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Copy pathRWI_ModNegExp_v_LTS_Segmented.R
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RWI_ModNegExp_v_LTS_Segmented.R
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#####RWI (ModNegExp) vs. LTS (%CC) #####
##### Set up and Data #####
library(dplR)
library(segmented)
library(dplyr)
library(trend)
library(modifiedmk)
library(zyp)
library(ggplot2)
library(patchwork)
setwd("C:/Users/mitch/Google Drive/PHD/Thesis Research/Landsat Time-Series/Validate/Change Validation/Dendrochronology")
#Laptop
#setwd("C:/Users/bonneymi/Google Drive/PHD/Thesis Research/Landsat Time-Series/Validate/Change Validation/Dendrochronology")
# Desktop
##### Bring in RWLs and apply ModNegExp detrending #####
#####
# F07Dec
#rwl.F07Dec = read.rwl("Ring Width Chronologies/F07/F07_12.rwl")
#rwl.report(rwl.F07Dec)
#rwl.F07Dec.ids = read.ids(rwl.F07Dec, stc = c(4, 2, 1))
#yr.F07Dec = time(rwl.F07Dec)
#rwl.F07Dec.mne = detrend(rwl.F07Dec, method = "ModNegExp")
#rwl.F07Dec.mne.sss = sss(rwl.F07Dec.mne, rwl.F07Dec.ids)
#cut.F07Dec.mne = max(yr.F07Dec[rwl.F07Dec.mne.sss < 0.85])
#yr.cut.F07Dec.mne = yr.F07Dec[yr.F07Dec > cut.F07Dec.mne]
#rwl.F07Dec.mne.crn = chron(detrend(rwl.F07Dec[yr.F07Dec > cut.F07Dec.mne,],
# method = "ModNegExp"))
#plot(rwl.F07Dec.mne.crn, add.spline = TRUE, nyrs = 10)
# F07Ace (Site 08)
rwl.F07Ace = read.rwl("Ring Width Chronologies/F07/F07_Acer_12.rwl")
rwl.report(rwl.F07Ace)
rwl.F07Ace.ids = read.ids(rwl.F07Ace, stc = c(4, 2, 1))
yr.F07Ace = time(rwl.F07Ace)
rwl.F07Ace.mne = detrend(rwl.F07Ace, method = "ModNegExp")
rwl.F07Ace.mne.sss = sss(rwl.F07Ace.mne, rwl.F07Ace.ids)
cut.F07Ace.mne = max(yr.F07Ace[rwl.F07Ace.mne.sss < 0.85])
yr.cut.F07Ace.mne = yr.F07Ace[yr.F07Ace > cut.F07Ace.mne]
rwl.F07Ace.mne.crn = chron(detrend(rwl.F07Ace[yr.F07Ace > cut.F07Ace.mne,],
method = "ModNegExp"))
plot(rwl.F07Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F15Dec
#rwl.F15Dec = read.rwl("Ring Width Chronologies/F15/F15_4.rwl")
#rwl.report(rwl.F15Dec)
#rwl.F15Dec.ids = read.ids(rwl.F15Dec, stc = c(4, 2, 1))
#yr.F15Dec = time(rwl.F15Dec)
#rwl.F15Dec.mne = detrend(rwl.F15Dec, method = "ModNegExp")
#rwl.F15Dec.mne.sss = sss(rwl.F15Dec.mne, rwl.F15Dec.ids)
#cut.F15Dec.mne = max(yr.F15Dec[rwl.F15Dec.mne.sss < 0.85])
#yr.cut.F15Dec.mne = yr.F15Dec[yr.F15Dec > cut.F15Dec.mne]
#rwl.F15Dec.mne.crn = chron(detrend(rwl.F15Dec[yr.F15Dec > cut.F15Dec.mne,],
# method = "ModNegExp"))
#plot(rwl.F15Dec.mne.crn, add.spline = TRUE, nyrs = 10)
# F15Ace (Site 02)
rwl.F15Ace = read.rwl("Ring Width Chronologies/F15/F15_Acer_1.rwl")
rwl.report(rwl.F15Ace)
rwl.F15Ace.ids = read.ids(rwl.F15Ace, stc = c(4, 2, 1))
yr.F15Ace = time(rwl.F15Ace)
rwl.F15Ace.mne = detrend(rwl.F15Ace, method = "ModNegExp")
rwl.F15Ace.mne.sss = sss(rwl.F15Ace.mne, rwl.F15Ace.ids)
