332 51 146 982 310 762 #> [114,] 432 436 778 969 774 359 895 784 90 379 #> [115,] 857 351 176 487 662 879 669 14 485 855 #> [116,] 875 61 579 189 335 702 451 727 964 668 #> [117,] 284 640 544 101 445 704 249 978 837 335 #> [118,] 593 545 22 306 943 961 438 470 72 796 #> [119,] 456 294 708 434 277 218 699 917 59 73 #> [120,] 551 190 658 17 876 386 502 105 643 257 #> [121,] 938 871 129 95 245 856 278 234 534 49 #> [122,] 29 24 178 334 847 742 756 816 586 813 #> [123,] 404 672 325 42 205 929 894 540 799 107 #> [124,] 163 523 506 181 192 829 786 541 954 872 #> [125,] 369 734 512 674 983 265 40 720 817 632 #> [126,] 973 143 64 366 552 288 140 671 243 490 #> [127,] 667 336 520 745 252 573 852 552 515 313 #> [128,] 559 833 473 217 182 405 350 381 803 38 #> [129,] 278 162 245 121 938 95 583 67 849 164 #> [130,] 632 615 756 262 334 753 122 367 596 265 #> [131,] 500 137 169 659 858 832 736 618 944 296 #> [132,] 146 297 826 624 227 409 956 7 441 876 #> [133,] 770 906 594 186 721 508 264 308 457 737 #> [134,] 844 373 967 617 344 308 264 955 235 331 #> [135,] 309 631 943 470 72 581 507 810 22 824 #> [136,] 194 477 655 984 315 20 871 462 797 802 #> [137,] 131 500 786 659 736 937 944 858 541 169 #> [138,] 675 821 614 348 645 602 783 574 415 437 #> [139,] 108 798 216 714 420 152 771 681 701 537 #> [140,] 552 143 243 64 16 745 520 490 366 252 #> [141,] 880 980 76 578 490 788 836 910 67 628 #> [142,] 759 544 730 146 722 297 624 958 956 105 #> [143,] 140 243 552 973 64 16 823 126 478 109 #> [144,] 415 411 485 662 138 614 979 675 204 783 #> [145,] 693 269 647 188 798 253 526 83 263 208 #> [146,] 722 132 544 142 759 297 876 737 658 826 #> [147,] 407 737 906 457 696 789 594 713 388 887 #> [148,] 282 406 491 283 637 705 869 982 576 242 #> [149,] 503 358 370 381 260 355 951 281 524 324 #> [150,] 346 762 453 332 977 754 413 371 742 621 #> [151,] 721 835 713 457 810 594 257 79 205 32 #> [152,] 681 657 300 216 907 690 15 798 981 389 #> [153,] 154 11 895 2 379 529 972 33 850 221 #> [154,] 153 11 895 157 2 391 972 289 570 529 #> [155,] 616 603 588 4 392 596 185 556 428 396 #> [156,] 726 345 567 472 935 183 413 598 68 260 #> [157,] 746 11 972 154 570 224 391 633 934 153 #> [158,] 321 196 465 93 78 891 251 634 26 380 #> [159,] 401 792 204 677 42 538 460 290 923 241 #> [160,] 859 967 994 716 344 867 866 700 428 398 #> [161,] 692 405 796 460 538 819 168 602 545 105 #> [162,] 16 129 278 164 85 918 583 849 230 64 #> [163,] 124 523 506 825 181 992 260 87 192 829 #> [164,] 16 478 823 162 143 583 490 109 140 141 #> [165,] 845 986 532 494 247 187 291 782 838 743 #> [166,] 840 312 392 27 715 170 428 814 69 89 #> [167,] 606 387 504 521 455 745 961 288 451 243 #> [168,] 538 796 792 49 405 534 587 241 315 48 #> [169,] 500 131 25 820 858 244 944 137 921 916 #> [170,] 443 69 482 89 342 706 912 36 947 556 #> [171,] 959 950 605 55 317 801 249 702 875 958 #> [172,] 271 582 568 768 560 399 924 698 926 463 #> [173,] 960 374 228 903 287 104 997 175 613 804 #> [174,] 637 200 576 406 305 873 144 148 415 751 #> [175,] 613 903 804 229 353 515 173 997 667 429 #> [176,] 857 879 351 433 669 487 585 783 115 799 #> [177,] 371 193 621 932 453 231 385 254 661 452 #> [178,] 756 982 122 51 847 113 920 214 334 24 #> [179,] 653 33 463 479 924 361 221 95 279 938 #> [180,] 795 237 285 69 706 892 223 27 393 342 #> [181,] 825 182 66 425 124 163 79 523 260 726 #> [182,] 181 425 381 128 260 833 825 350 726 324 #> [183,] 935 567 345 952 754 156 100 413 68 977 #> [184,] 368 940 419 752 15 356 448 690 475 511 #> [185,] 396 596 616 761 450 155 293 738 965 603 #> [186,] 508 133 518 763 546 388 594 890 628 721 #> [187,] 401 635 986 532 874 471 159 923 204 460 #> [188,] 572 269 999 263 641 145 234 693 83 274 #> [189,] 702 482 116 464 861 680 211 280 966 791 #> [190,] 707 821 120 602 437 105 460 658 107 551 #> [191,] 367 275 412 636 854 632 600 816 110 262 #> [192,] 560 557 124 506 541 172 786 271 698 296 #> [193,] 371 621 452 453 899 611 380 39 223 150 #> [194,] 802 136 462 797 477 871 829 88 655 63 #> [195,] 780 705 704 883 282 239 837 769 240 445 #> [196,] 158 251 891 939 26 77 380 322 93 634 #> [197,] 813 91 818 983 51 586 636 674 178 990 #> [198,] 548 272 316 352 987 739 325 896 672 328 #> [199,] 851 896 684 608 987 417 414 274 941 548 #> [200,] 576 873 675 415 174 406 637 535 990 409 #> [201,] 936 416 46 886 23 843 807 295 458 946 #> [202,] 530 137 250 131 500 296 858 311 169 659 #> [203,] 747 720 265 327 357 691 571 839 319 536 #> [204,] 42 401 159 460 107 437 602 540 622 799 #> [205,] 32 835 375 953 810 151 290 315 656 123 #> [206,] 773 976 300 438 819 118 681 545 263 306 #> [207,] 96 179 302 970 509 467 550 279 326 479 #> [208,] 677 894 656 226 526 587 269 792 290 375 #> [209,] 806 275 888 412 191 775 561 911 258 70 #> [210,] 725 418 717 591 649 476 668 213 455 474 #> [211,] 964 4 791 189 89 482 499 702 556 688 #> [212,] 330 522 785 730 283 869 645 692 142 297 #> [213,] 717 476 320 418 556 455 725 892 927 964 #> [214,] 40 242 809 756 37 982 178 297 624 640 #> [215,] 701 232 798 420 540 647 929 714 108 86 #> [216,] 681 564 976 426 860 865 300 714 819 389 #> [217,] 241 908 803 595 128 52 709 850 28 833 #> [218,] 294 708 60 699 508 902 119 456 514 794 #> [219,] 346 611 332 453 977 334 816 150 371 646 #> [220,] 58 862 601 949 919 87 937 267 988 354 #> [221,] 361 48 379 359 463 33 583 871 776 915 #> [222,] 8 613 604 577 175 903 740 225 229 822 #> [223,] 285 98 39 661 292 795 254 27 36 193 #> [224,] 746 633 934 157 349 111 648 363 834 782 #> [225,] 577 697 25 429 671 921 794 916 514 629 #> [226,] 375 28 677 208 315 792 587 534 217 241 #> [227,] 580 868 990 535 409 441 132 826 360 945 #> [228,] 287 104 848 960 374 569 236 256 652 804 #> [229,] 794 804 515 902 514 65 218 60 628 8 #> [230,] 824 422 336 16 652 773 374 54 881 745 #> [231,] 255 50 254 397 101 284 177 464 453 338 #> [232,] 714 929 215 574 540 7 108 798 86 212 #> [233,] 566 951 324 248 87 531 549 687 517 523 #> [234,] 570 587 534 121 972 49 856 188 391 864 #> [235,] 615 753 331 747 955 600 12 134 265 967 #> [236,] 256 848 907 104 287 772 573 356 228 678 #> [237,] 292 69 36 795 285 912 180 27 931 768 #> [238,] 612 1 995 919 992 905 390 528 465 985 #> [239,] 883 329 780 588 195 704 117 37 640 978 #> [240,] 837 704 727 579 445 978 335 431 668 730 #> [241,] 217 923 792 635 52 908 168 28 538 595 #> [242,] 297 624 214 785 982 132 809 553 956 283 #> [243,] 552 140 745 252 143 455 606 973 320 127 #> [244,] 169 820 500 25 131 766 858 137 427 815 #> [245,] 129 938 121 95 529 278 642 234 570 11 #> [246,] 777 750 344 866 719 39 331 991 731 617 #> [247,] 253 349 106 633 187 934 532 43 71 165 #> [248,] 281 951 324 517 233 728 355 358 549 260 #> [249,] 605 544 759 722 146 171 801 310 640 959 #> [250,] 202 832 131 550 296 748 137 530 500 169 #> [251,] 322 196 158 634 340 710 486 321 93 911 #> [252,] 320 243 476 745 261 455 127 649 552 667 #> [253,] 247 349 145 693 647 633 187 421 401 111 #> [254,] 98 397 223 899 50 285 621 582 317 706 #> [255,] 231 338 683 757 50 385 177 464 254 827 #> [256,] 236 848 356 772 287 907 104 228 960 274 #> [257,] 713 643 876 457 407 594 721 386 835 105 #> [258,] 888 682 561 939 610 729 998 77 97 19 #> [259,] 110 946 458 807 844 629 753 967 46 319 #> [260,] 825 324 381 728 951 726 182 523 156 472 #> [261,] 476 320 474 252 927 852 649 388 660 745 #> [262,] 130 334 632 646 588 219 101 756 623 611 #> [263,] 83 300 798 999 206 681 819 526 22 824 #> [264,] 308 955 770 133 580 882 826 571 388 747 #> [265,] 753 615 747 674 293 235 571 203 130 720 #> [266,] 10 575 863 291 268 638 376 838 469 808 #> [267,] 862 462 949 136 620 477 58 803 194 870 #> [268,] 575 808 484 863 469 298 827 266 838 491 #> [269,] 145 188 208 417 526 274 677 693 894 647 #> [270,] 800 917 631 810 749 678 459 581 423 135 #> [271,] 172 582 842 926 663 899 350 872 741 473 #> [272,] 352 325 316 14 208 894 548 555 677 540 #> [273,] 559 774 405 128 503 969 381 549 741 923 #> [274,] 526 656 417 631 896 199 894 72 269 208 #> [275,] 623 758 600 816 191 412 611 331 367 911 #> [276,] 494 614 144 491 783 348 735 662 461 138 #> [277,] 314 119 528 73 917 321 151 390 721 699 #> [278,] 129 67 849 162 938 95 245 121 54 578 #> [279,] 479 529 179 642 394 653 33 56 311 630 #> [280,] 90 689 21 702 171 189 524 436 778 950 #> [281,] 248 355 728 673 517 324 84 951 149 260 #> [282,] 869 148 283 491 846 481 446 195 785 780 #> [283,] 869 785 212 330 730 481 645 282 297 242 #> [284,] 464 706 117 101 13 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400 979 744 996 592 14 724 #> [673,] 728 281 355 619 998 542 472 248 609 260 #> [674,] 586 983 813 734 265 91 632 636 122 615 #> [675,] 645 138 574 415 576 821 200 409 920 658 #> [676,] 70 806 843 78 93 820 23 373 617 710 #> [677,] 792 208 587 159 226 534 538 28 290 894 #> [678,] 767 270 800 356 236 459 516 581 877 104 #> [679,] 498 402 651 91 410 535 468 990 712 818 #> [680,] 861 326 791 189 21 96 922 482 702 947 #> [681,] 216 976 300 564 426 152 389 881 657 206 #> [682,] 729 258 100 977 754 888 935 183 998 219 #> [683,] 757 338 255 454 808 827 863 231 503 484 #> [684,] 851 400 199 205 996 896 749 32 656 548 #> [685,] 347 608 505 864 971 34 760 547 414 554 #> [686,] 448 368 410 651 886 936 449 679 744 328 #> [687,] 549 969 525 774 358 951 517 248 784 566 #> [688,] 556 791 439 499 4 973 717 964 213 288 #> [689,] 931 280 90 21 912 399 524 359 950 702 #> [690,] 15 511 35 907 562 772 420 152 86 701 #> [691,] 357 516 767 882 319 884 747 313 563 536 #> [692,] 522 161 330 212 626 460 105 860 707 933 #> [693,] 647 145 798 111 83 263 269 188 834 962 #> [694,] 900 82 94 365 933 489 469 471 291 774 #> [695,] 502 724 441 151 904 533 658 567 382 17 #> [696,] 887 333 842 906 926 317 737 927 663 147 #> [697,] 225 629 398 577 994 429 160 671 25 967 #> [698,] 399 560 568 957 924 172 463 403 192 339 #> [699,] 73 294 218 60 119 373 877 277 508 314 #> [700,] 991 866 930 916 344 892 867 444 921 910 #> [701,] 215 420 798 377 232 647 108 139 86 690 #> [702,] 189 482 280 464 171 706 443 116 284 689 #> [703,] 761 239 185 588 329 603 965 396 155 616 #> [704,] 837 445 240 195 117 978 640 730 727 705 #> [705,] 195 769 780 40 790 214 704 148 282 242 #> [706,] 443 284 180 170 482 702 69 464 254 98 #> [707,] 602 190 460 437 821 107 348 559 105 120 #> [708,] 119 456 218 890 294 434 989 514 902 766 #> [709,] 483 908 803 217 52 620 862 595 425 62 #> [710,] 612 843 486 251 322 528 158 465 93 995 #> [711,] 70 815 465 676 820 612 736 775 304 137 #> [712,] 402 314 744 592 400 486 665 277 996 449 #> 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723 610 304 932 496 901 841 898 #> [734,] 674 839 720 983 91 586 265 813 535 873 #> [735,] 461 68 974 952 821 310 484 345 190 348 #> [736,] 304 137 557 131 296 74 192 519 901 500 #> [737,] 906 876 722 147 696 663 826 407 594 887 #> [738,] 450 102 299 396 185 536 596 811 369 831 #> [739,] 198 328 548 383 115 14 316 352 272 879 #> [740,] 766 493 434 456 8 708 794 218 44 229 #> [741,] 350 473 381 582 568 872 801 899 551 271 #> [742,] 29 24 346 122 847 382 611 380 758 150 #> [743,] 365 942 431 446 846 828 82 481 626 845 #> [744,] 410 672 543 328 996 712 402 979 404 400 #> [745,] 649 660 243 455 961 606 591 252 881 552 #> [746,] 157 224 972 111 11 570 363 834 633 188 #> [747,] 571 955 235 319 264 753 580 308 265 615 #> [748,] 550 832 627 342 126 109 973 393 99 814 #> [749,] 631 309 652 943 372 270 72 518 20 135 #> [750,] 777 246 719 362 758 39 78 731 93 519 #> [751,] 873 35 360 3 562 553 5 637 839 86 #> [752,] 475 368 751 411 701 637 421 174 690 420 #> [753,] 615 235 293 265 747 130 571 596 367 331 #> [754,] 977 935 150 100 183 413 453 346 567 729 #> [755,] 74 36 292 618 519 237 931 912 69 661 #> [756,] 178 214 122 37 982 334 24 847 130 113 #> [757,] 683 338 255 454 863 231 50 827 808 575 #> [758,] 380 623 611 600 26 742 331 452 371 346 #> [759,] 544 142 730 146 958 249 722 297 624 605 #> [760,] 547 554 408 493 941 984 608 849 864 685 #> [761,] 4 616 185 603 396 703 155 964 211 805 #> [762,] 150 453 346 621 413 382 742 371 899 386 #> [763,] 388 628 65 186 902 520 508 991 910 514 #> [764,] 440 878 62 52 975 28 43 607 709 908 #> [765,] 905 612 238 650 919 59 528 710 995 843 #> [766,] 740 434 708 858 989 456 514 493 8 76 #> [767,] 678 516 563 691 313 800 270 236 581 884 #> [768,] 99 618 627 172 444 237 931 912 109 271 #> [769,] 790 705 925 831 5 553 86 242 283 195 #> [770,] 264 133 826 308 955 594 906 457 737 441 #> [771,] 860 522 537 819 834 579 108 963 865 648 #> [772,] 511 356 459 597 907 256 562 236 690 581 #> [773,] 206 438 306 504 593 118 387 545 167 976 #> [774,] 784 273 289 787 687 893 549 969 31 471 #> [775,] 70 304 750 343 806 820 711 519 676 736 #> [776,] 55 789 48 950 407 801 663 959 473 797 #> [777,] 246 719 750 39 866 344 78 991 452 617 #> [778,] 436 90 524 358 359 969 399 568 280 689 #> [779,] 467 307 286 897 21 970 403 524 436 96 #> [780,] 195 883 705 239 37 704 40 282 148 640 #> [781,] 928 233 339 913 394 517 248 354 403 87 #> [782,] 537 111 532 349 771 828 648 365 834 633 #> [783,] 348 614 799 138 622 669 433 437 461 176 #> [784,] 774 289 893 687 489 525 114 969 787 31 #> [785,] 283 645 297 212 242 730 869 624 146 920 #> [786,] 541 829 659 937 954 124 601 192 137 944 #> [787,] 850 595 31 566 774 379 531 217 549 273 #> [788,] 63 836 546 910 880 797 141 655 628 518 #> [789,] 407 776 663 55 926 842 801 147 722 887 #> [790,] 831 769 445 925 624 553 705 956 809 102 #> [791,] 326 482 922 680 688 211 499 443 189 947 #> [792,] 159 538 677 241 168 587 290 534 923 796 #> [793,] 45 268 883 384 338 827 942 683 575 329 #> [794,] 60 229 514 218 515 902 763 921 516 740 #> [795,] 842 237 180 292 223 285 39 926 317 930 #> [796,] 507 22 168 49 290 545 470 538 161 118 #> [797,] 63 194 315 788 833 473 136 477 776 546 #> [798,] 420 647 83 263 215 701 139 693 232 714 #> [799,] 622 669 38 783 904 433 42 348 437 107 #> [800,] 270 459 423 877 917 581 678 665 810 631 #> [801,] 605 317 55 899 959 789 663 741 551 842 #> [802,] 88 194 871 803 957 833 924 136 473 797 #> [803,] 217 595 802 850 833 709 128 267 182 315 #> [804,] 515 313 104 229 127 563 336 667 516 573 #> [805,] 499 4 885 89 428 688 616 556 603 761 #> [806,] 676 70 911 93 26 561 78 12 617 710 #> [807,] 458 46 886 448 259 516 629 319 203 327 #> [808,] 484 370 863 503 310 268 683 827 575 735 #> [809,] 214 945 299 624 956 242 571 102 297 756 #> [810,] 917 457 721 835 508 270 713 151 594 205 #> [811,] 852 299 667 261 319 313 127 536 252 571 #> [812,] 953 879 14 375 592 620 622 325 433 205 #> [813,] 586 197 91 983 674 818 51 178 636 122 #> [814,] 840 428 867 342 859 27 312 166 393 362 #> [815,] 711 70 820 676 244 775 736 612 137 765 #> [816,] 334 122 742 29 332 412 847 636 911 219 #> [817,] 734 498 983 125 674 91 197 854 651 586 #> [818,] 51 197 990 91 813 920 200 535 178 576 #> [819,] 834 545 206 860 83 865 771 438 161 976 #> [820,] 169 70 500 676 244 25 944 711 775 78 #> [821,] 190 461 658 138 707 735 645 310 120 602 #> [822,] 578 8 604 76 547 554 67 311 760 880 #> [823,] 478 109 143 164 490 99 788 444 627 910 #> [824,] 72 22 300 309 943 230 881 118 573 135 #> [825,] 260 181 66 182 163 726 472 523 425 324 #> [826,] 770 580 737 24 132 441 876 906 146 955 #> [827,] 454 484 249 335 284 808 683 310 966 605 #> [828,] 968 365 743 782 537 625 648 845 82 301 #> [829,] 541 786 462 194 937 58 924 954 124 601 #> [830,] 941 493 294 456 6 851 678 218 119 44 #> [831,] 790 769 553 86 956 102 981 925 624 945 #> [832,] 550 627 131 618 99 748 755 768 916 74 #> [833,] 473 128 315 350 802 803 797 643 182 194 #> [834,] 819 111 771 83 889 49 438 538 206 161 #> [835,] 713 32 205 810 721 457 151 507 257 315 #> [836,] 655 880 788 63 984 628 141 518 902 372 #> [837,] 240 704 445 727 978 668 579 117 335 730 #> [838,] 291 900 986 575 471 469 638 974 626 863 #> [839,] 720 734 3 751 945 873 35 553 357 203 #> [840,] 428 814 342 166 27 867 69 312 170 392 #> [841,] 610 97 901 163 992 355 77 103 932 825 #> [842,] 926 663 317 795 696 271 899 582 887 789 #> [843,] 710 612 676 23 416 486 989 78 528 806 #> [844,] 134 373 967 23 60 617 344 921 25 319 #> [845,] 165 743 986 968 900 532 828 291 365 782 #> [846,] 481 942 446 431 626 869 330 743 522 283 #> [847,] 332 113 334 122 742 346 178 150 920 24 #> [848,] 287 236 104 228 256 960 374 336 569 824 #> [849,] 408 54 554 278 652 67 372 856 578 836 #> [850,] 595 787 803 379 566 217 802 241 88 871 #> [851,] 199 684 896 987 941 548 996 608 400 414 #> [852,] 811 667 261 127 252 319 313 320 515 429 #> [853,] 432 114 307 861 96 21 680 94 509 359 #> [854,] 946 367 636 12 191 412 110 586 936 674 #> [855,] 351 598 472 857 487 30 18 176 533 909 #> [856,] 20 22 372 72 507 943 796 315 49 656 #> [857,] 115 351 176 487 855 669 879 662 783 585 #> [858,] 76 500 514 880 980 944 659 921 141 169 #> [859,] 160 716 867 428 731 344 994 967 814 246 #> [860,] 865 771 522 819 564 216 958 714 579 142 #> [861,] 680 189 326 791 116 21 96 702 482 211 #> [862,] 267 620 220 483 87 949 58 709 919 803 #> [863,] 575 808 266 503 268 10 683 484 469 370 #> [864,] 971 34 347 685 1000 539 408 234 856 849 #> [865,] 860 451 958 564 819 61 545 976 875 216 #> [866,] 991 344 700 916 930 921 246 867 777 444 #> [867,] 393 700 916 342 866 814 237 428 930 344 #> [868,] 665 227 423 409 441 360 535 990 457 877 #> [869,] 283 481 330 212 785 282 846 446 730 626 #> [870,] 462 58 949 937 984 194 829 136 630 267 #> [871,] 802 194 88 136 121 95 797 938 33 924 #> [872,] 506 899 621 741 582 271 350 193 523 381 #> [873,] 535 751 200 409 360 651 576 990 415 637 #> [874,] 497 187 589 986 635 471 975 52 555 62 #> [875,] 61 116 958 451 865 579 959 668 171 950 #> [876,] 737 722 257 386 441 120 105 658 906 594 #> [877,] 665 423 800 73 270 917 868 699 996 314 #> [878,] 914 71 28 43 62 391 1000 440 52 764 #> [879,] 176 812 433 14 622 351 669 799 953 908 #> [880,] 141 836 980 76 578 628 788 910 902 65 #> [881,] 591 649 573 976 660 336 745 564 824 681 #> [882,] 580 581 357 264 884 474 571 308 691 945 #> [883,] 239 780 329 195 640 704 45 442 793 978 #> [884,] 581 35 562 357 3 360 423 882 511 907 #> [885,] 994 398 428 716 616 805 160 639 859 4 #> [886,] 327 448 699 458 23 73 877 516 936 60 #> [887,] 696 333 317 927 842 737 926 906 147 789 #> [888,] 258 682 561 610 97 209 998 939 729 733 #> [889,] 438 963 363 915 644 834 49 545 306 819 #> [890,] 708 186 546 989 944 508 321 954 477 628 #> [891,] 17 196 386 380 158 634 413 643 321 502 #> [892,] 320 439 393 700 696 213 476 180 887 930 #> [893,] 31 784 774 531 589 497 975 289 549 787 #> [894,] 526 656 208 540 929 290 677 123 42 205 #> [895,] 153 379 363 432 289 154 11 889 915 221 #> [896,] 987 199 851 548 417 274 684 597 996 608 #> [897,] 403 399 524 931 689 755 467 698 98 36 #> [898,] 103 913 733 385 723 41 355 403 97 998 #> [899,] 621 872 582 801 317 397 842 271 741 663 #> [900,] 694 291 469 838 471 986 82 489 365 933 #> [901,] 97 841 103 610 736 733 163 992 304 932 #> [902,] 628 65 514 763 880 836 518 218 910 520 #> [903,] 613 175 604 173 366 64 804 229 353 997 #> [904,] 38 953 799 533 669 437 724 107 695 433 #> [905,] 919 238 985 620 765 995 1 862 220 601 #> [906,] 737 133 594 663 696 147 926 770 876 457 #> [907,] 236 772 573 581 981 884 690 511 300 152 #> [908,] 52 217 433 62 709 589 622 440 28 241 #> [909,] 341 18 340 598 533 487 322 724 995 251 #> [910,] 546 444 788 628 930 880 991 141 763 902 #> [911,] 26 380 93 816 758 12 617 251 29 600 #> [912,] 931 237 36 69 768 443 170 689 482 292 #> [913,] 403 517 248 355 781 281 358 103 841 233 #> [914,] 878 347 1000 570 539 57 43 864 391 234 #> [915,] 963 48 933 889 61 405 161 545 776 168 #> [916,] 930 700 991 866 867 444 344 921 627 25 #> [917,] 810 270 508 800 721 665 457 835 314 277 #> [918,] 162 129 164 85 245 583 278 654 479 47 #> [919,] 601 905 238 220 985 620 862 949 181 267 #> [920,] 113 658 51 132 146 847 178 382 645 675 #> [921,] 991 866 25 344 514 916 930 700 763 944 #> [922,] 947 791 326 499 680 482 89 970 443 211 #> [923,] 635 241 538 792 471 405 159 168 161 217 #> [924,] 88 957 463 339 698 802 172 194 829 568 #> [925,] 769 377 869 108 790 5 283 831 86 139 #> [926,] 663 842 696 906 789 271 546 795 317 887 #> [927,] 333 476 887 261 320 696 474 213 892 388 #> [928,] 781 354 394 87 233 220 339 630 483 58 #> [929,] 540 232 526 894 123 459 656 574 42 290 #> [930,] 916 700 444 991 866 910 627 99 867 344 #> [931,] 912 689 36 98 237 768 69 897 292 443 #> [932,] 77 452 610 304 506 193 371 177 621 872 #> [933,] 963 915 82 692 363 365 694 94 161 61 #> [934,] 633 349 224 247 106 157 111 746 253 782 #> [935,] 754 183 567 413 156 345 100 977 726 150 #> [936,] 416 23 468 886 327 946 12 201 843 486 #> [937,] 58 949 601 462 829 786 541 870 659 220 #> [938,] 95 121 245 67 278 971 871 653 129 33 #> [939,] 196 77 561 322 992 340 935 251 26 158 #> [940,] 6 987 356 256 896 323 569 597 543 851 #> [941,] 830 493 199 851 760 414 547 6 554 456 #> [942,] 431 846 481 446 626 743 330 522 869 365 #> [943,] 309 72 372 22 135 20 518 507 631 118 #> [944,] 989 890 921 786 465 858 78 500 321 659 #> [945,] 3 956 553 580 7 809 35 624 571 882 #> [946,] 110 854 12 259 367 936 235 23 753 844 #> [947,] 922 443 170 970 89 482 791 326 680 286 #> [948,] 569 54 572 408 274 75 374 417 228 960 #> [949,] 58 937 601 462 870 267 220 829 862 541 #> [950,] 55 959 171 776 789 801 359 875 48 842 #> [951,] 324 381 566 248 233 549 260 728 182 358 #> [952,] 68 345 183 461 735 567 821 370 413 310 #> [953,] 38 375 904 812 205 32 622 799 833 79 #> [954,] 643 79 541 321 350 473 477 546 829 786 #> [955,] 264 308 770 580 747 571 826 29 235 24 #> [956,] 624 7 945 297 553 142 474 132 3 86 #> [957,] 88 568 924 802 698 463 350 473 172 339 #> [958,] 875 759 865 544 142 668 860 959 451 61 #> [959,] 171 950 55 605 801 317 789 776 842 887 #> [960,] 374 287 228 848 569 104 256 236 173 572 #> [961,] 660 745 455 649 593 118 520 167 306 881 #> [962,] 420 599 693 647 798 139 701 145 152 15 #> [963,] 933 915 61 889 363 82 771 579 94 834 #> [964,] 668 211 213 4 717 556 725 688 418 670 #> [965,] 185 603 596 588 155 761 616 703 293 396 #> [966,] 53 335 189 727 978 454 827 464 702 116 #> [967,] 134 319 160 308 844 344 264 747 235 955 #> [968,] 828 845 743 365 165 782 625 537 446 82 #> [969,] 687 358 436 778 525 549 273 774 517 114 #> [970,] 947 912 286 326 443 931 680 170 922 482 #> [971,] 864 34 938 95 67 1000 849 278 121 539 #> [972,] 11 570 746 234 49 157 121 889 153 534 #> [973,] 143 126 439 109 243 288 393 688 140 552 #> [974,] 735 68 461 585 348 732 345 783 707 821 #> [975,] 9 31 589 893 62 52 531 874 908 440 #> [976,] 564 681 216 881 206 300 118 426 865 649 #> [977,] 754 150 346 453 729 332 935 100 183 219 #> [978,] 727 837 13 240 966 117 704 335 211 464 #> [979,] 662 415 485 404 672 799 669 123 487 144 #> [980,] 141 490 880 76 578 910 858 788 836 444 #> [981,] 881 907 657 573 591 426 681 152 564 336 #> [982,] 178 242 756 214 113 51 406 920 37 785 #> [983,] 674 813 586 734 197 91 178 632 982 756 #> [984,] 655 477 136 836 462 194 870 63 372 20 #> [985,] 1 919 992 435 238 905 66 220 620 181 #> [986,] 471 532 187 635 874 838 497 900 923 291 #> [987,] 896 199 548 851 417 414 608 6 274 684 #> [988,] 607 220 862 505 620 539 483 267 34 1000 #> [989,] 944 890 708 373 528 78 321 119 921 218 #> [990,] 535 227 51 409 818 91 868 441 920 873 #> [991,] 866 700 930 344 916 921 444 910 763 546 #> [992,] 1 939 163 238 825 181 66 985 435 77 #> [993,] 769 637 148 705 5 751 839 174 40 925 #> [994,] 716 885 160 398 859 697 428 629 671 429 #> [995,] 390 18 592 341 238 909 612 322 251 340 #> [996,] 400 543 800 449 877 684 665 672 459 270 #> [997,] 613 173 323 175 903 228 804 104 960 287 #> [998,] 355 673 841 281 682 542 258 610 100 97 #> [999,] 572 641 188 263 300 206 773 374 83 824 #> [1000,] 539 34 864 971 226 803 878 595 850 28 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.37440774 0.5196210 0.6317683 0.6323538 0.8849260 0.8892560 0.9006937 #> [2,] 0.71849076 0.8712022 0.8970041 0.9249884 0.9328638 0.9559555 0.9912308 #> [3,] 0.33816647 0.3531914 0.4294108 0.5196530 0.5297383 0.5577980 0.6443834 #> [4,] 0.54020961 0.6153700 0.6328276 0.6360577 0.6708464 0.6854089 0.7296972 #> [5,] 0.39339787 0.4755666 0.6066615 0.7330094 0.7611787 0.7646775 0.7696904 #> [6,] 0.81778517 0.8393568 0.8864097 0.8899425 0.8930137 0.9004203 0.9592937 #> [7,] 0.31336098 0.4734230 0.4790972 0.5125694 0.5484259 0.5577980 0.5839837 #> [8,] 0.69429370 0.6999343 0.8040632 0.8374759 0.8581814 0.8620620 0.9037422 #> [9,] 0.49870522 0.5159316 0.5161472 0.6504423 0.7226073 0.7458232 0.8078439 #> [10,] 0.43286308 0.6523323 0.8222796 0.8646028 0.9897671 1.0654198 1.0962131 #> [11,] 0.41437972 0.5560717 0.6561674 0.7609491 0.7669037 0.8590798 0.8752580 #> [12,] 0.67937879 0.7172980 0.7480645 0.7544614 0.8050573 0.8083640 0.8481635 #> [13,] 0.52070502 0.6684482 0.7688076 0.7823320 0.8670848 0.9316208 0.9584593 #> [14,] 0.41137800 0.5708362 0.5865694 0.6090094 0.6456530 0.6700576 0.7213540 #> [15,] 0.59022782 0.7694994 0.8918625 1.0814758 1.1290545 1.1519124 1.1534854 #> [16,] 0.45092441 0.4958946 0.5104549 0.5122970 0.5530641 0.6073729 0.6617331 #> [17,] 0.21549985 0.3255899 0.3544750 0.4664532 0.4715509 0.4860027 0.4879230 #> [18,] 0.28419387 0.3691577 0.5267358 0.5449690 0.5967973 0.5985593 0.6283001 #> [19,] 0.60423508 0.9040169 0.9165124 1.0119967 1.0878704 1.1063906 1.1405633 #> [20,] 0.28229287 0.3660425 0.4033861 0.4280612 0.4487782 0.4534048 0.4866329 #> [21,] 0.53505778 0.5400066 0.6122105 0.7306741 0.7555545 0.8497530 0.8610543 #> [22,] 0.21870056 0.3441216 0.3792610 0.4279712 0.4352831 0.4734599 0.4760026 #> [23,] 0.69971236 0.7776224 0.7876056 0.7994573 0.8586295 0.8605815 0.8819863 #> [24,] 0.38762218 0.4285535 0.4381970 0.5334751 0.6623435 0.6643578 0.6768378 #> [25,] 0.53268027 0.6801124 0.6909970 0.7254717 0.7283049 0.7764582 0.7892815 #> [26,] 0.43329051 0.5178387 0.5360601 0.5642680 0.5666451 0.5944099 0.6307465 #> [27,] 0.30426516 0.3859418 0.4251838 0.4719293 0.5115982 0.5455742 0.5471404 #> [28,] 0.42629419 0.4520204 0.4723838 0.5216885 0.5984226 0.6227925 0.6259317 #> [29,] 0.35403636 0.3840472 0.3876222 0.5544435 0.5753758 0.6441560 0.6696159 #> [30,] 0.70399445 0.8185857 0.8728922 0.8887481 0.9390512 0.9504278 0.9534198 #> [31,] 0.54620478 0.5678647 0.5794177 0.5859331 0.6016578 0.6631736 0.6973421 #> [32,] 0.32203562 0.3870360 0.4510068 0.4565310 0.5181098 0.5219980 0.5803326 #> [33,] 0.59842366 0.6252209 0.6259422 0.6469902 0.6731264 0.6884798 0.6987678 #> [34,] 0.42295739 0.4799947 0.4822970 0.5100279 0.7521974 0.7531811 0.7617941 #> [35,] 0.35319143 0.4240218 0.5113439 0.5796505 0.5815733 0.6434926 0.6531255 #> [36,] 0.28798006 0.3680010 0.4005744 0.4271074 0.4391532 0.4795506 0.4806273 #> [37,] 0.46960421 0.5704743 0.5713747 0.7433508 0.8263683 0.9032159 0.9188813 #> [38,] 0.16379892 0.4343415 0.4634704 0.4812532 0.5262166 0.5456857 0.5900957 #> [39,] 0.32942541 0.3488723 0.4544843 0.4706264 0.4737524 0.5055472 0.5182899 #> [40,] 0.46960421 0.5094448 0.8000923 0.8405120 0.8940577 0.9118995 0.9520868 #> [41,] 0.56070439 0.8039226 0.8458749 0.9259973 0.9706053 1.0019942 1.0221179 #> [42,] 0.26218164 0.3335003 0.4401235 0.4973035 0.5241576 0.5242244 0.5406525 #> [43,] 0.63571193 0.7382393 0.9638590 1.0666145 1.0770948 1.0874487 1.0945852 #> [44,] 0.98042680 0.9936126 1.0111360 1.0134749 1.0909802 1.1755655 1.2292283 #> [45,] 0.96471043 1.1360815 1.2717714 1.5203067 1.5482659 1.7086648 1.7576296 #> [46,] 0.92897311 1.0798204 1.2356634 1.2538734 1.2887295 1.3175664 1.3497319 #> [47,] 0.89238862 0.9257327 0.9620712 0.9739201 0.9943237 1.1340280 1.1505984 #> [48,] 0.44108247 0.4464113 0.4928736 0.5033910 0.5245417 0.5959135 0.6397210 #> [49,] 0.39469461 0.3966216 0.4374424 0.4706606 0.4847577 0.4929153 0.6373957 #> [50,] 0.62219362 0.6576409 0.7388219 0.7729828 0.7943559 0.8368663 0.8794120 #> [51,] 0.30483279 0.4510571 0.5991109 0.6021361 0.6431851 0.6820095 0.6822274 #> [52,] 0.16342217 0.3812533 0.4723838 0.4917449 0.5672412 0.6012372 0.6016578 #> [53,] 0.47146486 0.6398191 0.6982818 0.7187265 0.7796118 0.9114307 0.9247849 #> [54,] 0.32811395 0.4597374 0.5698973 0.6545683 0.7488184 0.7636551 0.8007657 #> [55,] 0.26351243 0.2898434 0.3279429 0.3904213 0.4334976 0.4823644 0.5486852 #> [56,] 0.77615810 0.8922126 0.9410711 0.9435230 0.9454779 0.9498508 0.9649450 #> [57,] 1.01660350 1.0576989 1.0578482 1.0948736 1.1998130 1.2010383 1.2067120 #> [58,] 0.31354966 0.3359187 0.4454083 0.5013944 0.5523149 0.5649255 0.6207553 #> [59,] 0.54093924 0.6735608 0.6915943 0.7171179 0.7925492 0.8258154 0.8404956 #> [60,] 0.39442631 0.5581936 0.6069573 0.6558298 0.6593543 0.6951015 0.7095590 #> [61,] 0.42184296 0.4722252 0.4810669 0.5213441 0.5457783 0.5978246 0.6324206 #> [62,] 0.16342217 0.4269498 0.4520204 0.5193114 0.6631736 0.7057671 0.7128491 #> [63,] 0.22126147 0.3532780 0.4414033 0.4531874 0.4647006 0.5107906 0.5602252 #> [64,] 0.33416754 0.4265877 0.4359787 0.4958946 0.5433790 0.6881546 0.7001640 #> [65,] 0.23679109 0.2815989 0.3487280 0.4295142 0.5025708 0.5573627 0.6129167 #> [66,] 0.44637144 0.5003019 0.5529245 0.6283844 0.6367535 0.6652489 0.7487575 #> [67,] 0.33537141 0.5092862 0.5898424 0.5930201 0.6001093 0.6728713 0.6872553 #> [68,] 0.37747986 0.4014896 0.4185361 0.4482289 0.5682722 0.5703633 0.6131069 #> [69,] 0.28798006 0.3927377 0.4251838 0.4288953 0.4558640 0.4906690 0.5028235 #> [70,] 0.54611205 0.6926403 0.7215123 0.7658092 0.8311058 0.9560666 1.0400445 #> [71,] 0.47206866 0.6357119 0.6360646 0.6926937 0.6989833 0.7644924 0.7893722 #> [72,] 0.21870056 0.2927399 0.2999043 0.3591081 0.4487782 0.4585332 0.4617932 #> [73,] 0.30035462 0.5001202 0.5593482 0.5631557 0.6212020 0.6350773 0.6398905 #> [74,] 0.30645224 0.5000964 0.5348828 0.5447165 0.5564983 0.5910982 0.6571445 #> [75,] 0.73137409 0.7876294 0.7927559 0.9257327 0.9287029 0.9333650 0.9570342 #> [76,] 0.40150325 0.4310171 0.4984940 0.5758994 0.5810228 0.5926704 0.7203417 #> [77,] 0.42677834 0.5655940 0.5780670 0.5869566 0.5955668 0.6814462 0.6902135 #> [78,] 0.24603251 0.4255550 0.5281018 0.6320025 0.6766829 0.6816221 0.7057718 #> [79,] 0.44042104 0.5181098 0.5632566 0.5853790 0.5921010 0.6301329 0.6573707 #> [80,] 1.17838407 1.4621528 1.4891457 1.6146337 1.6568477 1.6983617 1.7513378 #> [81,] 0.97605309 1.0511801 1.0518557 1.0868473 1.1142342 1.1686700 1.1819750 #> [82,] 0.30897320 0.4068803 0.5589393 0.5719344 0.6460923 0.6503385 0.7187265 #> [83,] 0.29770340 0.5268685 0.5810016 0.5937661 0.6260777 0.6646483 0.6767827 #> [84,] 0.73278181 0.8382064 0.8712902 0.9346618 0.9439012 1.1061611 1.2219257 #> [85,] 0.63396681 0.7252764 0.8155012 0.8245226 0.8380611 0.9923770 0.9993802 #> [86,] 0.32458341 0.3933979 0.5125694 0.6410320 0.6680742 0.6691817 0.6752053 #> [87,] 0.66478616 0.7107108 0.7159460 0.7237704 0.7282900 0.7332213 0.7434692 #> [88,] 0.30996952 0.3251140 0.3526699 0.5128921 0.5429316 0.6395256 0.6586995 #> [89,] 0.57281868 0.5758453 0.7006440 0.7207689 0.7258189 0.7296972 0.7965332 #> [90,] 0.39852216 0.4400132 0.4526669 0.5161778 0.5400066 0.5461164 0.6188844 #> [91,] 0.48933920 0.6051880 0.6382770 0.6502117 0.6693098 0.7108046 0.7692118 #> [92,] 0.62260206 0.9165124 1.3085745 1.3806910 1.5870406 1.6285594 1.6554045 #> [93,] 0.24603251 0.3878538 0.4591714 0.5178387 0.6585143 0.6621358 0.6810870 #> [94,] 0.30897320 0.6290901 0.6398191 0.6496431 0.7823336 0.7843597 0.8580708 #> [95,] 0.04497083 0.5930201 0.6119075 0.6270330 0.6429569 0.6600823 0.6656487 #> [96,] 0.62583943 0.7505155 0.9832066 1.0235993 1.0636559 1.1201775 1.1666406 #> [97,] 0.35144018 0.6382641 0.7079532 0.7095187 0.7659251 0.8091797 0.8513275 #> [98,] 0.31801358 0.3401989 0.4208660 0.4271074 0.5054884 0.5429898 0.5665091 #> [99,] 0.21601832 0.3354368 0.3484150 0.3856758 0.5485465 0.5505203 0.5557009 #> [100,] 0.41366461 0.5126158 0.5594749 0.5739571 0.6100644 0.6774000 0.7450978 #> [101,] 0.66441475 0.7549090 0.7557675 0.7765707 0.7936885 0.7990604 0.8064913 #> [102,] 0.46871727 0.5296746 0.6837906 0.7008518 0.7655323 0.8326505 0.8770413 #> [103,] 0.76556108 0.7659251 0.7682346 0.9744743 1.0745172 1.1330448 1.1399362 #> [104,] 0.30557984 0.3708418 0.3794128 0.5570809 0.5645912 0.6157789 0.6886539 #> [105,] 0.42014793 0.4929323 0.5075120 0.5230954 0.5328842 0.5333565 0.5456871 #> [106,] 0.60144678 0.9305805 0.9306688 0.9501760 0.9659964 1.0453325 1.0542669 #> [107,] 0.26191941 0.3335003 0.3864874 0.4739008 0.4901712 0.5262166 0.5277461 #> [108,] 0.53526753 0.5824204 0.5919049 0.6908379 0.6932066 0.6949184 0.7062489 #> [109,] 0.32366596 0.3816655 0.5062977 0.5485465 0.6035720 0.6073536 0.6347685 #> [110,] 0.65787838 0.8523714 1.1802698 1.3560210 1.3630859 1.3759184 1.3968075 #> [111,] 0.50862957 0.6866783 0.7279623 0.7948560 0.8165282 0.8374849 0.8375440 #> [112,] 1.55852407 1.5673626 1.6632869 1.6701479 1.7246864 1.7747710 1.7934094 #> [113,] 0.36644829 0.4296335 0.5638167 0.6098110 0.6319255 0.6431851 0.6599731 #> [114,] 0.27515565 0.7833691 0.7984154 0.8626899 0.8700936 0.8907433 0.9139923 #> [115,] 0.38214516 0.6842062 0.7184307 0.8236605 0.8753309 0.9271065 0.9900302 #> [116,] 0.45027271 0.5457783 0.6602727 0.6741195 0.6863318 0.7091045 0.7172088 #> [117,] 0.56160326 0.6429118 0.7370743 0.7549090 0.7710974 0.7860382 0.7889454 #> [118,] 0.23376156 0.3276499 0.3441216 0.3508093 0.5015056 0.5053651 0.5176712 #> [119,] 0.31583768 0.3737276 0.4558761 0.5437083 0.5458634 0.5974658 0.6560051 #> [120,] 0.34765690 0.3987382 0.4601763 0.4879230 0.5018809 0.5067646 0.5416199 #> [121,] 0.58701147 0.5952777 0.6049412 0.6119075 0.6268535 0.6821388 0.7100069 #> [122,] 0.38404719 0.4381970 0.4623692 0.4922980 0.5168948 0.5305939 0.5628925 #> [123,] 0.35215632 0.3951595 0.5682765 0.5992893 0.6292636 0.6469049 0.6536885 #> [124,] 0.37061007 0.3711900 0.3871380 0.5807237 0.5986405 0.6415935 0.6531730 #> [125,] 1.16275048 1.2245416 1.2926636 1.3602846 1.3698820 1.4084729 1.4638888 #> [126,] 0.50990188 0.6099109 0.7001640 0.7341859 0.7767447 0.7813376 0.7953612 #> [127,] 0.38172029 0.5355710 0.5596657 0.5633162 0.5823183 0.5929949 0.6202899 #> [128,] 0.32678210 0.4734693 0.5506425 0.5621268 0.5665520 0.5690209 0.6215555 #> [129,] 0.32240302 0.5219120 0.5762402 0.6049412 0.6920584 0.7313320 0.7474944 #> [130,] 0.49883170 0.6053445 0.7167148 0.7190791 0.7574800 0.7613518 0.7938191 #> [131,] 0.53564742 0.6130721 0.6764559 0.7958312 0.8108554 0.8286614 0.8786131 #> [132,] 0.42579509 0.5151876 0.5341619 0.5671062 0.5755747 0.5903237 0.6075006 #> [133,] 0.35147778 0.3636692 0.3647389 0.4423911 0.5608529 0.5691907 0.5779306 #> [134,] 0.39681078 0.4251424 0.5178433 0.5361624 0.6805308 0.6867334 0.7135838 #> [135,] 0.30942474 0.4231522 0.4267138 0.5000046 0.5050926 0.5430369 0.5739390 #> [136,] 0.41118245 0.4267375 0.4318495 0.4339700 0.4768650 0.5465379 0.5586927 #> [137,] 0.61307211 0.6772350 0.7043720 0.7118153 0.8149391 0.8453201 0.8556896 #> [138,] 0.43229124 0.5113181 0.5293639 0.5827702 0.5852424 0.5925780 0.6027970 #> [139,] 0.53526753 0.6705211 0.7233836 0.7638404 0.7897678 0.7912746 0.8126103 #> [140,] 0.25761193 0.3248417 0.3820281 0.4265877 0.4509244 0.6123456 0.6371633 #> [141,] 0.21551060 0.2478560 0.4984940 0.5043320 0.5153898 0.5377246 0.5380901 #> [142,] 0.23215070 0.3977784 0.4000386 0.4393166 0.4936062 0.5116403 0.5226528 #> [143,] 0.32484169 0.4430272 0.4500779 0.4606841 0.5433790 0.5530641 0.5891766 #> [144,] 0.70360030 0.7115000 0.7115202 0.7263464 0.7909070 0.8401681 0.8523100 #> [145,] 0.53097493 0.5426041 0.5660205 0.8089592 0.9680723 0.9860777 1.0241528 #> [146,] 0.36684834 0.4257951 0.4369957 0.4393166 0.4547368 0.5026818 0.5330085 #> [147,] 0.34840672 0.4549819 0.4564327 0.5024490 0.5335413 0.5341757 0.5377761 #> [148,] 0.68439506 0.7249098 0.8863247 0.9094872 0.9198462 0.9435541 0.9756199 #> [149,] 0.48461201 0.6281582 0.7102363 0.7320435 0.7869123 0.8343406 0.8597976 #> [150,] 0.27767287 0.2814221 0.2841270 0.4878354 0.4954478 0.4975521 0.5313888 #> [151,] 0.37046721 0.4650588 0.4816673 0.4861472 0.5433768 0.5727964 0.5752032 #> [152,] 0.56256667 0.5770576 0.6789019 0.7157721 0.7351366 0.7589517 0.7694994 #> [153,] 0.54053002 0.5560717 0.5999067 0.7184908 0.7479200 0.7761110 0.8660131 #> [154,] 0.54053002 0.6561674 0.8307043 0.8541981 0.8712022 0.9810323 0.9903409 #> [155,] 0.37489759 0.4336603 0.7790019 0.8323242 0.8554640 0.8577206 0.9099627 #> [156,] 0.30569236 0.3262743 0.3579049 0.3630452 0.4926383 0.5405824 0.6052169 #> [157,] 0.64667460 0.7669037 0.8257021 0.8541981 0.9243470 0.9655508 1.0201939 #> [158,] 0.37740552 0.4020145 0.4190839 0.4591714 0.5281018 0.5550570 0.5788570 #> [159,] 0.33547237 0.3463819 0.4977218 0.5180707 0.5242244 0.5635180 0.5817520 #> [160,] 0.35829338 0.5718766 0.5747591 0.5779698 0.6522162 0.7144527 0.7876369 #> [161,] 0.50669338 0.5422210 0.5631950 0.5689447 0.6092181 0.6320838 0.6627306 #> [162,] 0.51045490 0.5219120 0.5858995 0.5937850 0.7252764 0.8000330 0.8418996 #> [163,] 0.37061007 0.4042716 0.5416616 0.6091344 0.6365370 0.7418901 0.7538221 #> [164,] 0.51229703 0.5239113 0.5903342 0.5937850 0.6369687 0.6395027 0.7197476 #> [165,] 0.54908168 0.9813251 1.1372137 1.1695009 1.2031595 1.2249379 1.2830620 #> [166,] 0.61272048 0.7611578 0.7883182 0.8482370 0.8973138 0.8973912 0.9098426 #> [167,] 0.24308049 0.2554877 0.3695717 0.4638763 0.4841923 0.5672204 0.5678705 #> [168,] 0.38041963 0.4770930 0.4780638 0.4847577 0.5092416 0.5473634 0.5519264 #> [169,] 0.26265001 0.6764559 0.6801124 0.7182474 0.8034945 0.8243029 0.9143408 #> [170,] 0.26578713 0.4558640 0.5643588 0.5758453 0.6031906 0.6038029 0.6317046 #> [171,] 0.17370456 0.4308061 0.4805783 0.4823644 0.5499564 0.5587297 0.6249264 #> [172,] 0.26941305 0.4017908 0.5415747 0.5470445 0.5757414 0.5872002 0.6124346 #> [173,] 0.87618145 0.9526264 0.9565228 0.9713093 1.0146814 1.0413455 1.1054185 #> [174,] 0.61152392 0.7089670 0.7760067 0.9366138 0.9987533 1.0345146 1.0764891 #> [175,] 0.55117629 0.6955637 0.8672199 0.9175538 1.0857738 1.0870652 1.1226844 #> [176,] 0.51603535 0.5265768 0.5388508 0.5712551 0.5801553 0.6707166 0.7064780 #> [177,] 0.57667920 0.6744389 0.7106979 0.7830243 0.8084727 0.8349234 0.8471915 #> [178,] 0.35086757 0.4558084 0.4623692 0.5991109 0.5998957 0.6098110 0.6568489 #> [179,] 0.59394151 0.6259422 0.6412760 0.7072447 0.9181475 0.9528680 0.9805333 #> [180,] 0.45973796 0.5236926 0.5413020 0.5590320 0.5625617 0.5949277 0.6129280 #> [181,] 0.48466368 0.4966760 0.5003019 0.5597563 0.5807237 0.6365370 0.6573707 #> [182,] 0.49667604 0.5257725 0.5446453 0.5665520 0.5922374 0.5939369 0.6009061 #> [183,] 0.44863069 0.4890375 0.4972940 0.5129245 0.5339640 0.5405824 0.5594749 #> [184,] 1.54969454 1.6058166 1.7903249 1.8486250 1.9415717 1.9861202 1.9921299 #> [185,] 0.46840057 0.5643122 0.7830058 0.8577187 0.9074240 0.9099627 0.9585011 #> [186,] 0.21503155 0.4423911 0.4427498 0.4470150 0.4800804 0.4852145 0.5140889 #> [187,] 0.54488025 0.5484068 0.6032080 0.6201989 0.6812469 0.6997092 0.7071064 #> [188,] 0.55211975 0.5650773 0.6230229 0.7717706 0.7985339 0.8089592 0.8240324 #> [189,] 0.38410060 0.6421988 0.6741195 0.6748141 0.7148040 0.7160836 0.7192255 #> [190,] 0.29678858 0.3855973 0.3987382 0.4023448 0.4417458 0.4929323 0.5541087 #> [191,] 0.83653302 0.9011914 1.1703316 1.1948539 1.3102195 1.3296849 1.3600533 #> [192,] 0.25389733 0.3532200 0.5986405 0.6218608 0.6532183 0.6823172 0.6910984 #> [193,] 0.22947858 0.3398413 0.4375047 0.5370750 0.5849706 0.5936046 0.5978990 #> [194,] 0.40798941 0.4111824 0.4547087 0.4821675 0.5014282 0.5111314 0.5392919 #> [195,] 0.42611375 0.5618296 0.6964333 0.8322004 0.9590741 0.9636064 0.9893164 #> [196,] 0.40201451 0.4776085 0.5035358 0.5344354 0.5360601 0.5955668 0.6228103 #> [197,] 0.46147713 0.6051880 0.6178127 0.7489781 0.7982087 0.8318041 0.9819342 #> [198,] 0.90254645 1.1476636 1.2435210 1.2555075 1.2713360 1.2953993 1.2964965 #> [199,] 0.35295760 0.4725503 0.6273540 0.6553937 0.6742267 0.7099813 0.7272521 #> [200,] 0.22003989 0.6339161 0.6577880 0.7028651 0.7089670 0.7099221 0.7364444 #> [201,] 1.30424483 1.3801370 1.6572063 1.6867596 1.7135375 1.7356769 1.7962741 #> [202,] 0.96348909 1.1220217 1.2021038 1.2176376 1.3685976 1.4039036 1.4407800 #> [203,] 0.72611264 0.8111915 0.8342606 0.8372628 0.8585798 0.8709668 1.0290069 #> [204,] 0.26218164 0.3876053 0.4977218 0.5012997 0.5277461 0.5384173 0.5414872 #> [205,] 0.32203562 0.4016753 0.4300057 0.5546442 0.5858126 0.5875525 0.6082550 #> [206,] 0.37711935 0.4536032 0.5418304 0.5571568 0.5603790 0.5993868 0.6085266 #> [207,] 1.29870348 1.4759336 1.5193207 1.6065617 1.6335442 1.6481263 1.6744164 #> [208,] 0.41828293 0.4505434 0.5149747 0.5504608 0.5652963 0.6662338 0.6896558 #> [209,] 1.09602517 1.3455591 1.3968001 1.4076033 1.4311583 1.4532473 1.4562446 #> [210,] 0.23259817 0.2557204 0.3945140 0.4285865 0.5073673 0.5450982 0.5976732 #> [211,] 0.57035503 0.6708464 0.6737630 0.7192255 0.7207689 0.7496445 0.7887201 #> [212,] 0.39122971 0.4981192 0.5229825 0.5269430 0.5292013 0.5795555 0.5996315 #> [213,] 0.35637992 0.4327384 0.4632784 0.4996657 0.5418139 0.5668559 0.5799450 #> [214,] 0.50944477 0.5110197 0.5355926 0.5386034 0.5713747 0.5859775 0.6705592 #> [215,] 0.34791594 0.5351012 0.5943828 0.6316720 0.6639791 0.7338786 0.7368381 #> [216,] 0.27087776 0.2839025 0.3817311 0.5293472 0.5908624 0.6269716 0.6411617 #> [217,] 0.39744238 0.4157032 0.4207887 0.5008248 0.5621268 0.5672412 0.6119784 #> [218,] 0.33093201 0.5025979 0.5581936 0.5705094 0.5770479 0.5935215 0.5974658 #> [219,] 0.47130002 0.4807848 0.5290914 0.6324410 0.6944847 0.7013259 0.7344657 #> [220,] 0.50139444 0.6117694 0.6213244 0.6379400 0.6655542 0.7627878 0.7797618 #> [221,] 0.17049041 0.4410825 0.4612374 0.4953889 0.5724994 0.6252209 0.7118564 #> [222,] 0.95693748 1.1132546 1.1244440 1.1303904 1.2668734 1.3012856 1.3358036 #> [223,] 0.28983255 0.3401989 0.3488723 0.4446433 0.4599618 0.4971177 0.5089164 #> [224,] 0.68358791 0.7398657 0.8153097 0.9655508 1.0165439 1.0306387 1.0955916 #> [225,] 0.52787810 0.6537160 0.7764582 0.8385543 0.8581661 0.9558473 0.9584774 #> [226,] 0.47700844 0.5216885 0.5275571 0.5504608 0.6699999 0.6742696 0.6864118 #> [227,] 0.40229399 0.4330564 0.4653143 0.4910223 0.5192757 0.5500262 0.5755747 #> [228,] 0.16765161 0.3794128 0.3972535 0.4516268 0.5227325 0.5984751 0.7192160 #> [229,] 0.56725251 0.5680787 0.5804492 0.6965240 0.7132296 0.7339512 0.7397689 #> [230,] 0.53481399 0.6018809 0.6580955 0.6617331 0.6902225 0.7393891 0.7903442 #> [231,] 0.56754181 0.6221936 0.7386678 0.7643205 0.7936885 0.8099410 0.8349234 #> [232,] 0.33706501 0.5086939 0.5351012 0.5672952 0.6229590 0.6575454 0.7062489 #> [233,] 0.51643360 0.5335565 0.5571488 0.5878117 0.7780923 0.8003594 0.8174984 #> [234,] 0.38920400 0.6969675 0.7000426 0.7243613 0.7523700 0.7669118 0.7962175 #> [235,] 0.37712295 0.3991980 0.5946666 0.6124585 0.6698246 0.7125412 0.7172980 #> [236,] 0.34385430 0.3561176 0.5080554 0.5570809 0.5827833 0.6707290 0.6944958 #> [237,] 0.35853973 0.3927377 0.4391532 0.4562070 0.4633042 0.4707987 0.5236926 #> [238,] 0.59462859 0.6323538 0.6356349 0.6532442 0.7442849 0.7569891 0.8079612 #> [239,] 0.59284156 0.6264562 0.8839687 0.8899777 0.9636064 0.9918935 1.0633670 #> [240,] 0.24728549 0.5359625 0.5978341 0.6676988 0.7735319 0.7764392 0.7795528 #> [241,] 0.39744238 0.4338809 0.4520274 0.5868525 0.6012372 0.6065631 0.6152847 #> [242,] 0.37716013 0.4836585 0.5110197 0.5421546 0.5715196 0.6801882 0.6905674 #> [243,] 0.28394795 0.3820281 0.3876899 0.3974501 0.4430272 0.5167141 0.5183246 #> [244,] 0.82430292 0.9597073 0.9680445 1.3547966 1.3548550 1.3607090 1.3921615 #> [245,] 0.57624017 0.5876477 0.6268535 0.6270330 0.6747779 0.6848974 0.7931200 #> [246,] 0.31299205 0.5034560 0.5043867 0.5419253 0.5535397 0.5575974 0.7085888 #> [247,] 0.64262965 0.8921008 0.9305805 0.9339677 1.0069366 1.0193305 1.1414821 #> [248,] 0.48782602 0.5189966 0.5340295 0.5627590 0.5878117 0.7211326 0.7482774 #> [249,] 0.29951135 0.4259536 0.4972859 0.6099927 0.6159928 0.6249264 0.6333368 #> [250,] 1.20210381 1.5519966 1.7618684 1.9120597 1.9633872 2.0200774 2.0272466 #> [251,] 0.35031851 0.4776085 0.5788570 0.6083226 0.6146147 0.6818975 0.7005643 #> [252,] 0.32958296 0.3974501 0.4641185 0.4644079 0.4666262 0.5781133 0.5823183 #> [253,] 0.64262965 0.7900258 0.9860777 0.9909285 1.0919575 1.1173512 1.1255470 #> [254,] 0.31801358 0.3946047 0.5089164 0.6378590 0.6576409 0.6856637 0.6870499 #> [255,] 0.56754181 0.6402904 0.7009357 0.7173158 0.7729828 0.9631997 1.0913812 #> [256,] 0.34385430 0.5192776 0.6300583 0.6377344 0.7117716 0.7386289 0.7809774 #> [257,] 0.22952545 0.3700009 0.4237522 0.4269215 0.4446646 0.4706503 0.4720348 #> [258,] 0.44150652 0.8000399 0.9923017 1.1193029 1.2113795 1.2947642 1.3275814 #> [259,] 0.85237136 1.0701032 1.2366975 1.4143776 1.5372939 1.5401955 1.6497181 #> [260,] 0.34800816 0.4439292 0.4792568 0.5645975 0.5678585 0.5823287 0.5922374 #> [261,] 0.35396396 0.4361154 0.4656768 0.4666262 0.5121672 0.5249327 0.5773453 #> [262,] 0.71907907 0.7288427 0.7508510 0.7767205 0.7890183 0.9310220 0.9435470 #> [263,] 0.29770340 0.4141110 0.5676076 0.6408758 0.6567339 0.6658709 0.7265516 #> [264,] 0.19402179 0.2678304 0.3317761 0.5779306 0.5793657 0.5922482 0.6011110 #> [265,] 0.52654089 0.5694851 0.7143330 0.7409058 0.7444994 0.7695044 0.8202393 #> [266,] 0.43286308 0.6539635 0.7156137 1.0338277 1.0880982 1.1014870 1.2061600 #> [267,] 0.36675682 0.5501011 0.6080251 0.6144638 0.6671930 0.6740908 0.6979966 #> [268,] 0.59586692 0.7958265 0.8402377 0.8576936 0.9461251 0.9771705 1.0383945 #> [269,] 0.54260412 0.5650773 0.6896558 0.7013672 0.8024806 0.8243659 0.8619546 #> [270,] 0.22500973 0.4578056 0.5132158 0.5347966 0.5573906 0.5618274 0.5656646 #> [271,] 0.26941305 0.2701207 0.4742903 0.5017003 0.5199536 0.5307726 0.5483506 #> [272,] 0.48405150 0.5934796 0.7977922 0.8339237 0.8528246 0.9053046 0.9145482 #> [273,] 0.50643364 0.6628639 0.7460642 0.7497779 0.7905453 0.8047521 0.8092070 #> [274,] 0.56440130 0.6220324 0.6527405 0.7055652 0.7417904 0.7430240 0.7638724 #> [275,] 0.64111107 0.7362176 0.7411347 0.8071888 0.9011914 0.9139025 0.9205592 #> [276,] 0.48583868 1.1128216 1.1280921 1.2256428 1.2970298 1.3186694 1.3719151 #> [277,] 0.27111546 0.5458634 0.5522195 0.5631557 0.6328689 0.6480226 0.6719849 #> [278,] 0.32240302 0.5092862 0.5285431 0.5858995 0.6302899 0.6656487 0.6848974 #> [279,] 0.54272310 0.9510885 1.0333731 1.0378200 1.0842766 1.0886630 1.1258774 #> [280,] 0.44001319 0.4482383 0.5350578 0.5680075 0.7093066 0.7229251 0.7477676 #> [281,] 0.48782602 0.5382450 0.6441751 0.6856915 0.7746998 0.8234598 0.8382064 #> [282,] 0.63216176 0.6843951 0.7033122 0.7206454 0.8457100 0.8556248 0.9346566 #> [283,] 0.20119578 0.4330050 0.5292013 0.6444889 0.6596934 0.6674573 0.7008368 #> [284,] 0.41791948 0.5500002 0.5616033 0.6644147 0.6684482 0.7115284 0.7714425 #> [285,] 0.28122955 0.2898325 0.3042652 0.3680010 0.4208660 0.4288953 0.4544843 #> [286,] 0.83117402 0.9536993 1.0759830 1.0794063 1.0797320 1.1290180 1.2017439 #> [287,] 0.16765161 0.2524951 0.3055798 0.4410636 0.4944418 0.5827833 0.6699247 #> [288,] 0.56499395 0.6104109 0.6494149 0.6533586 0.6589826 0.6912301 0.7058457 #> [289,] 0.56128240 0.6060546 0.6891124 0.7835165 0.8987008 0.9107711 0.9328638 #> [290,] 0.44254775 0.4739008 0.4938839 0.4973035 0.5154688 0.5364357 0.5900000 #> [291,] 0.43114166 0.5037232 0.7502026 0.8022048 0.8222796 0.9218647 0.9456269 #> [292,] 0.28122955 0.3585397 0.4005744 0.4599618 0.4719293 0.4737524 0.4941077 #> [293,] 0.45982563 0.4627462 0.6790274 0.7352688 0.7444994 0.7550201 0.8146329 #> [294,] 0.33093201 0.3737276 0.4075892 0.4965954 0.5459429 0.6212020 0.6413108 #> [295,] 1.22922834 1.6228459 1.7320149 1.8213842 1.9268269 2.0307088 2.0696552 #> [296,] 0.69835149 0.7256993 0.7417198 0.8211896 0.8380895 0.8780206 0.8932582 #> [297,] 0.24047378 0.3771601 0.4302289 0.4714800 0.5026818 0.5079815 0.5116403 #> [298,] 0.97717053 1.0341550 1.1530179 1.1615413 1.1964910 1.2109834 1.2236306 #> [299,] 0.46871727 0.5940345 0.6718302 0.6963547 0.7474774 0.7541661 0.7727392 #> [300,] 0.40337394 0.4141110 0.4921739 0.4987909 0.5418304 0.6260777 0.6305279 #> [301,] 0.38832700 0.5591449 0.7470331 0.8280040 0.9345298 1.0007794 1.0097257 #> [302,] 1.05544757 1.0864902 1.2096848 1.2278592 1.2559593 1.3363969 1.3384323 #> [303,] 1.05931979 1.4083979 1.5475604 1.5779953 1.5884435 1.6557957 1.7377046 #> [304,] 0.61747662 0.6944256 0.7058491 0.7141267 0.7232051 0.7310167 0.7342439 #> [305,] 0.99875331 1.1196969 1.1629325 1.1712476 1.1934590 1.2020231 1.2147471 #> [306,] 0.16044299 0.3508093 0.3586839 0.3947949 0.4432703 0.5215421 0.5766561 #> [307,] 0.75555445 0.7985300 0.9673132 1.0086205 1.0165799 1.0217857 1.0825259 #> [308,] 0.19402179 0.3555067 0.4337352 0.5723201 0.5901790 0.6238143 0.6588093 #> [309,] 0.15739874 0.2927399 0.3094247 0.3420135 0.4140120 0.4279712 0.4617395 #> [310,] 0.42526236 0.5833116 0.6245978 0.6534410 0.6637959 0.6728224 0.7151564 #> [311,] 0.65948086 0.7622319 0.7696701 0.7931384 0.8242023 0.8477518 0.9194493 #> [312,] 0.38594183 0.4376983 0.4930348 0.6038293 0.6202930 0.6476761 0.6973202 #> [313,] 0.29619568 0.5594014 0.5688117 0.5864253 0.5870340 0.6421234 0.6713917 #> [314,] 0.27111546 0.5001202 0.5686945 0.6167773 0.6353030 0.7064299 0.7158896 #> [315,] 0.45340478 0.4768650 0.4864367 0.4908308 0.4909328 0.5154688 0.5219980 #> [316,] 0.62039678 0.7758929 0.7894831 0.7977922 0.8005508 0.8373456 0.8664193 #> [317,] 0.34725973 0.4317180 0.4657972 0.4728753 0.4784125 0.4947778 0.5146067 #> [318,] 0.66774590 1.0664273 1.2667554 1.2998875 1.3524285 1.3923727 1.4089270 #> [319,] 0.53683528 0.6458406 0.6819790 0.6968820 0.7221679 0.7954932 0.8013005 #> [320,] 0.27692957 0.3295830 0.4315167 0.4361154 0.4632784 0.5194180 0.5731871 #> [321,] 0.37740552 0.5298873 0.5674285 0.6048902 0.6227255 0.6320025 0.6411027 #> [322,] 0.35031851 0.5399719 0.6108232 0.6269043 0.6609895 0.7051456 0.7055877 #> [323,] 0.95372941 0.9592937 0.9818004 0.9823114 1.0534956 1.0553641 1.0968400 #> [324,] 0.18607074 0.4439292 0.5175486 0.5340295 0.5501744 0.5571488 0.5711820 #> [325,] 0.41137800 0.5682765 0.5934796 0.6069523 0.7628360 0.7758584 0.7772044 #> [326,] 0.49029828 0.5084776 0.5933649 0.6258394 0.8065527 0.8477603 0.8576452 #> [327,] 0.67399602 0.6808892 0.7301544 0.7776224 0.7818618 0.7955500 0.8297262 #> [328,] 0.83159959 0.9046579 1.3424826 1.3942088 1.3985994 1.4262390 1.4263432 #> [329,] 0.62645619 0.8139930 1.0637702 1.1379397 1.2330201 1.2445042 1.2717714 #> [330,] 0.34718697 0.3912297 0.4194307 0.4439014 0.5228171 0.5727556 0.6406330 #> [331,] 0.26035703 0.5753758 0.5946666 0.6105969 0.6638629 0.6697832 0.6705406 #> [332,] 0.30855503 0.3710229 0.4878354 0.5026781 0.5290914 0.5319935 0.5828565 #> [333,] 0.32922558 0.3842350 0.4455320 0.5087297 0.5693583 0.5817674 0.6002576 #> [334,] 0.43580695 0.4922980 0.5026781 0.5831288 0.5879639 0.6053585 0.6735620 #> [335,] 0.57875264 0.6839511 0.6853032 0.6863318 0.6982818 0.7301646 0.7401596 #> [336,] 0.23416207 0.4904582 0.5355710 0.5864253 0.5963909 0.6157789 0.6580955 #> [337,] 0.56117283 0.9724704 1.0097257 1.2080045 1.3264815 1.4421302 1.4433116 #> [338,] 0.46558168 0.4841030 0.6402904 0.9160282 0.9315429 0.9379916 1.0990903 #> [339,] 0.56536588 0.6463297 0.6586995 0.6940481 0.7130196 0.7237704 0.7773880 #> [340,] 0.40700908 0.5399719 0.5425732 0.5478941 0.5985593 0.6146147 0.6517178 #> [341,] 0.23253009 0.2841939 0.5425732 0.5526583 0.5707032 0.6098153 0.6189824 #> [342,] 0.33335252 0.4853275 0.5666667 0.5674679 0.5784783 0.5826087 0.5944991 #> [343,] 0.91191200 0.9235948 0.9993258 1.0262315 1.0356451 1.0573300 1.0660545 #> [344,] 0.21286690 0.3670160 0.4329317 0.5043867 0.5509312 0.5822610 0.6361994 #> [345,] 0.32627428 0.4014896 0.4233858 0.4694141 0.4925961 0.4972940 0.5446308 #> [346,] 0.27767287 0.2783384 0.3710229 0.3929441 0.4713000 0.4934612 0.4958311 #> [347,] 0.49907726 0.6222739 0.7091526 0.8132572 0.8963111 0.9075966 1.0341483 #> [348,] 0.21792988 0.5361539 0.5793089 0.5827702 0.5841615 0.5937395 0.6079173 #> [349,] 0.49561025 0.7515701 0.7900258 0.7948560 0.8526947 0.8921008 0.9249440 #> [350,] 0.15272180 0.3451188 0.5312809 0.5427308 0.5455492 0.5483506 0.5682192 #> [351,] 0.50111140 0.5290405 0.5388508 0.5638882 0.6842062 0.6856766 0.7081862 #> [352,] 0.48405150 0.9997366 1.0063590 1.0186303 1.0258218 1.0370866 1.0842196 #> [353,] 0.85506147 0.9796234 0.9997460 1.0331354 1.0857738 1.1596754 1.1766655 #> [354,] 0.33886686 0.7598023 0.7773880 0.8075970 0.8530453 0.8797843 0.9533309 #> [355,] 0.53824498 0.7482774 0.8041277 0.8343406 0.8527889 0.8808184 0.9057698 #> [356,] 0.39460346 0.4735638 0.5339004 0.5406958 0.6300583 0.6759686 0.6760014 #> [357,] 0.36786814 0.4943045 0.5858722 0.6555239 0.6883619 0.7011929 0.7140176 #> [358,] 0.54467172 0.6008991 0.6133631 0.6281582 0.7045401 0.7285143 0.7305726 #> [359,] 0.49538891 0.5077185 0.5161778 0.6036898 0.6470808 0.6509040 0.6822214 #> [360,] 0.33360306 0.3951510 0.5297383 0.5445057 0.5796505 0.6006658 0.6055141 #> [361,] 0.17049041 0.5033910 0.5077185 0.6048211 0.6167268 0.6264364 0.6731264 #> [362,] 0.30195725 0.4376983 0.5229335 0.5471404 0.5548141 0.5564983 0.5812133 #> [363,] 0.60005117 0.6010209 0.6738717 0.7179053 0.8279851 0.8281021 0.8510822 #> [364,] 0.66774590 1.3163298 1.4704140 1.4948077 1.4982230 1.5405538 1.5528732 #> [365,] 0.40688031 0.5753531 0.6290901 0.6897839 0.7227850 0.7229793 0.7529551 #> [366,] 0.33416754 0.6339668 0.7072508 0.7096127 0.7341859 0.7492985 0.7689684 #> [367,] 0.77150740 0.8281860 0.8365330 0.8415089 0.8481635 0.8571622 0.8613339 #> [368,] 0.93525318 0.9501049 1.0214541 1.0440251 1.0497149 1.0786428 1.1421249 #> [369,] 0.75502011 0.8643716 0.8916613 0.8925923 0.9750515 1.1627505 1.1896890 #> [370,] 0.41366461 0.5810245 0.6492271 0.6670795 0.7102363 0.7141773 0.7190354 #> [371,] 0.22947858 0.4005975 0.4329269 0.4377666 0.4958311 0.5414789 0.5570704 #> [372,] 0.28229287 0.3060863 0.4140120 0.4617932 0.4860057 0.4901277 0.5107906 #> [373,] 0.42234308 0.4251424 0.6440649 0.6532035 0.6593543 0.6997124 0.7057718 #> [374,] 0.35705946 0.4944418 0.5227325 0.6337435 0.7313741 0.7448075 0.7467064 #> [375,] 0.43000568 0.4510068 0.4537805 0.4770084 0.4908308 0.6091098 0.6605649 #> [376,] 0.70550896 0.8658663 1.0162033 1.1059807 1.1066440 1.1351071 1.1641759 #> [377,] 0.73231115 0.7330094 0.7646062 0.7711267 0.8413085 0.8651759 0.8653694 #> [378,] 0.51593162 0.6416642 0.7694242 0.8225500 0.8955663 0.9084808 0.9439012 #> [379,] 0.46123736 0.5245417 0.6048211 0.6248637 0.6372920 0.7045773 0.7268233 #> [380,] 0.41684967 0.4332905 0.5386192 0.5518292 0.5708686 0.5978990 0.6011880 #> [381,] 0.47919111 0.4792568 0.4871539 0.5175486 0.5446453 0.5682192 0.6344623 #> [382,] 0.48667127 0.5048615 0.5272385 0.5503752 0.5544435 0.5757508 0.6004304 #> [383,] 1.39237913 1.4389659 1.4775812 1.5511146 1.6401882 1.6666768 1.7071872 #> [384,] 1.11081929 1.1862048 1.2363080 1.3240156 1.3580938 1.3619310 1.3934562 #> [385,] 0.84719154 0.9257695 0.9259973 0.9631997 1.0528749 1.1507484 1.2607130 #> [386,] 0.21549985 0.2927903 0.4528098 0.4554006 0.4847586 0.5067646 0.5147386 #> [387,] 0.25548771 0.2627143 0.3012077 0.3369672 0.5852312 0.6533586 0.6665172 #> [388,] 0.38313595 0.4852145 0.5667683 0.5705758 0.5723201 0.5816681 0.5817390 #> [389,] 0.36776757 0.3820516 0.5654415 0.6478635 0.6581621 0.7580703 0.7614613 #> [390,] 0.44042104 0.4462403 0.4861098 0.6844823 0.6898711 0.6920427 0.7149602 #> [391,] 0.37755185 0.4720687 0.4748450 0.6676814 0.6830605 0.7232436 0.7517080 #> [392,] 0.49303475 0.6282661 0.6352669 0.7280407 0.7822611 0.7883182 0.8423544 #> [393,] 0.24156076 0.3333525 0.4894152 0.4907465 0.6046438 0.6096974 0.6198758 #> [394,] 0.75980226 0.9182698 1.0389936 1.0786253 1.0842766 1.1125976 1.1204943 #> [395,] 0.79131716 0.8450893 0.8701179 0.9600946 0.9620011 1.1613341 1.1650136 #> [396,] 0.46840057 0.6900040 0.7541661 0.7634109 0.8923103 0.9210998 0.9261443 #> [397,] 0.39460470 0.4769464 0.4993423 0.5605158 0.5964247 0.6108362 0.6189705 #> [398,] 0.54175410 0.6101697 0.6214295 0.7347792 0.7857347 0.8037321 0.8482199 #> [399,] 0.42443846 0.4946278 0.4988318 0.4991808 0.5728891 0.5872002 0.6422448 #> [400,] 0.40589754 0.5994650 0.6405285 0.6597749 0.7341665 0.7384536 0.7440294 #> [401,] 0.33547237 0.3876053 0.5448803 0.5742499 0.5754628 0.6805831 0.7048972 #> [402,] 0.52774869 0.7536813 0.7796129 0.8243505 0.8382165 0.8643576 0.9220884 #> [403,] 0.61366148 0.6602462 0.7427637 0.7735705 0.8246115 0.8442161 0.8613048 #> [404,] 0.35215632 0.4564511 0.6322821 0.6395455 0.7042088 0.7094070 0.7266282 #> [405,] 0.50924156 0.5422210 0.5544217 0.5690209 0.5959135 0.6045659 0.6229129 #> [406,] 0.57640862 0.7099221 0.7166143 0.7249098 0.7888289 0.8557293 0.8600432 #> [407,] 0.27916916 0.3484067 0.4446646 0.4531318 0.4875835 0.5116676 0.5139554 #> [408,] 0.32811395 0.3350727 0.3440006 0.4969256 0.6380791 0.6789776 0.7619706 #> [409,] 0.33360306 0.4789346 0.4905156 0.5192757 0.5432577 0.5903237 0.6025242 #> [410,] 0.51734179 0.8135626 0.9046579 0.9546533 0.9682437 1.0339429 1.0568210 #> [411,] 0.51230855 0.7115000 0.7758929 0.7828679 0.9054288 1.0238655 1.1780441 #> [412,] 0.56052284 0.6773700 0.9139025 0.9216663 1.0707815 1.0758797 1.1157012 #> [413,] 0.27429430 0.4174707 0.4715509 0.4769901 0.5313888 0.5681010 0.5757508 #> [414,] 0.65431994 0.7272521 0.7962587 0.8683901 0.8706834 1.0330764 1.0452828 #> [415,] 0.52130015 0.5345360 0.6173324 0.6322821 0.6335443 0.7028651 0.7036003 #> [416,] 0.80280506 0.8307399 0.8473081 0.9751862 0.9957300 1.0787663 1.2395073 #> [417,] 0.63880966 0.6527405 0.7013672 0.7099813 0.7667526 0.7864823 0.8593760 #> [418,] 0.17091574 0.2557204 0.4154745 0.4707840 0.4996657 0.5305223 0.6249691 #> [419,] 0.91355647 0.9407524 1.1901579 1.2092175 1.2215077 1.2843890 1.2951655 #> [420,] 0.44077352 0.4423909 0.6316720 0.6967569 0.7555210 0.7897678 0.7911129 #> [421,] 1.18129626 1.1820783 1.2192175 1.2663894 1.4015970 1.4078856 1.5088903 #> [422,] 0.60188092 0.7306779 0.7614894 0.8201824 0.8507603 0.8563820 0.8624440 #> [423,] 0.37105317 0.4343819 0.4415877 0.4975265 0.4978055 0.5155221 0.6055141 #> [424,] 1.03306172 1.0416197 1.0467315 1.1375541 1.2039755 1.2894666 1.2903858 #> [425,] 0.44637144 0.5257725 0.5597563 0.6685950 0.6792135 