cut.F15Ace.mne = max(yr.F15Ace[rwl.F15Ace.mne.sss < 0.85])
yr.cut.F15Ace.mne = yr.F15Ace[yr.F15Ace > cut.F15Ace.mne]
rwl.F15Ace.mne.crn = chron(detrend(rwl.F15Ace[yr.F15Ace > cut.F15Ace.mne,],
method = "ModNegExp"))
plot(rwl.F15Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F15Car
#rwl.F15Car = read.rwl("Ring Width Chronologies/F15/F15_Carya_1.rwl")
#rwl.report(rwl.F15Car)
#rwl.F15Car.ids = read.ids(rwl.F15Car, stc = c(4, 2, 1))
#yr.F15Car = time(rwl.F15Car)
#rwl.F15Car.mne = detrend(rwl.F15Car, method = "ModNegExp")
#rwl.F15Car.mne.sss = sss(rwl.F15Car.mne, rwl.F15Car.ids)
#cut.F15Car.mne = max(yr.F15Car[rwl.F15Car.mne.sss < 0.85])
#yr.cut.F15Car.mne = yr.F15Car[yr.F15Car > cut.F15Car.mne]
#rwl.F15Car.mne.crn = chron(detrend(rwl.F15Car[yr.F15Car > cut.F15Car.mne,],
# method = "ModNegExp"))
#plot(rwl.F15Car.mne.crn, add.spline = TRUE, nyrs = 10)
# F15Que (Site 02)
rwl.F15Que = read.rwl("Ring Width Chronologies/F15/F15_Quercus_2.rwl")
rwl.report(rwl.F15Que)
rwl.F15Que.ids = read.ids(rwl.F15Que, stc = c(4, 2, 1))
yr.F15Que = time(rwl.F15Que)
rwl.F15Que.mne = detrend(rwl.F15Que, method = "ModNegExp")
rwl.F15Que.mne.sss = sss(rwl.F15Que.mne, rwl.F15Que.ids)
cut.F15Que.mne = max(yr.F15Que[rwl.F15Que.mne.sss < 0.85])
yr.cut.F15Que.mne = yr.F15Que[yr.F15Que > cut.F15Que.mne]
rwl.F15Que.mne.crn = chron(detrend(rwl.F15Que[yr.F15Que > cut.F15Que.mne,],
method = "ModNegExp"))
plot(rwl.F15Que.mne.crn, add.spline = TRUE, nyrs = 10)
# F21Dec (Site 06)
rwl.F21Dec = read.rwl("Ring Width Chronologies/F21/F21_11.rwl")
rwl.report(rwl.F21Dec)
rwl.F21Dec.ids = read.ids(rwl.F21Dec, stc = c(4, 2, 1))
yr.F21Dec = time(rwl.F21Dec)
rwl.F21Dec.mne = detrend(rwl.F21Dec, method = "ModNegExp")
rwl.F21Dec.mne.sss = sss(rwl.F21Dec.mne, rwl.F21Dec.ids)
cut.F21Dec.mne = max(yr.F21Dec[rwl.F21Dec.mne.sss < 0.85])
yr.cut.F21Dec.mne = yr.F21Dec[yr.F21Dec > cut.F21Dec.mne]
rwl.F21Dec.mne.crn = chron(detrend(rwl.F21Dec[yr.F21Dec > cut.F21Dec.mne,],
method = "ModNegExp"))
plot(rwl.F21Dec.mne.crn, add.spline = TRUE, nyrs = 10)
# F21Ace
#rwl.F21Ace = read.rwl("Ring Width Chronologies/F21/F21_Acer_7.rwl")
#rwl.report(rwl.F21Ace)
#rwl.F21Ace.ids = read.ids(rwl.F21Ace, stc = c(4, 2, 1))
#yr.F21Ace = time(rwl.F21Ace)
#rwl.F21Ace.mne = detrend(rwl.F21Ace, method = "ModNegExp")
#rwl.F21Ace.mne.sss = sss(rwl.F21Ace.mne, rwl.F21Ace.ids)
#cut.F21Ace.mne = max(yr.F21Ace[rwl.F21Ace.mne.sss < 0.85])
#yr.cut.F21Ace.mne = yr.F21Ace[yr.F21Ace > cut.F21Ace.mne]
#rwl.F21Ace.mne.crn = chron(detrend(rwl.F21Ace[yr.F21Ace > cut.F21Ace.mne,],
# method = "ModNegExp"))
#plot(rwl.F21Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F23Ace (Site 05)
rwl.F23Ace = read.rwl("Ring Width Chronologies/F23/F23_7.rwl")
rwl.report(rwl.F23Ace)
rwl.F23Ace.ids = read.ids(rwl.F23Ace, stc = c(4, 2, 1))
yr.F23Ace = time(rwl.F23Ace)
rwl.F23Ace.mne = detrend(rwl.F23Ace, method = "ModNegExp")
rwl.F23Ace.mne.sss = sss(rwl.F23Ace.mne, rwl.F23Ace.ids)
cut.F23Ace.mne = max(yr.F23Ace[rwl.F23Ace.mne.sss < 0.85])
yr.cut.F23Ace.mne = yr.F23Ace[yr.F23Ace > cut.F23Ace.mne]
rwl.F23Ace.mne.crn = chron(detrend(rwl.F23Ace[yr.F23Ace > cut.F23Ace.mne,],
method = "ModNegExp"))
plot(rwl.F23Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F25Ace (Site 11)
rwl.F25Ace = read.rwl("Ring Width Chronologies/F25/F25_6.rwl")
rwl.report(rwl.F25Ace)
rwl.F25Ace.ids = read.ids(rwl.F25Ace, stc = c(4, 2, 1))
yr.F25Ace = time(rwl.F25Ace)
rwl.F25Ace.mne = detrend(rwl.F25Ace, method = "ModNegExp")
rwl.F25Ace.mne.sss = sss(rwl.F25Ace.mne, rwl.F25Ace.ids)
cut.F25Ace.mne = max(yr.F25Ace[rwl.F25Ace.mne.sss < 0.85])
yr.cut.F25Ace.mne = yr.F25Ace[yr.F25Ace > cut.F25Ace.mne]
rwl.F25Ace.mne.crn = chron(detrend(rwl.F25Ace[yr.F25Ace > cut.F25Ace.mne,],
method = "ModNegExp"))
plot(rwl.F25Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F30Ace (Site 15)
rwl.F30Ace = read.rwl("Ring Width Chronologies/F30/F30_Acer_5.rwl")
rwl.report(rwl.F30Ace)
rwl.F30Ace.ids = read.ids(rwl.F30Ace, stc = c(4, 2, 1))
yr.F30Ace = time(rwl.F30Ace)
rwl.F30Ace.mne = detrend(rwl.F30Ace, method = "ModNegExp")
rwl.F30Ace.mne.sss = sss(rwl.F30Ace.mne, rwl.F30Ace.ids)
cut.F30Ace.mne = max(yr.F30Ace[rwl.F30Ace.mne.sss < 0.85])
yr.cut.F30Ace.mne = yr.F30Ace[yr.F30Ace > cut.F30Ace.mne]
rwl.F30Ace.mne.crn = chron(detrend(rwl.F30Ace[yr.F30Ace > cut.F30Ace.mne,],
method = "ModNegExp"))
plot(rwl.F30Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# F30Bet (Site 15)
rwl.F30Bet = read.rwl("Ring Width Chronologies/F30/F30_Betula_1.rwl")
rwl.report(rwl.F30Bet)
rwl.F30Bet.ids = read.ids(rwl.F30Bet, stc = c(4, 2, 1))
yr.F30Bet = time(rwl.F30Bet)
rwl.F30Bet.mne = detrend(rwl.F30Bet, method = "ModNegExp")
rwl.F30Bet.mne.sss = sss(rwl.F30Bet.mne, rwl.F30Bet.ids)
cut.F30Bet.mne = max(yr.F30Bet[rwl.F30Bet.mne.sss < 0.85])
yr.cut.F30Bet.mne = yr.F30Bet[yr.F30Bet > cut.F30Bet.mne]
rwl.F30Bet.mne.crn = chron(detrend(rwl.F30Bet[yr.F30Bet > cut.F30Bet.mne,],
method = "ModNegExp"))
plot(rwl.F30Bet.mne.crn, add.spline = TRUE, nyrs = 10)
# F33Pic (Site 13)
rwl.F33Pic = read.rwl("Ring Width Chronologies/F33/F33_3.rwl")
rwl.report(rwl.F33Pic)
rwl.F33Pic.ids = read.ids(rwl.F33Pic, stc = c(4, 2, 1))
yr.F33Pic = time(rwl.F33Pic)
rwl.F33Pic.mne = detrend(rwl.F33Pic, method = "ModNegExp")
rwl.F33Pic.mne.sss = sss(rwl.F33Pic.mne, rwl.F33Pic.ids)
cut.F33Pic.mne = max(yr.F33Pic[rwl.F33Pic.mne.sss < 0.85])
yr.cut.F33Pic.mne = yr.F33Pic[yr.F33Pic > cut.F33Pic.mne]
rwl.F33Pic.mne.crn = chron(detrend(rwl.F33Pic[yr.F33Pic > cut.F33Pic.mne,],
method = "ModNegExp"))
plot(rwl.F33Pic.mne.crn, add.spline = TRUE, nyrs = 10)
# M01Pop (Site 12)
rwl.M01Pop = read.rwl("Ring Width Chronologies/M01/M01_11.rwl")
rwl.report(rwl.M01Pop)
rwl.M01Pop.ids = read.ids(rwl.M01Pop, stc = c(4, 2, 1))