0.7737699 0.7910788 #> [426,] 0.38205163 0.4427894 0.5293472 0.5416102 0.5452154 0.5486471 0.6135617 #> [427,] 0.92897311 1.4607150 1.7194306 1.7638615 1.8088267 1.8113200 1.8456745 #> [428,] 0.36893463 0.5189327 0.5674679 0.6249000 0.6716065 0.6980939 0.7404938 #> [429,] 0.31315190 0.6597764 0.7347792 0.8168133 0.8300385 0.8385543 0.8550615 #> [430,] 0.56117283 0.9280705 1.0769471 1.0922780 1.2842654 1.3766206 1.4703591 #> [431,] 0.35991731 0.6071260 0.6503385 0.6590381 0.6897839 0.7125915 0.7392944 #> [432,] 0.27515565 0.6780247 0.7875097 0.8063710 0.8114488 0.8716991 0.8797149 #> [433,] 0.32404141 0.4805979 0.5681275 0.5712551 0.5734678 0.5900957 0.6468046 #> [434,] 0.33579434 0.5409392 0.5437083 0.5716376 0.7179293 0.7368908 0.7704097 #> [435,] 0.51962096 0.8417451 0.8673370 0.9462979 0.9760531 1.0758839 1.1064294 #> [436,] 0.35551633 0.5461164 0.6081071 0.7107542 0.7305726 0.7630830 0.7731162 #> [437,] 0.22473774 0.2619194 0.3630418 0.3737517 0.4401235 0.4417458 0.4634704 #> [438,] 0.39662159 0.4432703 0.4436377 0.4566741 0.5134027 0.5176712 0.5475328 #> [439,] 0.24156076 0.4339481 0.4853275 0.5150846 0.5453835 0.5731871 0.6445660 #> [440,] 0.42629419 0.4269498 0.4917449 0.5967956 0.7033093 0.7925975 0.8220188 #> [441,] 0.47387022 0.4973497 0.5272385 0.5500262 0.5524280 0.5579955 0.5641639 #> [442,] 0.69234816 0.8231293 0.8600432 0.8741934 0.8783775 0.9141506 0.9162641 #> [443,] 0.26578713 0.3455166 0.5271173 0.5728187 0.6090574 0.6193783 0.6266733 #> [444,] 0.30916496 0.3856758 0.4017785 0.4945967 0.5464797 0.5544855 0.5725951 #> [445,] 0.50248079 0.5973751 0.6198012 0.7106098 0.7710974 0.7735319 0.7790543 #> [446,] 0.30696916 0.4064382 0.4439416 0.5047480 0.6406330 0.6714506 0.7422157 #> [447,] 0.94100371 1.0331354 1.0410825 1.0532556 1.1293918 1.1827213 1.2545704 #> [448,] 0.85654824 1.1190720 1.1199056 1.1775246 1.1937552 1.2525599 1.2540978 #> [449,] 0.72588120 0.9358353 0.9492661 1.0811068 1.0912420 1.1134560 1.1234979 #> [450,] 0.49268324 0.5296746 0.7474774 0.7683373 0.8557396 0.8643716 0.9074240 #> [451,] 0.40730826 0.4722252 0.4829929 0.5956886 0.6168834 0.6456856 0.6482295 #> [452,] 0.43750473 0.4411643 0.5386192 0.5570704 0.5666451 0.5785084 0.5869566 #> [453,] 0.27833841 0.2841270 0.3683553 0.4005975 0.4819972 0.5281680 0.5319935 #> [454,] 0.55997084 0.6393230 0.7498951 0.7731162 0.8337882 0.8384616 0.8678513 #> [455,] 0.39223456 0.4003260 0.4009120 0.4256820 0.4335879 0.4368795 0.4841923 #> [456,] 0.31583768 0.3357943 0.4075892 0.4683504 0.6180513 0.6915943 0.7676689 #> [457,] 0.19718617 0.2507500 0.2952804 0.4110020 0.4269215 0.4505302 0.4861472 #> [458,] 0.79403055 0.9079105 0.9744997 1.0245793 1.0396396 1.0684175 1.0798204 #> [459,] 0.41279177 0.4735638 0.4978055 0.5009526 0.5194236 0.5210512 0.5656646 #> [460,] 0.18365652 0.3565099 0.3737517 0.4901712 0.5012997 0.5406525 0.5541087 #> [461,] 0.33777507 0.4185361 0.4500143 0.5213931 0.5732926 0.5733587 0.6311623 #> [462,] 0.34000005 0.4547087 0.4803415 0.4979181 0.4981605 0.5139271 0.5324105 #> [463,] 0.50958632 0.5724994 0.5762256 0.5984237 0.6036898 0.6167249 0.6167268 #> [464,] 0.41791948 0.5207050 0.6310681 0.6748141 0.6753137 0.7997849 0.8257319 #> [465,] 0.41908392 0.5298873 0.6748081 0.6816221 0.7071701 0.7271169 0.7385645 #> [466,] 1.34494781 1.5233323 1.5635876 1.6090008 1.7593662 1.7745993 1.8503743 #> [467,] 0.79853002 0.8112394 0.9556263 1.0278128 1.0292884 1.0529251 1.0797320 #> [468,] 0.67399602 0.8083640 0.8330092 0.8502555 0.8880128 0.8915497 0.9213053 #> [469,] 0.63932303 0.6410085 0.7169540 0.8022048 0.8101539 0.8853278 0.8998839 #> [470,] 0.35517167 0.5000046 0.5068350 0.5085941 0.5143304 0.5251669 0.5482755 #> [471,] 0.36491652 0.3965035 0.5752258 0.6001995 0.6997092 0.7815906 0.8311178 #> [472,] 0.36304515 0.5643803 0.5915972 0.6311051 0.6366774 0.6652489 0.6658170 #> [473,] 0.15272180 0.3878751 0.4335768 0.4719275 0.5506425 0.5845838 0.5928785 #> [474,] 0.43303048 0.4391350 0.4656768 0.5288773 0.5328568 0.5817674 0.5961361 #> [475,] 0.53830065 1.1780441 1.1802566 1.1812963 1.3166412 1.3192053 1.3597177 #> [476,] 0.27692957 0.3539640 0.3934326 0.4327384 0.4391350 0.4641185 0.4707840 #> [477,] 0.30784366 0.4136345 0.4267375 0.4565310 0.5014282 0.5324105 0.5731336 #> [478,] 0.14874895 0.3816655 0.5239113 0.6164456 0.6900380 0.6975582 0.7122184 #> [479,] 0.54272310 0.6060152 0.6884798 0.7072447 0.7911542 0.7989374 0.8566173 #> [480,] 0.43637557 0.7652942 0.7878777 0.8075332 0.8276442 0.9581094 0.9721621 #> [481,] 0.30696916 0.3199699 0.3438252 0.4263777 0.4439014 0.5274743 0.6674573 #> [482,] 0.34551656 0.4346415 0.5532540 0.5643588 0.6144827 0.6421988 0.6648602 #> [483,] 0.50575352 0.7093871 0.7159460 0.7826749 0.8296502 0.8301152 0.8694828 #> [484,] 0.39196996 0.4252624 0.6739281 0.7272578 0.7433350 0.7891056 0.8303995 #> [485,] 0.32267120 0.5123086 0.5447285 0.6203968 0.7115202 0.8486390 0.8656488 #> [486,] 0.61082321 0.6598523 0.7005643 0.8473081 0.8643576 0.9271097 0.9627590 #> [487,] 0.37131494 0.5290405 0.6032977 0.6206803 0.6283001 0.6460744 0.6707166 #> [488,] 0.60423508 0.6226021 1.0455526 1.1612867 1.2450324 1.2661239 1.2846899 #> [489,] 0.60605455 0.7563084 0.8980437 0.9233367 1.0435580 1.0566689 1.0593198 #> [490,] 0.38529405 0.5153898 0.5944632 0.6035720 0.6498777 0.6666415 0.6975582 #> [491,] 0.69234816 0.7206454 0.7888289 0.8132450 0.8274062 0.8489513 0.8564503 #> [492,] 1.15191239 1.3899942 1.4500010 1.4885976 1.5228824 1.5852860 1.6965329 #> [493,] 0.56315141 0.6642650 0.6965077 0.6999140 0.7645853 0.7683081 0.7897385 #> [494,] 0.48583868 1.1109211 1.1695009 1.3133883 1.3257371 1.3295063 1.3357156 #> [495,] 0.78233201 1.0637702 1.1451020 1.2800477 1.3045123 1.3432057 1.3498040 #> [496,] 0.58121331 0.5842185 0.5934716 0.7158673 0.7288453 0.8039226 0.8781543 #> [497,] 0.60144678 0.6523766 0.7815906 0.7855782 0.7883097 0.9350030 0.9439458 #> [498,] 0.58608188 0.9931059 1.1399638 1.1783841 1.2097972 1.2181288 1.2365478 #> [499,] 0.63282757 0.6682973 0.6743829 0.7578613 0.7671347 0.7887201 0.8602626 #> [500,] 0.26265001 0.5356474 0.6421611 0.6772350 0.7457229 0.7951561 0.8453708 #> [501,] 1.38999419 1.5992446 1.8790500 2.3113869 2.3168782 2.3637341 2.3922669 #> [502,] 0.32558987 0.4174707 0.4371221 0.4554006 0.4772252 0.5048615 0.5416199 #> [503,] 0.48461201 0.7215688 0.7433350 0.7617940 0.7680742 0.7905453 0.7961259 #> [504,] 0.22486970 0.2627143 0.3695717 0.4947433 0.5551474 0.5586764 0.5956886 #> [505,] 0.68271114 0.8258154 0.8675523 0.9214783 0.9267411 0.9584324 0.9606637 #> [506,] 0.38713800 0.4160170 0.4167471 0.5416616 0.6201974 0.6218608 0.6513026 #> [507,] 0.35517167 0.4033861 0.4352831 0.4425477 0.4630983 0.4665478 0.4729722 #> [508,] 0.21503155 0.4364501 0.5066574 0.5068288 0.5470138 0.5629381 0.5653868 #> [509,] 1.35540675 1.3651020 1.4498864 1.4697750 1.4866551 1.6000226 1.6029319 #> [510,] 1.12729374 1.1422187 1.1849896 1.2738890 1.2847577 1.2913047 1.3467737 #> [511,] 0.33004290 0.3838357 0.5194236 0.5339004 0.5774565 0.6389957 0.6516153 #> [512,] 0.87751687 1.0572218 1.1211695 1.1232132 1.2340452 1.2617497 1.2899045 #> [513,] 1.26501290 1.2800477 1.3927728 1.4072841 1.4812533 1.4894414 1.5189249 #> [514,] 0.32349377 0.4353077 0.5567052 0.5573627 0.5938147 0.5940007 0.6354609 #> [515,] 0.46681568 0.4981513 0.5804492 0.5870340 0.6382426 0.6558298 0.6613072 #> [516,] 0.48176089 0.4981513 0.6124427 0.6951015 0.6968820 0.7107325 0.7603987 #> [517,] 0.56275897 0.7327818 0.7415040 0.7746998 0.7948028 0.8078147 0.8275757 #> [518,] 0.43645014 0.4427498 0.4582420 0.4617395 0.4696348 0.4860057 0.4869942 #> [519,] 0.30195725 0.3064522 0.4834773 0.5319833 0.5844000 0.5934716 0.6602826 #> [520,] 0.34872796 0.4696348 0.5094419 0.5328879 0.5508058 0.5596657 0.5667683 #> [521,] 0.22486970 0.3012077 0.4638763 0.4827970 0.5581506 0.5857969 0.6168834 #> [522,] 0.34718697 0.4734484 0.4759376 0.4821902 0.4981192 0.5919049 0.6765002 #> [523,] 0.37119003 0.4042716 0.4167471 0.6327125 0.6424517 0.6451651 0.6544454 #> [524,] 0.49883178 0.5564145 0.6133631 0.6188844 0.6548673 0.6671413 0.7107542 #> [525,] 0.51487828 0.7055090 0.7121909 0.7625581 0.8078147 0.8712902 0.9196127 #> [526,] 0.26233166 0.3301814 0.5477472 0.5644013 0.5652963 0.6095633 0.6419645 #> [527,] 0.44657063 0.5469588 0.6504423 0.7273693 0.9348490 0.9960069 1.0127452 #> [528,] 0.55221954 0.6227255 0.6748081 0.6885028 0.6898711 0.6936241 0.6971507 #> [529,] 0.60601516 0.6747779 0.7274967 0.7710522 0.7746791 0.7761110 0.7761581 #> [530,] 0.96348909 1.0005127 1.1531303 1.1819835 1.2705730 1.2881013 1.3097788 #> [531,] 0.51614723 0.5859331 0.6091953 0.6416642 0.6484656 0.7493636 0.7608141 #> [532,] 0.52734415 0.5752258 0.6201989 0.6509948 0.7475855 0.7848703 0.8374849 #> [533,] 0.22768949 0.4755288 0.5001598 0.5267358 0.5417933 0.5707032 0.6032977 #> [534,] 0.17308916 0.3775519 0.4374424 0.4842991 0.5473634 0.5522140 0.5544129 #> [535,] 0.27366290 0.4910223 0.5432577 0.5697910 0.6618646 0.7108046 0.7424222 #> [536,] 0.82986354 0.8525027 0.8694440 0.8770413 0.8785766 0.9407524 0.9698302 #> [537,] 0.50957678 0.5401574 0.5591449 0.5853550 0.6122867 0.8077789 0.8343363 #> [538,] 0.36353777 0.3804196 0.4706606 0.4781166 0.4842991 0.5207731 0.5435039 #> [539,] 0.12432811 0.4799947 0.7927098 0.8045342 0.8104586 0.8335825 0.8565542 #> [540,] 0.39267342 0.5104084 0.5539836 0.5571402 0.6095633 0.6229590 0.6637908 #> [541,] 0.28212285 0.2891919 0.4649396 0.5648903 0.5664279 0.5829420 0.6284229 #> [542,] 0.99208071 1.0640377 1.1408177 1.2567472 1.2610097 1.2973438 1.3444573 #> [543,] 0.48996733 0.5606936 0.5728755 0.6760014 0.7544363 0.7715961 0.7818113 #> [544,] 0.22723214 0.3977784 0.4259536 0.4369957 0.5030719 0.5087297 0.5148810 #> [545,] 0.32764986 0.3947949 0.3989610 0.4436377 0.5049733 0.5212736 0.5482755 #> [546,] 0.38903127 0.4299702 0.4647006 0.4800804 0.5080255 0.5473492 0.6005693 #> [547,] 0.22075267 0.3000063 0.6380791 0.6999140 0.7695203 0.7772380 0.7839562 #> [548,] 0.54444892 0.7026799 0.7865930 0.8482936 0.8593760 0.8771458 0.9025465 #> [549,] 0.41975680 0.5425809 0.6484656 0.6700281 0.6740985 0.7462301 0.7625581 #> [550,] 0.44410572 0.4786823 0.5505203 0.6208388 0.7245477 0.7528892 0.8241554 #> [551,] 0.34765690 0.4860027 0.5374429 0.5498394 0.5611247 0.5674918 0.5913260 #> [552,] 0.25761193 0.2839479 0.4359787 0.4500779 0.5492644 0.6073729 0.6101469 #> [553,] 0.32458341 0.4294108 0.4755666 0.4790972 0.5430447 0.5497086 0.5698945 #> [554,] 0.30000634 0.3440006 0.3895970 0.5069029 0.6480851 0.6545683 0.7117059 #> [555,] 0.57083623 0.6741173 0.7033093 0.7443693 0.7894831 0.7964902 0.8458878 #> [556,] 0.51508459 0.5199321 0.5402096 0.5418139 0.5784783 0.6096974 0.6549053 #> [557,] 0.35322004 0.4095795 0.6571445 0.7186039 0.7256993 0.7310167 0.7931390 #> [558,] 1.73915157 2.0895044 2.1799612 2.2052621 2.2889129 2.3213603 2.3241489 #> [559,] 0.32678210 0.5064336 0.5544217 0.6344623 0.6354370 0.6742242 0.6952326 #> [560,] 0.25389733 0.4095795 0.4867599 0.5728891 0.5757414 0.6128420 0.6201974 #> [561,] 0.57920643 0.6609895 0.7130723 0.7608231 0.8280994 0.8915774 0.9119757 #> [562,] 0.33004290 0.3951510 0.4803121 0.5113439 0.5922088 0.6058663 0.6105139 #> [563,] 0.29619568 0.6414745 0.6818945 0.7300882 0.7494630 0.7524761 0.8068922 #> [564,] 0.28390248 0.3352527 0.4368724 0.4797075 0.5452154 0.5546065 0.5663818 #> [565,] 1.70387685 1.8756984 1.9238217 1.9351767 2.0168327 2.0358703 2.0389652 #> [566,] 0.50368519 0.5164336 0.5711820 0.6091953 0.6135240 0.6179733 0.6527461 #> [567,] 0.27429430 0.3579049 0.4233858 0.4472829 0.4561306 0.4772252 0.4890375 #> [568,] 0.36176548 0.4923406 0.4991808 0.5343824 0.5415747 0.5455492 0.5481736 #> [569,] 0.58372918 0.5984751 0.6508299 0.6699247 0.7298699 0.7735197 0.8480297 #> [570,] 0.38920400 0.6251328 0.7517080 0.7609491 0.7827158 0.8434251 0.8563041 #> [571,] 0.44136721 0.5160107 0.5731316 0.5940345 0.6567246 0.6588093 0.6591535 #> [572,] 0.33373156 0.4814404 0.5521197 0.7448075 0.7741427 0.7927559 0.8362222 #> [573,] 0.23416207 0.4356454 0.5688117 0.5912199 0.5929949 0.5965354 0.6701969 #> [574,] 0.46386001 0.4905156 0.5672952 0.6115767 0.6319799 0.6335443 0.6418135 #> [575,] 0.44102183 0.5958669 0.6523323 0.6539635 0.7169540 0.7502026 0.7989667 #> [576,] 0.22003989 0.5764086 0.5800283 0.6960439 0.6992767 0.7734542 0.7760067 #> [577,] 0.52787810 0.8656258 1.0886843 1.1303904 1.1652766 1.1992359 1.2040200 #> [578,] 0.33537141 0.4310171 0.4855791 0.5043320 0.5506841 0.6286606 0.6449115 #> [579,] 0.48106688 0.6400865 0.6602727 0.6610452 0.6643364 0.6676988 0.6725714 #> [580,] 0.40229399 0.4831555 0.5020988 0.5160107 0.5657945 0.5793657 0.5832154 #> [581,] 0.35648607 0.5155221 0.5430369 0.5512025 0.5672378 0.5931377 0.6105139 #> [582,] 0.27012072 0.4017908 0.4559433 0.4892858 0.4946278 0.5128846 0.5350863 #> [583,] 0.35868385 0.4425961 0.6264364 0.6395027 0.6446911 0.6611604 0.6894536 #> [584,] 0.55815062 0.5852312 0.6701959 0.6814269 0.7058457 0.7367348 0.7549490 #> [585,] 0.44657063 0.4577985 0.7049280 0.7064780 0.7427919 0.7677238 0.7866158 #> [586,] 0.33284522 0.3968305 0.5929588 0.6385466 0.6502117 0.6619225 0.7842259 #> [587,] 0.17308916 0.3946946 0.4523318 0.4748450 0.4781166 0.5063630 0.5519264 #> [588,] 0.77900189 0.7890183 0.8378151 0.8851613 0.8899777 0.9409221 1.0251435 #> [589,] 0.56786474 0.5713880 0.6050779 0.6742909 0.6922978 0.7159223 0.7236839 #> [590,] 1.28990446 1.4368071 1.4372491 1.6684753 1.6883546 1.7540976 1.8215611 #> [591,] 0.34060381 0.3857767 0.4285865 0.4323910 0.5243204 0.5416102 0.5619075 #> [592,] 0.44624031 0.4964189 0.6007497 0.6597749 0.6793760 0.7046408 0.7064299 #> [593,] 0.16044299 0.2337616 0.3989610 0.4425961 0.4827286 0.5134027 0.5305207 #> [594,] 0.23433208 0.2507500 0.3647389 0.3863603 0.4060251 0.4638805 0.4706503 #> [595,] 0.17597548 0.4429149 0.4458199 0.5008248 0.6135240 0.6417925 0.6768346 #> [596,] 0.46274619 0.5643122 0.7634109 0.7683373 0.8060857 0.8275630 0.8577206 #> [597,] 0.48996733 0.5210512 0.5406958 0.5489483 0.5774565 0.7836787 0.8296072 #> [598,] 0.22768949 0.5449690 0.5478941 0.5777244 0.5915972 0.6030622 0.6073392 #> [599,] 0.83371889 0.8694239 0.8819580 0.9786088 0.9887986 1.0007794 1.1333100 #> [600,] 0.26035703 0.5574171 0.6283340 0.6793788 0.6816118 0.7125412 0.7249124 #> [601,] 0.37701435 0.3873969 0.4454083 0.5852220 0.6213244 0.6464179 0.6551963 #> [602,] 0.18365652 0.2247377 0.2577553 0.3864874 0.4023448 0.5241576 0.5414872 #> [603,] 0.43366031 0.6522712 0.8378151 0.8617993 0.8700834 0.9045392 1.0198042 #> [604,] 0.69993429 0.7284301 0.9402911 1.1137788 1.1158997 1.1169389 1.1244440 #> [605,] 0.29951135 0.3365124 0.4805783 0.4852801 0.4947778 0.5059454 0.5593715 #> [606,] 0.24308049 0.3369672 0.4299477 0.4368795 0.5183246 0.5551474 0.5619075 #> [607,] 0.44525949 0.8932878 0.9821860 1.0011803 1.0103385 1.0981862 1.1486406 #> [608,] 0.59405249 0.6543199 0.6553937 0.7091526 0.8328801 0.8486597 0.8609763 #> [609,] 1.02977260 1.2619621 1.2667554 1.4606998 1.5334449 1.5560651 1.5957530 #> [610,] 0.35144018 0.5686196 0.5780670 0.6654444 0.8031539 0.8919497 0.8937318 #> [611,] 0.39294411 0.4329269 0.4807848 0.4819972 0.4849148 0.5227979 0.5640177 #> [612,] 0.46886255 0.5946286 0.6971507 0.7394289 0.7854985 0.8576131 0.9304374 #> [613,] 0.51490569 0.5511763 0.9546355 1.1132546 1.1169389 1.1330129 1.2553011 #> [614,] 0.44559324 0.5293639 0.5361539 0.5733587 0.7285486 0.7346014 0.7350405 #> [615,] 0.33760424 0.3771229 0.5694851 0.6053445 0.6638629 0.7151035 0.7352688 #> [616,] 0.37489759 0.6153700 0.6522712 0.6900040 0.7691259 0.7830058 0.8490819 #> [617,] 0.38785383 0.4255550 0.5361624 0.6440649 0.6580717 0.6705406 0.6816118 #> [618,] 0.33543684 0.3541546 0.4724177 0.5725951 0.6208388 0.6248876 0.6351238 #> [619,] 0.49870522 0.5469588 0.7579067 0.7694242 0.7866158 0.8033215 0.9437138 #> [620,] 0.45415534 0.6671930 0.6792135 0.6862691 0.7155112 0.7678281 0.7826749 #> [621,] 0.33984133 0.4188059 0.4377666 0.4584733 0.4993423 0.5206577 0.5281680 #> [622,] 0.32404141 0.4348363 0.5456857 0.5670290 0.5897818 0.6090094 0.6204706 #> [623,] 0.43463282 0.4849148 0.6283340 0.6380633 0.6411111 0.6772921 0.7280407 #> [624,] 0.24047378 0.2949672 0.4734230 0.4836585 0.4855279 0.5226528 0.5671062 #> [625,] 0.38832700 0.6122867 0.6906560 0.8819580 0.9070997 0.9280705 0.9724704 #> [626,] 0.31996990 0.4064382 0.4194307 0.5095882 0.6187073 0.6682280 0.6765002 #> [627,] 0.21601832 0.4441057 0.4724177 0.4945967 0.5062977 0.5134793 0.5488952 #> [628,] 0.17402163 0.2367911 0.4004923 0.4353077 0.4847487 0.4869942 0.4950905 #> [629,] 0.80130051 0.8168133 0.8360590 0.9012890 0.9719824 0.9892314 1.0326790 #> [630,] 0.33886686 0.6463297 0.6651778 0.7060388 0.7318452 0.7538065 0.7692197 #> [631,] 0.34201350 0.3591081 0.4149334 0.4231522 0.4760790 0.4992524 0.5132158 #> [632,] 0.49883170 0.7197396 0.7218012 0.7508510 0.7842259 0.7848501 0.7930338 #> [633,] 0.40473610 0.4956102 0.7398657 0.8165282 0.9339677 0.9609719 1.0370421 #> [634,] 0.48667127 0.5674285 0.5851370 0.6052577 0.6083226 0.6202031 0.6234698 #> [635,] 0.32803844 0.3965035 0.5484068 0.5868525 0.6253015 0.6509948 0.6716849 #> [636,] 0.56052284 0.6385466 0.6993452 0.7218012 0.7321719 0.8379912 0.8791882 #> [637,] 0.60666145 0.6115239 0.6960439 0.7364444 0.7907865 0.8651759 0.9106117 #> [638,] 0.90824995 0.9976108 1.0257840 1.0490677 1.0991771 1.1008939 1.1014870 #> [639,] 0.74307655 0.9445222 0.9865465 1.0185952 1.0381068 1.0908697 1.1027061 #> [640,] 0.64291176 0.6669140 0.7011364 0.7486184 0.7557675 0.8058338 0.8103042 #> [641,] 0.35605581 0.4814404 0.7876294 0.7985339 0.8573493 0.9576553 0.9659746 #> [642,] 0.79311997 0.7989374 0.8668125 0.8767005 0.8896757 0.9194493 0.9435230 #> [643,] 0.29279031 0.3544750 0.3700009 0.4719275 0.5045860 0.5201948 0.5427308 #> [644,] 0.43637557 0.4827970 0.4947433 0.7024931 0.7082306 0.7102383 0.7503552 #> [645,] 0.38434723 0.4441885 0.5685905 0.5852424 0.5996315 0.6418135 0.6847756 #> [646,] 0.75806006 0.7767205 0.7881615 0.8075945 0.8449039 0.9816106 0.9991894 #> [647,] 0.34015775 0.4648615 0.5660205 0.6646483 0.6967569 0.7338786 0.7546331 #> [648,] 0.54015739 0.6906560 0.7470331 0.7691937 0.8596595 0.8851544 0.8855184 #> [649,] 0.21401924 0.3291718 0.3406038 0.4113986 0.4169955 0.4256820 0.4330305 #> [650,] 1.21571383 1.6228459 1.7779071 1.8151949 1.8291248 1.8450162 1.9125968 #> [651,] 0.73862833 0.7855138 0.8135626 0.9202073 0.9247456 1.0629530 1.1103865 #> [652,] 0.48695987 0.4969256 0.5372878 0.5698973 0.6480851 0.6495004 0.6571974 #> [653,] 0.59394151 0.6429569 0.6469902 0.6594809 0.6669274 0.6801128 0.7318452 #> [654,] 0.71059489 0.7913172 0.8570968 0.9106875 0.9401968 0.9611286 0.9756189 #> [655,] 0.19780375 0.3078437 0.3579763 0.4318495 0.4531874 0.5124364 0.5570000 #> [656,] 0.33018138 0.3567649 0.4992524 0.5149747 0.5353460 0.5900000 0.6121609 #> [657,] 0.36776757 0.4427894 0.5663868 0.5770576 0.6664361 0.7138950 0.7603336 #> [658,] 0.39142869 0.4601763 0.4863964 0.5127744 0.5638167 0.5713538 0.5737622 #> [659,] 0.46493959 0.5561894 0.7030695 0.7118153 0.7135524 0.7341962 0.7462063 #> [660,] 0.21401924 0.2281317 0.3397789 0.4009120 0.4742309 0.5243204 0.5328568 #> [661,] 0.44464332 0.4574371 0.4834773 0.5054884 0.5055472 0.5161832 0.5229335 #> [662,] 0.32267120 0.5252603 0.7263464 0.7772791 0.7828679 0.7894459 0.8005508 #> [663,] 0.23163085 0.3125665 0.3891389 0.4232646 0.4854022 0.5116676 0.5146067 #> [664,] 1.68507968 1.7320149 1.8154012 1.8993829 1.9175140 1.9931019 2.0122906 #> [665,] 0.37105317 0.3822649 0.4080247 0.5637485 0.5686945 0.5851032 0.6203053 #> [666,] 1.26596347 1.2987503 1.3163879 1.3481555 1.4350144 1.4667450 1.5794637 #> [667,] 0.38172029 0.5221992 0.6277827 0.6597764 0.6713917 0.6715206 0.7095495 #> [668,] 0.41705624 0.4903407 0.5305223 0.5740756 0.5962643 0.5976732 0.6198012 #> [669,] 0.37131494 0.4369859 0.4755288 0.5471576 0.5577912 0.5801553 0.6030622 #> [670,] 0.68142692 0.7803132 0.8450893 0.9206161 0.9284356 0.9300096 0.9300310 #> [671,] 0.31315190 0.5417541 0.7687094 0.7741071 0.8012836 0.8145714 0.8155763 #> [672,] 0.39515951 0.4564511 0.6069523 0.6405285 0.7031803 0.7632962 0.7708018 #> [673,] 0.66039001 0.6856915 0.8041277 0.9437138 0.9860807 0.9920807 1.0049737 #> [674,] 0.33284522 0.5010170 0.6251130 0.6716530 0.7409058 0.7854566 0.7930338 #> [675,] 0.38434723 0.4322912 0.4638600 0.5213002 0.5800283 0.6553089 0.6577880 #> [676,] 0.54611205 0.6339835 0.7537169 0.7589348 0.8409048 0.8528875 0.8586295 #> [677,] 0.36503326 0.4182829 0.4523318 0.5180707 0.5275571 0.5544129 0.6218384 #> [678,] 0.24648209 0.5618274 0.5625347 0.6759686 0.7198735 0.7292775 0.7603987 #> [679,] 0.58608188 0.7796129 0.7855138 0.8454423 0.9682437 1.0820030 1.0851767 #> [680,] 0.48666539 0.4902983 0.6471791 0.7160836 0.7306741 0.7505155 0.7845608 #> [681,] 0.27087776 0.3374291 0.4033739 0.4368724 0.5486471 0.5625667 0.5654415 #> [682,] 0.57372655 0.8000399 0.9717136 0.9758373 1.0200868 1.0596932 1.1610597 #> [683,] 0.45429280 0.4655817 0.7009357 0.7498951 0.8086566 0.8632858 0.8719987 #> [684,] 0.46307271 0.5994650 0.6273540 0.7221424 0.7638539 0.7792963 0.8033483 #> [685,] 0.49907726 0.5940525 0.6827111 0.7004509 0.8277587 0.8280008 0.8723819 #> [686,] 1.50596458 1.5657095 1.5754641 1.6338610 1.7295999 1.7931603 1.8113775 #> [687,] 0.41975680 0.4354461 0.5148783 0.7302572 0.7369999 0.7374324 0.7415040 #> [688,] 0.51993210 0.6507086 0.6613144 0.6682973 0.6854089 0.6890310 0.7054623 #> [689,] 0.41315968 0.4482383 0.4526669 0.6122105 0.6367922 0.6531001 0.6671413 #> [690,] 0.59022782 0.6389957 0.6434926 0.7062934 0.7356794 0.7406443 0.7555210 #> [691,] 0.36786814 0.4817609 0.6667033 0.6692368 0.7221679 0.7474076 0.7715780 #> [692,] 0.47593764 0.5066934 0.5228171 0.6133590 0.6682280 0.6710433 0.6933420 #> [693,] 0.34015775 0.5309749 0.6754314 0.6866783 0.7454285 0.8355015 0.8627002 #> [694,] 0.44287676 0.5719344 0.6496431 0.7229793 0.7558851 0.7563084 0.8101539 #> [695,] 0.43712215 0.4553386 0.5641639 0.6392469 0.6439245 0.6870271 0.7060563 #> [696,] 0.14706566 0.4455320 0.4473325 0.4511470 0.4564653 0.4728753 0.4769149 #> [697,] 0.65371596 0.8360590 0.8482199 0.8656258 0.8673346 0.9124285 0.9252556 #> [698,] 0.42443846 0.4867599 0.5343824 0.5512543 0.5709348 0.6297882 0.6391369 #> [699,] 0.30035462 0.4965954 0.5705094 0.6069573 0.6560051 0.7111043 0.7248677 #> [700,] 0.23566961 0.2962470 0.3037289 0.3804423 0.4329317 0.4957784 0.5218996 #> [701,] 0.34791594 0.4423909 0.6339192 0.7711267 0.8043722 0.8176418 0.8193722 #> [702,] 0.38410060 0.4346415 0.5680075 0.6310681 0.6459038 0.6606776 0.6892405 #> [703,] 0.89461960 1.1036450 1.1944454 1.2430173 1.2445042 1.3393180 1.3762091 #> [704,] 0.36525783 0.5024808 0.5359625 0.6964333 0.7860382 0.8233114 0.8387448 #> [705,] 0.56182959 0.5877482 0.7154758 0.8000923 0.8829692 0.9316102 0.9406988 #> [706,] 0.52711728 0.5500002 0.5625617 0.6038029 0.6144827 0.6606776 0.6733218 #> [707,] 0.25775535 0.2967886 0.3565099 0.3630418 0.5500049 0.5858786 0.5937395 #> [708,] 0.45587612 0.4683504 0.5025979 0.5197292 0.5459429 0.5716376 0.5886034 #> [709,] 0.50575352 0.5658166 0.5931351 0.6119784 0.6813854 0.6862691 0.7420654 #> [710,] 0.46886255 0.5376504 0.6598523 0.6818975 0.7356749 0.7961273 0.8070495 #> [711,] 0.69264027 0.7333815 0.9616452 1.1140323 1.1178551 1.1265248 1.1347521 #> [712,] 0.52774869 0.7639499 0.8649104 0.8873985 0.8907783 0.9271097 0.9462811 #> [713,] 0.22952545 0.2952804 0.2981954 0.3316853 0.4060251 0.4816673 0.5045860 #> [714,] 0.33706501 0.5484259 0.5824204 0.6410320 0.6418896 0.6458494 0.6616116 #> [715,] 0.89731375 1.0537978 1.0872180 1.1281765 1.2874463 1.3430162 1.4573941 #> [716,] 0.45879487 0.5779698 0.6209997 0.6790274 0.7966283 0.8060857 0.8867858 #> [717,] 0.35637992 0.3945140 0.4154745 0.4335879 0.4409417 0.5278541 0.5740247 #> [718,] 0.66195643 0.6783787 0.7290759 0.7430766 0.8105196 0.8730697 0.9213143 #> [719,] 0.32942541 0.3510939 0.5217597 0.5319833 0.5348828 0.5535397 0.5548141 #> [720,] 0.28226619 0.7007640 0.8111915 0.8873127 0.9021124 0.9370842 0.9768958 #> [721,] 0.19718617 0.2343321 0.3316853 0.3704672 0.4115678 0.4458681 0.4720348 #> [722,] 0.36214779 0.3668483 0.3811909 0.4201479 0.4531318 0.4936062 0.5059454 #> [723,] 0.56070439 0.5842185 0.7960508 0.8029320 0.8727750 0.9239359 0.9257695 #> [724,] 0.45533863 0.4964189 0.5417933 0.5471576 0.5526583 0.6159372 0.6252936 #> [725,] 0.17091574 0.2325982 0.4170562 0.4409417 0.5799450 0.5900520 0.6431570 #> [726,] 0.30569236 0.4472829 0.4694141 0.5611247 0.5643803 0.5823287 0.6055500 #> [727,] 0.49841470 0.5978341 0.6058621 0.7401596 0.7509420 0.7776361 0.8039289 #> [728,] 0.55017438 0.5645975 0.6441751 0.6565390 0.6603900 0.6950564 0.7211326 #> [729,] 0.56164157 0.5737265 0.6881103 0.7450978 0.7895455 0.9096213 0.9724750 #> [730,] 0.34414173 0.4000386 0.4302289 0.4855279 0.5030719 0.5269430 0.5926604 #> [731,] 0.62029301 0.6352669 0.6380633 0.7147159 0.7249124 0.7291427 0.7433481 #> [732,] 0.45779847 0.6922978 0.6950564 0.7096315 0.7226073 0.7273693 0.7833891 #> [733,] 0.63826410 0.7682346 0.7960508 0.8937318 1.0084275 1.1145695 1.1332786 #> [734,] 0.67165298 0.6951862 0.7007640 0.7114757 0.8941862 0.9199336 0.9738467 #> [735,] 0.33777507 0.4482289 0.5056059 0.5320780 0.5597726 0.6534410 0.6739281 #> [736,] 0.69442557 0.8149391 0.8167236 0.8786131 0.9513195 0.9557853 0.9811198 #> [737,] 0.26835689 0.3334680 0.3621478 0.4549819 0.4769149 0.4854022 0.4865940 #> [738,] 0.49268324 0.6837906 0.9234186 0.9441428 1.0225519 1.1003096 1.1280842 #> [739,] 1.29539934 1.8722235 1.9284252 2.0010740 2.0750523 2.1162269 2.1169820 #> [740,] 0.51734193 0.6642650 0.7704097 0.8122535 0.8581814 0.8711888 0.8950791 #> [741,] 0.34511877 0.3878751 0.4791911 0.4892858 0.4923406 0.4952032 0.5455918 #> [742,] 0.35403636 0.4285535 0.5277485 0.5305939 0.5498010 0.5503752 0.5640177 #> [743,] 0.57535311 0.6563266 0.6590381 0.6714506 0.7532099 0.8052437 0.8501118 #> [744,] 0.51734179 0.7632962 0.8085228 0.8315996 0.8591507 0.8649104 0.9220884 #> [745,] 0.32917180 0.3397789 0.3876899 0.3922346 0.4001949 0.4299477 0.4323910 #> [746,] 0.64667460 0.6835879 0.6979654 0.8804967 0.9374665 0.9778351 0.9948617 #> [747,] 0.44136721 0.5503266 0.6124585 0.6458406 0.6666177 0.6790456 0.6853860 #> [748,] 0.82415540 0.8868312 1.0763748 1.1582749 1.1680500 1.2156539 1.2208195 #> [749,] 0.41493342 0.4700809 0.4869599 0.5400945 0.5436498 0.5573906 0.5594085 #> [750,] 0.46799293 0.5034560 0.5217597 0.6771774 0.7306261 0.7400928 0.7437343 #> [751,] 0.58434221 0.6646158 0.6969564 0.7237858 0.7355400 0.7586271 0.7755287 #> [752,] 0.53830065 1.3152491 1.4967682 1.5136034 1.5573514 1.6377122 1.6517498 #> [753,] 0.33760424 0.3991980 0.4598256 0.5265409 0.6790456 0.7613518 0.8217289 #> [754,] 0.23008354 0.3640111 0.4975521 0.5126158 0.5339640 0.5841814 0.6017285 #> [755,] 0.54471650 0.6192503 0.6313910 0.6351238 0.7059582 0.7568730 0.7921023 #> [756,] 0.35086757 0.5386034 0.5628925 0.5704743 0.5749911 0.5831288 0.6768378 #> [757,] 0.45429280 0.4841030 0.7173158 1.0579114 1.1550930 1.1706918 1.2055242 #> [758,] 0.41684967 0.4346328 0.5227979 0.5574171 0.5642680 0.5748597 0.6105969 #> [759,] 0.22723214 0.2321507 0.3441417 0.4547368 0.4606442 0.4972859 0.5295162 #> [760,] 0.22075267 0.3895970 0.6789776 0.7645853 0.7901658 0.8013009 0.8486597 #> [761,] 0.81532947 0.8531185 0.8577187 0.8617993 0.8923103 0.8946196 0.9825679 #> [762,] 0.28142206 0.3683553 0.4934612 0.5206577 0.5777682 0.6004304 0.6115999 #> [763,] 0.38313595 0.4004923 0.4295142 0.4470150 0.4973108 0.5328879 0.5470138 #> [764,] 0.85570464 0.8876872 0.8948302 1.0199801 1.0230997 1.0844723 1.1143924 #> [765,] 0.92943659 0.9304374 1.0307765 1.2157138 1.2584467 1.2722371 1.3115574 #> [766,] 0.51734193 0.7179293 0.7347693 0.8160029 0.8686874 0.8732938 0.9418266 #> [767,] 0.24648209 0.6124427 0.6414745 0.6667033 0.6792825 0.7024752 0.7242682 #> [768,] 0.34841500 0.3541546 0.5134793 0.5470445 0.6012403 0.6016000 0.6136057 #> [769,] 0.52454319 0.5877482 0.6497930 0.6978403 0.8646325 0.9219122 0.9685776 #> [770,] 0.33177611 0.3514778 0.4325350 0.4337352 0.4599327 0.4638805 0.4926394 #> [771,] 0.46283211 0.4734484 0.5095768 0.6294922 0.6707089 0.6725714 0.6949184 #> [772,] 0.38383571 0.3946035 0.5009526 0.5489483 0.5762502 0.6377344 0.6440527 #> [773,] 0.37711935 0.4566741 0.5215421 0.5586764 0.5766874 0.6164888 0.6767031 #> [774,] 0.38993683 0.6628639 0.6891124 0.7015497 0.7302572 0.7341874 0.7989318 #> [775,] 0.83110582 0.9107652 0.9549498 1.0660545 1.0960889 1.1519532 1.1663112 #> [776,] 0.28984342 0.3565893 0.4464113 0.4694526 0.5826878 0.5859595 0.5972087 #> [777,] 0.31299205 0.3510939 0.4679929 0.4706264 0.5949997 0.6361994 0.6766829 #> [778,] 0.35551633 0.3985222 0.5564145 0.6008991 0.6470808 0.6550018 0.6857016 #> [779,] 0.95562635 1.2624698 1.4753414 1.7136959 1.7248187 1.7823646 1.8136329 #> [780,] 0.42611375 0.6624952 0.7154758 0.8839687 0.9812302 0.9990435 1.0114281 #> [781,] 0.79814003 0.9909280 1.0555764 1.0578797 1.1204943 1.1938586 1.1983328 #> [782,] 0.58535499 0.7279623 0.7475855 0.8526947 0.8637983 0.8680231 0.8855184 #> [783,] 0.21792988 0.4455932 0.4849461 0.6027970 0.6261556 0.6593693 0.6599585 #> [784,] 0.38993683 0.5612824 0.7160182 0.8346796 0.8980437 0.9196127 0.9264201 #> [785,] 0.43300503 0.4441885 0.4714800 0.5229825 0.5421546 0.5926604 0.6107581 #> [786,] 0.28212285 0.4773151 0.5561894 0.5812558 0.6418716 0.6531730 0.6551963 #> [787,] 0.38701647 0.4429149 0.5794177 0.6179733 0.7015497 0.7045773 0.7493636 #> [788,] 0.22126147 0.4290754 0.4299702 0.4498610 0.5164665 0.5368604 0.5377246 #> [789,] 0.27916916 0.3565893 0.3891389 0.3904213 0.4800280 0.5232374 0.5258567 #> [790,] 0.36237544 0.5245432 0.7106098 0.8368973 0.8374019 0.8462018 0.8829692 #> [791,] 0.50847764 0.5532540 0.5913410 0.6471791 0.6507086 0.6737630 0.7578613 #> [792,] 0.34638187 0.3635378 0.3650333 0.4520274 0.4780638 0.5063630 0.5364357 #> [793,] 0.96471043 1.1238617 1.1670605 1.2363080 1.3438715 1.4656662 1.5202666 #> [794,] 0.39442631 0.5672525 0.5940007 0.6562407 0.6613072 0.7771895 0.8676490 #> [795,] 0.44218243 0.4562070 0.4597380 0.4941077 0.4971177 0.5149583 0.5182899 #> [796,] 0.46309833 0.4734599 0.4770930 0.4929153 0.4938839 0.5049733 0.5068350 #> [797,] 0.35327797 0.4821675 0.5343740 0.5368604 0.5838635 0.5845838 0.5888128 #> [798,] 0.44077352 0.4648615 0.5268685 0.5676076 0.5943828 0.6339192 0.6705211 #> [799,] 0.43483633 0.4369859 0.4812532 0.4849461 0.4924158 0.5734678 0.5745182 #> [800,] 0.22500973 0.4127918 0.4415877 0.5176093 0.5338640 0.5512025 0.5625347 #> [801,] 0.33651243 0.4317180 0.4334976 0.4602431 0.4944497 0.5258567 0.5421749 #> [802,] 0.30996952 0.4079894 0.4082440 0.5007269 0.5075303 0.5519626 0.5956112 #> [803,] 0.42078874 0.4458199 0.5007269 0.5594309 0.5710308 0.5931351 0.6654464 #> [804,] 0.46681568 0.5594014 0.5645912 0.5680787 0.7353619 0.7524761 0.7629788 #> [805,] 0.67438290 0.8576244 0.8710689 1.0787180 1.0813341 1.1490374 1.1719507 #> [806,] 0.63398347 0.7658092 0.8245560 0.8568136 0.8746828 0.9278684 0.9469909 #> [807,] 0.79403055 1.2538734 1.3409049 1.3886341 1.4143776 1.6039930 1.6385793 #> [808,] 0.39196996 0.6492271 0.7006185 0.7617940 0.7707938 0.7958265 0.8086566 #> [809,] 0.53559259 0.5987499 0.6718302 0.6761828 0.6896680 0.6905674 0.6947258 #> [810,] 0.21910513 0.4110020 0.4115678 0.4192902 0.5068288 0.5347966 0.5386177 #> [811,] 0.31452038 0.6963547 0.7095495 0.7133073 0.7954932 0.8097445 0.8705029 #> [812,] 0.54548829 0.5467555 0.6456530 0.6605649 0.7046408 0.7678281 0.7826580 #> [813,] 0.39683053 0.4614771 0.4893392 0.5376430 0.6251130 0.6571221 0.7005399 #> [814,] 0.42103663 0.5189327 0.5921624 0.7932110 0.7953206 0.8013409 0.8744982 #> [815,] 0.73338145 1.1091642 1.3315277 1.5215019 1.5316756 1.5636798 1.5704113 #> [816,] 0.58796392 0.6011233 0.6173346 0.6441560 0.6747761 0.6773700 0.6797189 #> [817,] 1.36065079 1.5137655 1.5193820 1.5240171 1.5498771 1.5958677 1.6121502 #> [818,] 0.30483279 0.6178127 0.6380806 0.6382770 0.6571221 0.7243450 0.8326125 #> [819,] 0.40273042 0.5212736 0.5603790 0.5628116 0.5810016 0.5887656 0.6294922 #> [820,] 0.71824740 0.7215123 0.8453708 0.8528875 0.9597073 0.9738452 1.1058711 #> [821,] 0.38559728 0.4500143 0.4863964 0.5113181 0.5500049 0.5597726 0.5685905 #> [822,] 0.62866059 0.6942937 0.7284301 0.7386602 0.7695203 0.8087480 0.8161729 #> [823,] 0.14874895 0.3236660 0.5891766 0.5903342 0.5944632 0.6515879 0.6564730 #> [824,] 0.45853317 0.4877218 0.4921739 0.5230083 0.5250451 0.5348140 0.5552367 #> [825,] 0.34800816 0.4846637 0.5529245 0.6009061 0.6091344 0.6167648 0.6366774 #> [826,] 0.43253503 0.4831555 0.4865940 0.5334751 0.5341619 0.5524280 0.5628177 #> [827,] 0.55997084 0.7272578 0.7378849 0.7621492 0.8044038 0.8407370 0.8632858 #> [828,] 0.68204381 0.7227850 0.8052437 0.8680231 0.8800067 0.9070997 0.9344276 #> [829,] 0.28919190 0.4773151 0.4803415 0.5392919 0.5550533 0.6207553 0.6225913 #> [830,] 0.55774317 0.6965077 0.7545314 0.7733977 0.8177852 0.9057978 0.9184425 #> [831,] 0.36237544 0.6978403 0.7546528 0.7967500 0.8791270 0.8803966 0.8834623 #> [832,] 0.47868229 0.7783767 0.8286614 0.8366864 0.8746035 0.8868312 0.9465115 #> [833,] 0.43357679 0.4734693 0.4909328 0.5312809 0.5519626 0.5710308 0.5838635 #> [834,] 0.40273042 0.5086296 0.6707089 0.6912846 0.7190484 0.7190719 0.7273207 #> [835,] 0.29819539 0.3870360 0.4016753 0.4192902 0.4458681 0.4505302 0.4650588 #> [836,] 0.35797635 0.3874699 0.4290754 0.4414033 0.4415019 0.4950905 0.5380901 #> [837,] 0.24728549 0.3652578 0.5973751 0.6058621 0.6714878 0.7875077 0.8330189 #> [838,] 0.43114166 0.7145558 0.7729599 0.7989667 0.8771213 0.8853278 0.9082500 #> [839,] 0.28226619 0.6951862 0.7774018 0.8110518 0.8782227 0.8891517 0.9318547 #> [840,] 0.36893463 0.4210366 0.5944991 0.6127205 0.6323531 0.6557683 0.7007896 #> [841,] 0.66544443 0.7079532 0.7227111 0.8612166 0.8741109 0.8808184 0.9161539 #> [842,] 0.23240441 0.3125665 0.3472597 0.4421824 0.4473325 0.4742903 0.5013366 #> [843,] 0.53765037 0.7394289 0.7537169 0.7876056 0.8028051 0.9627590 1.0137468 #> [844,] 0.39681078 0.4223431 0.6354693 0.7994573 0.8189176 0.8444658 0.8649676 #> [845,] 0.54908168 0.8796845 0.9251165 0.9912498 1.0151577 1.0183992 1.0188981 #> [846,] 0.34382525 0.3694933 0.4439416 0.6071260 0.6187073 0.6506722 0.6954510 #> [847,] 0.30855503 0.4296335 0.4358069 0.5168948 0.5498010 0.5910771 0.5998957 #> [848,] 0.25249506 0.3561176 0.3708418 0.3972535 0.5192776 0.5754905 0.6337435 #> [849,] 0.33507272 0.4597374 0.5069029 0.5285431 0.5372878 0.6001093 0.6130547 #> [850,] 0.17597548 0.3870165 0.5594309 0.6248637 0.6527461 0.6582989 0.6609336 #> [851,] 0.35295760 0.4630727 0.5233075 0.7415390 0.7522670 0.7865930 0.8056994 #> [852,] 0.31452038 0.5221992 0.5249327 0.6202899 0.6636930 0.6819790 0.7412960 #> [853,] 1.06356035 1.0736661 1.0825259 1.0962366 1.2298611 1.2869292 1.2916757 #> [854,] 0.96573690 1.1756648 1.1910772 1.2641272 1.3102195 1.3528209 1.3630859 #> [855,] 0.56388819 0.6959618 0.7599807 0.7856879 0.8119880 0.8185857 0.8418481 #> [856,] 0.36604246 0.4760026 0.4901277 0.4980567 0.6008453 0.6148009 0.6341309 #> [857,] 0.38214516 0.5011114 0.5160354 0.6922482 0.7856879 0.8467618 0.9077031 #> [858,] 0.59267044 0.6421611 0.6810884 0.7180966 0.7294620 0.7447693 0.7462063 #> [859,] 0.35829338 0.6209997 0.6765025 0.6980939 0.7291427 0.7454981 0.7605660 #> [860,] 0.35107872 0.4628321 0.4821902 0.5628116 0.5663818 0.5908624 0.6366756 #> [861,] 0.48666539 0.7148040 0.8065527 0.8443996 0.8621241 0.9785859 0.9832066 #> [862,] 0.36675682 0.4541553 0.6117694 0.7093871 0.7282900 0.7284700 0.7376806 #> [863,] 0.44102183 0.7006185 0.7156137 0.8526129 0.8576936 0.8646028 0.8719987 #> [864,] 0.25530869 0.5100279 0.6222739 0.7004509 0.7518164 0.7927098 0.8247377 #> [865,] 0.35107872 0.4073083 0.4727952 0.4797075 0.5887656 0.5978246 0.6011425 #> [866,] 0.20711353 0.2128669 0.2962470 0.4271706 0.4383742 0.5015894 0.5419253 #> [867,] 0.49074646 0.5218996 0.5402898 0.5666667 0.5851182 0.5921624 0.6165753 #> [868,] 0.38226491 0.4330564 0.4343819 0.4789346 0.4973497 0.6238698 0.6618646 #> [869,] 0.20119578 0.5274743 0.5727556 0.5795555 0.6107581 0.6321618 0.6506722 #> [870,] 0.34000005 0.5523149 0.5835526 0.5923980 0.6091881 0.6279973 0.6840545 #> [871,] 0.40824403 0.5111314 0.5128921 0.5586927 0.5952777 0.6600823 0.6684693 #> [872,] 0.41601699 0.4297828 0.4584733 0.4952032 0.5350863 0.5674021 0.5813493 #> [873,] 0.56979095 0.5843422 0.6339161 0.6882297 0.7151055 0.7386283 0.7734542 #> [874,] 0.65237655 0.6812469 0.7236839 0.7454451 0.8110938 0.8311178 0.9366361 #> [875,] 0.42184296 0.4502727 0.4504206 0.4829929 0.6192943 0.6400865 0.6467567 #> [876,] 0.33346801 0.3811909 0.4237522 0.4528098 0.4738702 0.5018809 0.5075120 #> [877,] 0.40802474 0.4975265 0.5176093 0.5593482 0.6650764 0.6870879 0.6948454 #> [878,] 0.67952156 0.6989833 0.7085963 0.7382393 0.7438888 0.8188065 0.8575808 #> [879,] 0.52657678 0.5467555 0.5681275 0.5865694 0.7248411 0.7300095 0.7356286 #> [880,] 0.21551060 0.3874699 0.3986045 0.4015032 0.4855791 0.5016559 0.5164665 #> [881,] 0.38577668 0.4169955 0.4356454 0.4523469 0.4742309 0.4904582 0.5262045 #> [882,] 0.56579449 0.5672378 0.5858722 0.5922482 0.6274278 0.6352016 0.6591535 #> [883,] 0.59284156 0.6624952 0.8139930 0.8322004 0.9945305 1.0395810 1.1360815 #> [884,] 0.35648607 0.4240218 0.4803121 0.4943045 0.5196530 0.5445057 0.6082788 #> [885,] 0.53011276 0.6101697 0.6716065 0.7966283 0.8490819 0.8710689 0.9369868 #> [886,] 0.68088921 0.8565482 0.8652427 0.9079105 0.9379848 0.9565635 0.9650364 #> [887,] 0.14706566 0.3842350 0.4784125 0.5095337 0.5187470 0.5218255 0.5586692 #> [888,] 0.44150652 1.0596932 1.2575503 1.3015299 1.3514940 1.3968001 1.4491465 #> [889,] 0.54753283 0.5853666 0.6010209 0.6263943 0.7082306 0.7190484 0.7254835 #> [890,] 0.51972920 0.5291272 0.5473492 0.5569819 0.5688599 0.6242691 0.6411027 #> [891,] 0.46645317 0.5035358 0.5147386 0.5518292 0.5550570 0.5851370 0.6037221 #> [892,] 0.43151667 0.4339481 0.4894152 0.4957784 0.5787041 0.5879778 0.5921479 #> [893,] 0.54620478 0.7160182 0.7341874 0.7608141 0.7634351 0.7855782 0.8240498 #> [894,] 0.26233166 0.3567649 0.4505434 0.5104084 0.5520312 0.6066468 0.6306655 #> [895,] 0.59990667 0.6372920 0.6738717 0.6780247 0.7835165 0.8307043 0.8752580 #> [896,] 0.44276402 0.4725503 0.5233075 0.5444489 0.6388097 0.7417904 0.7792963 #> [897,] 0.61366148 0.6422448 0.6548673 0.6628541 0.7373350 0.8047808 0.8112394 #> [898,] 1.13993621 1.2238417 1.2944929 1.4138356 1.4223752 1.5587307 1.5715766 #> [899,] 0.41880586 0.4297828 0.4559433 0.4602431 0.4657972 0.4769464 0.5013366 #> [900,] 0.44287676 0.5037232 0.6410085 0.7145558 0.8573915 0.8691765 0.8887671 #> [901,] 0.70951869 0.7227111 0.7655611 0.9049409 1.1103051 1.1436977 1.1671287 #> [902,] 0.17402163 0.2815989 0.3234938 0.4973108 0.5245936 0.5454948 0.5846517 #> [903,] 0.51490569 0.6955637 0.9402911 0.9713093 0.9992002 1.0926981 1.1465395 #> [904,] 0.16379892 0.4782800 0.4924158 0.5001598 0.5577912 0.5829803 0.6159372 #> [905,] 0.64480070 0.7569891 0.9153377 0.9287700 0.9294366 1.0306542 1.1035669 #> [906,] 0.26835689 0.3636692 0.3863603 0.4232646 0.4511470 0.4564327 0.4722120 #> [907,] 0.50805535 0.5762502 0.5965354 0.6315937 0.6394524 0.6589275 0.7062934 #> [908,] 0.38125326 0.4157032 0.4805979 0.5193114 0.5658166 0.5713880 0.5897818 #> [909,] 0.23253009 0.3691577 0.4070091 0.5777244 0.6250401 0.6715531 0.7055877 #> [910,] 0.38903127 0.4017785 0.4498610 0.4847487 0.4936195 0.5238938 0.5465308 #> [911,] 0.59440990 0.6989068 0.6995980 0.7242203 0.7455946 0.7480645 0.7795465 #> [912,] 0.29872606 0.4707987 0.4806273 0.4906690 0.6176954 0.6193783 0.6317046 #> [913,] 0.82461152 0.8591310 0.8731408 0.9164328 1.0578797 1.0834695 1.1278420 #> [914,] 0.67952156 0.9075966 0.9496742 0.9526848 1.0082962 1.0166035 1.1136135 #> [915,] 0.44171162 0.4928736 0.5012118 0.6263943 0.6324206 0.6440154 0.6997098 #> [916,] 0.29134698 0.3804423 0.4118020 0.4271706 0.5402898 0.5544855 0.5822610 #> [917,] 0.21910513 0.4578056 0.5066574 0.5338640 0.5411180 0.5637485 0.5884009 #> [918,] 0.80003296 0.8520190 0.8595147 0.9993802 1.0713708 1.0777662 1.0786178 #> [919,] 0.58522196 0.6448007 0.6532442 0.6655542 0.7092594 0.7155112 0.7459836 #> [920,] 0.36644829 0.3914287 0.4510571 0.6330261 0.6349593 0.6370497 0.6568489 #> [921,] 0.43640664 0.5015894 0.5326803 0.5509312 0.5567052 0.5991453 0.6388802 #> [922,] 0.50561703 0.5913410 0.5933649 0.7671347 0.7845608 0.9111170 0.9142453 #> [923,] 0.32803844 0.4338809 0.5207731 0.5649189 0.6001995 0.6045659 0.6381594 #> [924,] 0.35266986 0.3835013 0.5095863 0.5653659 0.5709348 0.5956112 0.6124346 #> [925,] 0.64979301 0.7646062 0.8164882 0.8276377 0.8368973 0.8421748 0.8771384 #> [926,] 0.23163085 0.2324044 0.4564653 0.4722120 0.4800280 0.5017003 0.5080255 #> [927,] 0.32922558 0.3934326 0.5095337 0.5121672 0.5194180 0.5259880 0.5288773 #> [928,] 0.79814003 0.9533309 1.0389936 1.1709526 1.1749098 1.1781384 1.2001907 #> [929,] 0.39267342 0.5086939 0.5477472 0.5520312 0.6469049 0.6839461 0.6940983 #> [930,] 0.29134698 0.3037289 0.3091650 0.3305156 0.4383742 0.4936195 0.5488952 #> [931,] 0.29872606 0.4131597 0.4795506 0.5665091 0.5997251 0.6136057 0.6261643 #> [932,] 0.42677834 0.4411643 0.5686196 0.6174766 0.6513026 0.6731724 0.7777964 #> [933,] 0.39206430 0.5012118 0.5589393 0.7086554 0.7179053 0.7529551 0.7558851 #> [934,] 0.40473610 0.7515701 0.8153097 1.0193305 1.1129096 1.1454856 1.2042223 #> [935,] 0.36401105 0.4486307 0.4561306 0.4769901 0.4926383 0.5446308 0.5739571 #> [936,] 0.83073988 0.8819863 1.0641047 1.0782516 1.0969681 1.1447650 1.2193489 #> [937,] 0.33591867 0.3466916 0.3873969 0.5139271 0.5550533 0.5812558 0.5829420 #> [938,] 0.04497083 0.5870115 0.5876477 0.5898424 0.6302899 0.6714246 0.6754866 #> [939,] 0.53443537 0.5655940 0.5792064 0.7051456 0.7086461 0.7153976 0.8056811 #> [940,] 0.88640969 1.1744302 1.2819133 1.3042470 1.3137897 1.4205330 1.4232624 #> [941,] 0.55774317 0.5631514 0.7511748 0.7522670 0.7901658 0.7962587 0.8298927 #> [942,] 0.35991731 0.3694933 0.4263777 0.5047480 0.5095882 0.6563266 0.7308714 #> [943,] 0.15739874 0.2999043 0.3060863 0.3792610 0.4267138 0.4280612 0.4582420 #> [944,] 0.46932208 0.5688599 0.6562525 0.7224982 0.7271169 0.7447693 0.7451598 #> [945,] 0.33816647 0.5002608 0.5497086 0.5832154 0.5839837 0.5987499 0.6531255 #> [946,] 0.65787838 0.9657369 0.9883514 1.0701032 1.1374992 1.1447650 1.2451739 #> [947,] 0.50561703 0.6266733 0.6672829 0.6746814 0.7006440 0.7663563 0.7955146 #> [948,] 0.58372918 0.7636551 0.8544198 0.9077602 0.9096541 0.9287029 0.9601853 #> [949,] 0.31354966 0.3466916 0.3770144 0.4981605 0.5835526 0.6080251 0.6379400 #> [950,] 0.26351243 0.3076413 0.4308061 0.4694526 0.5942378 0.6482221 0.6509040 #> [951,] 0.18607074 0.4871539 0.5036852 0.5189966 0.5335565 0.5425809 0.5678585 #> [952,] 0.37747986 0.4925961 0.5129245 0.5213931 0.5320780 0.6816017 0.7071654 #> [953,] 0.43434151 0.4537805 0.4782800 0.5454883 0.5546442 0.5803326 0.6204706 #> [954,] 0.54966756 0.5632566 0.5648903 0.6048902 0.6158045 0.6161821 0.6182972 #> [955,] 0.26783037 0.3555067 0.4599327 0.5020988 0.5503266 0.5731316 0.5883047 #> [956,] 0.29496717 0.3133610 0.5002608 0.5079815 0.5430447 0.5891322 0.5961361 #> [957,] 0.32511397 0.3617655 0.3835013 0.5075303 0.5512543 0.5762256 0.5950332 #> [958,] 0.45042063 0.4606442 0.4727952 0.5148810 0.5263058 0.5962643 0.6366756 #> [959,] 0.17370456 0.3076413 0.3279429 0.4852801 0.4944497 0.5166436 0.5732934 #> [960,] 0.35705946 0.4410636 0.4516268 0.5754905 0.6508299 0.6886539 0.8302582 #> [961,] 0.22813174 0.4001949 0.4003260 0.4113986 0.4827286 0.5053651 0.5508058 #> [962,] 0.79111290 0.8337189 0.9096423 0.9653220 0.9693479 1.0392179 1.0942301 #> [963,] 0.39206430 0.4417116 0.5213441 0.5853666 0.6000512 0.6460923 0.6988536 #> [964,] 0.49034069 0.5703550 0.6110425 0.6360577 0.6465991 0.6549053 0.6917995 #> [965,] 1.03453276 1.0574077 1.1989866 1.2401557 1.2488760 1.2885182 1.3480125 #> [966,] 0.47146486 0.5787526 0.7349696 0.7509420 0.8018245 0.8678513 0.8806975 #> [967,] 0.51784333 0.5368353 0.5718766 0.6238143 0.6354693 0.6705881 0.7379584 #> [968,] 0.68204381 0.9912498 1.0265272 1.2226328 1.3495796 1.3968221 1.4309520 #> [969,] 0.43544614 0.5446717 0.6081071 0.6550018 0.7121909 0.7462301 0.8047521 #> [970,] 0.67468144 0.8115864 0.8311740 0.8576452 0.8817724 0.8970071 0.9092255 #> [971,] 0.25530869 0.4229574 0.6714246 0.6844769 0.7469655 0.7584307 0.7610679 #> [972,] 0.41437972 0.6251328 0.6979654 0.7523700 0.8096269 0.8257021 0.8363396 #> [973,] 0.46068409 0.5099019 0.5453835 0.6073536 0.6115334 0.6494149 0.6606911 #> [974,] 0.50560586 0.5682722 0.5732926 0.7049280 0.7061830 0.7096315 0.7706464 #> [975,] 0.74582316 0.7609546 0.8046738 0.8240498 0.8241563 0.8281400 0.8356967 #> [976,] 0.33525273 0.3374291 0.3817311 0.4523469 0.4536032 0.4987909 0.6120957 #> [977,] 0.23008354 0.4954478 0.5046153 0.5614417 0.5616416 0.5828565 0.5882024 #> [978,] 0.49841470 0.6714878 0.7688076 0.7764392 0.8018245 0.8105688 0.8233114 #> [979,] 0.52526029 0.5345360 0.5447285 0.6395455 0.7031803 0.7376332 0.7483666 #> [980,] 0.24785601 0.3852941 0.3986045 0.5758994 0.6449115 0.6810014 0.7294620 #> [981,] 0.63397320 0.6394524 0.6664361 0.6701969 0.6802607 0.7022022 0.7301337 #> [982,] 0.45580842 0.5715196 0.5749911 0.5859775 0.6681737 0.6822274 0.7166143 #> [983,] 0.50101698 0.5376430 0.5929588 0.7114757 0.7489781 0.7692118 0.7773591 #> [984,] 0.19780375 0.4136345 0.4339700 0.4415019 0.4979181 0.6075412 0.6091881 #> [985,] 0.63176827 0.7092594 0.8033928 0.8673370 0.9140608 0.9153377 1.0441789 #> [986,] 0.36491652 0.5273441 0.6032080 0.6253015 0.7454451 0.7729599 0.7883097 #> [987,] 0.44276402 0.6742267 0.7026799 0.7415390 0.7864823 0.8683901 0.9982356 #> [988,] 0.44525949 0.8690679 0.8875176 0.9267411 1.0588732 1.0614233 1.0699795 #> [989,] 0.46932208 0.5569819 0.5886034 0.6532035 0.6885028 0.7242253 0.7372330 #> [990,] 0.27366290 0.4653143 0.6021361 0.6025242 0.6380806 0.6693098 0.6744915 #> [991,] 0.20711353 0.2356696 0.3305156 0.3670160 0.4118020 0.4364066 0.5464797 #> [992,] 0.37440774 0.7086461 0.7418901 0.7442849 0.7787907 0.7836854 0.7855291 #> [993,] 1.04193676 1.0992335 1.1503974 1.1741563 1.2870854 1.3807208 1.4045472 #> [994,] 0.45879487 0.5301128 0.5747591 0.6214295 0.7605660 0.8673346 0.8709480 #> [995,] 0.48610978 0.5967973 0.6007497 0.6189824 0.6356349 0.7741783 0.7854985 #> [996,] 0.40589754 0.5606936 0.6975627 0.7258812 0.7362571 0.7638539 0.7670559 #> [997,] 0.95463549 1.1054185 1.1069747 1.1304412 1.2374547 1.4004799 1.4737834 #> [998,] 0.85278886 0.9860807 1.0845824 1.2091892 1.2122146 1.2567472 1.3275814 #> [999,] 0.33373156 0.3560558 0.6230229 0.6408758 0.6742086 0.7076926 0.8252286 #> [1000,] 0.12432811 0.4822970 0.7518164 0.7584307 0.8035997 0.8186917 0.8575808 #> [,8] [,9] [,10] #> [1,] 1.0220903 1.0506990 1.0647866 #> [2,] 1.0159804 1.0628464 1.0808911 #> [3,] 0.6555239 0.6691817 0.6939634 #> [4,] 0.8153295 0.8323242 0.8576244 #> [5,] 0.7755287 0.8105826 0.8419308 #> [6,] 0.9605567 0.9751774 0.9853637 #> [7,] 0.6006658 0.6082166 0.6575454 #> [8,] 0.9434125 0.9569375 0.9573127 #> [9,] 0.8578887 0.8690163 0.9022197 #> [10,] 1.1059807 1.1271065 1.2717197 #> [11,] 0.9275638 0.9374665 0.9629951 #> [12,] 0.8683344 0.8833672 0.8889993 #> [13,] 0.9806743 0.9860205 1.0262920 #> [14,] 0.8045830 0.8120747 0.8122041 #> [15,] 1.1613124 1.1683864 1.1694879 #> [16,] 0.7096127 0.7521758 0.7795502 #> [17,] 0.5288156 0.5698295 0.5967761 #> [18,] 0.6856766 0.7122816 0.7510326 #> [19,] 1.1743609 1.2663257 1.2730675 #> [20,] 0.5036007 0.5465379 0.5710962 #> [21,] 0.8614368 0.8647220 0.9100972 #> [22,] 0.4877218 0.5036007 0.5085941 #> [23,] 0.8889993 0.9078457 0.9205416 #> [24,] 0.6778038 0.6823220 0.6916917 #> [25,] 0.7951561 0.9031122 0.9116083 #> [26,] 0.7183941 0.7444101 0.7563863 #> [27,] 0.5881498 0.6069905 0.6132131 #> [28,] 0.6360646 0.6735662 0.7085963 #> [29,] 0.6846481 0.6876163 0.7291948 #> [30,] 1.0284256 1.0312844 1.0480669 #> [31,] 0.7609546 0.7702795 0.8113416 #> [32,] 0.5915577 0.6097883 0.6270160 #> [33,] 0.7015258 0.7143286 0.7274967 #> [34,] 0.8280008 0.8410305 0.8838150 #> [35,] 0.6646158 0.6752053 0.6759780 #> [36,] 0.5115982 0.5658013 0.6014117 #> [37,] 0.9767749 0.9812302 0.9813567 #> [38,] 0.6544361 0.6599681 0.6723698 #> [39,] 0.5575974 0.5998258 0.6160434 #> [40,] 1.0114281 1.0269782 1.0689315 #> [41,] 1.0221192 1.0405471 1.0802871 #> [42,] 0.5539836 0.5670290 0.5742499 #> [43,] 1.1136135 1.1143924 1.1565844 #> [44,] 1.2811282 1.3083940 1.3553939 #> [45,] 1.7795953 1.7843617 1.8282038 #> [46,] 1.3636859 1.3862933 1.4477444 #> [47,] 1.1656563 1.1768936 1.2009966 #> [48,] 0.6516211 0.6789018 0.6822214 #> [49,] 0.6571013 0.6922736 0.7112233 #> [50,] 0.9127270 0.9160051 0.9327613 #> [51,] 0.7005399 0.7818339 0.7852307 #> [52,] 0.6050779 0.6813854 0.7484952 #> [53,] 0.9623091 0.9637074 0.9640488 #> [54,] 0.9004985 0.9043681 0.9113352 #> [55,] 0.5764813 0.5861439 0.6036196 #> [56,] 0.9721654 0.9896678 1.0213423 #> [57,] 1.2183686 1.2607060 1.2665570 #> [58,] 0.6979966 0.7376806 0.7692197 #> [59,] 0.9177779 0.9393185 0.9394270 #> [60,] 0.7310089 0.7778650 0.8020409 #> [61,] 0.6713180 0.7570872 0.7576333 #> [62,] 0.7238832 0.7438888 0.7741449 #> [63,] 0.5693800 0.5731336 0.6171722 #> [64,] 0.8134684 0.8155012 0.8486851 #> [65,] 0.6314431 0.6620528 0.6695307 #> [66,] 0.7487702 0.7510326 0.7665815 #> [67,] 0.7037280 0.7139507 0.7341633 #> [68,] 0.6670795 0.7039944 0.7103590 #> [69,] 0.5590320 0.5826087 0.6090574 #> [70,] 1.0705625 1.0839160 1.1077849 #> [71,] 0.7925975 0.8267848 0.8600750 #> [72,] 0.4665478 0.4980567 0.5050926 #> [73,] 0.6832122 0.7127399 0.7301544 #> [74,] 0.6706489 0.7036290 0.7122281 #> [75,] 0.9944253 1.0938387 1.1284889 #> [76,] 0.7341633 0.7386602 0.7543114 #> [77,] 0.7444101 0.7878845 0.8116886 #> [78,] 0.7183941 0.7242253 0.7437343 #> [79,] 0.6669422 0.6838606 0.7015809 #> [80,] 1.7852066 1.9035128 1.9593996 #> [81,] 1.2074716 1.2937300 1.3053031 #> [82,] 0.8501118 0.8510822 0.8887671 #> [83,] 0.6912846 0.7224415 0.7439148 #> [84,] 1.2312541 1.2640422 1.2865493 #> [85,] 1.0518943 1.0554476 1.0708872 #> [86,] 0.6896308 0.7412201 0.7649269 #> [87,] 0.7517473 0.7538065 0.7627878 #> [88,] 0.6607499 0.6651778 0.6755015 #> [89,] 0.8049486 0.8602626 0.9045392 #> [90,] 0.7128878 0.7728106 0.8510530 #> [91,] 0.7852307 0.7854566 0.8330092 #> [92,] 1.7256766 1.7263170 1.7344674 #> [93,] 0.6820320 0.6995980 0.7385645 #> [94,] 0.9136147 1.0265890 1.0361744 #> [95,] 0.6844769 0.6987678 0.7313320 #> [96,] 1.1858038 1.2298611 1.2433319 #> [97,] 0.9149957 1.1553283 1.1715203 #> [98,] 0.6350549 0.6468977 0.6569167 #> [99,] 0.6095392 0.6515879 0.7125169 #> [100,] 0.7548809 0.7640128 0.7852657 #> [101,] 0.8339963 0.8746430 0.8802706 #> [102,] 0.8803966 0.9150893 0.9655447 #> [103,] 1.3828986 1.3844780 1.4077336 #> [104,] 0.6973268 0.7098145 0.7467064 #> [105,] 0.5697776 0.6048633 0.6226070 #> [106,] 1.0555791 1.0815560 1.0834790 #> [107,] 0.5333565 0.5858786 0.5920383 #> [108,] 0.7323111 0.7431155 0.7739945 #> [109,] 0.6553638 0.7204231 0.7339759 #> [110,] 1.4211427 1.5612817 1.5691502 #> [111,] 0.8804201 0.8804967 0.9133936 #> [112,] 1.8300943 1.8462969 1.8554780 #> [113,] 0.6681737 0.7201891 0.7234314 #> [114,] 0.9264201 0.9446354 0.9898591 #> [115,] 1.0305274 1.0873706 1.1108897 #> [116,] 0.7776361 0.8285550 0.8445627 #> [117,] 0.8105688 0.8334105 0.8344281 #> [118,] 0.5251669 0.5258499 0.5761898 #> [119,] 0.6680991 0.6735608 0.6832122 #> [120,] 0.5456871 0.5893277 0.5902274 #> [121,] 0.7243613 0.7391918 0.7430023 #> [122,] 0.6011233 0.6619225 0.7385597 #> [123,] 0.6637908 0.6920283 0.7221301 #> [124,] 0.6622352 0.6793424 0.6797925 #> [125,] 1.4978961 1.5240171 1.5591992 #> [126,] 0.8145714 0.8294044 0.8933134 #> [127,] 0.6213495 0.6382426 0.6421234 #> [128,] 0.6497090 0.6654464 0.6723698 #> [129,] 0.7529188 0.7531056 0.8286505 #> [130,] 0.8415089 0.8776250 0.8833272 #> [131,] 0.9066364 0.9705027 0.9867259 #> [132,] 0.6082166 0.6088717 0.6183626 #> [133,] 0.5901790 0.5906260 0.6049659 #> [134,] 0.7160267 0.7525095 0.7664416 #> [135,] 0.5871855 0.5888006 0.5958864 #> [136,] 0.5596206 0.5888128 0.5969488 #> [137,] 0.8632859 0.8702170 0.9241328 #> [138,] 0.6115767 0.6173324 0.6180640 #> [139,] 0.8219311 0.8441528 0.8611271 #> [140,] 0.7012192 0.7072508 0.7099819 #> [141,] 0.5743078 0.6728713 0.6747774 #> [142,] 0.5263058 0.5891322 0.6317845 #> [143,] 0.6099109 0.6164456 0.6347685 #> [144,] 0.8930163 0.9479502 0.9620478 #> [145,] 1.0256061 1.0524403 1.0587543 #> [146,] 0.5566784 0.5713538 0.5788591 #> [147,] 0.5543984 0.5705758 0.5721490 #> [148,] 0.9795626 1.0067947 1.0458673 #> [149,] 0.8637182 0.9008321 0.9043360 #> [150,] 0.5414789 0.5788055 0.5862132 #> [151,] 0.5853790 0.5875525 0.5915577 #> [152,] 0.7714320 0.7798431 0.7872667 #> [153,] 0.9014666 0.9705633 0.9777831 #> [154,] 1.0157654 1.0779330 1.0913184 #> [155,] 0.9316542 0.9658182 0.9670866 #> [156,] 0.6073392 0.6131069 0.6592461 #> [157,] 1.1114114 1.1454856 1.1555352 #> [158,] 0.6052577 0.6307465 0.7290489 #> [159,] 0.6285511 0.6381594 0.6443042 #> [160,] 0.7880060 0.8260032 0.8534824 #> [161,] 0.6633763 0.6657410 0.6797596 #> [162,] 0.8476128 0.8624028 0.8771263 #> [163,] 0.7845580 0.8266930 0.8318053 #> [164,] 0.7392637 0.7512895 0.8111425 #> [165,] 1.2922349 1.3063313 1.3084353 #> [166,] 0.9128304 0.9454870 1.0354152 #> [167,] 0.6104109 0.6456856 0.6493743 #> [168,] 0.6152847 0.6210218 0.6397210 #> [169,] 0.9241328 0.9419218 1.1478319 #> [170,] 0.6543718 0.6672829 0.7638711 #> [171,] 0.6459038 0.6709559 0.6813143 #> [172,] 0.6297882 0.6436494 0.6455486 #> [173,] 1.1226844 1.1330129 1.2040133 #> [174,] 1.0910784 1.1286312 1.1740047 #> [175,] 1.1304412 1.1901069 1.1943255 #> [176,] 0.7143797 0.7184307 0.7481196 #> [177,] 0.8513361 0.8603842 0.8674875 #> [178,] 0.6705592 0.6735620 0.6778038 #> [179,] 1.0194385 1.0333731 1.0398635 #> [180,] 0.6132131 0.6198758 0.6648093 #> [181,] 0.6900043 0.7318831 0.7641128 #> [182,] 0.6079576 0.6144660 0.6170095 #> [183,] 0.5681010 0.5703633 0.6560972 #> [184,] 2.0221354 2.0532234 2.0724232 #> [185,] 1.0225519 1.0345328 1.0356459 #> [186,] 0.5291272 0.5727759 0.5803860 #> [187,] 0.8187429 0.8696613 0.8934775 #> [188,] 0.8677932 0.8900682 0.9412017 #> [189,] 0.7229251 0.7349696 0.7951147 #> [190,] 0.5737622 0.5920383 0.6537043 #> [191,] 1.3769688 1.3968075 1.4065190 #> [192,] 0.6929701 0.6971881 0.6983515 #> [193,] 0.5998258 0.6034515 0.6136679 #> [194,] 0.5429316 0.5570000 0.5602252 #> [195,] 1.0188878 1.0793454 1.0858714 #> [196,] 0.6269043 0.6810870 0.6868403 #> [197,] 1.0075614 1.0431346 1.0821745 #> [198,] 1.3594604 1.4146364 1.4263432 #> [199,] 0.7430240 0.7511748 0.8771458 #> [200,] 0.7863389 0.8066169 0.8143462 #> [201,] 1.8213842 1.8912685 1.9962653 #> [202,] 1.4548162 1.4980519 1.4988507 #> [203,] 1.0292585 1.0564403 1.0715925 #> [204,] 0.5571402 0.6248315 0.6380658 #> [205,] 0.6196099 0.6290472 0.6292636 #> [206,] 0.6378416 0.6567339 0.6771992 #> [207,] 1.6953200 1.7081563 1.7206302 #> [208,] 0.7497781 0.7546189 0.7557900 #> [209,] 1.5233936 1.5288099 1.5962986 #> [210,] 0.6239921 0.6247975 0.6265817 #> [211,] 0.7915838 0.8227246 0.8271605 #> [212,] 0.6133590 0.6334791 0.6504994 #> [213,] 0.5879778 0.5889366 0.6110425 #> [214,] 0.7508885 0.8324732 0.8941450 #> [215,] 0.7468176 0.7739945 0.8493845 #> [216,] 0.6418896 0.6420441 0.6478635 #> [217,] 0.6582989 0.6735662 0.7013804 #> [218,] 0.6180513 0.6354609 0.6562407 #> [219,] 0.7390813 0.7445928 0.7881615 #> [220,] 0.8158097 0.8690679 0.8797843 #> [221,] 0.7647663 0.7783410 0.7837751 #> [222,] 1.3820083 1.3966081 1.4695749 #> [223,] 0.5455742 0.5658013 0.6034515 #> [224,] 1.1583985 1.2959913 1.3045000 #> [225,] 0.9930158 1.0073708 1.0326790 #> [226,] 0.7177860 0.7245656 0.7300216 #> [227,] 0.6152128 0.6768162 0.7500130 #> [228,] 0.8081437 0.8210160 0.8341311 #> [229,] 0.8173428 0.8390229 0.8620620 #> [230,] 0.8007657 0.8012049 0.8112083 #> [231,] 0.9177369 0.9279086 0.9315429 #> [232,] 0.7094825 0.7412201 0.7487212 #> [233,] 0.8801390 0.8919684 0.9048960 #> [234,] 0.8240324 0.8333244 0.8385498 #> [235,] 0.7525095 0.7695044 0.7708732 #> [236,] 0.7133467 0.7192160 0.7198735 #> [237,] 0.5881498 0.5997251 0.6016000 #> [238,] 0.8105593 0.9042581 0.9140608 #> [239,] 1.0796850 1.0905094 1.0981334 #> [240,] 0.8508098 0.8635917 0.8980599 #> [241,] 0.6227925 0.6280889 0.6417925 #> [242,] 0.7067708 0.7184563 0.7377581 #> [243,] 0.6115334 0.6430333 0.6456477 #> [244,] 1.4331381 1.4607150 1.5316756 #> [245,] 0.8501881 0.8563041 0.8590798 #> [246,] 0.7118094 0.7147159 0.7300108 #> [247,] 1.1565844 1.1967436 1.2031595 #> [248,] 0.7812252 0.7929947 0.8109086 #> [249,] 0.6637959 0.6669140 0.6801039 #> [250,] 2.0397549 2.0483508 2.0556776 #> [251,] 0.7355141 0.7445453 0.7960024 #> [252,] 0.6012655 0.6101469 0.6277827 #> [253,] 1.1820783 1.1844585 1.1852437 #> [254,] 0.7059918 0.7131183 0.7172770 #> [255,] 1.1761711 1.1954909 1.2139322 #> [256,] 0.8081437 0.8302582 0.8359470 #> [257,] 0.4847586 0.4983993 0.5328842 #> [258,] 1.3630821 1.3645951 1.4483778 #> [259,] 1.6683581 1.6846586 1.6916542 #> [260,] 0.6327125 0.6592461 0.6797594 #> [261,] 0.5816681 0.6651170 0.6719349 #> [262,] 1.0199230 1.0306691 1.0465887 #> [263,] 0.7328701 0.7366519 0.7488794 #> [264,] 0.6567246 0.6632021 0.6666177 #> [265,] 0.8342606 0.8833272 0.8873127 #> [266,] 1.2187290 1.2362250 1.3522488 #> [267,] 0.7023748 0.7102512 0.7225791 #> [268,] 1.0880982 1.0941461 1.0960332 #> [269,] 0.8627002 0.8743276 0.8751708 #> [270,] 0.6198851 0.6314126 0.6475378 #> [271,] 0.5674021 0.5693480 0.5928785 #> [272,] 0.9341928 0.9833019 0.9973478 #> [273,] 0.8096329 0.8292255 0.8555027 #> [274,] 0.7891404 0.8243659 0.8278745 #> [275,] 0.9317763 0.9409960 0.9466711 #> [276,] 1.3790276 1.3802968 1.4076604 #> [277,] 0.6844823 0.7143535 0.7407388 #> [278,] 0.7100069 0.7488184 0.7600098 #> [279,] 1.1506401 1.1908013 1.2663376 #> [280,] 0.7630830 0.7659436 0.7761110 #> [281,] 0.8431615 0.8637182 0.9226522 #> [282,] 0.9590741 1.0251275 1.0283219 #> [283,] 0.7033122 0.7052669 0.7377581 #> [284,] 0.8044038 0.8099410 0.8531779 #> [285,] 0.4574371 0.4633042 0.5149583 #> [286,] 