yr.M01Pop = time(rwl.M01Pop)
rwl.M01Pop.mne = detrend(rwl.M01Pop, method = "ModNegExp")
rwl.M01Pop.mne.sss = sss(rwl.M01Pop.mne, rwl.M01Pop.ids)
cut.M01Pop.mne = max(yr.M01Pop[rwl.M01Pop.mne.sss < 0.85])
yr.cut.M01Pop.mne = yr.M01Pop[yr.M01Pop > cut.M01Pop.mne]
rwl.M01Pop.mne.crn = chron(detrend(rwl.M01Pop[yr.M01Pop > cut.M01Pop.mne,],
method = "ModNegExp"))
plot(rwl.M01Pop.mne.crn, add.spline = TRUE, nyrs = 10)
# M05Thu (Site 07)
rwl.M05Thu = read.rwl("Ring Width Chronologies/M05/M05_6.rwl")
rwl.report(rwl.M05Thu)
rwl.M05Thu.ids = read.ids(rwl.M05Thu, stc = c(4, 2, 1))
yr.M05Thu = time(rwl.M05Thu)
rwl.M05Thu.mne = detrend(rwl.M05Thu, method = "ModNegExp")
rwl.M05Thu.mne.sss = sss(rwl.M05Thu.mne, rwl.M05Thu.ids)
cut.M05Thu.mne = max(yr.M05Thu[rwl.M05Thu.mne.sss < 0.85])
yr.cut.M05Thu.mne = yr.M05Thu[yr.M05Thu > cut.M05Thu.mne]
rwl.M05Thu.mne.crn = chron(detrend(rwl.M05Thu[yr.M05Thu > cut.M05Thu.mne,],
method = "ModNegExp"))
plot(rwl.M05Thu.mne.crn, add.spline = TRUE, nyrs = 10)
# M06Ace (Site 03)
rwl.M06Ace = read.rwl("Ring Width Chronologies/M06/M06_Acer_1.rwl")
rwl.report(rwl.M06Ace)
rwl.M06Ace.ids = read.ids(rwl.M06Ace, stc = c(4, 2, 1))
yr.M06Ace = time(rwl.M06Ace)
rwl.M06Ace.mne = detrend(rwl.M06Ace, method = "ModNegExp")
rwl.M06Ace.mne.sss = sss(rwl.M06Ace.mne, rwl.M06Ace.ids)
cut.M06Ace.mne = max(yr.M06Ace[rwl.M06Ace.mne.sss < 0.85])
yr.cut.M06Ace.mne = yr.M06Ace[yr.M06Ace > cut.M06Ace.mne]
rwl.M06Ace.mne.crn = chron(detrend(rwl.M06Ace[yr.M06Ace > cut.M06Ace.mne,],
method = "ModNegExp"))
plot(rwl.M06Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# M06Que (Site 03)
rwl.M06Que = read.rwl("Ring Width Chronologies/M06/M06_Quercus_1.rwl")
rwl.report(rwl.M06Que)
rwl.M06Que.ids = read.ids(rwl.M06Que, stc = c(4, 2, 1))
yr.M06Que = time(rwl.M06Que)
rwl.M06Que.mne = detrend(rwl.M06Que, method = "ModNegExp")
rwl.M06Que.mne.sss = sss(rwl.M06Que.mne, rwl.M06Que.ids)
cut.M06Que.mne = max(yr.M06Que[rwl.M06Que.mne.sss < 0.85])
yr.cut.M06Que.mne = yr.M06Que[yr.M06Que > cut.M06Que.mne]
rwl.M06Que.mne.crn = chron(detrend(rwl.M06Que[yr.M06Que > cut.M06Que.mne,],
method = "ModNegExp"))
plot(rwl.M06Que.mne.crn, add.spline = TRUE, nyrs = 10)
# M07Tsu (Site 04)
rwl.M07Tsu = read.rwl("Ring Width Chronologies/M07/M07_Tsuga_2.rwl")
rwl.report(rwl.M07Tsu)
rwl.M07Tsu.ids = read.ids(rwl.M07Tsu, stc = c(4, 2, 1))
yr.M07Tsu = time(rwl.M07Tsu)
rwl.M07Tsu.mne = detrend(rwl.M07Tsu, method = "ModNegExp")
rwl.M07Tsu.mne.sss = sss(rwl.M07Tsu.mne, rwl.M07Tsu.ids)
cut.M07Tsu.mne = max(yr.M07Tsu[rwl.M07Tsu.mne.sss < 0.85])
yr.cut.M07Tsu.mne = yr.M07Tsu[yr.M07Tsu > cut.M07Tsu.mne]
rwl.M07Tsu.mne.crn = chron(detrend(rwl.M07Tsu[yr.M07Tsu > cut.M07Tsu.mne,],
method = "ModNegExp"))
plot(rwl.M07Tsu.mne.crn, add.spline = TRUE, nyrs = 10)
# M13Tsu (Site 01)
rwl.M13Tsu = read.rwl("Ring Width Chronologies/M13/M13_Tsuga_2.rwl")