1.2142199 1.2325390 1.2486723 #> [287,] 0.7117716 0.7845475 0.8023944 #> [288,] 0.7105949 0.7146610 0.7506698 #> [289,] 0.9633728 0.9659964 1.0147951 #> [290,] 0.6066468 0.6082550 0.6091098 #> [291,] 1.0277905 1.0338277 1.0357108 #> [292,] 0.5000964 0.5028235 0.5161832 #> [293,] 0.8557396 0.8838206 0.9363732 #> [294,] 0.7162112 0.7261345 0.7368908 #> [295,] 2.1098008 2.1263382 2.1662556 #> [296,] 0.9174849 0.9391519 0.9513195 #> [297,] 0.5151876 0.5552198 0.6246004 #> [298,] 1.2236423 1.2574848 1.2584744 #> [299,] 0.8009335 0.8341694 0.8629700 #> [300,] 0.6411617 0.6742086 0.6789019 #> [301,] 1.0222484 1.0651726 1.0824499 #> [302,] 1.4780679 1.4931978 1.4998941 #> [303,] 1.7793757 1.8106507 1.8345273 #> [304,] 0.8031539 0.8513275 0.8791171 #> [305,] 1.2292597 1.2357294 1.2826214 #> [306,] 0.6505470 0.6771992 0.6788451 #> [307,] 1.1818239 1.1945705 1.2083771 #> [308,] 0.6593763 0.6723912 0.6867334 #> [309,] 0.4700809 0.4816955 0.4866329 #> [310,] 0.7201891 0.7371411 0.7627039 #> [311,] 0.9326344 1.0005127 1.0038540 #> [312,] 0.7288453 0.7482867 0.7527413 #> [313,] 0.6792825 0.7098145 0.7107325 #> [314,] 0.7188043 0.7305566 0.7341665 #> [315,] 0.5343740 0.5371986 0.6196099 #> [316,] 1.0993493 1.1180550 1.1213501 #> [317,] 0.5166436 0.5418754 0.5499564 #> [318,] 1.4359835 1.4469369 1.4834774 #> [319,] 0.8046689 0.8057893 0.8264896 #> [320,] 0.5838682 0.5972898 0.6249691 #> [321,] 0.6478044 0.6480226 0.6585143 #> [322,] 0.7356749 0.7797624 0.8027854 #> [323,] 1.0977021 1.1011990 1.1069747 #> [324,] 0.6170095 0.6424517 0.6700281 #> [325,] 0.7829631 0.7942367 0.7964902 #> [326,] 0.8682310 0.9106875 1.0473610 #> [327,] 0.8372628 0.8838178 0.9024265 #> [328,] 1.4275131 1.5188548 1.5214910 #> [329,] 1.2825670 1.3081612 1.3743889 #> [330,] 0.6444889 0.6908379 0.6954510 #> [331,] 0.6772921 0.7085888 0.7115589 #> [332,] 0.6055590 0.6319255 0.6535943 #> [333,] 0.6089178 0.6171698 0.6420984 #> [334,] 0.7013259 0.7197396 0.7201731 #> [335,] 0.7554972 0.7570872 0.7621492 #> [336,] 0.6903874 0.7099542 0.7263274 #> [337,] 1.4731754 1.4770385 1.4885976 #> [338,] 1.1171090 1.1343383 1.1866288 #> [339,] 0.7904959 0.8211896 0.8407411 #> [340,] 0.6571768 0.7153976 0.7344173 #> [341,] 0.6206803 0.7202563 0.7536813 #> [342,] 0.6031906 0.6348537 0.6648093 #> [343,] 1.0684635 1.1281765 1.2701446 #> [344,] 0.6379043 0.6522162 0.6533374 #> [345,] 0.5810245 0.5867504 0.5913260 #> [346,] 0.5046153 0.5277485 0.5910771 #> [347,] 1.0505311 1.0576989 1.0992925 #> [348,] 0.6311623 0.6560215 0.6822626 #> [349,] 1.0165439 1.1333100 1.1340170 #> [350,] 0.5813493 0.5950332 0.6079576 #> [351,] 0.7300095 0.7602339 0.7629245 #> [352,] 1.1053641 1.1180550 1.1312491 #> [353,] 1.1837643 1.1988522 1.2302067 #> [354,] 0.9533387 0.9564164 0.9649450 #> [355,] 0.9164328 0.9176535 1.0082143 #> [356,] 0.7133467 0.7360781 0.7797316 #> [357,] 0.7177315 0.7849657 0.7935783 #> [358,] 0.7369999 0.7680742 0.7718746 #> [359,] 0.6829276 0.7062740 0.7828293 #> [360,] 0.6238698 0.6319799 0.6712264 #> [361,] 0.8012627 0.8229591 0.8455997 #> [362,] 0.6160434 0.6178926 0.6240917 #> [363,] 0.9001376 0.9136147 0.9147746 #> [364,] 1.5932991 1.6157023 1.6168954 #> [365,] 0.8077789 0.8595672 0.8851544 #> [366,] 0.8105764 0.9519390 0.9627244 #> [367,] 0.8791882 0.8820581 0.9409960 #> [368,] 1.1542080 1.2095336 1.2174031 #> [369,] 1.2001517 1.2151301 1.2367852 #> [370,] 0.7215688 0.7504660 0.7612260 #> [371,] 0.5766792 0.6133128 0.6378261 #> [372,] 0.5124364 0.5342045 0.5436498 #> [373,] 0.7111043 0.7964800 0.8074443 #> [374,] 0.7735197 0.7903442 0.8573493 #> [375,] 0.6717405 0.6987919 0.7056988 #> [376,] 1.1960716 1.2061600 1.2549521 #> [377,] 0.9120929 0.9420186 1.0101190 #> [378,] 0.9728332 0.9873418 0.9990933 #> [379,] 0.7385341 0.7460797 0.7479200 #> [380,] 0.6228103 0.6252471 0.6378261 #> [381,] 0.6451651 0.6466886 0.6497090 #> [382,] 0.6164454 0.6313534 0.6431640 #> [383,] 1.7215563 1.7413460 1.7505421 #> [384,] 1.4146987 1.4767661 1.5423798 #> [385,] 1.2790766 1.3318983 1.3342449 #> [386,] 0.5253829 0.5374429 0.5478135 #> [387,] 0.6767031 0.6980473 0.7102383 #> [388,] 0.6058627 0.6468854 0.6582067 #> [389,] 0.7614894 0.7666488 0.7872667 #> [390,] 0.7636327 0.7665815 0.7671899 #> [391,] 0.7542396 0.7742806 0.8188065 #> [392,] 0.8449039 0.8477306 0.8554640 #> [393,] 0.6606911 0.6652344 0.7106932 #> [394,] 1.1621956 1.1644541 1.1986978 #> [395,] 1.1677274 1.2597720 1.2879303 #> [396,] 0.9441428 0.9562852 0.9670866 #> [397,] 0.6461569 0.6517059 0.6642091 #> [398,] 0.8534824 0.8593124 0.9181514 #> [399,] 0.6507469 0.6531001 0.6857016 #> [400,] 0.7627499 0.8010999 0.8162060 #> [401,] 0.7167615 0.7590209 0.7663757 #> [402,] 0.9385822 0.9546533 0.9735454 #> [403,] 0.9174849 0.9597572 0.9760358 #> [404,] 0.7314190 0.7399096 0.7420626 #> [405,] 0.6440154 0.6769841 0.6813038 #> [406,] 0.9042870 0.9199815 0.9366138 #> [407,] 0.5143304 0.5230954 0.5826878 #> [408,] 0.7751121 0.8150130 0.8160969 #> [409,] 0.6680155 0.6764425 0.6882297 #> [410,] 1.0683585 1.0714703 1.1421249 #> [411,] 1.2147471 1.2663894 1.2727458 #> [412,] 1.1390125 1.1560421 1.1667422 #> [413,] 0.5777682 0.5841814 0.5867504 #> [414,] 1.0770330 1.1461043 1.1826790 #> [415,] 0.7190763 0.7826114 0.8346353 #> [416,] 1.2734965 1.3180883 1.3209671 #> [417,] 0.8609763 0.8951846 0.9389049 #> [418,] 0.6299388 0.6377190 0.6472083 #> [419,] 1.3202222 1.3234820 1.3480002 #> [420,] 0.7911226 0.8918625 0.9113162 #> [421,] 1.5099517 1.5879799 1.6275022 #> [422,] 0.8648793 0.8801506 0.8962246 #> [423,] 0.6058663 0.6082788 0.6314126 #> [424,] 1.3163879 1.3328708 1.3662731 #> [425,] 0.7955620 0.7975097 0.8114964 #> [426,] 0.6418109 0.6934614 0.7022022 #> [427,] 1.8529715 1.9275538 1.9412448 #> [428,] 0.8037321 0.8180881 0.8260032 #> [429,] 0.8748160 0.9124285 0.9167675 #> [430,] 1.4781769 1.5195083 1.5757886 #> [431,] 0.7765806 0.7796118 0.7852608 #> [432,] 0.9012644 0.9333909 0.9559555 #> [433,] 0.6599585 0.6928193 0.6981878 #> [434,] 0.7897385 0.8571447 0.8957822 #> [435,] 1.1936272 1.2030167 1.2407876 #> [436,] 0.7833691 0.8614368 0.8945441 #> [437,] 0.5384173 0.5829803 0.5841615 #> [438,] 0.5571568 0.6174222 0.6316854 #> [439,] 0.6609872 0.6613144 0.7115270 #> [440,] 0.8451830 0.8557046 0.8650737 #> [441,] 0.5728784 0.5745183 0.5839120 #> [442,] 1.0467757 1.0550746 1.0574801 #> [443,] 0.6892405 0.7332718 0.7333728 #> [444,] 0.5805887 0.6012403 0.6268490 #> [445,] 0.7912182 0.8435330 0.8641570 #> [446,] 0.7852608 0.8762724 0.9075501 #> [447,] 1.3676645 1.3747721 1.4162996 #> [448,] 1.2725432 1.2803105 1.2833201 #> [449,] 1.1295200 1.2154808 1.2187803 #> [450,] 0.9210998 0.9405099 1.0160491 #> [451,] 0.6643364 0.6665172 0.6964813 #> [452,] 0.6314404 0.6404495 0.6684119 #> [453,] 0.5370750 0.5614417 0.6017285 #> [454,] 0.9114307 0.9131999 0.9218992 #> [455,] 0.5167141 0.5323207 0.5668559 #> [456,] 0.7683081 0.7733977 0.8122535 #> [457,] 0.5024490 0.5412091 0.5569929 #> [458,] 1.0857416 1.1236817 1.1830759 #> [459,] 0.5728755 0.5922088 0.5931377 #> [460,] 0.5689447 0.5754628 0.5817520 #> [461,] 0.6513352 0.7065268 0.7117218 #> [462,] 0.5501011 0.5596206 0.5649255 #> [463,] 0.6391369 0.6395256 0.6412760 #> [464,] 0.8306194 0.8751047 0.8794120 #> [465,] 0.7393981 0.8423412 0.8437964 #> [466,] 1.8836910 1.9095367 1.9135119 #> [467,] 1.1950243 1.2333716 1.2435436 #> [468,] 0.9667808 0.9857054 0.9916732 #> [469,] 0.9461251 0.9463880 0.9540307 #> [470,] 0.5763679 0.5815961 0.5851288 #> [471,] 0.8321809 0.8519922 0.8573915 #> [472,] 0.6797594 0.6974174 0.7011072 #> [473,] 0.6045314 0.6086948 0.6161821 #> [474,] 0.6178535 0.6265817 0.6299388 #> [475,] 1.4563943 1.4668533 1.4956531 #> [476,] 0.4759897 0.5278541 0.5323207 #> [477,] 0.5904797 0.5921010 0.6182972 #> [478,] 0.7475949 0.7595601 0.7750158 #> [479,] 0.8676342 0.9958579 1.0082914 #> [480,] 0.9988466 1.0379648 1.0873266 #> [481,] 0.6811404 0.7125915 0.7438543 #> [482,] 0.7233252 0.7258189 0.7496445 #> [483,] 0.8908971 0.9220001 0.9393187 #> [484,] 0.8402377 0.8782716 0.8924074 #> [485,] 0.8752551 0.9683506 0.9777102 #> [486,] 0.9860885 1.0097891 1.0138331 #> [487,] 0.6715531 0.6862316 0.6922482 #> [488,] 1.2949801 1.3199758 1.3314084 #> [489,] 1.0834790 1.1111410 1.1434177 #> [490,] 0.7012192 0.7035310 0.7197476 #> [491,] 0.8863247 0.9151983 0.9861853 #> [492,] 1.7159364 1.7248158 1.7670427 #> [493,] 0.8374759 0.9264355 0.9493804 #> [494,] 1.3369391 1.3572066 1.3602157 #> [495,] 1.3663135 1.3747328 1.3915746 #> [496,] 0.9388876 0.9787855 0.9818573 #> [497,] 0.9633728 0.9716273 1.0105809 #> [498,] 1.2406684 1.2583409 1.3507659 #> [499,] 0.8820550 1.0146170 1.0150288 #> [500,] 0.9055686 0.9680445 0.9941518 #> [501,] 2.4145756 2.4420108 2.4464436 #> [502,] 0.5520771 0.5745183 0.5826584 #> [503,] 0.8337882 0.8505912 0.8526129 #> [504,] 0.6882318 0.6964999 0.7367348 #> [505,] 0.9813650 0.9874835 1.0472607 #> [506,] 0.6814462 0.7060224 0.7076732 #> [507,] 0.4816955 0.4847935 0.4864367 #> [508,] 0.5691907 0.5770479 0.5780491 #> [509,] 1.6335442 1.6385179 1.6535956 #> [510,] 1.3528960 1.3563794 1.3611355 #> [511,] 0.6759780 0.7094116 0.7247117 #> [512,] 1.2926636 1.3023587 1.3749284 #> [513,] 1.5299030 1.5399956 1.6611706 #> [514,] 0.6710257 0.6810884 0.6950817 #> [515,] 0.7584446 0.8062355 0.8130625 #> [516,] 0.8178128 0.8178424 0.8205612 #> [517,] 0.8591310 0.8919684 0.9240162 #> [518,] 0.5025708 0.5418103 0.5693054 #> [519,] 0.6895236 0.6973202 0.7059582 #> [520,] 0.5773875 0.5787767 0.6029737 #> [521,] 0.7146610 0.7643505 0.7878777 #> [522,] 0.6803957 0.6811404 0.7170140 #> [523,] 0.6578455 0.6659730 0.6749460 #> [524,] 0.7388219 0.7477676 0.7486448 #> [525,] 0.9893545 1.0141225 1.0483922 #> [526,] 0.6767827 0.7060023 0.7315015 #> [527,] 1.0208764 1.0257840 1.0330603 #> [528,] 0.7171179 0.7334094 0.7448059 #> [529,] 0.8668125 0.9510885 0.9550453 #> [530,] 1.3518969 1.3648092 1.3849989 #> [531,] 0.7892308 0.8003594 0.8356967 #> [532,] 0.8611332 0.8613864 0.9164915 #> [533,] 0.6250401 0.6367535 0.6517178 #> [534,] 0.7000426 0.7177860 0.7378278 #> [535,] 0.7863389 0.8349013 0.8376880 #> [536,] 0.9794036 1.0058872 1.0216113 #> [537,] 0.8364121 0.8611271 0.8800067 #> [538,] 0.5635180 0.6092181 0.6218384 #> [539,] 0.8904440 0.8921551 0.8932878 #> [540,] 0.6639791 0.7167615 0.7429688 #> [541,] 0.6532183 0.6622352 0.6784005 #> [542,] 1.3483684 1.3507839 1.3894962 #> [543,] 0.8085228 0.8488790 0.8680777 #> [544,] 0.5340517 0.5593715 0.6246004 #> [545,] 0.5689344 0.6011425 0.6378416 #> [546,] 0.6056311 0.6089128 0.6109068 #> [547,] 0.7942207 0.8298927 0.8602639 #> [548,] 0.9145482 0.9155776 0.9157779 #> [549,] 0.7833891 0.7929947 0.7977212 #> [550,] 0.8923763 0.8936716 0.8943902 #> [551,] 0.6252989 0.6302890 0.6325989 #> [552,] 0.6213495 0.6715206 0.6738913 #> [553,] 0.5815733 0.6928156 0.7067708 #> [554,] 0.7766909 0.7895843 0.8063112 #> [555,] 0.8607480 0.8663636 0.8850935 #> [556,] 0.6819663 0.7233252 0.7428187 #> [557,] 0.8135751 0.8167236 0.8241794 #> [558,] 2.3432253 2.3690277 2.3950798 #> [559,] 0.7093489 0.7243632 0.7538692 #> [560,] 0.6790146 0.6813491 0.6987252 #> [561,] 0.9278684 0.9670014 0.9923017 #> [562,] 0.6440527 0.6939634 0.6946025 #> [563,] 0.8178424 0.8214247 0.8249273 #> [564,] 0.5854172 0.6030661 0.6703721 #> [565,] 2.2018303 2.2170551 2.2715903 #> [566,] 0.6740985 0.6961154 0.7107108 #> [567,] 0.5288156 0.6289047 0.6432439 #> [568,] 0.6086948 0.6167249 0.6182271 #> [569,] 0.8899425 0.8907170 0.9248265 #> [570,] 0.8781574 0.9243470 0.9436126 #> [571,] 0.6611107 0.6947258 0.7779487 #> [572,] 0.8544198 0.8607628 0.9113352 #> [573,] 0.6818945 0.6944958 0.6973268 #> [574,] 0.6450755 0.7042088 0.7045850 #> [575,] 0.8500746 0.9282655 1.0189408 #> [576,] 0.7818339 0.7826114 0.8231869 #> [577,] 1.2586485 1.2649063 1.4049734 #> [578,] 0.6757237 0.6791727 0.7117059 #> [579,] 0.6788451 0.6853032 0.7137189 #> [580,] 0.6333447 0.6695882 0.6853860 #> [581,] 0.6198851 0.6315937 0.6712264 #> [582,] 0.5481736 0.5764813 0.6090256 #> [583,] 0.6900380 0.6917397 0.7118564 #> [584,] 0.8031552 0.8159384 0.8325545 #> [585,] 0.8078439 0.8962927 0.9024955 #> [586,] 0.7863477 0.8318041 0.8382579 #> [587,] 0.6263667 0.6662338 0.6693745 #> [588,] 1.0664086 1.0867883 1.1595318 #> [589,] 0.7238832 0.7291073 0.7634351 #> [590,] 1.8472453 1.9284869 1.9455458 #> [591,] 0.6030661 0.6150027 0.6178535 #> [592,] 0.7067891 0.7202563 0.7385543 #> [593,] 0.5766874 0.6071901 0.6722267 #> [594,] 0.5037597 0.5140889 0.5278857 #> [595,] 0.6973421 0.7460797 0.7484519 #> [596,] 0.8587832 0.8776250 0.8925923 #> [597,] 0.8990254 0.8998505 0.9043764 #> [598,] 0.6098153 0.6283844 0.6289047 #> [599,] 1.1384414 1.1628123 1.1670567 #> [600,] 0.7396622 0.7411347 0.7429837 #> [601,] 0.6981834 0.7385372 0.7571183 #> [602,] 0.5697776 0.5925780 0.6079173 #> [603,] 1.0356459 1.0574077 1.0919283 #> [604,] 1.1836077 1.2198348 1.2271238 #> [605,] 0.6046276 0.6299998 0.6461569 #> [606,] 0.5649939 0.5740247 0.5826055 #> [607,] 1.1519656 1.1540375 1.1572590 #> [608,] 0.8675523 0.8853132 0.9111472 #> [609,] 1.6049527 1.6168954 1.6674373 #> [610,] 0.9049409 0.9094752 1.0089479 #> [611,] 0.5936046 0.6057138 0.6595870 #> [612,] 0.9681270 0.9843482 1.0041864 #> [613,] 1.2830695 1.3338333 1.3386482 #> [614,] 0.7772791 0.8072681 0.8344979 #> [615,] 0.7472657 0.7642566 0.7715074 #> [616,] 0.8531185 0.8587832 0.8961581 #> [617,] 0.7300108 0.7336099 0.7580219 #> [618,] 0.6519833 0.7036290 0.7411941 #> [619,] 0.9758230 1.0551752 1.0664273 #> [620,] 0.8801867 0.8923898 0.9039039 #> [621,] 0.5862132 0.6693059 0.6782872 #> [622,] 0.6219328 0.6248315 0.6261556 #> [623,] 0.7478602 0.7580601 0.7706583 #> [624,] 0.5698945 0.6299090 0.6403390 #> [625,] 1.0167930 1.0453051 1.1333771 #> [626,] 0.7392944 0.7548943 0.7563227 #> [627,] 0.6061947 0.6288226 0.6932874 #> [628,] 0.5016559 0.5094419 0.5727759 #> [629,] 1.0443008 1.0609972 1.0629319 #> [630,] 0.7998817 0.8195488 0.8678657 #> [631,] 0.5547715 0.6030755 0.6154857 #> [632,] 0.8144859 0.8281860 0.8697099 #> [633,] 1.0451144 1.0542669 1.1114114 #> [634,] 0.6419922 0.6432989 0.6447714 #> [635,] 0.6742909 0.7286320 0.7541595 #> [636,] 0.9607071 0.9640626 0.9819342 #> [637,] 0.9198462 0.9676332 0.9754811 #> [638,] 1.1200604 1.2439004 1.2668609 #> [639,] 1.1293918 1.1544637 1.1925736 #> [640,] 0.8213356 0.8387448 0.8433437 #> [641,] 0.9677572 0.9888431 1.0394407 #> [642,] 1.0378200 1.0572936 1.0864902 #> [643,] 0.5496676 0.5826584 0.5893277 #> [644,] 0.8202058 0.8503474 0.8628839 #> [645,] 0.6876549 0.6992767 0.7008368 #> [646,] 1.0006761 1.0891220 1.1000991 #> [647,] 0.7692970 0.8176418 0.8722259 #> [648,] 0.9001376 0.9306692 0.9344276 #> [649,] 0.4759897 0.5073673 0.5773453 #> [650,] 1.9382943 1.9416419 2.0260886 #> [651,] 1.1290313 1.2173869 1.2181288 #> [652,] 0.6658469 0.6770162 0.6778106 #> [653,] 0.7356093 0.7722727 0.7758287 #> [654,] 1.0047432 1.0131991 1.0317723 #> [655,] 0.5662782 0.5710962 0.5811230 #> [656,] 0.6184569 0.6220324 0.6290472 #> [657,] 0.7677005 0.7693379 0.7703628 #> [658,] 0.5899925 0.6245978 0.6298449 #> [659,] 0.7469655 0.7567931 0.7701006 #> [660,] 0.5644498 0.5773875 0.6264687 #> [661,] 0.5910982 0.6014117 0.6069905 #> [662,] 0.8325475 0.8360327 0.8718518 #> [663,] 0.5199536 0.5201948 0.5253829 #> [664,] 2.0411730 2.0795131 2.1723752 #> [665,] 0.6398905 0.7198262 0.7426566 #> [666,] 1.6206125 1.6538794 1.6952439 #> [667,] 0.7155574 0.7265504 0.7561248 #> [668,] 0.6236368 0.6674390 0.7730710 #> [669,] 0.6593693 0.6599681 0.7081862 #> [670,] 0.9392153 0.9818740 0.9936594 #> [671,] 0.8417960 0.8581661 0.9167841 #> [672,] 0.8022114 0.8045830 0.8129684 #> [673,] 1.0238475 1.0297726 1.0514701 #> [674,] 0.8379912 0.8492909 0.8665540 #> [675,] 0.6764425 0.7128808 0.7414976 #> [676,] 0.9237796 0.9572670 0.9769117 #> [677,] 0.6259317 0.6287526 0.6306655 #> [678,] 0.7745990 0.7968260 0.8289013 #> [679,] 1.1319135 1.1391750 1.1587812 #> [680,] 0.7995431 0.8797833 0.9001406 #> [681,] 0.5658163 0.5663868 0.6085266 #> [682,] 1.1814759 1.2122146 1.2527892 #> [683,] 0.9574515 0.9917246 1.0476483 #> [684,] 0.8202589 0.8405467 0.8482936 #> [685,] 1.0408116 1.0770330 1.0790551 #> [686,] 1.8179036 1.8340617 1.9116460 #> [687,] 0.8240114 0.8346796 0.8540282 #> [688,] 0.7111981 0.7168102 0.7506698 #> [689,] 0.7062740 0.7083143 0.7332891 #> [690,] 0.7589517 0.7814489 0.8743718 #> [691,] 0.7887940 0.8068922 0.8298635 #> [692,] 0.7042882 0.7073357 0.7086554 #> [693,] 0.8677932 0.9081009 0.9096423 #> [694,] 0.9426249 0.9456269 0.9996745 #> [695,] 0.7261435 0.7369679 0.7431326 #> [696,] 0.5259880 0.5325244 0.5335413 #> [697,] 0.9357843 1.0164852 1.1235415 #> [698,] 0.6602462 0.6971881 0.7130196 #> [699,] 0.7407388 0.7441478 0.7587615 #> [700,] 0.5805887 0.6411091 0.6493704 #> [701,] 0.8441528 0.8526117 0.8743718 #> [702,] 0.7091045 0.7115284 0.7332891 #> [703,] 1.4329723 1.4998115 1.5321308 #> [704,] 0.8716388 0.9324892 0.9406988 #> [705,] 0.9435541 1.0315119 1.0640330 #> [706,] 0.6753137 0.7172770 0.7210186 #> [707,] 0.6354370 0.6485246 0.6687828 #> [708,] 0.7013456 0.7131056 0.7347693 #> [709,] 0.7484519 0.7737699 0.7741449 #> [710,] 0.8423412 0.8555198 0.8749579 #> [711,] 1.1663112 1.2098689 1.2143511 #> [712,] 0.9481154 0.9482776 0.9492661 #> [713,] 0.5139554 0.5386177 0.5543984 #> [714,] 0.6937074 0.7245582 0.7468176 #> [715,] 1.4709076 1.4824762 1.4912133 #> [716,] 0.8951693 0.8961581 0.9878208 #> [717,] 0.5740756 0.5972898 0.6142427 #> [718,] 0.9250145 0.9261443 0.9285668 #> [719,] 0.5785084 0.6069989 0.6116374 #> [720,] 0.9843897 1.0550667 1.0561660 #> [721,] 0.5411180 0.5608529 0.5653868 #> [722,] 0.5295162 0.5340517 0.5555209 #> [723,] 0.9688074 1.0018221 1.0154645 #> [724,] 0.6862316 0.7080334 0.7092927 #> [725,] 0.6652081 0.6720352 0.6917995 #> [726,] 0.6144660 0.6167648 0.6466886 #> [727,] 0.8975746 0.9026949 0.9269203 #> [728,] 0.7297279 0.7441841 0.7579067 #> [729,] 0.9753392 0.9793578 0.9902628 #> [730,] 0.6466303 0.6596934 0.6713759 #> [731,] 0.7510750 0.8031048 0.8497506 #> [732,] 0.7844187 0.7910788 0.8033215 #> [733,] 1.1436977 1.2593077 1.2944929 #> [734,] 1.0233959 1.0988194 1.1192609 #> [735,] 0.6888786 0.7709872 0.8026905 #> [736,] 1.1020634 1.1103051 1.1116794 #> [737,] 0.4875835 0.5037597 0.5218255 #> [738,] 1.2087302 1.2367852 1.2630820 #> [739,] 2.1226628 2.1489737 2.1847682 #> [740,] 0.9699873 0.9936126 0.9981753 #> [741,] 0.5541666 0.5674918 0.5693480 #> [742,] 0.5708686 0.5748597 0.5788055 #> [743,] 0.8565868 0.8744701 0.8796845 #> [744,] 0.9290540 0.9548992 0.9762425 #> [745,] 0.4644079 0.5262045 0.5492644 #> [746,] 1.0158253 1.0370421 1.0656568 #> [747,] 0.7036279 0.7143330 0.7151035 #> [748,] 1.2295782 1.2701114 1.3213964 #> [749,] 0.5693054 0.6197711 0.6784878 #> [750,] 0.7510750 0.7788458 0.7880611 #> [751,] 0.7907865 0.8110518 0.8268999 #> [752,] 1.6528810 1.6852090 1.7012686 #> [753,] 0.8275630 0.8613339 0.8736702 #> [754,] 0.6022206 0.6873235 0.6881103 #> [755,] 0.7941230 0.7957952 0.7978296 #> [756,] 0.7081964 0.7167148 0.7362297 #> [757,] 1.2384498 1.2410470 1.3512977 #> [758,] 0.6404495 0.7010819 0.7160734 #> [759,] 0.5552198 0.6299090 0.6299998 #> [760,] 0.8529619 0.8699727 0.8723819 #> [761,] 1.1095331 1.1378732 1.1933431 #> [762,] 0.6133128 0.6134790 0.6152627 #> [763,] 0.5848662 0.5881791 0.5938147 #> [764,] 1.2015164 1.2033767 1.2357261 #> [765,] 1.3644598 1.3664599 1.3791541 #> [766,] 0.9682310 0.9753498 0.9834251 #> [767,] 0.7501720 0.7553391 0.7784621 #> [768,] 0.6176954 0.6553638 0.7037984 #> [769,] 0.9799616 0.9971581 1.0188878 #> [770,] 0.6067608 0.6166483 0.6215483 #> [771,] 0.6988536 0.7143979 0.7691937 #> [772,] 0.6707290 0.7406443 0.7925577 #> [773,] 0.6920620 0.7192525 0.7378939 #> [774,] 0.8061181 0.8335395 0.8519922 #> [775,] 1.2012728 1.2145928 1.2264780 #> [776,] 0.6036117 0.6045314 0.6074887 #> [777,] 0.7134724 0.7330610 0.7336099 #> [778,] 0.7357161 0.7659436 0.7765781 #> [779,] 1.9690753 1.9715492 1.9898267 #> [780,] 1.0283219 1.0732000 1.1302308 #> [781,] 1.2927882 1.2994474 1.3193296 #> [782,] 0.8892492 0.9349566 0.9609719 #> [783,] 0.7018778 0.7117218 0.7143797 #> [784,] 0.9308265 0.9329245 0.9864876 #> [785,] 0.6700494 0.6884271 0.7247373 #> [786,] 0.6910984 0.7043720 0.7224982 #> [787,] 0.8368802 0.8438580 0.8626579 #> [788,] 0.5811230 0.6050018 0.6342684 #> [789,] 0.5341757 0.5555209 0.5730101 #> [790,] 0.8837296 0.8878474 0.9150893 #> [791,] 0.7885298 0.7951147 0.7955146 #> [792,] 0.5522140 0.5649189 0.5940605 #> [793,] 1.5337373 1.5347884 1.5472385 #> [794,] 0.8744181 0.8762210 0.8950791 #> [795,] 0.5354529 0.6159843 0.6497799 #> [796,] 0.5435039 0.5631950 0.5761898 #> [797,] 0.5904797 0.6074887 0.6143992 #> [798,] 0.6754314 0.7094825 0.7628576 #> [799,] 0.5793089 0.5914720 0.6272817 #> [800,] 0.5851032 0.6079851 0.6545558 #> [801,] 0.5455918 0.5498394 0.5578871 #> [802,] 0.5969488 0.6414602 0.6594972 #> [803,] 0.7023748 0.7029518 0.7169213 #> [804,] 0.8185188 0.8205612 0.8326591 #> [805,] 1.1750752 1.1903984 1.1933431 #> [806,] 1.0302284 1.0537445 1.0748683 #> [807,] 1.6709016 1.7005440 1.7394570 #> [808,] 0.8407370 0.8500746 0.8637478 #> [809,] 0.7008518 0.7150811 0.7800171 #> [810,] 0.5433768 0.5853286 0.5858126 #> [811,] 0.8785766 0.8927469 0.9212768 #> [812,] 0.7942367 0.8017567 0.8151381 #> [813,] 0.7228474 0.7321719 0.7385597 #> [814,] 0.9128304 0.9264948 0.9299598 #> [815,] 1.5803471 1.5953455 1.6277466 #> [816,] 0.6993452 0.7242203 0.7344657 #> [817,] 1.6124134 1.6636003 1.6760532 #> [818,] 0.8376880 0.8426910 0.8453352 #> [819,] 0.6316854 0.6320838 0.6329795 #> [820,] 1.1178551 1.1519532 1.1632556 #> [821,] 0.5833116 0.6029864 0.6135050 #> [822,] 0.9326344 0.9529245 0.9951438 #> [823,] 0.6772256 0.6932874 0.7187178 #> [824,] 0.5820719 0.5912199 0.5958864 #> [825,] 0.6578455 0.6685950 0.6729128 #> [826,] 0.5650308 0.5788591 0.5883047 #> [827,] 0.8792121 0.8806975 0.8816077 #> [828,] 1.0188981 1.0945970 1.1559622 #> [829,] 0.6354521 0.6415935 0.6464179 #> [830,] 0.9306057 0.9731270 0.9804268 #> [831,] 0.8952237 0.9297569 0.9763403 #> [832,] 1.0843053 1.0897857 1.1173875 #> [833,] 0.5930187 0.5939369 0.6069079 #> [834,] 0.7465824 0.7697630 0.7820416 #> [835,] 0.4847935 0.4983993 0.5371986 #> [836,] 0.5418103 0.5454948 0.5471028 #> [837,] 0.8334105 0.8618845 0.9283201 #> [838,] 0.9496958 1.0138397 1.0735338 #> [839,] 0.9777460 1.0175556 1.0292585 #> [840,] 0.7527413 0.7735569 0.7822611 #> [841,] 0.9744743 1.0102749 1.0125469 #> [842,] 0.5128846 0.5187470 0.5232374 #> [843,] 1.0283224 1.0680810 1.0750131 #> [844,] 0.9416318 0.9482930 0.9629695 #> [845,] 1.0277905 1.0347361 1.1459346 #> [846,] 0.7532099 0.8083029 0.8129840 #> [847,] 0.6244790 0.6370497 0.6643578 #> [848,] 0.7263274 0.7298699 0.7523639 #> [849,] 0.6670763 0.6757237 0.7488167 #> [850,] 0.7400483 0.7407983 0.7678197 #> [851,] 0.8328801 0.8593535 0.8706834 #> [852,] 0.8042118 0.8181203 0.8300385 #> [853,] 1.3269187 1.3651020 1.3995892 #> [854,] 1.4041430 1.4212342 1.4334688 #> [855,] 0.8489212 0.8509315 0.9226693 #> [856,] 0.6371847 0.6373957 0.6489260 #> [857,] 0.9479864 1.0184980 1.0239902 #> [858,] 0.7565456 0.7837856 0.8034945 #> [859,] 0.7948078 0.7953206 0.8008431 #> [860,] 0.6458494 0.6610452 0.6853779 #> [861,] 0.9921289 1.0741951 1.0819476 #> [862,] 0.7420654 0.7459836 0.7656647 #> [863,] 0.9134277 0.9463880 0.9774547 #> [864,] 0.8385498 0.8457286 0.8462907 #> [865,] 0.6143098 0.6192943 0.6269716 #> [866,] 0.5851182 0.5949997 0.6690769 #> [867,] 0.6249000 0.6323681 0.6533374 #> [868,] 0.6744915 0.6873026 0.6948454 #> [869,] 0.7422157 0.7559815 0.7563227 #> [870,] 0.6857204 0.7060388 0.7225791 #> [871,] 0.6754866 0.7015258 0.7165442 #> [872,] 0.6624938 0.6659730 0.6665198 #> [873,] 0.7918862 0.8899351 0.9106117 #> [874,] 0.9897825 0.9967091 0.9998667 #> [875,] 0.6674390 0.6709559 0.6722478 #> [876,] 0.5127744 0.5200857 0.5278857 #> [877,] 0.7248677 0.7362571 0.7574972 #> [878,] 0.8650737 0.8673128 0.8876872 #> [879,] 0.8082818 0.8188058 0.8420042 #> [880,] 0.5238938 0.5245936 0.6314431 #> [881,] 0.5546065 0.5552367 0.5658163 #> [882,] 0.6593763 0.6692368 0.6819575 #> [883,] 1.1362660 1.1670605 1.1814908 #> [884,] 0.6274278 0.6516153 0.6589275 #> [885,] 0.9445222 0.9758457 0.9935428 #> [886,] 1.0196759 1.0782516 1.1500725 #> [887,] 0.5611785 0.5721490 0.5730101 #> [888,] 1.4525418 1.5335511 1.5650556 #> [889,] 0.7290624 0.7626622 0.7701266 #> [890,] 0.6575866 0.6631294 0.6709890 #> [891,] 0.6369631 0.6478044 0.6552745 #> [892,] 0.5949277 0.6268538 0.6411613 #> [893,] 0.8987008 0.8996465 0.9122463 #> [894,] 0.6536885 0.7169877 0.7448199 #> [895,] 0.8754574 0.8961878 0.9137037 #> [896,] 0.8990254 0.9600782 0.9845505 #> [897,] 0.8324243 0.8734084 0.8839719 #> [898,] 1.6232812 1.6329324 1.6843737 #> [899,] 0.5307726 0.5541666 0.5542382 #> [900,] 0.9233367 0.9341166 0.9619989 #> [901,] 1.1674370 1.2091835 1.2888790 #> [902,] 0.5935215 0.6133391 0.6145858 #> [903,] 1.1552582 1.1596754 1.2374547 #> [904,] 0.6360963 0.6439245 0.6468046 #> [905,] 1.1296944 1.1306698 1.1445650 #> [906,] 0.4926394 0.5200857 0.5569929 #> [907,] 0.7094116 0.7272964 0.7351366 #> [908,] 0.5967956 0.5984226 0.6065631 #> [909,] 0.7092927 0.7741783 0.8215954 #> [910,] 0.5743078 0.5881791 0.6133391 #> [911,] 0.7960024 0.8147715 0.8201958 #> [912,] 0.6367922 0.6648602 0.7074269 #> [913,] 1.1330448 1.1393728 1.2083914 #> [914,] 1.1261638 1.1363303 1.1381319 #> [915,] 0.7265642 0.7372435 0.7600814 #> [916,] 0.5991453 0.6288226 0.6909970 #> [917,] 0.5936259 0.6167773 0.6328689 #> [918,] 1.0889671 1.1266647 1.1340280 #> [919,] 0.8134718 0.8773818 0.8794205 #> [920,] 0.6634449 0.6847756 0.7128808 #> [921,] 0.6411091 0.6529900 0.6562525 #> [922,] 0.9428644 0.9479833 0.9766401 #> [923,] 0.6546695 0.7623191 0.7831897 #> [924,] 0.6176913 0.6225913 0.6360304 #> [925,] 0.8952237 0.8954024 0.9522548 #> [926,] 0.5354529 0.5418754 0.5586692 #> [927,] 0.5889366 0.6452530 0.6895276 #> [928,] 1.2360886 1.2645463 1.4156511 #> [929,] 0.7045850 0.7102461 0.7254271 #> [930,] 0.5557009 0.6323681 0.6379043 #> [931,] 0.6628541 0.7176973 0.7332718 #> [932,] 0.7830243 0.7958926 0.8087172 #> [933,] 0.7823336 0.7935952 0.8060961 #> [934,] 1.2170469 1.3016234 1.3293415 #> [935,] 0.5882024 0.6785070 0.6851937 #> [936,] 1.3042448 1.3067202 1.3290534 #> [937,] 0.5923980 0.7701006 0.7797618 #> [938,] 0.6801128 0.6920584 0.7143286 #> [939,] 0.8133878 0.8762922 0.8792833 #> [940,] 1.4887429 1.5084606 1.5273402 #> [941,] 0.8393568 0.9422893 0.9868203 #> [942,] 0.8230795 0.8939918 0.9083788 #> [943,] 0.4729722 0.4760790 0.5015056 #> [944,] 0.7457229 0.7612536 0.8136419 #> [945,] 0.6536390 0.6611107 0.6819575 #> [946,] 1.2655733 1.3786466 1.4154325 #> [947,] 0.8477603 0.9001406 0.9536993 #> [948,] 0.9881205 1.0120315 1.0276484 #> [949,] 0.7025475 0.7284700 0.8108278 #> [950,] 0.6722478 0.6789018 0.6876039 #> [951,] 0.6565390 0.6916976 0.7045401 #> [952,] 0.7141773 0.7169669 0.7371411 #> [953,] 0.6559141 0.6639398 0.6669422 #> [954,] 0.6337316 0.6354521 0.6418716 #> [955,] 0.6696159 0.6698246 0.6916917 #> [956,] 0.6075006 0.6443834 0.6680742 #> [957,] 0.6603293 0.6623050 0.6940481 #> [958,] 0.6436329 0.6482295 0.6713180 #> [959,] 0.6036117 0.6117387 0.6139310 #> [960,] 0.8303042 0.8761815 0.9232052 #> [961,] 0.5678705 0.5766561 0.5903886 #> [962,] 1.1215965 1.1605183 1.2426790 #> [963,] 0.7745578 0.7843597 0.8036236 #> [964,] 0.7111981 0.7140241 0.7803132 #> [965,] 1.3762091 1.4262877 1.4529862 #> [966,] 0.9081192 0.9342811 0.9352012 #> [967,] 0.7595652 0.7708732 0.7737753 #> [968,] 1.4909861 1.5724724 1.5742275 #> [969,] 0.8061181 0.8275757 0.8626899 #> [970,] 0.9417043 0.9428644 0.9652038 #> [971,] 0.7672127 0.7708118 0.8045342 #> [972,] 0.8512705 0.8660131 0.8675326 #> [973,] 0.6890310 0.7102459 0.7356957 #> [974,] 0.8293702 0.8737719 0.8771729 #> [975,] 0.9366361 0.9594017 1.0164096 #> [976,] 0.6135617 0.6143098 0.6145980 #> [977,] 0.6100644 0.6560972 0.6944847 #> [978,] 0.8658190 0.9287271 0.9562004 #> [979,] 0.8026439 0.8061461 0.8523100 #> [980,] 0.7544792 0.7700905 0.7944546 #> [981,] 0.7798431 0.7893466 0.7930837 #> [982,] 0.7172235 0.7433508 0.7481295 #> [983,] 0.8697099 0.8845862 0.8919917 #> [984,] 0.6171722 0.6253003 0.6681443 #> [985,] 1.0713757 1.0957445 1.1245306 #> [986,] 0.8691765 0.8912487 0.9218647 #> [987,] 1.0751697 1.0866799 1.0975572 #> [988,] 1.1608268 1.1729700 1.1819343 #> [989,] 0.7698354 0.7771256 0.8079784 #> [990,] 0.6881899 0.7428363 0.7918862 #> [991,] 0.5465308 0.5848662 0.6901507 #> [992,] 0.8033928 0.8417451 0.8513532 #> [993,] 1.4140997 1.4184929 1.4390561 #> [994,] 0.9012890 0.9167841 0.9214295 #> [995,] 0.8027854 0.8349415 0.8488035 #> [996,] 0.7708018 0.7850795 0.8009255 #> [997,] 1.5251204 1.5339383 1.5403993 #> [998,] 1.3328246 1.3609737 1.3649186 #> [999,] 0.8731058 0.8916354 0.9132031 #> [1000,] 0.8604964 0.8725707 0.9429566 #>"},{"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] 0"},{"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 16 0.00502 0.101 0.832 0.852 class2 class1 0 8.29 #> 2 67 0.0231 0.704 0.515 3.33 class2 class3 1 13.1 #> 3 56 0.0133 0.0196 0.197 -0.208 class2 class2 1 13.7 #> 4 21 0.247 0.111 0.172 1.92 class2 class1 0 10.0 #> 5 6 0.0996 0.337 0.679 1.65 class2 class1 0 11.3 #> 6 37 0.953 0.381 0.484 7.73 class1 class2 1 12.9 #> 7 43 0.853 0.423 0.128 2.52 class2 class2 1 12.0 #> 8 76 0.592 0.126 0.640 1.87 class2 class3 1 9.05 #> 9 10 0.616 0.478 0.199 1.71 class2 class1 0 11.3 #> 10 73 0.712 0.215 0.367 2.07 class2 class3 1 10.5 #> # 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,] 0.557496857 0.55254884 -1.38428152 #> [2,] 0.143171407 -0.77158277 -0.96941346 #> [3,] -1.502965899 1.57749290 0.43071065 #> [4,] -1.487689698 -1.05402386 0.26670370 #> [5,] 0.301986654 -1.41109437 0.62034936 #> [6,] -1.056499901 0.16076292 1.21403260 #> [7,] 1.182601035 1.16135604 -1.25154531 #> [8,] 0.175161417 0.26428063 1.46442058 #> [9,] -0.176386967 1.20998237 -0.73032910 #> [10,] 1.361071950 -1.39595395 -0.06137393 #> [11,] -0.705411677 -0.57942208 0.24049595 #> [12,] -1.115293290 -0.76255463 -1.04310500 #> [13,] 1.175452272 -1.22436156 -1.38640227 #> [14,] 1.163323464 0.93595248 -0.35468719 #> [15,] 0.740115180 -0.67577632 0.01731844 #> [16,] 1.601440012 -1.49938411 0.92018807 #> [17,] 0.240059379 -0.23790043 -0.96933282 #> [18,] -0.916705090 0.94753440 1.36072730 #> [19,] 1.618689241 -0.90362707 -0.04163005 #> [20,] 0.562887422 -0.80338410 -1.07272806 #> [21,] 0.020818643 0.07529508 0.56738816 #> [22,] -1.593153944 -0.36883048 -0.23429092 #> [23,] 1.406199371 -0.02143408 1.26338516 #> [24,] -1.429128536 0.46864660 -1.58328718 #> [25,] -0.934904314 -0.63212679 0.49228289 #> [26,] 1.433305360 0.42283996 1.39473103 #> [27,] 0.019767484 -1.28450984 -1.55476772 #> [28,] 0.508009412 0.38076053 1.20257860 #> [29,] -1.004761203 -0.13291545 1.32921921 #> [30,] 1.399130241 -1.31633977 0.26874764 #> [31,] 0.482602659 -1.02751941 -0.50095342 #> [32,] -0.543356713 1.28087863 1.23478788 #> [33,] -0.507422484 -1.35009733 1.22676601 #> [34,] 0.368655653 -0.52200992 -0.15475605 #> [35,] -0.081804366 -0.31056493 -0.85835736 #> [36,] 0.037561521 0.60339003 0.28293992 #> [37,] -1.491887476 1.21099572 -1.12477401 #> [38,] 0.005129352 -0.33981920 1.34288859 #> [39,] -1.302914735 -1.67374465 1.06058374 #> [40,] 1.305653373 0.61042250 -1.49525241 #> [41,] 0.482143429 0.06664269 0.45823394 #> [42,] -1.492688729 1.50206999 0.76974567 #> [43,] 0.617022851 0.40265098 1.36800527 #> [44,] -0.617457478 0.95260935 0.66416594 #> [45,] -1.606625323 -1.44734923 -0.88052376 #> [46,] -0.638064343 -1.31229252 -0.57006223 #> [47,] 1.090721278 1.66480604 -1.44087462 #> [48,] 1.486976621 1.31668060 -0.21521681 #> [49,] -1.468830013 0.77331120 -1.56317205 #> [50,] 1.455674529 1.44060148 1.11282839 #> [51,] -0.290803815 -0.05969706 -1.45511364 #> [52,] 0.733134872 -1.52929026 -0.97373036 #> [53,] 0.344436164 -1.32132534 -1.09050376 #> [54,] 0.025103475 -0.87335631 -0.37249570 #> [55,] 0.005565627 0.47061529 -0.90081899 #> [56,] 0.088159041 1.36984081 0.30931130 #> [57,] 1.479750148 1.66158548 0.57718636 #> [58,] 0.043417799 -1.24565767 -0.64769623 #> [59,] 0.822253646 0.76005166 -0.69140429 #> [60,] 0.329282537 -0.65838399 -1.01716064 #> [61,] 0.071329812 1.25781906 1.31319492 #> [62,] -0.855197996 0.70530147 -1.31843757 #> [63,] 0.658248655 0.73338389 1.49260712 #> [64,] 1.452178644 -0.85450389 0.61899811 #> [65,] -0.835366335 0.75340630 -0.44970310 #> [66,] 1.326492491 -1.51463056 1.40166921 #> [67,] 0.036750337 -0.50928817 1.52348777 #> [68,] -1.504910611 1.42806769 0.14878131 #> [69,] -0.821539840 -0.49322190 -0.22241568 #> [70,] -0.589478093 0.76056918 0.06020416 #> [71,] -1.500470526 0.84789696 1.18340728 #> [72,] -1.287270835 0.47255714 -0.38925795 #> [73,] 0.930305547 1.75773170 1.23534137 #> [74,] 0.884229017 -0.61870832 0.58878309 #> [75,] -0.671317579 -1.51476542 0.93921205 #> [76,] -1.457536947 -0.44484394 1.11904540 #> [77,] -1.217264634 -0.23667642 -1.27240224 #> [78,] -1.547997709 0.88847735 -0.47816578 #> [79,] 0.373987416 -1.68670147 -1.40146429 #> [80,] 1.594535000 -0.38942638 1.34122937 #> [81,] 0.707546080 0.76234299 1.44107612 #> [82,] -0.937221893 -1.28057014 -0.79493906 #> [83,] 0.359529230 1.48544158 0.54443627 #> [84,] 1.408014465 0.64311365 -1.48655835 #> [85,] 0.271926331 0.18442901 0.13738623 #> [86,] 0.581231249 -0.93864001 -0.66737277 #> [87,] -0.757940402 -0.38858262 -1.18463333 #> [88,] 0.642676960 -1.46812212 -0.99615908 #> [89,] -1.289230186 0.50206737 0.67252825 #> [90,] 0.361422796 0.33176833 0.49695858 #> [91,] -0.124556298 1.45847359 -0.42208603 #> [92,] -1.643635969 1.12659784 0.99615075 #> [93,] -0.401240219 -0.81190439 -1.56386233 #> [94,] 1.325629029 -1.58461564 -0.67454196 #> [95,] -1.609741631 -0.45822804 0.86180774 #> [96,] -0.299746586 0.51120513 0.79680852 #> [97,] -0.495243445 0.63245996 -1.05074541 #> [98,] 1.606864285 0.67175963 0.46781733 #> [99,] 0.812649785 1.18037103 1.46240172 #> [100,] -0.407814777 0.59991283 -1.34126926"},{"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] 5.07911944 1.05678953 2.23934946 4.90880410 3.94374665 6.53658525 #> [7] 2.57238069 2.92755504 1.93907094 3.77123417 3.82940916 3.26356318 #> [13] 3.84825760 2.40057374 4.40478254 6.02535041 5.44352997 2.95309313 #> [19] 3.00570147 5.14461239 4.39227241 2.98797491 6.23613147 2.86382843 #> [25] 1.65266879 -0.44598386 1.02615166 2.74603837 3.73981858 6.94316675 #> [31] 2.93417951 3.35549657 2.73999449 1.10539803 1.70830250 3.58635098 #> [37] 1.28806087 2.81915418 3.13872282 6.20266732 4.37399516 4.97421467 #> [43] 3.70563582 5.22433880 1.34701982 0.51430319 2.67376327 3.59549867 #> [49] 5.04825709 3.26804763 2.83442489 2.81225459 3.24932848 4.93538219 #> [55] 4.16472611 2.76181792 3.88390283 3.39162937 8.17669511 3.63533456 #> [61] 3.06576604 4.44669588 4.64586848 6.06715629 2.05589047 2.77448895 #> [67] 3.56069761 3.18679927 1.49435688 4.63091489 2.80400707 -0.08882069 #> [73] 1.69272079 3.83614544 3.20063962 2.38496150 4.77629459 2.06289074 #> [79] 3.67755742 4.86142919 -0.03458803 5.14039305 5.25721799 -0.11626374 #> [85] 0.38867651 4.09989691 3.99624665 2.11558569 2.90413662 4.74220971 #> [91] 2.43009103 2.66588818 2.77099636 6.15168702 2.77969727 2.99476337 #> [97] 4.04757098 2.87846362 2.71481505 4.03923739"},{"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 -0.252 -0.602 -0.501 0.324 h 1 #> 2 -0.752 -0.0650 0.604 -0.268 e 5 #> 3 -0.0158 0.846 1.19 -1.40 i 2 #> 4 -0.618 0.0271 0.255 0.415 o 2 #> 5 -1.18 0.184 0.734 0.456 h 1 #> 6 0.264 1.36 -0.490 0.274 o 2 #> 7 -1.55 0.855 2.99 0.997 e 5 #> 8 0.204 1.64 0.0465 0.939 n 1 #> 9 0.644 0.201 -0.208 0.853 u 1 #> 10 -0.218 1.30 0.435 -1.42 n 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 -0.252 -0.602 -0.501 0.324 h 2 #> 2 -0.752 -0.0650 0.604 -0.268 e 1 #> 3 -0.0158 0.846 1.19 -1.40 i 2 #> 4 -0.618 0.0271 0.255 0.415 o 2 #> 5 -1.18 0.184 0.734 0.456 h 2 #> 6 0.264 1.36 -0.490 0.274 o 2 #> 7 -1.55 0.855 2.99 0.997 e 1 #> 8 0.204 1.64 0.0465 0.939 n 3 #> 9 0.644 0.201 -0.208 0.853 u 1 #> 10 -0.218 1.30 0.435 -1.42 n 3 #> # 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 9 #> 2 7 #> 3 10 #> 4 5 #> 5 7 #> 6 5 #> 7 5 #> 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 5 #> 2 1 #> 3 2 #> 4 4 #> 5 1 #> 6 1 #> 7 2 #> 8 2 #> 9 7 #> 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 3 #> 3 1 #> 4 2 #> 5 3 #> 6 3 #> 7 1 #> 8 1 #> 9 3 #> 10 3 #> # 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 6 #> 2 4 #> 3 4 #> 4 5 #> 5 10 #> 6 4 #> 7 6 #> 8 6 #> 9 4 #> 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 8 #> 2 2 #> 3 8 #> 4 7 #> 5 7 #> 6 5 #> 7 4 #> 8 1 #> 9 5 #> 10 8 #> # 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 1 #> 2 4 #> 3 1 #> 4 3 #> 5 3 #> 6 2 #> 7 4 #> 8 4 #> 9 3 #> 10 1 #> # 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 0.597 #> 2 2.21 #> 3 6.20 #> 4 2.57 #> 5 0.414 #> 6 1.23 #> 7 4.79 #> 8 3.31 #> 9 1.82 #> 10 3.87 #> 11 3.24 #> 12 2.99 #> 13 3.13 #> 14 4.74 #> 15 2.12 #> 16 1.71 #> 17 3.21 #> 18 0.993 #> 19 4.12 #> 20 5.84"},{"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 2.13 0.705 -1.16 -1.37 -2.52 1.08 1.42 -0.377 #> 2 -2.16 0.719 0.784 -1.65 1.22 1.68 -0.397 1.87 #> 3 1.30 0.463 0.284 0.665 -0.973 -0.110 -0.212 -1.36 #> 4 0.266 -0.303 -0.681 -1.05 -0.475 0.600 1.34 0.277 #> 5 0.986 1.27 0.810 0.459 -0.971 0.420 -1.03 -1.05 #> 6 -0.0816 2.04 -0.00146 -0.362 -1.17 0.675 -1.04 0.700 #> 7 -0.0698 0.612 1.75 -0.132 0.510 1.16 -1.02 -0.737 #> 8 -0.390 1.50 -0.156 0.269 -0.693 -0.0874 -0.915 0.577 #> 9 -1.32 0.986 0.0330 -0.910 0.206 0.815 -0.341 1.44 #> 10 0.117 0.275 -1.00 -0.487 -0.819 0.0165 0.819 0.519 #> # 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 2.13 0.705 -1.16 -1.37 4.34 1.87 #> 2 -2.16 0.719 0.784 -1.65 -0.889 -4.03 #> 3 1.30 0.463 0.284 0.665 0.463 3.67 #> 4 0.266 -0.303 -0.681 -1.05 2.77 1.60 #> 5 0.986 1.27 0.810 0.459 0.562 4.25 #> 6 -0.0816 2.04 -0.00146 -0.362 2.41 -3.16 #> 7 -0.0698 0.612 1.75 -0.132 -3.58 -2.75 #> 8 -0.390 1.50 -0.156 0.269 2.36 -2.21 #> 9 -1.32 0.986 0.0330 -0.910 -0.201 -3.36 #> 10 0.117 0.275 -1.00 -0.487 2.87 0.441 #> # 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 2.13 0.705 -1.16 -1.37 -2.29 -0.706 #> 2 -2.16 0.719 0.784 -1.65 -0.666 3.13 #> 3 1.30 0.463 0.284 0.665 0.858 -3.05 #> 4 0.266 -0.303 -0.681 -1.05 -1.79 -2.01 #> 5 0.986 1.27 0.810 0.459 1.45 -3.23 #> 6 -0.0816 2.04 -0.00146 -0.362 -1.91 2.35 #> 7 -0.0698 0.612 1.75 -0.132 2.53 -0.873 #> 8 -0.390 1.50 -0.156 0.269 -1.56 2.07 #> 9 -1.32 0.986 0.0330 -0.910 -0.667 2.86 #> 10 0.117 0.275 -1.00 -0.487 -2.09 0.537 #> # 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 -1.18 -0.437 #> 2 0.230 -0.548 #> 3 2.73 -0.128 #> 4 0.772 0.965 #> 5 0.122 1.24 #> 6 -0.878 1.50 #> 7 0.104 -0.543 #> 8 0.295 2.14 #> 9 0.712 -0.784 #> 10 0.723 0.0671 #> # 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 -0.735 1.24 1.48 -0.128 #> 2 -1.05 -1.17 0.753 0.296 #> 3 1.32 -1.42 1.41 -1.47 #> 4 -0.454 -0.144 -0.479 -0.0116 #> 5 1.91 -0.175 1.27 -0.345 #> 6 1.01 -0.480 0.184 0.309 #> 7 -0.134 -0.107 0.0852 0.968 #> 8 -0.203 0.748 -0.966 0.658 #> 9 -1.95 -1.78 -1.25 -0.0726 #> 10 1.14 0.0262 -0.00742 -0.297 #> # 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 8.08 -7.66 #> 2 2.37 -4.12 #> 3 6.81 -4.01 #> 4 -3.07 4.23 #> 5 3.07 3.35 #> 6 -6.88 -6.09 #> 7 10.3 1.02 #> 8 2.62 4.12 #> 9 -0.307 -2.68 #> 10 0.692 -6.27 #> # 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 -6.38 -3.76 #> 2 1.55 0.251 #> 3 -6.33 2.92 #> 4 -0.516 -0.728 #> 5 0.278 5.36 #> 6 5.36 2.85 #> 7 -8.58 -2.40 #> 8 -1.77 4.14 #> 9 3.73 -1.01 #> 10 5.09 1.20 #> # 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 -2.96 1.60 #> 2 -0.206 -2.37 #> 3 -0.635 -0.560 #> 4 -3.37 1.09 #> 5 4.26 0.397 #> 6 3.30 1.62 #> 7 -3.49 0.106 #> 8 -0.0529 1.23 #> 9 -3.13 1.31 #> 10 4.73 -0.264 #> # 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 2.75 6.43 #> 2 -2.40 -6.28 #> 3 5.86 -2.11 #> 4 4.77 5.55 #> 5 -6.98 -1.11 #> 6 -7.44 -2.29 #> 7 2.70 -7.05 #> 8 5.13 -6.46 #> 9 2.85 6.16 #> 10 -9.17 0.0725 #> # 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 0.184 -2.79 #> 2 1.51 1.19 #> 3 -0.476 -0.746 #> 4 1.90 -2.91 #> 5 -0.0406 2.33 #> 6 1.92 -2.74 #> 7 -0.471 2.95 #> 8 1.71 2.39 #> 9 -3.73 0.00121 #> 10 -2.18 -2.45 #> # 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 0.5 #> 2 0 #> 3 0.167 #> 4 0 #> 5 0.167 #> 6 0.333 #> 7 0.333 #> 8 0 #> 9 0.333 #> 10 0.5 #> # 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 1 #> 2 1 #> 3 1 #> 4 0.5 #> 5 1 #> 6 0.3 #> 7 1 #> 8 1 #> 9 1 #> 10 0.5 #> # 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 0 and a penalty parameter (lambda) of 1e-10 #> # A tibble: 4 × 2 #> feature coefficient #> #> 1 (Intercept) 3.35 #> 2 pstat5 1.09 #> 3 cd45 0.710 #> 4 cd34 -0.0956 # 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 -0.0588 #> 2 (Intercept) 0.00000102 # 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 1e+00 #> # A tibble: 0 × 2 #> # ℹ 2 variables: feature , coefficient "},{"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 0.391 0.908 -0.584 -0.281 b #> 2 0.805 -1.23 0.799 -0.472 b #> 3 0.943 0.371 -0.575 0.928 b #> 4 -0.631 -1.17 -0.0679 -0.0230 a #> 5 -0.189 -0.638 2.32 -0.734 b #> 6 -0.701 0.263 -0.179 0.972 a #> 7 -0.422 0.707 1.03 -2.52 a #> 8 -0.453 0.614 0.806 2.03 a #> 9 -1.67 1.09 -0.390 -0.473 a #> 10 -1.17 -1.38 0.599 0.635 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 0.391 0.908 -0.584 -0.281 a #> 2 0.805 -1.23 0.799 -0.472 a #> 3 0.943 0.371 -0.575 0.928 a #> 4 -0.631 -1.17 -0.0679 -0.0230 a #> 5 -0.189 -0.638 2.32 -0.734 b #> 6 -0.701 0.263 -0.179 0.972 b #> 7 -0.422 0.707 1.03 -2.52 a #> 8 -0.453 0.614 0.806 2.03 b #> 9 -1.67 1.09 -0.390 -0.473 a #> 10 -1.17 -1.38 0.599 0.635 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 a #> 2 b #> 3 a #> 4 b #> 5 a #> 6 a #> 7 a #> 8 a #> 9 a #> 10 a #> # 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 b #> 2 b #> 3 b #> 4 b #> 5 a #> 6 a #> 7 b #> 8 a #> 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 b #> 6 a #> 7 a #> 8 b #> 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 a #> 3 a #> 4 a #> 5 b #> 6 a #> 7 a #> 8 a #> 9 b #> 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-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_0.99.0 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.0 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_0.6.34 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.0 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.8 #> [19] tools_4.3.3 igraph_2.0.2 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_2.14.0 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_1.2.7 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.1 #> [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_0.6.1 #> [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_0.6.34 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_0.4.8 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_1.2.7 bslib_0.6.1 #> [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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [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_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] labeling_0.4.3 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 highr_0.10 #> [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] data.table_1.15.2 hms_1.1.3 #> [73] tidygraph_1.3.1 utf8_1.2.4 #> [75] XVector_0.42.0 ggrepel_0.9.5 #> [77] BiocVersion_3.18.1 foreach_1.5.2 #> [79] pillar_1.9.0 RcppHNSW_0.6.0 #> [81] later_1.3.2 splines_4.3.3 #> [83] tweenr_2.0.3 lattice_0.22-5 #> [85] survival_3.5-8 bit_4.0.5 #> [87] RProtoBufLib_2.14.0 tidyselect_1.2.0 #> [89] Biostrings_2.70.2 knitr_1.45 #> [91] gridExtra_2.3 xfun_0.42 #> [93] graphlayouts_1.1.1 hardhat_1.3.1 #> [95] timeDate_4032.109 stringi_1.8.3 #> [97] yaml_2.3.8 evaluate_0.23 #> [99] codetools_0.2-19 ggraph_2.2.1 #> [101] tibble_3.2.1 BiocManager_1.30.22 #> [103] cli_3.6.2 rpart_4.1.23 #> [105] xtable_1.8-4 systemfonts_1.0.6 #> [107] munsell_0.5.0 jquerylib_0.1.4 #> [109] Rcpp_1.0.12 globals_0.16.3 #> [111] png_0.1-8 parallel_4.3.3 #> [113] ellipsis_0.3.2 pkgdown_2.0.7 #> [115] gower_1.0.1 readr_2.1.5 #> [117] blob_1.2.4 listenv_0.9.1 #> [119] glmnet_4.1-8 viridisLite_0.4.2 #> [121] ipred_0.9-14 scales_1.3.0 #> [123] prodlim_2023.08.28 purrr_1.0.2 #> [125] crayon_1.5.2 rlang_1.1.3 #> [127] 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 5.30 6.71 #> 2 8.98 7.55 #> 3 35.3 4.97 #> 4 13.0 15.5 #> 5 7.03 10.4 #> 6 22.2 8.18 phenograph_data |> tof_preprocess() |> tof_reduce_dimensions(method = \"tsne\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .tsne1 .tsne2 #> #> 1 9.10 10.6 #> 2 -0.610 12.8 #> 3 4.70 35.3 #> 4 0.467 22.0 #> 5 8.97 17.7 #> 6 -5.29 23.7 #> 7 -3.58 13.8 #> 8 -11.6 23.4 #> 9 -4.22 21.2 #> 10 1.12 7.05 #> # ℹ 2,990 more rows phenograph_data |> tof_reduce_dimensions(method = \"umap\", augment = FALSE) #> # A tibble: 3,000 × 2 #> .umap1 .umap2 #> #> 1 9.68 4.59 #> 2 8.90 3.55 #> 3 3.06 -0.0897 #> 4 2.76 -1.80 #> 5 9.87 4.21 #> 6 0.317 -2.52 #> 7 9.92 3.76 #> 8 2.23 -0.769 #> 9 5.43 -0.381 #> 10 8.30 5.75 #> # 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_0.99.0 #> #> 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] png_0.1-8 systemfonts_1.0.6 vctrs_0.6.5 #> [10] stringr_1.5.1 pkgconfig_2.0.3 shape_1.4.6.1 #> [13] fastmap_1.1.1 ellipsis_0.3.2 labeling_0.4.3 #> [16] ggraph_2.2.1 utf8_1.2.4 rmarkdown_2.26 #> [19] prodlim_2023.08.28 tzdb_0.4.0 ragg_1.2.7 #> [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 keras_2.13.0 tweenr_2.0.3 #> [31] irlba_2.3.5.1 tensorflow_2.15.0 parallel_4.3.3 #> [34] R6_2.5.1 bslib_0.6.1 stringi_1.8.3 #> [37] reticulate_1.35.0 parallelly_1.37.1 rpart_4.1.23 #> [40] lubridate_1.9.3 jquerylib_0.1.4 Rcpp_1.0.12 #> [43] iterators_1.0.14 knitr_1.45 future.apply_1.11.1 #> [46] base64enc_0.1-3 readr_2.1.5 flowCore_2.14.1 #> [49] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [52] igraph_2.0.2 timechange_0.3.0 tidyselect_1.2.0 #> [55] yaml_2.3.8 viridis_0.6.5 timeDate_4032.109 #> [58] doParallel_1.0.17 codetools_0.2-19 listenv_0.9.1 #> [61] lattice_0.22-5 tibble_3.2.1 Biobase_2.62.0 #> [64] withr_3.0.0 evaluate_0.23 Rtsne_0.17 #> [67] future_1.33.1 desc_1.4.3 survival_3.5-8 #> [70] polyclip_1.10-6 embed_1.1.3 pillar_1.9.0 #> [73] whisker_0.4.1 foreach_1.5.2 stats4_4.3.3 #> [76] generics_0.1.3 RcppHNSW_0.6.0 S4Vectors_0.40.2 #> [79] hms_1.1.3 munsell_0.5.0 scales_1.3.0 #> [82] globals_0.16.3 class_7.3-22 glue_1.7.0 #> [85] tools_4.3.3 data.table_1.15.2 gower_1.0.1 #> [88] fs_1.6.3 graphlayouts_1.1.1 tidygraph_1.3.1 #> [91] grid_4.3.3 yardstick_1.3.0 tidyr_1.3.1 #> [94] RProtoBufLib_2.14.0 ipred_0.9-14 colorspace_2.1-0 #> [97] ggforce_0.4.2 cli_3.6.2 tfruns_1.5.2 #> [100] textshaping_0.3.7 fansi_1.0.6 cytolib_2.14.1 #> [103] viridisLite_0.4.2 lava_1.8.0 uwot_0.1.16 #> [106] gtable_0.3.4 zeallot_0.1.0 sass_0.4.8 #> [109] digest_0.6.34 BiocGenerics_0.48.1 ggrepel_0.9.5 #> [112] farver_2.1.1 memoise_2.0.1 htmltools_0.5.7 #> [115] pkgdown_2.0.7 lifecycle_1.0.4 hardhat_1.3.1 #> [118] 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_0.99.0 #> #> 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_1.2.7 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_0.6.1 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_2.14.1 #> [40] Matrix_1.6-5 splines_4.3.3 nnet_7.3-19 #> [43] igraph_2.0.2 timechange_0.3.0 tidyselect_1.2.0 #> [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_1.3.0 tidyr_1.3.1 #> [82] RProtoBufLib_2.14.0 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_0.4.8 #> [94] digest_0.6.34 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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [15] BiocGenerics_0.48.1 stringr_1.5.1 #> [17] dplyr_1.1.4 tidytof_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] philentropy_0.8.0 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 ggforce_0.4.2 #> [53] MASS_7.3-60.0.1 lava_1.8.0 #> [55] rappdirs_0.3.3 DelayedArray_0.28.0 #> [57] tools_4.3.3 interactiveDisplayBase_1.40.0 #> [59] httpuv_1.6.14 future.apply_1.11.1 #> [61] nnet_7.3-19 glue_1.7.0 #> [63] promises_1.2.1 grid_4.3.3 #> [65] generics_0.1.3 recipes_1.0.10 #> [67] gtable_0.3.4 tzdb_0.4.0 #> [69] class_7.3-22 tidyr_1.3.1 #> [71] data.table_1.15.2 hms_1.1.3 #> [73] tidygraph_1.3.1 utf8_1.2.4 #> [75] XVector_0.42.0 ggrepel_0.9.5 #> [77] BiocVersion_3.18.1 foreach_1.5.2 #> [79] pillar_1.9.0 RcppHNSW_0.6.0 #> [81] later_1.3.2 splines_4.3.3 #> [83] tweenr_2.0.3 lattice_0.22-5 #> [85] survival_3.5-8 bit_4.0.5 #> [87] emdist_0.3-3 RProtoBufLib_2.14.0 #> [89] tidyselect_1.2.0 Biostrings_2.70.2 #> [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] ggplot2_3.5.0 readr_2.1.5 #> [119] blob_1.2.4 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/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_0.99.0 #> #> 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_1.2.7 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_1.2.0 bslib_0.6.1 #> [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_2.14.1 Matrix_1.6-5 #> [46] splines_4.3.3 nnet_7.3-19 igraph_2.0.2 #> [49] timechange_0.3.0 tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 RProtoBufLib_2.14.0 #> [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_0.4.8 #> [103] digest_0.6.34 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. 3.18 0.862 #> 2 229. -0.949 10.6 #> 3 293. 4.93 1.35 #> 4 431. 0.0843 13.0 #> # 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_0.99.0 #> #> loaded via a namespace (and not attached): #> [1] tidyselect_1.2.0 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_0.6.34 timechange_0.3.0 lifecycle_1.0.4 #> [13] yardstick_1.3.0 survival_3.5-8 magrittr_2.0.3 #> [16] compiler_4.3.3 rlang_1.1.3 sass_0.4.8 #> [19] tools_4.3.3 igraph_2.0.2 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_2.14.0 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_1.2.7 generics_0.1.3 future.apply_1.11.1 #> [49] tzdb_0.4.0 cachem_1.0.8 flowCore_2.14.1 #> [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_0.6.1 #> [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_1.22.0 flowCore_2.14.1 #> [3] SummarizedExperiment_1.32.0 Biobase_2.62.0 #> [5] GenomicRanges_1.54.1 GenomeInfoDb_1.38.7 #> [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_2.4.0 #> [15] BiocGenerics_0.48.1 forcats_1.0.0 #> [17] ggplot2_3.5.0 dplyr_1.1.4 #> [19] tidytof_0.99.0 #> #> 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_1.48.0 ragg_1.2.7 #> [9] vctrs_0.6.5 memoise_2.0.1 #> [11] htmltools_0.5.7 S4Arrays_1.2.1 #> [13] curl_5.2.1 SparseArray_1.2.4 #> [15] sass_0.4.8 parallelly_1.37.1 #> [17] bslib_0.6.1 desc_1.4.3 #> [19] lubridate_1.9.3 cachem_1.0.8 #> [21] igraph_2.0.2 mime_0.12 #> [23] lifecycle_1.0.4 iterators_1.0.14 #> [25] pkgconfig_2.0.3 Matrix_1.6-5 #> [27] R6_2.5.1 fastmap_1.1.1 #> [29] shiny_1.8.0 GenomeInfoDbData_1.2.11 #> [31] future_1.33.1 digest_0.6.34 #> [33] colorspace_2.1-0 AnnotationDbi_1.64.1 #> [35] textshaping_0.3.7 RSQLite_2.3.5 #> [37] labeling_0.4.3 filelock_1.0.3 #> [39] cytolib_2.14.1 fansi_1.0.6 #> [41] yardstick_1.3.0 timechange_0.3.0 #> [43] httr_1.4.7 polyclip_1.10-6 #> [45] abind_1.4-5 compiler_4.3.3 #> [47] bit64_4.0.5 withr_3.0.0 #> [49] doParallel_1.0.17 viridis_0.6.5 #> [51] DBI_1.2.2 highr_0.10 #> [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] stringr_1.5.1 RcppHNSW_0.6.0 #> [83] later_1.3.2 splines_4.3.3 #> [85] tweenr_2.0.3 lattice_0.22-5 #> [87] survival_3.5-8 bit_4.0.5 #> [89] RProtoBufLib_2.14.0 tidyselect_1.2.0 #> [91] Biostrings_2.70.2 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 readr_2.1.5 #> [119] blob_1.2.4 listenv_0.9.1 #> [121] glmnet_4.1-8 viridisLite_0.4.2 #> [123] ipred_0.9-14 ggridges_0.5.6 #> [125] scales_1.3.0 prodlim_2023.08.28 #> [127] purrr_1.0.2 crayon_1.5.2 #> [129] 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_0.99.0 #> #> 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_1.2.7 #> [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_0.6.1 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_2.14.1 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.2 timechange_0.3.0 #> [46] tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.0 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_0.4.8 #> [97] digest_0.6.34 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_0.99.0 #> #> 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_1.2.7 #> [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_0.6.1 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_2.14.1 Matrix_1.6-5 splines_4.3.3 #> [43] nnet_7.3-19 igraph_2.0.2 timechange_0.3.0 #> [46] tidyselect_1.2.0 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_1.3.0 tidyr_1.3.1 #> [85] RProtoBufLib_2.14.0 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_0.4.8 digest_0.6.34 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 (2024). tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles. R package version 0.99.0, https://keyes-timothy.github.io/tidytof/, https://keyes-timothy.github.io/tidytof.","code":"@Manual{, title = {tidytof: Analyze High-dimensional Cytometry Data Using Tidy Data Principles}, author = {Timothy Keyes}, year = {2024}, note = {R package version 0.99.0, https://keyes-timothy.github.io/tidytof/}, url = {https://keyes-timothy.github.io/tidytof}, }"},{"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 directory","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/get_extension.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Find the extension for a file — get_extension","text":"file extension `filename`","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":null,"dir":"Reference","previous_headings":"","what":"L2 normalize an input vector x to a length of 1 — l2_normalize","title":"L2 normalize an input vector x to a length of 1 — l2_normalize","text":"L2 normalize input vector x length 1","code":""},{"path":"https://keyes-timothy.github.io/tidytof/reference/l2_normalize.