rwl.report(rwl.M13Tsu)
rwl.M13Tsu.ids = read.ids(rwl.M13Tsu, stc = c(4, 2, 1))
yr.M13Tsu = time(rwl.M13Tsu)
rwl.M13Tsu.mne = detrend(rwl.M13Tsu, method = "ModNegExp")
rwl.M13Tsu.mne.sss = sss(rwl.M13Tsu.mne, rwl.M13Tsu.ids)
cut.M13Tsu.mne = max(yr.M13Tsu[rwl.M13Tsu.mne.sss < 0.85])
yr.cut.M13Tsu.mne = yr.M13Tsu[yr.M13Tsu > cut.M13Tsu.mne]
rwl.M13Tsu.mne.crn = chron(detrend(rwl.M13Tsu[yr.M13Tsu > cut.M13Tsu.mne,],
method = "ModNegExp"))
plot(rwl.M13Tsu.mne.crn, add.spline = TRUE, nyrs = 10)
# M17Thu (Site 16)
rwl.M17Thu = read.rwl("Ring Width Chronologies/M17/M17_1.rwl")
rwl.report(rwl.M17Thu)
rwl.M17Thu.ids = read.ids(rwl.M17Thu, stc = c(4, 2, 1))
yr.M17Thu = time(rwl.M17Thu)
rwl.M17Thu.mne = detrend(rwl.M17Thu, method = "ModNegExp")
rwl.M17Thu.mne.sss = sss(rwl.M17Thu.mne, rwl.M17Thu.ids)
cut.M17Thu.mne = max(yr.M17Thu[rwl.M17Thu.mne.sss < 0.85])
yr.cut.M17Thu.mne = yr.M17Thu[yr.M17Thu > cut.M17Thu.mne]
rwl.M17Thu.mne.crn = chron(detrend(rwl.M17Thu[yr.M17Thu > cut.M17Thu.mne,],
method = "ModNegExp"))
plot(rwl.M17Thu.mne.crn, add.spline = TRUE, nyrs = 10)
# M20Con
#rwl.M20Con = read.rwl("Ring Width Chronologies/M20/M20_3.rwl")
#rwl.report(rwl.M20Con)
#rwl.M20Con.ids = read.ids(rwl.M20Con, stc = c(4, 2, 1))
#yr.M20Con = time(rwl.M20Con)
#rwl.M20Con.mne = detrend(rwl.M20Con, method = "ModNegExp")
#rwl.M20Con.mne.sss = sss(rwl.M20Con.mne, rwl.M20Con.ids)
#cut.M20Con.mne = max(yr.M20Con[rwl.M20Con.mne.sss < 0.85])
#yr.cut.M20Con.mne = yr.M20Con[yr.M20Con > cut.M20Con.mne]
#rwl.M20Con.mne.crn = chron(detrend(rwl.M20Con[yr.M20Con > cut.M20Con.mne,],
# method = "ModNegExp"))
#plot(rwl.M20Con.mne.crn, add.spline = TRUE, nyrs = 10)
# M20Thu (Site 14)
rwl.M20Thu = read.rwl("Ring Width Chronologies/M20/M20_Thuja_3.rwl")
rwl.report(rwl.M20Thu)
rwl.M20Thu.ids = read.ids(rwl.M20Thu, stc = c(4, 2, 1))
yr.M20Thu = time(rwl.M20Thu)
rwl.M20Thu.mne = detrend(rwl.M20Thu, method = "ModNegExp")
rwl.M20Thu.mne.sss = sss(rwl.M20Thu.mne, rwl.M20Thu.ids)
cut.M20Thu.mne = max(yr.M20Thu[rwl.M20Thu.mne.sss < 0.85])
yr.cut.M20Thu.mne = yr.M20Thu[yr.M20Thu > cut.M20Thu.mne]
rwl.M20Thu.mne.crn = chron(detrend(rwl.M20Thu[yr.M20Thu > cut.M20Thu.mne,],
method = "ModNegExp"))
plot(rwl.M20Thu.mne.crn, add.spline = TRUE, nyrs = 10)
# M26Dec (Site 10)
rwl.M26Dec = read.rwl("Ring Width Chronologies/M26/M26_Deciduous_1.rwl")
rwl.report(rwl.M26Dec)
rwl.M26Dec.ids = read.ids(rwl.M26Dec, stc = c(4, 2, 1))
yr.M26Dec = time(rwl.M26Dec)
rwl.M26Dec.mne = detrend(rwl.M26Dec, method = "ModNegExp")
rwl.M26Dec.mne.sss = sss(rwl.M26Dec.mne, rwl.M26Dec.ids)
cut.M26Dec.mne = max(yr.M26Dec[rwl.M26Dec.mne.sss < 0.85])
yr.cut.M26Dec.mne = yr.M26Dec[yr.M26Dec > cut.M26Dec.mne]
rwl.M26Dec.mne.crn = chron(detrend(rwl.M26Dec[yr.M26Dec > cut.M26Dec.mne,],
method = "ModNegExp"))
plot(rwl.M26Dec.mne.crn, add.spline = TRUE, nyrs = 10)