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"L2 normalize an input vector x to a length of 1 — 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/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 16 #> 2 13 #> 3 9 #> 4 19 #> 5 19 #> 6 9 #> 7 9 #> 8 9 #> 9 20 #> 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 9 #> 2 6 #> 3 18 #> 4 12 #> 5 5 #> 6 6 #> 7 6 #> 8 2 #> 9 14 #> 10 19 #> # 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 1 #> 2 7 #> 3 3 #> 4 2 #> 5 1 #> 6 8 #> 7 4 #> 8 1 #> 9 2 #> 10 6 #> # ℹ 990 more rows tof_cluster_phenograph(tof_tibble = sim_data, cluster_cols = c(cd45, cd19)) #> # A tibble: 1,000 × 1 #> .phenograph_cluster #> #> 1 5 #> 2 6 #> 3 2 #> 4 2 #> 5 3 #> 6 10 #> 7 10 #> 8 9 #> 9 1 #> 10 12 #> # 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 -0.0692 0.285 1.75 -0.343 a #> 2 0.302 2.12 0.830 0.868 s #> 3 0.564 -0.337 -0.804 1.05 i #> 4 -0.732 0.0620 -0.747 0.463 z #> 5 -0.280 1.33 0.405 -2.20 v #> 6 0.891 1.15 -0.613 0.358 r #> 7 -0.285 0.192 -0.505 -0.123 t #> 8 -1.28 0.837 0.399 0.329 t #> 9 0.532 1.61 -0.373 0.693 b #> 10 -0.391 -1.59 1.16 -0.363 y #> # 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 0.416 -0.0549 -0.695 0.226 t #> 2 -0.811 0.802 0.674 -0.247 d #> 3 1.13 -0.218 -1.17 -0.354 l #> 4 0.514 -0.853 1.11 -0.959 j #> 5 -0.127 -0.115 0.840 0.696 p #> 6 0.00882 1.18 0.463 0.0690 l #> 7 -0.483 0.728 -0.210 -0.456 n #> 8 1.59 -1.13 0.00641 0.909 y #> 9 0.315 0.0125 -0.150 0.00140 s #> 10 0.147 0.00315 -1.83 0.743 j #> # 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: 95 × 5 #> cd45 cd38 cd34 cd19 cluster_id #> #> 1 -1.18 -0.355 -2.25 -0.514 g #> 2 -1.39 -0.266 0.671 -0.916 r #> 3 -0.0662 -0.249 0.407 -0.0341 s #> 4 0.324 0.379 0.697 -0.186 a #> 5 -2.17 -2.24 -0.895 0.114 a #> 6 -0.532 1.68 0.507 0.445 c #> 7 -0.848 -0.836 0.340 -0.572 h #> 8 -0.805 -1.24 0.00874 0.480 r #> 9 -0.298 -1.97 1.40 -0.383 z #> 10 -0.921 0.747 1.42 -1.29 w #> # ℹ 85 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 -0.299 -1.19 0.223 1.02 m #> 2 0.726 1.12 0.860 0.970 a #> 3 -0.361 1.01 0.00652 -0.159 b #> 4 -0.947 0.396 -0.836 -0.952 k #> 5 1.51 2.26 0.961 0.164 j #> 6 -0.209 0.126 -1.25 -1.08 e #> 7 1.22 1.00 -1.42 -0.400 u #> 8 0.534 2.94 -0.222 0.675 j #> 9 0.143 0.236 -1.29 -1.27 r #> 10 0.238 0.709 -0.00808 1.59 a #> # 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 0.392 1.62 -1.94 1.01 c #> 2 0.726 1.12 0.860 0.970 a #> 3 -0.920 -1.39 -0.800 -0.920 j #> 4 0.0644 -0.297 -1.60 0.520 z #> 5 -0.587 0.152 -1.27 3.11 o #> 6 -1.23 0.513 -1.32 0.973 d #> 7 0.615 1.89 -1.57 -0.552 t #> 8 -0.707 1.13 0.536 1.08 l #> 9 1.22 1.00 -1.42 -0.400 u #> 10 0.534 2.94 -0.222 0.675 j #> # 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: 509 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -1.00 0.353 0.283 -1.57 #> 2 -0.886 0.199 1.64 -0.417 #> 3 0.386 1.87 0.0963 -0.325 #> 4 -1.28 -1.74 1.02 -1.64 #> 5 -0.428 1.97 -0.960 -0.886 #> 6 -0.696 -0.0458 0.825 0.594 #> 7 -0.0576 1.67 0.984 -0.0994 #> 8 1.39 0.920 1.87 0.798 #> 9 2.40 0.000711 1.10 -0.262 #> 10 -2.04 -1.19 -0.184 -1.20 #> # ℹ 499 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: 200 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -1.00 0.353 0.283 -1.57 #> 2 -0.428 1.97 -0.960 -0.886 #> 3 -2.04 -1.19 -0.184 -1.20 #> 4 -1.52 -0.327 -0.195 0.177 #> 5 -0.398 -0.112 -1.63 0.182 #> 6 -0.643 0.493 -0.672 0.266 #> 7 -1.02 1.37 -1.63 -0.311 #> 8 2.26 -0.788 -1.67 -0.281 #> 9 0.180 -0.00392 -1.88 0.582 #> 10 -0.529 0.870 2.02 -0.0415 #> # ℹ 190 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: 190 × 4 #> cd45 cd38 cd34 cd19 #> #> 1 -0.315 0.160 1.03 0.820 #> 2 -1.34 0.138 1.64 -0.306 #> 3 -1.28 -1.74 1.02 -1.64 #> 4 0.487 -1.20 -0.728 -2.00 #> 5 1.39 0.920 1.87 0.798 #> 6 -0.0217 0.359 0.876 -0.866 #> 7 1.11 0.197 -0.411 0.936 #> 8 0.145 1.44 -0.984 1.05 #> 9 0.310 -1.39 0.195 1.41 #> 10 -0.281 0.265 1.11 -0.438 #> # ℹ 180 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 0.644 -1.20 -0.232 -1.17 v #> 2 2.04 1.48 -0.209 -1.32 j #> 3 0.452 -0.0795 0.0191 1.38 x #> 4 -0.886 -0.0508 0.359 -0.399 i #> 5 0.0550 -0.366 0.696 0.765 p #> 6 -0.747 -1.26 0.764 0.225 k #> 7 1.18 0.667 0.981 -0.288 j #> 8 0.870 -0.572 0.431 -0.0517 y #> 9 1.35 0.408 0.328 0.775 x #> 10 0.144 1.40 0.0902 -0.249 o #> # 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 -0.350 -1.94 -0.824 1.21 a #> 2 -0.302 0.423 -0.871 0.0792 a #> 3 -1.29 -0.273 -1.44 0.806 a #> 4 -0.376 -0.750 -0.0136 -1.13 a #> 5 0.187 -1.31 0.439 -1.15 b #> 6 0.422 -0.801 0.251 -1.35 b #> 7 -0.397 -0.394 -2.26 -0.0954 b #> 8 0.839 0.631 -0.724 2.08 b #> 9 -0.260 -1.09 1.92 -1.23 c #> 10 -0.534 -0.521 1.15 1.12 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 -1.36 0.411 0.548 1.12 1 #> 2 -0.0788 -1.01 2.17 1.62 1 #> 3 -0.0631 -0.366 0.203 1.89 1 #> 4 0.225 0.467 -1.29 0.559 1 #> 5 1.20 1.28 1.03 0.828 1 #> 6 -0.786 -0.349 -0.575 0.980 1 #> 7 -0.562 1.67 0.808 -0.668 1 #> 8 0.0671 -1.00 1.24 -0.923 1 #> 9 1.45 -1.51 -1.56 -1.52 1 #> 10 1.48 0.414 1.22 -0.0824 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 -1.36 0.411 0.548 1.12 0.6 #> 2 -0.0788 -1.01 2.17 1.62 0.2 #> 3 -0.0631 -0.366 0.203 1.89 0.9 #> 4 0.225 0.467 -1.29 0.559 1 #> 5 1.20 1.28 1.03 0.828 0.4 #> 6 -0.786 -0.349 -0.575 0.980 1 #> 7 -0.562 1.67 0.808 -0.668 0.6 #> 8 0.0671 -1.00 1.24 -0.923 1 #> 9 1.45 -1.51 -1.56 -1.52 0 #> 10 1.48 0.414 1.22 -0.0824 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 0.324 0.0821 -0.108 0.0729 0.0187 #> 2 mario 0.174 0.0791 -0.352 0.162 0.000622 #> # 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 0.324 #> 2 kirby a cd38 0.0821 #> 3 kirby a cd34 -0.108 #> 4 kirby a cd19 0.0729 #> 5 kirby b cd45 0.0187 #> 6 kirby b cd38 -0.102 #> 7 kirby b cd34 0.0402 #> 8 kirby b cd19 -0.408 #> 9 kirby c cd45 -0.389 #> 10 kirby c cd38 0.410 #> # 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@q_emd` `stim_cd38@q_emd` `stim_cd34@q_emd` #> #> 1 mario NA NA NA #> 2 kirby NA NA NA #> # ℹ 101 more variables: `stim_cd19@q_emd` , `stim_cd45@z_emd` , #> # `stim_cd38@z_emd` , `stim_cd34@z_emd` , `stim_cd19@z_emd` , #> # `stim_cd45@n_emd` , `stim_cd38@n_emd` , `stim_cd34@n_emd` , #> # `stim_cd19@n_emd` , `stim_cd45@i_emd` , `stim_cd38@i_emd` , #> # `stim_cd34@i_emd` , `stim_cd19@i_emd` , `stim_cd45@h_emd` , #> # `stim_cd38@h_emd` , `stim_cd34@h_emd` , `stim_cd19@h_emd` , #> # `stim_cd45@d_emd` , `stim_cd38@d_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 q cd45 stim 8.56 #> 2 q cd38 stim 14.1 #> 3 q cd34 stim 7.86 #> 4 q cd19 stim 17.9 #> 5 z cd45 stim 5.38 #> 6 z cd38 stim 8.33 #> 7 z cd34 stim 9.05 #> 8 z cd19 stim 4.89 #> 9 n cd45 stim 8.50 #> 10 n cd38 stim 4.99 #> # 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 0.0365 0.0385 0.0325 0.0385 0.0588 0.0487 0.0264 #> 2 mario 0.0138 0.0493 0.0335 0.0237 0.0256 0.0434 0.0296 #> # 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 0.0365 0.0385 0.0325 0.0385 0.0588 0.0487 0.0264 #> 2 mario 0.0138 0.0493 0.0335 0.0237 0.0256 0.0434 0.0296 #> # 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 kirby NA NA NA #> 2 mario NA NA NA #> # ℹ 101 more variables: `stim_cd19@e_jsd` , `stim_cd45@j_jsd` , #> # `stim_cd38@j_jsd` , `stim_cd34@j_jsd` , `stim_cd19@j_jsd` , #> # `stim_cd45@q_jsd` , `stim_cd38@q_jsd` , `stim_cd34@q_jsd` , #> # `stim_cd19@q_jsd` , `stim_cd45@h_jsd` , `stim_cd38@h_jsd` , #> # `stim_cd34@h_jsd` , `stim_cd19@h_jsd` , `stim_cd45@c_jsd` , #> # `stim_cd38@c_jsd` , `stim_cd34@c_jsd` , `stim_cd19@c_jsd` , #> # `stim_cd45@i_jsd` , `stim_cd38@i_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 0.763 #> 2 e cd38 stim 0.764 #> 3 e cd34 stim 0.730 #> 4 e cd19 stim 0.764 #> 5 j cd45 stim 0.680 #> 6 j cd38 stim 0.893 #> 7 j cd34 stim 0.840 #> 8 j cd19 stim 0.924 #> 9 q cd45 stim 0.862 #> 10 q cd38 stim 0.862 #> # 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 0.05 0.042 0.03 0.032 0.028 0.036 0.04 #> 2 mario 0.054 0.04 0.038 0.034 0.04 0.036 0.042 #> # 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 0.05 #> 2 kirby b 0.042 #> 3 kirby c 0.03 #> 4 kirby d 0.032 #> 5 kirby e 0.028 #> 6 kirby f 0.036 #> 7 kirby g 0.04 #> 8 kirby h 0.032 #> 9 kirby i 0.052 #> 10 kirby j 0.038 #> # 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 0.0769 0.0769 #> 2 mario 0.0714 0 0.143 #> # 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 #> 2 kirby a cd38 0.0769 #> 3 kirby a cd34 0.0769 #> 4 kirby a cd19 0 #> 5 kirby b cd45 0.111 #> 6 kirby b cd38 0.148 #> 7 kirby b cd34 0.259 #> 8 kirby b cd19 0.0741 #> 9 kirby c cd45 0 #> 10 kirby c cd38 0.0588 #> # 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,] 442 230 784 689 53 137 330 836 996 69 #> [2,] 874 179 540 168 223 720 504 824 885 383 #> [3,] 213 97 262 688 569 583 921 711 455 436 #> [4,] 691 167 268 281 235 746 958 524 275 449 #> [5,] 681 33 966 976 295 698 239 740 59 993 #> [6,] 641 921 467 213 884 514 552 410 711 3 #> [7,] 735 797 69 294 53 226 238 330 907 129 #> [8,] 525 884 433 469 305 678 332 833 591 6 #> [9,] 975 358 333 394 526 199 692 888 828 48 #> [10,] 159 743 81 440 297 368 985 480 54 456 #> [11,] 278 476 906 232 995 600 272 172 349 73 #> [12,] 821 641 554 514 293 467 725 478 780 702 #> [13,] 88 906 989 349 703 142 11 216 596 147 #> [14,] 724 993 523 59 376 422 496 123 647 976 #> [15,] 375 585 893 249 178 301 967 179 978 250 #> [16,] 636 231 204 175 121 181 377 370 998 960 #> [17,] 590 928 631 901 620 317 388 770 466 529 #> [18,] 853 71 936 822 309 959 989 29 227 597 #> [19,] 807 271 545 727 434 960 679 977 453 302 #> [20,] 77 468 610 520 144 109 156 167 942 235 #> [21,] 958 536 443 158 673 240 347 556 355 285 #> [22,] 648 63 781 176 671 499 725 569 852 436 #> [23,] 363 282 207 985 413 115 882 454 788 381 #> [24,] 457 86 802 267 877 681 943 124 548 740 #> [25,] 623 234 220 279 32 352 662 166 530 185 #> [26,] 141 346 785 221 404 830 868 786 60 833 #> [27,] 487 317 780 89 621 918 554 61 58 171 #> [28,] 353 484 809 587 493 699 49 919 83 156 #> [29,] 172 272 227 936 216 11 675 476 788 989 #> [30,] 72 62 459 576 793 600 344 274 995 127 #> [31,] 712 66 491 494 326 322 51 636 488 472 #> [32,] 64 621 662 508 946 234 4 443 61 487 #> [33,] 5 976 59 845 186 698 539 263 873 772 #> [34,] 42 561 231 181 35 609 16 719 545 977 #> [35,] 42 571 34 889 561 977 379 945 577 545 #> [36,] 655 343 427 357 567 521 882 909 741 309 #> [37,] 907 531 982 762 562 143 318 850 497 135 #> [38,] 866 320 114 529 857 559 149 348 546 254 #> [39,] 284 164 969 259 165 230 400 974 112 595 #> [40,] 666 687 857 611 854 348 559 866 593 669 #> [41,] 871 849 470 361 890 250 717 485 649 847 #> [42,] 561 392 545 19 960 34 35 231 577 977 #> [43,] 524 128 954 846 778 459 148 344 296 127 #> [44,] 522 899 892 419 705 437 653 576 146 513 #> [45,] 93 913 299 598 110 409 294 626 408 334 #> [46,] 402 465 86 976 978 124 350 681 261 848 #> [47,] 290 722 707 206 753 533 328 512 750 198 #> [48,] 59 594 394 374 647 248 724 518 526 5 #> [49,] 156 109 601 116 77 383 587 484 942 504 #> [50,] 794 158 443 240 558 744 155 748 879 536 #> [51,] 494 377 693 365 644 745 712 55 972 121 #> [52,] 318 965 201 137 182 143 907 602 568 37 #> [53,] 512 69 330 154 7 784 143 707 441 797 #> [54,] 138 440 203 783 612 67 368 115 10 78 #> [55,] 181 204 721 365 609 104 377 51 448 121 #> [56,] 984 842 827 897 564 713 312 415 96 190 #> [57,] 310 437 419 930 742 705 895 932 44 899 #> [58,] 694 668 551 918 75 89 751 409 778 652 #> [59,] 724 976 48 33 523 14 993 5 394 647 #> [60,] 26 830 989 983 868 141 633 404 386 902 #> [61,] 275 64 758 825 840 554 471 487 800 27 #> [62,] 459 778 30 110 102 72 335 817 203 385 #> [63,] 499 648 852 731 436 22 947 569 129 584 #> [64,] 61 32 532 534 275 487 846 148 233 443 #> [65,] 686 334 451 464 735 629 41 373 663 690 #> [66,] 712 31 491 693 51 494 854 130 326 488 #> [67,] 203 122 296 247 440 704 335 138 459 102 #> [68,] 106 861 815 557 816 851 640 398 910 937 #> [69,] 970 53 7 133 441 784 330 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220 915 352 622 604 508 820 949 234 #> [766,] 505 507 999 117 699 715 424 236 410 981 #> [767,] 947 700 129 803 499 584 750 334 725 629 #> [768,] 398 861 244 212 937 412 816 992 107 798 #> [769,] 579 911 509 676 477 104 500 561 980 79 #> [770,] 466 17 380 140 101 590 588 990 371 901 #> [771,] 575 888 229 819 818 933 549 991 253 430 #> [772,] 186 539 952 217 277 482 315 647 434 642 #> [773,] 225 507 213 262 929 6 603 8 904 988 #> [774,] 601 248 518 310 818 761 419 610 653 779 #> [775,] 461 92 922 403 169 639 378 560 267 310 #> [776,] 386 902 658 633 387 324 147 973 349 432 #> [777,] 111 323 843 926 222 938 519 193 205 886 #> [778,] 459 62 102 110 122 299 918 704 43 67 #> [779,] 698 618 761 372 109 828 295 374 869 248 #> [780,] 27 89 947 918 129 554 171 852 725 12 #> [781,] 22 803 584 628 725 431 176 499 671 855 #> [782,] 935 480 479 646 557 517 297 237 90 418 #> [783,] 644 440 335 138 54 745 203 972 67 755 #> [784,] 330 474 53 137 689 69 797 7 836 161 #> [785,] 141 26 346 221 786 427 830 833 413 404 #> [786,] 404 830 479 995 476 413 906 785 517 141 #> [787,] 194 715 384 806 64 623 166 840 758 672 #> [788,] 557 227 782 100 476 216 413 917 839 479 #> [789,] 672 185 806 840 229 273 924 167 758 275 #> [790,] 430 530 682 575 549 240 536 234 794 185 #> [791,] 360 843 199 390 952 341 642 953 333 362 #> [792,] 389 605 751 254 529 155 694 145 197 58 #> [793,] 455 576 600 127 516 232 515 30 522 265 #> [794,] 158 50 744 240 443 536 556 682 430 21 #> [795,] 723 485 951 649 624 528 369 890 665 87 #> [796,] 936 853 877 18 548 364 720 29 227 831 #> [797,] 294 143 907 474 925 330 7 735 562 137 #> [798,] 418 311 325 861 910 398 313 640 782 730 #> [799,] 986 117 833 838 303 134 490 184 425 898 #> [800,] 852 746 171 604 820 61 508 471 758 489 #> [801,] 219 90 615 961 492 313 935 423 480 839 #> [802,] 831 160 457 876 267 675 24 877 496 615 #> [803,] 584 700 431 767 499 436 781 725 821 988 #> [804,] 291 677 863 76 340 302 862 875 930 961 #> [805,] 894 791 187 360 199 362 736 843 473 498 #> [806,] 840 672 789 999 275 758 787 64 924 532 #> [807,] 727 271 19 434 453 900 315 212 960 679 #> [808,] 717 373 663 849 925 470 361 73 858 574 #> [809,] 353 493 28 919 580 699 885 658 484 387 #> [810,] 297 368 102 460 306 122 480 98 150 299 #> [811,] 891 659 695 396 236 986 114 425 134 660 #> [812,] 983 125 76 860 364 492 895 517 830 705 #> [813,] 581 878 477 924 920 50 748 794 879 509 #> [814,] 339 196 298 112 970 893 671 464 710 614 #> [815,] 851 106 839 971 90 160 100 615 917 492 #> [816,] 910 640 937 861 414 359 68 412 354 798 #> [817,] 385 502 81 62 335 469 270 962 459 684 #> [818,] 888 601 248 774 518 933 372 779 594 956 #> [819,] 888 549 510 575 199 771 647 372 526 217 #> [820,] 508 746 411 99 489 171 716 800 268 393 #> [821,] 467 12 514 641 436 725 711 803 305 569 #> [822,] 71 959 309 100 407 917 18 788 851 661 #> [823,] 604 915 765 613 701 968 622 949 489 800 #> [824,] 760 301 250 555 871 847 513 218 383 890 #> [825,] 471 554 641 61 852 569 758 688 194 800 #> [826,] 280 841 987 535 606 292 746 674 994 171 #> [827,] 897 56 984 412 414 300 244 338 96 564 #> [828,] 342 526 618 356 333 779 394 186 698 845 #> [829,] 209 429 630 357 343 868 221 655 560 251 #> [830,] 983 404 786 221 26 479 785 812 141 364 #> [831,] 802 160 877 639 267 615 675 125 169 876 #> [832,] 314 150 98 306 423 463 619 810 612 961 #> [833,] 184 270 141 785 525 8 26 678 425 469 #> [834,] 603 929 399 904 262 988 196 614 22 569 #> [835,] 263 732 119 224 350 482 568 182 992 135 #> [836,] 837 137 189 120 996 330 118 354 784 474 #> [837,] 836 189 354 120 137 543 118 113 474 330 #> [838,] 909 741 542 799 36 357 697 343 490 184 #> [839,] 90 492 313 815 615 851 971 801 730 935 #> [840,] 806 758 275 999 97 789 520 61 954 410 #> [841,] 826 535 987 606 280 674 171 756 746 626 #> [842,] 564 56 312 95 984 415 713 190 96 328 #> [843,] 390 777 205 341 202 642 926 111 953 323 #> [844,] 647 956 217 372 277 376 406 248 123 48 #> [845,] 394 848 33 342 526 828 397 976 59 186 #> [846,] 148 443 43 247 558 534 128 64 155 532 #> [847,] 871 250 760 361 470 849 301 513 41 824 #> [848,] 845 394 976 59 33 192 523 402 465 46 #> [849,] 361 470 717 41 871 513 847 808 250 649 #> [850,] 135 224 182 568 37 531 263 627 974 201 #> [851,] 815 971 106 839 100 917 407 90 68 313 #> [852,] 63 471 569 648 554 171 266 800 436 825 #> [853,] 18 597 936 703 989 29 172 796 272 227 #> [854,] 94 130 351 559 444 348 857 66 40 320 #> [855,] 645 603 988 431 803 781 821 467 628 711 #> [856,] 927 177 589 599 656 200 910 426 120 934 #> [857,] 666 348 40 559 866 529 254 38 320 854 #> [858,] 237 646 643 738 808 717 887 543 925 675 #> [859,] 216 413 454 476 934 788 906 23 786 227 #> [860,] 521 635 420 78 630 812 567 340 76 427 #> [861,] 398 798 768 68 816 640 910 412 418 313 #> [862,] 577 863 406 804 657 378 291 875 922 677 #> [863,] 862 577 677 804 406 378 657 302 151 865 #> [864,] 379 421 316 403 92 922 862 657 378 863 #> [865,] 285 556 883 347 302 956 151 536 863 21 #> [866,] 529 320 348 254 38 857 694 792 693 559 #> [867,] 192 498 402 360 257 627 165 465 261 835 #> [868,] 221 983 346 630 26 829 429 211 560 404 #> [869,] 347 761 217 372 180 964 295 779 186 698 #> [870,] 258 578 385 81 444 817 944 656 962 308 #> [871,] 250 847 41 361 849 555 470 890 513 760 #> [872,] 307 771 571 253 103 511 575 123 933 991 #> [873,] 366 632 681 105 887 85 738 361 740 943 #> [874,] 540 2 179 168 223 720 978 528 15 951 #> [875,] 291 76 550 804 912 801 582 862 900 406 #> [876,] 802 475 637 831 160 496 457 997 877 267 #> [877,] 831 169 548 639 267 802 24 457 160 796 #> [878,] 581 477 813 924 79 579 748 509 980 920 #> [879,] 748 215 532 500 534 233 558 50 79 924 #> [880,] 163 329 957 200 750 722 710 290 996 408 #> [881,] 960 326 453 463 998 488 718 306 807 727 #> [882,] 427 567 521 115 170 592 183 23 860 763 #> [883,] 151 556 382 865 347 991 148 956 273 677 #> [884,] 433 469 305 525 467 8 514 6 173 921 #> [885,] 614 249 432 139 179 399 353 218 375 809 #> [886,] 908 667 519 111 938 174 685 946 446 323 #> [887,] 943 366 738 873 105 717 858 632 675 555 #> [888,] 819 818 771 372 549 510 594 48 779 575 #> [889,] 565 683 920 35 509 337 477 307 980 379 #> [890,] 485 41 871 649 250 978 178 555 375 849 #> [891,] 811 236 659 194 986 384 695 546 114 149 #> [892,] 522 44 634 387 942 468 899 653 793 386 #> [893,] 15 112 375 585 969 178 967 617 405 259 #> [894,] 805 187 791 473 193 843 360 737 362 202 #> [895,] 274 612 517 98 78 932 314 423 150 420 #> [896,] 819 647 510 724 277 199 527 549 888 395 #> [897,] 827 56 96 244 300 564 984 412 338 414 #> [898,] 134 184 537 8 833 525 542 428 332 108 #> [899,] 44 522 419 146 437 761 964 180 610 892 #> [900,] 727 912 153 212 664 807 992 434 550 582 #> [901,] 393 716 388 17 371 990 631 570 317 916 #> [902,] 387 386 776 633 973 919 658 892 914 246 #> [903,] 450 563 370 381 282 16 363 82 636 289 #> [904,] 256 225 139 583 506 616 658 399 973 633 #> [905,] 505 920 581 424 715 924 878 452 337 672 #> [906,] 995 476 11 173 216 786 232 13 413 349 #> [907,] 143 37 318 562 797 474 531 762 925 982 #> [908,] 886 519 938 667 685 946 111 691 411 820 #> [909,] 741 838 36 697 357 567 654 343 427 80 #> [910,] 640 816 599 798 927 325 177 861 311 354 #> [911,] 769 579 676 509 980 477 79 337 188 104 #> [912,] 153 632 664 582 219 105 900 423 150 738 #> [913,] 409 45 93 299 252 598 562 762 75 264 #> [914,] 265 981 591 733 999 552 97 410 892 386 #> [915,] 823 968 166 765 352 728 604 662 508 25 #> [916,] 570 458 441 176 512 707 226 129 750 680 #> [917,] 407 100 309 822 661 125 71 851 815 839 #> [918,] 58 551 778 89 694 668 780 478 459 62 #> [919,] 387 504 942 653 963 902 246 892 973 699 #> [920,] 581 565 878 813 337 477 509 980 905 683 #> [921,] 552 455 410 97 344 266 6 591 3 514 #> [922,] 378 92 775 403 461 657 862 406 677 863 #> [923,] 222 931 926 197 355 488 673 553 323 679 #> [924,] 878 672 581 813 806 748 789 715 879 532 #> [925,] 797 808 294 531 474 907 373 37 327 319 #> [926,] 323 673 777 953 923 519 843 111 222 744 #> [927,] 599 589 177 856 910 426 120 640 656 189 #> [928,] 631 620 279 17 662 739 590 32 621 946 #> [929,] 603 262 569 641 213 773 834 6 471 711 #> [930,] 677 57 310 151 314 437 76 895 804 419 #> [931,] 355 331 923 488 673 197 718 255 222 208 #> [932,] 437 705 274 895 423 643 105 742 98 522 #> [933,] 481 229 818 991 382 714 260 774 273 771 #> [934,] 859 216 749 703 13 454 308 476 906 88 #> [935,] 782 480 297 517 219 479 801 90 615 418 #> [936,] 29 227 853 172 18 675 272 796 71 548 #> [937,] 816 768 414 861 107 244 359 68 910 412 #> [938,] 588 519 908 886 371 685 777 323 111 926 #> [939,] 530 682 242 744 486 794 790 240 430 380 #> [940,] 170 730 998 231 115 370 586 121 719 313 #> [941,] 587 692 484 728 49 949 604 109 401 156 #> [942,] 653 504 919 892 77 387 246 20 419 468 #> [943,] 555 740 717 887 366 649 681 871 849 361 #> [944,] 126 308 551 753 416 385 668 870 918 767 #> [945,] 511 422 577 379 657 862 35 42 376 864 #> [946,] 519 240 621 691 958 323 32 886 667 908 #> [947,] 129 767 780 725 499 731 63 803 436 626 #> [948,] 505 715 608 905 766 787 83 672 194 806 #> [949,] 501 622 941 692 823 604 765 405 613 726 #> [950,] 593 695 126 336 669 478 243 12 944 293 #> [951,] 795 432 528 485 890 649 464 369 451 624 #> [952,] 772 315 953 186 362 277 642 539 510 647 #> [953,] 673 642 926 952 323 843 341 285 536 958 #> [954,] 520 43 128 468 733 524 148 97 273 275 #> [955,] 589 177 670 445 927 157 856 354 426 599 #> [956,] 372 217 844 347 991 869 865 285 883 151 #> [957,] 163 880 200 996 589 189 329 120 836 837 #> [958,] 21 691 536 667 162 443 4 347 946 240 #> [959,] 822 71 309 100 407 917 18 788 661 851 #> [960,] 881 392 998 727 302 807 463 19 745 453 #> [961,] 150 423 340 801 420 517 612 76 297 78 #> [962,] 684 660 979 502 495 702 573 335 210 706 #> [963,] 246 919 942 653 387 902 419 481 310 504 #> [964,] 295 180 761 146 899 698 869 98 105 610 #> [965,] 52 483 283 696 132 318 91 137 201 143 #> [966,] 239 740 366 681 105 5 295 267 873 964 #> [967,] 708 15 292 969 764 994 178 397 280 893 #> [968,] 716 662 393 166 915 701 823 388 84 279 #> [969,] 112 284 617 238 967 259 708 893 261 327 #> [970,] 69 689 133 298 710 339 112 238 441 7 #> [971,] 851 815 839 313 90 730 398 106 661 550 #> [972,] 644 684 704 783 578 440 203 979 694 67 #> [973,] 633 699 902 591 658 387 776 225 386 506 #> [974,] 627 850 135 446 318 397 257 131 201 360 #> [975,] 9 358 394 333 526 828 48 199 888 845 #> [976,] 59 33 5 848 350 993 523 465 845 86 #> [977,] 367 721 561 19 545 377 392 355 42 55 #> [978,] 890 375 485 178 617 871 555 41 250 46 #> [979,] 210 706 684 495 660 335 962 502 233 247 #> [980,] 260 382 911 477 683 878 769 337 151 991 #> [981,] 410 552 733 999 678 914 265 921 72 954 #> [982,] 762 651 37 174 264 162 562 205 497 913 #> [983,] 830 404 705 364 812 786 868 274 386 221 #> [984,] 56 312 842 827 713 415 435 190 289 897 #> [985,] 207 311 10 81 258 159 381 23 282 418 #> [986,] 117 799 833 236 134 678 659 811 8 425 #> [987,] 606 826 535 841 634 280 674 218 574 144 #> [988,] 645 603 711 855 431 596 803 305 616 142 #> [989,] 703 597 13 216 147 172 60 88 29 272 #> [990,] 140 570 901 198 388 533 916 17 458 611 #> [991,] 382 883 956 556 307 151 229 933 260 185 #> [992,] 664 212 153 119 732 900 244 912 632 482 #> [993,] 14 59 5 724 976 496 376 123 239 48 #> [994,] 292 708 280 535 356 618 764 826 847 342 #> [995,] 906 476 173 232 11 786 600 195 30 404 #> [996,] 836 189 957 722 837 880 137 47 329 290 #> [997,] 438 160 831 125 876 815 877 407 917 802 #> [998,] 730 940 370 170 960 881 313 418 745 115 #> [999,] 733 981 410 840 806 914 552 97 273 954 #> [1000,] 429 424 80 251 209 829 683 452 211 630 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.75123324 0.7728336 0.8535100 0.9610233 0.9988456 1.0241624 1.0346625 #> [2,] 0.72794121 0.8192607 1.0118221 1.0396370 1.0447104 1.0736597 1.1356458 #> [3,] 0.31837803 0.4564968 0.4829120 0.5219559 0.5329834 0.6032451 0.6036069 #> [4,] 0.31047166 0.3921811 0.4326476 0.4630233 0.5398038 0.5555416 0.6151065 #> [5,] 0.46734788 0.4760593 0.5466231 0.5606464 0.5640889 0.5726195 0.5783608 #> [6,] 0.51152215 0.5275568 0.5497865 0.5556941 0.5624806 0.6122315 0.6278000 #> [7,] 0.29359887 0.5086648 0.5542773 0.5716974 0.5869704 0.5940340 0.5959630 #> [8,] 0.26143033 0.4718084 0.5603310 0.5707832 0.6882329 0.7308097 0.7341715 #> [9,] 0.24984831 0.5609735 0.8632232 0.9219492 1.0170726 1.0442705 1.0520745 #> [10,] 0.28986430 0.4537154 0.5204006 0.5421002 0.5423238 0.5463735 0.5587194 #> [11,] 0.46652531 0.5084248 0.5263341 0.5266163 0.5446352 0.5495054 0.5850168 #> [12,] 0.37170190 0.4381048 0.5241100 0.5423993 0.5424052 0.5655038 0.6071722 #> [13,] 0.57222011 0.6124874 0.6357935 0.6396935 0.6462147 0.6708131 0.7391421 #> [14,] 0.34717912 0.5838188 0.5962627 0.6141551 0.6745910 0.8141714 0.8167852 #> [15,] 0.58059369 0.6224053 0.6662234 0.6803393 0.7407235 0.8389967 0.8429282 #> [16,] 0.63314368 0.8630664 0.8939270 1.0213772 1.0458141 1.0663115 1.1116785 #> [17,] 0.54835213 0.7198504 0.7467861 0.8400073 0.8529885 0.9064278 0.9411035 #> [18,] 0.66924590 0.6764030 0.8256855 0.8928497 0.9098371 0.9177645 0.9264857 #> [19,] 0.47391219 0.5187272 0.6039699 0.6313239 0.7718839 0.8130187 0.8617744 #> [20,] 0.15081454 0.3758961 0.3943047 0.4202105 0.4597621 0.5058134 0.5526436 #> [21,] 0.26227964 0.3306440 0.4625359 0.4748135 0.5735866 0.5903636 0.6051760 #> [22,] 0.43233503 0.5294653 0.5807848 0.6182965 0.6219620 0.6649769 0.6820446 #> [23,] 0.51569242 0.5551573 0.6019943 0.6874485 0.7129012 0.7578704 0.7810091 #> [24,] 0.26564505 0.8384672 0.9027206 0.9060224 0.9245897 0.9398366 0.9480771 #> [25,] 0.58665844 0.8659828 0.8981276 0.9708535 1.0050467 1.0329852 1.0364884 #> [26,] 0.19951012 0.3092625 0.3686001 0.4240887 0.6186630 0.6479357 0.7211515 #> [27,] 0.36583627 0.4208790 0.4291136 0.5052316 0.5424372 0.6643204 0.6768602 #> [28,] 0.63749376 0.6643837 0.7336474 0.8632298 0.9630081 1.0143946 1.0585654 #> [29,] 0.22773632 0.2810413 0.3024392 0.6371490 0.7398366 0.7736565 0.7775577 #> [30,] 0.41843597 0.4328745 0.4340449 0.5067279 0.5268015 0.5302476 0.5624027 #> [31,] 0.44109626 0.5183360 0.6860934 0.9339130 0.9629930 1.0579500 1.0943981 #> [32,] 0.51867312 0.5645729 0.6016788 0.6357531 0.6454604 0.6971910 0.7115832 #> [33,] 0.47605929 0.5257607 0.5566021 0.5824304 0.6490195 0.7135536 0.7156801 #> [34,] 0.97263621 1.1064219 1.1444155 1.1538665 1.1596231 1.2692750 1.3001680 #> [35,] 0.98260936 1.1123529 1.1596231 1.1717316 1.1850187 1.2225809 1.2601779 #> [36,] 0.49461149 0.5933300 0.6646068 0.6683092 0.8323921 0.8352585 0.8584428 #> [37,] 0.33752748 0.3668717 0.3733893 0.3738390 0.4858712 0.5329507 0.5359664 #> [38,] 0.63792553 0.6805158 0.8052105 0.8253294 0.9434459 0.9543999 0.9622925 #> [39,] 0.79115486 0.9026125 1.0002906 1.0816304 1.1440999 1.2319974 1.2718061 #> [40,] 0.43594864 0.7188591 0.7189500 0.8269726 1.0044478 1.0197690 1.0395219 #> [41,] 0.29776484 0.3110705 0.3169707 0.3448560 0.3540405 0.3739873 0.4252863 #> [42,] 0.56407060 0.8340099 0.9236600 0.9486578 0.9507656 0.9726362 0.9826094 #> [43,] 0.35070927 0.4212043 0.4399540 0.5016230 0.5245879 0.5408275 0.5485840 #> [44,] 0.13879613 0.2449583 0.3716588 0.3857744 0.4513943 0.4539900 0.4953717 #> [45,] 0.27674962 0.3248874 0.3792540 0.4061885 0.4685934 0.5096766 0.5394649 #> [46,] 0.70931322 0.7762396 0.7874432 0.8142466 0.8157303 0.8707487 0.8863619 #> [47,] 0.34194145 0.6358041 0.6792359 0.6991985 0.7115954 0.7373865 0.8197065 #> [48,] 0.54036550 0.6094105 0.6267325 0.6674362 0.7317710 0.7325700 0.7442621 #> [49,] 0.53167438 0.6307679 0.7248237 0.7447617 0.7648825 0.7757223 0.7758032 #> [50,] 0.34302240 0.4088468 0.4949305 0.6649502 0.6736528 0.6801958 0.6810368 #> [51,] 0.44016974 0.5076383 0.5175767 0.7000366 0.7683283 0.7880880 0.8114595 #> [52,] 0.34683737 0.4084745 0.5982558 0.5990630 0.6392620 0.6435954 0.6734625 #> [53,] 0.43617718 0.4965451 0.5327786 0.5778905 0.5869704 0.5978329 0.6719906 #> [54,] 0.30248329 0.3537647 0.3890210 0.5052213 0.5101134 0.5352680 0.5394586 #> [55,] 0.52308478 0.5402685 0.5593313 0.5874356 0.7559557 0.7821695 0.7822892 #> [56,] 0.69637689 0.7825293 0.8849706 0.9317456 0.9860083 1.0912192 1.1467084 #> [57,] 0.16587260 0.2957155 0.3413110 0.4116207 0.4205946 0.5540923 0.6107552 #> [58,] 0.21674498 0.2655099 0.3790429 0.3932341 0.4436496 0.5844425 0.6218011 #> [59,] 0.40454188 0.4800348 0.5403655 0.5566021 0.5771780 0.6141551 0.6305519 #> [60,] 0.75561149 0.7588936 0.7819681 0.7975871 0.8945171 0.9152283 0.9162163 #> [61,] 0.30100470 0.4964479 0.5864232 0.5975842 0.6878447 0.6894267 0.6965180 #> [62,] 0.16282452 0.3341115 0.4328745 0.4532430 0.4765493 0.5213406 0.5403077 #> [63,] 0.27072483 0.3851949 0.4612409 0.5142720 0.5167208 0.5294653 0.6030569 #> [64,] 0.49644789 0.5186731 0.5261169 0.5533974 0.5655843 0.6290988 0.6358013 #> [65,] 0.35509133 0.5000259 0.5016800 0.5273160 0.5482631 0.6101186 0.6657547 #> [66,] 0.51826095 0.5183360 0.6878115 0.9025787 0.9928195 1.0014826 1.0029237 #> [67,] 0.20842890 0.3411604 0.3568485 0.4068925 0.4214355 0.4377764 0.4642517 #> [68,] 0.47750098 0.6644961 0.8175037 0.8371260 0.8424111 0.8428174 0.8493960 #> [69,] 0.45928747 0.4965451 0.5542773 0.5917991 0.6237566 0.6343305 0.6550373 #> [70,] 1.00949093 1.1650926 1.2078393 1.2169444 1.2341341 1.2526122 1.3587940 #> [71,] 0.22038706 0.3698291 0.5063805 0.6682706 0.6764030 0.6803568 0.7025122 #> [72,] 0.41824000 0.4184360 0.4325937 0.4377125 0.4848735 0.5098943 0.5213406 #> [73,] 0.36595529 0.4150283 0.4230495 0.4715471 0.4820203 0.4893759 0.5093840 #> [74,] 0.48657110 0.5810022 0.7735514 0.8210664 0.8910608 0.8927631 0.9176643 #> [75,] 0.40020124 0.4232959 0.4436496 0.4596097 0.5149030 0.5152366 0.5463399 #> [76,] 0.48817965 0.4927598 0.5107277 0.5623835 0.5859066 0.5935301 0.5946564 #> [77,] 0.15081454 0.4335340 0.4363955 0.5182849 0.5205824 0.5418517 0.5425802 #> [78,] 0.30995948 0.3273770 0.4254990 0.4672448 0.5121810 0.5136565 0.5154224 #> [79,] 0.47970446 0.4947340 0.6647554 0.7263034 0.7535873 0.7609947 0.7625186 #> [80,] 0.79618626 0.8698408 0.8936291 0.8975484 0.9456737 0.9510369 0.9690743 #> [81,] 0.47623620 0.4877438 0.4971451 0.5032684 0.5146468 0.5204006 0.5501698 #> [82,] 0.93583477 1.0071221 1.0885044 1.1050823 1.1755327 1.1912636 1.2374069 #> [83,] 0.70018889 0.8879985 0.9946745 1.0809744 1.1726914 1.2589149 1.3267449 #> [84,] 0.85574145 0.9550328 0.9788586 0.9906871 1.0431189 1.0460363 1.0573611 #> [85,] 0.32254407 0.4354345 0.4755552 0.4913438 0.5488552 0.5558120 0.6032168 #> [86,] 0.58625836 0.7660761 0.7706474 0.7710468 0.7778610 0.7782860 0.7874432 #> [87,] 0.39066227 0.4763041 0.5491078 0.6551946 0.6561217 0.6865512 0.7407140 #> [88,] 0.40030939 0.5722201 0.6381720 0.6390878 0.6411925 0.6611606 0.6664912 #> [89,] 0.46587098 0.5052316 0.5139742 0.5400055 0.5485160 0.5844425 0.5922445 #> [90,] 0.19996637 0.2139283 0.3522130 0.3596801 0.5070188 0.5268149 0.5578403 #> [91,] 0.34578357 0.3713855 0.5607744 0.5739698 0.5832266 0.5948363 0.6034775 #> [92,] 0.13118949 0.4307515 0.5089294 0.6219634 0.7261603 0.7798434 0.8711605 #> [93,] 0.15515797 0.2767496 0.3113777 0.3347892 0.4146456 0.4380970 0.4573444 #> [94,] 0.40949008 0.7662003 0.8612254 0.8767726 0.8964731 1.1548959 1.1752675 #> [95,] 0.81376542 0.8750154 0.8946274 0.8990536 0.9284389 0.9383646 0.9488876 #> [96,] 0.36398921 0.7536512 0.7638342 0.8374410 0.8986590 0.9269765 0.9570827 #> [97,] 0.40763037 0.4564968 0.4906205 0.4921939 0.4925928 0.5062591 0.5259640 #> [98,] 0.27744439 0.3237234 0.4171864 0.4533024 0.4574603 0.5073590 0.5079862 #> [99,] 0.44986007 0.5442689 0.6053123 0.6070115 0.6822141 0.6850527 0.8025247 #> [100,] 0.36217182 0.4002402 0.5479470 0.6148331 0.6682706 0.6987118 0.7176815 #> [101,] 1.16034211 1.1830457 1.4589260 1.4813706 1.5482614 1.6614958 1.7219734 #> [102,] 0.20121330 0.2754540 0.3096327 0.3457012 0.3905825 0.3975459 0.4304232 #> [103,] 1.20255514 1.2330142 1.2589149 1.3426098 1.3481100 1.5117781 1.6069093 #> [104,] 0.44558674 0.5320156 0.5873528 0.5957944 0.6836550 0.6969275 0.7006954 #> [105,] 0.28454807 0.4706502 0.4755460 0.5303708 0.5371465 0.5512903 0.5558524 #> [106,] 0.47750098 0.4851378 0.4973154 0.5868762 0.7283071 0.8416151 0.8593591 #> [107,] 0.98216529 1.0064208 1.1040333 1.2141759 1.2774960 1.2957623 1.3022217 #> [108,] 0.47869309 0.8343755 0.9840538 1.0110834 1.0577510 1.0926077 1.1350025 #> [109,] 0.49710326 0.5006748 0.5058134 0.5114418 0.5418517 0.5850060 0.5912959 #> [110,] 0.19702653 0.3113777 0.3457012 0.4007505 0.4532430 0.4683849 0.4685934 #> [111,] 0.36008810 0.4174495 0.4408895 0.4548463 0.4828149 0.5061000 0.5141196 #> [112,] 0.57458749 0.6909275 0.7177089 0.7218150 0.7744658 0.8237228 0.8288619 #> [113,] 0.57147892 0.6049098 0.6478061 0.6816491 0.7262376 0.7536512 0.8159034 #> [114,] 0.53953399 0.6014050 0.6576091 0.6786399 0.6947844 0.7110033 0.7216347 #> [115,] 0.48599759 0.5154224 0.5558413 0.5789364 0.6354666 0.6547929 0.6603546 #> [116,] 0.33628768 0.4650064 0.5263157 0.7069239 0.7447617 0.7891283 0.8259153 #> [117,] 0.69958193 0.9283156 1.0939061 1.1216403 1.1250323 1.1288179 1.1515432 #> [118,] 0.36398921 0.5714789 0.6894734 0.7014631 0.7260693 0.7619641 0.8240498 #> [119,] 0.29785763 0.3904106 0.5147426 0.5658674 0.5798745 0.5887206 0.5896853 #> [120,] 0.35909924 0.4613995 0.4725982 0.5263326 0.5385190 0.5893542 0.6825840 #> [121,] 0.35748401 0.4455867 0.5154027 0.6343369 0.6851376 0.7147886 0.7230207 #> [122,] 0.26383426 0.2754540 0.2988481 0.2991627 0.3411604 0.4419143 0.4423288 #> [123,] 0.62395483 0.6685404 0.7950016 0.8067952 0.8221489 0.8340085 0.8765175 #> [124,] 0.82937244 0.8707487 0.9551027 1.0103346 1.0519770 1.0832535 1.2253165 #> [125,] 0.55221468 0.5857830 0.6497453 0.6719993 0.6832759 0.6859977 0.7828531 #> [126,] 0.45402224 0.6171313 0.6190679 0.7087893 0.7216347 0.7515560 0.7782790 #> [127,] 0.27028389 0.3572052 0.3993061 0.4557764 0.4626339 0.4726576 0.4993475 #> [128,] 0.28309416 0.4212043 0.4574603 0.4870235 0.5599778 0.5800733 0.5826591 #> [129,] 0.25146438 0.4186988 0.5370477 0.5654464 0.5749203 0.6088727 0.6190746 #> [130,] 0.49033543 0.5993018 0.6014718 0.6776866 0.7389885 0.7588429 0.7662003 #> [131,] 0.35233048 0.4090712 0.5369264 0.6189582 0.6317574 0.6684878 0.6798811 #> [132,] 0.48681494 0.5408715 0.6034775 0.6051837 0.6162894 0.6389791 0.6392017 #> [133,] 0.20289628 0.5917991 0.7040529 0.8219994 0.8745047 0.9693476 0.9888539 #> [134,] 0.86087471 0.8734834 0.9566091 1.0393017 1.0486299 1.1115831 1.1149204 #> [135,] 0.27865399 0.2969847 0.3897075 0.4253274 0.4470561 0.4950304 0.5887206 #> [136,] 0.73790913 0.7727693 0.7880136 0.8494167 0.9279067 1.0006806 1.0803707 #> [137,] 0.45981635 0.4838382 0.5119779 0.5679398 0.5857583 0.5990630 0.6031911 #> [138,] 0.30248329 0.3120646 0.3843879 0.3865320 0.4880859 0.4925018 0.5331744 #> [139,] 0.36139975 0.3821192 0.3884705 0.4467783 0.6854213 0.6990176 0.7259507 #> [140,] 0.79370421 1.1505867 1.1890757 1.1910424 1.2222432 1.3167299 1.3355435 #> [141,] 0.19951012 0.2417104 0.2781614 0.4680106 0.6250949 0.6525452 0.6603980 #> [142,] 0.37893352 0.4015229 0.4064958 0.4696581 0.4792169 0.4909026 0.5502156 #> [143,] 0.31849457 0.3427354 0.3941537 0.4860365 0.5329507 0.5679398 0.5687805 #> [144,] 0.36318027 0.4053421 0.4399584 0.4597621 0.5413375 0.5425802 0.5480974 #> [145,] 0.38772521 0.4192009 0.4871528 0.5026201 0.5073320 0.6164275 0.6390106 #> [146,] 0.26642362 0.3237234 0.4394007 0.4495239 0.4681588 0.4948503 0.5472648 #> [147,] 0.61755802 0.6381720 0.6557861 0.7506527 0.7529128 0.7540028 0.7591031 #> [148,] 0.18634156 0.5009155 0.5485840 0.5599778 0.5931087 0.5965084 0.6349871 #> [149,] 0.12046345 0.5411184 0.6786399 0.6798061 0.6833299 0.7009866 0.7406080 #> [150,] 0.17188245 0.3712625 0.3743934 0.3987000 0.4416277 0.5023142 0.5079862 #> [151,] 0.37547110 0.4474790 0.4828926 0.5041523 0.6416367 0.6856432 0.7161804 #> [152,] 0.79937319 0.8241613 0.8907011 1.0241436 1.1297508 1.1573210 1.1658958 #> [153,] 0.19419128 0.2061225 0.3007250 0.4605015 0.5260537 0.5295352 0.5749598 #> [154,] 0.30085979 0.3841699 0.5687805 0.5778905 0.6377990 0.6564521 0.6819878 #> [155,] 0.19513800 0.4827132 0.5439209 0.5786936 0.5950256 0.6442066 0.6687645 #> [156,] 0.46868378 0.5114418 0.5316744 0.5526436 0.5607080 0.6290993 0.6300735 #> [157,] 0.59045114 0.9869900 1.1535666 1.1582809 1.1780062 1.2135801 1.2585351 #> [158,] 0.24545491 0.3333003 0.3969425 0.4088468 0.4721369 0.4748135 0.5520819 #> [159,] 0.28986430 0.3544125 0.3764880 0.3992212 0.5491586 0.5680241 0.5835483 #> [160,] 0.43780840 0.4498638 0.7033036 0.7877849 0.8172419 0.8357316 0.8577205 #> [161,] 0.40582631 0.5054017 0.7030924 0.7243780 0.7407140 0.7705307 0.7930608 #> [162,] 0.35234103 0.4147042 0.4795022 0.4952720 0.4962613 0.4989207 0.5185135 #> [163,] 0.54300594 0.5583139 0.6795859 0.8095291 0.8206264 0.9728522 1.1327863 #> [164,] 0.52367020 0.5885379 0.8717503 0.8939719 0.9026125 0.9614319 1.0410679 #> [165,] 0.96143186 1.0199945 1.0232013 1.0551349 1.0902843 1.1010699 1.1045643 #> [166,] 0.61571818 0.7040191 0.7058041 0.7656191 0.7734775 0.7824822 0.8215516 #> [167,] 0.35229033 0.3921811 0.5471192 0.5639015 0.5672174 0.5735262 0.5753703 #> [168,] 0.96825642 1.0396370 1.1898986 1.2800371 1.2881848 1.3270543 1.3435666 #> [169,] 0.66673361 0.6783771 0.6894816 0.7395991 0.7798434 0.8020988 0.8409993 #> [170,] 0.28743920 0.4859976 0.6151464 0.6426209 0.6501716 0.6633979 0.7000754 #> [171,] 0.31719653 0.4426525 0.4802058 0.5162458 0.5402894 0.5484489 0.5699656 #> [172,] 0.08045996 0.2277363 0.4479699 0.6083513 0.7456291 0.7558175 0.7626539 #> [173,] 0.26393459 0.3593245 0.3717283 0.3820295 0.4015229 0.4740713 0.5161408 #> [174,] 0.30338965 0.3445281 0.3752012 0.3833929 0.4174495 0.4185740 0.5224324 #> [175,] 0.60837870 0.6760452 0.7997734 0.8205199 0.8496036 0.8767713 0.8946601 #> [176,] 0.61829653 0.6366517 0.6997558 0.7136063 0.7383797 0.7610103 0.8476542 #> [177,] 0.29403821 0.3544237 0.5263326 0.5589544 0.6405376 0.6407043 0.6920594 #> [178,] 0.30753941 0.4389056 0.4819657 0.5267271 0.5626776 0.5839383 0.6590841 #> [179,] 0.61451410 0.8065479 0.8074916 0.8192607 0.8541983 0.8731405 0.9437775 #> [180,] 0.26642362 0.3097676 0.3796937 0.3842518 0.5073590 0.5130520 0.5358143 #> [181,] 0.47013970 0.5230848 0.5470321 0.8496036 0.8674290 0.9467661 0.9817323 #> [182,] 0.36021158 0.4470561 0.4515219 0.4767623 0.5241343 0.5364844 0.5865542 #> [183,] 0.53317440 0.6307292 0.6375269 0.6506767 0.6550839 0.6692890 0.6958393 #> [184,] 0.59828158 0.7636470 0.8197293 0.8356096 0.8501639 0.8775427 0.9832344 #> [185,] 0.51230390 0.5204844 0.5648963 0.7084205 0.7865361 0.7996885 0.8246749 #> [186,] 0.38596645 0.4145507 0.5691145 0.5989056 0.6002112 0.6328652 0.6405012 #> [187,] 0.96030647 1.0761704 1.0808862 1.0870889 1.1122201 1.1279417 1.1959206 #> [188,] 0.49473402 0.6299515 0.6832576 0.6942725 0.6958393 0.7062105 0.7064561 #> [189,] 0.35909924 0.4255819 0.5056986 0.5103887 0.6192480 0.7221886 0.7384616 #> [190,] 0.48660327 0.7114507 0.7246401 0.7313637 0.8284993 1.0153069 1.0305508 #> [191,] 0.65050082 0.6816491 0.6842797 0.7600037 0.7705307 0.7733659 0.8202470 #> [192,] 0.81182782 0.8194373 0.8417935 1.1055276 1.1253849 1.1289979 1.1556449 #> [193,] 0.66753911 0.7257133 0.7266951 0.7743539 0.8705561 0.9086395 0.9237531 #> [194,] 0.62045966 0.7111853 0.7449372 0.8068628 0.8219124 0.8335637 0.8411046 #> [195,] 0.32084307 0.3593245 0.3889879 0.4064958 0.4534542 0.5020450 0.5061745 #> [196,] 0.59162905 0.6261190 0.6519473 0.6742100 0.7273983 0.7396244 0.7504098 #> [197,] 0.25774459 0.3922203 0.4871528 0.5731440 0.5860091 0.6188489 0.6274038 #> [198,] 0.55964953 0.6301278 0.7006146 0.7704925 0.8103618 0.8187889 0.8411695 #> [199,] 0.56526701 0.5919769 0.7933394 0.8006608 0.8031460 0.8174900 0.9500898 #> [200,] 0.63850522 0.6481022 0.7466410 0.8095291 0.8236840 0.8689609 0.8885308 #> [201,] 0.27865399 0.2923046 0.5241343 0.5313052 0.5860547 0.5909128 0.5959543 #> [202,] 0.42631331 0.5904569 0.5998869 0.7362744 0.7605896 0.8071532 0.8210664 #> [203,] 0.20842890 0.2428933 0.2988481 0.3402142 0.3697308 0.3865320 0.3890210 #> [204,] 0.47013970 0.5402685 0.6083787 0.7876943 0.8457159 0.8897081 0.8939270 #> [205,] 0.34090425 0.4108805 0.4379580 0.5384481 0.5431463 0.5516274 0.5550702 #> [206,] 0.52132482 0.5291135 0.5549221 0.6991985 0.7043297 0.7597526 0.7809424 #> [207,] 0.21587317 0.5501698 0.5944061 0.6019943 0.6036912 0.6121399 0.6232368 #> [208,] 0.19508862 0.3493520 0.4058071 0.5062397 0.5209490 0.5405174 0.5476367 #> [209,] 0.34835901 0.3519596 0.4355776 0.7052760 0.7261054 0.7961863 0.7980408 #> [210,] 0.15472873 0.2741477 0.3276143 0.3874743 0.4512306 0.4848735 0.5456915 #> [211,] 0.30083629 0.5030022 0.5802170 0.6049625 0.6124386 0.6855360 0.7587768 #> [212,] 0.50905999 0.5763424 0.6077715 0.6494028 0.6571812 0.7116446 0.7209059 #> [213,] 0.31837803 0.3607279 0.5027575 0.5151727 0.5491726 0.5556941 0.6165135 #> [214,] 0.58913260 0.8071999 0.9856087 0.9869900 1.0638696 1.1631375 1.1634441 #> [215,] 0.31003120 0.3585382 0.3890705 0.3975790 0.4177120 0.5668593 0.5714364 #> [216,] 0.40446900 0.4795265 0.5715888 0.5716924 0.6813370 0.7271226 0.7320339 #> [217,] 0.41890859 0.4878582 0.5088305 0.5258547 0.5416196 0.5602363 0.5909218 #> [218,] 0.40846562 0.4148849 0.4390372 0.4579331 0.4686838 0.5092006 0.5149004 #> [219,] 0.31027223 0.3580854 0.4416277 0.4557797 0.4635054 0.4980985 0.5223489 #> [220,] 0.67821809 0.7269518 0.7884421 0.8981276 0.9696410 1.0095566 1.0242894 #> [221,] 0.29113310 0.4240887 0.4680106 0.5413932 0.5551246 0.6033871 0.6445696 #> [222,] 0.31200656 0.3922203 0.5089802 0.6606336 0.6940500 0.7074970 0.7195269 #> [223,] 0.80654788 0.8376506 0.9906952 1.0447104 1.0594308 1.1200518 1.1231975 #> [224,] 0.29785763 0.3634108 0.3946379 0.4253274 0.5136025 0.5313052 0.5568903 #> [225,] 0.51517267 0.6075172 0.6088218 0.6133793 0.6376472 0.6498677 0.6685887 #> [226,] 0.41347323 0.4914823 0.5701759 0.5940340 0.6190746 0.6238119 0.6348328 #> [227,] 0.30243922 0.4479699 0.4755517 0.5638175 0.6171614 0.6514300 0.7090876 #> [228,] 0.90555460 0.9918885 1.1684544 1.1782701 1.2056914 1.2159078 1.2336533 #> [229,] 0.51230390 0.6631301 0.6653664 0.7335557 0.7518248 0.8405305 0.9252294 #> [230,] 0.77283362 0.8079880 0.8467161 0.8528566 0.8555573 0.9325761 0.9362719 #> [231,] 0.59961280 0.6343369 0.7535997 0.7794280 0.8462224 0.8493717 0.8586241 #> [232,] 0.26393459 0.2679328 0.2736122 0.3208431 0.3571812 0.4792169 0.4811862 #> [233,] 0.17875079 0.3975790 0.4053346 0.4628703 0.5497720 0.5765510 0.6245413 #> [234,] 0.67821809 0.6819256 0.6971910 0.7084205 0.7768582 0.8004708 0.8130695 #> [235,] 0.35229033 0.4113023 0.4561031 0.4946848 0.4971033 0.5271013 0.5398038 #> [236,] 0.62906531 0.7234641 0.7449372 0.7810325 0.8642700 0.8649101 0.8839147 #> [237,] 0.20395837 0.3844797 0.4306182 0.5561102 0.5694909 0.5874698 0.5900798 #> [238,] 0.57458749 0.5959630 0.6238756 0.6348328 0.6590748 0.6690772 0.7566615 #> [239,] 0.24032959 0.5017250 0.5050407 0.5692543 0.5727376 0.5783608 0.6350611 #> [240,] 0.41672649 0.4448618 0.4721369 0.5199025 0.5258675 0.5286509 0.5563468 #> [241,] 0.98405382 1.1375119 1.1976571 1.2046299 1.2651680 1.3673162 1.3923991 #> [242,] 0.57243631 0.9198506 1.0032346 1.1074054 1.1169257 1.1289227 1.1497500 #> [243,] 0.55313581 0.6545472 0.7726849 0.8180935 0.8738729 0.8899513 0.9580921 #> [244,] 0.57634239 0.6158662 0.6329802 0.6512504 0.6656699 0.6795726 0.7843879 #> [245,] 0.40030939 0.4047201 0.6347717 0.6518593 0.6594240 0.6682516 0.6796197 #> [246,] 0.20957563 0.5535769 0.5850788 0.5888863 0.5982422 0.6195995 0.6572079 #> [247,] 0.32538741 0.3764248 0.4068925 0.4827132 0.5377097 0.5388781 0.5564219 #> [248,] 0.07991648 0.3969090 0.4554043 0.5958814 0.6328125 0.6462949 0.6542727 #> [249,] 0.40846562 0.5515295 0.6300735 0.6669118 0.6803393 0.7171484 0.7440268 #> [250,] 0.24531337 0.2920796 0.3045802 0.3529613 0.3733389 0.3739873 0.4389056 #> [251,] 0.30083629 0.3895963 0.5889364 0.6204556 0.6560988 0.6840995 0.7052760 #> [252,] 0.35356658 0.4333924 0.4380970 0.4901973 0.5463399 0.5705713 0.5717453 #> [253,] 0.71555951 0.8531945 0.9589535 1.0147257 1.0754897 1.0893189 1.0893488 #> [254,] 0.32560846 0.3436373 0.5602555 0.6310238 0.7119669 0.7121295 0.8242852 #> [255,] 0.19508862 0.3335111 0.3359325 0.4334802 0.4795022 0.5193626 0.5880298 #> [256,] 0.33217500 0.5334556 0.5916505 0.7259507 0.7293199 0.7529128 0.7547055 #> [257,] 0.34844429 0.3698551 0.4515219 0.6835393 0.7039375 0.7097591 0.7388971 #> [258,] 0.48774381 0.5034437 0.6036912 0.6462597 0.6528830 0.6738408 0.7292561 #> [259,] 0.50387183 0.5885379 0.8716588 0.9163334 0.9436734 0.9756567 0.9843683 #> [260,] 0.31083119 0.5392876 0.5584831 0.6788038 0.6856432 0.6939049 0.7587768 #> [261,] 0.50540167 0.6094466 0.6106162 0.7100869 0.7474043 0.7548418 0.7657958 #> [262,] 0.36072787 0.4829120 0.6355625 0.6428138 0.6685887 0.7080839 0.7910339 #> [263,] 0.39463790 0.3948480 0.5147426 0.5186912 0.5888827 0.6300291 0.6367832 #> [264,] 0.39988625 0.4002021 0.4185740 0.4483313 0.4727237 0.4871645 0.4962613 #> [265,] 0.23993803 0.5224575 0.5456090 0.5960820 0.6028731 0.6078198 0.6157228 #> [266,] 0.37926665 0.4468730 0.4925928 0.4942371 0.5473633 0.5825448 0.6047881 #> [267,] 0.46645854 0.5050407 0.6158712 0.7140490 0.7180839 0.7395991 0.7456979 #> [268,] 0.38487972 0.4326476 0.4426525 0.4727237 0.4775865 0.4810028 0.5031708 #> [269,] 1.06380439 1.1005776 1.1676122 1.1770550 1.2246484 1.2639915 1.3374345 #> [270,] 0.35760660 0.5306228 0.6001879 0.6131775 0.6140907 0.6375269 0.6378469 #> [271,] 0.37007701 0.3978038 0.4291071 0.5187272 0.5306961 0.5880864 0.6199867 #> [272,] 0.08045996 0.2810413 0.4755517 0.5850168 0.7445571 0.7490401 0.7683102 #> [273,] 0.48999353 0.5196985 0.5392876 0.6215960 0.6416367 0.6426553 0.6913845 #> [274,] 0.24010441 0.3874158 0.4525542 0.4539625 0.4949872 0.5261021 0.5265085 #> [275,] 0.30100470 0.5029816 0.5127194 0.5510996 0.5639015 0.5655843 0.6384658 #> [276,] 0.54449025 1.0617308 1.1690400 1.2214387 1.2731221 1.2739128 1.4960939 #> [277,] 0.29910942 0.5714456 0.5963457 0.6705824 0.6787581 0.7043262 0.7580649 #> [278,] 0.35718116 0.4193687 0.4433985 0.4534542 0.4665253 0.5451624 0.5502156 #> [279,] 0.41394639 0.6325459 0.6699654 0.7836029 0.7851510 0.8182959 0.8425422 #> [280,] 0.22475057 0.3396683 0.4055814 0.4175321 0.4377923 0.4471095 0.4585823 #> [281,] 0.29444451 0.4113023 0.4141784 0.4404892 0.4630233 0.5031708 0.5333971 #> [282,] 0.31644694 0.5551573 0.5961214 0.6221072 0.6550789 0.7101610 0.7283993 #> [283,] 0.34578357 0.3468282 0.3687117 0.4835465 0.5648596 0.6162894 0.6381854 #> [284,] 0.50387183 0.5236702 0.7701056 0.7911549 0.8739263 0.8836861 0.9411378 #> [285,] 0.41119252 0.5622579 0.6023753 0.6042996 0.6207918 0.6479959 0.6823808 #> [286,] 0.85567071 1.0204548 1.0805443 1.1364532 1.3088280 1.3428671 1.3523249 #> [287,] 0.52439153 0.7499065 0.9440280 0.9675250 1.0153069 1.2163210 1.2222432 #> [288,] 0.54239075 0.6429094 0.8806943 1.3620115 1.4205182 1.4246796 1.4566494 #> [289,] 0.94204478 0.9724045 0.9750135 1.0133426 1.1141167 1.1172216 1.1293275 #> [290,] 0.34194145 0.4542825 0.5073444 0.5291135 0.7688035 0.7853064 0.8207269 #> [291,] 0.26594121 0.4145992 0.5107277 0.5382576 0.5775870 0.6428034 0.6433268 #> [292,] 0.33966834 0.3705808 0.4968724 0.5614452 0.5912959 0.6030132 0.6323524 #> [293,] 0.54240523 0.5685634 0.6545472 0.7480650 0.7893958 0.7894660 0.8047075 #> [294,] 0.26927678 0.4298212 0.4530965 0.4804884 0.5015095 0.5394649 0.5716974 #> [295,] 0.26343145 0.2979331 0.4463409 0.5502562 0.5519391 0.5640889 0.5888744 #> [296,] 0.28309416 0.3568485 0.4377125 0.4419143 0.4674355 0.4870335 0.5484208 #> [297,] 0.23681564 0.3086668 0.4232261 0.4378483 0.4828435 0.5027134 0.5113881 #> [298,] 0.44123648 0.4935362 0.5216645 0.5916291 0.6151879 0.7155653 0.7452075 #> [299,] 0.15515797 0.1970265 0.3792540 0.3921772 0.4304232 0.4333924 0.4406006 #> [300,] 0.35029758 0.3859824 0.6158662 0.6386173 0.6545420 0.6789222 0.6912502 #> [301,] 0.17672129 0.2920796 0.3487168 0.4119493 0.4148849 0.4666492 0.5515295 #> [302,] 0.53302955 0.5382576 0.5505231 0.6596260 0.6688616 0.6746336 0.6863087 #> [303,] 1.26360735 1.2688591 1.4489720 1.5091963 1.5111890 1.6452195 1.7570417 #> [304,] 0.32254407 0.3594234 0.4605015 0.4645395 0.5425355 0.5434271 0.5556632 #> [305,] 0.24866835 0.3789335 0.3792134 0.4561757 0.5161408 0.5266475 0.5673960 #> [306,] 0.26740534 0.3208248 0.4667387 0.4988816 0.5023142 0.5210939 0.5911413 #> [307,] 0.68948956 0.8591377 0.8632621 0.8758765 0.9509291 0.9731328 1.0001691 #> [308,] 0.51152798 0.7055470 0.7202057 0.7660243 0.7819818 0.7912739 0.8258615 #> [309,] 0.50638053 0.5179364 0.5468375 0.6046618 0.6985545 0.7881233 0.8091576 #> [310,] 0.16587260 0.3356574 0.3871199 0.4471608 0.4613834 0.5982422 0.6546082 #> [311,] 0.43717069 0.4465631 0.5035108 0.6308780 0.6550789 0.6917758 0.7570848 #> [312,] 0.35346968 0.4866033 0.4911228 0.9278780 0.9597156 0.9675250 0.9823241 #> [313,] 0.46884574 0.4715928 0.4735928 0.5765781 0.5976214 0.6209378 0.6795702 #> [314,] 0.18842874 0.2774444 0.3743934 0.4975694 0.5050988 0.5495635 0.5568096 #> [315,] 0.48747026 0.5115595 0.5776815 0.5810022 0.5833092 0.5961116 0.6199867 #> [316,] 0.71882339 0.7298102 0.7980408 0.8027938 0.8137303 0.8317879 0.8445150 #> [317,] 0.42087897 0.4312480 0.5634303 0.6422605 0.6566718 0.7048257 0.7623149 #> [318,] 0.34683737 0.4090368 0.4860365 0.5359664 0.5383853 0.6108532 0.6336967 #> [319,] 0.37138546 0.4117716 0.4785496 0.4868149 0.5997890 0.6381854 0.6638874 #> [320,] 0.46517599 0.5368974 0.6422321 0.6776026 0.6805158 0.7277149 0.7588429 #> [321,] 0.09697109 0.3934919 0.3992212 0.4013733 0.4841724 0.5219711 0.6044576 #> [322,] 0.61354902 0.7331312 0.8358797 0.8843709 0.9381280 0.9839040 0.9903867 #> [323,] 0.37300207 0.3944722 0.4408895 0.5156462 0.6216648 0.6234677 0.6260663 #> [324,] 0.30975111 0.5447037 0.5730397 0.5768426 0.6028126 0.6794550 0.6901274 #> [325,] 0.49046188 0.5118977 0.5345808 0.5487884 0.6182018 0.6263767 0.6269757 #> [326,] 0.48914700 0.5917555 0.6135490 0.8236793 0.8388834 0.8661062 0.8718881 #> [327,] 0.47466248 0.6094466 0.6433676 0.6557585 0.6625708 0.6628817 0.6690772 #> [328,] 0.73136370 0.8106643 0.8197065 0.9443775 1.0060133 1.0388432 1.0488026 #> [329,] 0.56057729 0.5963301 0.6683562 0.6795859 0.7329504 0.8120086 0.8243287 #> [330,] 0.27949333 0.4054250 0.4598163 0.4782392 0.5327786 0.6019534 0.6318225 #> [331,] 0.33351112 0.3356392 0.3441288 0.4058071 0.4617185 0.4678582 0.5969929 #> [332,] 0.50641307 0.5115280 0.5247714 0.5806314 0.6723671 0.7161356 0.7278448 #> [333,] 0.52374859 0.5609209 0.5919769 0.7004560 0.7470308 0.7624804 0.7892986 #> [334,] 0.42795110 0.4530965 0.4612184 0.4931298 0.5000259 0.5240484 0.5290541 #> [335,] 0.32761430 0.3475464 0.3843879 0.3949626 0.4205965 0.4415721 0.4535849 #> [336,] 0.50451422 0.8163552 1.0484713 1.0540369 1.1106780 1.1204779 1.1334821 #> [337,] 0.56262031 0.7564921 0.8078945 0.8326922 0.8352566 0.8653057 0.8771891 #> [338,] 0.35029758 0.6987542 0.7735514 0.8113368 0.8188095 0.8228033 0.8238807 #> [339,] 0.49353624 0.6602226 0.7272707 0.7396244 0.7642973 0.8857255 0.9054262 #> [340,] 0.26385543 0.4138580 0.4591137 0.4841722 0.4971807 0.5357776 0.5859066 #> [341,] 0.25334228 0.4108805 0.4143892 0.4465461 0.5185135 0.5862135 0.6011670 #> [342,] 0.23535424 0.3844714 0.3873494 0.4871865 0.5377632 0.5901825 0.6002112 #> [343,] 0.49333622 0.5099957 0.5933300 0.6932422 0.7805514 0.7853750 0.9455148 #> [344,] 0.42777114 0.4942371 0.4996934 0.5374261 0.5378361 0.5491287 0.5572177 #> [345,] 0.43543452 0.4731234 0.5097656 0.5154537 0.5501798 0.5601731 0.5648554 #> [346,] 0.27816143 0.2911331 0.3092625 0.4558091 0.6182421 0.6550839 0.6832576 #> [347,] 0.31133296 0.5416196 0.5924440 0.5951297 0.6042996 0.6043597 0.6051760 #> [348,] 0.32560846 0.4348900 0.5081217 0.5956861 0.7277149 0.8389705 0.8713819 #> [349,] 0.27110632 0.2736122 0.3717283 0.4193687 0.4696581 0.5124989 0.5188817 #> [350,] 0.46804512 0.5862584 0.6132736 0.6300291 0.6931258 0.7347957 0.8070378 #> [351,] 0.59930178 0.6040936 0.6243774 0.7537008 0.8512212 0.8618755 0.8767726 #> [352,] 0.72695182 0.8925216 1.0102435 1.0211662 1.0279754 1.0329852 1.2980993 #> [353,] 0.31170307 0.6374938 0.7231658 0.8318084 0.9315260 0.9454788 1.0235594 #> [354,] 0.47014037 0.5311183 0.5807093 0.5893542 0.6192480 0.6478061 0.6894734 #> [355,] 0.31605501 0.5687865 0.6450369 0.6562205 0.6602688 0.6698618 0.6733962 #> [356,] 0.21566331 0.3844714 0.4404892 0.4961319 0.5078930 0.5369264 0.5770353 #> [357,] 0.44777486 0.5099957 0.5576428 0.6046208 0.6613447 0.6683092 0.6720861 #> [358,] 0.44599817 0.5609735 0.7096210 0.8861194 0.8862931 0.8908547 0.9431895 #> [359,] 0.59854713 0.7551560 0.7975922 0.8340979 0.9488876 0.9678762 1.0104143 #> [360,] 0.69343238 0.8115715 0.8518886 0.8595648 0.8660315 0.9372885 1.0002448 #> [361,] 0.18945147 0.3203283 0.3361130 0.3448560 0.3493174 0.3777877 0.4662042 #> [362,] 0.48657110 0.6705820 0.7001198 0.7362744 0.8745113 0.9834923 0.9946699 #> [363,] 0.31644694 0.5156924 0.6910260 0.7869916 0.8203473 0.8254108 0.8631331 #> [364,] 0.57077554 0.6313865 0.6719993 0.6959316 0.7819634 0.8589755 0.8680502 #> [365,] 0.58735280 0.5874356 0.6407729 0.6911030 0.7000366 0.7373071 0.7389704 #> [366,] 0.28454807 0.3498404 0.3726540 0.3987329 0.4167784 0.4767417 0.4883674 #> [367,] 0.24909537 0.6217602 0.8683734 0.9142125 0.9621563 0.9622823 0.9941056 #> [368,] 0.20121330 0.2632861 0.2991627 0.3697308 0.4134017 0.4259888 0.5201796 #> [369,] 0.24136134 0.6594240 0.6920199 0.7117756 0.7880136 0.8037376 0.8159284 #> [370,] 0.49568273 0.6052306 0.6221072 0.6234920 0.6700591 0.7000754 0.7869916 #> [371,] 0.47399983 0.6625547 0.7281672 0.7694525 0.7743539 0.8051282 0.8220837 #> [372,] 0.36567019 0.4189086 0.5234874 0.5771078 0.5944776 0.5951297 0.7530624 #> [373,] 0.18460255 0.2060190 0.3782162 0.3791834 0.4426923 0.4612184 0.4794289 #> [374,] 0.46500636 0.5511406 0.5911722 0.5958814 0.6242367 0.6417307 0.6562524 #> [375,] 0.30753941 0.5299665 0.5805937 0.5926426 0.6214401 0.6687676 0.7171484 #> [376,] 0.58633486 0.6239548 0.6599681 0.6648714 0.6745910 0.7043262 0.7398995 #> [377,] 0.50763829 0.5663951 0.6228037 0.7147886 0.7307823 0.7430718 0.7822892 #> [378,] 0.30361897 0.3430907 0.5306958 0.5863829 0.6143961 0.6843397 0.7215748 #> [379,] 0.62529285 0.9373032 0.9589361 0.9839291 1.0569027 1.1136162 1.1334750 #> [380,] 0.31004980 0.6990266 0.9175304 1.0122607 1.1262807 1.2133086 1.2242808 #> [381,] 0.49568273 0.5961214 0.6232368 0.6734077 0.8202901 0.8360364 0.8509438 #> [382,] 0.31083119 0.4828926 0.4899935 0.5448074 0.5461205 0.5871392 0.7828199 #> [383,] 0.33628768 0.5511406 0.5918048 0.6436058 0.6527433 0.6718151 0.7163910 #> [384,] 0.44077776 0.5411184 0.6801610 0.7706525 0.7962910 0.8294079 0.8335637 #> [385,] 0.15604009 0.4762362 0.5680078 0.5814557 0.6462597 0.6486402 0.6494302 #> [386,] 0.38532828 0.4359686 0.5209098 0.5689635 0.5757741 0.5904509 0.5991853 #> [387,] 0.33470661 0.3647560 0.5047202 0.5630189 0.5689635 0.5829721 0.5891104 #> [388,] 0.65667184 0.6750756 0.6775991 0.7616078 0.7726849 0.9088996 0.9411035 #> [389,] 0.31575585 0.5786936 0.6070519 0.6149352 0.7121295 0.7450731 0.7560740 #> [390,] 0.40636897 0.4379580 0.4465461 0.5390755 0.5909128 0.5998869 0.6395035 #> [391,] 0.71637391 0.9092624 0.9917192 1.0071221 1.0974428 1.1297508 1.1602162 #> [392,] 0.51834711 0.5505231 0.5846558 0.5926610 0.7513978 0.7774382 0.7794280 #> [393,] 0.14605421 0.6075121 0.6775991 0.7048257 0.7444487 0.7576389 0.8104532 #> [394,] 0.46638959 0.5294214 0.5480739 0.6234146 0.6267325 0.6959634 0.7060311 #> [395,] 1.04920444 1.1822441 1.2197848 1.3257101 1.3857820 1.3957540 1.3989331 #> [396,] 0.50074816 0.9368811 1.0471965 1.0540369 1.0659909 1.0825885 1.0936585 #> [397,] 0.35233048 0.3636660 0.4969829 0.5377632 0.5908810 0.6138392 0.7085501 #> [398,] 0.33018724 0.5976214 0.6068353 0.6865432 0.7154680 0.7209059 0.7564411 #> [399,] 0.32685154 0.3821192 0.5760403 0.6261190 0.6692866 0.7112562 0.7409202 #> [400,] 0.99219177 1.0147922 1.1050029 1.1439902 1.2163210 1.2301288 1.2718061 #> [401,] 0.50067478 0.5271013 0.5735262 0.6820610 0.6876870 0.7728214 0.7903175 #> [402,] 0.61061623 0.6870153 0.7093132 0.7347957 0.8417935 0.9099727 0.9207612 #> [403,] 0.13118949 0.5519855 0.5731555 0.6801153 0.8020988 0.8270089 0.9160206 #> [404,] 0.35945348 0.3929751 0.4128428 0.4949872 0.5282306 0.6033871 0.6078198 #> [405,] 0.47216259 0.6429094 0.8313961 0.9559077 1.1019036 1.1677363 1.2491498 #> [406,] 0.52840771 0.5306958 0.5440199 0.5557236 0.5803419 0.5863349 0.5922188 #> [407,] 0.18051066 0.4002402 0.5468375 0.5576100 0.5873958 0.6803568 0.7861184 #> [408,] 0.42982120 0.4931298 0.5219711 0.5226653 0.5355817 0.5618288 0.6011145 #> [409,] 0.23307038 0.5096766 0.5400055 0.5411882 0.5479479 0.5729807 0.5868046 #> [410,] 0.22690537 0.3102786 0.4061678 0.4906205 0.5086148 0.5583424 0.6063374 #> [411,] 0.59354400 0.6070115 0.6145340 0.6739434 0.6935096 0.6939493 0.7555520 #> [412,] 0.45311652 0.4835465 0.6051837 0.6545420 0.6795726 0.6883190 0.7681392 #> [413,] 0.52874860 0.5485678 0.5557123 0.5716924 0.6452023 0.7073426 0.7129012 #> [414,] 0.59854713 0.7433678 0.7931495 1.1149152 1.1309300 1.2472189 1.2511145 #> [415,] 1.00915273 1.0397144 1.0531656 1.0897849 1.1299244 1.2105582 1.2705682 #> [416,] 0.44685385 0.4802813 0.4841724 0.5951493 0.6011145 0.6486402 0.6837553 #> [417,] 0.39066227 0.4058263 0.5686761 0.5893919 0.7694337 0.7836191 0.8194804 #> [418,] 0.30577483 0.4465631 0.5118977 0.5633698 0.5763036 0.6474377 0.6518129 #> [419,] 0.33565738 0.3413110 0.3634738 0.3796179 0.3857744 0.4868420 0.5030333 #> [420,] 0.26385543 0.3050787 0.3099595 0.4493096 0.5142780 0.5426927 0.5432273 #> [421,] 0.72981016 0.8191095 1.0391618 1.0635243 1.1439234 1.1497564 1.1579309 #> [422,] 0.77901084 0.8141714 0.8880066 0.9686581 0.9776988 0.9981969 1.0372120 #> [423,] 0.17188245 0.3102722 0.4068494 0.4948228 0.5050988 0.5092787 0.5166929 #> [424,] 0.58021695 0.6560988 0.8278941 0.8310321 0.8902744 0.9046550 0.9497534 #> [425,] 0.50074816 0.7472879 0.7886271 0.8055432 0.8072300 0.8322109 0.8544600 #> [426,] 0.65841128 0.7551560 0.8793542 0.8990536 0.9343049 0.9647801 1.0091527 #> [427,] 0.40360403 0.4477749 0.4521097 0.5402954 0.5465505 0.6198224 0.6646068 #> [428,] 0.47869309 0.9571049 1.1259877 1.1363119 1.2431542 1.2535680 1.2883587 #> [429,] 0.34027172 0.3519596 0.3895963 0.5030022 0.5741917 0.5803987 0.7337276 #> [430,] 0.21863794 0.5792501 0.5799310 0.6055756 0.6098908 0.6619151 0.7052326 #> [431,] 0.42262902 0.5161931 0.5211340 0.5809020 0.6295294 0.6742100 0.7002335 #> [432,] 0.57267127 0.5730397 0.6557861 0.6854213 0.6911311 0.6957793 0.7067249 #> [433,] 0.24866835 0.2996319 0.4200978 0.4258462 0.4310830 0.5603310 0.5667096 #> [434,] 0.39780376 0.4386172 0.4797447 0.4874703 0.4933392 0.5682878 0.6303169 #> [435,] 0.49112284 0.6390106 0.6759421 0.7114507 0.7823791 0.7902742 0.7998991 #> [436,] 0.36738405 0.4135458 0.5167208 0.5334669 0.5473633 0.5482934 0.5957012 #> [437,] 0.29571554 0.3634738 0.3637507 0.3814911 0.3871199 0.3876728 0.4539900 #> [438,] 0.49119061 0.7828531 0.8027938 0.8172419 0.9485036 1.0184482 1.0185377 #> [439,] 0.83437551 0.9571049 1.0803707 1.1735669 1.1976571 1.2111689 1.3843671 #> [440,] 0.24289334 0.3120646 0.3537647 0.3949626 0.4134017 0.4214355 0.4319502 #> [441,] 0.20289628 0.6237566 0.6844604 0.7369896 0.8385844 0.8803578 0.8962578 #> [442,] 0.75123324 0.9219082 1.0496610 1.1107187 1.1215952 1.1425865 1.1637278 #> [443,] 0.24545491 0.4625359 0.4660731 0.4949305 0.5009155 0.5330862 0.5507820 #> [444,] 0.67768660 0.7038044 0.7892187 0.8172974 0.8343469 0.8612254 0.8907011 #> [445,] 1.22623347 1.2420842 1.3854303 1.3953893 1.4621698 1.5357017 1.5569942 #> [446,] 0.47289305 0.6145340 0.6684878 0.6760726 0.6819878 0.6879263 0.6969249 #> [447,] 1.45228667 1.6043613 1.6490182 1.8686264 1.8838568 1.8952957 1.9006795 #> [448,] 0.57243631 0.7067615 0.8611921 1.0199524 1.0782641 1.1621953 1.1898859 #> [449,] 0.24863800 0.3598209 0.3796937 0.4948503 0.5142888 0.5173598 0.5306004 #> [450,] 0.44366791 0.7192560 1.2657779 1.2739660 1.3403521 1.3811454 1.5902023 #> [451,] 0.50167997 0.5237082 0.5300692 0.5317664 0.5451624 0.5490896 0.5717995 #> [452,] 0.64144872 0.8653057 1.1236420 1.2908359 1.3073813 1.3171347 1.3352933 #> [453,] 0.40963634 0.4797447 0.5306961 0.6045138 0.6223325 0.6235444 0.6778300 #> [454,] 0.57965193 0.7163739 0.7299407 0.7894505 0.9358348 1.0104482 1.0190099 #> [455,] 0.33731955 0.3678879 0.3792666 0.4171944 0.5020450 0.5062591 0.5226281 #> [456,] 0.09697109 0.3223750 0.3544125 0.4270109 0.4468538 0.5355817 0.5816812 #> [457,] 0.26564505 0.7653629 0.8236488 0.9174340 0.9267543 0.9272176 0.9374222 #> [458,] 0.48470066 0.5052092 0.5596495 0.6027456 0.6594966 0.6607136 0.6997558 #> [459,] 0.16282452 0.2891480 0.4182400 0.4340449 0.4996934 0.5051723 0.5113368 #> [460,] 0.44060061 0.4449831 0.4557764 0.4573444 0.4681588 0.5077163 0.5246272 #> [461,] 0.27996485 0.5602452 0.6143961 0.6219634 0.6801153 0.7140490 0.7973198 #> [462,] 0.70380443 0.8241613 1.1755510 1.1950636 1.2795708 1.3081447 1.3256072 #> [463,] 0.26740534 0.3117459 0.4585147 0.4678582 0.5209490 0.5859090 0.5880765 #> [464,] 0.52166448 0.5273160 0.6314860 0.6347717 0.6591848 0.6602226 0.6829279 #> [465,] 0.46804512 0.6870153 0.7545626 0.7706474 0.7762396 0.8118278 0.9270370 #> [466,] 0.31004980 0.7137102 0.8195100 0.8730117 0.9593647 1.0354425 1.1030984 #> [467,] 0.29292073 0.3314803 0.4310830 0.4425622 0.4561757 0.5497865 0.5655038 #> [468,] 0.27981317 0.3631803 0.3758961 0.4363955 0.4510979 0.4921939 0.5126823 #> [469,] 0.34390429 0.3668414 0.4200978 0.5247714 0.5707832 0.5720249 0.6123558 #> [470,] 0.21164504 0.2930111 0.3169707 0.3361130 0.3613120 0.3687756 0.3733389 #> [471,] 0.20417561 0.4786287 0.5084755 0.5140385 0.5410413 0.5428946 0.6918404 #> [472,] 0.83587974 0.8890259 0.9255049 0.9664203 1.0821634 1.0852990 1.1843941 #> [473,] 0.76058958 0.9090353 0.9256838 0.9697771 1.0202062 1.0394324 1.0615033 #> [474,] 0.40542497 0.4449027 0.4514099 0.5119779 0.5300852 0.5545604 0.5947904 #> [475,] 0.87266945 1.0586150 1.0839485 1.0860132 1.1040333 1.1843674 1.3202047 #> [476,] 0.39915058 0.4044690 0.4656943 0.5084248 0.5287486 0.5500657 0.6266317 #> [477,] 0.51479463 0.5694424 0.6092113 0.6469373 0.6859729 0.6948786 0.6948917 #> [478,] 0.33332358 0.4540222 0.6165999 0.6311446 0.6354071 0.6525144 0.6576091 #> [479,] 0.32348784 0.4167936 0.4622053 0.4756200 0.4828435 0.4856521 0.5458660 #> [480,] 0.20774666 0.2368156 0.3148796 0.3318808 0.4167936 0.5171208 0.5223489 #> [481,] 0.64265530 0.6615948 0.6939049 0.7691026 0.7837144 0.8313751 0.8374920 #> [482,] 0.31191358 0.5115595 0.5186912 0.5568903 0.5658674 0.5805367 0.6526331 #> [483,] 0.36871174 0.4752569 0.4946232 0.5832266 0.6778089 0.6828681 0.7059491 #> [484,] 0.61434841 0.6643837 0.7710334 0.8202840 0.8318084 1.0382065 1.1255773 #> [485,] 0.32282065 0.4160175 0.4522604 0.4870973 0.5622388 0.5837745 0.6021089 #> [486,] 0.69902660 0.9255049 0.9593647 0.9603065 1.0848989 1.1964572 1.2806124 #> [487,] 0.36583627 0.4312480 0.5921655 0.6179367 0.6290988 0.6688491 0.6798061 #> [488,] 0.48914700 0.5809788 0.5860091 0.6228037 0.6670446 0.7151664 0.7331312 #> [489,] 0.54426886 0.5818935 0.6092135 0.6802616 0.7296064 0.7703605 0.7863003 #> [490,] 0.64144872 1.1515432 1.2574863 1.3068810 1.3621837 1.4400532 1.5037535 #> [491,] 0.68609343 0.6878115 1.0190392 1.1752675 1.2500793 1.4017630 1.4175909 #> [492,] 0.19996637 0.2641698 0.4474902 0.4658086 0.5312450 0.5522147 0.5635916 #> [493,] 0.55034352 0.6327033 0.7231658 0.9630081 1.1152965 1.1166193 1.2305216 #> [494,] 0.44016974 0.7307823 0.8092806 0.8182231 0.8593062 0.8993036 0.9097510 #> [495,] 0.27017635 0.3699957 0.4096830 0.4512306 0.4612165 0.4628703 0.4941352 #> [496,] 0.73232324 0.7710468 0.8167852 0.8925772 0.9230566 0.9272176 0.9380741 #> [497,] 0.35942336 0.4049898 0.4334802 0.4913438 0.5031696 0.5197681 0.5405174 #> [498,] 1.03943243 1.0721897 1.0905577 1.1089035 1.1314227 1.1912541 1.2253874 #> [499,] 0.27072483 0.3865848 0.4292685 0.5141644 0.5370477 0.5482934 0.5624287 #> [500,] 0.37642480 0.3978315 0.3998387 0.5439209 0.5668593 0.6262270 0.6268746 #> [501,] 0.47216259 0.5423907 0.7833552 0.9727131 1.1413017 1.1811073 1.2372009 #> [502,] 0.34754640 0.3576066 0.3862040 0.3874743 0.4431694 0.4952917 0.5221971 #> [503,] 1.25593536 1.3176629 1.3585019 1.3851334 1.3869811 1.4232511 1.4684821 #> [504,] 0.41890633 0.4389962 0.4798121 0.6254812 0.6718151 0.6731796 0.7411969 #> [505,] 0.78545275 0.8847553 1.0815974 1.1519782 1.3383411 1.4112341 1.5026801 #> [506,] 0.37644458 0.5334556 0.5487723 0.7187771 0.7560823 0.7745370 0.9103913 #> [507,] 0.70927902 0.7398905 0.8395667 0.8904261 0.8994460 0.9188252 0.9655937 #> [508,] 0.51459128 0.6009983 0.6357531 0.6474427 0.6819256 0.7059153 0.7111395 #> [509,] 0.43697204 0.4958141 0.5626203 0.5694424 0.5959871 0.6326815 0.7707595 #> [510,] 0.40476741 0.5652670 0.5761168 0.6490482 0.6917122 0.7084974 0.7534034 #> [511,] 0.82162781 0.9675018 0.9981969 1.0832153 1.2422757 1.2847455 1.2862323 #> [512,] 0.30085979 0.4361772 0.5251689 0.5338096 0.5823034 0.6650514 0.7381164 #> [513,] 0.29301114 0.3731118 0.4421991 0.4486907 0.4543662 0.4699835 0.4710620 #> [514,] 0.29292073 0.4098502 0.4798583 0.5378361 0.5423993 0.5514421 0.5667096 #> [515,] 0.26793281 0.2711063 0.3820295 0.3889879 0.4171944 0.4261615 0.4433985 #> [516,] 0.27028389 0.3415438 0.3734846 0.3840070 0.4426923 0.4609179 0.4715471 #> [517,] 0.30866684 0.3234878 0.3318808 0.4254990 0.4446930 0.4488586 0.4648980 #> [518,] 0.07991648 0.4038128 0.4762512 0.5911722 0.6350611 0.6912811 0.6981930 #> [519,] 0.37025403 0.3944722 0.4546167 0.5061000 0.5270976 0.5509830 0.5810267 #> [520,] 0.27981317 0.4053421 0.4202105 0.4315446 0.5017654 0.5127194 0.5205824 #> [521,] 0.34512046 0.4042546 0.4521097 0.5453375 0.5888239 0.6046208 0.6074047 #> [522,] 0.13879613 0.3419732 0.3665608 0.4178758 0.4448724 0.4893759 0.5030333 #> [523,] 0.54363676 0.5771780 0.5962627 0.7278883 0.8893138 0.9051578 0.9270370 #> [524,] 0.33184739 0.3507093 0.3598209 0.5017654 0.5346923 0.5475948 0.5485160 #> [525,] 0.26143033 0.3439043 0.3928074 0.4258462 0.5064131 0.5837491 0.7095298 #> [526,] 0.28883648 0.4871865 0.5237486 0.5480739 0.6635628 0.6831837 0.6882478 #> [527,] 0.77901084 1.1327007 1.2185269 1.2789434 1.2862323 1.3177358 1.4660522 #> [528,] 0.38801771 0.3950953 0.4755505 0.5452861 0.5768426 0.6079400 0.6224556 #> [529,] 0.27912903 0.3436373 0.4348900 0.6630490 0.6776026 0.7560740 0.8175933 #> [530,] 0.45669225 0.5905829 0.6098908 0.8362491 0.8714916 0.8802147 0.9061382 #> [531,] 0.36687170 0.4117716 0.4755552 0.5197681 0.5227071 0.5425355 0.5607744 #> [532,] 0.33962879 0.3890705 0.4053346 0.5261169 0.6142398 0.6576187 0.6897535 #> [533,] 0.73738653 0.8103618 0.8189857 0.8284993 0.8510004 0.8795685 0.8951109 #> [534,] 0.17875079 0.3396288 0.3585382 0.5533974 0.5907733 0.6179367 0.6273748 #> [535,] 0.32500550 0.3266515 0.3547841 0.3792344 0.4175321 0.4422283 0.4517481 #> [536,] 0.33064403 0.3969425 0.5079901 0.5199025 0.5263975 0.5330862 0.5579990 #> [537,] 0.86476367 0.9477399 0.9832344 1.0586136 1.2534981 1.3615438 1.3869811 #> [538,] 1.51838574 1.6109467 1.7708302 1.8085940 1.9709012 1.9897454 2.1130770 #> [539,] 0.31191358 0.4145507 0.4342199 0.5136025 0.5556632 0.5776815 0.5888827 #> [540,] 0.48520405 0.6145141 0.9431510 0.9827255 1.0118221 1.0709103 1.1359482 #> [541,] 0.40472014 0.4864877 0.6390878 0.6591848 0.7155856 0.7660701 0.7727693 #> [542,] 0.76364702 0.7853750 0.8647637 1.0036165 1.0303411 1.0931007 1.0985984 #> [543,] 0.58070930 0.6279492 0.6657246 0.6752513 0.6872129 0.7148179 0.7352546 #> [544,] 0.61975077 0.9142557 0.9437180 1.0027772 1.0378565 1.1022966 1.2146529 #> [545,] 0.60396993 0.8556707 0.8925672 0.9236600 0.9622823 0.9663578 0.9743262 #> [546,] 0.12046345 0.4407778 0.6910868 0.7058085 0.7183874 0.7288252 0.7548969 #> [547,] 1.17888285 1.2565012 1.3092816 1.3485371 1.3592511 1.3868103 1.3965006 #> [548,] 0.55249129 0.5894451 0.6794550 0.6825160 0.7403855 0.7499781 0.7685453 #> [549,] 0.40476741 0.5235680 0.5698808 0.6055756 0.6478767 0.7933394 0.8048888 #> [550,] 0.59574307 0.6550834 0.7081091 0.8176899 0.8303848 0.8308750 0.8356224 #> [551,] 0.36883720 0.3790429 0.4196418 0.4813874 0.6354071 0.7087893 0.7210488 #> [552,] 0.22690537 0.3318503 0.3474324 0.3919076 0.5672823 0.5812286 0.5816418 #> [553,] 0.25774459 0.3120066 0.5073320 0.6759421 0.6994948 0.7042752 0.7059491 #> [554,] 0.48465152 0.5241100 0.5428946 0.5505425 0.5685634 0.5802675 0.6282014 #> [555,] 0.28688126 0.3224021 0.4224186 0.4696288 0.4751730 0.4799773 0.4841708 #> [556,] 0.42682207 0.4577456 0.5079901 0.5520819 0.6207918 0.6501106 0.6740800 #> [557,] 0.51817417 0.5442637 0.5868762 0.6314365 0.6477322 0.6781473 0.6986159 #> [558,] 0.19513800 0.3253874 0.3978315 0.5390598 0.5714364 0.5780151 0.6273748 #> [559,] 0.60147181 0.6422321 0.6654582 0.7315526 0.7750984 0.8343469 0.8389705 #> [560,] 0.58039868 0.6049625 0.6840995 0.7140941 0.7261054 0.7437403 0.7692705 #> [561,] 0.56407060 0.5846558 0.8028055 0.8462224 0.8719151 0.8746693 0.8830011 #> [562,] 0.34273538 0.3841699 0.4105118 0.4383739 0.4858712 0.5065422 0.5251689 #> [563,] 0.44366791 0.7238057 1.1969462 1.3358588 1.3543267 1.3886413 1.5250261 #> [564,] 0.63184141 0.7544464 0.7638342 0.8534673 0.9591824 0.9823241 0.9860083 #> [565,] 0.53892468 0.6336688 0.8073962 0.8078945 0.9509165 0.9673514 1.0400164 #> [566,] 1.40517342 1.4799383 1.5337433 1.6619145 1.6645287 1.6742392 1.6770461 #> [567,] 0.40425462 0.5054819 0.5465505 0.6699691 0.6847809 0.7834679 0.7970432 #> [568,] 0.29230462 0.2969847 0.3602116 0.3634108 0.3904106 0.5878294 0.6034387 #> [569,] 0.41354584 0.4359867 0.4786287 0.5156658 0.5329834 0.5480443 0.5491726 #> [570,] 0.40415075 0.6027456 0.6301278 0.6499022 0.6650514 0.7953267 0.8599447 #> [571,] 1.01472574 1.1123529 1.1923692 1.3073333 1.3876238 1.5133920 1.5342320 #> [572,] 1.41605561 1.5498688 1.7465202 1.7507080 1.8019820 1.8112516 1.8627146 #> [573,] 0.59337182 0.6013404 0.6559592 0.6794917 0.7073037 0.7472879 0.7559327 #> [574,] 0.20601898 0.2138112 0.2836252 0.3734846 0.3993061 0.4641697 0.5026104 #> [575,] 0.56988075 0.5799310 0.6264804 0.6338821 0.7155595 0.7585791 0.8802147 #> [576,] 0.24010441 0.3451244 0.3776825 0.4448724 0.5067279 0.5093840 0.5153017 #> [577,] 0.44522840 0.4637061 0.5803419 0.6279280 0.7888915 0.8112898 0.8401171 #> [578,] 0.46177516 0.5032684 0.5560926 0.6384115 0.6558610 0.6738408 0.6795696 #> [579,] 0.25183425 0.4369720 0.4405283 0.4724317 0.6092113 0.6395914 0.6836550 #> [580,] 0.55034352 0.8932676 0.9048609 0.9122109 0.9204655 1.0020373 1.0966724 #> [581,] 0.27957885 0.4029978 0.5986835 0.6859729 0.7006915 0.8743741 0.9495803 #> [582,] 0.46009541 0.5295352 0.5377479 0.5922188 0.5985136 0.6594093 0.6648714 #> [583,] 0.16973124 0.3884705 0.4261615 0.4953801 0.5302664 0.6032451 0.6088218 #> [584,] 0.36096082 0.4292685 0.4315726 0.5161931 0.5861869 0.6151879 0.6519473 #> [585,] 0.62240532 0.8313961 0.8539695 1.0598134 1.0899057 1.1185382 1.1891441 #> [586,] 0.53355768 0.6586069 0.7373932 0.8106935 0.8137303 0.8560083 0.8586241 #> [587,] 0.61434841 0.6742504 0.7758032 0.8632298 0.9866009 0.9946745 1.0171249 #> [588,] 0.50994700 0.7137102 0.7281672 0.8292282 0.8709165 0.9152130 0.9175304 #> [589,] 0.35442371 0.4167385 0.4613995 0.4975055 0.5103887 0.7466410 0.7591619 #> [590,] 0.54835213 0.7573177 0.7731458 0.7745561 0.7955375 0.8407062 0.8494256 #> [591,] 0.48960202 0.5124989 0.5754902 0.5816418 0.5968568 0.6040564 0.6065801 #> [592,] 0.40807386 0.5852049 0.5944061 0.6151464 0.6305765 0.6354666 0.6405491 #> [593,] 0.50451422 0.5435610 0.8748761 0.9055023 1.0413266 1.0492853 1.1193411 #> [594,] 0.60941048 0.8286666 0.8362473 0.8475980 0.8652686 0.9248949 0.9401446 #> [595,] 1.02764981 1.0675792 1.1726157 1.1737706 1.2117283 1.2122693 1.2136554 #> [596,] 0.48648770 0.6071182 0.6077914 0.6451965 0.6664912 0.6796197 0.6990176 #> [597,] 0.31537054 0.4401888 0.7049388 0.7611104 0.7926868 0.8169729 0.8347674 #> [598,] 0.33478924 0.3535666 0.3921772 0.3934919 0.4061885 0.4270109 0.4683849 #> [599,] 0.29403821 0.4011936 0.4167385 0.4725982 0.5336573 0.5487884 0.5762095 #> [600,] 0.34154377 0.3776825 0.4150283 0.4241536 0.4626339 0.4811862 0.5238636 #> [601,] 0.29062669 0.3969090 0.4038128 0.6562524 0.6739732 0.6791804 0.7069239 #> [602,] 0.36985508 0.4767623 0.6505008 0.6659052 0.6908772 0.6949283 0.7074873 #> [603,] 0.46510675 0.5777555 0.6102530 0.6860259 0.6954669 0.8290772 0.8351042 #> [604,] 0.58189352 0.6589000 0.6686780 0.6893716 0.8203466 0.8461130 0.8599066 #> [605,] 0.28459575 0.3376415 0.4192009 0.5731440 0.6105425 0.6149352 0.6214294 #> [606,] 0.11608719 0.3638735 0.3792344 0.3799525 0.3879428 0.4390372 0.4585823 #> [607,] 0.80719987 0.8293724 1.0997222 1.2354086 1.3177878 1.3927904 1.4089550 #> [608,] 0.70018889 1.2503321 1.2780366 1.3383411 1.4643526 1.5313353 1.5387897 #> [609,] 0.54703206 0.6326815 0.7559557 0.8459306 0.8506859 0.8917204 0.9307966 #> [610,] 0.27361090 0.3943047 0.4335340 0.5142888 0.5265425 0.5371743 0.5416264 #> [611,] 0.82697259 0.8951109 0.9285000 0.9448010 1.0888297 1.2039308 1.2067116 #> [612,] 0.30507871 0.3273770 0.4344419 0.4591137 0.4751239 0.4870335 0.5101134 #> [613,] 0.54105950 0.7512295 0.8366602 0.9077201 1.0226334 1.0241518 1.1789045 #> [614,] 0.32685154 0.4467783 0.6437903 0.6957793 0.7111202 0.7504098 0.7703679 #> [615,] 0.21392826 0.2641698 0.4380816 0.4557797 0.5332089 0.5424867 0.5470235 #> [616,] 0.16973124 0.3613998 0.4432428 0.4559979 0.5624052 0.5760403 0.6043345 #> [617,] 0.54910784 0.5686761 0.6911297 0.6945931 0.7177089 0.7198688 0.7597752 #> [618,] 0.21566331 0.3873494 0.4141784 0.4209737 0.4946848 0.5121898 0.5441918 #> [619,] 0.31174585 0.3208248 0.3302222 0.3356392 0.3359325 0.3493520 0.4049898 #> [620,] 0.48416998 0.6688491 0.6699654 0.7330502 0.7655117 0.7961905 0.8147810 #> [621,] 0.54243723 0.5634303 0.5645729 0.5921655 0.5974787 0.6009983 0.6831728 #> [622,] 0.44986007 0.6802616 0.6893716 0.8791343 0.8939719 0.9821576 0.9837626 #> [623,] 0.58665844 0.8052953 0.8582436 0.9003027 0.9672961 0.9803843 1.0155728 #> [624,] 0.24136134 0.5790625 0.6168309 0.6571811 0.6682516 0.7423945 0.7494334 #> [625,] 0.52631572 0.6946062 0.8203172 0.8286666 0.8862931 1.0503166 1.0956282 #> [626,] 0.28362515 0.3572052 0.3782162 0.4301535 0.4609179 0.4736896 0.4951976 #> [627,] 0.34844429 0.4708596 0.4950304 0.5364844 0.5959543 0.6394717 0.6509156 #> [628,] 0.70116622 0.8738729 0.9232706 0.9257615 0.9500668 1.0094909 1.0283225 #> [629,] 0.41787351 0.5240484 0.6101186 0.6395927 0.6518593 0.6559082 0.6670681 #> [630,] 0.34027172 0.3483590 0.5775658 0.5876252 0.6204556 0.6855360 0.7085435 #> [631,] 0.48183801 0.7126242 0.7467861 0.7836029 0.8784556 0.8980112 0.8987570 #> [632,] 0.19419128 0.2839768 0.3802278 0.4279197 0.4645395 0.4706502 0.4918564 #> [633,] 0.23167235 0.4764203 0.4957492 0.5487723 0.5527083 0.5540839 0.6585077 #> [634,] 0.37995251 0.4259055 0.4356021 0.4399584 0.4510979 0.5149004 0.6219822 #> [635,] 0.39363928 0.4841722 0.5142780 0.5335577 0.5623835 0.5888239 0.6495236 #> [636,] 0.63314368 1.0503016 1.1153360 1.1235266 1.1364532 1.1380188 1.1802990 #> [637,] 0.85546966 0.8595941 0.8765175 0.8949732 0.9058662 0.9230566 0.9535782 #> [638,] 0.54449025 1.2893829 1.4274326 1.4313215 1.4664327 1.6344595 1.6742548 #> [639,] 0.46645854 0.4802830 0.6528180 0.6667336 0.6717098 0.6732536 0.6761374 #> [640,] 0.35965794 0.4701404 0.5471553 0.5762095 0.6405376 0.6763833 0.6825840 #> [641,] 0.43598666 0.4381048 0.5115222 0.5120130 0.5410413 0.5505425 0.5846763 #> [642,] 0.25334228 0.5324512 0.5390755 0.5392326 0.5592968 0.5691145 0.5746819 #> [643,] 0.36595529 0.3844797 0.4811879 0.5233490 0.5263663 0.5361204 0.5486290 #> [644,] 0.34235251 0.5245471 0.5290194 0.5560926 0.5857627 0.6764634 0.6833624 #> [645,] 0.36347243 0.4590754 0.6295294 0.6487658 0.6860259 0.7038720 0.7096475 #> [646,] 0.20395837 0.4657923 0.4935208 0.5361204 0.5476945 0.5932247 0.6263767 #> [647,] 0.29910942 0.4783190 0.5909218 0.6377302 0.6388986 0.7084974 0.7317710 #> [648,] 0.38519494 0.4323350 0.5596796 0.6020497 0.6978972 0.7114342 0.7876461 #> [649,] 0.35376145 0.4160175 0.4224186 0.4543662 0.4552867 0.4556376 0.4574192 #> [650,] 1.26516795 1.2810324 1.5369662 1.5829576 1.5865118 1.6985075 1.7013157 #> [651,] 0.30338965 0.3409042 0.3426225 0.3523410 0.3687144 0.4049237 0.4143892 #> [652,] 0.38772521 0.4232959 0.5335816 0.5549221 0.5569106 0.6105425 0.6243774 #> [653,] 0.37150479 0.4389962 0.4868420 0.4953717 0.5373714 0.5376232 0.5427501 #> [654,] 0.78346792 0.7997734 0.8752845 0.9425318 0.9635824 0.9930162 0.9941969 #> [655,] 0.49333622 0.4946115 0.5402954 0.5576428 0.6074047 0.7881233 0.8216602 #> [656,] 0.81634704 0.8325586 0.9138814 0.9159168 0.9335997 0.9660048 1.0039897 #> [657,] 0.30361897 0.5440199 0.5652880 0.6127630 0.6279280 0.6685404 0.7063750 #> [658,] 0.37644458 0.4764203 0.5026760 0.5916505 0.6111725 0.6610760 0.7162576 #> [659,] 0.46121647 0.4937272 0.6014050 0.6290653 0.6328639 0.7201901 0.7545655 #> [660,] 0.27017635 0.4135228 0.4148534 0.4937272 0.5395340 0.5418916 0.5552143 #> [661,] 0.58739582 0.6725229 0.7993370 0.9048243 0.9329313 0.9839407 1.0221971 #> [662,] 0.41394639 0.6016788 0.6157182 0.6422605 0.6474427 0.6831728 0.7126242 #> [663,] 0.18460255 0.2138112 0.3822591 0.3840070 0.3862574 0.4230495 0.4736896 #> [664,] 0.30072497 0.3464240 0.3802278 0.3995926 0.6015810 0.6372212 0.6519142 #> [665,] 0.30975111 0.3880177 0.4556376 0.5139233 0.5483299 0.5894451 0.6325165 #> [666,] 0.43594864 0.5407081 0.8323526 0.9285000 0.9896595 1.0405765 1.0770914 #> [667,] 0.34452809 0.3600881 0.3892187 0.4049237 0.4952720 0.5409081 0.5506858 #> [668,] 0.25073339 0.2655099 0.3688372 0.4002012 0.5335816 0.5430112 0.5562233 #> [669,] 0.55313581 0.8070900 0.8268404 0.9176893 0.9838827 1.0484415 1.0492853 #> [670,] 0.77684925 0.7780350 0.8705382 0.8738428 0.9277626 0.9402255 0.9558444 #> [671,] 0.62196198 0.6366517 0.6978972 0.8322545 0.8497819 0.9580972 1.0241518 #> [672,] 0.30084255 0.5388117 0.5648963 0.5803478 0.7518248 0.7807733 0.8681542 #> [673,] 0.43749453 0.5505227 0.5672890 0.5687865 0.5735866 0.5894259 0.5947715 #> [674,] 0.24863800 0.3318474 0.4301535 0.4310850 0.4422283 0.4810028 0.4870022 #> [675,] 0.53320894 0.6171614 0.6680559 0.6984509 0.7053176 0.7166298 0.7309727 #> [676,] 0.39983873 0.4405283 0.4797045 0.5156670 0.5388781 0.5728042 0.6942725 #> [677,] 0.35368533 0.4474790 0.4496334 0.4678426 0.6593369 0.7176423 0.7300486 #> [678,] 0.39190757 0.4899965 0.5306228 0.5583424 0.6397865 0.6539415 0.6601470 #> [679,] 0.42631331 0.4291071 0.5682878 0.6235444 0.6862337 0.7036078 0.7612428 #> [680,] 0.40020213 0.4387530 0.4914823 0.5162458 0.5411882 0.5538449 0.5839871 #> [681,] 0.27824019 0.3971149 0.4167784 0.4673479 0.4958828 0.5184400 0.5692543 #> [682,] 0.44486184 0.5100506 0.5699179 0.5792501 0.5905829 0.6962281 0.6983024 #> [683,] 0.63366877 0.7138283 0.8833182 0.9951903 0.9986345 1.0016824 1.0486325 #> [684,] 0.32908268 0.3829368 0.4572627 0.4617752 0.5499103 0.5675280 0.5688572 #> [685,] 0.47289305 0.4739998 0.6328005 0.6521120 0.6720328 0.6739434 0.7266951 #> [686,] 0.35509133 0.4265314 0.6051751 0.6168309 0.6865512 0.7016635 0.7183189 #> [687,] 0.71885913 0.8323526 0.9055023 0.9176893 0.9448010 1.1334821 1.1536165 #> [688,] 0.51403851 0.5219559 0.5413375 0.5480443 0.6355625 0.6555136 0.6644830 #> [689,] 0.61265766 0.6874574 0.7786590 0.8613076 0.9503610 0.9610233 1.0182848 #> [690,] 0.42653138 0.4625601 0.4763041 0.5893919 0.6858187 0.6941150 0.6977133 #> [691,] 0.29444451 0.3104717 0.4775865 0.5029744 0.5991155 0.6026409 0.6093745 #> [692,] 0.69054547 0.8296649 0.8948670 0.9504654 1.0171249 1.0282789 1.0300253 #> [693,] 0.51757671 0.5368974 0.7315526 0.7389885 0.8092806 0.8729183 0.8828692 #> [694,] 0.21674498 0.2507334 0.4196418 0.4596097 0.5467713 0.5569106 0.6194824 #> [695,] 0.69062254 0.8392936 1.1106780 1.1193411 1.1366575 1.1374524 1.1464533 #> [696,] 0.34682822 0.3859824 0.4531165 0.4946232 0.5408715 0.5927076 0.5948363 #> [697,] 0.95620290 1.1272844 1.1292652 1.3601725 1.4219230 1.4411477 1.4704535 #> [698,] 0.26343145 0.4435184 0.5219604 0.5413950 0.5726195 0.6162140 0.6336426 #> [699,] 0.42701073 0.5527083 0.8069478 0.8241123 0.8512284 0.8574372 0.8688199 #> [700,] 0.36336045 0.4141241 0.4178735 0.4315726 0.5211340 0.6684754 0.6770869 #> [701,] 0.54105950 0.9021926 0.9023072 1.1646467 1.1690400 1.1740738 1.2017255 #> [702,] 0.33332358 0.4941352 0.5491287 0.5514421 0.5823703 0.6171313 0.6328639 #> [703,] 0.31537054 0.4176534 0.6411925 0.6462147 0.7403294 0.7454633 0.7899675 #> [704,] 0.26328614 0.2638343 0.3096327 0.3402142 0.4377764 0.4409665 0.5062397 #> [705,] 0.38149111 0.3966306 0.4026726 0.4178758 0.4513943 0.4525542 0.5153017 #> [706,] 0.15472873 0.3533348 0.3699957 0.3862040 0.4205965 0.4325937 0.5418916 #> [707,] 0.43000173 0.5073444 0.5338096 0.5381579 0.6499022 0.6594966 0.6792359 #> [708,] 0.33323683 0.4416356 0.4746625 0.4969829 0.5978585 0.6048488 0.6156391 #> [709,] 0.60490980 0.6197508 0.6389791 0.6671744 0.6842797 0.8041659 0.8240498 #> [710,] 0.44123648 0.5963301 0.6770869 0.6782984 0.7436061 0.7591996 0.7596100 #> [711,] 0.36738405 0.4432428 0.4953801 0.5027575 0.5266475 0.5653288 0.5719470 #> [712,] 0.44109626 0.5182610 0.8114595 0.8182231 0.8704664 0.8718881 0.8750923 #> [713,] 0.35346968 0.7246401 0.7499065 0.7823791 0.8305809 0.9931664 1.0404170 #> [714,] 0.58893642 0.6124386 0.6788038 0.7691026 0.7834769 0.8118543 0.8310321 #> [715,] 0.66340973 0.8016984 0.8089124 0.8649101 0.8843676 0.9499100 1.0330265 #> [716,] 0.14605421 0.6347718 0.6725681 0.7590693 0.7616078 0.7722215 0.7821952 #> [717,] 0.25735799 0.3537615 0.3695420 0.3777877 0.4214002 0.4252863 0.4281232 #> [718,] 0.33022224 0.3441288 0.4096363 0.4386172 0.4585147 0.5031696 0.5193626 #> [719,] 0.65860686 0.8196058 0.8914782 0.9086368 0.9839407 1.0014516 1.0230460 #> [720,] 0.39509527 0.5139233 0.5447037 0.5524913 0.8080131 0.8155186 0.8215988 #> [721,] 0.55933129 0.6217602 0.6965583 0.7067615 0.7703424 0.8276266 0.8368484 #> [722,] 0.63580413 0.7688035 0.8106643 0.9246408 0.9380414 0.9439554 1.0680439 #> [723,] 0.46256011 0.4801519 0.5622388 0.6051751 0.6279492 0.6551946 0.6571811 #> [724,] 0.34717912 0.4045419 0.5436368 0.6388986 0.7033968 0.7442621 0.7580649 #> [725,] 0.51181402 0.5664642 0.6071722 0.6465218 0.6607136 0.6631087 0.6820446 #> [726,] 0.75122951 0.7833552 0.8497819 0.8599066 0.8806943 0.9128641 0.9215414 #> [727,] 0.22735807 0.5369339 0.5880864 0.6313239 0.6365637 0.6494028 0.6978221 #> [728,] 0.69858196 0.8308600 0.8988011 0.9483479 0.9529792 0.9557612 0.9866009 #> [729,] 0.58913260 0.5904511 1.0997222 1.2397709 1.3674834 1.3693199 1.3854303 #> [730,] 0.47359279 0.5291050 0.5563820 0.6234920 0.6501716 0.6651793 0.6910260 #> [731,] 0.38658483 0.4641697 0.4951976 0.5142720 0.5381997 0.5654464 0.5733830 #> [732,] 0.53774224 0.6493739 0.6512504 0.6671744 0.7118943 0.7749027 0.7782724 #> [733,] 0.40440349 0.4300163 0.5196985 0.5397259 0.6101514 0.6157228 0.6182368 #> [734,] 1.35351303 1.6109467 1.7051863 1.7236095 1.8262364 1.9428414 2.0175106 #> [735,] 0.29359887 0.4279511 0.4804884 0.5369971 0.5482631 0.5865848 0.5953025 #> [736,] 0.90613818 0.9500898 0.9713147 1.0095566 1.0338680 1.1879064 1.2013942 #> [737,] 0.52439153 0.6675391 0.8305809 1.0501612 1.0567316 1.1273738 1.1840653 #> [738,] 0.42791970 0.4785496 0.5145920 0.5260537 0.5268112 0.5434271 0.5488552 #> [739,] 0.77256093 0.8926279 1.0544813 1.1382764 1.1418979 1.1830457 1.2970655 #> [740,] 0.27824019 0.3109608 0.3987329 0.4258963 0.5017250 0.5105818 0.5888744 #> [741,] 0.27773200 0.8213555 0.8559930 0.9525178 0.9838940 1.0723668 1.1206739 #> [742,] 0.38767281 0.4026726 0.4205946 0.4471608 0.4802830 0.5507127 0.5815310 #> [743,] 0.32237498 0.3764880 0.4013733 0.4537154 0.4802813 0.4904619 0.5476945 #> [744,] 0.49446897 0.5258675 0.5579990 0.5600066 0.5672890 0.5699179 0.6562205 #> [745,] 0.35748401 0.5123131 0.5290194 0.5663951 0.5957944 0.6198647 0.6897391 #> [746,] 0.31719653 0.3848797 0.4561031 0.4979489 0.5333971 0.5371761 0.5391688 #> [747,] 0.67604518 0.9275340 1.0278988 1.0327197 1.2267464 1.2375629 1.3199692 #> [748,] 0.12222005 0.4177120 0.6268746 0.7180089 0.7371032 0.7431611 0.7436420 #> [749,] 0.74546332 0.7926868 0.8942326 1.0656954 1.1363119 1.1613837 1.1821319 #> [750,] 0.43000173 0.5052092 0.5965068 0.6683562 0.7105456 0.7366041 0.7704925 #> [751,] 0.28459575 0.3694457 0.5026201 0.6070519 0.6188489 0.6194824 0.6218011 #> [752,] 0.70730375 0.7993732 0.8869694 0.9236479 0.9618237 1.0278988 1.0328210 #> [753,] 0.45428253 0.5213248 0.5381579 0.5430112 0.6351318 0.6863909 0.6964509 #> [754,] 0.73790913 0.9146707 0.9268745 0.9441675 0.9728522 0.9886703 1.0041557 #> [755,] 0.51540272 0.5320156 0.5852049 0.6104892 0.6833624 0.6897391 0.6911030 #> [756,] 0.41347323 0.4387530 0.4678809 0.4871645 0.4934944 0.5402894 0.5618440 #> [757,] 1.17827006 1.2534981 1.2886819 1.3553739 1.3561492 1.4709502 1.4736376 #> [758,] 0.48695844 0.5510996 0.5864232 0.6654570 0.6918404 0.6931311 0.6985820 #> [759,] 1.60098152 1.6043613 1.6645287 1.8161045 1.8887888 1.9565765 2.0145514 #> [760,] 0.17672129 0.2947138 0.3361387 0.3529613 0.4579331 0.4654775 0.5653037 #> [761,] 0.27361090 0.3842518 0.3946160 0.4463409 0.4822301 0.4970682 0.5173598 #> [762,] 0.09831649 0.3687144 0.3738390 0.3752012 0.4383739 0.4483313 0.4989207 #> [763,] 0.40807386 0.6692890 0.7407087 0.7445498 0.7617625 0.7812317 0.8029511 #> [764,] 0.33323683 0.3636660 0.4090712 0.4678809 0.5384396 0.5770353 0.6342586 #> [765,] 0.74149780 0.7884421 0.8924413 0.8925216 1.0852243 1.1068269 1.1279884 #> [766,] 0.88475525 1.0307034 1.1031474 1.1288179 1.1767783 1.2611665 1.3072379 #> [767,] 0.39276221 0.4141241 0.4186988 0.5460700 0.5804832 0.5861869 0.5965068 #> [768,] 0.60683531 0.6239664 0.6329802 0.6571812 0.7014294 0.7681392 0.9043524 #> [769,] 0.25183425 0.3287616 0.4958141 0.5156670 0.6469373 0.7240607 0.7927622 #> [770,] 0.81951004 1.0033135 1.0122607 1.1505867 1.1603421 1.1781983 1.1982659 #> [771,] 0.63388207 0.7708441 0.8405305 0.8498749 0.9045846 0.9466005 0.9803991 #> [772,] 0.38596645 0.4342199 0.4684409 0.5602363 0.5714456 0.5805367 0.5833092 #> [773,] 0.63764715 0.7092790 0.8200161 0.8512293 0.8517123 0.9066578 0.9243237 #> [774,] 0.29062669 0.4554043 0.4762512 0.6821908 0.6890077 0.7307151 0.7315235 #> [775,] 0.27996485 0.5089294 0.5409551 0.5731555 0.6783771 0.7118041 0.7215748 #> [776,] 0.38532828 0.4681975 0.5026760 0.5540839 0.5630189 0.6028126 0.6175580 #> [777,] 0.51411964 0.5156462 0.5420390 0.5666462 0.6606336 0.6987629 0.7096344 #> [778,] 0.28914798 0.3341115 0.3905825 0.4007505 0.4423288 0.4925744 0.5092038 #> [779,] 0.44351835 0.5441918 0.5751142 0.5771078 0.5850060 0.6032333 0.6109984 #> [780,] 0.42911358 0.4658710 0.4704423 0.5636624 0.5749203 0.6282014 0.6493615 #> [781,] 0.58078479 0.6287389 0.6727315 0.7011662 0.7128726 0.7296091 0.7383797 #> [782,] 0.17515967 0.3148796 0.4622053 0.4935208 0.5181742 0.5225545 0.5405678 #> [783,] 0.34235251 0.4319502 0.4692709 0.4880859 0.5052213 0.5123131 0.5347047 #> [784,] 0.27949333 0.5545604 0.5978329 0.6031911 0.6126577 0.6343305 0.7437618 #> [785,] 0.24171039 0.3686001 0.4558091 0.5551246 0.6074524 0.6198224 0.6720896 #> [786,] 0.39297509 0.3943971 0.4756200 0.5476847 0.5500657 0.5557123 0.5801731 #> [787,] 0.62045966 0.6634097 0.6801610 0.7152110 0.7696425 0.8052953 0.8215516 #> [788,] 0.54426365 0.5638175 0.6749506 0.6987118 0.7195750 0.7425097 0.7472007 #> [789,] 0.30084255 0.5204844 0.5390357 0.6601272 0.6631301 0.7861197 0.7917998 #> [790,] 0.21863794 0.4566923 0.5100506 0.6264804 0.6478767 0.7233856 0.7629658 #> [791,] 0.69343238 0.7916039 0.8174900 0.8209361 0.8597213 0.8956150 0.9191062 #> [792,] 0.31575585 0.3376415 0.3694457 0.6310238 0.6630490 0.6687645 0.6753017 #> [793,] 0.33731955 0.3451244 0.4241536 0.4726576 0.4765648 0.4850090 0.5104808 #> [794,] 0.33330030 0.3430224 0.4944690 0.5286509 0.5507820 0.5691711 0.6740800 #> [795,] 0.48015193 0.4870973 0.5434333 0.5681859 0.5790625 0.6224556 0.6920199 #> [796,] 0.91545188 0.9851428 0.9985390 1.2401699 1.2706049 1.4116211 1.4267028 #> [797,] 0.26927678 0.3941537 0.4221518 0.4449027 0.4632372 0.4782392 0.5086648 #> [798,] 0.30577483 0.4371707 0.5345808 0.6105168 0.6122402 0.6865432 0.7719839 #> [799,] 0.85802249 0.9283156 1.2431804 1.2509933 1.2636073 1.2909028 1.3068810 #> [800,] 0.60499899 0.6353063 0.6588731 0.6686780 0.6783649 0.7031266 0.7059153 #> [801,] 0.35808541 0.3596801 0.4380816 0.4387298 0.4474902 0.4715928 0.4920686 #> [802,] 0.42497290 0.4498638 0.7653629 0.8186734 0.8834345 0.8955751 0.9027206 #> [803,] 0.36096082 0.3633604 0.4226290 0.5460700 0.5624287 0.6155368 0.6287389 #> [804,] 0.41459923 0.4496334 0.4880470 0.4927598 0.4971807 0.5330295 0.5530242 #> [805,] 0.87209495 1.2580143 1.5146902 1.5192367 1.6207223 1.7483121 1.7926297 #> [806,] 0.32580181 0.5388117 0.5390357 0.6096010 0.6983498 0.7022300 0.7152110 #> [807,] 0.22735807 0.3700770 0.4739122 0.4933392 0.6045138 0.6468782 0.7561484 #> [808,] 0.36954200 0.3791834 0.3862574 0.4063097 0.4711711 0.4919237 0.4926647 #> [809,] 0.31170307 0.6327033 0.7336474 0.9043976 0.9048609 0.9850208 1.0023837 #> [810,] 0.42322607 0.4259888 0.4446164 0.4449831 0.4667387 0.4983489 0.5171208 #> [811,] 0.47742160 0.7927973 0.8392936 0.9368811 0.9712860 1.0107862 1.0526832 #> [812,] 0.57426260 0.5857830 0.6128149 0.6252175 0.6313865 0.6448859 0.6608414 #> [813,] 0.40299783 0.5424989 0.6948786 0.7302712 0.8294974 0.9501195 0.9531711 #> [814,] 0.72727075 0.8829206 0.9873766 1.0244392 1.0398980 1.2170482 1.2480076 #> [815,] 0.24949429 0.4851378 0.4852129 0.5202777 0.6531028 0.7033036 0.7176815 #> [816,] 0.51507672 0.5471553 0.5615487 0.6850255 0.7931495 0.7975922 0.8424111 #> [817,] 0.15604009 0.4431694 0.5146468 0.5624673 0.5727703 0.6123558 0.6131775 #> [818,] 0.65828602 0.6739732 0.6864811 0.6890077 0.7181508 0.7231773 0.7530624 #> [819,] 0.51564803 0.5235680 0.5761168 0.7585791 0.8006608 0.8498749 0.9355411 #> [820,] 0.51459128 0.5371761 0.5935440 0.6053123 0.6092135 0.6686627 0.6725681 #> [821,] 0.33148026 0.3717019 0.4098502 0.5120130 0.5334669 0.5664642 0.6158515 #> [822,] 0.22038706 0.2836823 0.5179364 0.5479470 0.5576100 0.6203619 0.8928497 #> [823,] 0.65890004 0.6900357 0.7414978 0.8366602 0.9023072 0.9483216 0.9959242 #> [824,] 0.29471381 0.3487168 0.4474640 0.4799773 0.4854754 0.4999563 0.5302813 #> [825,] 0.20417561 0.4846515 0.5846763 0.5975842 0.6272176 0.6513768 0.6654570 #> [826,] 0.22475057 0.2403118 0.3006863 0.3547841 0.3638735 0.4968724 0.4979489 #> [827,] 0.33702873 0.8849706 1.0227250 1.2455886 1.2472189 1.2625984 1.2728276 #> [828,] 0.23535424 0.2888365 0.4209737 0.5078930 0.5609209 0.6032333 0.6234146 #> [829,] 0.43557756 0.5741917 0.5876252 0.6720861 0.6932422 0.7299574 0.8358283 #> [830,] 0.34242776 0.3594535 0.3943971 0.6445696 0.6479357 0.6531994 0.6720896 #> [831,] 0.42497290 0.4378084 0.6613781 0.6717098 0.7180839 0.8036945 0.8090444 #> [832,] 0.18842874 0.3712625 0.4171864 0.4988816 0.5338943 0.5859090 0.6002829 #> [833,] 0.59828158 0.6140907 0.6609668 0.6916844 0.7095298 0.7533940 0.7615608 #> [834,] 0.85086686 0.8693708 1.0402638 1.0913809 1.1162744 1.1580728 1.1882179 #> [835,] 0.39484804 0.5377422 0.5896853 0.5998148 0.6132736 0.7345074 0.7400159 #> [836,] 0.28200459 0.4838382 0.5056986 0.7097586 0.7118269 0.7154967 0.7260693 #> [837,] 0.28200459 0.4255819 0.5311183 0.5385190 0.5857583 0.6752513 0.7014631 #> [838,] 0.72854123 0.8213555 1.2479845 1.2509933 1.3578865 1.3883013 1.4704535 #> [839,] 0.35221305 0.4658086 0.4688457 0.4852129 0.5470235 0.5481457 0.5696647 #> [840,] 0.32580181 0.4869584 0.5029816 0.5929361 0.6505765 0.6601272 0.6815772 #> [841,] 0.24031182 0.3266515 0.3289196 0.3879428 0.4055814 0.4310850 0.4802058 #> [842,] 0.63184141 0.7825293 0.9278780 0.9812174 0.9830406 1.0397144 1.0555835 #> [843,] 0.40636897 0.5420390 0.5550702 0.5862135 0.5904569 0.6071488 0.6836963 #> [844,] 0.47831904 0.4992977 0.5258547 0.5944776 0.5963457 0.6599681 0.7640140 #> [845,] 0.46638959 0.5044446 0.5824304 0.6530868 0.6635628 0.6810039 0.7583725 #> [846,] 0.18634156 0.4660731 0.5016230 0.5377097 0.5390598 0.5907733 0.6081748 #> [847,] 0.29038507 0.3045802 0.3361387 0.3493174 0.3687756 0.3766028 0.4119493 #> [848,] 0.50444460 0.5294214 0.6075915 0.7424908 0.8023538 0.8194373 0.9051578 #> [849,] 0.18945147 0.2116450 0.2573580 0.3110705 0.3223047 0.3731118 0.3766028 #> [850,] 0.38970748 0.5717071 0.5865542 0.5878294 0.5908949 0.6282007 0.6367832 #> [851,] 0.24949429 0.4835535 0.4973154 0.5481457 0.6148331 0.7302801 0.7861184 #> [852,] 0.46124088 0.5084755 0.5156658 0.5596796 0.5802675 0.6023530 0.6047881 #> [853,] 0.66924590 0.7049388 0.8096001 0.8637338 0.8647272 0.8857966 0.9629770 #> [854,] 0.40949008 0.4903354 0.6040936 0.6654582 0.7892187 0.9190275 0.9911303 #> [855,] 0.45907545 0.5777555 0.5780089 0.7002335 0.7745468 0.8503036 0.8679628 #> [856,] 0.60555636 0.7423913 0.7591619 0.7960978 0.9138814 0.9284428 1.1086736 #> [857,] 0.54070810 0.5956861 0.7189500 0.7750984 0.7991863 0.8518376 0.8815974 #> [858,] 0.43061825 0.4657923 0.4811879 0.5268112 0.5396213 0.5876804 0.5909889 #> [859,] 0.47952646 0.5485678 0.5796519 0.7068683 0.7246722 0.7839269 0.8756713 #> [860,] 0.34512046 0.3936393 0.5426927 0.5617833 0.5775658 0.6252175 0.6699691 #> [861,] 0.33018724 0.6105168 0.6239664 0.6644961 0.6850255 0.7567525 0.7708239 #> [862,] 0.44522840 0.4481359 0.5284077 0.5530242 0.5652880 0.5863829 0.6606663 #> [863,] 0.44813593 0.4637061 0.4678426 0.4880470 0.5557236 0.6843397 0.7063750 #> [864,] 0.62529285 0.8191095 1.0646795 1.0720833 1.0783879 1.1276875 1.1329767 #> [865,] 0.41119252 0.4577456 0.5452479 0.6297758 0.6929260 0.7024502 0.7180998 #> [866,] 0.27912903 0.4651760 0.5081217 0.5602555 0.6379255 0.7991863 0.8322765 #> [867,] 1.15661048 1.3462438 1.4471441 1.5645124 1.5808967 1.6063300 1.6320500 #> [868,] 0.54139322 0.6773988 0.7105605 0.7149278 0.7211515 0.7299574 0.7337276 #> [869,] 0.31133296 0.4822301 0.5088305 0.5234874 0.5358143 0.5401556 0.5946605 #> [870,] 0.50344372 0.6795696 0.7893724 0.8130988 0.8172974 0.8690556 0.8829630 #> [871,] 0.24531337 0.2903851 0.2977648 0.3203283 0.3223047 0.3224021 0.3613120 #> [872,] 1.09268104 1.1382408 1.1923692 1.2465492 1.3481100 1.4001927 1.4189594 #> [873,] 0.37265397 0.4918564 0.4958828 0.5303708 0.5321874 0.5558120 0.5829550 #> [874,] 0.48520405 0.7279412 0.8074916 0.9682564 1.1254497 1.1566882 1.1824327 #> [875,] 0.26594121 0.5953002 0.5957431 0.6172237 0.6716399 0.6896890 0.6984653 #> [876,] 0.81867341 0.8726695 0.9058662 0.9245956 0.9318064 1.0447502 1.0583478 #> [877,] 0.66137812 0.6894816 0.8342098 0.8775902 0.9044680 0.9185322 0.9245897 #> [878,] 0.27957885 0.5147946 0.5424989 0.5528173 0.7625186 0.7667228 0.7693490 #> [879,] 0.12222005 0.3100312 0.6142398 0.6262270 0.6349211 0.6857133 0.7149717 #> [880,] 0.55831393 0.5605773 0.6047171 0.6481022 0.9314802 0.9439554 0.9520623 #> [881,] 0.49736419 0.5917555 0.6223325 0.6957458 0.7322672 0.7716297 0.7745400 #> [882,] 0.40360403 0.5054819 0.5453375 0.5558413 0.6633979 0.7233839 0.7326161 #> [883,] 0.37547110 0.4268221 0.5448074 0.5452479 0.6043597 0.6078415 0.6495392 #> [884,] 0.29963193 0.3668414 0.3792134 0.3928074 0.4425622 0.4718084 0.4798583 #> [885,] 0.64379029 0.7468705 0.8206195 0.8262232 0.8541983 0.8743703 0.9315260 #> [886,] 0.30096399 0.3892187 0.4546167 0.4548463 0.5983123 0.6060355 0.6328005 #> [887,] 0.46703537 0.4767417 0.5145920 0.5321874 0.5712901 0.5777134 0.5909889 #> [888,] 0.51564803 0.6582860 0.7708441 0.7788900 0.8357769 0.8599289 0.8652686 #> [889,] 0.53892468 1.0486325 1.1644871 1.1717316 1.2046336 1.2400584 1.2450309 #> [890,] 0.32282065 0.3540405 0.4008547 0.4574192 0.4597129 0.4749997 0.4819657 #> [891,] 0.47742160 0.7234641 0.8592318 1.0554974 1.0620862 1.1109065 1.1366575 #> [892,] 0.34197322 0.3716588 0.4356021 0.5047202 0.5166129 0.5323596 0.5344388 #> [893,] 0.66622344 0.6909275 0.8534496 0.8539695 0.9764982 0.9863673 0.9984615 #> [894,] 0.87209495 1.0808862 1.3254115 1.3404295 1.4891477 1.5381128 1.5951257 #> [895,] 0.38741583 0.4344419 0.4488586 0.4533024 0.4672448 0.4848637 0.4975694 #> [896,] 1.03245278 1.1925389 1.2063668 1.2165550 1.2345554 1.2772759 1.3177358 #> [897,] 0.33702873 0.9317456 1.1587109 1.2196853 1.2244513 1.2387571 1.2509943 #> [898,] 0.86087471 0.8775427 1.0586136 1.2316273 1.2486592 1.3250910 1.3474000 #> [899,] 0.24495826 0.3665608 0.3796179 0.4394007 0.4699607 0.4970682 0.4973593 #> [900,] 0.53693393 0.5509196 0.5758092 0.6077715 0.6372212 0.6468782 0.6495503 #> [901,] 0.60751210 0.6347718 0.6750756 0.8400073 0.8639459 0.8938192 0.8987570 #> [902,] 0.36475602 0.4359686 0.4681975 0.4957492 0.5509920 0.6065373 0.6610760 #> [903,] 0.71925597 0.7238057 1.4242708 1.4808959 1.6356863 1.7811930 1.8049915 #> [904,] 0.33217500 0.6075172 0.7308439 0.7573067 0.7745370 0.8331341 0.8373647 #> [905,] 1.08159736 1.0956074 1.1598090 1.2341912 1.2433489 1.2582241 1.2708966 #> [906,] 0.29083287 0.3991506 0.5263341 0.5313566 0.5715888 0.5801731 0.5866417 #> [907,] 0.31849457 0.3375275 0.4090368 0.4105118 0.4221518 0.4514099 0.5643394 #> [908,] 0.30096399 0.5509830 0.5541190 0.6393489 0.6521120 0.6928918 0.7301782 #> [909,] 0.27773200 0.7285412 0.9501224 0.9562029 0.9740056 1.0353836 1.1208545 #> [910,] 0.35965794 0.5150767 0.5336573 0.6122402 0.6531913 0.6848237 0.6920594 #> [911,] 0.32876163 0.4724317 0.5728042 0.5959871 0.6017204 0.7424823 0.7535873 #> [912,] 0.20612255 0.2839768 0.3995926 0.4600954 0.4980985 0.5371465 0.5509196 #> [913,] 0.23307038 0.3248874 0.4146456 0.4839062 0.4901973 0.5347778 0.5501868 #> [914,] 0.23993803 0.5254370 0.6040564 0.6101514 0.6314974 0.6320861 0.6400916 #> [915,] 0.69003570 0.8739171 0.8805306 0.8924413 1.0279754 1.0449738 1.1265626 #> [916,] 0.40415075 0.4847007 0.6844604 0.7136063 0.7381164 0.7421462 0.7458784 #> [917,] 0.18051066 0.3621718 0.6046618 0.6203619 0.6725229 0.6859977 0.7025122 #> [918,] 0.39323414 0.4813874 0.5092038 0.5139742 0.5467713 0.5562233 0.5636624 #> [919,] 0.33470661 0.4189063 0.4808973 0.5373714 0.5594251 0.6065373 0.6195995 #> [920,] 0.70069148 0.8073962 0.8207193 0.8294974 0.8352566 0.8714288 0.9656345 #> [921,] 0.34743241 0.3678879 0.4061678 0.4076304 0.4277711 0.4468730 0.5275568 #> [922,] 0.34309068 0.4307515 0.5409551 0.5519855 0.5602452 0.6127630 0.7134870 #> [923,] 0.50898016 0.5401785 0.6252212 0.6393644 0.6450369 0.6670446 0.6746208 #> [924,] 0.55281735 0.5803478 0.5986835 0.7302712 0.7642574 0.7886514 0.7917998 #> [925,] 0.46323720 0.4711711 0.5015095 0.5227071 0.5300852 0.6057881 0.6281770 #> [926,] 0.37300207 0.5505227 0.5666462 0.5839332 0.6252212 0.6690963 0.6836963 #> [927,] 0.40119359 0.4975055 0.5589544 0.6055564 0.6531913 0.6584113 0.7604565 #> [928,] 0.48183801 0.4841700 0.6325459 0.7198504 0.7318016 0.7725609 0.7955375 #> [929,] 0.61025299 0.7080839 0.8064294 0.8291200 0.8344719 0.8517123 0.8693708 #> [930,] 0.35368533 0.4116207 0.4613834 0.5041523 0.5568096 0.5683251 0.6135434 #> [931,] 0.31605501 0.4617185 0.5401785 0.5809788 0.5947715 0.6274038 0.6835825 #> [932,] 0.36375067 0.3966306 0.4539625 0.4848637 0.5092787 0.5233490 0.5512903 #> [933,] 0.66159483 0.6653664 0.7231773 0.7462066 0.7828199 0.7834769 0.8125567 #> [934,] 0.72467222 0.8892709 0.8942326 0.9342293 1.0019782 1.0190099 1.0817150 #> [935,] 0.17515967 0.2077467 0.4378483 0.4446930 0.4635054 0.4856521 0.4920686 #> [936,] 0.63714896 0.6514300 0.8096001 0.8222615 0.8256855 0.8450948 0.8645342 #> [937,] 0.56154873 0.7014294 0.7433678 0.8551267 1.0064208 1.0101535 1.0104143 #> [938,] 0.50994700 0.5270976 0.5541190 0.5983123 0.6625547 0.6720328 0.6987629 #> [939,] 1.13746210 1.1554039 1.1709452 1.2012700 1.3270357 1.3400856 1.4142670 #> [940,] 0.28743920 0.5563820 0.5856901 0.5996128 0.6547929 0.6700591 0.7373932 #> [941,] 0.67425040 0.6905455 0.7710334 0.9557612 0.9796315 1.0197606 1.1882176 #> [942,] 0.37150479 0.4798121 0.4808973 0.5166129 0.5182849 0.5829721 0.5888863 #> [943,] 0.28688126 0.4258963 0.4281232 0.4670354 0.4883674 0.5060454 0.5184400 #> [944,] 0.77827903 0.7819818 0.8017048 0.8162861 0.8181842 0.8197966 0.8473799 #> [945,] 0.82162781 1.0372120 1.1002160 1.1334750 1.2121846 1.2270154 1.2946210 #> [946,] 0.37025403 0.4167265 0.5974787 0.6093745 0.6213680 0.6260663 0.6454604 #> [947,] 0.25146438 0.3927622 0.4704423 0.5118140 0.5141644 0.5981667 0.6030569 #> [948,] 0.78545275 1.4532707 1.5313353 1.5427409 1.6086276 1.7504291 1.7885411 #> [949,] 0.97271309 0.9821576 1.0197606 1.0300253 1.0468094 1.0555918 1.2018503 #> [950,] 0.54356099 0.6906225 0.7515560 0.8163552 0.8268404 1.0246197 1.0835439 #> [951,] 0.54343326 0.5726713 0.6079400 0.6362282 0.6411489 0.6882258 0.7099575 #> [952,] 0.46844091 0.5961116 0.6552774 0.6681492 0.6705820 0.6705824 0.6773838 #> [953,] 0.43749453 0.5324512 0.5839332 0.6552774 0.6928143 0.7366193 0.7452899 #> [954,] 0.43154461 0.4399540 0.4870235 0.5126823 0.5397259 0.5775316 0.5931087 #> [955,] 1.02021825 1.1881607 1.1982660 1.2262335 1.2503180 1.2585351 1.2634868 #> [956,] 0.36567019 0.4878582 0.4992977 0.5924440 0.6344151 0.6589955 0.7024502 #> [957,] 0.54300594 0.6047171 0.6385052 0.8357751 0.8688679 0.8910615 0.9210669 #> [958,] 0.26227964 0.5029744 0.5263975 0.5506858 0.5875720 0.5980812 0.6151065 #> [959,] 0.28368227 0.3698291 0.6985545 0.7824819 0.8126316 0.8950014 0.9177645 #> [960,] 0.49736419 0.5183471 0.6872977 0.7351015 0.7555208 0.7721703 0.8106311 #> [961,] 0.39870005 0.4068494 0.4138580 0.4387298 0.4493096 0.4648980 0.4751239 #> [962,] 0.32908268 0.4148534 0.4663373 0.4952917 0.5148448 0.5823703 0.5933718 #> [963,] 0.20957563 0.5594251 0.6219183 0.6602812 0.7078393 0.7360118 0.7703560 #> [964,] 0.29793315 0.3097676 0.3946160 0.4495239 0.4973593 0.5219604 0.5401556 #> [965,] 0.40847446 0.4752569 0.5648596 0.5927076 0.6392017 0.6626822 0.6950428 #> [966,] 0.24032959 0.3109608 0.3498404 0.3971149 0.4755460 0.5466231 0.5519391 #> [967,] 0.83996762 0.8429282 0.8430529 0.8506495 0.8805370 0.9030809 0.9297668 #> [968,] 0.75906932 0.7979691 0.8549952 0.8591549 0.8739171 0.9021926 0.9483216 #> [969,] 0.72181504 0.7701056 0.8012313 0.8261697 0.8506495 0.9436734 0.9631076 #> [970,] 0.45928747 0.6874574 0.7040529 0.7155653 0.7596100 0.7642973 0.8288619 #> [971,] 0.48355347 0.5202777 0.5696647 0.6209378 0.8020739 0.8293285 0.8423353 #> [972,] 0.52454708 0.5688572 0.5876460 0.5896110 0.6384115 0.6425840 0.6848581 #> [973,] 0.23167235 0.4270107 0.5509920 0.6065801 0.6111725 0.6277603 0.6409072 #> [974,] 0.47085961 0.6588610 0.6596290 0.6879263 0.7065132 0.7088534 0.7781482 #> [975,] 0.24984831 0.4459982 0.7060311 0.8481894 0.8836024 0.9899705 0.9995362 #> [976,] 0.48003483 0.5257607 0.5606464 0.6075915 0.6931258 0.7270252 0.7278883 #> [977,] 0.24909537 0.6965583 0.8719151 0.8899695 0.8925672 0.9524394 0.9771197 #> [978,] 0.47499973 0.5926426 0.6021089 0.6590841 0.6911297 0.7376685 0.7610334 #> [979,] 0.27414771 0.3533348 0.3829368 0.4096830 0.4135228 0.4415721 0.4663373 #> [980,] 0.55848306 0.5871392 0.6017204 0.6948917 0.7138283 0.7796337 0.8115816 #> [981,] 0.31027863 0.3318503 0.4300163 0.4788421 0.4899965 0.5254370 0.5960820 #> [982,] 0.09831649 0.3426225 0.3733893 0.3833929 0.3998862 0.4147042 0.5065422 #> [983,] 0.34242776 0.4128428 0.5373668 0.5707755 0.5742626 0.6556826 0.6773988 #> [984,] 0.69637689 0.9597156 0.9830406 1.0227250 1.0404170 1.0897849 1.1604625 #> [985,] 0.21587317 0.5035108 0.5587194 0.6152778 0.6528830 0.6680267 0.6734077 #> [986,] 0.69958193 0.8580225 0.8754899 0.9156674 0.9566091 0.9670229 0.9763510 #> [987,] 0.11608719 0.3006863 0.3250055 0.3289196 0.4259055 0.4377923 0.4870022 #> [988,] 0.36347243 0.4651067 0.5653288 0.5780089 0.5809020 0.6451965 0.6682168 #> [989,] 0.41765340 0.4401888 0.6357935 0.6813370 0.7506527 0.7766402 0.7819681 #> [990,] 0.79370421 0.8864215 0.8938192 0.9058586 1.0523116 1.1082587 1.1725493 #> [991,] 0.54612053 0.6078415 0.6344151 0.6866979 0.6894896 0.7161804 0.7335557 #> [992,] 0.34642398 0.5090600 0.5749598 0.5798745 0.6493739 0.6495503 0.6656699 #> [993,] 0.58381883 0.6305519 0.6584598 0.7033968 0.7270252 0.7323232 0.7672032 #> [994,] 0.37058080 0.4416356 0.4471095 0.4517481 0.4961319 0.5121898 0.5384396 #> [995,] 0.29083287 0.4656943 0.4740713 0.5162538 0.5446352 0.5476847 0.5501051 #> [996,] 0.71182690 0.8015365 0.8357751 0.9246408 0.9344621 1.0045098 1.0209222 #> [997,] 0.49119061 0.8981545 1.0922723 1.0947023 1.1007515 1.1540649 1.1801942 #> [998,] 0.52910495 0.5856901 0.6052306 0.6426209 0.6872977 0.7322672 0.7591426 #> [999,] 0.40440349 0.4788421 0.5086148 0.5929361 0.6096010 0.6314974 0.6784615 #> [1000,] 0.95906486 0.9779007 0.9891183 0.9967242 1.0371942 1.0655567 1.1123559 #> [,8] [,9] [,10] #> [1,] 1.1277723 1.1816461 1.1990002 #> [2,] 1.2216679 1.2352019 1.2608456 #> [3,] 0.6189972 0.6357971 0.6409345 #> [4,] 0.6563752 0.6593882 0.6812084 #> [5,] 0.6451487 0.6575858 0.6584598 #> [6,] 0.6787011 0.7148674 0.7266524 #> [7,] 0.6318225 0.6663029 0.6779481 #> [8,] 0.7533940 0.7807431 0.7992978 #> [9,] 1.0873969 1.0961940 1.1964236 #> [10,] 0.5822407 0.5883231 0.5917679 #> [11,] 0.6083513 0.6091392 0.6192702 #> [12,] 0.7107465 0.7228639 0.7490380 #> [13,] 0.7498239 0.7559947 0.7591031 #> [14,] 0.8340085 0.8789927 0.8985272 #> [15,] 0.8731405 0.8742264 0.9712348 #> [16,] 1.1797889 1.1845552 1.2039234 #> [17,] 1.0033135 1.0354425 1.0443826 #> [18,] 0.9330399 0.9576537 0.9757561 #> [19,] 0.8899695 0.9015966 0.9232722 #> [20,] 0.5672174 0.5912759 0.6029007 #> [21,] 0.6501106 0.6602688 0.6823808 #> [22,] 0.7445577 0.7545314 0.7877731 #> [23,] 0.7894505 0.8406640 0.8509438 #> [24,] 0.9551027 0.9654747 1.0030288 #> [25,] 1.0730757 1.0733707 1.0883246 #> [26,] 0.7280532 0.7556115 0.7615608 #> [27,] 0.7040471 0.7180793 0.7558149 #> [28,] 1.1124655 1.1726914 1.2164293 #> [29,] 0.7843067 0.7848351 0.8214750 #> [30,] 0.5764293 0.6088673 0.6187806 #> [31,] 1.1153360 1.1448984 1.1843941 #> [32,] 0.7166056 0.7172308 0.7263161 #> [33,] 0.7250852 0.7477290 0.7512148 #> [34,] 1.3742790 1.4484635 1.4505247 #> [35,] 1.2946210 1.2946745 1.3529488 #> [36,] 0.9501224 0.9525178 0.9781398 #> [37,] 0.5908949 0.6201243 0.6238170 #> [38,] 0.9860467 1.0113623 1.0792367 #> [39,] 1.3823259 1.4800667 1.4810435 #> [40,] 1.0405158 1.0413266 1.0484415 #> [41,] 0.4522604 0.4552867 0.4983990 #> [42,] 1.0056548 1.0083335 1.0214761 #> [43,] 0.5572177 0.5785664 0.6033729 #> [44,] 0.5246025 0.5514685 0.5659403 #> [45,] 0.5520987 0.5618288 0.5626486 #> [46,] 0.9168416 0.9505614 0.9540991 #> [47,] 0.9491927 0.9748667 1.0007042 #> [48,] 0.7468701 0.7627787 0.7740428 #> [49,] 0.8202840 0.8203177 0.8369207 #> [50,] 0.7371032 0.7728698 0.7842856 #> [51,] 0.8279323 0.8695652 0.8741601 #> [52,] 0.6908772 0.7031286 0.7333217 #> [53,] 0.6937080 0.7369896 0.7374129 #> [54,] 0.5789364 0.5883231 0.5903267 #> [55,] 0.8279323 0.8611921 0.8671132 #> [56,] 1.2105582 1.3609712 1.4209254 #> [57,] 0.6228348 0.6295301 0.6422188 #> [58,] 0.6590690 0.6728996 0.6740843 #> [59,] 0.6575858 0.6959634 0.7385297 #> [60,] 0.9164001 0.9352856 0.9474459 #> [61,] 0.6986781 0.7031266 0.7040471 #> [62,] 0.5624673 0.5656930 0.5680078 #> [63,] 0.6356963 0.6507316 0.6567545 #> [64,] 0.6932837 0.7119049 0.7338189 #> [65,] 0.6698017 0.6710056 0.6977133 #> [66,] 1.0568114 1.0584024 1.0628807 #> [67,] 0.4925018 0.5051723 0.5192192 #> [68,] 0.8972152 0.9264264 1.0154784 #> [69,] 0.7329504 0.7436061 0.7528723 #> [70,] 1.3642570 1.4449321 1.4667406 #> [71,] 0.8690166 0.9903226 1.0250856 #> [72,] 0.5239980 0.5479711 0.5800733 #> [73,] 0.5115099 0.5237082 0.5635509 #> [74,] 0.9470885 0.9779822 1.0129587 #> [75,] 0.5990171 0.6098017 0.6144892 #> [76,] 0.5953002 0.6128149 0.6135434 #> [77,] 0.5607080 0.6527787 0.6877265 #> [78,] 0.5357776 0.5617833 0.5903267 #> [79,] 0.7854694 0.8062140 0.8121136 #> [80,] 0.9891183 0.9941969 0.9993140 #> [81,] 0.5521847 0.5686514 0.5835483 #> [82,] 1.2409497 1.2821540 1.3338516 #> [83,] 1.3557836 1.3559887 1.3816546 #> [84,] 1.1189773 1.1394716 1.2007114 #> [85,] 0.6464739 0.6472974 0.6520667 #> [86,] 0.8236488 0.8243628 0.8298920 #> [87,] 0.7494334 0.7898359 0.8762251 #> [88,] 0.7067036 0.7517859 0.7558175 #> [89,] 0.6324472 0.6384841 0.6393787 #> [90,] 0.5765781 0.6253036 0.6467292 #> [91,] 0.6217369 0.6738939 0.6883190 #> [92,] 0.9049822 0.9530349 1.0050902 #> [93,] 0.5503597 0.5701566 0.5904144 #> [94,] 1.1755510 1.2042861 1.2316526 #> [95,] 0.9724045 0.9812174 0.9884258 #> [96,] 0.9592701 0.9728378 1.0266564 #> [97,] 0.5480974 0.5812286 0.6118587 #> [98,] 0.5246272 0.5265085 0.5475677 #> [99,] 0.8461130 0.8477330 0.8717503 #> [100,] 0.7628803 0.7824819 0.7993370 #> [101,] 1.7962282 1.8124344 1.8204522 #> [102,] 0.4446164 0.4626767 0.4765493 #> [103,] 1.6171545 1.6189861 1.6381932 #> [104,] 0.7240607 0.7821695 0.8235358 #> [105,] 0.5712901 0.5985136 0.6067016 #> [106,] 0.8766191 0.8782562 0.8872883 #> [107,] 1.3221504 1.3258106 1.3595579 #> [108,] 1.2497242 1.2797769 1.2847999 #> [109,] 0.6307679 0.6548610 0.6825140 #> [110,] 0.5077163 0.5152366 0.5201796 #> [111,] 0.5897513 0.6775438 0.7249468 #> [112,] 0.8716588 0.9054262 0.9855289 #> [113,] 0.8448758 0.8714976 0.9142557 #> [114,] 0.7288252 0.8052105 0.8151615 #> [115,] 0.6718406 0.6722277 0.6948328 #> [116,] 0.8939690 0.9073273 0.9459113 #> [117,] 1.1866277 1.2049009 1.2131600 #> [118,] 0.9652541 0.9756351 0.9942206 #> [119,] 0.5998649 0.6091943 0.6123165 #> [120,] 0.6872129 0.7097586 0.7301151 #> [121,] 0.7390389 0.7604702 0.7630935 #> [122,] 0.4938862 0.4983489 0.5466885 #> [123,] 0.9209192 0.9381286 0.9578028 #> [124,] 1.2576783 1.3457942 1.3543061 #> [125,] 0.8000203 0.8167809 0.8301323 #> [126,] 0.8340372 0.8514435 0.8515045 #> [127,] 0.5103176 0.5475948 0.5882427 #> [128,] 0.5827694 0.5828899 0.5951991 #> [129,] 0.6340862 0.6482273 0.6507316 #> [130,] 0.7738277 0.8869694 0.8988091 #> [131,] 0.6822141 0.6939493 0.6992247 #> [132,] 0.6574931 0.7422702 0.7461058 #> [133,] 0.9973896 1.0184821 1.0237132 #> [134,] 1.1486590 1.1613028 1.1837634 #> [135,] 0.6238170 0.6395035 0.6520195 #> [136,] 1.0973044 1.1709569 1.2332039 #> [137,] 0.6108532 0.6429611 0.6630001 #> [138,] 0.5521847 0.5843494 0.6012981 #> [139,] 0.7308439 0.7434359 0.7581942 #> [140,] 1.3928817 1.4068608 1.4439310 #> [141,] 0.6609668 0.6850597 0.7299244 #> [142,] 0.5915916 0.6050565 0.6077914 #> [143,] 0.5823034 0.5947904 0.6019534 #> [144,] 0.5553071 0.5645280 0.6446181 #> [145,] 0.6940500 0.7282091 0.7310340 #> [146,] 0.5495635 0.5514685 0.5826591 #> [147,] 0.7611104 0.8040045 0.8043343 #> [148,] 0.6495392 0.6776919 0.6854624 #> [149,] 0.7480650 0.8191044 0.8823637 #> [150,] 0.5411869 0.5502896 0.5583409 #> [151,] 0.7180998 0.7336379 0.7341086 #> [152,] 1.1912636 1.2095629 1.2352239 #> [153,] 0.5758092 0.6116671 0.6165203 #> [154,] 0.7524017 0.7819264 0.7953267 #> [155,] 0.6733962 0.6810368 0.6899465 #> [156,] 0.6521463 0.6840823 0.7100448 #> [157,] 1.3620943 1.3688559 1.3708892 #> [158,] 0.5600066 0.6737385 0.6776919 #> [159,] 0.5901161 0.5951493 0.6357927 #> [160,] 0.8658120 0.8981545 0.9256894 #> [161,] 0.7964822 0.7980622 0.8108124 #> [162,] 0.5224324 0.5541192 0.5592968 #> [163,] 1.1540350 1.1687959 1.1734590 #> [164,] 1.0675792 1.1022426 1.1054776 #> [165,] 1.1229310 1.1440999 1.1454136 #> [166,] 0.8308600 0.8457055 0.8557414 #> [167,] 0.5969554 0.5991155 0.6028723 #> [168,] 1.3838868 1.3842514 1.3956961 #> [169,] 0.8712192 0.9154238 0.9272356 #> [170,] 0.7390389 0.7535997 0.7970432 #> [171,] 0.6023530 0.6238119 0.6355553 #> [172,] 0.7766402 0.7777657 0.7899675 #> [173,] 0.5313566 0.5754902 0.5769897 #> [174,] 0.5431463 0.5839871 0.6055436 #> [175,] 0.9994798 1.0213772 1.0685104 #> [176,] 0.8997026 0.9091246 0.9232706 #> [177,] 0.7352546 0.7384616 0.7423913 #> [178,] 0.6937898 0.6945931 0.7143432 #> [179,] 1.0741032 1.0937999 1.0980578 #> [180,] 0.5416264 0.5501798 0.5502562 #> [181,] 0.9843336 1.0336066 1.0590858 #> [182,] 0.6091943 0.6363898 0.6373057 #> [183,] 0.7003897 0.7075189 0.7136140 #> [184,] 0.9884055 1.0074228 1.0174219 #> [185,] 0.8916240 0.8993724 0.9282640 #> [186,] 0.6490195 0.6628685 0.6681492 #> [187,] 1.2156100 1.2544759 1.2724148 #> [188,] 0.7837702 0.8218098 0.8225141 #> [189,] 0.7619641 0.8015365 0.8407601 #> [190,] 1.0560959 1.1273738 1.1434620 #> [191,] 0.8261643 0.8521884 0.9099727 #> [192,] 1.1566105 1.1855532 1.1886661 #> [193,] 0.9440280 0.9661984 0.9921918 #> [194,] 0.8645134 0.8670063 0.9061379 #> [195,] 0.5188817 0.5238636 0.5719127 #> [196,] 0.8252084 0.8782798 0.8829206 #> [197,] 0.6393644 0.7216211 0.7395698 #> [198,] 0.9058586 0.9194339 0.9787950 #> [199,] 0.9836911 1.0023737 1.0260539 #> [200,] 0.8919486 0.9284428 0.9431832 #> [201,] 0.5982558 0.6336967 0.6477662 #> [202,] 0.8238807 0.8295729 0.8532570 #> [203,] 0.3975459 0.4535849 0.4674355 #> [204,] 0.8965174 0.8982999 0.8993036 #> [205,] 0.5746819 0.5860547 0.5897513 #> [206,] 0.7902742 0.8046735 0.8447648 #> [207,] 0.6917758 0.7283993 0.7666928 #> [208,] 0.5911413 0.5921035 0.5962793 #> [209,] 0.8112749 0.8424908 0.8805676 #> [210,] 0.5499103 0.5552143 0.5724341 #> [211,] 0.7664883 0.7766420 0.8158615 #> [212,] 0.7401987 0.7662884 0.7797529 #> [213,] 0.6575542 0.6580014 0.6768431 #> [214,] 1.2166165 1.2253165 1.2261671 #> [215,] 0.6186600 0.6671991 0.6732922 #> [216,] 0.7398366 0.7403294 0.7425097 #> [217,] 0.5989056 0.6023753 0.6490482 #> [218,] 0.5521809 0.5585820 0.6238076 #> [219,] 0.5268149 0.5624969 0.5636207 #> [220,] 1.1284222 1.1794205 1.1963549 #> [221,] 0.6613447 0.6957399 0.7197768 #> [222,] 0.7376104 0.7534260 0.7571064 #> [223,] 1.1254497 1.1310411 1.1359482 #> [224,] 0.5717071 0.5998148 0.6373057 #> [225,] 0.7136713 0.7231501 0.7289041 #> [226,] 0.6541174 0.7063086 0.7362559 #> [227,] 0.7185699 0.7271226 0.7680247 #> [228,] 1.2394236 1.2587553 1.3192218 #> [229,] 0.9253467 0.9658028 0.9916496 #> [230,] 0.9420330 0.9539283 0.9658795 #> [231,] 0.8630664 0.8804020 0.8901090 #> [232,] 0.4850090 0.5162538 0.5226281 #> [233,] 0.6355172 0.6559131 0.6857133 #> [234,] 0.8232688 0.8362491 0.8615428 #> [235,] 0.5794818 0.5823488 0.5878504 #> [236,] 0.8986594 0.9156674 0.9355886 #> [237,] 0.6070168 0.6146959 0.6385568 #> [238,] 0.7800092 0.7919962 0.8261697 #> [239,] 0.6446008 0.6517779 0.6528180 #> [240,] 0.5903636 0.6649502 0.6650598 #> [241,] 1.5181657 1.5191653 1.6806529 #> [242,] 1.1709452 1.2108411 1.2212352 #> [243,] 0.9794785 1.0148312 1.0588020 #> [244,] 0.8228033 0.9406653 0.9412112 #> [245,] 0.7355959 0.7427091 0.8169518 #> [246,] 0.6605395 0.7010432 0.7101375 #> [247,] 0.5724341 0.5965084 0.6013598 #> [248,] 0.6581380 0.6864811 0.7007856 #> [249,] 0.7468705 0.7483195 0.7560462 #> [250,] 0.4474640 0.4590751 0.4597129 #> [251,] 0.8388607 0.9121455 0.9395272 #> [252,] 0.6237672 0.6324957 0.6378056 #> [253,] 1.1115367 1.1695811 1.2465492 #> [254,] 0.8507018 0.8815974 0.9280725 #> [255,] 0.5880765 0.6234843 0.6359189 #> [256,] 0.7837060 0.7840352 0.8021130 #> [257,] 0.7432229 0.7474043 0.7781482 #> [258,] 0.8157246 0.8163470 0.8207238 #> [259,] 1.0071526 1.0514318 1.0816304 #> [260,] 0.7723188 0.8073125 0.8125567 #> [261,] 0.7733659 0.7765029 0.7781799 #> [262,] 0.8045442 0.8443363 0.8512293 #> [263,] 0.6691929 0.6805346 0.7250852 #> [264,] 0.5409081 0.5479479 0.5533832 #> [265,] 0.6240639 0.6376946 0.6383766 #> [266,] 0.6140820 0.6224795 0.6236054 #> [267,] 0.7967967 0.8110177 0.8293811 #> [268,] 0.5538449 0.5592717 0.5692131 #> [269,] 1.3894109 1.3983734 1.4073322 #> [270,] 0.6532931 0.6535509 0.6603980 #> [271,] 0.6954934 0.8071532 0.8544645 #> [272,] 0.7997930 0.8001390 0.8008286 #> [273,] 0.7323858 0.7352067 0.7820764 #> [274,] 0.5457597 0.5458660 0.5603155 #> [275,] 0.6593882 0.6684101 0.6983498 #> [276,] 1.5282590 1.5432321 1.5518024 #> [277,] 0.7831023 0.8821525 0.8906814 #> [278,] 0.5610538 0.5769897 0.6043345 #> [279,] 0.8457055 0.8582436 0.9107608 #> [280,] 0.5585820 0.5978585 0.6342586 #> [281,] 0.5753703 0.6876870 0.7038946 #> [282,] 0.7290462 0.8648736 0.8648975 #> [283,] 0.6778300 0.6789222 0.7318722 #> [284,] 0.9411683 0.9855289 1.0050375 #> [285,] 0.7043195 0.7432437 0.7502960 #> [286,] 1.3651561 1.3892015 1.3943802 #> [287,] 1.2771909 1.2880192 1.3741400 #> [288,] 1.5075022 1.5866471 1.6241959 #> [289,] 1.1508820 1.2054700 1.2060725 #> [290,] 0.8510004 0.8512387 0.8942729 #> [291,] 0.6606663 0.6876140 0.7072640 #> [292,] 0.6391744 0.6506912 0.6697097 #> [293,] 0.8054279 0.8070900 0.8415829 #> [294,] 0.6004037 0.6064343 0.6525837 #> [295,] 0.5946605 0.6109984 0.6217659 #> [296,] 0.5564219 0.5682669 0.5785664 #> [297,] 0.5121810 0.5405678 0.5407712 #> [298,] 0.8108245 0.8151554 0.8167585 #> [299,] 0.4839062 0.4925744 0.5149030 #> [300,] 0.7401987 0.7627323 0.7641001 #> [301,] 0.5626776 0.6190075 0.6316696 #> [302,] 0.6929260 0.7291426 0.7316196 #> [303,] 1.9051118 1.9100229 1.9334947 #> [304,] 0.5817847 0.5997890 0.6207748 #> [305,] 0.5720249 0.5837491 0.5898708 #> [306,] 0.5965174 0.6005517 0.6137788 #> [307,] 1.0034240 1.0314873 1.0320467 #> [308,] 0.8265704 0.8445410 0.8636050 #> [309,] 0.8943133 0.9098371 0.9781398 #> [310,] 0.6571992 0.6586063 0.6722925 #> [311,] 0.8053866 0.8088441 0.8141323 #> [312,] 0.9904840 1.0488026 1.0567316 #> [313,] 0.6881557 0.7582715 0.7591426 #> [314,] 0.5568581 0.5827694 0.6204041 #> [315,] 0.6862337 0.7001198 0.7561484 #> [316,] 0.8492696 0.8711605 0.8899702 #> [317,] 0.7655117 0.7722215 0.7753759 #> [318,] 0.6363898 0.6404232 0.6509156 #> [319,] 0.6670985 0.6672640 0.6962625 #> [320,] 0.8233239 0.8242852 0.8484660 #> [321,] 0.6495988 0.6508068 0.6542615 #> [322,] 1.0180832 1.0250659 1.0266634 #> [323,] 0.6837625 0.6928143 0.6936870 #> [324,] 0.7342534 0.7540028 0.7679974 #> [325,] 0.6308780 0.6407043 0.6508068 #> [326,] 0.9350917 0.9420448 0.9533092 #> [327,] 0.6695906 0.6926156 0.7533284 #> [328,] 1.0668279 1.0853428 1.2035339 #> [329,] 0.8267822 0.8279529 0.8500662 #> [330,] 0.6328065 0.6525837 0.6550373 #> [331,] 0.6137788 0.6349687 0.6698618 #> [332,] 0.7303193 0.7322579 0.7341715 #> [333,] 0.8000697 0.8059705 0.8269345 #> [334,] 0.5379070 0.5381997 0.5402068 #> [335,] 0.4572627 0.4642517 0.4692709 #> [336,] 1.1938257 1.2105804 1.2646468 #> [337,] 0.8833182 0.8913455 0.9097912 #> [338,] 0.8483925 0.8485289 0.8739361 #> [339,] 0.9268745 0.9301437 0.9494924 #> [340,] 0.5926610 0.6433268 0.6688616 #> [341,] 0.6328652 0.6697167 0.6775438 #> [342,] 0.6189582 0.6336426 0.6530868 #> [343,] 0.9829755 0.9838940 1.0288515 #> [344,] 0.5624027 0.5672823 0.5751450 #> [345,] 0.5785100 0.5880912 0.6032128 #> [346,] 0.7105605 0.7254459 0.7285652 #> [347,] 0.6198076 0.6297758 0.6598254 #> [348,] 0.8729183 0.8793803 0.9190275 #> [349,] 0.5209098 0.5302664 0.5624052 #> [350,] 0.8863619 0.9420280 0.9459780 #> [351,] 0.8880786 0.9228884 0.9664693 #> [352,] 1.3276280 1.3284202 1.3549191 #> [353,] 1.0438588 1.0442103 1.0701039 #> [354,] 0.7461058 0.7690134 0.7864186 #> [355,] 0.7151664 0.7450731 0.7689244 #> [356,] 0.5878504 0.5908810 0.6090971 #> [357,] 0.6847809 0.8065705 0.8067604 #> [358,] 1.0136404 1.0154122 1.0282789 #> [359,] 1.0531656 1.0646987 1.1138957 #> [360,] 1.0397803 1.0461697 1.0537380 #> [361,] 0.4926647 0.5154537 0.5237127 #> [362,] 0.9955914 1.0392933 1.0474345 #> [363,] 0.8972945 0.8996744 0.9027318 #> [364,] 0.9159105 0.9435594 0.9511380 #> [365,] 0.7430718 0.7604702 0.7980560 #> [366,] 0.5674692 0.5727376 0.6627259 #> [367,] 1.0199524 1.0204548 1.0471335 #> [368,] 0.5353742 0.5394586 0.5463735 #> [369,] 0.8494756 0.8770611 0.8840677 #> [370,] 0.9045793 0.9141658 0.9328294 #> [371,] 0.8484185 0.8639459 0.9237728 #> [372,] 0.7616295 0.7788900 0.8071625 #> [373,] 0.5097656 0.5103176 0.5450248 #> [374,] 0.6674362 0.6946062 0.8056381 #> [375,] 0.7198688 0.7292259 0.7533332 #> [376,] 0.7404467 0.7665664 0.7672032 #> [377,] 0.8388834 0.8573182 0.8683734 #> [378,] 0.7261603 0.7888915 0.8065603 #> [379,] 1.1768365 1.2174035 1.2453905 #> [380,] 1.2598092 1.3246854 1.3305341 #> [381,] 0.8631331 0.8777154 0.8879424 #> [382,] 0.8118543 0.8243492 0.8374920 #> [383,] 0.7757223 0.7796205 0.8203172 #> [384,] 0.8415829 0.8425422 0.8427918 #> [385,] 0.6887134 0.6924916 0.6935413 #> [386,] 0.6301571 0.6443765 0.6467538 #> [387,] 0.6254812 0.6277603 0.6707994 #> [388,] 0.9504140 0.9787950 0.9788155 #> [389,] 0.7573177 0.7574468 0.7613383 #> [390,] 0.6926296 0.7004906 0.7667403 #> [391,] 1.1676943 1.2179838 1.2325915 #> [392,] 0.8188247 0.8241835 0.8339341 #> [393,] 0.8329418 0.8386294 0.8460252 #> [394,] 0.7096210 0.7292739 0.7584514 #> [395,] 1.4799383 1.5064008 1.5235157 #> [396,] 1.1022143 1.1110085 1.1184343 #> [397,] 0.7088534 0.7127603 0.7214720 #> [398,] 0.7625693 0.7998142 0.8176899 #> [399,] 0.7830381 0.8207924 0.8252420 #> [400,] 1.2736809 1.3238208 1.3259648 #> [401,] 0.7904562 0.8157185 0.8233814 #> [402,] 0.9497575 0.9665054 0.9835361 #> [403,] 0.9377258 1.0256769 1.0350779 #> [404,] 0.6182421 0.6186630 0.6250949 #> [405,] 1.3379349 1.3868488 1.4096641 #> [406,] 0.6810388 0.7326497 0.7436179 #> [407,] 0.8000203 0.8126316 0.8261912 #> [408,] 0.6668383 0.6772600 0.7011845 #> [409,] 0.5904144 0.6055436 0.6144892 #> [410,] 0.6182368 0.6483972 0.6787011 #> [411,] 0.7589513 0.7703605 0.7758075 #> [412,] 0.7763655 0.7924663 0.8046182 #> [413,] 0.7299407 0.7307987 0.7472007 #> [414,] 1.2705682 1.3039116 1.3173147 #> [415,] 1.3978243 1.4099353 1.4368023 #> [416,] 0.7055470 0.7556795 0.7571401 #> [417,] 0.8557944 0.9016194 0.9258674 #> [418,] 0.6795702 0.6936138 0.7203690 #> [419,] 0.5507127 0.5616786 0.5664522 #> [420,] 0.5946564 0.6271226 0.6589521 #> [421,] 1.2660488 1.2935256 1.3210175 #> [422,] 1.0777358 1.1552969 1.2204435 #> [423,] 0.5313030 0.5338943 0.5407712 #> [424,] 0.9779007 1.0178157 1.0591399 #> [425,] 0.8950220 0.9454731 0.9560139 #> [426,] 1.0097005 1.0485973 1.1099787 #> [427,] 0.6957399 0.7095744 0.7805514 #> [428,] 1.3067943 1.3501308 1.3673162 #> [429,] 0.8278941 0.8936291 0.9025688 #> [430,] 0.7547116 0.7996885 0.8211158 #> [431,] 0.7154578 0.7296091 0.7660701 #> [432,] 0.7421226 0.7538467 0.8155186 #> [433,] 0.5806314 0.5915916 0.6548079 #> [434,] 0.6349687 0.6365637 0.6500784 #> [435,] 0.8473934 0.8574420 0.9175749 #> [436,] 0.6155368 0.6198042 0.6409345 #> [437,] 0.4699607 0.5248791 0.5486645 #> [438,] 1.0221971 1.0283199 1.0576939 #> [439,] 1.4077905 1.4078477 1.4427959 #> [440,] 0.4409665 0.4626767 0.4938862 #> [441,] 0.9048603 0.9087658 0.9091246 #> [442,] 1.1669433 1.1732456 1.1790484 #> [443,] 0.5563468 0.5780151 0.5950256 #> [444,] 0.9618237 0.9843987 1.0340714 #> [445,] 1.6385503 1.6417239 1.6554494 #> [446,] 0.7878675 0.7962567 0.7979834 #> [447,] 2.0097095 2.0525425 2.0587405 #> [448,] 1.1924949 1.2178915 1.2492241 #> [449,] 0.5403095 0.5631900 0.5737795 #> [450,] 1.6583900 1.7281786 1.7370262 #> [451,] 0.6314860 0.6396309 0.6518001 #> [452,] 1.3654383 1.3918685 1.4056749 #> [453,] 0.6978221 0.7011060 0.7079638 #> [454,] 1.0720183 1.1228689 1.1266475 #> [455,] 0.5374261 0.5787567 0.5882427 #> [456,] 0.5917679 0.6269757 0.6475247 #> [457,] 1.0103346 1.0583478 1.0754355 #> [458,] 0.7366074 0.7696810 0.8113648 #> [459,] 0.5130750 0.5307737 0.5408275 #> [460,] 0.5350491 0.5520000 0.5657748 #> [461,] 0.8358193 0.8409993 0.8453590 #> [462,] 1.3948239 1.4562316 1.4921572 #> [463,] 0.5976927 0.6159258 0.6270187 #> [464,] 0.7016635 0.7099575 0.7111202 #> [465,] 0.9276422 0.9977400 1.0455170 #> [466,] 1.1230235 1.1773242 1.1921464 #> [467,] 0.5719470 0.6084708 0.6584985 #> [468,] 0.5323596 0.5371743 0.6082670 #> [469,] 0.6305895 0.6475206 0.6699390 #> [470,] 0.3822591 0.4214002 0.4794289 #> [471,] 0.6965180 0.7162136 0.7932884 #> [472,] 1.2037354 1.2412579 1.2781834 #> [473,] 1.1528347 1.1575442 1.1691273 #> [474,] 0.6404232 0.6628817 0.6633204 #> [475,] 1.3680536 1.3806925 1.4502315 #> [476,] 0.7013151 0.7044657 0.7068683 #> [477,] 0.7424823 0.8617070 0.8710939 #> [478,] 0.6833299 0.7013946 0.7057739 #> [479,] 0.5670320 0.5874698 0.6266317 #> [480,] 0.5411971 0.5717583 0.5764336 #> [481,] 0.8700075 0.8761098 0.8879985 #> [482,] 0.6665749 0.6856635 0.6897393 #> [483,] 0.7095349 0.7123296 0.7411007 #> [484,] 1.2568529 1.3388399 1.3498595 #> [485,] 0.6030901 0.6106986 0.6362282 #> [486,] 1.3270357 1.3446570 1.3753723 #> [487,] 0.6944722 0.6986781 0.7058085 #> [488,] 0.7442717 0.7716297 0.8258617 #> [489,] 0.8303598 0.8338579 0.8443118 #> [490,] 1.5111890 1.5445189 1.5564387 #> [491,] 1.4287770 1.4338115 1.4534610 #> [492,] 0.6028583 0.6085813 0.6364568 #> [493,] 1.2444039 1.2724466 1.2782419 #> [494,] 0.9186961 0.9288731 0.9339130 #> [495,] 0.5148448 0.5639078 0.6331913 #> [496,] 0.9704159 0.9718047 0.9869484 #> [497,] 0.5541192 0.5699598 0.5739698 #> [498,] 1.2294258 1.2405399 1.2632070 #> [499,] 0.5804832 0.6020497 0.6649769 #> [500,] 0.6395914 0.6407729 0.6647554 #> [501,] 1.2427244 1.2891885 1.3755788 #> [502,] 0.5479711 0.5639078 0.5675280 #> [503,] 1.5388021 1.6372228 1.6697375 #> [504,] 0.7529697 0.7724098 0.7991795 #> [505,] 1.5642651 1.5753041 1.5896113 #> [506,] 0.9122109 0.9163889 0.9183538 #> [507,] 0.9718225 0.9727489 0.9946086 #> [508,] 0.7348817 0.7541810 0.7824822 #> [509,] 0.8368484 0.8441140 0.8712227 #> [510,] 0.7831023 0.8224706 0.8269345 #> [511,] 1.2972052 1.2995585 1.3610271 #> [512,] 0.7433959 0.7674406 0.8095525 #> [513,] 0.4820203 0.4914494 0.5091375 #> [514,] 0.5673960 0.6052295 0.6122315 #> [515,] 0.4559979 0.4896020 0.4909026 #> [516,] 0.4765648 0.5061745 0.5290541 #> [517,] 0.5166929 0.5225545 0.5312450 #> [518,] 0.7012764 0.7025821 0.7181508 #> [519,] 0.6690963 0.6722143 0.7096344 #> [520,] 0.5259640 0.5403095 0.5471192 #> [521,] 0.7425617 0.7567662 0.7822832 #> [522,] 0.5248791 0.5320507 0.5325069 #> [523,] 0.9687019 0.9696667 1.0605354 #> [524,] 0.5592717 0.5775316 0.6013149 #> [525,] 0.7134244 0.7180454 0.7746114 #> [526,] 0.6894268 0.6917122 0.7388943 #> [527,] 1.5320355 1.5359497 1.5481314 #> [528,] 0.6239519 0.6647352 0.6698996 #> [529,] 0.8253294 0.8426068 0.8512405 #> [530,] 0.9175670 0.9586984 0.9696410 #> [531,] 0.5643394 0.6282007 0.6373988 #> [532,] 0.7024892 0.7180089 0.7402136 #> [533,] 0.9412042 0.9443775 0.9501321 #> [534,] 0.6331913 0.6349211 0.6712309 #> [535,] 0.4731234 0.5026104 0.5306004 #> [536,] 0.5622579 0.5691711 0.5894259 #> [537,] 1.3938269 1.4201260 1.4538800 #> [538,] 2.1562114 2.1712210 2.1791550 #> [539,] 0.5998649 0.6303169 0.6841752 #> [540,] 1.2021338 1.2431104 1.2434704 #> [541,] 0.8252084 0.8266875 0.8453320 #> [542,] 1.1119488 1.1136882 1.1416424 #> [543,] 0.7668405 0.7668689 0.7780350 #> [544,] 1.2257260 1.2418458 1.3271819 #> [545,] 0.9903867 1.0414513 1.0425481 #> [546,] 0.7894660 0.7979414 0.8440595 #> [547,] 1.4748032 1.5288034 1.6200355 #> [548,] 0.8342098 0.8576379 0.8589755 #> [549,] 0.8357769 0.8472275 0.8531945 #> [550,] 0.8899300 0.9027397 0.9051167 #> [551,] 0.7655653 0.7879630 0.8017048 #> [552,] 0.6278000 0.6282079 0.6320861 #> [553,] 0.7442717 0.7569825 0.7942681 #> [554,] 0.6768602 0.6894267 0.7283503 #> [555,] 0.4914494 0.5066574 0.5105818 #> [556,] 0.6791070 0.6866979 0.7237962 #> [557,] 0.7090876 0.7166298 0.7232070 #> [558,] 0.6349871 0.6720933 0.6736528 #> [559,] 0.8838416 0.8964731 0.9543999 #> [560,] 0.7819956 0.8213843 0.8433024 #> [561,] 0.8987376 0.9224726 0.9437928 #> [562,] 0.5383853 0.5501868 0.5729807 #> [563,] 1.6153224 1.6803922 1.7070157 #> [564,] 0.9931664 1.0353998 1.0435389 #> [565,] 1.1575457 1.2387429 1.2800923 #> [566,] 1.7779091 1.7815292 1.8143845 #> [567,] 0.8014217 0.8219595 0.8323921 #> [568,] 0.6558496 0.6684397 0.6691929 #> [569,] 0.5976967 0.6356963 0.6428138 #> [570,] 0.8780852 0.8807945 0.8864215 #> [571,] 1.5694029 1.6226836 1.6559844 #> [572,] 1.8867064 1.8946499 1.8951040 #> [573,] 0.7954502 0.8146452 0.8159977 #> [574,] 0.5106542 0.5251450 0.5402068 #> [575,] 0.9253467 0.9494333 0.9523070 #> [576,] 0.5224575 0.5246025 0.5282306 #> [577,] 0.8574534 0.8758765 0.9156594 #> [578,] 0.6972932 0.7002176 0.7072410 #> [579,] 0.7263034 0.7667228 0.7703424 #> [580,] 1.1658215 1.2123779 1.2285181 #> [581,] 0.9509007 0.9569566 0.9901120 #> [582,] 0.6984653 0.7123291 0.7231011 #> [583,] 0.6165135 0.6549497 0.6617059 #> [584,] 0.6559082 0.6567545 0.6708039 #> [585,] 1.2372009 1.2431104 1.3414206 #> [586,] 0.8837551 0.8870166 0.8986792 #> [587,] 1.1300497 1.1378241 1.1534381 #> [588,] 0.9341930 0.9854642 0.9955070 #> [589,] 0.7817602 0.7950334 0.8359405 #> [590,] 0.8507018 0.8532815 0.8706052 #> [591,] 0.6070573 0.6123936 0.6133793 #> [592,] 0.6506767 0.6857493 0.6886219 #> [593,] 1.1799360 1.2041982 1.2203544 #> [594,] 0.9431895 1.0075567 1.0447846 #> [595,] 1.2599315 1.2657402 1.2736123 #> [596,] 0.7112562 0.7154578 0.7300252 #> [597,] 0.9327264 0.9697106 0.9757561 #> [598,] 0.5226653 0.5347778 0.6004037 #> [599,] 0.7221886 0.7933000 0.7960978 #> [600,] 0.5261021 0.5263663 0.5302476 #> [601,] 0.7163910 0.7248237 0.7679930 #> [602,] 0.7257560 0.7548418 0.7964255 #> [603,] 0.8378586 0.8414736 0.8446556 #> [604,] 0.9077201 0.9923359 1.0106972 #> [605,] 0.6994948 0.7045753 0.7119669 #> [606,] 0.5645280 0.5647955 0.5910950 #> [607,] 1.4204439 1.4740305 1.4971383 #> [608,] 1.5498688 1.6069093 1.7384498 #> [609,] 0.9435022 0.9635824 0.9656355 #> [610,] 0.5616786 0.5708692 0.6117274 #> [611,] 1.2172076 1.2886264 1.3355435 #> [612,] 0.5113881 0.5568581 0.5676932 #> [613,] 1.1811073 1.2101745 1.2565012 #> [614,] 0.8288751 0.8812426 0.9096677 #> [615,] 0.5957393 0.6231754 0.6320876 #> [616,] 0.6050565 0.6071182 0.6480847 #> [617,] 0.7711614 0.7980622 0.8012313 #> [618,] 0.5614452 0.6487696 0.6725718 #> [619,] 0.5817847 0.6002829 0.6276241 #> [620,] 0.8364844 0.8494256 0.8512405 #> [621,] 0.7005655 0.7149314 0.7576389 #> [622,] 0.9959242 1.0071526 1.0788781 #> [623,] 1.0235715 1.0478058 1.0535272 #> [624,] 0.7768493 0.7793272 0.8205517 #> [625,] 1.1115848 1.1248822 1.1475012 #> [626,] 0.5346923 0.5503597 0.5520000 #> [627,] 0.6558496 0.6659052 0.7765029 #> [628,] 1.0708415 1.1446938 1.2266482 #> [629,] 0.6829279 0.7051407 0.7155856 #> [630,] 0.7140941 0.7149278 0.7381929 #> [631,] 0.9039229 0.9152130 0.9270557 #> [632,] 0.5377479 0.5674692 0.6032168 #> [633,] 0.6707994 0.6877516 0.7379386 #> [634,] 0.6339956 0.6650379 0.6674789 #> [635,] 0.6655739 0.6722275 0.7188234 #> [636,] 1.2039409 1.2538158 1.3004335 #> [637,] 0.9567400 0.9805564 1.0181206 #> [638,] 1.7680531 1.7727131 1.8282199 #> [639,] 0.7118041 0.7131544 0.7225123 #> [640,] 0.7567525 0.7756439 0.7817602 #> [641,] 0.5957012 0.6084708 0.6490912 #> [642,] 0.6071488 0.6660500 0.6773838 #> [643,] 0.5670320 0.5772271 0.6008826 #> [644,] 0.6868794 0.6977937 0.7150017 #> [645,] 0.7461080 0.7604850 0.7808844 #> [646,] 0.6266361 0.6314365 0.6341940 #> [647,] 0.7385297 0.7404467 0.7519838 #> [648,] 0.8369020 0.8398624 0.8476542 #> [649,] 0.4740971 0.4755505 0.5060454 #> [650,] 1.7220624 1.7519612 1.7616198 #> [651,] 0.4828149 0.5392326 0.5533832 #> [652,] 0.6740843 0.6871881 0.7243692 #> [653,] 0.5535769 0.5727801 0.5891104 #> [654,] 1.0375054 1.0590858 1.0765221 #> [655,] 0.8332197 0.8422317 0.8527906 #> [656,] 1.0206574 1.0517808 1.0649530 #> [657,] 0.7398995 0.8453590 0.8595941 #> [658,] 0.8261406 0.8307582 0.8373647 #> [659,] 0.7806629 0.7927973 0.7979414 #> [660,] 0.5940258 0.6013404 0.6245413 #> [661,] 1.0329767 1.0371129 1.0452618 #> [662,] 0.7318016 0.7900017 0.7961905 #> [663,] 0.4782290 0.4993475 0.5221172 #> [664,] 0.6718837 0.6856635 0.7067344 #> [665,] 0.6518001 0.6655306 0.6815493 #> [666,] 1.1155896 1.1403134 1.1439860 #> [667,] 0.5810267 0.5871655 0.6011670 #> [668,] 0.7238108 0.7438533 0.7550931 #> [669,] 1.0504722 1.0550247 1.0723984 #> [670,] 0.9731496 0.9735136 0.9877512 #> [671,] 1.0529481 1.0845242 1.1297322 #> [672,] 0.8843676 0.8887155 0.9268679 #> [673,] 0.6216648 0.6479959 0.6746208 #> [674,] 0.5251450 0.5284033 0.5484489 #> [675,] 0.7403855 0.7486979 0.7514374 #> [676,] 0.6969275 0.7127841 0.7689411 #> [677,] 0.7326497 0.7373563 0.7384600 #> [678,] 0.6684066 0.6699390 0.6867790 #> [679,] 0.7660027 0.7884803 0.7889959 #> [680,] 0.6324472 0.6806701 0.6925097 #> [681,] 0.6062568 0.6252554 0.6853151 #> [682,] 0.7331707 0.7955418 0.8179501 #> [683,] 1.0878071 1.1123559 1.1526806 #> [684,] 0.5940258 0.6229879 0.6271196 #> [685,] 0.8574649 0.8976148 0.9387797 #> [686,] 0.7263605 0.7401842 0.7438612 #> [687,] 1.3116721 1.3278361 1.3876646 #> [688,] 0.6817233 0.6931311 0.7211141 #> [689,] 1.0200061 1.0265487 1.0418415 #> [690,] 0.7148179 0.7231723 0.7243780 #> [691,] 0.6315268 0.6431302 0.6574496 #> [692,] 1.0381903 1.0520745 1.0933699 #> [693,] 0.9025787 0.9038915 0.9198327 #> [694,] 0.6214294 0.6753017 0.7139887 #> [695,] 1.1763199 1.1795062 1.2006747 #> [696,] 0.6034387 0.6123165 0.6987542 #> [697,] 1.4813297 1.6121141 1.7260634 #> [698,] 0.6417307 0.6438171 0.6462949 #> [699,] 0.8838049 0.9152433 0.9183538 #> [700,] 0.7079312 0.7122314 0.7785440 #> [701,] 1.2274216 1.3371795 1.3521911 #> [702,] 0.6383516 0.6398683 0.6559131 #> [703,] 0.8225748 0.8324250 0.8455299 #> [704,] 0.5142809 0.5876460 0.6074908 #> [705,] 0.5373668 0.5540923 0.5664522 #> [706,] 0.5765510 0.5853877 0.6001879 #> [707,] 0.6937080 0.7006146 0.7421462 #> [708,] 0.6238756 0.6882597 0.6976726 #> [709,] 0.8374410 0.9248079 0.9406653 #> [710,] 0.7652198 0.8181279 0.8206264 #> [711,] 0.5948992 0.5976967 0.6158515 #> [712,] 0.9381280 0.9597892 0.9664203 #> [713,] 1.0555835 1.0615033 1.0912192 #> [714,] 0.8848121 0.9112138 0.9500410 #> [715,] 1.0850121 1.0871142 1.0940913 #> [716,] 0.7843407 0.8227103 0.8617159 #> [717,] 0.4696288 0.4699835 0.4738325 #> [718,] 0.5210939 0.5921035 0.6556833 #> [719,] 1.0706496 1.0765221 1.1663353 #> [720,] 0.8406039 0.8808923 0.8958698 #> [721,] 0.8506859 0.8674290 0.8749529 #> [722,] 1.1576949 1.1636063 1.1886189 #> [723,] 0.7265791 0.7418144 0.7951605 #> [724,] 0.7665664 0.8252081 0.8266375 #> [725,] 0.6844479 0.6874830 0.7128726 #> [726,] 0.9559077 1.1242090 1.1646467 #> [727,] 0.7351015 0.7832581 0.8303848 #> [728,] 0.9923359 1.0102435 1.0381903 #> [729,] 1.4331600 1.4771536 1.4932030 #> [730,] 0.6936138 0.7290462 0.7386993 #> [731,] 0.5865848 0.5928012 0.5935847 #> [732,] 0.8202470 0.8342600 0.8562082 #> [733,] 0.6299515 0.7325734 0.7354251 #> [734,] 2.0558621 2.0864969 2.1005553 #> [735,] 0.6088727 0.6541174 0.6590748 #> [736,] 1.2058457 1.2621229 1.2702962 #> [737,] 1.1890757 1.2943075 1.2991210 #> [738,] 0.5636207 0.5829550 0.6010974 #> [739,] 1.2999900 1.3123376 1.3535130 #> [740,] 0.5961094 0.6115983 0.6147504 #> [741,] 1.1292652 1.1476807 1.1993746 #> [742,] 0.6389123 0.6490562 0.6605395 #> [743,] 0.5694909 0.6220328 0.6521282 #> [744,] 0.6801958 0.6913184 0.7516634 #> [745,] 0.7373071 0.7444701 0.7774382 #> [746,] 0.5555416 0.5618440 0.5831496 #> [747,] 1.3390375 1.3843793 1.4050764 #> [748,] 0.7693490 0.7886514 0.8007839 #> [749,] 1.2160325 1.2310418 1.2612687 #> [750,] 0.7772398 0.7835136 0.7853064 #> [751,] 0.6469686 0.6871881 0.6893706 #> [752,] 1.0467176 1.0487921 1.0535688 #> [753,] 0.7115954 0.7243692 0.7353212 #> [754,] 1.0067329 1.1439401 1.1663841 #> [755,] 0.7445498 0.8205199 0.8258803 #> [756,] 0.5692131 0.5878257 0.6048488 #> [757,] 1.5059130 1.5141408 1.5370495 #> [758,] 0.7022300 0.7040191 0.7266179 #> [759,] 2.0418621 2.1325963 2.2567825 #> [760,] 0.5918048 0.5966892 0.6095008 #> [761,] 0.5413950 0.5472648 0.5751142 #> [762,] 0.5384481 0.5705695 0.5868046 #> [763,] 0.8146452 0.8153426 0.8235265 #> [764,] 0.6850527 0.6924807 0.7120703 #> [765,] 1.1885719 1.2018503 1.2018910 #> [766,] 1.3260409 1.3341675 1.3347847 #> [767,] 0.6248858 0.6631087 0.6670681 #> [768,] 0.9237803 0.9821653 1.0128317 #> [769,] 0.8028055 0.8115816 0.8469829 #> [770,] 1.3735787 1.4586779 1.4587643 #> [771,] 1.0487313 1.1115367 1.1278697 #> [772,] 0.6377302 0.7069487 0.7291969 #> [773,] 0.9324725 0.9695837 1.0005072 #> [774,] 0.7340137 0.7686250 0.7910865 #> [775,] 0.7437403 0.7456979 0.7878617 #> [776,] 0.6409072 0.6948549 0.7067249 #> [777,] 0.7257133 0.7526207 0.8145793 #> [778,] 0.5142809 0.5245879 0.5252494 #> [779,] 0.6242367 0.6256287 0.6328125 #> [780,] 0.6837660 0.6874830 0.7228639 #> [781,] 0.8125125 0.8322545 0.8503036 #> [782,] 0.5561102 0.5578403 0.5763036 #> [783,] 0.5896110 0.5958654 0.6104892 #> [784,] 0.7866859 0.7935557 0.7964822 #> [785,] 0.6916844 0.7073426 0.7114112 #> [786,] 0.6074524 0.6283285 0.6525452 #> [787,] 0.8485117 0.8661158 0.8681542 #> [788,] 0.7669146 0.7704553 0.7730321 #> [789,] 0.7936552 0.8056731 0.8282903 #> [790,] 0.7768582 0.7846403 0.7865361 #> [791,] 0.9218025 0.9309645 0.9834923 #> [792,] 0.7310340 0.7395698 0.7413927 #> [793,] 0.5268015 0.5320507 0.5456090 #> [794,] 0.6962281 0.7052326 0.7597433 #> [795,] 0.7230854 0.7567452 0.7898359 #> [796,] 1.4279747 1.4767257 1.4891995 #> [797,] 0.5369971 0.6397847 0.6429611 #> [798,] 0.7756439 0.7789369 0.7825231 #> [799,] 1.3097582 1.4824465 1.5489940 #> [800,] 0.7162136 0.7266179 0.7296064 #> [801,] 0.4948228 0.5717583 0.5764634 #> [802,] 0.9185322 0.9380741 0.9420223 #> [803,] 0.6465218 0.6621150 0.6682168 #> [804,] 0.6172237 0.6478300 0.6539777 #> [805,] 1.8593783 1.8708800 1.9053267 #> [806,] 0.7604921 0.7642574 0.7730720 #> [807,] 0.7662884 0.7721703 0.7889959 #> [808,] 0.5115099 0.5396213 0.5483203 #> [809,] 1.0282668 1.0382065 1.0560935 #> [810,] 0.5475677 0.5502896 0.5973181 #> [811,] 1.1429708 1.1613028 1.2231753 #> [812,] 0.6638050 0.6828485 0.6834797 #> [813,] 0.9778346 1.0510234 1.1124590 #> [814,] 1.2542723 1.3124297 1.3212982 #> [815,] 0.7370478 0.7417071 0.7623365 #> [816,] 0.8667483 0.8711702 0.8878093 #> [817,] 0.6253059 0.6553342 0.6873358 #> [818,] 0.8282982 0.8362473 0.8699008 #> [819,] 0.9744774 0.9890063 1.0132698 #> [820,] 0.6783649 0.7362620 0.7444487 #> [821,] 0.6621150 0.6987482 0.7166870 #> [822,] 0.9290550 1.0580104 1.0725154 #> [823,] 1.0468094 1.0712767 1.0978278 #> [824,] 0.6427528 0.6436058 0.6524632 #> [825,] 0.6817233 0.7111853 0.7402580 #> [826,] 0.5284033 0.5541392 0.5699656 #> [827,] 1.3117818 1.3270609 1.3359374 #> [828,] 0.6628685 0.6713565 0.6810039 #> [829,] 0.8527906 0.8812073 0.9528009 #> [830,] 0.6828485 0.6850597 0.6959316 #> [831,] 0.8301323 0.8712192 0.9245956 #> [832,] 0.6132856 0.6421572 0.6431027 #> [833,] 0.7869176 0.8055432 0.8226591 #> [834,] 1.2076362 1.2278662 1.2417090 #> [835,] 0.7902266 0.8018794 0.8600771 #> [836,] 0.7864186 0.7935557 0.8255778 #> [837,] 0.7262376 0.7844301 0.7885148 #> [838,] 1.4799255 1.5037535 1.5076612 #> [839,] 0.5764634 0.6651793 0.6815833 #> [840,] 0.6878447 0.6985192 0.7296800 #> [841,] 0.4934944 0.5391688 0.5655502 #> [842,] 1.0560959 1.0712848 1.0853428 #> [843,] 0.7249468 0.7366193 0.7376150 #> [844,] 0.7874349 0.7950016 0.8099821 #> [845,] 0.7664371 0.7807477 0.8116326 #> [846,] 0.6358013 0.6442066 0.6576187 #> [847,] 0.4486907 0.4983990 0.4999563 #> [848,] 0.9207612 0.9276422 0.9540991 #> [849,] 0.4063097 0.4590751 0.4740971 #> [850,] 0.6394717 0.6588610 0.6600258 #> [851,] 0.8149142 0.8428174 0.8796094 #> [852,] 0.6049990 0.6198042 0.6272176 #> [853,] 0.9851428 1.0335664 1.0680627 #> [854,] 1.0029237 1.0044478 1.0091058 #> [855,] 0.9468798 0.9500668 0.9856499 #> [856,] 1.1343272 1.1434065 1.1500361 #> [857,] 0.9434459 0.9806701 0.9911303 #> [858,] 0.6657246 0.6823429 0.6984509 #> [859,] 0.9392122 1.0334538 1.0380556 #> [860,] 0.6851883 0.6919852 0.7095744 #> [861,] 0.7763655 0.7913457 0.8270811 #> [862,] 0.6989579 0.7134870 0.7771113 #> [863,] 0.7291426 0.7341086 0.7359247 #> [864,] 1.2080432 1.2282035 1.3640246 #> [865,] 0.7292787 0.7359247 0.8104232 #> [866,] 0.8350270 0.8828692 0.8838416 #> [867,] 1.6801516 1.7936242 1.8107486 #> [868,] 0.7664883 0.7692705 0.7853750 #> [869,] 0.6256287 0.6405012 0.6438171 #> [870,] 0.9159168 0.9412794 0.9424081 #> [871,] 0.4008547 0.4421991 0.4654775 #> [872,] 1.5398663 1.5488528 1.5865198 #> [873,] 0.5946995 0.5961094 0.5985655 #> [874,] 1.2174384 1.2866041 1.3608177 #> [875,] 0.6989579 0.7028270 0.7502891 #> [876,] 1.1007515 1.1159988 1.1618348 #> [877,] 0.9267543 0.9303417 0.9985390 #> [878,] 0.7707595 0.7796337 0.8207193 #> [879,] 0.7728698 0.8062140 0.8064596 #> [880,] 0.9699226 1.0045098 1.0449545 #> [881,] 0.8332732 0.8572955 0.8811205 #> [882,] 0.7810091 0.7894367 0.8153426 #> [883,] 0.7170823 0.7352067 0.7384600 #> [884,] 0.5624806 0.5863302 0.6213767 #> [885,] 0.9655477 0.9727644 1.0023837 #> [886,] 0.6547229 0.6760726 0.6837625 #> [887,] 0.6487290 0.6680559 0.7312532 #> [888,] 0.8688664 0.9291881 0.9494333 #> [889,] 1.3239755 1.3330514 1.3419673 #> [890,] 0.4841708 0.5299665 0.5941560 #> [891,] 1.1557669 1.2092693 1.2211860 #> [892,] 0.5427501 0.5509455 0.5757741 #> [893,] 1.0055165 1.1019036 1.1303557 #> [894,] 1.5957744 1.6656746 1.6704343 #> [895,] 0.5313030 0.5411869 0.5432273 #> [896,] 1.3521568 1.3854506 1.3989331 #> [897,] 1.2729158 1.2869336 1.3173147 #> [898,] 1.3501308 1.4088867 1.4431790 #> [899,] 0.5130520 0.5265425 0.5344388 #> [900,] 0.6500784 0.6550834 0.6594093 #> [901,] 0.9341268 0.9882515 1.0482291 #> [902,] 0.6887986 0.6949705 0.7101375 #> [903,] 1.8382930 1.8593577 1.8821169 #> [904,] 0.8450184 0.8584939 0.8900323 #> [905,] 1.3352933 1.3647099 1.3707155 #> [906,] 0.6124874 0.6452023 0.6502552 #> [907,] 0.6015697 0.6057881 0.6303679 #> [908,] 0.7397444 0.7589513 0.7761755 #> [909,] 1.1331706 1.1684123 1.2233917 #> [910,] 0.7708239 0.8088441 0.8090672 #> [911,] 0.7564921 0.8313556 0.8853633 #> [912,] 0.5590943 0.5693136 0.6010974 #> [913,] 0.5705695 0.5990171 0.5997920 #> [914,] 0.6483972 0.6809148 0.6938318 #> [915,] 1.1443646 1.1713342 1.1896728 #> [916,] 0.7747097 0.7772398 0.8024150 #> [917,] 0.7302801 0.7417071 0.7470587 #> [918,] 0.6165999 0.6218071 0.6254043 #> [919,] 0.6983487 0.8245811 0.8574372 #> [920,] 1.0107482 1.0956074 1.1528632 #> [921,] 0.5968568 0.6036069 0.6052295 #> [922,] 0.7816347 0.8317877 0.8353579 #> [923,] 0.7042752 0.7556773 0.7612428 #> [924,] 0.8016984 0.8064596 0.8279561 #> [925,] 0.6581170 0.6625708 0.6638874 #> [926,] 0.7545084 0.7692472 0.8111023 #> [927,] 0.8234208 0.8325586 0.8762490 #> [928,] 0.8036009 0.8617396 0.9095927 #> [929,] 0.9777802 0.9867707 0.9916101 #> [930,] 0.6246838 0.6478300 0.6710057 #> [931,] 0.7047821 0.7074970 0.7094189 #> [932,] 0.5815310 0.5894735 0.5981939 #> [933,] 0.8676934 0.8809400 0.9466005 #> [934,] 1.0968149 1.1379194 1.1401699 #> [935,] 0.5070188 0.5424867 0.5633698 #> [936,] 0.9154519 0.9903226 0.9985880 #> [937,] 1.0154784 1.0198059 1.0558893 #> [938,] 0.7369948 0.7536130 0.8294904 #> [939,] 1.4303140 1.4317122 1.4805684 #> [940,] 0.8062438 0.8196058 0.8569086 #> [941,] 1.1892653 1.2261792 1.2324396 #> [942,] 0.5912759 0.5969571 0.6082670 #> [943,] 0.5314154 0.5326785 0.5801175 #> [944,] 0.8829630 0.8910723 0.8967822 #> [945,] 1.3858221 1.4302707 1.4316539 #> [946,] 0.6547229 0.6886322 0.6928918 #> [947,] 0.6877890 0.7001301 0.7057125 #> [948,] 1.8357152 1.9045001 1.9065517 #> [949,] 1.3379349 1.4021315 1.4079049 #> [950,] 1.0840138 1.1094766 1.1308136 #> [951,] 0.7117756 0.7177677 0.7423945 #> [952,] 0.7438321 0.7534034 0.7842284 #> [953,] 0.7502960 0.7789664 0.7982690 #> [954,] 0.6118587 0.6215960 0.6384658 #> [955,] 1.2871531 1.3225393 1.3418591 #> [956,] 0.7043195 0.7170823 0.7336379 #> [957,] 1.0358191 1.0774448 1.1165770 #> [958,] 0.6198076 0.6213680 0.6650598 #> [959,] 1.1471868 1.2669688 1.2800816 #> [960,] 0.8130187 0.8631036 0.8891762 #> [961,] 0.4881796 0.5027134 0.5136565 #> [962,] 0.5982893 0.6004924 0.6157327 #> [963,] 0.7837144 0.7919935 0.7991795 #> [964,] 0.6104453 0.6329952 0.6364906 #> [965,] 0.7174851 0.7278104 0.7323772 #> [966,] 0.6158712 0.6274870 0.6436952 #> [967,] 0.9331233 0.9648185 0.9984615 #> [968,] 0.9794996 0.9906871 1.0181875 #> [969,] 0.9764982 0.9772140 0.9803378 #> [970,] 0.8332279 0.8385844 0.8746902 #> [971,] 0.8782562 0.9048243 0.9051167 #> [972,] 0.7135772 0.7139887 0.7223360 #> [973,] 0.6498677 0.6716114 0.7187771 #> [974,] 0.7911595 0.7980903 0.8115715 #> [975,] 1.0262068 1.0452156 1.0937855 #> [976,] 0.7545626 0.7664371 0.7778610 #> [977,] 0.9817203 1.0214761 1.0233572 #> [978,] 0.7640607 0.7933599 0.8157303 #> [979,] 0.5221971 0.5497720 0.6275281 #> [980,] 0.8326922 0.8450174 0.8531720 #> [981,] 0.6079576 0.6749162 0.6866674 #> [982,] 0.5516274 0.5699598 0.6100876 #> [983,] 0.6790706 0.7139076 0.7197768 #> [984,] 1.2032878 1.2213903 1.2509943 #> [985,] 0.6874485 0.7101610 0.7203690 #> [986,] 1.0107862 1.0289652 1.0302160 #> [987,] 0.5092006 0.5504881 0.5553071 #> [988,] 0.6925121 0.7282713 0.7690535 #> [989,] 0.7949332 0.8214750 0.8386153 #> [990,] 1.1753586 1.2128637 1.2172076 #> [991,] 0.7462066 0.8073125 0.8246749 #> [992,] 0.6815427 0.7020037 0.7071514 #> [993,] 0.8221489 0.8382346 0.8520056 #> [994,] 0.5541392 0.5859897 0.5901825 #> [995,] 0.6030581 0.6088673 0.6820477 #> [996,] 1.0221451 1.0348177 1.0581953 #> [997,] 1.1938107 1.2440572 1.2494514 #> [998,] 0.7631284 0.8002346 0.8086030 #> [999,] 0.6835776 0.6913845 0.6999382 #> [1000,] 1.1236420 1.1943301 1.2301638 #> # 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,] 442 230 784 689 53 137 330 836 996 69 #> [2,] 874 179 540 168 223 720 504 824 885 383 #> [3,] 213 97 262 688 569 583 921 711 455 436 #> [4,] 691 167 268 281 235 746 958 524 275 449 #> [5,] 681 33 966 976 295 698 239 740 59 993 #> [6,] 641 921 467 213 884 514 552 410 711 3 #> [7,] 735 797 69 294 53 226 238 330 907 129 #> [8,] 525 884 433 469 305 678 332 833 591 6 #> [9,] 975 358 333 394 526 199 692 888 828 48 #> [10,] 159 743 81 440 297 368 985 480 54 456 #> [11,] 278 476 906 232 995 600 272 172 349 73 #> [12,] 821 641 554 514 293 467 725 478 780 702 #> [13,] 88 906 989 349 703 142 11 216 596 147 #> [14,] 724 993 523 59 376 422 496 123 647 976 #> [15,] 375 585 893 249 178 301 967 179 978 250 #> [16,] 636 231 204 175 121 181 377 370 998 960 #> [17,] 590 928 631 901 620 317 388 770 466 529 #> [18,] 853 71 936 822 309 959 989 29 227 597 #> [19,] 807 271 545 727 434 960 679 977 453 302 #> [20,] 77 468 610 520 144 109 156 167 942 235 #> [21,] 958 536 443 158 673 240 347 556 355 285 #> [22,] 648 63 781 176 671 499 725 569 852 436 #> [23,] 363 282 207 985 413 115 882 454 788 381 #> [24,] 457 86 802 267 877 681 943 124 548 740 #> [25,] 623 234 220 279 32 352 662 166 530 185 #> [26,] 141 346 785 221 404 830 868 786 60 833 #> [27,] 487 317 780 89 621 918 554 61 58 171 #> [28,] 353 484 809 587 493 699 49 919 83 156 #> [29,] 172 272 227 936 216 11 675 476 788 989 #> [30,] 72 62 459 576 793 600 344 274 995 127 #> [31,] 712 66 491 494 326 322 51 636 488 472 #> [32,] 64 621 662 508 946 234 4 443 61 487 #> [33,] 5 976 59 845 186 698 539 263 873 772 #> [34,] 42 561 231 181 35 609 16 719 545 977 #> [35,] 42 571 34 889 561 977 379 945 577 545 #> [36,] 655 343 427 357 567 521 882 909 741 309 #> [37,] 907 531 982 762 562 143 318 850 497 135 #> [38,] 866 320 114 529 857 559 149 348 546 254 #> [39,] 284 164 969 259 165 230 400 974 112 595 #> [40,] 666 687 857 611 854 348 559 866 593 669 #> [41,] 871 849 470 361 890 250 717 485 649 847 #> [42,] 561 392 545 19 960 34 35 231 577 977 #> [43,] 524 128 954 846 778 459 148 344 296 127 #> [44,] 522 899 892 419 705 437 653 576 146 513 #> [45,] 93 913 299 598 110 409 294 626 408 334 #> [46,] 402 465 86 976 978 124 350 681 261 848 #> [47,] 290 722 707 206 753 533 328 512 750 198 #> [48,] 59 594 394 374 647 248 724 518 526 5 #> [49,] 156 109 601 116 77 383 587 484 942 504 #> [50,] 794 158 443 240 558 744 155 748 879 536 #> [51,] 494 377 693 365 644 745 712 55 972 121 #> [52,] 318 965 201 137 182 143 907 602 568 37 #> [53,] 512 69 330 154 7 784 143 707 441 797 #> [54,] 138 440 203 783 612 67 368 115 10 78 #> [55,] 181 204 721 365 609 104 377 51 448 121 #> [56,] 984 842 827 897 564 713 312 415 96 190 #> [57,] 310 437 419 930 742 705 895 932 44 899 #> [58,] 694 668 551 918 75 89 751 409 778 652 #> [59,] 724 976 48 33 523 14 993 5 394 647 #> [60,] 26 830 989 983 868 141 633 404 386 902 #> [61,] 275 64 758 825 840 554 471 487 800 27 #> [62,] 459 778 30 110 102 72 335 817 203 385 #> [63,] 499 648 852 731 436 22 947 569 129 584 #> [64,] 61 32 532 534 275 487 846 148 233 443 #> [65,] 686 334 451 464 735 629 41 373 663 690 #> [66,] 712 31 491 693 51 494 854 130 326 488 #> [67,] 203 122 296 247 440 704 335 138 459 102 #> [68,] 106 861 815 557 816 851 640 398 910 937 #> [69,] 970 53 7 133 441 784 330 329 710 735 #> [70,] 628 243 198 458 990 176 388 669 916 570 #> [71,] 822 959 309 100 18 407 917 788 936 227 #> [72,] 459 30 706 296 210 335 62 67 502 128 #> [73,] 643 600 663 516 513 522 576 808 451 717 #> [74,] 362 315 338 202 679 271 952 482 434 224 #> [75,] 668 652 58 694 299 110 252 913 93 409 #> [76,] 961 804 291 635 340 423 420 875 812 930 #> [77,] 20 610 468 942 520 109 144 156 167 761 #> [78,] 420 612 517 895 297 961 115 340 860 54 #> [79,] 676 188 500 579 911 215 878 247 879 706 #> [80,] 209 630 429 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267 310 #> [776,] 386 902 658 633 387 324 147 973 349 432 #> [777,] 111 323 843 926 222 938 519 193 205 886 #> [778,] 459 62 102 110 122 299 918 704 43 67 #> [779,] 698 618 761 372 109 828 295 374 869 248 #> [780,] 27 89 947 918 129 554 171 852 725 12 #> [781,] 22 803 584 628 725 431 176 499 671 855 #> [782,] 935 480 479 646 557 517 297 237 90 418 #> [783,] 644 440 335 138 54 745 203 972 67 755 #> [784,] 330 474 53 137 689 69 797 7 836 161 #> [785,] 141 26 346 221 786 427 830 833 413 404 #> [786,] 404 830 479 995 476 413 906 785 517 141 #> [787,] 194 715 384 806 64 623 166 840 758 672 #> [788,] 557 227 782 100 476 216 413 917 839 479 #> [789,] 672 185 806 840 229 273 924 167 758 275 #> [790,] 430 530 682 575 549 240 536 234 794 185 #> [791,] 360 843 199 390 952 341 642 953 333 362 #> [792,] 389 605 751 254 529 155 694 145 197 58 #> [793,] 455 576 600 127 516 232 515 30 522 265 #> [794,] 158 50 744 240 443 536 556 682 430 21 #> [795,] 723 485 951 649 624 528 369 890 665 87 #> [796,] 936 853 877 18 548 364 720 29 227 831 #> [797,] 294 143 907 474 925 330 7 735 562 137 #> [798,] 418 311 325 861 910 398 313 640 782 730 #> [799,] 986 117 833 838 303 134 490 184 425 898 #> [800,] 852 746 171 604 820 61 508 471 758 489 #> [801,] 219 90 615 961 492 313 935 423 480 839 #> [802,] 831 160 457 876 267 675 24 877 496 615 #> [803,] 584 700 431 767 499 436 781 725 821 988 #> [804,] 291 677 863 76 340 302 862 875 930 961 #> [805,] 894 791 187 360 199 362 736 843 473 498 #> [806,] 840 672 789 999 275 758 787 64 924 532 #> [807,] 727 271 19 434 453 900 315 212 960 679 #> [808,] 717 373 663 849 925 470 361 73 858 574 #> [809,] 353 493 28 919 580 699 885 658 484 387 #> [810,] 297 368 102 460 306 122 480 98 150 299 #> [811,] 891 659 695 396 236 986 114 425 134 660 #> [812,] 983 125 76 860 364 492 895 517 830 705 #> [813,] 581 878 477 924 920 50 748 794 879 509 #> [814,] 339 196 298 112 970 893 671 464 710 614 #> [815,] 851 106 839 971 90 160 100 615 917 492 #> [816,] 910 640 937 861 414 359 68 412 354 798 #> [817,] 385 502 81 62 335 469 270 962 459 684 #> [818,] 888 601 248 774 518 933 372 779 594 956 #> [819,] 888 549 510 575 199 771 647 372 526 217 #> [820,] 508 746 411 99 489 171 716 800 268 393 #> [821,] 467 12 514 641 436 725 711 803 305 569 #> [822,] 71 959 309 100 407 917 18 788 851 661 #> [823,] 604 915 765 613 701 968 622 949 489 800 #> [824,] 760 301 250 555 871 847 513 218 383 890 #> [825,] 471 554 641 61 852 569 758 688 194 800 #> [826,] 280 841 987 535 606 292 746 674 994 171 #> [827,] 897 56 984 412 414 300 244 338 96 564 #> [828,] 342 526 618 356 333 779 394 186 698 845 #> [829,] 209 429 630 357 343 868 221 655 560 251 #> [830,] 983 404 786 221 26 479 785 812 141 364 #> [831,] 802 160 877 639 267 615 675 125 169 876 #> [832,] 314 150 98 306 423 463 619 810 612 961 #> [833,] 184 270 141 785 525 8 26 678 425 469 #> [834,] 603 929 399 904 262 988 196 614 22 569 #> [835,] 263 732 119 224 350 482 568 182 992 135 #> [836,] 837 137 189 120 996 330 118 354 784 474 #> [837,] 836 189 354 120 137 543 118 113 474 330 #> [838,] 909 741 542 799 36 357 697 343 490 184 #> [839,] 90 492 313 815 615 851 971 801 730 935 #> [840,] 806 758 275 999 97 789 520 61 954 410 #> [841,] 826 535 987 606 280 674 171 756 746 626 #> [842,] 564 56 312 95 984 415 713 190 96 328 #> [843,] 390 777 205 341 202 642 926 111 953 323 #> [844,] 647 956 217 372 277 376 406 248 123 48 #> [845,] 394 848 33 342 526 828 397 976 59 186 #> [846,] 148 443 43 247 558 534 128 64 155 532 #> [847,] 871 250 760 361 470 849 301 513 41 824 #> [848,] 845 394 976 59 33 192 523 402 465 46 #> [849,] 361 470 717 41 871 513 847 808 250 649 #> [850,] 135 224 182 568 37 531 263 627 974 201 #> [851,] 815 971 106 839 100 917 407 90 68 313 #> [852,] 63 471 569 648 554 171 266 800 436 825 #> [853,] 18 597 936 703 989 29 172 796 272 227 #> [854,] 94 130 351 559 444 348 857 66 40 320 #> [855,] 645 603 988 431 803 781 821 467 628 711 #> [856,] 927 177 589 599 656 200 910 426 120 934 #> [857,] 666 348 40 559 866 529 254 38 320 854 #> [858,] 237 646 643 738 808 717 887 543 925 675 #> [859,] 216 413 454 476 934 788 906 23 786 227 #> [860,] 521 635 420 78 630 812 567 340 76 427 #> [861,] 398 798 768 68 816 640 910 412 418 313 #> [862,] 577 863 406 804 657 378 291 875 922 677 #> [863,] 862 577 677 804 406 378 657 302 151 865 #> [864,] 379 421 316 403 92 922 862 657 378 863 #> [865,] 285 556 883 347 302 956 151 536 863 21 #> [866,] 529 320 348 254 38 857 694 792 693 559 #> [867,] 192 498 402 360 257 627 165 465 261 835 #> [868,] 221 983 346 630 26 829 429 211 560 404 #> [869,] 347 761 217 372 180 964 295 779 186 698 #> [870,] 258 578 385 81 444 817 944 656 962 308 #> [871,] 250 847 41 361 849 555 470 890 513 760 #> [872,] 307 771 571 253 103 511 575 123 933 991 #> [873,] 366 632 681 105 887 85 738 361 740 943 #> [874,] 540 2 179 168 223 720 978 528 15 951 #> [875,] 291 76 550 804 912 801 582 862 900 406 #> [876,] 802 475 637 831 160 496 457 997 877 267 #> [877,] 831 169 548 639 267 802 24 457 160 796 #> [878,] 581 477 813 924 79 579 748 509 980 920 #> [879,] 748 215 532 500 534 233 558 50 79 924 #> [880,] 163 329 957 200 750 722 710 290 996 408 #> [881,] 960 326 453 463 998 488 718 306 807 727 #> [882,] 427 567 521 115 170 592 183 23 860 763 #> [883,] 151 556 382 865 347 991 148 956 273 677 #> [884,] 433 469 305 525 467 8 514 6 173 921 #> [885,] 614 249 432 139 179 399 353 218 375 809 #> [886,] 908 667 519 111 938 174 685 946 446 323 #> [887,] 943 366 738 873 105 717 858 632 675 555 #> [888,] 819 818 771 372 549 510 594 48 779 575 #> [889,] 565 683 920 35 509 337 477 307 980 379 #> [890,] 485 41 871 649 250 978 178 555 375 849 #> [891,] 811 236 659 194 986 384 695 546 114 149 #> [892,] 522 44 634 387 942 468 899 653 793 386 #> [893,] 15 112 375 585 969 178 967 617 405 259 #> [894,] 805 187 791 473 193 843 360 737 362 202 #> [895,] 274 612 517 98 78 932 314 423 150 420 #> [896,] 819 647 510 724 277 199 527 549 888 395 #> [897,] 827 56 96 244 300 564 984 412 338 414 #> [898,] 134 184 537 8 833 525 542 428 332 108 #> [899,] 44 522 419 146 437 761 964 180 610 892 #> [900,] 727 912 153 212 664 807 992 434 550 582 #> [901,] 393 716 388 17 371 990 631 570 317 916 #> [902,] 387 386 776 633 973 919 658 892 914 246 #> [903,] 450 563 370 381 282 16 363 82 636 289 #> [904,] 256 225 139 583 506 616 658 399 973 633 #> [905,] 505 920 581 424 715 924 878 452 337 672 #> [906,] 995 476 11 173 216 786 232 13 413 349 #> [907,] 143 37 318 562 797 474 531 762 925 982 #> [908,] 886 519 938 667 685 946 111 691 411 820 #> [909,] 741 838 36 697 357 567 654 343 427 80 #> [910,] 640 816 599 798 927 325 177 861 311 354 #> [911,] 769 579 676 509 980 477 79 337 188 104 #> [912,] 153 632 664 582 219 105 900 423 150 738 #> [913,] 409 45 93 299 252 598 562 762 75 264 #> [914,] 265 981 591 733 999 552 97 410 892 386 #> [915,] 823 968 166 765 352 728 604 662 508 25 #> [916,] 570 458 441 176 512 707 226 129 750 680 #> [917,] 407 100 309 822 661 125 71 851 815 839 #> [918,] 58 551 778 89 694 668 780 478 459 62 #> [919,] 387 504 942 653 963 902 246 892 973 699 #> [920,] 581 565 878 813 337 477 509 980 905 683 #> [921,] 552 455 410 97 344 266 6 591 3 514 #> [922,] 378 92 775 403 461 657 862 406 677 863 #> [923,] 222 931 926 197 355 488 673 553 323 679 #> [924,] 878 672 581 813 806 748 789 715 879 532 #> [925,] 797 808 294 531 474 907 373 37 327 319 #> [926,] 323 673 777 953 923 519 843 111 222 744 #> [927,] 599 589 177 856 910 426 120 640 656 189 #> [928,] 631 620 279 17 662 739 590 32 621 946 #> [929,] 603 262 569 641 213 773 834 6 471 711 #> [930,] 677 57 310 151 314 437 76 895 804 419 #> [931,] 355 331 923 488 673 197 718 255 222 208 #> [932,] 437 705 274 895 423 643 105 742 98 522 #> [933,] 481 229 818 991 382 714 260 774 273 771 #> [934,] 859 216 749 703 13 454 308 476 906 88 #> [935,] 782 480 297 517 219 479 801 90 615 418 #> [936,] 29 227 853 172 18 675 272 796 71 548 #> [937,] 816 768 414 861 107 244 359 68 910 412 #> [938,] 588 519 908 886 371 685 777 323 111 926 #> [939,] 530 682 242 744 486 794 790 240 430 380 #> [940,] 170 730 998 231 115 370 586 121 719 313 #> [941,] 587 692 484 728 49 949 604 109 401 156 #> [942,] 653 504 919 892 77 387 246 20 419 468 #> [943,] 555 740 717 887 366 649 681 871 849 361 #> [944,] 126 308 551 753 416 385 668 870 918 767 #> [945,] 511 422 577 379 657 862 35 42 376 864 #> [946,] 519 240 621 691 958 323 32 886 667 908 #> [947,] 129 767 780 725 499 731 63 803 436 626 #> [948,] 505 715 608 905 766 787 83 672 194 806 #> [949,] 501 622 941 692 823 604 765 405 613 726 #> [950,] 593 695 126 336 669 478 243 12 944 293 #> [951,] 795 432 528 485 890 649 464 369 451 624 #> [952,] 772 315 953 186 362 277 642 539 510 647 #> [953,] 673 642 926 952 323 843 341 285 536 958 #> [954,] 520 43 128 468 733 524 148 97 273 275 #> [955,] 589 177 670 445 927 157 856 354 426 599 #> [956,] 372 217 844 347 991 869 865 285 883 151 #> [957,] 163 880 200 996 589 189 329 120 836 837 #> [958,] 21 691 536 667 162 443 4 347 946 240 #> [959,] 822 71 309 100 407 917 18 788 661 851 #> [960,] 881 392 998 727 302 807 463 19 745 453 #> [961,] 150 423 340 801 420 517 612 76 297 78 #> [962,] 684 660 979 502 495 702 573 335 210 706 #> [963,] 246 919 942 653 387 902 419 481 310 504 #> [964,] 295 180 761 146 899 698 869 98 105 610 #> [965,] 52 483 283 696 132 318 91 137 201 143 #> [966,] 239 740 366 681 105 5 295 267 873 964 #> [967,] 708 15 292 969 764 994 178 397 280 893 #> [968,] 716 662 393 166 915 701 823 388 84 279 #> [969,] 112 284 617 238 967 259 708 893 261 327 #> [970,] 69 689 133 298 710 339 112 238 441 7 #> [971,] 851 815 839 313 90 730 398 106 661 550 #> [972,] 644 684 704 783 578 440 203 979 694 67 #> [973,] 633 699 902 591 658 387 776 225 386 506 #> [974,] 627 850 135 446 318 397 257 131 201 360 #> [975,] 9 358 394 333 526 828 48 199 888 845 #> [976,] 59 33 5 848 350 993 523 465 845 86 #> [977,] 367 721 561 19 545 377 392 355 42 55 #> [978,] 890 375 485 178 617 871 555 41 250 46 #> [979,] 210 706 684 495 660 335 962 502 233 247 #> [980,] 260 382 911 477 683 878 769 337 151 991 #> [981,] 410 552 733 999 678 914 265 921 72 954 #> [982,] 762 651 37 174 264 162 562 205 497 913 #> [983,] 830 404 705 364 812 786 868 274 386 221 #> [984,] 56 312 842 827 713 415 435 190 289 897 #> [985,] 207 311 10 81 258 159 381 23 282 418 #> [986,] 117 799 833 236 134 678 659 811 8 425 #> [987,] 606 826 535 841 634 280 674 218 574 144 #> [988,] 645 603 711 855 431 596 803 305 616 142 #> [989,] 703 597 13 216 147 172 60 88 29 272 #> [990,] 140 570 901 198 388 533 916 17 458 611 #> [991,] 382 883 956 556 307 151 229 933 260 185 #> [992,] 664 212 153 119 732 900 244 912 632 482 #> [993,] 14 59 5 724 976 496 376 123 239 48 #> [994,] 292 708 280 535 356 618 764 826 847 342 #> [995,] 906 476 173 232 11 786 600 195 30 404 #> [996,] 836 189 957 722 837 880 137 47 329 290 #> [997,] 438 160 831 125 876 815 877 407 917 802 #> [998,] 730 940 370 170 960 881 313 418 745 115 #> [999,] 733 981 410 840 806 914 552 97 273 954 #> [1000,] 429 424 80 251 209 829 683 452 211 630 #> #> $neighbor_distances #> [,1] [,2] [,3] [,4] [,5] [,6] [,7] #> [1,] 0.75123324 0.7728336 0.8535100 0.9610233 0.9988456 1.0241624 1.0346625 #> [2,] 0.72794121 0.8192607 1.0118221 1.0396370 1.0447104 1.0736597 1.1356458 #> [3,] 0.31837803 0.4564968 0.4829120 0.5219559 0.5329834 0.6032451 0.6036069 #> [4,] 0.31047166 0.3921811 0.4326476 0.4630233 0.5398038 0.5555416 0.6151065 #> [5,] 0.46734788 0.4760593 0.5466231 0.5606464 0.5640889 0.5726195 0.5783608 #> [6,] 0.51152215 0.5275568 0.5497865 0.5556941 0.5624806 0.6122315 0.6278000 #> [7,] 0.29359887 0.5086648 0.5542773 0.5716974 0.5869704 0.5940340 0.5959630 #> [8,] 0.26143033 0.4718084 0.5603310 0.5707832 0.6882329 0.7308097 0.7341715 #> [9,] 0.24984831 0.5609735 0.8632232 0.9219492 1.0170726 1.0442705 1.0520745 #> [10,] 0.28986430 0.4537154 0.5204006 0.5421002 0.5423238 0.5463735 0.5587194 #> [11,] 0.46652531 0.5084248 0.5263341 0.5266163 0.5446352 0.5495054 0.5850168 #> [12,] 0.37170190 0.4381048 0.5241100 0.5423993 0.5424052 0.5655038 0.6071722 #> [13,] 0.57222011 0.6124874 0.6357935 0.6396935 0.6462147 0.6708131 0.7391421 #> [14,] 0.34717912 0.5838188 0.5962627 0.6141551 0.6745910 0.8141714 0.8167852 #> [15,] 0.58059369 0.6224053 0.6662234 0.6803393 0.7407235 0.8389967 0.8429282 #> [16,] 0.63314368 0.8630664 0.8939270 1.0213772 1.0458141 1.0663115 1.1116785 #> [17,] 0.54835213 0.7198504 0.7467861 0.8400073 0.8529885 0.9064278 0.9411035 #> [18,] 0.66924590 0.6764030 0.8256855 0.8928497 0.9098371 0.9177645 0.9264857 #> [19,] 0.47391219 0.5187272 0.6039699 0.6313239 0.7718839 0.8130187 0.8617744 #> [20,] 0.15081454 0.3758961 0.3943047 0.4202105 0.4597621 0.5058134 0.5526436 #> [21,] 0.26227964 0.3306440 0.4625359 0.4748135 0.5735866 0.5903636 0.6051760 #> [22,] 0.43233503 0.5294653 0.5807848 0.6182965 0.6219620 0.6649769 0.6820446 #> [23,] 0.51569242 0.5551573 0.6019943 0.6874485 0.7129012 0.7578704 0.7810091 #> [24,] 0.26564505 0.8384672 0.9027206 0.9060224 0.9245897 0.9398366 0.9480771 #> [25,] 0.58665844 0.8659828 0.8981276 0.9708535 1.0050467 1.0329852 1.0364884 #> [26,] 0.19951012 0.3092625 0.3686001 0.4240887 0.6186630 0.6479357 0.7211515 #> [27,] 0.36583627 0.4208790 0.4291136 0.5052316 0.5424372 0.6643204 0.6768602 #> [28,] 0.63749376 0.6643837 0.7336474 0.8632298 0.9630081 1.0143946 1.0585654 #> [29,] 0.22773632 0.2810413 0.3024392 0.6371490 0.7398366 0.7736565 0.7775577 #> [30,] 0.41843597 0.4328745 0.4340449 0.5067279 0.5268015 0.5302476 0.5624027 #> [31,] 0.44109626 0.5183360 0.6860934 0.9339130 0.9629930 1.0579500 1.0943981 #> [32,] 0.51867312 0.5645729 0.6016788 0.6357531 0.6454604 0.6971910 0.7115832 #> [33,] 0.47605929 0.5257607 0.5566021 0.5824304 0.6490195 0.7135536 0.7156801 #> [34,] 0.97263621 1.1064219 1.1444155 1.1538665 1.1596231 1.2692750 1.3001680 #> [35,] 0.98260936 1.1123529 1.1596231 1.1717316 1.1850187 1.2225809 1.2601779 #> [36,] 0.49461149 0.5933300 0.6646068 0.6683092 0.8323921 0.8352585 0.8584428 #> [37,] 0.33752748 0.3668717 0.3733893 0.3738390 0.4858712 0.5329507 0.5359664 #> [38,] 0.63792553 0.6805158 0.8052105 0.8253294 0.9434459 0.9543999 0.9622925 #> [39,] 0.79115486 0.9026125 1.0002906 1.0816304 1.1440999 1.2319974 1.2718061 #> [40,] 0.43594864 0.7188591 0.7189500 0.8269726 1.0044478 1.0197690 1.0395219 #> [41,] 0.29776484 0.3110705 0.3169707 0.3448560 0.3540405 0.3739873 0.4252863 #> [42,] 0.56407060 0.8340099 0.9236600 0.9486578 0.9507656 0.9726362 0.9826094 #> [43,] 0.35070927 0.4212043 0.4399540 0.5016230 0.5245879 0.5408275 0.5485840 #> [44,] 0.13879613 0.2449583 0.3716588 0.3857744 0.4513943 0.4539900 0.4953717 #> [45,] 0.27674962 0.3248874 0.3792540 0.4061885 0.4685934 0.5096766 0.5394649 #> [46,] 0.70931322 0.7762396 0.7874432 0.8142466 0.8157303 0.8707487 0.8863619 #> [47,] 0.34194145 0.6358041 0.6792359 0.6991985 0.7115954 0.7373865 0.8197065 #> [48,] 0.54036550 0.6094105 0.6267325 0.6674362 0.7317710 0.7325700 0.7442621 #> [49,] 0.53167438 0.6307679 0.7248237 0.7447617 0.7648825 0.7757223 0.7758032 #> [50,] 0.34302240 0.4088468 0.4949305 0.6649502 0.6736528 0.6801958 0.6810368 #> [51,] 0.44016974 0.5076383 0.5175767 0.7000366 0.7683283 0.7880880 0.8114595 #> [52,] 0.34683737 0.4084745 0.5982558 0.5990630 0.6392620 0.6435954 0.6734625 #> [53,] 0.43617718 0.4965451 0.5327786 0.5778905 0.5869704 0.5978329 0.6719906 #> [54,] 0.30248329 0.3537647 0.3890210 0.5052213 0.5101134 0.5352680 0.5394586 #> [55,] 0.52308478 0.5402685 0.5593313 0.5874356 0.7559557 0.7821695 0.7822892 #> [56,] 0.69637689 0.7825293 0.8849706 0.9317456 0.9860083 1.0912192 1.1467084 #> [57,] 0.16587260 0.2957155 0.3413110 0.4116207 0.4205946 0.5540923 0.6107552 #> [58,] 0.21674498 0.2655099 0.3790429 0.3932341 0.4436496 0.5844425 0.6218011 #> [59,] 0.40454188 0.4800348 0.5403655 0.5566021 0.5771780 0.6141551 0.6305519 #> [60,] 0.75561149 0.7588936 0.7819681 0.7975871 0.8945171 0.9152283 0.9162163 #> [61,] 0.30100470 0.4964479 0.5864232 0.5975842 0.6878447 0.6894267 0.6965180 #> [62,] 0.16282452 0.3341115 0.4328745 0.4532430 0.4765493 0.5213406 0.5403077 #> [63,] 0.27072483 0.3851949 0.4612409 0.5142720 0.5167208 0.5294653 0.6030569 #> [64,] 0.49644789 0.5186731 0.5261169 0.5533974 0.5655843 0.6290988 0.6358013 #> [65,] 0.35509133 0.5000259 0.5016800 0.5273160 0.5482631 0.6101186 0.6657547 #> [66,] 0.51826095 0.5183360 0.6878115 0.9025787 0.9928195 1.0014826 1.0029237 #> [67,] 0.20842890 0.3411604 0.3568485 0.4068925 0.4214355 0.4377764 0.4642517 #> [68,] 0.47750098 0.6644961 0.8175037 0.8371260 0.8424111 0.8428174 0.8493960 #> [69,] 0.45928747 0.4965451 0.5542773 0.5917991 0.6237566 0.6343305 0.6550373 #> [70,] 1.00949093 1.1650926 1.2078393 1.2169444 1.2341341 1.2526122 1.3587940 #> [71,] 0.22038706 0.3698291 0.5063805 0.6682706 0.6764030 0.6803568 0.7025122 #> [72,] 0.41824000 0.4184360 0.4325937 0.4377125 0.4848735 0.5098943 0.5213406 #> [73,] 0.36595529 0.4150283 0.4230495 0.4715471 0.4820203 0.4893759 0.5093840 #> [74,] 0.48657110 0.5810022 0.7735514 0.8210664 0.8910608 0.8927631 0.9176643 #> [75,] 0.40020124 0.4232959 0.4436496 0.4596097 0.5149030 0.5152366 0.5463399 #> [76,] 0.48817965 0.4927598 0.5107277 0.5623835 0.5859066 0.5935301 0.5946564 #> [77,] 0.15081454 0.4335340 0.4363955 0.5182849 0.5205824 0.5418517 0.5425802 #> [78,] 0.30995948 0.3273770 0.4254990 0.4672448 0.5121810 0.5136565 0.5154224 #> [79,] 0.47970446 0.4947340 0.6647554 0.7263034 0.7535873 0.7609947 0.7625186 #> [80,] 0.79618626 0.8698408 0.8936291 0.8975484 0.9456737 0.9510369 0.9690743 #> [81,] 0.47623620 0.4877438 0.4971451 0.5032684 0.5146468 0.5204006 0.5501698 #> [82,] 0.93583477 1.0071221 1.0885044 1.1050823 1.1755327 1.1912636 1.2374069 #> [83,] 0.70018889 0.8879985 0.9946745 1.0809744 1.1726914 1.2589149 1.3267449 #> [84,] 0.85574145 0.9550328 0.9788586 0.9906871 1.0431189 1.0460363 1.0573611 #> [85,] 0.32254407 0.4354345 0.4755552 0.4913438 0.5488552 0.5558120 0.6032168 #> [86,] 0.58625836 0.7660761 0.7706474 0.7710468 0.7778610 0.7782860 0.7874432 #> [87,] 0.39066227 0.4763041 0.5491078 0.6551946 0.6561217 0.6865512 0.7407140 #> [88,] 0.40030939 0.5722201 0.6381720 0.6390878 0.6411925 0.6611606 0.6664912 #> [89,] 0.46587098 0.5052316 0.5139742 0.5400055 0.5485160 0.5844425 0.5922445 #> [90,] 0.19996637 0.2139283 0.3522130 0.3596801 0.5070188 0.5268149 0.5578403 #> [91,] 0.34578357 0.3713855 0.5607744 0.5739698 0.5832266 0.5948363 0.6034775 #> [92,] 0.13118949 0.4307515 0.5089294 0.6219634 0.7261603 0.7798434 0.8711605 #> [93,] 0.15515797 0.2767496 0.3113777 0.3347892 0.4146456 0.4380970 0.4573444 #> [94,] 0.40949008 0.7662003 0.8612254 0.8767726 0.8964731 1.1548959 1.1752675 #> [95,] 0.81376542 0.8750154 0.8946274 0.8990536 0.9284389 0.9383646 0.9488876 #> [96,] 0.36398921 0.7536512 0.7638342 0.8374410 0.8986590 0.9269765 0.9570827 #> [97,] 0.40763037 0.4564968 0.4906205 0.4921939 0.4925928 0.5062591 0.5259640 #> [98,] 0.27744439 0.3237234 0.4171864 0.4533024 0.4574603 0.5073590 0.5079862 #> [99,] 0.44986007 0.5442689 0.6053123 0.6070115 0.6822141 0.6850527 0.8025247 #> [100,] 0.36217182 0.4002402 0.5479470 0.6148331 0.6682706 0.6987118 0.7176815 #> [101,] 1.16034211 1.1830457 1.4589260 1.4813706 1.5482614 1.6614958 1.7219734 #> [102,] 0.20121330 0.2754540 0.3096327 0.3457012 0.3905825 0.3975459 0.4304232 #> [103,] 1.20255514 1.2330142 1.2589149 1.3426098 1.3481100 1.5117781 1.6069093 #> [104,] 0.44558674 0.5320156 0.5873528 0.5957944 0.6836550 0.6969275 0.7006954 #> [105,] 0.28454807 0.4706502 0.4755460 0.5303708 0.5371465 0.5512903 0.5558524 #> [106,] 0.47750098 0.4851378 0.4973154 0.5868762 0.7283071 0.8416151 0.8593591 #> [107,] 0.98216529 1.0064208 1.1040333 1.2141759 1.2774960 1.2957623 1.3022217 #> [108,] 0.47869309 0.8343755 0.9840538 1.0110834 1.0577510 1.0926077 1.1350025 #> [109,] 0.49710326 0.5006748 0.5058134 0.5114418 0.5418517 0.5850060 0.5912959 #> [110,] 0.19702653 0.3113777 0.3457012 0.4007505 0.4532430 0.4683849 0.4685934 #> [111,] 0.36008810 0.4174495 0.4408895 0.4548463 0.4828149 0.5061000 0.5141196 #> [112,] 0.57458749 0.6909275 0.7177089 0.7218150 0.7744658 0.8237228 0.8288619 #> [113,] 0.57147892 0.6049098 0.6478061 0.6816491 0.7262376 0.7536512 0.8159034 #> [114,] 0.53953399 0.6014050 0.6576091 0.6786399 0.6947844 0.7110033 0.7216347 #> [115,] 0.48599759 0.5154224 0.5558413 0.5789364 0.6354666 0.6547929 0.6603546 #> [116,] 0.33628768 0.4650064 0.5263157 0.7069239 0.7447617 0.7891283 0.8259153 #> [117,] 0.69958193 0.9283156 1.0939061 1.1216403 1.1250323 1.1288179 1.1515432 #> [118,] 0.36398921 0.5714789 0.6894734 0.7014631 0.7260693 0.7619641 0.8240498 #> [119,] 0.29785763 0.3904106 0.5147426 0.5658674 0.5798745 0.5887206 0.5896853 #> [120,] 0.35909924 0.4613995 0.4725982 0.5263326 0.5385190 0.5893542 0.6825840 #> [121,] 0.35748401 0.4455867 0.5154027 0.6343369 0.6851376 0.7147886 0.7230207 #> [122,] 0.26383426 0.2754540 0.2988481 0.2991627 0.3411604 0.4419143 0.4423288 #> [123,] 0.62395483 0.6685404 0.7950016 0.8067952 0.8221489 0.8340085 0.8765175 #> [124,] 0.82937244 0.8707487 0.9551027 1.0103346 1.0519770 1.0832535 1.2253165 #> [125,] 0.55221468 0.5857830 0.6497453 0.6719993 0.6832759 0.6859977 0.7828531 #> [126,] 0.45402224 0.6171313 0.6190679 0.7087893 0.7216347 0.7515560 0.7782790 #> [127,] 0.27028389 0.3572052 0.3993061 0.4557764 0.4626339 0.4726576 0.4993475 #> [128,] 0.28309416 0.4212043 0.4574603 0.4870235 0.5599778 0.5800733 0.5826591 #> [129,] 0.25146438 0.4186988 0.5370477 0.5654464 0.5749203 0.6088727 0.6190746 #> [130,] 0.49033543 0.5993018 0.6014718 0.6776866 0.7389885 0.7588429 0.7662003 #> [131,] 0.35233048 0.4090712 0.5369264 0.6189582 0.6317574 0.6684878 0.6798811 #> [132,] 0.48681494 0.5408715 0.6034775 0.6051837 0.6162894 0.6389791 0.6392017 #> [133,] 0.20289628 0.5917991 0.7040529 0.8219994 0.8745047 0.9693476 0.9888539 #> [134,] 0.86087471 0.8734834 0.9566091 1.0393017 1.0486299 1.1115831 1.1149204 #> [135,] 0.27865399 0.2969847 0.3897075 0.4253274 0.4470561 0.4950304 0.5887206 #> [136,] 0.73790913 0.7727693 0.7880136 0.8494167 0.9279067 1.0006806 1.0803707 #> [137,] 0.45981635 0.4838382 0.5119779 0.5679398 0.5857583 0.5990630 0.6031911 #> [138,] 0.30248329 0.3120646 0.3843879 0.3865320 0.4880859 0.4925018 0.5331744 #> [139,] 0.36139975 0.3821192 0.3884705 0.4467783 0.6854213 0.6990176 0.7259507 #> [140,] 0.79370421 1.1505867 1.1890757 1.1910424 1.2222432 1.3167299 1.3355435 #> [141,] 0.19951012 0.2417104 0.2781614 0.4680106 0.6250949 0.6525452 0.6603980 #> [142,] 0.37893352 0.4015229 0.4064958 0.4696581 0.4792169 0.4909026 0.5502156 #> [143,] 0.31849457 0.3427354 0.3941537 0.4860365 0.5329507 0.5679398 0.5687805 #> [144,] 0.36318027 0.4053421 0.4399584 0.4597621 0.5413375 0.5425802 0.5480974 #> [145,] 0.38772521 0.4192009 0.4871528 0.5026201 0.5073320 0.6164275 0.6390106 #> [146,] 0.26642362 0.3237234 0.4394007 0.4495239 0.4681588 0.4948503 0.5472648 #> [147,] 0.61755802 0.6381720 0.6557861 0.7506527 0.7529128 0.7540028 0.7591031 #> [148,] 0.18634156 0.5009155 0.5485840 0.5599778 0.5931087 0.5965084 0.6349871 #> [149,] 0.12046345 0.5411184 0.6786399 0.6798061 0.6833299 0.7009866 0.7406080 #> [150,] 0.17188245 0.3712625 0.3743934 0.3987000 0.4416277 0.5023142 0.5079862 #> [151,] 0.37547110 0.4474790 0.4828926 0.5041523 0.6416367 0.6856432 0.7161804 #> [152,] 0.79937319 0.8241613 0.8907011 1.0241436 1.1297508 1.1573210 1.1658958 #> [153,] 0.19419128 0.2061225 0.3007250 0.4605015 0.5260537 0.5295352 0.5749598 #> [154,] 0.30085979 0.3841699 0.5687805 0.5778905 0.6377990 0.6564521 0.6819878 #> [155,] 0.19513800 0.4827132 0.5439209 0.5786936 0.5950256 0.6442066 0.6687645 #> [156,] 0.46868378 0.5114418 0.5316744 0.5526436 0.5607080 0.6290993 0.6300735 #> [157,] 0.59045114 0.9869900 1.1535666 1.1582809 1.1780062 1.2135801 1.2585351 #> [158,] 0.24545491 0.3333003 0.3969425 0.4088468 0.4721369 0.4748135 0.5520819 #> [159,] 0.28986430 0.3544125 0.3764880 0.3992212 0.5491586 0.5680241 0.5835483 #> [160,] 0.43780840 0.4498638 0.7033036 0.7877849 0.8172419 0.8357316 0.8577205 #> [161,] 0.40582631 0.5054017 0.7030924 0.7243780 0.7407140 0.7705307 0.7930608 #> [162,] 0.35234103 0.4147042 0.4795022 0.4952720 0.4962613 0.4989207 0.5185135 #> [163,] 0.54300594 0.5583139 0.6795859 0.8095291 0.8206264 0.9728522 1.1327863 #> [164,] 0.52367020 0.5885379 0.8717503 0.8939719 0.9026125 0.9614319 1.0410679 #> [165,] 0.96143186 1.0199945 1.0232013 1.0551349 1.0902843 1.1010699 1.1045643 #> [166,] 0.61571818 0.7040191 0.7058041 0.7656191 0.7734775 0.7824822 0.8215516 #> [167,] 0.35229033 0.3921811 0.5471192 0.5639015 0.5672174 0.5735262 0.5753703 #> [168,] 0.96825642 1.0396370 1.1898986 1.2800371 1.2881848 1.3270543 1.3435666 #> [169,] 0.66673361 0.6783771 0.6894816 0.7395991 0.7798434 0.8020988 0.8409993 #> [170,] 0.28743920 0.4859976 0.6151464 0.6426209 0.6501716 0.6633979 0.7000754 #> [171,] 0.31719653 0.4426525 0.4802058 0.5162458 0.5402894 0.5484489 0.5699656 #> [172,] 0.08045996 0.2277363 0.4479699 0.6083513 0.7456291 0.7558175 0.7626539 #> [173,] 0.26393459 0.3593245 0.3717283 0.3820295 0.4015229 0.4740713 0.5161408 #> [174,] 0.30338965 0.3445281 0.3752012 0.3833929 0.4174495 0.4185740 0.5224324 #> [175,] 0.60837870 0.6760452 0.7997734 0.8205199 0.8496036 0.8767713 0.8946601 #> [176,] 0.61829653 0.6366517 0.6997558 0.7136063 0.7383797 0.7610103 0.8476542 #> [177,] 0.29403821 0.3544237 0.5263326 0.5589544 0.6405376 0.6407043 0.6920594 #> [178,] 0.30753941 0.4389056 0.4819657 0.5267271 0.5626776 0.5839383 0.6590841 #> [179,] 0.61451410 0.8065479 0.8074916 0.8192607 0.8541983 0.8731405 0.9437775 #> [180,] 0.26642362 0.3097676 0.3796937 0.3842518 0.5073590 0.5130520 0.5358143 #> [181,] 0.47013970 0.5230848 0.5470321 0.8496036 0.8674290 0.9467661 0.9817323 #> [182,] 0.36021158 0.4470561 0.4515219 0.4767623 0.5241343 0.5364844 0.5865542 #> [183,] 0.53317440 0.6307292 0.6375269 0.6506767 0.6550839 0.6692890 0.6958393 #> [184,] 0.59828158 0.7636470 0.8197293 0.8356096 0.8501639 0.8775427 0.9832344 #> [185,] 0.51230390 0.5204844 0.5648963 0.7084205 0.7865361 0.7996885 0.8246749 #> [186,] 0.38596645 0.4145507 0.5691145 0.5989056 0.6002112 0.6328652 0.6405012 #> [187,] 0.96030647 1.0761704 1.0808862 1.0870889 1.1122201 1.1279417 1.1959206 #> [188,] 0.49473402 0.6299515 0.6832576 0.6942725 0.6958393 0.7062105 0.7064561 #> [189,] 0.35909924 0.4255819 0.5056986 0.5103887 0.6192480 0.7221886 0.7384616 #> [190,] 0.48660327 0.7114507 0.7246401 0.7313637 0.8284993 1.0153069 1.0305508 #> [191,] 0.65050082 0.6816491 0.6842797 0.7600037 0.7705307 0.7733659 0.8202470 #> [192,] 0.81182782 0.8194373 0.8417935 1.1055276 1.1253849 1.1289979 1.1556449 #> [193,] 0.66753911 0.7257133 0.7266951 0.7743539 0.8705561 0.9086395 0.9237531 #> [194,] 0.62045966 0.7111853 0.7449372 0.8068628 0.8219124 0.8335637 0.8411046 #> [195,] 0.32084307 0.3593245 0.3889879 0.4064958 0.4534542 0.5020450 0.5061745 #> [196,] 0.59162905 0.6261190 0.6519473 0.6742100 0.7273983 0.7396244 0.7504098 #> [197,] 0.25774459 0.3922203 0.4871528 0.5731440 0.5860091 0.6188489 0.6274038 #> [198,] 0.55964953 0.6301278 0.7006146 0.7704925 0.8103618 0.8187889 0.8411695 #> [199,] 0.56526701 0.5919769 0.7933394 0.8006608 0.8031460 0.8174900 0.9500898 #> [200,] 0.63850522 0.6481022 0.7466410 0.8095291 0.8236840 0.8689609 0.8885308 #> [201,] 0.27865399 0.2923046 0.5241343 0.5313052 0.5860547 0.5909128 0.5959543 #> [202,] 0.42631331 0.5904569 0.5998869 0.7362744 0.7605896 0.8071532 0.8210664 #> [203,] 0.20842890 0.2428933 0.2988481 0.3402142 0.3697308 0.3865320 0.3890210 #> [204,] 0.47013970 0.5402685 0.6083787 0.7876943 0.8457159 0.8897081 0.8939270 #> [205,] 0.34090425 0.4108805 0.4379580 0.5384481 0.5431463 0.5516274 0.5550702 #> [206,] 0.52132482 0.5291135 0.5549221 0.6991985 0.7043297 0.7597526 0.7809424 #> [207,] 0.21587317 0.5501698 0.5944061 0.6019943 0.6036912 0.6121399 0.6232368 #> [208,] 0.19508862 0.3493520 0.4058071 0.5062397 0.5209490 0.5405174 0.5476367 #> [209,] 0.34835901 0.3519596 0.4355776 0.7052760 0.7261054 0.7961863 0.7980408 #> [210,] 0.15472873 0.2741477 0.3276143 0.3874743 0.4512306 0.4848735 0.5456915 #> [211,] 0.30083629 0.5030022 0.5802170 0.6049625 0.6124386 0.6855360 0.7587768 #> [212,] 0.50905999 0.5763424 0.6077715 0.6494028 0.6571812 0.7116446 0.7209059 #> [213,] 0.31837803 0.3607279 0.5027575 0.5151727 0.5491726 0.5556941 0.6165135 #> [214,] 0.58913260 0.8071999 0.9856087 0.9869900 1.0638696 1.1631375 1.1634441 #> [215,] 0.31003120 0.3585382 0.3890705 0.3975790 0.4177120 0.5668593 0.5714364 #> [216,] 0.40446900 0.4795265 0.5715888 0.5716924 0.6813370 0.7271226 0.7320339 #> [217,] 0.41890859 0.4878582 0.5088305 0.5258547 0.5416196 0.5602363 0.5909218 #> [218,] 0.40846562 0.4148849 0.4390372 0.4579331 0.4686838 0.5092006 0.5149004 #> [219,] 0.31027223 0.3580854 0.4416277 0.4557797 0.4635054 0.4980985 0.5223489 #> [220,] 0.67821809 0.7269518 0.7884421 0.8981276 0.9696410 1.0095566 1.0242894 #> [221,] 0.29113310 0.4240887 0.4680106 0.5413932 0.5551246 0.6033871 0.6445696 #> [222,] 0.31200656 0.3922203 0.5089802 0.6606336 0.6940500 0.7074970 0.7195269 #> [223,] 0.80654788 0.8376506 0.9906952 1.0447104 1.0594308 1.1200518 1.1231975 #> [224,] 0.29785763 0.3634108 0.3946379 0.4253274 0.5136025 0.5313052 0.5568903 #> [225,] 0.51517267 0.6075172 0.6088218 0.6133793 0.6376472 0.6498677 0.6685887 #> [226,] 0.41347323 0.4914823 0.5701759 0.5940340 0.6190746 0.6238119 0.6348328 #> [227,] 0.30243922 0.4479699 0.4755517 0.5638175 0.6171614 0.6514300 0.7090876 #> [228,] 0.90555460 0.9918885 1.1684544 1.1782701 1.2056914 1.2159078 1.2336533 #> [229,] 0.51230390 0.6631301 0.6653664 0.7335557 0.7518248 0.8405305 0.9252294 #> [230,] 0.77283362 0.8079880 0.8467161 0.8528566 0.8555573 0.9325761 0.9362719 #> [231,] 0.59961280 0.6343369 0.7535997 0.7794280 0.8462224 0.8493717 0.8586241 #> [232,] 0.26393459 0.2679328 