# M26Ace
#rwl.M26Ace = read.rwl("Ring Width Chronologies/M26/M26_Acer_1.rwl")
#rwl.report(rwl.M26Ace)
#rwl.M26Ace.ids = read.ids(rwl.M26Ace, stc = c(4, 2, 1))
#yr.M26Ace = time(rwl.M26Ace)
#rwl.M26Ace.mne = detrend(rwl.M26Ace, method = "ModNegExp")
#rwl.M26Ace.mne.sss = sss(rwl.M26Ace.mne, rwl.M26Ace.ids)
#cut.M26Ace.mne = max(yr.M26Ace[rwl.M26Ace.mne.sss < 0.85])
#yr.cut.M26Ace.mne = yr.M26Ace[yr.M26Ace > cut.M26Ace.mne]
#rwl.M26Ace.mne.crn = chron(detrend(rwl.M26Ace[yr.M26Ace > cut.M26Ace.mne,],
# method = "ModNegExp"))
#plot(rwl.M26Ace.mne.crn, add.spline = TRUE, nyrs = 10)
# M26Thu (Site 10)
rwl.M26Thu = read.rwl("Ring Width Chronologies/M26/M26_Thuja_1.rwl")
rwl.report(rwl.M26Thu)
rwl.M26Thu.ids = read.ids(rwl.M26Thu, stc = c(4, 2, 1))
yr.M26Thu = time(rwl.M26Thu)
rwl.M26Thu.mne = detrend(rwl.M26Thu, method = "ModNegExp")
rwl.M26Thu.mne.sss = sss(rwl.M26Thu.mne, rwl.M26Thu.ids)
cut.M26Thu.mne = max(yr.M26Thu[rwl.M26Thu.mne.sss < 0.85])
yr.cut.M26Thu.mne = yr.M26Thu[yr.M26Thu > cut.M26Thu.mne]
rwl.M26Thu.mne.crn = chron(detrend(rwl.M26Thu[yr.M26Thu > cut.M26Thu.mne,],
method = "ModNegExp"))
plot(rwl.M26Thu.mne.crn, add.spline = TRUE, nyrs = 10)
# M27Pin (Site 09)
rwl.M27Pin = read.rwl("Ring Width Chronologies/M27/M27_6.rwl")
rwl.report(rwl.M27Pin)
rwl.M27Pin.ids = read.ids(rwl.M27Pin, stc = c(4, 2, 1))
yr.M27Pin = time(rwl.M27Pin)
rwl.M27Pin.mne = detrend(rwl.M27Pin, method = "ModNegExp")
rwl.M27Pin.mne.sss = sss(rwl.M27Pin.mne, rwl.M27Pin.ids)
cut.M27Pin.mne = max(yr.M27Pin[rwl.M27Pin.mne.sss < 0.85])
yr.cut.M27Pin.mne = yr.M27Pin[yr.M27Pin > cut.M27Pin.mne]
rwl.M27Pin.mne.crn = chron(detrend(rwl.M27Pin[yr.M27Pin > cut.M27Pin.mne,],
method = "ModNegExp"))
plot(rwl.M27Pin.mne.crn, add.spline = TRUE, nyrs = 10)
#####
# RWI names
#write.csv(rwl.F07Dec.mne.crn, "F07Dec_ModNegExp.csv") #rwl.F07Dec.mne.crn
#write.csv(rwl.F07Ace.mne.crn, "F07Ace_ModNegExp.csv") #rwl.F07Ace.mne.crn
#write.csv(rwl.F15Dec.mne.crn, "F15Dec_ModNegExp.csv") #rwl.F15Dec.mne.crn
#write.csv(rwl.F15Ace.mne.crn, "F15Ace_ModNegExp.csv") #rwl.F15Ace.mne.crn
#write.csv(rwl.F15Car.mne.crn, "F15Car_ModNegExp.csv") #rwl.F15Car.mne.crn
#write.csv(rwl.F15Que.mne.crn, "F15Que_ModNegExp.csv") #rwl.F15Que.mne.crn
#write.csv(rwl.F21Dec.mne.crn, "F21Dec_ModNegExp.csv") #rwl.F21Dec.mne.crn
#write.csv(rwl.F21Ace.mne.crn, "F21Ace_ModNegExp.csv") #rwl.F21Ace.mne.crn
#write.csv(rwl.F23Ace.mne.crn, "F23Ace_ModNegExp.csv") #rwl.F23Ace.mne.crn
#write.csv(rwl.F25Ace.mne.crn, "F25Ace_ModNegExp.csv") #rwl.F25Ace.mne.crn
#write.csv(rwl.F30Ace.mne.crn, "F30Ace_ModNegExp.csv") #rwl.F30Ace.mne.crn
#write.csv(rwl.F30Bet.mne.crn, "F30Bet_ModNegExp.csv") #rwl.F30Bet.mne.crn
#write.csv(rwl.F33Pic.mne.crn, "F33Pic_ModNegExp.csv") #rwl.F33Pic.mne.crn
#write.csv(rwl.M01Pop.mne.crn, "M01Pop_ModNegExp.csv") #rwl.M01Pop.mne.crn