0.2736122 0.3208431 0.3571812 0.4792169 0.4811862 #> [233,] 0.17875079 0.3975790 0.4053346 0.4628703 0.5497720 0.5765510 0.6245413 #> [234,] 0.67821809 0.6819256 0.6971910 0.7084205 0.7768582 0.8004708 0.8130695 #> [235,] 0.35229033 0.4113023 0.4561031 0.4946848 0.4971033 0.5271013 0.5398038 #> [236,] 0.62906531 0.7234641 0.7449372 0.7810325 0.8642700 0.8649101 0.8839147 #> [237,] 0.20395837 0.3844797 0.4306182 0.5561102 0.5694909 0.5874698 0.5900798 #> [238,] 0.57458749 0.5959630 0.6238756 0.6348328 0.6590748 0.6690772 0.7566615 #> [239,] 0.24032959 0.5017250 0.5050407 0.5692543 0.5727376 0.5783608 0.6350611 #> [240,] 0.41672649 0.4448618 0.4721369 0.5199025 0.5258675 0.5286509 0.5563468 #> [241,] 0.98405382 1.1375119 1.1976571 1.2046299 1.2651680 1.3673162 1.3923991 #> [242,] 0.57243631 0.9198506 1.0032346 1.1074054 1.1169257 1.1289227 1.1497500 #> [243,] 0.55313581 0.6545472 0.7726849 0.8180935 0.8738729 0.8899513 0.9580921 #> [244,] 0.57634239 0.6158662 0.6329802 0.6512504 0.6656699 0.6795726 0.7843879 #> [245,] 0.40030939 0.4047201 0.6347717 0.6518593 0.6594240 0.6682516 0.6796197 #> [246,] 0.20957563 0.5535769 0.5850788 0.5888863 0.5982422 0.6195995 0.6572079 #> [247,] 0.32538741 0.3764248 0.4068925 0.4827132 0.5377097 0.5388781 0.5564219 #> [248,] 0.07991648 0.3969090 0.4554043 0.5958814 0.6328125 0.6462949 0.6542727 #> [249,] 0.40846562 0.5515295 0.6300735 0.6669118 0.6803393 0.7171484 0.7440268 #> [250,] 0.24531337 0.2920796 0.3045802 0.3529613 0.3733389 0.3739873 0.4389056 #> [251,] 0.30083629 0.3895963 0.5889364 0.6204556 0.6560988 0.6840995 0.7052760 #> [252,] 0.35356658 0.4333924 0.4380970 0.4901973 0.5463399 0.5705713 0.5717453 #> [253,] 0.71555951 0.8531945 0.9589535 1.0147257 1.0754897 1.0893189 1.0893488 #> [254,] 0.32560846 0.3436373 0.5602555 0.6310238 0.7119669 0.7121295 0.8242852 #> [255,] 0.19508862 0.3335111 0.3359325 0.4334802 0.4795022 0.5193626 0.5880298 #> [256,] 0.33217500 0.5334556 0.5916505 0.7259507 0.7293199 0.7529128 0.7547055 #> [257,] 0.34844429 0.3698551 0.4515219 0.6835393 0.7039375 0.7097591 0.7388971 #> [258,] 0.48774381 0.5034437 0.6036912 0.6462597 0.6528830 0.6738408 0.7292561 #> [259,] 0.50387183 0.5885379 0.8716588 0.9163334 0.9436734 0.9756567 0.9843683 #> [260,] 0.31083119 0.5392876 0.5584831 0.6788038 0.6856432 0.6939049 0.7587768 #> [261,] 0.50540167 0.6094466 0.6106162 0.7100869 0.7474043 0.7548418 0.7657958 #> [262,] 0.36072787 0.4829120 0.6355625 0.6428138 0.6685887 0.7080839 0.7910339 #> [263,] 0.39463790 0.3948480 0.5147426 0.5186912 0.5888827 0.6300291 0.6367832 #> [264,] 0.39988625 0.4002021 0.4185740 0.4483313 0.4727237 0.4871645 0.4962613 #> [265,] 0.23993803 0.5224575 0.5456090 0.5960820 0.6028731 0.6078198 0.6157228 #> [266,] 0.37926665 0.4468730 0.4925928 0.4942371 0.5473633 0.5825448 0.6047881 #> [267,] 0.46645854 0.5050407 0.6158712 0.7140490 0.7180839 0.7395991 0.7456979 #> [268,] 0.38487972 0.4326476 0.4426525 0.4727237 0.4775865 0.4810028 0.5031708 #> [269,] 1.06380439 1.1005776 1.1676122 1.1770550 1.2246484 1.2639915 1.3374345 #> [270,] 0.35760660 0.5306228 0.6001879 0.6131775 0.6140907 0.6375269 0.6378469 #> [271,] 0.37007701 0.3978038 0.4291071 0.5187272 0.5306961 0.5880864 0.6199867 #> [272,] 0.08045996 0.2810413 0.4755517 0.5850168 0.7445571 0.7490401 0.7683102 #> [273,] 0.48999353 0.5196985 0.5392876 0.6215960 0.6416367 0.6426553 0.6913845 #> [274,] 0.24010441 0.3874158 0.4525542 0.4539625 0.4949872 0.5261021 0.5265085 #> [275,] 0.30100470 0.5029816 0.5127194 0.5510996 0.5639015 0.5655843 0.6384658 #> [276,] 0.54449025 1.0617308 1.1690400 1.2214387 1.2731221 1.2739128 1.4960939 #> [277,] 0.29910942 0.5714456 0.5963457 0.6705824 0.6787581 0.7043262 0.7580649 #> [278,] 0.35718116 0.4193687 0.4433985 0.4534542 0.4665253 0.5451624 0.5502156 #> [279,] 0.41394639 0.6325459 0.6699654 0.7836029 0.7851510 0.8182959 0.8425422 #> [280,] 0.22475057 0.3396683 0.4055814 0.4175321 0.4377923 0.4471095 0.4585823 #> [281,] 0.29444451 0.4113023 0.4141784 0.4404892 0.4630233 0.5031708 0.5333971 #> [282,] 0.31644694 0.5551573 0.5961214 0.6221072 0.6550789 0.7101610 0.7283993 #> [283,] 0.34578357 0.3468282 0.3687117 0.4835465 0.5648596 0.6162894 0.6381854 #> [284,] 0.50387183 0.5236702 0.7701056 0.7911549 0.8739263 0.8836861 0.9411378 #> [285,] 0.41119252 0.5622579 0.6023753 0.6042996 0.6207918 0.6479959 0.6823808 #> [286,] 0.85567071 1.0204548 1.0805443 1.1364532 1.3088280 1.3428671 1.3523249 #> [287,] 0.52439153 0.7499065 0.9440280 0.9675250 1.0153069 1.2163210 1.2222432 #> [288,] 0.54239075 0.6429094 0.8806943 1.3620115 1.4205182 1.4246796 1.4566494 #> [289,] 0.94204478 0.9724045 0.9750135 1.0133426 1.1141167 1.1172216 1.1293275 #> [290,] 0.34194145 0.4542825 0.5073444 0.5291135 0.7688035 0.7853064 0.8207269 #> [291,] 0.26594121 0.4145992 0.5107277 0.5382576 0.5775870 0.6428034 0.6433268 #> [292,] 0.33966834 0.3705808 0.4968724 0.5614452 0.5912959 0.6030132 0.6323524 #> [293,] 0.54240523 0.5685634 0.6545472 0.7480650 0.7893958 0.7894660 0.8047075 #> [294,] 0.26927678 0.4298212 0.4530965 0.4804884 0.5015095 0.5394649 0.5716974 #> [295,] 0.26343145 0.2979331 0.4463409 0.5502562 0.5519391 0.5640889 0.5888744 #> [296,] 0.28309416 0.3568485 0.4377125 0.4419143 0.4674355 0.4870335 0.5484208 #> [297,] 0.23681564 0.3086668 0.4232261 0.4378483 0.4828435 0.5027134 0.5113881 #> [298,] 0.44123648 0.4935362 0.5216645 0.5916291 0.6151879 0.7155653 0.7452075 #> [299,] 0.15515797 0.1970265 0.3792540 0.3921772 0.4304232 0.4333924 0.4406006 #> [300,] 0.35029758 0.3859824 0.6158662 0.6386173 0.6545420 0.6789222 0.6912502 #> [301,] 0.17672129 0.2920796 0.3487168 0.4119493 0.4148849 0.4666492 0.5515295 #> [302,] 0.53302955 0.5382576 0.5505231 0.6596260 0.6688616 0.6746336 0.6863087 #> [303,] 1.26360735 1.2688591 1.4489720 1.5091963 1.5111890 1.6452195 1.7570417 #> [304,] 0.32254407 0.3594234 0.4605015 0.4645395 0.5425355 0.5434271 0.5556632 #> [305,] 0.24866835 0.3789335 0.3792134 0.4561757 0.5161408 0.5266475 0.5673960 #> [306,] 0.26740534 0.3208248 0.4667387 0.4988816 0.5023142 0.5210939 0.5911413 #> [307,] 0.68948956 0.8591377 0.8632621 0.8758765 0.9509291 0.9731328 1.0001691 #> [308,] 0.51152798 0.7055470 0.7202057 0.7660243 0.7819818 0.7912739 0.8258615 #> [309,] 0.50638053 0.5179364 0.5468375 0.6046618 0.6985545 0.7881233 0.8091576 #> [310,] 0.16587260 0.3356574 0.3871199 0.4471608 0.4613834 0.5982422 0.6546082 #> [311,] 0.43717069 0.4465631 0.5035108 0.6308780 0.6550789 0.6917758 0.7570848 #> [312,] 0.35346968 0.4866033 0.4911228 0.9278780 0.9597156 0.9675250 0.9823241 #> [313,] 0.46884574 0.4715928 0.4735928 0.5765781 0.5976214 0.6209378 0.6795702 #> [314,] 0.18842874 0.2774444 0.3743934 0.4975694 0.5050988 0.5495635 0.5568096 #> [315,] 0.48747026 0.5115595 0.5776815 0.5810022 0.5833092 0.5961116 0.6199867 #> [316,] 0.71882339 0.7298102 0.7980408 0.8027938 0.8137303 0.8317879 0.8445150 #> [317,] 0.42087897 0.4312480 0.5634303 0.6422605 0.6566718 0.7048257 0.7623149 #> [318,] 0.34683737 0.4090368 0.4860365 0.5359664 0.5383853 0.6108532 0.6336967 #> [319,] 0.37138546 0.4117716 0.4785496 0.4868149 0.5997890 0.6381854 0.6638874 #> [320,] 0.46517599 0.5368974 0.6422321 0.6776026 0.6805158 0.7277149 0.7588429 #> [321,] 0.09697109 0.3934919 0.3992212 0.4013733 0.4841724 0.5219711 0.6044576 #> [322,] 0.61354902 0.7331312 0.8358797 0.8843709 0.9381280 0.9839040 0.9903867 #> [323,] 0.37300207 0.3944722 0.4408895 0.5156462 0.6216648 0.6234677 0.6260663 #> [324,] 0.30975111 0.5447037 0.5730397 0.5768426 0.6028126 0.6794550 0.6901274 #> [325,] 0.49046188 0.5118977 0.5345808 0.5487884 0.6182018 0.6263767 0.6269757 #> [326,] 0.48914700 0.5917555 0.6135490 0.8236793 0.8388834 0.8661062 0.8718881 #> [327,] 0.47466248 0.6094466 0.6433676 0.6557585 0.6625708 0.6628817 0.6690772 #> [328,] 0.73136370 0.8106643 0.8197065 0.9443775 1.0060133 1.0388432 1.0488026 #> [329,] 0.56057729 0.5963301 0.6683562 0.6795859 0.7329504 0.8120086 0.8243287 #> [330,] 0.27949333 0.4054250 0.4598163 0.4782392 0.5327786 0.6019534 0.6318225 #> [331,] 0.33351112 0.3356392 0.3441288 0.4058071 0.4617185 0.4678582 0.5969929 #> [332,] 0.50641307 0.5115280 0.5247714 0.5806314 0.6723671 0.7161356 0.7278448 #> [333,] 0.52374859 0.5609209 0.5919769 0.7004560 0.7470308 0.7624804 0.7892986 #> [334,] 0.42795110 0.4530965 0.4612184 0.4931298 0.5000259 0.5240484 0.5290541 #> [335,] 0.32761430 0.3475464 0.3843879 0.3949626 0.4205965 0.4415721 0.4535849 #> [336,] 0.50451422 0.8163552 1.0484713 1.0540369 1.1106780 1.1204779 1.1334821 #> [337,] 0.56262031 0.7564921 0.8078945 0.8326922 0.8352566 0.8653057 0.8771891 #> [338,] 0.35029758 0.6987542 0.7735514 0.8113368 0.8188095 0.8228033 0.8238807 #> [339,] 0.49353624 0.6602226 0.7272707 0.7396244 0.7642973 0.8857255 0.9054262 #> [340,] 0.26385543 0.4138580 0.4591137 0.4841722 0.4971807 0.5357776 0.5859066 #> [341,] 0.25334228 0.4108805 0.4143892 0.4465461 0.5185135 0.5862135 0.6011670 #> [342,] 0.23535424 0.3844714 0.3873494 0.4871865 0.5377632 0.5901825 0.6002112 #> [343,] 0.49333622 0.5099957 0.5933300 0.6932422 0.7805514 0.7853750 0.9455148 #> [344,] 0.42777114 0.4942371 0.4996934 0.5374261 0.5378361 0.5491287 0.5572177 #> [345,] 0.43543452 0.4731234 0.5097656 0.5154537 0.5501798 0.5601731 0.5648554 #> [346,] 0.27816143 0.2911331 0.3092625 0.4558091 0.6182421 0.6550839 0.6832576 #> [347,] 0.31133296 0.5416196 0.5924440 0.5951297 0.6042996 0.6043597 0.6051760 #> [348,] 0.32560846 0.4348900 0.5081217 0.5956861 0.7277149 0.8389705 0.8713819 #> [349,] 0.27110632 0.2736122 0.3717283 0.4193687 0.4696581 0.5124989 0.5188817 #> [350,] 0.46804512 0.5862584 0.6132736 0.6300291 0.6931258 0.7347957 0.8070378 #> [351,] 0.59930178 0.6040936 0.6243774 0.7537008 0.8512212 0.8618755 0.8767726 #> [352,] 0.72695182 0.8925216 1.0102435 1.0211662 1.0279754 1.0329852 1.2980993 #> [353,] 0.31170307 0.6374938 0.7231658 0.8318084 0.9315260 0.9454788 1.0235594 #> [354,] 0.47014037 0.5311183 0.5807093 0.5893542 0.6192480 0.6478061 0.6894734 #> [355,] 0.31605501 0.5687865 0.6450369 0.6562205 0.6602688 0.6698618 0.6733962 #> [356,] 0.21566331 0.3844714 0.4404892 0.4961319 0.5078930 0.5369264 0.5770353 #> [357,] 0.44777486 0.5099957 0.5576428 0.6046208 0.6613447 0.6683092 0.6720861 #> [358,] 0.44599817 0.5609735 0.7096210 0.8861194 0.8862931 0.8908547 0.9431895 #> [359,] 0.59854713 0.7551560 0.7975922 0.8340979 0.9488876 0.9678762 1.0104143 #> [360,] 0.69343238 0.8115715 0.8518886 0.8595648 0.8660315 0.9372885 1.0002448 #> [361,] 0.18945147 0.3203283 0.3361130 0.3448560 0.3493174 0.3777877 0.4662042 #> [362,] 0.48657110 0.6705820 0.7001198 0.7362744 0.8745113 0.9834923 0.9946699 #> [363,] 0.31644694 0.5156924 0.6910260 0.7869916 0.8203473 0.8254108 0.8631331 #> [364,] 0.57077554 0.6313865 0.6719993 0.6959316 0.7819634 0.8589755 0.8680502 #> [365,] 0.58735280 0.5874356 0.6407729 0.6911030 0.7000366 0.7373071 0.7389704 #> [366,] 0.28454807 0.3498404 0.3726540 0.3987329 0.4167784 0.4767417 0.4883674 #> [367,] 0.24909537 0.6217602 0.8683734 0.9142125 0.9621563 0.9622823 0.9941056 #> [368,] 0.20121330 0.2632861 0.2991627 0.3697308 0.4134017 0.4259888 0.5201796 #> [369,] 0.24136134 0.6594240 0.6920199 0.7117756 0.7880136 0.8037376 0.8159284 #> [370,] 0.49568273 0.6052306 0.6221072 0.6234920 0.6700591 0.7000754 0.7869916 #> [371,] 0.47399983 0.6625547 0.7281672 0.7694525 0.7743539 0.8051282 0.8220837 #> [372,] 0.36567019 0.4189086 0.5234874 0.5771078 0.5944776 0.5951297 0.7530624 #> [373,] 0.18460255 0.2060190 0.3782162 0.3791834 0.4426923 0.4612184 0.4794289 #> [374,] 0.46500636 0.5511406 0.5911722 0.5958814 0.6242367 0.6417307 0.6562524 #> [375,] 0.30753941 0.5299665 0.5805937 0.5926426 0.6214401 0.6687676 0.7171484 #> [376,] 0.58633486 0.6239548 0.6599681 0.6648714 0.6745910 0.7043262 0.7398995 #> [377,] 0.50763829 0.5663951 0.6228037 0.7147886 0.7307823 0.7430718 0.7822892 #> [378,] 0.30361897 0.3430907 0.5306958 0.5863829 0.6143961 0.6843397 0.7215748 #> [379,] 0.62529285 0.9373032 0.9589361 0.9839291 1.0569027 1.1136162 1.1334750 #> [380,] 0.31004980 0.6990266 0.9175304 1.0122607 1.1262807 1.2133086 1.2242808 #> [381,] 0.49568273 0.5961214 0.6232368 0.6734077 0.8202901 0.8360364 0.8509438 #> [382,] 0.31083119 0.4828926 0.4899935 0.5448074 0.5461205 0.5871392 0.7828199 #> [383,] 0.33628768 0.5511406 0.5918048 0.6436058 0.6527433 0.6718151 0.7163910 #> [384,] 0.44077776 0.5411184 0.6801610 0.7706525 0.7962910 0.8294079 0.8335637 #> [385,] 0.15604009 0.4762362 0.5680078 0.5814557 0.6462597 0.6486402 0.6494302 #> [386,] 0.38532828 0.4359686 0.5209098 0.5689635 0.5757741 0.5904509 0.5991853 #> [387,] 0.33470661 0.3647560 0.5047202 0.5630189 0.5689635 0.5829721 0.5891104 #> [388,] 0.65667184 0.6750756 0.6775991 0.7616078 0.7726849 0.9088996 0.9411035 #> [389,] 0.31575585 0.5786936 0.6070519 0.6149352 0.7121295 0.7450731 0.7560740 #> [390,] 0.40636897 0.4379580 0.4465461 0.5390755 0.5909128 0.5998869 0.6395035 #> [391,] 0.71637391 0.9092624 0.9917192 1.0071221 1.0974428 1.1297508 1.1602162 #> [392,] 0.51834711 0.5505231 0.5846558 0.5926610 0.7513978 0.7774382 0.7794280 #> [393,] 0.14605421 0.6075121 0.6775991 0.7048257 0.7444487 0.7576389 0.8104532 #> [394,] 0.46638959 0.5294214 0.5480739 0.6234146 0.6267325 0.6959634 0.7060311 #> [395,] 1.04920444 1.1822441 1.2197848 1.3257101 1.3857820 1.3957540 1.3989331 #> [396,] 0.50074816 0.9368811 1.0471965 1.0540369 1.0659909 1.0825885 1.0936585 #> [397,] 0.35233048 0.3636660 0.4969829 0.5377632 0.5908810 0.6138392 0.7085501 #> [398,] 0.33018724 0.5976214 0.6068353 0.6865432 0.7154680 0.7209059 0.7564411 #> [399,] 0.32685154 0.3821192 0.5760403 0.6261190 0.6692866 0.7112562 0.7409202 #> [400,] 0.99219177 1.0147922 1.1050029 1.1439902 1.2163210 1.2301288 1.2718061 #> [401,] 0.50067478 0.5271013 0.5735262 0.6820610 0.6876870 0.7728214 0.7903175 #> [402,] 0.61061623 0.6870153 0.7093132 0.7347957 0.8417935 0.9099727 0.9207612 #> [403,] 0.13118949 0.5519855 0.5731555 0.6801153 0.8020988 0.8270089 0.9160206 #> [404,] 0.35945348 0.3929751 0.4128428 0.4949872 0.5282306 0.6033871 0.6078198 #> [405,] 0.47216259 0.6429094 0.8313961 0.9559077 1.1019036 1.1677363 1.2491498 #> [406,] 0.52840771 0.5306958 0.5440199 0.5557236 0.5803419 0.5863349 0.5922188 #> [407,] 0.18051066 0.4002402 0.5468375 0.5576100 0.5873958 0.6803568 0.7861184 #> [408,] 0.42982120 0.4931298 0.5219711 0.5226653 0.5355817 0.5618288 0.6011145 #> [409,] 0.23307038 0.5096766 0.5400055 0.5411882 0.5479479 0.5729807 0.5868046 #> [410,] 0.22690537 0.3102786 0.4061678 0.4906205 0.5086148 0.5583424 0.6063374 #> [411,] 0.59354400 0.6070115 0.6145340 0.6739434 0.6935096 0.6939493 0.7555520 #> [412,] 0.45311652 0.4835465 0.6051837 0.6545420 0.6795726 0.6883190 0.7681392 #> [413,] 0.52874860 0.5485678 0.5557123 0.5716924 0.6452023 0.7073426 0.7129012 #> [414,] 0.59854713 0.7433678 0.7931495 1.1149152 1.1309300 1.2472189 1.2511145 #> [415,] 1.00915273 1.0397144 1.0531656 1.0897849 1.1299244 1.2105582 1.2705682 #> [416,] 0.44685385 0.4802813 0.4841724 0.5951493 0.6011145 0.6486402 0.6837553 #> [417,] 0.39066227 0.4058263 0.5686761 0.5893919 0.7694337 0.7836191 0.8194804 #> [418,] 0.30577483 0.4465631 0.5118977 0.5633698 0.5763036 0.6474377 0.6518129 #> [419,] 0.33565738 0.3413110 0.3634738 0.3796179 0.3857744 0.4868420 0.5030333 #> [420,] 0.26385543 0.3050787 0.3099595 0.4493096 0.5142780 0.5426927 0.5432273 #> [421,] 0.72981016 0.8191095 1.0391618 1.0635243 1.1439234 1.1497564 1.1579309 #> [422,] 0.77901084 0.8141714 0.8880066 0.9686581 0.9776988 0.9981969 1.0372120 #> [423,] 0.17188245 0.3102722 0.4068494 0.4948228 0.5050988 0.5092787 0.5166929 #> [424,] 0.58021695 0.6560988 0.8278941 0.8310321 0.8902744 0.9046550 0.9497534 #> [425,] 0.50074816 0.7472879 0.7886271 0.8055432 0.8072300 0.8322109 0.8544600 #> [426,] 0.65841128 0.7551560 0.8793542 0.8990536 0.9343049 0.9647801 1.0091527 #> [427,] 0.40360403 0.4477749 0.4521097 0.5402954 0.5465505 0.6198224 0.6646068 #> [428,] 0.47869309 0.9571049 1.1259877 1.1363119 1.2431542 1.2535680 1.2883587 #> [429,] 0.34027172 0.3519596 0.3895963 0.5030022 0.5741917 0.5803987 0.7337276 #> [430,] 0.21863794 0.5792501 0.5799310 0.6055756 0.6098908 0.6619151 0.7052326 #> [431,] 0.42262902 0.5161931 0.5211340 0.5809020 0.6295294 0.6742100 0.7002335 #> [432,] 0.57267127 0.5730397 0.6557861 0.6854213 0.6911311 0.6957793 0.7067249 #> [433,] 0.24866835 0.2996319 0.4200978 0.4258462 0.4310830 0.5603310 0.5667096 #> [434,] 0.39780376 0.4386172 0.4797447 0.4874703 0.4933392 0.5682878 0.6303169 #> [435,] 0.49112284 0.6390106 0.6759421 0.7114507 0.7823791 0.7902742 0.7998991 #> [436,] 0.36738405 0.4135458 0.5167208 0.5334669 0.5473633 0.5482934 0.5957012 #> [437,] 0.29571554 0.3634738 0.3637507 0.3814911 0.3871199 0.3876728 0.4539900 #> [438,] 0.49119061 0.7828531 0.8027938 0.8172419 0.9485036 1.0184482 1.0185377 #> [439,] 0.83437551 0.9571049 1.0803707 1.1735669 1.1976571 1.2111689 1.3843671 #> [440,] 0.24289334 0.3120646 0.3537647 0.3949626 0.4134017 0.4214355 0.4319502 #> [441,] 0.20289628 0.6237566 0.6844604 0.7369896 0.8385844 0.8803578 0.8962578 #> [442,] 0.75123324 0.9219082 1.0496610 1.1107187 1.1215952 1.1425865 1.1637278 #> [443,] 0.24545491 0.4625359 0.4660731 0.4949305 0.5009155 0.5330862 0.5507820 #> [444,] 0.67768660 0.7038044 0.7892187 0.8172974 0.8343469 0.8612254 0.8907011 #> [445,] 1.22623347 1.2420842 1.3854303 1.3953893 1.4621698 1.5357017 1.5569942 #> [446,] 0.47289305 0.6145340 0.6684878 0.6760726 0.6819878 0.6879263 0.6969249 #> [447,] 1.45228667 1.6043613 1.6490182 1.8686264 1.8838568 1.8952957 1.9006795 #> [448,] 0.57243631 0.7067615 0.8611921 1.0199524 1.0782641 1.1621953 1.1898859 #> [449,] 0.24863800 0.3598209 0.3796937 0.4948503 0.5142888 0.5173598 0.5306004 #> [450,] 0.44366791 0.7192560 1.2657779 1.2739660 1.3403521 1.3811454 1.5902023 #> [451,] 0.50167997 0.5237082 0.5300692 0.5317664 0.5451624 0.5490896 0.5717995 #> [452,] 0.64144872 0.8653057 1.1236420 1.2908359 1.3073813 1.3171347 1.3352933 #> [453,] 0.40963634 0.4797447 0.5306961 0.6045138 0.6223325 0.6235444 0.6778300 #> [454,] 0.57965193 0.7163739 0.7299407 0.7894505 0.9358348 1.0104482 1.0190099 #> [455,] 0.33731955 0.3678879 0.3792666 0.4171944 0.5020450 0.5062591 0.5226281 #> [456,] 0.09697109 0.3223750 0.3544125 0.4270109 0.4468538 0.5355817 0.5816812 #> [457,] 0.26564505 0.7653629 0.8236488 0.9174340 0.9267543 0.9272176 0.9374222 #> [458,] 0.48470066 0.5052092 0.5596495 0.6027456 0.6594966 0.6607136 0.6997558 #> [459,] 0.16282452 0.2891480 0.4182400 0.4340449 0.4996934 0.5051723 0.5113368 #> [460,] 0.44060061 0.4449831 0.4557764 0.4573444 0.4681588 0.5077163 0.5246272 #> [461,] 0.27996485 0.5602452 0.6143961 0.6219634 0.6801153 0.7140490 0.7973198 #> [462,] 0.70380443 0.8241613 1.1755510 1.1950636 1.2795708 1.3081447 1.3256072 #> [463,] 0.26740534 0.3117459 0.4585147 0.4678582 0.5209490 0.5859090 0.5880765 #> [464,] 0.52166448 0.5273160 0.6314860 0.6347717 0.6591848 0.6602226 0.6829279 #> [465,] 0.46804512 0.6870153 0.7545626 0.7706474 0.7762396 0.8118278 0.9270370 #> [466,] 0.31004980 0.7137102 0.8195100 0.8730117 0.9593647 1.0354425 1.1030984 #> [467,] 0.29292073 0.3314803 0.4310830 0.4425622 0.4561757 0.5497865 0.5655038 #> [468,] 0.27981317 0.3631803 0.3758961 0.4363955 0.4510979 0.4921939 0.5126823 #> [469,] 0.34390429 0.3668414 0.4200978 0.5247714 0.5707832 0.5720249 0.6123558 #> [470,] 0.21164504 0.2930111 0.3169707 0.3361130 0.3613120 0.3687756 0.3733389 #> [471,] 0.20417561 0.4786287 0.5084755 0.5140385 0.5410413 0.5428946 0.6918404 #> [472,] 0.83587974 0.8890259 0.9255049 0.9664203 1.0821634 1.0852990 1.1843941 #> [473,] 0.76058958 0.9090353 0.9256838 0.9697771 1.0202062 1.0394324 1.0615033 #> [474,] 0.40542497 0.4449027 0.4514099 0.5119779 0.5300852 0.5545604 0.5947904 #> [475,] 0.87266945 1.0586150 1.0839485 1.0860132 1.1040333 1.1843674 1.3202047 #> [476,] 0.39915058 0.4044690 0.4656943 0.5084248 0.5287486 0.5500657 0.6266317 #> [477,] 0.51479463 0.5694424 0.6092113 0.6469373 0.6859729 0.6948786 0.6948917 #> [478,] 0.33332358 0.4540222 0.6165999 0.6311446 0.6354071 0.6525144 0.6576091 #> [479,] 0.32348784 0.4167936 0.4622053 0.4756200 0.4828435 0.4856521 0.5458660 #> [480,] 0.20774666 0.2368156 0.3148796 0.3318808 0.4167936 0.5171208 0.5223489 #> [481,] 0.64265530 0.6615948 0.6939049 0.7691026 0.7837144 0.8313751 0.8374920 #> [482,] 0.31191358 0.5115595 0.5186912 0.5568903 0.5658674 0.5805367 0.6526331 #> [483,] 0.36871174 0.4752569 0.4946232 0.5832266 0.6778089 0.6828681 0.7059491 #> [484,] 0.61434841 0.6643837 0.7710334 0.8202840 0.8318084 1.0382065 1.1255773 #> [485,] 0.32282065 0.4160175 0.4522604 0.4870973 0.5622388 0.5837745 0.6021089 #> [486,] 0.69902660 0.9255049 0.9593647 0.9603065 1.0848989 1.1964572 1.2806124 #> [487,] 0.36583627 0.4312480 0.5921655 0.6179367 0.6290988 0.6688491 0.6798061 #> [488,] 0.48914700 0.5809788 0.5860091 0.6228037 0.6670446 0.7151664 0.7331312 #> [489,] 0.54426886 0.5818935 0.6092135 0.6802616 0.7296064 0.7703605 0.7863003 #> [490,] 0.64144872 1.1515432 1.2574863 1.3068810 1.3621837 1.4400532 1.5037535 #> [491,] 0.68609343 0.6878115 1.0190392 1.1752675 1.2500793 1.4017630 1.4175909 #> [492,] 0.19996637 0.2641698 0.4474902 0.4658086 0.5312450 0.5522147 0.5635916 #> [493,] 0.55034352 0.6327033 0.7231658 0.9630081 1.1152965 1.1166193 1.2305216 #> [494,] 0.44016974 0.7307823 0.8092806 0.8182231 0.8593062 0.8993036 0.9097510 #> [495,] 0.27017635 0.3699957 0.4096830 0.4512306 0.4612165 0.4628703 0.4941352 #> [496,] 0.73232324 0.7710468 0.8167852 0.8925772 0.9230566 0.9272176 0.9380741 #> [497,] 0.35942336 0.4049898 0.4334802 0.4913438 0.5031696 0.5197681 0.5405174 #> [498,] 1.03943243 1.0721897 1.0905577 1.1089035 1.1314227 1.1912541 1.2253874 #> [499,] 0.27072483 0.3865848 0.4292685 0.5141644 0.5370477 0.5482934 0.5624287 #> [500,] 0.37642480 0.3978315 0.3998387 0.5439209 0.5668593 0.6262270 0.6268746 #> [501,] 0.47216259 0.5423907 0.7833552 0.9727131 1.1413017 1.1811073 1.2372009 #> [502,] 0.34754640 0.3576066 0.3862040 0.3874743 0.4431694 0.4952917 0.5221971 #> [503,] 1.25593536 1.3176629 1.3585019 1.3851334 1.3869811 1.4232511 1.4684821 #> [504,] 0.41890633 0.4389962 0.4798121 0.6254812 0.6718151 0.6731796 0.7411969 #> [505,] 0.78545275 0.8847553 1.0815974 1.1519782 1.3383411 1.4112341 1.5026801 #> [506,] 0.37644458 0.5334556 0.5487723 0.7187771 0.7560823 0.7745370 0.9103913 #> [507,] 0.70927902 0.7398905 0.8395667 0.8904261 0.8994460 0.9188252 0.9655937 #> [508,] 0.51459128 0.6009983 0.6357531 0.6474427 0.6819256 0.7059153 0.7111395 #> [509,] 0.43697204 0.4958141 0.5626203 0.5694424 0.5959871 0.6326815 0.7707595 #> [510,] 0.40476741 0.5652670 0.5761168 0.6490482 0.6917122 0.7084974 0.7534034 #> [511,] 0.82162781 0.9675018 0.9981969 1.0832153 1.2422757 1.2847455 1.2862323 #> [512,] 0.30085979 0.4361772 0.5251689 0.5338096 0.5823034 0.6650514 0.7381164 #> [513,] 0.29301114 0.3731118 0.4421991 0.4486907 0.4543662 0.4699835 0.4710620 #> [514,] 0.29292073 0.4098502 0.4798583 0.5378361 0.5423993 0.5514421 0.5667096 #> [515,] 0.26793281 0.2711063 0.3820295 0.3889879 0.4171944 0.4261615 0.4433985 #> [516,] 0.27028389 0.3415438 0.3734846 0.3840070 0.4426923 0.4609179 0.4715471 #> [517,] 0.30866684 0.3234878 0.3318808 0.4254990 0.4446930 0.4488586 0.4648980 #> [518,] 0.07991648 0.4038128 0.4762512 0.5911722 0.6350611 0.6912811 0.6981930 #> [519,] 0.37025403 0.3944722 0.4546167 0.5061000 0.5270976 0.5509830 0.5810267 #> [520,] 0.27981317 0.4053421 0.4202105 0.4315446 0.5017654 0.5127194 0.5205824 #> [521,] 0.34512046 0.4042546 0.4521097 0.5453375 0.5888239 0.6046208 0.6074047 #> [522,] 0.13879613 0.3419732 0.3665608 0.4178758 0.4448724 0.4893759 0.5030333 #> [523,] 0.54363676 0.5771780 0.5962627 0.7278883 0.8893138 0.9051578 0.9270370 #> [524,] 0.33184739 0.3507093 0.3598209 0.5017654 0.5346923 0.5475948 0.5485160 #> [525,] 0.26143033 0.3439043 0.3928074 0.4258462 0.5064131 0.5837491 0.7095298 #> [526,] 0.28883648 0.4871865 0.5237486 0.5480739 0.6635628 0.6831837 0.6882478 #> [527,] 0.77901084 1.1327007 1.2185269 1.2789434 1.2862323 1.3177358 1.4660522 #> [528,] 0.38801771 0.3950953 0.4755505 0.5452861 0.5768426 0.6079400 0.6224556 #> [529,] 0.27912903 0.3436373 0.4348900 0.6630490 0.6776026 0.7560740 0.8175933 #> [530,] 0.45669225 0.5905829 0.6098908 0.8362491 0.8714916 0.8802147 0.9061382 #> [531,] 0.36687170 0.4117716 0.4755552 0.5197681 0.5227071 0.5425355 0.5607744 #> [532,] 0.33962879 0.3890705 0.4053346 0.5261169 0.6142398 0.6576187 0.6897535 #> [533,] 0.73738653 0.8103618 0.8189857 0.8284993 0.8510004 0.8795685 0.8951109 #> [534,] 0.17875079 0.3396288 0.3585382 0.5533974 0.5907733 0.6179367 0.6273748 #> [535,] 0.32500550 0.3266515 0.3547841 0.3792344 0.4175321 0.4422283 0.4517481 #> [536,] 0.33064403 0.3969425 0.5079901 0.5199025 0.5263975 0.5330862 0.5579990 #> [537,] 0.86476367 0.9477399 0.9832344 1.0586136 1.2534981 1.3615438 1.3869811 #> [538,] 1.51838574 1.6109467 1.7708302 1.8085940 1.9709012 1.9897454 2.1130770 #> [539,] 0.31191358 0.4145507 0.4342199 0.5136025 0.5556632 0.5776815 0.5888827 #> [540,] 0.48520405 0.6145141 0.9431510 0.9827255 1.0118221 1.0709103 1.1359482 #> [541,] 0.40472014 0.4864877 0.6390878 0.6591848 0.7155856 0.7660701 0.7727693 #> [542,] 0.76364702 0.7853750 0.8647637 1.0036165 1.0303411 1.0931007 1.0985984 #> [543,] 0.58070930 0.6279492 0.6657246 0.6752513 0.6872129 0.7148179 0.7352546 #> [544,] 0.61975077 0.9142557 0.9437180 1.0027772 1.0378565 1.1022966 1.2146529 #> [545,] 0.60396993 0.8556707 0.8925672 0.9236600 0.9622823 0.9663578 0.9743262 #> [546,] 0.12046345 0.4407778 0.6910868 0.7058085 0.7183874 0.7288252 0.7548969 #> [547,] 1.17888285 1.2565012 1.3092816 1.3485371 1.3592511 1.3868103 1.3965006 #> [548,] 0.55249129 0.5894451 0.6794550 0.6825160 0.7403855 0.7499781 0.7685453 #> [549,] 0.40476741 0.5235680 0.5698808 0.6055756 0.6478767 0.7933394 0.8048888 #> [550,] 0.59574307 0.6550834 0.7081091 0.8176899 0.8303848 0.8308750 0.8356224 #> [551,] 0.36883720 0.3790429 0.4196418 0.4813874 0.6354071 0.7087893 0.7210488 #> [552,] 0.22690537 0.3318503 0.3474324 0.3919076 0.5672823 0.5812286 0.5816418 #> [553,] 0.25774459 0.3120066 0.5073320 0.6759421 0.6994948 0.7042752 0.7059491 #> [554,] 0.48465152 0.5241100 0.5428946 0.5505425 0.5685634 0.5802675 0.6282014 #> [555,] 0.28688126 0.3224021 0.4224186 0.4696288 0.4751730 0.4799773 0.4841708 #> [556,] 0.42682207 0.4577456 0.5079901 0.5520819 0.6207918 0.6501106 0.6740800 #> [557,] 0.51817417 0.5442637 0.5868762 0.6314365 0.6477322 0.6781473 0.6986159 #> [558,] 0.19513800 0.3253874 0.3978315 0.5390598 0.5714364 0.5780151 0.6273748 #> [559,] 0.60147181 0.6422321 0.6654582 0.7315526 0.7750984 0.8343469 0.8389705 #> [560,] 0.58039868 0.6049625 0.6840995 0.7140941 0.7261054 0.7437403 0.7692705 #> [561,] 0.56407060 0.5846558 0.8028055 0.8462224 0.8719151 0.8746693 0.8830011 #> [562,] 0.34273538 0.3841699 0.4105118 0.4383739 0.4858712 0.5065422 0.5251689 #> [563,] 0.44366791 0.7238057 1.1969462 1.3358588 1.3543267 1.3886413 1.5250261 #> [564,] 0.63184141 0.7544464 0.7638342 0.8534673 0.9591824 0.9823241 0.9860083 #> [565,] 0.53892468 0.6336688 0.8073962 0.8078945 0.9509165 0.9673514 1.0400164 #> [566,] 1.40517342 1.4799383 1.5337433 1.6619145 1.6645287 1.6742392 1.6770461 #> [567,] 0.40425462 0.5054819 0.5465505 0.6699691 0.6847809 0.7834679 0.7970432 #> [568,] 0.29230462 0.2969847 0.3602116 0.3634108 0.3904106 0.5878294 0.6034387 #> [569,] 0.41354584 0.4359867 0.4786287 0.5156658 0.5329834 0.5480443 0.5491726 #> [570,] 0.40415075 0.6027456 0.6301278 0.6499022 0.6650514 0.7953267 0.8599447 #> [571,] 1.01472574 1.1123529 1.1923692 1.3073333 1.3876238 1.5133920 1.5342320 #> [572,] 1.41605561 1.5498688 1.7465202 1.7507080 1.8019820 1.8112516 1.8627146 #> [573,] 0.59337182 0.6013404 0.6559592 0.6794917 0.7073037 0.7472879 0.7559327 #> [574,] 0.20601898 0.2138112 0.2836252 0.3734846 0.3993061 0.4641697 0.5026104 #> [575,] 0.56988075 0.5799310 0.6264804 0.6338821 0.7155595 0.7585791 0.8802147 #> [576,] 0.24010441 0.3451244 0.3776825 0.4448724 0.5067279 0.5093840 0.5153017 #> [577,] 0.44522840 0.4637061 0.5803419 0.6279280 0.7888915 0.8112898 0.8401171 #> [578,] 0.46177516 0.5032684 0.5560926 0.6384115 0.6558610 0.6738408 0.6795696 #> [579,] 0.25183425 0.4369720 0.4405283 0.4724317 0.6092113 0.6395914 0.6836550 #> [580,] 0.55034352 0.8932676 0.9048609 0.9122109 0.9204655 1.0020373 1.0966724 #> [581,] 0.27957885 0.4029978 0.5986835 0.6859729 0.7006915 0.8743741 0.9495803 #> [582,] 0.46009541 0.5295352 0.5377479 0.5922188 0.5985136 0.6594093 0.6648714 #> [583,] 0.16973124 0.3884705 0.4261615 0.4953801 0.5302664 0.6032451 0.6088218 #> [584,] 0.36096082 0.4292685 0.4315726 0.5161931 0.5861869 0.6151879 0.6519473 #> [585,] 0.62240532 0.8313961 0.8539695 1.0598134 1.0899057 1.1185382 1.1891441 #> [586,] 0.53355768 0.6586069 0.7373932 0.8106935 0.8137303 0.8560083 0.8586241 #> [587,] 0.61434841 0.6742504 0.7758032 0.8632298 0.9866009 0.9946745 1.0171249 #> [588,] 0.50994700 0.7137102 0.7281672 0.8292282 0.8709165 0.9152130 0.9175304 #> [589,] 0.35442371 0.4167385 0.4613995 0.4975055 0.5103887 0.7466410 0.7591619 #> [590,] 0.54835213 0.7573177 0.7731458 0.7745561 0.7955375 0.8407062 0.8494256 #> [591,] 0.48960202 0.5124989 0.5754902 0.5816418 0.5968568 0.6040564 0.6065801 #> [592,] 0.40807386 0.5852049 0.5944061 0.6151464 0.6305765 0.6354666 0.6405491 #> [593,] 0.50451422 0.5435610 0.8748761 0.9055023 1.0413266 1.0492853 1.1193411 #> [594,] 0.60941048 0.8286666 0.8362473 0.8475980 0.8652686 0.9248949 0.9401446 #> [595,] 1.02764981 1.0675792 1.1726157 1.1737706 1.2117283 1.2122693 1.2136554 #> [596,] 0.48648770 0.6071182 0.6077914 0.6451965 0.6664912 0.6796197 0.6990176 #> [597,] 0.31537054 0.4401888 0.7049388 0.7611104 0.7926868 0.8169729 0.8347674 #> [598,] 0.33478924 0.3535666 0.3921772 0.3934919 0.4061885 0.4270109 0.4683849 #> [599,] 0.29403821 0.4011936 0.4167385 0.4725982 0.5336573 0.5487884 0.5762095 #> [600,] 0.34154377 0.3776825 0.4150283 0.4241536 0.4626339 0.4811862 0.5238636 #> [601,] 0.29062669 0.3969090 0.4038128 0.6562524 0.6739732 0.6791804 0.7069239 #> [602,] 0.36985508 0.4767623 0.6505008 0.6659052 0.6908772 0.6949283 0.7074873 #> [603,] 0.46510675 0.5777555 0.6102530 0.6860259 0.6954669 0.8290772 0.8351042 #> [604,] 0.58189352 0.6589000 0.6686780 0.6893716 0.8203466 0.8461130 0.8599066 #> [605,] 0.28459575 0.3376415 0.4192009 0.5731440 0.6105425 0.6149352 0.6214294 #> [606,] 0.11608719 0.3638735 0.3792344 0.3799525 0.3879428 0.4390372 0.4585823 #> [607,] 0.80719987 0.8293724 1.0997222 1.2354086 1.3177878 1.3927904 1.4089550 #> [608,] 0.70018889 1.2503321 1.2780366 1.3383411 1.4643526 1.5313353 1.5387897 #> [609,] 0.54703206 0.6326815 0.7559557 0.8459306 0.8506859 0.8917204 0.9307966 #> [610,] 0.27361090 0.3943047 0.4335340 0.5142888 0.5265425 0.5371743 0.5416264 #> [611,] 0.82697259 0.8951109 0.9285000 0.9448010 1.0888297 1.2039308 1.2067116 #> [612,] 0.30507871 0.3273770 0.4344419 0.4591137 0.4751239 0.4870335 0.5101134 #> [613,] 0.54105950 0.7512295 0.8366602 0.9077201 1.0226334 1.0241518 1.1789045 #> [614,] 0.32685154 0.4467783 0.6437903 0.6957793 0.7111202 0.7504098 0.7703679 #> [615,] 0.21392826 0.2641698 0.4380816 0.4557797 0.5332089 0.5424867 0.5470235 #> [616,] 0.16973124 0.3613998 0.4432428 0.4559979 0.5624052 0.5760403 0.6043345 #> [617,] 0.54910784 0.5686761 0.6911297 0.6945931 0.7177089 0.7198688 0.7597752 #> [618,] 0.21566331 0.3873494 0.4141784 0.4209737 0.4946848 0.5121898 0.5441918 #> [619,] 0.31174585 0.3208248 0.3302222 0.3356392 0.3359325 0.3493520 0.4049898 #> [620,] 0.48416998 0.6688491 0.6699654 0.7330502 0.7655117 0.7961905 0.8147810 #> [621,] 0.54243723 0.5634303 0.5645729 0.5921655 0.5974787 0.6009983 0.6831728 #> [622,] 0.44986007 0.6802616 0.6893716 0.8791343 0.8939719 0.9821576 0.9837626 #> [623,] 0.58665844 0.8052953 0.8582436 0.9003027 0.9672961 0.9803843 1.0155728 #> [624,] 0.24136134 0.5790625 0.6168309 0.6571811 0.6682516 0.7423945 0.7494334 #> [625,] 0.52631572 0.6946062 0.8203172 0.8286666 0.8862931 1.0503166 1.0956282 #> [626,] 0.28362515 0.3572052 0.3782162 0.4301535 0.4609179 0.4736896 0.4951976 #> [627,] 0.34844429 0.4708596 0.4950304 0.5364844 0.5959543 0.6394717 0.6509156 #> [628,] 0.70116622 0.8738729 0.9232706 0.9257615 0.9500668 1.0094909 1.0283225 #> [629,] 0.41787351 0.5240484 0.6101186 0.6395927 0.6518593 0.6559082 0.6670681 #> [630,] 0.34027172 0.3483590 0.5775658 0.5876252 0.6204556 0.6855360 0.7085435 #> [631,] 0.48183801 0.7126242 0.7467861 0.7836029 0.8784556 0.8980112 0.8987570 #> [632,] 0.19419128 0.2839768 0.3802278 0.4279197 0.4645395 0.4706502 0.4918564 #> [633,] 0.23167235 0.4764203 0.4957492 0.5487723 0.5527083 0.5540839 0.6585077 #> [634,] 0.37995251 0.4259055 0.4356021 0.4399584 0.4510979 0.5149004 0.6219822 #> [635,] 0.39363928 0.4841722 0.5142780 0.5335577 0.5623835 0.5888239 0.6495236 #> [636,] 0.63314368 1.0503016 1.1153360 1.1235266 1.1364532 1.1380188 1.1802990 #> [637,] 0.85546966 0.8595941 0.8765175 0.8949732 0.9058662 0.9230566 0.9535782 #> [638,] 0.54449025 1.2893829 1.4274326 1.4313215 1.4664327 1.6344595 1.6742548 #> [639,] 0.46645854 0.4802830 0.6528180 0.6667336 0.6717098 0.6732536 0.6761374 #> [640,] 0.35965794 0.4701404 0.5471553 0.5762095 0.6405376 0.6763833 0.6825840 #> [641,] 0.43598666 0.4381048 0.5115222 0.5120130 0.5410413 0.5505425 0.5846763 #> [642,] 0.25334228 0.5324512 0.5390755 0.5392326 0.5592968 0.5691145 0.5746819 #> [643,] 0.36595529 0.3844797 0.4811879 0.5233490 0.5263663 0.5361204 0.5486290 #> [644,] 0.34235251 0.5245471 0.5290194 0.5560926 0.5857627 0.6764634 0.6833624 #> [645,] 0.36347243 0.4590754 0.6295294 0.6487658 0.6860259 0.7038720 0.7096475 #> [646,] 0.20395837 0.4657923 0.4935208 0.5361204 0.5476945 0.5932247 0.6263767 #> [647,] 0.29910942 0.4783190 0.5909218 0.6377302 0.6388986 0.7084974 0.7317710 #> [648,] 0.38519494 0.4323350 0.5596796 0.6020497 0.6978972 0.7114342 0.7876461 #> [649,] 0.35376145 0.4160175 0.4224186 0.4543662 0.4552867 0.4556376 0.4574192 #> [650,] 1.26516795 1.2810324 1.5369662 1.5829576 1.5865118 1.6985075 1.7013157 #> [651,] 0.30338965 0.3409042 0.3426225 0.3523410 0.3687144 0.4049237 0.4143892 #> [652,] 0.38772521 0.4232959 0.5335816 0.5549221 0.5569106 0.6105425 0.6243774 #> [653,] 0.37150479 0.4389962 0.4868420 0.4953717 0.5373714 0.5376232 0.5427501 #> [654,] 0.78346792 0.7997734 0.8752845 0.9425318 0.9635824 0.9930162 0.9941969 #> [655,] 0.49333622 0.4946115 0.5402954 0.5576428 0.6074047 0.7881233 0.8216602 #> [656,] 0.81634704 0.8325586 0.9138814 0.9159168 0.9335997 0.9660048 1.0039897 #> [657,] 0.30361897 0.5440199 0.5652880 0.6127630 0.6279280 0.6685404 0.7063750 #> [658,] 0.37644458 0.4764203 0.5026760 0.5916505 0.6111725 0.6610760 0.7162576 #> [659,] 0.46121647 0.4937272 0.6014050 0.6290653 0.6328639 0.7201901 0.7545655 #> [660,] 0.27017635 0.4135228 0.4148534 0.4937272 0.5395340 0.5418916 0.5552143 #> [661,] 0.58739582 0.6725229 0.7993370 0.9048243 0.9329313 0.9839407 1.0221971 #> [662,] 0.41394639 0.6016788 0.6157182 0.6422605 0.6474427 0.6831728 0.7126242 #> [663,] 0.18460255 0.2138112 0.3822591 0.3840070 0.3862574 0.4230495 0.4736896 #> [664,] 0.30072497 0.3464240 0.3802278 0.3995926 0.6015810 0.6372212 0.6519142 #> [665,] 0.30975111 0.3880177 0.4556376 0.5139233 0.5483299 0.5894451 0.6325165 #> [666,] 0.43594864 0.5407081 0.8323526 0.9285000 0.9896595 1.0405765 1.0770914 #> [667,] 0.34452809 0.3600881 0.3892187 0.4049237 0.4952720 0.5409081 0.5506858 #> [668,] 0.25073339 0.2655099 0.3688372 0.4002012 0.5335816 0.5430112 0.5562233 #> [669,] 0.55313581 0.8070900 0.8268404 0.9176893 0.9838827 1.0484415 1.0492853 #> [670,] 0.77684925 0.7780350 0.8705382 0.8738428 0.9277626 0.9402255 0.9558444 #> [671,] 0.62196198 0.6366517 0.6978972 0.8322545 0.8497819 0.9580972 1.0241518 #> [672,] 0.30084255 0.5388117 0.5648963 0.5803478 0.7518248 0.7807733 0.8681542 #> [673,] 0.43749453 0.5505227 0.5672890 0.5687865 0.5735866 0.5894259 0.5947715 #> [674,] 0.24863800 0.3318474 0.4301535 0.4310850 0.4422283 0.4810028 0.4870022 #> [675,] 0.53320894 0.6171614 0.6680559 0.6984509 0.7053176 0.7166298 0.7309727 #> [676,] 0.39983873 0.4405283 0.4797045 0.5156670 0.5388781 0.5728042 0.6942725 #> [677,] 0.35368533 0.4474790 0.4496334 0.4678426 0.6593369 0.7176423 0.7300486 #> [678,] 0.39190757 0.4899965 0.5306228 0.5583424 0.6397865 0.6539415 0.6601470 #> [679,] 0.42631331 0.4291071 0.5682878 0.6235444 0.6862337 0.7036078 0.7612428 #> [680,] 0.40020213 0.4387530 0.4914823 0.5162458 0.5411882 0.5538449 0.5839871 #> [681,] 0.27824019 0.3971149 0.4167784 0.4673479 0.4958828 0.5184400 0.5692543 #> [682,] 0.44486184 0.5100506 0.5699179 0.5792501 0.5905829 0.6962281 0.6983024 #> [683,] 0.63366877 0.7138283 0.8833182 0.9951903 0.9986345 1.0016824 1.0486325 #> [684,] 0.32908268 0.3829368 0.4572627 0.4617752 0.5499103 0.5675280 0.5688572 #> [685,] 0.47289305 0.4739998 0.6328005 0.6521120 0.6720328 0.6739434 0.7266951 #> [686,] 0.35509133 0.4265314 0.6051751 0.6168309 0.6865512 0.7016635 0.7183189 #> [687,] 0.71885913 0.8323526 0.9055023 0.9176893 0.9448010 1.1334821 1.1536165 #> [688,] 0.51403851 0.5219559 0.5413375 0.5480443 0.6355625 0.6555136 0.6644830 #> [689,] 0.61265766 0.6874574 0.7786590 0.8613076 0.9503610 0.9610233 1.0182848 #> [690,] 0.42653138 0.4625601 0.4763041 0.5893919 0.6858187 0.6941150 0.6977133 #> [691,] 0.29444451 0.3104717 0.4775865 0.5029744 0.5991155 0.6026409 0.6093745 #> [692,] 0.69054547 0.8296649 0.8948670 0.9504654 1.0171249 1.0282789 1.0300253 #> [693,] 0.51757671 0.5368974 0.7315526 0.7389885 0.8092806 0.8729183 0.8828692 #> [694,] 0.21674498 0.2507334 0.4196418 0.4596097 0.5467713 0.5569106 0.6194824 #> [695,] 0.69062254 0.8392936 1.1106780 1.1193411 1.1366575 1.1374524 1.1464533 #> [696,] 0.34682822 0.3859824 0.4531165 0.4946232 0.5408715 0.5927076 0.5948363 #> [697,] 0.95620290 1.1272844 1.1292652 1.3601725 1.4219230 1.4411477 1.4704535 #> [698,] 0.26343145 0.4435184 0.5219604 0.5413950 0.5726195 0.6162140 0.6336426 #> [699,] 0.42701073 0.5527083 0.8069478 0.8241123 0.8512284 0.8574372 0.8688199 #> [700,] 0.36336045 0.4141241 0.4178735 0.4315726 0.5211340 0.6684754 0.6770869 #> [701,] 0.54105950 0.9021926 0.9023072 1.1646467 1.1690400 1.1740738 1.2017255 #> [702,] 0.33332358 0.4941352 0.5491287 0.5514421 0.5823703 0.6171313 0.6328639 #> [703,] 0.31537054 0.4176534 0.6411925 0.6462147 0.7403294 0.7454633 0.7899675 #> [704,] 0.26328614 0.2638343 0.3096327 0.3402142 0.4377764 0.4409665 0.5062397 #> [705,] 0.38149111 0.3966306 0.4026726 0.4178758 0.4513943 0.4525542 0.5153017 #> [706,] 0.15472873 0.3533348 0.3699957 0.3862040 0.4205965 0.4325937 0.5418916 #> [707,] 0.43000173 0.5073444 0.5338096 0.5381579 0.6499022 0.6594966 0.6792359 #> [708,] 0.33323683 0.4416356 0.4746625 0.4969829 0.5978585 0.6048488 0.6156391 #> [709,] 0.60490980 0.6197508 0.6389791 0.6671744 0.6842797 0.8041659 0.8240498 #> [710,] 0.44123648 0.5963301 0.6770869 0.6782984 0.7436061 0.7591996 0.7596100 #> [711,] 0.36738405 0.4432428 0.4953801 0.5027575 0.5266475 0.5653288 0.5719470 #> [712,] 0.44109626 0.5182610 0.8114595 0.8182231 0.8704664 0.8718881 0.8750923 #> [713,] 0.35346968 0.7246401 0.7499065 0.7823791 0.8305809 0.9931664 1.0404170 #> [714,] 0.58893642 0.6124386 0.6788038 0.7691026 0.7834769 0.8118543 0.8310321 #> [715,] 0.66340973 0.8016984 0.8089124 0.8649101 0.8843676 0.9499100 1.0330265 #> [716,] 0.14605421 0.6347718 0.6725681 0.7590693 0.7616078 0.7722215 0.7821952 #> [717,] 0.25735799 0.3537615 0.3695420 0.3777877 0.4214002 0.4252863 0.4281232 #> [718,] 0.33022224 0.3441288 0.4096363 0.4386172 0.4585147 0.5031696 0.5193626 #> [719,] 0.65860686 0.8196058 0.8914782 0.9086368 0.9839407 1.0014516 1.0230460 #> [720,] 0.39509527 0.5139233 0.5447037 0.5524913 0.8080131 0.8155186 0.8215988 #> [721,] 0.55933129 0.6217602 0.6965583 0.7067615 0.7703424 0.8276266 0.8368484 #> [722,] 0.63580413 0.7688035 0.8106643 0.9246408 0.9380414 0.9439554 1.0680439 #> [723,] 0.46256011 0.4801519 0.5622388 0.6051751 0.6279492 0.6551946 0.6571811 #> [724,] 0.34717912 0.4045419 0.5436368 0.6388986 0.7033968 0.7442621 0.7580649 #> [725,] 0.51181402 0.5664642 0.6071722 0.6465218 0.6607136 0.6631087 0.6820446 #> [726,] 0.75122951 0.7833552 0.8497819 0.8599066 0.8806943 0.9128641 0.9215414 #> [727,] 0.22735807 0.5369339 0.5880864 0.6313239 0.6365637 0.6494028 0.6978221 #> [728,] 0.69858196 0.8308600 0.8988011 0.9483479 0.9529792 0.9557612 0.9866009 #> [729,] 0.58913260 0.5904511 1.0997222 1.2397709 1.3674834 1.3693199 1.3854303 #> [730,] 0.47359279 0.5291050 0.5563820 0.6234920 0.6501716 0.6651793 0.6910260 #> [731,] 0.38658483 0.4641697 0.4951976 0.5142720 0.5381997 0.5654464 0.5733830 #> [732,] 0.53774224 0.6493739 0.6512504 0.6671744 0.7118943 0.7749027 0.7782724 #> [733,] 0.40440349 0.4300163 0.5196985 0.5397259 0.6101514 0.6157228 0.6182368 #> [734,] 1.35351303 1.6109467 1.7051863 1.7236095 1.8262364 1.9428414 2.0175106 #> [735,] 0.29359887 0.4279511 0.4804884 0.5369971 0.5482631 0.5865848 0.5953025 #> [736,] 0.90613818 0.9500898 0.9713147 1.0095566 1.0338680 1.1879064 1.2013942 #> [737,] 0.52439153 0.6675391 0.8305809 1.0501612 1.0567316 1.1273738 1.1840653 #> [738,] 0.42791970 0.4785496 0.5145920 0.5260537 0.5268112 0.5434271 0.5488552 #> [739,] 0.77256093 0.8926279 1.0544813 1.1382764 1.1418979 1.1830457 1.2970655 #> [740,] 0.27824019 0.3109608 0.3987329 0.4258963 0.5017250 0.5105818 0.5888744 #> [741,] 0.27773200 0.8213555 0.8559930 0.9525178 0.9838940 1.0723668 1.1206739 #> [742,] 0.38767281 0.4026726 0.4205946 0.4471608 0.4802830 0.5507127 0.5815310 #> [743,] 0.32237498 0.3764880 0.4013733 0.4537154 0.4802813 0.4904619 0.5476945 #> [744,] 0.49446897 0.5258675 0.5579990 0.5600066 0.5672890 0.5699179 0.6562205 #> [745,] 0.35748401 0.5123131 0.5290194 0.5663951 0.5957944 0.6198647 0.6897391 #> [746,] 0.31719653 0.3848797 0.4561031 0.4979489 0.5333971 0.5371761 0.5391688 #> [747,] 0.67604518 0.9275340 1.0278988 1.0327197 1.2267464 1.2375629 1.3199692 #> [748,] 0.12222005 0.4177120 0.6268746 0.7180089 0.7371032 0.7431611 0.7436420 #> [749,] 0.74546332 0.7926868 0.8942326 1.0656954 1.1363119 1.1613837 1.1821319 #> [750,] 0.43000173 0.5052092 0.5965068 0.6683562 0.7105456 0.7366041 0.7704925 #> [751,] 0.28459575 0.3694457 0.5026201 0.6070519 0.6188489 0.6194824 0.6218011 #> [752,] 0.70730375 0.7993732 0.8869694 0.9236479 0.9618237 1.0278988 1.0328210 #> [753,] 0.45428253 0.5213248 0.5381579 0.5430112 0.6351318 0.6863909 0.6964509 #> [754,] 0.73790913 0.9146707 0.9268745 0.9441675 0.9728522 0.9886703 1.0041557 #> [755,] 0.51540272 0.5320156 0.5852049 0.6104892 0.6833624 0.6897391 0.6911030 #> [756,] 0.41347323 0.4387530 0.4678809 0.4871645 0.4934944 0.5402894 0.5618440 #> [757,] 1.17827006 1.2534981 1.2886819 1.3553739 1.3561492 1.4709502 1.4736376 #> [758,] 0.48695844 0.5510996 0.5864232 0.6654570 0.6918404 0.6931311 0.6985820 #> [759,] 1.60098152 1.6043613 1.6645287 1.8161045 1.8887888 1.9565765 2.0145514 #> [760,] 0.17672129 0.2947138 0.3361387 0.3529613 0.4579331 0.4654775 0.5653037 #> [761,] 0.27361090 0.3842518 0.3946160 0.4463409 0.4822301 0.4970682 0.5173598 #> [762,] 0.09831649 0.3687144 0.3738390 0.3752012 0.4383739 0.4483313 0.4989207 #> [763,] 0.40807386 0.6692890 0.7407087 0.7445498 0.7617625 0.7812317 0.8029511 #> [764,] 0.33323683 0.3636660 0.4090712 0.4678809 0.5384396 0.5770353 0.6342586 #> [765,] 0.74149780 0.7884421 0.8924413 0.8925216 1.0852243 1.1068269 1.1279884 #> [766,] 0.88475525 1.0307034 1.1031474 1.1288179 1.1767783 1.2611665 1.3072379 #> [767,] 0.39276221 0.4141241 0.4186988 0.5460700 0.5804832 0.5861869 0.5965068 #> [768,] 0.60683531 0.6239664 0.6329802 0.6571812 0.7014294 0.7681392 0.9043524 #> [769,] 0.25183425 0.3287616 0.4958141 0.5156670 0.6469373 0.7240607 0.7927622 #> [770,] 0.81951004 1.0033135 1.0122607 1.1505867 1.1603421 1.1781983 1.1982659 #> [771,] 0.63388207 0.7708441 0.8405305 0.8498749 0.9045846 0.9466005 0.9803991 #> [772,] 0.38596645 0.4342199 0.4684409 0.5602363 0.5714456 0.5805367 0.5833092 #> [773,] 0.63764715 0.7092790 0.8200161 0.8512293 0.8517123 0.9066578 0.9243237 #> [774,] 0.29062669 0.4554043 0.4762512 0.6821908 0.6890077 0.7307151 0.7315235 #> [775,] 0.27996485 0.5089294 0.5409551 0.5731555 0.6783771 0.7118041 0.7215748 #> [776,] 0.38532828 0.4681975 0.5026760 0.5540839 0.5630189 0.6028126 0.6175580 #> [777,] 0.51411964 0.5156462 0.5420390 0.5666462 0.6606336 0.6987629 0.7096344 #> [778,] 0.28914798 0.3341115 0.3905825 0.4007505 0.4423288 0.4925744 0.5092038 #> [779,] 0.44351835 0.5441918 0.5751142 0.5771078 0.5850060 0.6032333 0.6109984 #> [780,] 0.42911358 0.4658710 0.4704423 0.5636624 0.5749203 0.6282014 0.6493615 #> [781,] 0.58078479 0.6287389 0.6727315 0.7011662 0.7128726 0.7296091 0.7383797 #> [782,] 0.17515967 0.3148796 0.4622053 0.4935208 0.5181742 0.5225545 0.5405678 #> [783,] 0.34235251 0.4319502 0.4692709 0.4880859 0.5052213 0.5123131 0.5347047 #> [784,] 0.27949333 0.5545604 0.5978329 0.6031911 0.6126577 0.6343305 0.7437618 #> [785,] 0.24171039 0.3686001 0.4558091 0.5551246 0.6074524 0.6198224 0.6720896 #> [786,] 0.39297509 0.3943971 0.4756200 0.5476847 0.5500657 0.5557123 0.5801731 #> [787,] 0.62045966 0.6634097 0.6801610 0.7152110 0.7696425 0.8052953 0.8215516 #> [788,] 0.54426365 0.5638175 0.6749506 0.6987118 0.7195750 0.7425097 0.7472007 #> [789,] 0.30084255 0.5204844 0.5390357 0.6601272 0.6631301 0.7861197 0.7917998 #> [790,] 0.21863794 0.4566923 0.5100506 0.6264804 0.6478767 0.7233856 0.7629658 #> [791,] 0.69343238 0.7916039 0.8174900 0.8209361 0.8597213 0.8956150 0.9191062 #> [792,] 0.31575585 0.3376415 0.3694457 0.6310238 0.6630490 0.6687645 0.6753017 #> [793,] 0.33731955 0.3451244 0.4241536 0.4726576 0.4765648 0.4850090 0.5104808 #> [794,] 0.33330030 0.3430224 0.4944690 0.5286509 0.5507820 0.5691711 0.6740800 #> [795,] 0.48015193 0.4870973 0.5434333 0.5681859 0.5790625 0.6224556 0.6920199 #> [796,] 0.91545188 0.9851428 0.9985390 1.2401699 1.2706049 1.4116211 1.4267028 #> [797,] 0.26927678 0.3941537 0.4221518 0.4449027 0.4632372 0.4782392 0.5086648 #> [798,] 0.30577483 0.4371707 0.5345808 0.6105168 0.6122402 0.6865432 0.7719839 #> [799,] 0.85802249 0.9283156 1.2431804 1.2509933 1.2636073 1.2909028 1.3068810 #> [800,] 0.60499899 0.6353063 0.6588731 0.6686780 0.6783649 0.7031266 0.7059153 #> [801,] 0.35808541 0.3596801 0.4380816 0.4387298 0.4474902 0.4715928 0.4920686 #> [802,] 0.42497290 0.4498638 0.7653629 0.8186734 0.8834345 0.8955751 0.9027206 #> [803,] 0.36096082 0.3633604 0.4226290 0.5460700 0.5624287 0.6155368 0.6287389 #> [804,] 0.41459923 0.4496334 0.4880470 0.4927598 0.4971807 0.5330295 0.5530242 #> [805,] 0.87209495 1.2580143 1.5146902 1.5192367 1.6207223 1.7483121 1.7926297 #> [806,] 0.32580181 0.5388117 0.5390357 0.6096010 0.6983498 0.7022300 0.7152110 #> [807,] 0.22735807 0.3700770 0.4739122 0.4933392 0.6045138 0.6468782 0.7561484 #> [808,] 0.36954200 0.3791834 0.3862574 0.4063097 0.4711711 0.4919237 0.4926647 #> [809,] 0.31170307 0.6327033 0.7336474 0.9043976 0.9048609 0.9850208 1.0023837 #> [810,] 0.42322607 0.4259888 0.4446164 0.4449831 0.4667387 0.4983489 0.5171208 #> [811,] 0.47742160 0.7927973 0.8392936 0.9368811 0.9712860 1.0107862 1.0526832 #> [812,] 0.57426260 0.5857830 0.6128149 0.6252175 0.6313865 0.6448859 0.6608414 #> [813,] 0.40299783 0.5424989 0.6948786 0.7302712 0.8294974 0.9501195 0.9531711 #> [814,] 0.72727075 0.8829206 0.9873766 1.0244392 1.0398980 1.2170482 1.2480076 #> [815,] 0.24949429 0.4851378 0.4852129 0.5202777 0.6531028 0.7033036 0.7176815 #> [816,] 0.51507672 0.5471553 0.5615487 0.6850255 0.7931495 0.7975922 0.8424111 #> [817,] 0.15604009 0.4431694 0.5146468 0.5624673 0.5727703 0.6123558 0.6131775 #> [818,] 0.65828602 0.6739732 0.6864811 0.6890077 0.7181508 0.7231773 0.7530624 #> [819,] 0.51564803 0.5235680 0.5761168 0.7585791 0.8006608 0.8498749 0.9355411 #> [820,] 0.51459128 0.5371761 0.5935440 0.6053123 0.6092135 0.6686627 0.6725681 #> [821,] 0.33148026 0.3717019 0.4098502 0.5120130 0.5334669 0.5664642 0.6158515 #> [822,] 0.22038706 0.2836823 0.5179364 0.5479470 0.5576100 0.6203619 0.8928497 #> [823,] 0.65890004 0.6900357 0.7414978 0.8366602 0.9023072 0.9483216 0.9959242 #> [824,] 0.29471381 0.3487168 0.4474640 0.4799773 0.4854754 0.4999563 0.5302813 #> [825,] 0.20417561 0.4846515 0.5846763 0.5975842 0.6272176 0.6513768 0.6654570 #> [826,] 0.22475057 0.2403118 0.3006863 0.3547841 0.3638735 0.4968724 0.4979489 #> [827,] 0.33702873 0.8849706 1.0227250 1.2455886 1.2472189 1.2625984 1.2728276 #> [828,] 0.23535424 0.2888365 0.4209737 0.5078930 0.5609209 0.6032333 0.6234146 #> [829,] 0.43557756 0.5741917 0.5876252 0.6720861 0.6932422 0.7299574 0.8358283 #> [830,] 0.34242776 0.3594535 0.3943971 0.6445696 0.6479357 0.6531994 0.6720896 #> [831,] 0.42497290 0.4378084 0.6613781 0.6717098 0.7180839 0.8036945 0.8090444 #> [832,] 0.18842874 0.3712625 0.4171864 0.4988816 0.5338943 0.5859090 0.6002829 #> [833,] 0.59828158 0.6140907 0.6609668 0.6916844 0.7095298 0.7533940 0.7615608 #> [834,] 0.85086686 0.8693708 1.0402638 1.0913809 1.1162744 1.1580728 1.1882179 #> [835,] 0.39484804 0.5377422 0.5896853 0.5998148 0.6132736 0.7345074 0.7400159 #> [836,] 0.28200459 0.4838382 0.5056986 0.7097586 0.7118269 0.7154967 0.7260693 #> [837,] 0.28200459 0.4255819 0.5311183 0.5385190 0.5857583 0.6752513 0.7014631 #> [838,] 0.72854123 0.8213555 1.2479845 1.2509933 1.3578865 1.3883013 1.4704535 #> [839,] 0.35221305 0.4658086 0.4688457 0.4852129 0.5470235 0.5481457 0.5696647 #> [840,] 0.32580181 0.4869584 0.5029816 0.5929361 0.6505765 0.6601272 0.6815772 #> [841,] 0.24031182 0.3266515 0.3289196 0.3879428 0.4055814 0.4310850 0.4802058 #> [842,] 0.63184141 0.7825293 0.9278780 0.9812174 0.9830406 1.0397144 1.0555835 #> [843,] 0.40636897 0.5420390 0.5550702 0.5862135 0.5904569 0.6071488 0.6836963 #> [844,] 0.47831904 0.4992977 0.5258547 0.5944776 0.5963457 0.6599681 0.7640140 #> [845,] 0.46638959 0.5044446 0.5824304 0.6530868 0.6635628 0.6810039 0.7583725 #> [846,] 0.18634156 0.4660731 0.5016230 0.5377097 0.5390598 0.5907733 0.6081748 #> [847,] 0.29038507 0.3045802 0.3361387 0.3493174 0.3687756 0.3766028 0.4119493 #> [848,] 0.50444460 0.5294214 0.6075915 0.7424908 0.8023538 0.8194373 0.9051578 #> [849,] 0.18945147 0.2116450 0.2573580 0.3110705 0.3223047 0.3731118 0.3766028 #> [850,] 0.38970748 0.5717071 0.5865542 0.5878294 0.5908949 0.6282007 0.6367832 #> [851,] 0.24949429 0.4835535 0.4973154 0.5481457 0.6148331 0.7302801 0.7861184 #> [852,] 0.46124088 0.5084755 0.5156658 0.5596796 0.5802675 0.6023530 0.6047881 #> [853,] 0.66924590 0.7049388 0.8096001 0.8637338 0.8647272 0.8857966 0.9629770 #> [854,] 0.40949008 0.4903354 0.6040936 0.6654582 0.7892187 0.9190275 0.9911303 #> [855,] 0.45907545 0.5777555 0.5780089 0.7002335 0.7745468 0.8503036 0.8679628 #> [856,] 0.60555636 0.7423913 0.7591619 0.7960978 0.9138814 0.9284428 1.1086736 #> [857,] 0.54070810 0.5956861 0.7189500 0.7750984 0.7991863 0.8518376 0.8815974 #> [858,] 0.43061825 0.4657923 0.4811879 0.5268112 0.5396213 0.5876804 0.5909889 #> [859,] 0.47952646 0.5485678 0.5796519 0.7068683 0.7246722 0.7839269 0.8756713 #> [860,] 0.34512046 0.3936393 0.5426927 0.5617833 0.5775658 0.6252175 0.6699691 #> [861,] 0.33018724 0.6105168 0.6239664 0.6644961 0.6850255 0.7567525 0.7708239 #> [862,] 0.44522840 0.4481359 0.5284077 0.5530242 0.5652880 0.5863829 0.6606663 #> [863,] 0.44813593 0.4637061 0.4678426 0.4880470 0.5557236 0.6843397 0.7063750 #> [864,] 0.62529285 0.8191095 1.0646795 1.0720833 1.0783879 1.1276875 1.1329767 #> [865,] 0.41119252 0.4577456 0.5452479 0.6297758 0.6929260 0.7024502 0.7180998 #> [866,] 0.27912903 0.4651760 0.5081217 0.5602555 0.6379255 0.7991863 0.8322765 #> [867,] 1.15661048 1.3462438 1.4471441 1.5645124 1.5808967 1.6063300 1.6320500 #> [868,] 0.54139322 0.6773988 0.7105605 0.7149278 0.7211515 0.7299574 0.7337276 #> [869,] 0.31133296 0.4822301 0.5088305 0.5234874 0.5358143 0.5401556 0.5946605 #> [870,] 0.50344372 0.6795696 0.7893724 0.8130988 0.8172974 0.8690556 0.8829630 #> [871,] 0.24531337 0.2903851 0.2977648 0.3203283 0.3223047 0.3224021 0.3613120 #> [872,] 1.09268104 1.1382408 1.1923692 1.2465492 1.3481100 1.4001927 1.4189594 #> [873,] 0.37265397 0.4918564 0.4958828 0.5303708 0.5321874 0.5558120 0.5829550 #> [874,] 0.48520405 0.7279412 0.8074916 0.9682564 1.1254497 1.1566882 1.1824327 #> [875,] 0.26594121 0.5953002 0.5957431 0.6172237 0.6716399 0.6896890 0.6984653 #> [876,] 0.81867341 0.8726695 0.9058662 0.9245956 0.9318064 1.0447502 1.0583478 #> [877,] 0.66137812 0.6894816 0.8342098 0.8775902 0.9044680 0.9185322 0.9245897 #> [878,] 0.27957885 0.5147946 0.5424989 0.5528173 0.7625186 0.7667228 0.7693490 #> [879,] 0.12222005 0.3100312 0.6142398 0.6262270 0.6349211 0.6857133 0.7149717 #> [880,] 0.55831393 0.5605773 0.6047171 0.6481022 0.9314802 0.9439554 0.9520623 #> [881,] 0.49736419 0.5917555 0.6223325 0.6957458 0.7322672 0.7716297 0.7745400 #> [882,] 0.40360403 0.5054819 0.5453375 0.5558413 0.6633979 0.7233839 0.7326161 #> [883,] 0.37547110 0.4268221 0.5448074 0.5452479 0.6043597 0.6078415 0.6495392 #> [884,] 0.29963193 0.3668414 0.3792134 0.3928074 0.4425622 0.4718084 0.4798583 #> [885,] 0.64379029 0.7468705 0.8206195 0.8262232 0.8541983 0.8743703 0.9315260 #> [886,] 0.30096399 0.3892187 0.4546167 0.4548463 0.5983123 0.6060355 0.6328005 #> [887,] 0.46703537 0.4767417 0.5145920 0.5321874 0.5712901 0.5777134 0.5909889 #> [888,] 0.51564803 0.6582860 0.7708441 0.7788900 0.8357769 0.8599289 0.8652686 #> [889,] 0.53892468 1.0486325 1.1644871 1.1717316 1.2046336 1.2400584 1.2450309 #> [890,] 0.32282065 0.3540405 0.4008547 0.4574192 0.4597129 0.4749997 0.4819657 #> [891,] 0.47742160 0.7234641 0.8592318 1.0554974 1.0620862 1.1109065 1.1366575 #> [892,] 0.34197322 0.3716588 0.4356021 0.5047202 0.5166129 0.5323596 0.5344388 #> [893,] 0.66622344 0.6909275 0.8534496 0.8539695 0.9764982 0.9863673 0.9984615 #> [894,] 0.87209495 1.0808862 1.3254115 1.3404295 1.4891477 1.5381128 1.5951257 #> [895,] 0.38741583 0.4344419 0.4488586 0.4533024 0.4672448 0.4848637 0.4975694 #> [896,] 1.03245278 1.1925389 1.2063668 1.2165550 1.2345554 1.2772759 1.3177358 #> [897,] 0.33702873 0.9317456 1.1587109 1.2196853 1.2244513 1.2387571 1.2509943 #> [898,] 0.86087471 0.8775427 1.0586136 1.2316273 1.2486592 1.3250910 1.3474000 #> [899,] 0.24495826 0.3665608 0.3796179 0.4394007 0.4699607 0.4970682 0.4973593 #> [900,] 0.53693393 0.5509196 0.5758092 0.6077715 0.6372212 0.6468782 0.6495503 #> [901,] 0.60751210 0.6347718 0.6750756 0.8400073 0.8639459 0.8938192 0.8987570 #> [902,] 0.36475602 0.4359686 0.4681975 0.4957492 0.5509920 0.6065373 0.6610760 #> [903,] 0.71925597 0.7238057 1.4242708 1.4808959 1.6356863 1.7811930 1.8049915 #> [904,] 0.33217500 0.6075172 0.7308439 0.7573067 0.7745370 0.8331341 0.8373647 #> [905,] 1.08159736 1.0956074 1.1598090 1.2341912 1.2433489 1.2582241 1.2708966 #> [906,] 0.29083287 0.3991506 0.5263341 0.5313566 0.5715888 0.5801731 0.5866417 #> [907,] 0.31849457 0.3375275 0.4090368 0.4105118 0.4221518 0.4514099 0.5643394 #> [908,] 0.30096399 0.5509830 0.5541190 0.6393489 0.6521120 0.6928918 0.7301782 #> [909,] 0.27773200 0.7285412 0.9501224 0.9562029 0.9740056 1.0353836 1.1208545 #> [910,] 0.35965794 0.5150767 0.5336573 0.6122402 0.6531913 0.6848237 0.6920594 #> [911,] 0.32876163 0.4724317 0.5728042 0.5959871 0.6017204 0.7424823 0.7535873 #> [912,] 0.20612255 0.2839768 0.3995926 0.4600954 0.4980985 0.5371465 0.5509196 #> [913,] 0.23307038 0.3248874 0.4146456 0.4839062 0.4901973 0.5347778 0.5501868 #> [914,] 0.23993803 0.5254370 0.6040564 0.6101514 0.6314974 0.6320861 0.6400916 #> [915,] 0.69003570 0.8739171 0.8805306 0.8924413 1.0279754 1.0449738 1.1265626 #> [916,] 0.40415075 0.4847007 0.6844604 0.7136063 0.7381164 0.7421462 0.7458784 #> [917,] 0.18051066 0.3621718 0.6046618 0.6203619 0.6725229 0.6859977 0.7025122 #> [918,] 0.39323414 0.4813874 0.5092038 0.5139742 0.5467713 0.5562233 0.5636624 #> [919,] 0.33470661 0.4189063 0.4808973 0.5373714 0.5594251 0.6065373 0.6195995 #> [920,] 0.70069148 0.8073962 0.8207193 0.8294974 0.8352566 0.8714288 0.9656345 #> [921,] 0.34743241 0.3678879 0.4061678 0.4076304 0.4277711 0.4468730 0.5275568 #> [922,] 0.34309068 0.4307515 0.5409551 0.5519855 0.5602452 0.6127630 0.7134870 #> [923,] 0.50898016 0.5401785 0.6252212 0.6393644 0.6450369 0.6670446 0.6746208 #> [924,] 0.55281735 0.5803478 0.5986835 0.7302712 0.7642574 0.7886514 0.7917998 #> [925,] 0.46323720 0.4711711 0.5015095 0.5227071 0.5300852 0.6057881 0.6281770 #> [926,] 0.37300207 0.5505227 0.5666462 0.5839332 0.6252212 0.6690963 0.6836963 #> [927,] 0.40119359 0.4975055 0.5589544 0.6055564 0.6531913 0.6584113 0.7604565 #> [928,] 0.48183801 0.4841700 0.6325459 0.7198504 0.7318016 0.7725609 0.7955375 #> [929,] 0.61025299 0.7080839 0.8064294 0.8291200 0.8344719 0.8517123 0.8693708 #> [930,] 0.35368533 0.4116207 0.4613834 0.5041523 0.5568096 0.5683251 0.6135434 #> [931,] 0.31605501 0.4617185 0.5401785 0.5809788 0.5947715 0.6274038 0.6835825 #> [932,] 0.36375067 0.3966306 0.4539625 0.4848637 0.5092787 0.5233490 0.5512903 #> [933,] 0.66159483 0.6653664 0.7231773 0.7462066 0.7828199 0.7834769 0.8125567 #> [934,] 0.72467222 0.8892709 0.8942326 0.9342293 1.0019782 1.0190099 1.0817150 #> [935,] 0.17515967 0.2077467 0.4378483 0.4446930 0.4635054 0.4856521 0.4920686 #> [936,] 0.63714896 0.6514300 0.8096001 0.8222615 0.8256855 0.8450948 0.8645342 #> [937,] 0.56154873 0.7014294 0.7433678 0.8551267 1.0064208 1.0101535 1.0104143 #> [938,] 0.50994700 0.5270976 0.5541190 0.5983123 0.6625547 0.6720328 0.6987629 #> [939,] 1.13746210 1.1554039 1.1709452 1.2012700 1.3270357 1.3400856 1.4142670 #> [940,] 0.28743920 0.5563820 0.5856901 0.5996128 0.6547929 0.6700591 0.7373932 #> [941,] 0.67425040 0.6905455 0.7710334 0.9557612 0.9796315 1.0197606 1.1882176 #> [942,] 0.37150479 0.4798121 0.4808973 0.5166129 0.5182849 0.5829721 0.5888863 #> [943,] 0.28688126 0.4258963 0.4281232 0.4670354 0.4883674 0.5060454 0.5184400 #> [944,] 0.77827903 0.7819818 0.8017048 0.8162861 0.8181842 0.8197966 0.8473799 #> [945,] 0.82162781 1.0372120 1.1002160 1.1334750 1.2121846 1.2270154 1.2946210 #> [946,] 0.37025403 0.4167265 0.5974787 0.6093745 0.6213680 0.6260663 0.6454604 #> [947,] 0.25146438 0.3927622 