#write.csv(rwl.M05Thu.mne.crn, "M05Thu_ModNegExp.csv") #rwl.M05Thu.mne.crn
#write.csv(rwl.M06Ace.mne.crn, "M06Ace_ModNegExp.csv") #rwl.M06Ace.mne.crn
#write.csv(rwl.M06Que.mne.crn, "M06Que_ModNegExp.csv") #rwl.M06Que.mne.crn
#write.csv(rwl.M07Tsu.mne.crn, "M07Tsu_ModNegExp.csv") #rwl.M07Tsu.mne.crn
#write.csv(rwl.M13Tsu.mne.crn, "M13Tsu_ModNegExp.csv") #rwl.M13Tsu.mne.crn
#write.csv(rwl.M17Thu.mne.crn, "M17Thu_ModNegExp.csv") #rwl.M17Thu.mne.crn
#write.csv(rwl.M20Con.mne.crn, "M20Con_ModNegExp.csv") #rwl.M20Con.mne.crn
#write.csv(rwl.M20Thu.mne.crn, "M20Thu_ModNegExp.csv") #rwl.M20Thu.mne.crn
#write.csv(rwl.M26Dec.mne.crn, "M26Dec_ModNegExp.csv") #rwl.M26Dec.mne.crn
#write.csv(rwl.M26Ace.mne.crn, "M26Ace_ModNegExp.csv") #rwl.M26Ace.mne.crn
#write.csv(rwl.M26Thu.mne.crn, "M26Thu_ModNegExp.csv") #rwl.M26Thu.mne.crn
#write.csv(rwl.M27Pin.mne.crn, "M27Pin_ModNegExp.csv") #rwl.M27Pin.mne.crn
##### Bring in %CC LTS #####
#####
# F07
lts.F07 = read.csv("Ring Width Chronologies/F07/DetrendingLandsat/F07_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F07), lts.F07$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F07), lts.F07$Avg_CC_Fitted)
# F15
lts.F15 = read.csv("Ring Width Chronologies/F15/DetrendingLandsat/F15_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F15), lts.F15$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F15), lts.F15$Avg_CC_Fitted)
# F21
lts.F21 = read.csv("Ring Width Chronologies/F21/DetrendingLandsat/F21_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F21), lts.F21$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F21), lts.F21$Avg_CC_Fitted)
# F23
lts.F23 = read.csv("Ring Width Chronologies/F23/DetrendingLandsat/F23_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F23), lts.F23$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F23), lts.F23$Avg_CC_Fitted)
# F25
lts.F25 = read.csv("Ring Width Chronologies/F25/DetrendingLandsat/F25_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F25), lts.F25$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F25), lts.F25$Avg_CC_Fitted)
# F30
lts.F30 = read.csv("Ring Width Chronologies/F30/DetrendingLandsat/F30_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F30), lts.F30$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F30), lts.F30$Avg_CC_Fitted)
# F33
lts.F33 = read.csv("Ring Width Chronologies/F33/DetrendingLandsat/F33_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.F33), lts.F33$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.F33), lts.F33$Avg_CC_Fitted)
# M01
lts.M01 = read.csv("Ring Width Chronologies/M01/DetrendingLandsat/M01_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M01), lts.M01$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M01), lts.M01$Avg_CC_Fitted)