0.4704423 0.5118140 0.5141644 0.5981667 0.6030569 #> [948,] 0.78545275 1.4532707 1.5313353 1.5427409 1.6086276 1.7504291 1.7885411 #> [949,] 0.97271309 0.9821576 1.0197606 1.0300253 1.0468094 1.0555918 1.2018503 #> [950,] 0.54356099 0.6906225 0.7515560 0.8163552 0.8268404 1.0246197 1.0835439 #> [951,] 0.54343326 0.5726713 0.6079400 0.6362282 0.6411489 0.6882258 0.7099575 #> [952,] 0.46844091 0.5961116 0.6552774 0.6681492 0.6705820 0.6705824 0.6773838 #> [953,] 0.43749453 0.5324512 0.5839332 0.6552774 0.6928143 0.7366193 0.7452899 #> [954,] 0.43154461 0.4399540 0.4870235 0.5126823 0.5397259 0.5775316 0.5931087 #> [955,] 1.02021825 1.1881607 1.1982660 1.2262335 1.2503180 1.2585351 1.2634868 #> [956,] 0.36567019 0.4878582 0.4992977 0.5924440 0.6344151 0.6589955 0.7024502 #> [957,] 0.54300594 0.6047171 0.6385052 0.8357751 0.8688679 0.8910615 0.9210669 #> [958,] 0.26227964 0.5029744 0.5263975 0.5506858 0.5875720 0.5980812 0.6151065 #> [959,] 0.28368227 0.3698291 0.6985545 0.7824819 0.8126316 0.8950014 0.9177645 #> [960,] 0.49736419 0.5183471 0.6872977 0.7351015 0.7555208 0.7721703 0.8106311 #> [961,] 0.39870005 0.4068494 0.4138580 0.4387298 0.4493096 0.4648980 0.4751239 #> [962,] 0.32908268 0.4148534 0.4663373 0.4952917 0.5148448 0.5823703 0.5933718 #> [963,] 0.20957563 0.5594251 0.6219183 0.6602812 0.7078393 0.7360118 0.7703560 #> [964,] 0.29793315 0.3097676 0.3946160 0.4495239 0.4973593 0.5219604 0.5401556 #> [965,] 0.40847446 0.4752569 0.5648596 0.5927076 0.6392017 0.6626822 0.6950428 #> [966,] 0.24032959 0.3109608 0.3498404 0.3971149 0.4755460 0.5466231 0.5519391 #> [967,] 0.83996762 0.8429282 0.8430529 0.8506495 0.8805370 0.9030809 0.9297668 #> [968,] 0.75906932 0.7979691 0.8549952 0.8591549 0.8739171 0.9021926 0.9483216 #> [969,] 0.72181504 0.7701056 0.8012313 0.8261697 0.8506495 0.9436734 0.9631076 #> [970,] 0.45928747 0.6874574 0.7040529 0.7155653 0.7596100 0.7642973 0.8288619 #> [971,] 0.48355347 0.5202777 0.5696647 0.6209378 0.8020739 0.8293285 0.8423353 #> [972,] 0.52454708 0.5688572 0.5876460 0.5896110 0.6384115 0.6425840 0.6848581 #> [973,] 0.23167235 0.4270107 0.5509920 0.6065801 0.6111725 0.6277603 0.6409072 #> [974,] 0.47085961 0.6588610 0.6596290 0.6879263 0.7065132 0.7088534 0.7781482 #> [975,] 0.24984831 0.4459982 0.7060311 0.8481894 0.8836024 0.9899705 0.9995362 #> [976,] 0.48003483 0.5257607 0.5606464 0.6075915 0.6931258 0.7270252 0.7278883 #> [977,] 0.24909537 0.6965583 0.8719151 0.8899695 0.8925672 0.9524394 0.9771197 #> [978,] 0.47499973 0.5926426 0.6021089 0.6590841 0.6911297 0.7376685 0.7610334 #> [979,] 0.27414771 0.3533348 0.3829368 0.4096830 0.4135228 0.4415721 0.4663373 #> [980,] 0.55848306 0.5871392 0.6017204 0.6948917 0.7138283 0.7796337 0.8115816 #> [981,] 0.31027863 0.3318503 0.4300163 0.4788421 0.4899965 0.5254370 0.5960820 #> [982,] 0.09831649 0.3426225 0.3733893 0.3833929 0.3998862 0.4147042 0.5065422 #> [983,] 0.34242776 0.4128428 0.5373668 0.5707755 0.5742626 0.6556826 0.6773988 #> [984,] 0.69637689 0.9597156 0.9830406 1.0227250 1.0404170 1.0897849 1.1604625 #> [985,] 0.21587317 0.5035108 0.5587194 0.6152778 0.6528830 0.6680267 0.6734077 #> [986,] 0.69958193 0.8580225 0.8754899 0.9156674 0.9566091 0.9670229 0.9763510 #> [987,] 0.11608719 0.3006863 0.3250055 0.3289196 0.4259055 0.4377923 0.4870022 #> [988,] 0.36347243 0.4651067 0.5653288 0.5780089 0.5809020 0.6451965 0.6682168 #> [989,] 0.41765340 0.4401888 0.6357935 0.6813370 0.7506527 0.7766402 0.7819681 #> [990,] 0.79370421 0.8864215 0.8938192 0.9058586 1.0523116 1.1082587 1.1725493 #> [991,] 0.54612053 0.6078415 0.6344151 0.6866979 0.6894896 0.7161804 0.7335557 #> [992,] 0.34642398 0.5090600 0.5749598 0.5798745 0.6493739 0.6495503 0.6656699 #> [993,] 0.58381883 0.6305519 0.6584598 0.7033968 0.7270252 0.7323232 0.7672032 #> [994,] 0.37058080 0.4416356 0.4471095 0.4517481 0.4961319 0.5121898 0.5384396 #> [995,] 0.29083287 0.4656943 0.4740713 0.5162538 0.5446352 0.5476847 0.5501051 #> [996,] 0.71182690 0.8015365 0.8357751 0.9246408 0.9344621 1.0045098 1.0209222 #> [997,] 0.49119061 0.8981545 1.0922723 1.0947023 1.1007515 1.1540649 1.1801942 #> [998,] 0.52910495 0.5856901 0.6052306 0.6426209 0.6872977 0.7322672 0.7591426 #> [999,] 0.40440349 0.4788421 0.5086148 0.5929361 0.6096010 0.6314974 0.6784615 #> [1000,] 0.95906486 0.9779007 0.9891183 0.9967242 1.0371942 1.0655567 1.1123559 #> [,8] [,9] [,10] #> [1,] 1.1277723 1.1816461 1.1990002 #> [2,] 1.2216679 1.2352019 1.2608456 #> [3,] 0.6189972 0.6357971 0.6409345 #> [4,] 0.6563752 0.6593882 0.6812084 #> [5,] 0.6451487 0.6575858 0.6584598 #> [6,] 0.6787011 0.7148674 0.7266524 #> [7,] 0.6318225 0.6663029 0.6779481 #> [8,] 0.7533940 0.7807431 0.7992978 #> [9,] 1.0873969 1.0961940 1.1964236 #> [10,] 0.5822407 0.5883231 0.5917679 #> [11,] 0.6083513 0.6091392 0.6192702 #> [12,] 0.7107465 0.7228639 0.7490380 #> [13,] 0.7498239 0.7559947 0.7591031 #> [14,] 0.8340085 0.8789927 0.8985272 #> [15,] 0.8731405 0.8742264 0.9712348 #> [16,] 1.1797889 1.1845552 1.2039234 #> [17,] 1.0033135 1.0354425 1.0443826 #> [18,] 0.9330399 0.9576537 0.9757561 #> [19,] 0.8899695 0.9015966 0.9232722 #> [20,] 0.5672174 0.5912759 0.6029007 #> [21,] 0.6501106 0.6602688 0.6823808 #> [22,] 0.7445577 0.7545314 0.7877731 #> [23,] 0.7894505 0.8406640 0.8509438 #> [24,] 0.9551027 0.9654747 1.0030288 #> [25,] 1.0730757 1.0733707 1.0883246 #> [26,] 0.7280532 0.7556115 0.7615608 #> [27,] 0.7040471 0.7180793 0.7558149 #> [28,] 1.1124655 1.1726914 1.2164293 #> [29,] 0.7843067 0.7848351 0.8214750 #> [30,] 0.5764293 0.6088673 0.6187806 #> [31,] 1.1153360 1.1448984 1.1843941 #> [32,] 0.7166056 0.7172308 0.7263161 #> [33,] 0.7250852 0.7477290 0.7512148 #> [34,] 1.3742790 1.4484635 1.4505247 #> [35,] 1.2946210 1.2946745 1.3529488 #> [36,] 0.9501224 0.9525178 0.9781398 #> [37,] 0.5908949 0.6201243 0.6238170 #> [38,] 0.9860467 1.0113623 1.0792367 #> [39,] 1.3823259 1.4800667 1.4810435 #> [40,] 1.0405158 1.0413266 1.0484415 #> [41,] 0.4522604 0.4552867 0.4983990 #> [42,] 1.0056548 1.0083335 1.0214761 #> [43,] 0.5572177 0.5785664 0.6033729 #> [44,] 0.5246025 0.5514685 0.5659403 #> [45,] 0.5520987 0.5618288 0.5626486 #> [46,] 0.9168416 0.9505614 0.9540991 #> [47,] 0.9491927 0.9748667 1.0007042 #> [48,] 0.7468701 0.7627787 0.7740428 #> [49,] 0.8202840 0.8203177 0.8369207 #> [50,] 0.7371032 0.7728698 0.7842856 #> [51,] 0.8279323 0.8695652 0.8741601 #> [52,] 0.6908772 0.7031286 0.7333217 #> [53,] 0.6937080 0.7369896 0.7374129 #> [54,] 0.5789364 0.5883231 0.5903267 #> [55,] 0.8279323 0.8611921 0.8671132 #> [56,] 1.2105582 1.3609712 1.4209254 #> [57,] 0.6228348 0.6295301 0.6422188 #> [58,] 0.6590690 0.6728996 0.6740843 #> [59,] 0.6575858 0.6959634 0.7385297 #> [60,] 0.9164001 0.9352856 0.9474459 #> [61,] 0.6986781 0.7031266 0.7040471 #> [62,] 0.5624673 0.5656930 0.5680078 #> [63,] 0.6356963 0.6507316 0.6567545 #> [64,] 0.6932837 0.7119049 0.7338189 #> [65,] 0.6698017 0.6710056 0.6977133 #> [66,] 1.0568114 1.0584024 1.0628807 #> [67,] 0.4925018 0.5051723 0.5192192 #> [68,] 0.8972152 0.9264264 1.0154784 #> [69,] 0.7329504 0.7436061 0.7528723 #> [70,] 1.3642570 1.4449321 1.4667406 #> [71,] 0.8690166 0.9903226 1.0250856 #> [72,] 0.5239980 0.5479711 0.5800733 #> [73,] 0.5115099 0.5237082 0.5635509 #> [74,] 0.9470885 0.9779822 1.0129587 #> [75,] 0.5990171 0.6098017 0.6144892 #> [76,] 0.5953002 0.6128149 0.6135434 #> [77,] 0.5607080 0.6527787 0.6877265 #> [78,] 0.5357776 0.5617833 0.5903267 #> [79,] 0.7854694 0.8062140 0.8121136 #> [80,] 0.9891183 0.9941969 0.9993140 #> [81,] 0.5521847 0.5686514 0.5835483 #> [82,] 1.2409497 1.2821540 1.3338516 #> [83,] 1.3557836 1.3559887 1.3816546 #> [84,] 1.1189773 1.1394716 1.2007114 #> [85,] 0.6464739 0.6472974 0.6520667 #> [86,] 0.8236488 0.8243628 0.8298920 #> [87,] 0.7494334 0.7898359 0.8762251 #> [88,] 0.7067036 0.7517859 0.7558175 #> [89,] 0.6324472 0.6384841 0.6393787 #> [90,] 0.5765781 0.6253036 0.6467292 #> [91,] 0.6217369 0.6738939 0.6883190 #> [92,] 0.9049822 0.9530349 1.0050902 #> [93,] 0.5503597 0.5701566 0.5904144 #> [94,] 1.1755510 1.2042861 1.2316526 #> [95,] 0.9724045 0.9812174 0.9884258 #> [96,] 0.9592701 0.9728378 1.0266564 #> [97,] 0.5480974 0.5812286 0.6118587 #> [98,] 0.5246272 0.5265085 0.5475677 #> [99,] 0.8461130 0.8477330 0.8717503 #> [100,] 0.7628803 0.7824819 0.7993370 #> [101,] 1.7962282 1.8124344 1.8204522 #> [102,] 0.4446164 0.4626767 0.4765493 #> [103,] 1.6171545 1.6189861 1.6381932 #> [104,] 0.7240607 0.7821695 0.8235358 #> [105,] 0.5712901 0.5985136 0.6067016 #> [106,] 0.8766191 0.8782562 0.8872883 #> [107,] 1.3221504 1.3258106 1.3595579 #> [108,] 1.2497242 1.2797769 1.2847999 #> [109,] 0.6307679 0.6548610 0.6825140 #> [110,] 0.5077163 0.5152366 0.5201796 #> [111,] 0.5897513 0.6775438 0.7249468 #> [112,] 0.8716588 0.9054262 0.9855289 #> [113,] 0.8448758 0.8714976 0.9142557 #> [114,] 0.7288252 0.8052105 0.8151615 #> [115,] 0.6718406 0.6722277 0.6948328 #> [116,] 0.8939690 0.9073273 0.9459113 #> [117,] 1.1866277 1.2049009 1.2131600 #> [118,] 0.9652541 0.9756351 0.9942206 #> [119,] 0.5998649 0.6091943 0.6123165 #> [120,] 0.6872129 0.7097586 0.7301151 #> [121,] 0.7390389 0.7604702 0.7630935 #> [122,] 0.4938862 0.4983489 0.5466885 #> [123,] 0.9209192 0.9381286 0.9578028 #> [124,] 1.2576783 1.3457942 1.3543061 #> [125,] 0.8000203 0.8167809 0.8301323 #> [126,] 0.8340372 0.8514435 0.8515045 #> [127,] 0.5103176 0.5475948 0.5882427 #> [128,] 0.5827694 0.5828899 0.5951991 #> [129,] 0.6340862 0.6482273 0.6507316 #> [130,] 0.7738277 0.8869694 0.8988091 #> [131,] 0.6822141 0.6939493 0.6992247 #> [132,] 0.6574931 0.7422702 0.7461058 #> [133,] 0.9973896 1.0184821 1.0237132 #> [134,] 1.1486590 1.1613028 1.1837634 #> [135,] 0.6238170 0.6395035 0.6520195 #> [136,] 1.0973044 1.1709569 1.2332039 #> [137,] 0.6108532 0.6429611 0.6630001 #> [138,] 0.5521847 0.5843494 0.6012981 #> [139,] 0.7308439 0.7434359 0.7581942 #> [140,] 1.3928817 1.4068608 1.4439310 #> [141,] 0.6609668 0.6850597 0.7299244 #> [142,] 0.5915916 0.6050565 0.6077914 #> [143,] 0.5823034 0.5947904 0.6019534 #> [144,] 0.5553071 0.5645280 0.6446181 #> [145,] 0.6940500 0.7282091 0.7310340 #> [146,] 0.5495635 0.5514685 0.5826591 #> [147,] 0.7611104 0.8040045 0.8043343 #> [148,] 0.6495392 0.6776919 0.6854624 #> [149,] 0.7480650 0.8191044 0.8823637 #> [150,] 0.5411869 0.5502896 0.5583409 #> [151,] 0.7180998 0.7336379 0.7341086 #> [152,] 1.1912636 1.2095629 1.2352239 #> [153,] 0.5758092 0.6116671 0.6165203 #> [154,] 0.7524017 0.7819264 0.7953267 #> [155,] 0.6733962 0.6810368 0.6899465 #> [156,] 0.6521463 0.6840823 0.7100448 #> [157,] 1.3620943 1.3688559 1.3708892 #> [158,] 0.5600066 0.6737385 0.6776919 #> [159,] 0.5901161 0.5951493 0.6357927 #> [160,] 0.8658120 0.8981545 0.9256894 #> [161,] 0.7964822 0.7980622 0.8108124 #> [162,] 0.5224324 0.5541192 0.5592968 #> [163,] 1.1540350 1.1687959 1.1734590 #> [164,] 1.0675792 1.1022426 1.1054776 #> [165,] 1.1229310 1.1440999 1.1454136 #> [166,] 0.8308600 0.8457055 0.8557414 #> [167,] 0.5969554 0.5991155 0.6028723 #> [168,] 1.3838868 1.3842514 1.3956961 #> [169,] 0.8712192 0.9154238 0.9272356 #> [170,] 0.7390389 0.7535997 0.7970432 #> [171,] 0.6023530 0.6238119 0.6355553 #> [172,] 0.7766402 0.7777657 0.7899675 #> [173,] 0.5313566 0.5754902 0.5769897 #> [174,] 0.5431463 0.5839871 0.6055436 #> [175,] 0.9994798 1.0213772 1.0685104 #> [176,] 0.8997026 0.9091246 0.9232706 #> [177,] 0.7352546 0.7384616 0.7423913 #> [178,] 0.6937898 0.6945931 0.7143432 #> [179,] 1.0741032 1.0937999 1.0980578 #> [180,] 0.5416264 0.5501798 0.5502562 #> [181,] 0.9843336 1.0336066 1.0590858 #> [182,] 0.6091943 0.6363898 0.6373057 #> [183,] 0.7003897 0.7075189 0.7136140 #> [184,] 0.9884055 1.0074228 1.0174219 #> [185,] 0.8916240 0.8993724 0.9282640 #> [186,] 0.6490195 0.6628685 0.6681492 #> [187,] 1.2156100 1.2544759 1.2724148 #> [188,] 0.7837702 0.8218098 0.8225141 #> [189,] 0.7619641 0.8015365 0.8407601 #> [190,] 1.0560959 1.1273738 1.1434620 #> [191,] 0.8261643 0.8521884 0.9099727 #> [192,] 1.1566105 1.1855532 1.1886661 #> [193,] 0.9440280 0.9661984 0.9921918 #> [194,] 0.8645134 0.8670063 0.9061379 #> [195,] 0.5188817 0.5238636 0.5719127 #> [196,] 0.8252084 0.8782798 0.8829206 #> [197,] 0.6393644 0.7216211 0.7395698 #> [198,] 0.9058586 0.9194339 0.9787950 #> [199,] 0.9836911 1.0023737 1.0260539 #> [200,] 0.8919486 0.9284428 0.9431832 #> [201,] 0.5982558 0.6336967 0.6477662 #> [202,] 0.8238807 0.8295729 0.8532570 #> [203,] 0.3975459 0.4535849 0.4674355 #> [204,] 0.8965174 0.8982999 0.8993036 #> [205,] 0.5746819 0.5860547 0.5897513 #> [206,] 0.7902742 0.8046735 0.8447648 #> [207,] 0.6917758 0.7283993 0.7666928 #> [208,] 0.5911413 0.5921035 0.5962793 #> [209,] 0.8112749 0.8424908 0.8805676 #> [210,] 0.5499103 0.5552143 0.5724341 #> [211,] 0.7664883 0.7766420 0.8158615 #> [212,] 0.7401987 0.7662884 0.7797529 #> [213,] 0.6575542 0.6580014 0.6768431 #> [214,] 1.2166165 1.2253165 1.2261671 #> [215,] 0.6186600 0.6671991 0.6732922 #> [216,] 0.7398366 0.7403294 0.7425097 #> [217,] 0.5989056 0.6023753 0.6490482 #> [218,] 0.5521809 0.5585820 0.6238076 #> [219,] 0.5268149 0.5624969 0.5636207 #> [220,] 1.1284222 1.1794205 1.1963549 #> [221,] 0.6613447 0.6957399 0.7197768 #> [222,] 0.7376104 0.7534260 0.7571064 #> [223,] 1.1254497 1.1310411 1.1359482 #> [224,] 0.5717071 0.5998148 0.6373057 #> [225,] 0.7136713 0.7231501 0.7289041 #> [226,] 0.6541174 0.7063086 0.7362559 #> [227,] 0.7185699 0.7271226 0.7680247 #> [228,] 1.2394236 1.2587553 1.3192218 #> [229,] 0.9253467 0.9658028 0.9916496 #> [230,] 0.9420330 0.9539283 0.9658795 #> [231,] 0.8630664 0.8804020 0.8901090 #> [232,] 0.4850090 0.5162538 0.5226281 #> [233,] 0.6355172 0.6559131 0.6857133 #> [234,] 0.8232688 0.8362491 0.8615428 #> [235,] 0.5794818 0.5823488 0.5878504 #> [236,] 0.8986594 0.9156674 0.9355886 #> [237,] 0.6070168 0.6146959 0.6385568 #> [238,] 0.7800092 0.7919962 0.8261697 #> [239,] 0.6446008 0.6517779 0.6528180 #> [240,] 0.5903636 0.6649502 0.6650598 #> [241,] 1.5181657 1.5191653 1.6806529 #> [242,] 1.1709452 1.2108411 1.2212352 #> [243,] 0.9794785 1.0148312 1.0588020 #> [244,] 0.8228033 0.9406653 0.9412112 #> [245,] 0.7355959 0.7427091 0.8169518 #> [246,] 0.6605395 0.7010432 0.7101375 #> [247,] 0.5724341 0.5965084 0.6013598 #> [248,] 0.6581380 0.6864811 0.7007856 #> [249,] 0.7468705 0.7483195 0.7560462 #> [250,] 0.4474640 0.4590751 0.4597129 #> [251,] 0.8388607 0.9121455 0.9395272 #> [252,] 0.6237672 0.6324957 0.6378056 #> [253,] 1.1115367 1.1695811 1.2465492 #> [254,] 0.8507018 0.8815974 0.9280725 #> [255,] 0.5880765 0.6234843 0.6359189 #> [256,] 0.7837060 0.7840352 0.8021130 #> [257,] 0.7432229 0.7474043 0.7781482 #> [258,] 0.8157246 0.8163470 0.8207238 #> [259,] 1.0071526 1.0514318 1.0816304 #> [260,] 0.7723188 0.8073125 0.8125567 #> [261,] 0.7733659 0.7765029 0.7781799 #> [262,] 0.8045442 0.8443363 0.8512293 #> [263,] 0.6691929 0.6805346 0.7250852 #> [264,] 0.5409081 0.5479479 0.5533832 #> [265,] 0.6240639 0.6376946 0.6383766 #> [266,] 0.6140820 0.6224795 0.6236054 #> [267,] 0.7967967 0.8110177 0.8293811 #> [268,] 0.5538449 0.5592717 0.5692131 #> [269,] 1.3894109 1.3983734 1.4073322 #> [270,] 0.6532931 0.6535509 0.6603980 #> [271,] 0.6954934 0.8071532 0.8544645 #> [272,] 0.7997930 0.8001390 0.8008286 #> [273,] 0.7323858 0.7352067 0.7820764 #> [274,] 0.5457597 0.5458660 0.5603155 #> [275,] 0.6593882 0.6684101 0.6983498 #> [276,] 1.5282590 1.5432321 1.5518024 #> [277,] 0.7831023 0.8821525 0.8906814 #> [278,] 0.5610538 0.5769897 0.6043345 #> [279,] 0.8457055 0.8582436 0.9107608 #> [280,] 0.5585820 0.5978585 0.6342586 #> [281,] 0.5753703 0.6876870 0.7038946 #> [282,] 0.7290462 0.8648736 0.8648975 #> [283,] 0.6778300 0.6789222 0.7318722 #> [284,] 0.9411683 0.9855289 1.0050375 #> [285,] 0.7043195 0.7432437 0.7502960 #> [286,] 1.3651561 1.3892015 1.3943802 #> [287,] 1.2771909 1.2880192 1.3741400 #> [288,] 1.5075022 1.5866471 1.6241959 #> [289,] 1.1508820 1.2054700 1.2060725 #> [290,] 0.8510004 0.8512387 0.8942729 #> [291,] 0.6606663 0.6876140 0.7072640 #> [292,] 0.6391744 0.6506912 0.6697097 #> [293,] 0.8054279 0.8070900 0.8415829 #> [294,] 0.6004037 0.6064343 0.6525837 #> [295,] 0.5946605 0.6109984 0.6217659 #> [296,] 0.5564219 0.5682669 0.5785664 #> [297,] 0.5121810 0.5405678 0.5407712 #> [298,] 0.8108245 0.8151554 0.8167585 #> [299,] 0.4839062 0.4925744 0.5149030 #> [300,] 0.7401987 0.7627323 0.7641001 #> [301,] 0.5626776 0.6190075 0.6316696 #> [302,] 0.6929260 0.7291426 0.7316196 #> [303,] 1.9051118 1.9100229 1.9334947 #> [304,] 0.5817847 0.5997890 0.6207748 #> [305,] 0.5720249 0.5837491 0.5898708 #> [306,] 0.5965174 0.6005517 0.6137788 #> [307,] 1.0034240 1.0314873 1.0320467 #> [308,] 0.8265704 0.8445410 0.8636050 #> [309,] 0.8943133 0.9098371 0.9781398 #> [310,] 0.6571992 0.6586063 0.6722925 #> [311,] 0.8053866 0.8088441 0.8141323 #> [312,] 0.9904840 1.0488026 1.0567316 #> [313,] 0.6881557 0.7582715 0.7591426 #> [314,] 0.5568581 0.5827694 0.6204041 #> [315,] 0.6862337 0.7001198 0.7561484 #> [316,] 0.8492696 0.8711605 0.8899702 #> [317,] 0.7655117 0.7722215 0.7753759 #> [318,] 0.6363898 0.6404232 0.6509156 #> [319,] 0.6670985 0.6672640 0.6962625 #> [320,] 0.8233239 0.8242852 0.8484660 #> [321,] 0.6495988 0.6508068 0.6542615 #> [322,] 1.0180832 1.0250659 1.0266634 #> [323,] 0.6837625 0.6928143 0.6936870 #> [324,] 0.7342534 0.7540028 0.7679974 #> [325,] 0.6308780 0.6407043 0.6508068 #> [326,] 0.9350917 0.9420448 0.9533092 #> [327,] 0.6695906 0.6926156 0.7533284 #> [328,] 1.0668279 1.0853428 1.2035339 #> [329,] 0.8267822 0.8279529 0.8500662 #> [330,] 0.6328065 0.6525837 0.6550373 #> [331,] 0.6137788 0.6349687 0.6698618 #> [332,] 0.7303193 0.7322579 0.7341715 #> [333,] 0.8000697 0.8059705 0.8269345 #> [334,] 0.5379070 0.5381997 0.5402068 #> [335,] 0.4572627 0.4642517 0.4692709 #> [336,] 1.1938257 1.2105804 1.2646468 #> [337,] 0.8833182 0.8913455 0.9097912 #> [338,] 0.8483925 0.8485289 0.8739361 #> [339,] 0.9268745 0.9301437 0.9494924 #> [340,] 0.5926610 0.6433268 0.6688616 #> [341,] 0.6328652 0.6697167 0.6775438 #> [342,] 0.6189582 0.6336426 0.6530868 #> [343,] 0.9829755 0.9838940 1.0288515 #> [344,] 0.5624027 0.5672823 0.5751450 #> [345,] 0.5785100 0.5880912 0.6032128 #> [346,] 0.7105605 0.7254459 0.7285652 #> [347,] 0.6198076 0.6297758 0.6598254 #> [348,] 0.8729183 0.8793803 0.9190275 #> [349,] 0.5209098 0.5302664 0.5624052 #> [350,] 0.8863619 0.9420280 0.9459780 #> [351,] 0.8880786 0.9228884 0.9664693 #> [352,] 1.3276280 1.3284202 1.3549191 #> [353,] 1.0438588 1.0442103 1.0701039 #> [354,] 0.7461058 0.7690134 0.7864186 #> [355,] 0.7151664 0.7450731 0.7689244 #> [356,] 0.5878504 0.5908810 0.6090971 #> [357,] 0.6847809 0.8065705 0.8067604 #> [358,] 1.0136404 1.0154122 1.0282789 #> [359,] 1.0531656 1.0646987 1.1138957 #> [360,] 1.0397803 1.0461697 1.0537380 #> [361,] 0.4926647 0.5154537 0.5237127 #> [362,] 0.9955914 1.0392933 1.0474345 #> [363,] 0.8972945 0.8996744 0.9027318 #> [364,] 0.9159105 0.9435594 0.9511380 #> [365,] 0.7430718 0.7604702 0.7980560 #> [366,] 0.5674692 0.5727376 0.6627259 #> [367,] 1.0199524 1.0204548 1.0471335 #> [368,] 0.5353742 0.5394586 0.5463735 #> [369,] 0.8494756 0.8770611 0.8840677 #> [370,] 0.9045793 0.9141658 0.9328294 #> [371,] 0.8484185 0.8639459 0.9237728 #> [372,] 0.7616295 0.7788900 0.8071625 #> [373,] 0.5097656 0.5103176 0.5450248 #> [374,] 0.6674362 0.6946062 0.8056381 #> [375,] 0.7198688 0.7292259 0.7533332 #> [376,] 0.7404467 0.7665664 0.7672032 #> [377,] 0.8388834 0.8573182 0.8683734 #> [378,] 0.7261603 0.7888915 0.8065603 #> [379,] 1.1768365 1.2174035 1.2453905 #> [380,] 1.2598092 1.3246854 1.3305341 #> [381,] 0.8631331 0.8777154 0.8879424 #> [382,] 0.8118543 0.8243492 0.8374920 #> [383,] 0.7757223 0.7796205 0.8203172 #> [384,] 0.8415829 0.8425422 0.8427918 #> [385,] 0.6887134 0.6924916 0.6935413 #> [386,] 0.6301571 0.6443765 0.6467538 #> [387,] 0.6254812 0.6277603 0.6707994 #> [388,] 0.9504140 0.9787950 0.9788155 #> [389,] 0.7573177 0.7574468 0.7613383 #> [390,] 0.6926296 0.7004906 0.7667403 #> [391,] 1.1676943 1.2179838 1.2325915 #> [392,] 0.8188247 0.8241835 0.8339341 #> [393,] 0.8329418 0.8386294 0.8460252 #> [394,] 0.7096210 0.7292739 0.7584514 #> [395,] 1.4799383 1.5064008 1.5235157 #> [396,] 1.1022143 1.1110085 1.1184343 #> [397,] 0.7088534 0.7127603 0.7214720 #> [398,] 0.7625693 0.7998142 0.8176899 #> [399,] 0.7830381 0.8207924 0.8252420 #> [400,] 1.2736809 1.3238208 1.3259648 #> [401,] 0.7904562 0.8157185 0.8233814 #> [402,] 0.9497575 0.9665054 0.9835361 #> [403,] 0.9377258 1.0256769 1.0350779 #> [404,] 0.6182421 0.6186630 0.6250949 #> [405,] 1.3379349 1.3868488 1.4096641 #> [406,] 0.6810388 0.7326497 0.7436179 #> [407,] 0.8000203 0.8126316 0.8261912 #> [408,] 0.6668383 0.6772600 0.7011845 #> [409,] 0.5904144 0.6055436 0.6144892 #> [410,] 0.6182368 0.6483972 0.6787011 #> [411,] 0.7589513 0.7703605 0.7758075 #> [412,] 0.7763655 0.7924663 0.8046182 #> [413,] 0.7299407 0.7307987 0.7472007 #> [414,] 1.2705682 1.3039116 1.3173147 #> [415,] 1.3978243 1.4099353 1.4368023 #> [416,] 0.7055470 0.7556795 0.7571401 #> [417,] 0.8557944 0.9016194 0.9258674 #> [418,] 0.6795702 0.6936138 0.7203690 #> [419,] 0.5507127 0.5616786 0.5664522 #> [420,] 0.5946564 0.6271226 0.6589521 #> [421,] 1.2660488 1.2935256 1.3210175 #> [422,] 1.0777358 1.1552969 1.2204435 #> [423,] 0.5313030 0.5338943 0.5407712 #> [424,] 0.9779007 1.0178157 1.0591399 #> [425,] 0.8950220 0.9454731 0.9560139 #> [426,] 1.0097005 1.0485973 1.1099787 #> [427,] 0.6957399 0.7095744 0.7805514 #> [428,] 1.3067943 1.3501308 1.3673162 #> [429,] 0.8278941 0.8936291 0.9025688 #> [430,] 0.7547116 0.7996885 0.8211158 #> [431,] 0.7154578 0.7296091 0.7660701 #> [432,] 0.7421226 0.7538467 0.8155186 #> [433,] 0.5806314 0.5915916 0.6548079 #> [434,] 0.6349687 0.6365637 0.6500784 #> [435,] 0.8473934 0.8574420 0.9175749 #> [436,] 0.6155368 0.6198042 0.6409345 #> [437,] 0.4699607 0.5248791 0.5486645 #> [438,] 1.0221971 1.0283199 1.0576939 #> [439,] 1.4077905 1.4078477 1.4427959 #> [440,] 0.4409665 0.4626767 0.4938862 #> [441,] 0.9048603 0.9087658 0.9091246 #> [442,] 1.1669433 1.1732456 1.1790484 #> [443,] 0.5563468 0.5780151 0.5950256 #> [444,] 0.9618237 0.9843987 1.0340714 #> [445,] 1.6385503 1.6417239 1.6554494 #> [446,] 0.7878675 0.7962567 0.7979834 #> [447,] 2.0097095 2.0525425 2.0587405 #> [448,] 1.1924949 1.2178915 1.2492241 #> [449,] 0.5403095 0.5631900 0.5737795 #> [450,] 1.6583900 1.7281786 1.7370262 #> [451,] 0.6314860 0.6396309 0.6518001 #> [452,] 1.3654383 1.3918685 1.4056749 #> [453,] 0.6978221 0.7011060 0.7079638 #> [454,] 1.0720183 1.1228689 1.1266475 #> [455,] 0.5374261 0.5787567 0.5882427 #> [456,] 0.5917679 0.6269757 0.6475247 #> [457,] 1.0103346 1.0583478 1.0754355 #> [458,] 0.7366074 0.7696810 0.8113648 #> [459,] 0.5130750 0.5307737 0.5408275 #> [460,] 0.5350491 0.5520000 0.5657748 #> [461,] 0.8358193 0.8409993 0.8453590 #> [462,] 1.3948239 1.4562316 1.4921572 #> [463,] 0.5976927 0.6159258 0.6270187 #> [464,] 0.7016635 0.7099575 0.7111202 #> [465,] 0.9276422 0.9977400 1.0455170 #> [466,] 1.1230235 1.1773242 1.1921464 #> [467,] 0.5719470 0.6084708 0.6584985 #> [468,] 0.5323596 0.5371743 0.6082670 #> [469,] 0.6305895 0.6475206 0.6699390 #> [470,] 0.3822591 0.4214002 0.4794289 #> [471,] 0.6965180 0.7162136 0.7932884 #> [472,] 1.2037354 1.2412579 1.2781834 #> [473,] 1.1528347 1.1575442 1.1691273 #> [474,] 0.6404232 0.6628817 0.6633204 #> [475,] 1.3680536 1.3806925 1.4502315 #> [476,] 0.7013151 0.7044657 0.7068683 #> [477,] 0.7424823 0.8617070 0.8710939 #> [478,] 0.6833299 0.7013946 0.7057739 #> [479,] 0.5670320 0.5874698 0.6266317 #> [480,] 0.5411971 0.5717583 0.5764336 #> [481,] 0.8700075 0.8761098 0.8879985 #> [482,] 0.6665749 0.6856635 0.6897393 #> [483,] 0.7095349 0.7123296 0.7411007 #> [484,] 1.2568529 1.3388399 1.3498595 #> [485,] 0.6030901 0.6106986 0.6362282 #> [486,] 1.3270357 1.3446570 1.3753723 #> [487,] 0.6944722 0.6986781 0.7058085 #> [488,] 0.7442717 0.7716297 0.8258617 #> [489,] 0.8303598 0.8338579 0.8443118 #> [490,] 1.5111890 1.5445189 1.5564387 #> [491,] 1.4287770 1.4338115 1.4534610 #> [492,] 0.6028583 0.6085813 0.6364568 #> [493,] 1.2444039 1.2724466 1.2782419 #> [494,] 0.9186961 0.9288731 0.9339130 #> [495,] 0.5148448 0.5639078 0.6331913 #> [496,] 0.9704159 0.9718047 0.9869484 #> [497,] 0.5541192 0.5699598 0.5739698 #> [498,] 1.2294258 1.2405399 1.2632070 #> [499,] 0.5804832 0.6020497 0.6649769 #> [500,] 0.6395914 0.6407729 0.6647554 #> [501,] 1.2427244 1.2891885 1.3755788 #> [502,] 0.5479711 0.5639078 0.5675280 #> [503,] 1.5388021 1.6372228 1.6697375 #> [504,] 0.7529697 0.7724098 0.7991795 #> [505,] 1.5642651 1.5753041 1.5896113 #> [506,] 0.9122109 0.9163889 0.9183538 #> [507,] 0.9718225 0.9727489 0.9946086 #> [508,] 0.7348817 0.7541810 0.7824822 #> [509,] 0.8368484 0.8441140 0.8712227 #> [510,] 0.7831023 0.8224706 0.8269345 #> [511,] 1.2972052 1.2995585 1.3610271 #> [512,] 0.7433959 0.7674406 0.8095525 #> [513,] 0.4820203 0.4914494 0.5091375 #> [514,] 0.5673960 0.6052295 0.6122315 #> [515,] 0.4559979 0.4896020 0.4909026 #> [516,] 0.4765648 0.5061745 0.5290541 #> [517,] 0.5166929 0.5225545 0.5312450 #> [518,] 0.7012764 0.7025821 0.7181508 #> [519,] 0.6690963 0.6722143 0.7096344 #> [520,] 0.5259640 0.5403095 0.5471192 #> [521,] 0.7425617 0.7567662 0.7822832 #> [522,] 0.5248791 0.5320507 0.5325069 #> [523,] 0.9687019 0.9696667 1.0605354 #> [524,] 0.5592717 0.5775316 0.6013149 #> [525,] 0.7134244 0.7180454 0.7746114 #> [526,] 0.6894268 0.6917122 0.7388943 #> [527,] 1.5320355 1.5359497 1.5481314 #> [528,] 0.6239519 0.6647352 0.6698996 #> [529,] 0.8253294 0.8426068 0.8512405 #> [530,] 0.9175670 0.9586984 0.9696410 #> [531,] 0.5643394 0.6282007 0.6373988 #> [532,] 0.7024892 0.7180089 0.7402136 #> [533,] 0.9412042 0.9443775 0.9501321 #> [534,] 0.6331913 0.6349211 0.6712309 #> [535,] 0.4731234 0.5026104 0.5306004 #> [536,] 0.5622579 0.5691711 0.5894259 #> [537,] 1.3938269 1.4201260 1.4538800 #> [538,] 2.1562114 2.1712210 2.1791550 #> [539,] 0.5998649 0.6303169 0.6841752 #> [540,] 1.2021338 1.2431104 1.2434704 #> [541,] 0.8252084 0.8266875 0.8453320 #> [542,] 1.1119488 1.1136882 1.1416424 #> [543,] 0.7668405 0.7668689 0.7780350 #> [544,] 1.2257260 1.2418458 1.3271819 #> [545,] 0.9903867 1.0414513 1.0425481 #> [546,] 0.7894660 0.7979414 0.8440595 #> [547,] 1.4748032 1.5288034 1.6200355 #> [548,] 0.8342098 0.8576379 0.8589755 #> [549,] 0.8357769 0.8472275 0.8531945 #> [550,] 0.8899300 0.9027397 0.9051167 #> [551,] 0.7655653 0.7879630 0.8017048 #> [552,] 0.6278000 0.6282079 0.6320861 #> [553,] 0.7442717 0.7569825 0.7942681 #> [554,] 0.6768602 0.6894267 0.7283503 #> [555,] 0.4914494 0.5066574 0.5105818 #> [556,] 0.6791070 0.6866979 0.7237962 #> [557,] 0.7090876 0.7166298 0.7232070 #> [558,] 0.6349871 0.6720933 0.6736528 #> [559,] 0.8838416 0.8964731 0.9543999 #> [560,] 0.7819956 0.8213843 0.8433024 #> [561,] 0.8987376 0.9224726 0.9437928 #> [562,] 0.5383853 0.5501868 0.5729807 #> [563,] 1.6153224 1.6803922 1.7070157 #> [564,] 0.9931664 1.0353998 1.0435389 #> [565,] 1.1575457 1.2387429 1.2800923 #> [566,] 1.7779091 1.7815292 1.8143845 #> [567,] 0.8014217 0.8219595 0.8323921 #> [568,] 0.6558496 0.6684397 0.6691929 #> [569,] 0.5976967 0.6356963 0.6428138 #> [570,] 0.8780852 0.8807945 0.8864215 #> [571,] 1.5694029 1.6226836 1.6559844 #> [572,] 1.8867064 1.8946499 1.8951040 #> [573,] 0.7954502 0.8146452 0.8159977 #> [574,] 0.5106542 0.5251450 0.5402068 #> [575,] 0.9253467 0.9494333 0.9523070 #> [576,] 0.5224575 0.5246025 0.5282306 #> [577,] 0.8574534 0.8758765 0.9156594 #> [578,] 0.6972932 0.7002176 0.7072410 #> [579,] 0.7263034 0.7667228 0.7703424 #> [580,] 1.1658215 1.2123779 1.2285181 #> [581,] 0.9509007 0.9569566 0.9901120 #> [582,] 0.6984653 0.7123291 0.7231011 #> [583,] 0.6165135 0.6549497 0.6617059 #> [584,] 0.6559082 0.6567545 0.6708039 #> [585,] 1.2372009 1.2431104 1.3414206 #> [586,] 0.8837551 0.8870166 0.8986792 #> [587,] 1.1300497 1.1378241 1.1534381 #> [588,] 0.9341930 0.9854642 0.9955070 #> [589,] 0.7817602 0.7950334 0.8359405 #> [590,] 0.8507018 0.8532815 0.8706052 #> [591,] 0.6070573 0.6123936 0.6133793 #> [592,] 0.6506767 0.6857493 0.6886219 #> [593,] 1.1799360 1.2041982 1.2203544 #> [594,] 0.9431895 1.0075567 1.0447846 #> [595,] 1.2599315 1.2657402 1.2736123 #> [596,] 0.7112562 0.7154578 0.7300252 #> [597,] 0.9327264 0.9697106 0.9757561 #> [598,] 0.5226653 0.5347778 0.6004037 #> [599,] 0.7221886 0.7933000 0.7960978 #> [600,] 0.5261021 0.5263663 0.5302476 #> [601,] 0.7163910 0.7248237 0.7679930 #> [602,] 0.7257560 0.7548418 0.7964255 #> [603,] 0.8378586 0.8414736 0.8446556 #> [604,] 0.9077201 0.9923359 1.0106972 #> [605,] 0.6994948 0.7045753 0.7119669 #> [606,] 0.5645280 0.5647955 0.5910950 #> [607,] 1.4204439 1.4740305 1.4971383 #> [608,] 1.5498688 1.6069093 1.7384498 #> [609,] 0.9435022 0.9635824 0.9656355 #> [610,] 0.5616786 0.5708692 0.6117274 #> [611,] 1.2172076 1.2886264 1.3355435 #> [612,] 0.5113881 0.5568581 0.5676932 #> [613,] 1.1811073 1.2101745 1.2565012 #> [614,] 0.8288751 0.8812426 0.9096677 #> [615,] 0.5957393 0.6231754 0.6320876 #> [616,] 0.6050565 0.6071182 0.6480847 #> [617,] 0.7711614 0.7980622 0.8012313 #> [618,] 0.5614452 0.6487696 0.6725718 #> [619,] 0.5817847 0.6002829 0.6276241 #> [620,] 0.8364844 0.8494256 0.8512405 #> [621,] 0.7005655 0.7149314 0.7576389 #> [622,] 0.9959242 1.0071526 1.0788781 #> [623,] 1.0235715 1.0478058 1.0535272 #> [624,] 0.7768493 0.7793272 0.8205517 #> [625,] 1.1115848 1.1248822 1.1475012 #> [626,] 0.5346923 0.5503597 0.5520000 #> [627,] 0.6558496 0.6659052 0.7765029 #> [628,] 1.0708415 1.1446938 1.2266482 #> [629,] 0.6829279 0.7051407 0.7155856 #> [630,] 0.7140941 0.7149278 0.7381929 #> [631,] 0.9039229 0.9152130 0.9270557 #> [632,] 0.5377479 0.5674692 0.6032168 #> [633,] 0.6707994 0.6877516 0.7379386 #> [634,] 0.6339956 0.6650379 0.6674789 #> [635,] 0.6655739 0.6722275 0.7188234 #> [636,] 1.2039409 1.2538158 1.3004335 #> [637,] 0.9567400 0.9805564 1.0181206 #> [638,] 1.7680531 1.7727131 1.8282199 #> [639,] 0.7118041 0.7131544 0.7225123 #> [640,] 0.7567525 0.7756439 0.7817602 #> [641,] 0.5957012 0.6084708 0.6490912 #> [642,] 0.6071488 0.6660500 0.6773838 #> [643,] 0.5670320 0.5772271 0.6008826 #> [644,] 0.6868794 0.6977937 0.7150017 #> [645,] 0.7461080 0.7604850 0.7808844 #> [646,] 0.6266361 0.6314365 0.6341940 #> [647,] 0.7385297 0.7404467 0.7519838 #> [648,] 0.8369020 0.8398624 0.8476542 #> [649,] 0.4740971 0.4755505 0.5060454 #> [650,] 1.7220624 1.7519612 1.7616198 #> [651,] 0.4828149 0.5392326 0.5533832 #> [652,] 0.6740843 0.6871881 0.7243692 #> [653,] 0.5535769 0.5727801 0.5891104 #> [654,] 1.0375054 1.0590858 1.0765221 #> [655,] 0.8332197 0.8422317 0.8527906 #> [656,] 1.0206574 1.0517808 1.0649530 #> [657,] 0.7398995 0.8453590 0.8595941 #> [658,] 0.8261406 0.8307582 0.8373647 #> [659,] 0.7806629 0.7927973 0.7979414 #> [660,] 0.5940258 0.6013404 0.6245413 #> [661,] 1.0329767 1.0371129 1.0452618 #> [662,] 0.7318016 0.7900017 0.7961905 #> [663,] 0.4782290 0.4993475 0.5221172 #> [664,] 0.6718837 0.6856635 0.7067344 #> [665,] 0.6518001 0.6655306 0.6815493 #> [666,] 1.1155896 1.1403134 1.1439860 #> [667,] 0.5810267 0.5871655 0.6011670 #> [668,] 0.7238108 0.7438533 0.7550931 #> [669,] 1.0504722 1.0550247 1.0723984 #> [670,] 0.9731496 0.9735136 0.9877512 #> [671,] 1.0529481 1.0845242 1.1297322 #> [672,] 0.8843676 0.8887155 0.9268679 #> [673,] 0.6216648 0.6479959 0.6746208 #> [674,] 0.5251450 0.5284033 0.5484489 #> [675,] 0.7403855 0.7486979 0.7514374 #> [676,] 0.6969275 0.7127841 0.7689411 #> [677,] 0.7326497 0.7373563 0.7384600 #> [678,] 0.6684066 0.6699390 0.6867790 #> [679,] 0.7660027 0.7884803 0.7889959 #> [680,] 0.6324472 0.6806701 0.6925097 #> [681,] 0.6062568 0.6252554 0.6853151 #> [682,] 0.7331707 0.7955418 0.8179501 #> [683,] 1.0878071 1.1123559 1.1526806 #> [684,] 0.5940258 0.6229879 0.6271196 #> [685,] 0.8574649 0.8976148 0.9387797 #> [686,] 0.7263605 0.7401842 0.7438612 #> [687,] 1.3116721 1.3278361 1.3876646 #> [688,] 0.6817233 0.6931311 0.7211141 #> [689,] 1.0200061 1.0265487 1.0418415 #> [690,] 0.7148179 0.7231723 0.7243780 #> [691,] 0.6315268 0.6431302 0.6574496 #> [692,] 1.0381903 1.0520745 1.0933699 #> [693,] 0.9025787 0.9038915 0.9198327 #> [694,] 0.6214294 0.6753017 0.7139887 #> [695,] 1.1763199 1.1795062 1.2006747 #> [696,] 0.6034387 0.6123165 0.6987542 #> [697,] 1.4813297 1.6121141 1.7260634 #> [698,] 0.6417307 0.6438171 0.6462949 #> [699,] 0.8838049 0.9152433 0.9183538 #> [700,] 0.7079312 0.7122314 0.7785440 #> [701,] 1.2274216 1.3371795 1.3521911 #> [702,] 0.6383516 0.6398683 0.6559131 #> [703,] 0.8225748 0.8324250 0.8455299 #> [704,] 0.5142809 0.5876460 0.6074908 #> [705,] 0.5373668 0.5540923 0.5664522 #> [706,] 0.5765510 0.5853877 0.6001879 #> [707,] 0.6937080 0.7006146 0.7421462 #> [708,] 0.6238756 0.6882597 0.6976726 #> [709,] 0.8374410 0.9248079 0.9406653 #> [710,] 0.7652198 0.8181279 0.8206264 #> [711,] 0.5948992 0.5976967 0.6158515 #> [712,] 0.9381280 0.9597892 0.9664203 #> [713,] 1.0555835 1.0615033 1.0912192 #> [714,] 0.8848121 0.9112138 0.9500410 #> [715,] 1.0850121 1.0871142 1.0940913 #> [716,] 0.7843407 0.8227103 0.8617159 #> [717,] 0.4696288 0.4699835 0.4738325 #> [718,] 0.5210939 0.5921035 0.6556833 #> [719,] 1.0706496 1.0765221 1.1663353 #> [720,] 0.8406039 0.8808923 0.8958698 #> [721,] 0.8506859 0.8674290 0.8749529 #> [722,] 1.1576949 1.1636063 1.1886189 #> [723,] 0.7265791 0.7418144 0.7951605 #> [724,] 0.7665664 0.8252081 0.8266375 #> [725,] 0.6844479 0.6874830 0.7128726 #> [726,] 0.9559077 1.1242090 1.1646467 #> [727,] 0.7351015 0.7832581 0.8303848 #> [728,] 0.9923359 1.0102435 1.0381903 #> [729,] 1.4331600 1.4771536 1.4932030 #> [730,] 0.6936138 0.7290462 0.7386993 #> [731,] 0.5865848 0.5928012 0.5935847 #> [732,] 0.8202470 0.8342600 0.8562082 #> [733,] 0.6299515 0.7325734 0.7354251 #> [734,] 2.0558621 2.0864969 2.1005553 #> [735,] 0.6088727 0.6541174 0.6590748 #> [736,] 1.2058457 1.2621229 1.2702962 #> [737,] 1.1890757 1.2943075 1.2991210 #> [738,] 0.5636207 0.5829550 0.6010974 #> [739,] 1.2999900 1.3123376 1.3535130 #> [740,] 0.5961094 0.6115983 0.6147504 #> [741,] 1.1292652 1.1476807 1.1993746 #> [742,] 0.6389123 0.6490562 0.6605395 #> [743,] 0.5694909 0.6220328 0.6521282 #> [744,] 0.6801958 0.6913184 0.7516634 #> [745,] 0.7373071 0.7444701 0.7774382 #> [746,] 0.5555416 0.5618440 0.5831496 #> [747,] 1.3390375 1.3843793 1.4050764 #> [748,] 0.7693490 0.7886514 0.8007839 #> [749,] 1.2160325 1.2310418 1.2612687 #> [750,] 0.7772398 0.7835136 0.7853064 #> [751,] 0.6469686 0.6871881 0.6893706 #> [752,] 1.0467176 1.0487921 1.0535688 #> [753,] 0.7115954 0.7243692 0.7353212 #> [754,] 1.0067329 1.1439401 1.1663841 #> [755,] 0.7445498 0.8205199 0.8258803 #> [756,] 0.5692131 0.5878257 0.6048488 #> [757,] 1.5059130 1.5141408 1.5370495 #> [758,] 0.7022300 0.7040191 0.7266179 #> [759,] 2.0418621 2.1325963 2.2567825 #> [760,] 0.5918048 0.5966892 0.6095008 #> [761,] 0.5413950 0.5472648 0.5751142 #> [762,] 0.5384481 0.5705695 0.5868046 #> [763,] 0.8146452 0.8153426 0.8235265 #> [764,] 0.6850527 0.6924807 0.7120703 #> [765,] 1.1885719 1.2018503 1.2018910 #> [766,] 1.3260409 1.3341675 1.3347847 #> [767,] 0.6248858 0.6631087 0.6670681 #> [768,] 0.9237803 0.9821653 1.0128317 #> [769,] 0.8028055 0.8115816 0.8469829 #> [770,] 1.3735787 1.4586779 1.4587643 #> [771,] 1.0487313 1.1115367 1.1278697 #> [772,] 0.6377302 0.7069487 0.7291969 #> [773,] 0.9324725 0.9695837 1.0005072 #> [774,] 0.7340137 0.7686250 0.7910865 #> [775,] 0.7437403 0.7456979 0.7878617 #> [776,] 0.6409072 0.6948549 0.7067249 #> [777,] 0.7257133 0.7526207 0.8145793 #> [778,] 0.5142809 0.5245879 0.5252494 #> [779,] 0.6242367 0.6256287 0.6328125 #> [780,] 0.6837660 0.6874830 0.7228639 #> [781,] 0.8125125 0.8322545 0.8503036 #> [782,] 0.5561102 0.5578403 0.5763036 #> [783,] 0.5896110 0.5958654 0.6104892 #> [784,] 0.7866859 0.7935557 0.7964822 #> [785,] 0.6916844 0.7073426 0.7114112 #> [786,] 0.6074524 0.6283285 0.6525452 #> [787,] 0.8485117 0.8661158 0.8681542 #> [788,] 0.7669146 0.7704553 0.7730321 #> [789,] 0.7936552 0.8056731 0.8282903 #> [790,] 0.7768582 0.7846403 0.7865361 #> [791,] 0.9218025 0.9309645 0.9834923 #> [792,] 0.7310340 0.7395698 0.7413927 #> [793,] 0.5268015 0.5320507 0.5456090 #> [794,] 0.6962281 0.7052326 0.7597433 #> [795,] 0.7230854 0.7567452 0.7898359 #> [796,] 1.4279747 1.4767257 1.4891995 #> [797,] 0.5369971 0.6397847 0.6429611 #> [798,] 0.7756439 0.7789369 0.7825231 #> [799,] 1.3097582 1.4824465 1.5489940 #> [800,] 0.7162136 0.7266179 0.7296064 #> [801,] 0.4948228 0.5717583 0.5764634 #> [802,] 0.9185322 0.9380741 0.9420223 #> [803,] 0.6465218 0.6621150 0.6682168 #> [804,] 0.6172237 0.6478300 0.6539777 #> [805,] 1.8593783 1.8708800 1.9053267 #> [806,] 0.7604921 0.7642574 0.7730720 #> [807,] 0.7662884 0.7721703 0.7889959 #> [808,] 0.5115099 0.5396213 0.5483203 #> [809,] 1.0282668 1.0382065 1.0560935 #> [810,] 0.5475677 0.5502896 0.5973181 #> [811,] 1.1429708 1.1613028 1.2231753 #> [812,] 0.6638050 0.6828485 0.6834797 #> [813,] 0.9778346 1.0510234 1.1124590 #> [814,] 1.2542723 1.3124297 1.3212982 #> [815,] 0.7370478 0.7417071 0.7623365 #> [816,] 0.8667483 0.8711702 0.8878093 #> [817,] 0.6253059 0.6553342 0.6873358 #> [818,] 0.8282982 0.8362473 0.8699008 #> [819,] 0.9744774 0.9890063 1.0132698 #> [820,] 0.6783649 0.7362620 0.7444487 #> [821,] 0.6621150 0.6987482 0.7166870 #> [822,] 0.9290550 1.0580104 1.0725154 #> [823,] 1.0468094 1.0712767 1.0978278 #> [824,] 0.6427528 0.6436058 0.6524632 #> [825,] 0.6817233 0.7111853 0.7402580 #> [826,] 0.5284033 0.5541392 0.5699656 #> [827,] 1.3117818 1.3270609 1.3359374 #> [828,] 0.6628685 0.6713565 0.6810039 #> [829,] 0.8527906 0.8812073 0.9528009 #> [830,] 0.6828485 0.6850597 0.6959316 #> [831,] 0.8301323 0.8712192 0.9245956 #> [832,] 0.6132856 0.6421572 0.6431027 #> [833,] 0.7869176 0.8055432 0.8226591 #> [834,] 1.2076362 1.2278662 1.2417090 #> [835,] 0.7902266 0.8018794 0.8600771 #> [836,] 0.7864186 0.7935557 0.8255778 #> [837,] 0.7262376 0.7844301 0.7885148 #> [838,] 1.4799255 1.5037535 1.5076612 #> [839,] 0.5764634 0.6651793 0.6815833 #> [840,] 0.6878447 0.6985192 0.7296800 #> [841,] 0.4934944 0.5391688 0.5655502 #> [842,] 1.0560959 1.0712848 1.0853428 #> [843,] 0.7249468 0.7366193 0.7376150 #> [844,] 0.7874349 0.7950016 0.8099821 #> [845,] 0.7664371 0.7807477 0.8116326 #> [846,] 0.6358013 0.6442066 0.6576187 #> [847,] 0.4486907 0.4983990 0.4999563 #> [848,] 0.9207612 0.9276422 0.9540991 #> [849,] 0.4063097 0.4590751 0.4740971 #> [850,] 0.6394717 0.6588610 0.6600258 #> [851,] 0.8149142 0.8428174 0.8796094 #> [852,] 0.6049990 0.6198042 0.6272176 #> [853,] 0.9851428 1.0335664 1.0680627 #> [854,] 1.0029237 1.0044478 1.0091058 #> [855,] 0.9468798 0.9500668 0.9856499 #> [856,] 1.1343272 1.1434065 1.1500361 #> [857,] 0.9434459 0.9806701 0.9911303 #> [858,] 0.6657246 0.6823429 0.6984509 #> [859,] 0.9392122 1.0334538 1.0380556 #> [860,] 0.6851883 0.6919852 0.7095744 #> [861,] 0.7763655 0.7913457 0.8270811 #> [862,] 0.6989579 0.7134870 0.7771113 #> [863,] 0.7291426 0.7341086 0.7359247 #> [864,] 1.2080432 1.2282035 1.3640246 #> [865,] 0.7292787 0.7359247 0.8104232 #> [866,] 0.8350270 0.8828692 0.8838416 #> [867,] 1.6801516 1.7936242 1.8107486 #> [868,] 0.7664883 0.7692705 0.7853750 #> [869,] 0.6256287 0.6405012 0.6438171 #> [870,] 0.9159168 0.9412794 0.9424081 #> [871,] 0.4008547 0.4421991 0.4654775 #> [872,] 1.5398663 1.5488528 1.5865198 #> [873,] 0.5946995 0.5961094 0.5985655 #> [874,] 1.2174384 1.2866041 1.3608177 #> [875,] 0.6989579 0.7028270 0.7502891 #> [876,] 1.1007515 1.1159988 1.1618348 #> [877,] 0.9267543 0.9303417 0.9985390 #> [878,] 0.7707595 0.7796337 0.8207193 #> [879,] 0.7728698 0.8062140 0.8064596 #> [880,] 0.9699226 1.0045098 1.0449545 #> [881,] 0.8332732 0.8572955 0.8811205 #> [882,] 0.7810091 0.7894367 0.8153426 #> [883,] 0.7170823 0.7352067 0.7384600 #> [884,] 0.5624806 0.5863302 0.6213767 #> [885,] 0.9655477 0.9727644 1.0023837 #> [886,] 0.6547229 0.6760726 0.6837625 #> [887,] 0.6487290 0.6680559 0.7312532 #> [888,] 0.8688664 0.9291881 0.9494333 #> [889,] 1.3239755 1.3330514 1.3419673 #> [890,] 0.4841708 0.5299665 0.5941560 #> [891,] 1.1557669 1.2092693 1.2211860 #> [892,] 0.5427501 0.5509455 0.5757741 #> [893,] 1.0055165 1.1019036 1.1303557 #> [894,] 1.5957744 1.6656746 1.6704343 #> [895,] 0.5313030 0.5411869 0.5432273 #> [896,] 1.3521568 1.3854506 1.3989331 #> [897,] 1.2729158 1.2869336 1.3173147 #> [898,] 1.3501308 1.4088867 1.4431790 #> [899,] 0.5130520 0.5265425 0.5344388 #> [900,] 0.6500784 0.6550834 0.6594093 #> [901,] 0.9341268 0.9882515 1.0482291 #> [902,] 0.6887986 0.6949705 0.7101375 #> [903,] 1.8382930 1.8593577 1.8821169 #> [904,] 0.8450184 0.8584939 0.8900323 #> [905,] 1.3352933 1.3647099 1.3707155 #> [906,] 0.6124874 0.6452023 0.6502552 #> [907,] 0.6015697 0.6057881 0.6303679 #> [908,] 0.7397444 0.7589513 0.7761755 #> [909,] 1.1331706 1.1684123 1.2233917 #> [910,] 0.7708239 0.8088441 0.8090672 #> [911,] 0.7564921 0.8313556 0.8853633 #> [912,] 0.5590943 0.5693136 0.6010974 #> [913,] 0.5705695 0.5990171 0.5997920 #> [914,] 0.6483972 0.6809148 0.6938318 #> [915,] 1.1443646 1.1713342 1.1896728 #> [916,] 0.7747097 0.7772398 0.8024150 #> [917,] 0.7302801 0.7417071 0.7470587 #> [918,] 0.6165999 0.6218071 0.6254043 #> [919,] 0.6983487 0.8245811 0.8574372 #> [920,] 1.0107482 1.0956074 1.1528632 #> [921,] 0.5968568 0.6036069 0.6052295 #> [922,] 0.7816347 0.8317877 0.8353579 #> [923,] 0.7042752 0.7556773 0.7612428 #> [924,] 0.8016984 0.8064596 0.8279561 #> [925,] 0.6581170 0.6625708 0.6638874 #> [926,] 0.7545084 0.7692472 0.8111023 #> [927,] 0.8234208 0.8325586 0.8762490 #> [928,] 0.8036009 0.8617396 0.9095927 #> [929,] 0.9777802 0.9867707 0.9916101 #> [930,] 0.6246838 0.6478300 0.6710057 #> [931,] 0.7047821 0.7074970 0.7094189 #> [932,] 0.5815310 0.5894735 0.5981939 #> [933,] 0.8676934 0.8809400 0.9466005 #> [934,] 1.0968149 1.1379194 1.1401699 #> [935,] 0.5070188 0.5424867 0.5633698 #> [936,] 0.9154519 0.9903226 0.9985880 #> [937,] 1.0154784 1.0198059 1.0558893 #> [938,] 0.7369948 0.7536130 0.8294904 #> [939,] 1.4303140 1.4317122 1.4805684 #> [940,] 0.8062438 0.8196058 0.8569086 #> [941,] 1.1892653 1.2261792 1.2324396 #> [942,] 0.5912759 0.5969571 0.6082670 #> [943,] 0.5314154 0.5326785 0.5801175 #> [944,] 0.8829630 0.8910723 0.8967822 #> [945,] 1.3858221 1.4302707 1.4316539 #> [946,] 0.6547229 0.6886322 0.6928918 #> [947,] 0.6877890 0.7001301 0.7057125 #> [948,] 1.8357152 1.9045001 1.9065517 #> [949,] 1.3379349 1.4021315 1.4079049 #> [950,] 1.0840138 1.1094766 1.1308136 #> [951,] 0.7117756 0.7177677 0.7423945 #> [952,] 0.7438321 0.7534034 0.7842284 #> [953,] 0.7502960 0.7789664 0.7982690 #> [954,] 0.6118587 0.6215960 0.6384658 #> [955,] 1.2871531 1.3225393 1.3418591 #> [956,] 0.7043195 0.7170823 0.7336379 #> [957,] 1.0358191 1.0774448 1.1165770 #> [958,] 0.6198076 0.6213680 0.6650598 #> [959,] 1.1471868 1.2669688 1.2800816 #> [960,] 0.8130187 0.8631036 0.8891762 #> [961,] 0.4881796 0.5027134 0.5136565 #> [962,] 0.5982893 0.6004924 0.6157327 #> [963,] 0.7837144 0.7919935 0.7991795 #> [964,] 0.6104453 0.6329952 0.6364906 #> [965,] 0.7174851 0.7278104 0.7323772 #> [966,] 0.6158712 0.6274870 0.6436952 #> [967,] 0.9331233 0.9648185 0.9984615 #> [968,] 0.9794996 0.9906871 1.0181875 #> [969,] 0.9764982 0.9772140 0.9803378 #> [970,] 0.8332279 0.8385844 0.8746902 #> [971,] 0.8782562 0.9048243 0.9051167 #> [972,] 0.7135772 0.7139887 0.7223360 #> [973,] 0.6498677 0.6716114 0.7187771 #> [974,] 0.7911595 0.7980903 0.8115715 #> [975,] 1.0262068 1.0452156 1.0937855 #> [976,] 0.7545626 0.7664371 0.7778610 #> [977,] 0.9817203 1.0214761 1.0233572 #> [978,] 0.7640607 0.7933599 0.8157303 #> [979,] 0.5221971 0.5497720 0.6275281 #> [980,] 0.8326922 0.8450174 0.8531720 #> [981,] 0.6079576 0.6749162 0.6866674 #> [982,] 0.5516274 0.5699598 0.6100876 #> [983,] 0.6790706 0.7139076 0.7197768 #> [984,] 1.2032878 1.2213903 1.2509943 #> [985,] 0.6874485 0.7101610 0.7203690 #> [986,] 1.0107862 1.0289652 1.0302160 #> [987,] 0.5092006 0.5504881 0.5553071 #> [988,] 0.6925121 0.7282713 0.7690535 #> [989,] 0.7949332 0.8214750 0.8386153 #> [990,] 1.1753586 1.2128637 1.2172076 #> [991,] 0.7462066 0.8073125 0.8246749 #> [992,] 0.6815427 0.7020037 0.7071514 #> [993,] 0.8221489 0.8382346 0.8520056 #> [994,] 0.5541392 0.5859897 0.5901825 #> [995,] 0.6030581 0.6088673 0.6820477 #> [996,] 1.0221451 1.0348177 1.0581953 #> [997,] 1.1938107 1.2440572 1.2494514 #> [998,] 0.7631284 0.8002346 0.8086030 #> [999,] 0.6835776 0.6913845 0.6999382 #> [1000,] 1.1236420 1.1943301 1.2301638 #>"},{"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] 0"},{"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] 1"},{"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 85 0.788 0.914 0.440 6.14 class1 class3 1 6.43 #> 2 31 0.806 0.240 0.782 3.61 class2 class2 1 9.90 #> 3 48 0.375 0.825 0.0230 4.55 class1 class2 1 12.2 #> 4 61 0.0185 0.946 0.314 3.68 class2 class3 1 8.11 #> 5 22 0.196 0.691 0.347 2.99 class2 class1 0 8.27 #> 6 79 0.535 0.249 0.523 2.49 class2 class3 1 10.2 #> 7 74 0.689 0.856 0.907 6.98 class1 class3 1 10.9 #> 8 9 0.239 0.408 0.344 3.82 class1 class1 0 10.9 #> 9 53 0.134 0.906 0.919 5.34 class1 class2 1 8.89 #> 10 81 0.169 0.947 0.178 4.31 class1 class3 1 6.35 #> # 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,] -1.78317206 -1.0787379605 1.333560457 #> [2,] 1.10354848 1.0628629638 -0.007971152 #> [3,] 0.28101225 -0.9863854712 0.943167367 #> [4,] 0.15853500 0.9648649256 1.478340692 #> [5,] -0.19131391 -0.5968674218 -1.511126613 #> [6,] 0.46303565 0.9286830086 -0.295172477 #> [7,] -1.70231141 1.5557823494 1.379459045 #> [8,] 1.49148700 -1.4755548574 1.159147834 #> [9,] 1.72386496 0.5710371564 0.330228937 #> [10,] -0.39782305 0.2049274774 0.357080999 #> [11,] 0.44302282 1.3419463533 0.167546255 #> [12,] -1.76651518 0.8491942626 0.976864801 #> [13,] -0.63356464 -1.4433745349 0.332350031 #> [14,] -0.77306937 -1.1757549265 -0.945803345 #> [15,] -0.63717571 -0.8130631050 -1.004776766 #> [16,] -1.45990328 -1.6729066815 -1.581385700 #> [17,] 0.10951455 0.0323062376 0.230590132 #> [18,] 0.70539364 0.6626321482 0.559850122 #> [19,] 0.33545021 -0.0490838722 -0.260766820 #> [20,] -0.76876632 -1.3719608221 0.685592057 #> [21,] 1.18076987 0.9757881406 0.409331098 #> [22,] 0.29693186 -1.5711703850 -0.593965239 #> [23,] 1.06160014 1.9560886989 -0.359778415 #> [24,] 0.83531440 0.2409114010 -1.332756134 #> [25,] 0.66249487 -1.2375144652 -1.575283965 #> [26,] -0.98960673 -0.6669653896 -1.340737940 #> [27,] 1.41488135 -0.9242453876 0.725438340 #> [28,] -0.59096372 1.0250240427 -1.545310478 #> [29,] 1.75503156 -0.2734310127 0.267503896 #> [30,] -0.44195731 -0.0417721597 0.552188957 #> [31,] 0.63496759 -1.3558765673 -0.836229703 #> [32,] -1.40325154 -0.4987362393 1.478943176 #> [33,] -0.41326441 -0.7409474669 1.386029524 #> [34,] -0.11448838 -1.0214624007 -1.602191748 #> [35,] -0.16017563 -0.8513593263 1.137506946 #> [36,] 0.58728012 1.5438732777 -0.832493326 #> [37,] -1.08957913 -0.5714635307 -0.817002991 #> [38,] 0.56855878 1.6475160444 1.245318999 #> [39,] -0.17115678 0.5475570558 -0.644908178 #> [40,] 0.02686691 0.9672587787 -1.026616390 #> [41,] 0.33192934 0.0145352092 -0.173320975 #> [42,] -1.05596792 -1.0748526758 -0.191256247 #> [43,] 0.25905544 -0.7499622282 -0.087657555 #> [44,] 0.15440636 1.3524495620 0.168353586 #> [45,] -1.16808454 -0.7985231102 -0.803837572 #> [46,] -1.45180107 -1.3360255127 -1.439330467 #> [47,] -1.69432680 1.6889785690 1.329175952 #> [48,] 0.28276938 -0.0993963932 0.013122238 #> [49,] 0.12893504 -0.4431576358 -0.229954891 #> [50,] 0.98692593 1.0557804701 1.190741254 #> [51,] 0.92667438 0.1577735525 -1.669680934 #> [52,] -1.27372723 0.5730161197 0.053478552 #> [53,] 1.06871379 -1.2095639195 -0.451003047 #> [54,] 0.77182004 0.3530487708 -1.059214355 #> [55,] 1.29626841 0.8187685274 -0.478643532 #> [56,] 1.74791062 0.9897908331 -0.425375490 #> [57,] -0.16235393 -0.0910664797 1.705414173 #> [58,] -0.15738729 1.8711936081 -0.126729294 #> [59,] -0.29535792 -0.1358952865 1.397848103 #> [60,] 1.79191607 0.6458904947 -1.638537173 #> [61,] -0.66798290 -1.5840545159 -0.419631398 #> [62,] -0.32974182 1.1886707737 -0.173524097 #> [63,] -0.83681468 0.6331643597 0.082403326 #> [64,] -0.73922866 -0.2322845260 0.101457935 #> [65,] -0.57623004 -1.3937666103 1.443584669 #> [66,] -0.42359729 1.5135844751 0.072547217 #> [67,] -0.32929042 -0.0006291011 -1.692261786 #> [68,] -0.24088988 1.8261944212 0.224511135 #> [69,] -0.95149649 -1.1081043364 -0.378855087 #> [70,] 1.91723194 -0.3502579310 -1.844824658 #> [71,] 1.25176008 1.2581136351 1.233394505 #> [72,] 0.07080782 -0.8866318099 0.638288680 #> [73,] -1.54302889 1.6857596143 -0.287625849 #> [74,] 1.18695821 -0.5113548744 0.394427408 #> [75,] -0.82486995 -1.6204020019 -0.588203677 #> [76,] -1.79645181 -0.6864040144 -1.311847684 #> [77,] -1.55872438 -1.0220035939 1.789038093 #> [78,] -0.77897578 0.3692559643 0.428812512 #> [79,] 1.57042159 -1.4346590275 0.833725974 #> [80,] -0.06382824 0.2857970510 1.481791590 #> [81,] -1.61397628 -0.8465167432 -1.648507113 #> [82,] 0.20313852 0.0542426137 -0.118507950 #> [83,] 0.62050888 0.3346826055 1.023535635 #> [84,] -1.14703558 -1.5574576712 1.355170017 #> [85,] 0.42846179 -0.1753167314 1.112048926 #> [86,] 0.41997232 0.4549634910 -0.711286973 #> [87,] -0.19044873 0.8933333770 -0.474693737 #> [88,] -0.81555060 1.0777003059 -0.877067329 #> [89,] 1.17350911 -0.4341643860 0.698854727 #> [90,] 1.71932353 1.0695474903 0.804058565 #> [91,] -0.20881194 0.9738611349 1.699522445 #> [92,] 0.81303834 -0.4329034118 -1.108390019 #> [93,] 0.81484298 -0.3213290673 -0.321048497 #> [94,] 1.65329116 0.6674428429 -0.108223498 #> [95,] 1.26883535 0.1571099065 -1.479695708 #> [96,] -1.37947623 0.1786102124 0.793738394 #> [97,] 0.03251312 0.3384958529 1.722195881 #> [98,] -1.52463054 -1.2938448569 0.866870876 #> [99,] 0.72690694 1.0860309387 -0.340135386 #> [100,] -0.17026210 -0.3968786695 -1.021002674"},{"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] 3.52157326 4.18706346 4.99297032 4.17405690 6.40545747 2.50865483 #> [7] 2.33612014 5.33430165 2.56476097 3.01796819 2.89283573 3.66406721 #> [13] 5.22714046 4.35298919 2.58000727 2.15550594 3.60309971 4.89536032 #> [19] 3.81554420 6.30328847 3.53770881 1.86969723 4.09551566 7.24970111 #> [25] 2.86504606 3.55623919 3.95237545 4.11631444 5.47602150 0.28915723 #> [31] 3.07282122 5.27719683 3.72704122 6.58541327 5.98730966 3.61953499 #> [37] 3.87629426 2.33057302 5.04979749 3.18911819 3.49611601 3.47001035 #> [43] 7.14360147 3.63460106 4.13321827 5.18586447 2.17984783 3.86569248 #> [49] 5.01891594 0.53959254 6.47362294 4.17886077 4.79635337 3.38241659 #> [55] 3.88628723 1.25895231 3.16017574 4.52602869 1.12086352 4.14951568 #> [61] 3.76323375 4.80787027 7.01629099 3.03324953 3.10142062 4.20861853 #> [67] 2.47186593 4.27612420 4.45194221 7.21234030 3.62721676 4.33038331 #> [73] 3.34296498 3.24989602 3.46735076 0.29506689 0.03436764 1.68920782 #> [79] 5.35307405 5.71910412 2.83330794 0.24626349 2.92575949 0.42468688 #> [85] 1.52052028 3.57405227 8.28915490 7.86521083 4.00969324 4.79126650 #> [91] 1.25838678 2.55059481 2.77286430 1.54709918 1.48236052 0.40922086 #> [97] 3.08609250 3.17320626 3.97903992 2.75743110"},{"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 1.85 -1.32 -1.84 -0.719 y 2 #> 2 1.32 -0.265 0.545 -1.95 i 4 #> 3 1.12 1.42 0.995 0.844 h 2 #> 4 -1.07 -0.107 0.620 0.0522 r 4 #> 5 0.474 -0.470 0.874 -0.0629 b 2 #> 6 0.985 0.559 0.408 -0.162 w 2 #> 7 -0.321 0.307 1.96 1.64 p 2 #> 8 0.145 -1.28 -0.451 -1.04 s 2 #> 9 -0.378 -0.390 0.0341 -0.130 a 1 #> 10 0.387 -0.367 0.873 -0.678 m 2 #> # 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 1.85 -1.32 -1.84 -0.719 y 1 #> 2 1.32 -0.265 0.545 -1.95 i 4 #> 3 1.12 1.42 0.995 0.844 h 2 #> 4 -1.07 -0.107 0.620 0.0522 r 4 #> 5 0.474 -0.470 0.874 -0.0629 b 3 #> 6 0.985 0.559 0.408 -0.162 w 3 #> 7 -0.321 0.307 1.96 1.64 p 1 #> 8 0.145 -1.28 -0.451 -1.04 s 2 #> 9 -0.378 -0.390 0.0341 -0.130 a 2 #> 10 0.387 -0.367 0.873 -0.678 m 2 #> # 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 8 #> 2 7 #> 3 8 #> 4 1 #> 5 1 #> 6 6 #> 7 1 #> 8 6 #> 9 8 #> 10 3 #> # 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 2 #> 2 3 #> 3 4 #> 4 4 #> 5 3 #> 6 5 #> 7 1 #> 8 3 #> 9 4 #> 10 4 #> # 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 1 #> 2 2 #> 3 3 #> 4 3 #> 5 1 #> 6 4 #> 7 1 #> 8 3 #> 9 3 #> 10 3 #> # 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 1 #> 2 2 #> 3 9 #> 4 5 #> 5 2 #> 6 1 #> 7 7 #> 8 3 #> 9 3 #> 10 8 #> # 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 6 #> 2 3 #> 3 4 #> 4 6 #> 5 9 #> 6 6 #> 7 6 #> 8 4 #> 9 9 #> 10 9 #> # 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 1 #> 2 3 #> 3 3 #> 4 2 #> 5 3 #> 6 3 #> 7 5 #> 8 1 #> 9 1 #> 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 4.06 #> 2 1.01 #> 3 5.13 #> 4 4.07 #> 5 4.76 #> 6 4.54 #> 7 2.65 #> 8 2.13 #> 9 2.95 #> 10 5.19 #> 11 4.63 #> 12 1.27 #> 13 3.74 #> 14 3.48 #> 15 2.03 #> 16 3.08 #> 17 1.20 #> 18 5.99 #> 19 0.665 #> 20 0.118"},{"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 1.39 -0.189 -0.202 -0.956 -0.752 0.370 -1.34 -0.395 #> 2 1.39 -1.25 -2.00 -0.963 0.656 -0.729 -2.61 -0.274 #> 3 -0.688 -0.896 0.0756 0.610 0.743 -0.189 0.720 -0.455 #> 4 -1.18 -0.0477 -1.78 -0.249 1.17 -0.839 -0.456 1.45 #> 5 0.925 0.198 -0.208 -0.0243 -0.801 -0.288 -0.504 -0.217 #> 6 0.388 -0.689 0.252 1.34 -0.118 -0.791 0.672 -1.17 #> 7 -0.207 -1.14 1.05 0.931 0.259 0.189 1.18 -1.39 #> 8 0.947 2.46 0.174 1.87 -2.63 -1.68 1.15 0.439 #> 9 -0.258 -2.54 -0.769 -1.52 2.07 0.916 -1.49 -0.655 #> 10 1.77 0.659 0.726 -0.134 -1.95 0.245 -0.390 -0.724 #> # 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 1.39 -0.189 -0.202 -0.956 1.44 -0.343 #> 2 1.39 -1.25 -2.00 -0.963 -2.51 2.47 #> 3 -0.688 -0.896 0.0756 0.610 -2.30 -1.68 #> 4 -1.18 -0.0477 -1.78 -0.249 -2.58 0.667 #> 5 0.925 0.198 -0.208 -0.0243 1.65 1.49 #> 6 0.388 -0.689 0.252 1.34 -2.63 -3.18 #> 7 -0.207 -1.14 1.05 0.931 -1.81 -3.71 #> 8 0.947 2.46 0.174 1.87 2.89 4.60 #> 9 -0.258 -2.54 -0.769 -1.52 0.474 -3.26 #> 10 1.77 0.659 0.726 -0.134 3.42 2.05 #> # 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 1.39 -0.189 -0.202 -0.956 -3.80 -2.20 #> 2 1.39 -1.25 -2.00 -0.963 1.54 -1.89 #> 3 -0.688 -0.896 0.0756 0.610 1.52 0.938 #> 4 -1.18 -0.0477 -1.78 -0.249 0.418 -0.149 #> 5 0.925 0.198 -0.208 -0.0243 -3.80 -1.13 #> 6 0.388 -0.689 0.252 1.34 3.91 -2.45 #> 7 -0.207 -1.14 1.05 0.931 4.12 -3.12 #> 8 0.947 2.46 0.174 1.87 -2.15 1.50 #> 9 -0.258 -2.54 -0.769 -1.52 -1.83 -2.71 #> 10 1.77 0.659 0.726 -0.134 -4.28 0.887 #> # 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 -0.324 -2.56 #> 2 -1.03 0.829 #> 3 0.657 -0.668 #> 4 0.511 -0.681 #> 5 0.250 0.361 #> 6 -0.448 -0.371 #> 7 -1.13 1.54 #> 8 -0.259 -1.95 #> 9 -0.0261 0.277 #> 10 0.0256 0.445 #> # 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 1.06 1.68 -0.145 0.324 #> 2 -0.295 0.281 -0.912 1.73 #> 3 0.459 -0.686 -2.27 0.921 #> 4 2.57 -0.607 -0.349 -0.0860 #> 5 -0.637 -1.86 1.13 0.370 #> 6 -0.265 -0.281 0.562 -0.349 #> 7 -0.806 -0.395 -0.558 1.36 #> 8 -0.421 1.33 -0.410 -1.35 #> 9 0.667 -0.798 1.59 -0.0229 #> 10 -0.329 -0.494 0.0244 -0.315 #> # 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 5.71 -1.94 #> 2 6.79 -3.89 #> 3 4.59 1.47 #> 4 -5.97 3.60 #> 5 1.44 -6.66 #> 6 6.40 -4.35 #> 7 0.858 6.37 #> 8 3.04 0.757 #> 9 -4.07 -1.17 #> 10 2.57 -7.17 #> # 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 -6.78 4.69 #> 2 -3.11 6.73 #> 3 -1.96 4.62 #> 4 8.84 -2.51 #> 5 6.52 2.62 #> 6 -3.57 2.33 #> 7 8.31 1.91 #> 8 1.10 6.10 #> 9 -0.347 -5.74 #> 10 4.42 6.23 #> # 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 -0.107 -2.62 #> 2 1.28 0.775 #> 3 -3.33 1.56 #> 4 1.09 0.991 #> 5 -2.92 -3.27 #> 6 -3.02 -3.33 #> 7 1.37 2.20 #> 8 0.917 2.54 #> 9 -0.459 -1.25 #> 10 2.54 2.57 #> # 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 1.89 -6.18 #> 2 -7.23 -0.302 #> 3 2.95 1.72 #> 4 -7.82 -0.0960 #> 5 -0.989 -5.83 #> 6 -0.605 -5.28 #> 7 -3.50 3.27 #> 8 -4.11 4.16 #> 9 6.96 -3.82 #> 10 -4.94 8.10 #> # 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 -0.411 1.91 #> 2 -0.689 -1.06 #> 3 -5.01 -0.0412 #> 4 5.71 -0.340 #> 5 -4.68 0.0799 #> 6 -4.96 -0.0157 #> 7 4.94 3.30 #> 8 -5.05 -0.222 #> 9 4.13 0.650 #> 10 3.40 2.58 #> # 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 0.375 #> 2 0.25 #> 3 0.25 #> 4 0 #> 5 0.625 #> 6 0.125 #> 7 0.375 #> 8 0.125 #> 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 1 #> 2 1 #> 3 0.7 #> 4 0.1 #> 5 0.3 #> 6 0.9 #> 7 1 #> 8 0.5 #> 9 1 #> 10 0.1 #> # 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-08 #> # A tibble: 4 × 2 #> feature coefficient #> #> 1 (Intercept) 3.44 #> 2 pstat5 1.18 #> 3 cd45 0.958 #> 4 cd34 0.133 # 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 -1.53 #> 2 cd45 -1.21 #> 3 cd34 -0.233 #> 4 (Intercept) -0.0361 # 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 -0.175 #> 2 pstat5 0.0684 #> 3 cd34 -0.0448"},{"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 -1.24 0.373 -0.937 -0.704 b #> 2 0.269 1.52 1.81 0.506 a #> 3 -0.736 -0.0917 -0.830 0.821 b #> 4 0.638 0.608 0.564 -0.428 a #> 5 1.29 -0.926 -0.153 0.324 b #> 6 -0.634 -0.00913 -0.996 0.145 b #> 7 -0.481 -0.782 -0.704 -0.812 b #> 8 0.924 1.21 -1.44 -1.48 a #> 9 -1.24 0.128 0.107 -0.509 a #> 10 0.255 -0.885 1.41 1.78 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 -1.24 0.373 -0.937 -0.704 b #> 2 0.269 1.52 1.81 0.506 a #> 3 -0.736 -0.0917 -0.830 0.821 a #> 4 0.638 0.608 0.564 -0.428 a #> 5 1.29 -0.926 -0.153 0.324 a #> 6 -0.634 -0.00913 -0.996 0.145 a #> 7 -0.481 -0.782 -0.704 -0.812 a #> 8 0.924 1.21 -1.44 -1.48 b #> 9 -1.24 0.128 0.107 -0.509 a #> 10 0.255 -0.885 1.41 1.78 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 a #> 2 a #> 3 b #> 4 a #> 5 b #> 6 a #> 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 b #> 3 a #> 4 a #> 5 a #> 6 a #> 7 a #> 8 b #> 9 b #> 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 a #> 3 b #> 4 b #> 5 a #> 6 b #> 7 a #> 8 b #> 9 b #> 10 a #> # 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 a #> 6 a #> 7 a #> 8 b #> 9 b #> 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-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":""}]