# M05
lts.M05 = read.csv("Ring Width Chronologies/M05/DetrendingLandsat/M05_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M05), lts.M05$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M05), lts.M05$Avg_CC_Fitted)
# M06
lts.M06 = read.csv("Ring Width Chronologies/M06/DetrendingLandsat/M06_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M06), lts.M06$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M06), lts.M06$Avg_CC_Fitted)
# M07
lts.M07 = read.csv("Ring Width Chronologies/M07/DetrendingLandsat/M07_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M07), lts.M07$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M07), lts.M07$Avg_CC_Fitted)
# M13
lts.M13 = read.csv("Ring Width Chronologies/M13/DetrendingLandsat/M13_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M13), lts.M13$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M13), lts.M13$Avg_CC_Fitted)
# M17
lts.M17 = read.csv("Ring Width Chronologies/M17/DetrendingLandsat/M17_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M17), lts.M17$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M17), lts.M17$Avg_CC_Fitted)
# M20
lts.M20 = read.csv("Ring Width Chronologies/M20/DetrendingLandsat/M20_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M20), lts.M20$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M20), lts.M20$Avg_CC_Fitted)
# M26
lts.M26 = read.csv("Ring Width Chronologies/M26/DetrendingLandsat/M26_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M26), lts.M26$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M26), lts.M26$Avg_CC_Fitted)
# M27
lts.M27 = read.csv("Ring Width Chronologies/M27/DetrendingLandsat/M27_Landsat_1.csv",
row.names = 1)
plot(rownames(lts.M27), lts.M27$Avg_CC_Median, ylab = "Landsat-derived %CC", xlab = "")
lines(rownames(lts.M27), lts.M27$Avg_CC_Fitted)
#####
# LTS names
#lts.F07
#lts.F15
#lts.F21
#lts.F23
#lts.F25
#lts.F30
#lts.F33
#lts.M01
#lts.M05
#lts.M06
#lts.M07
#lts.M13
#lts.M17
#lts.M20
#lts.M26
#lts.M27
##### Updated lines.segmented function #####
my.seglines = function (x, term, bottom = TRUE, shift = TRUE, conf.level = 0.95,
k = 50, pch = 18, rev.sgn = FALSE, ...)
{
if (missing(term)) {
if (length(x$nameUV$Z) > 1) {
stop("please, specify `term'")
}
else {
term <- x$nameUV$Z
}
}
ss <- list(...)
metodo <- if (!is.null(ss$method))
ss$method
else "delta"
colore <- if (is.null(ss$col))
1
else ss$col
usr <- par("usr")
h <- (usr[4] - usr[3])/abs(k)
y <- if (bottom)
usr[3] + h
else usr[4] - h
m <- confint.segmented(object = x, parm = term, level = conf.level,
rev.sgn = rev.sgn, digits = 4, method = metodo) # digits = 15
m <- matrix(m, ncol = 3)
if (nrow(m) > 1)
m <- m[order(m[, 1]), ]
est.psi <- m[, 1]
lower.psi <- m[, 2]
upper.psi <- m[, 3]
if (length(est.psi) > 1) {
y <- if (shift)
y + seq(-h/2, h/2, length = length(est.psi))
else rep(y, length(est.psi))
}
segments(lower.psi, y, upper.psi, y, ...)
points(est.psi, y, type = "p", pch = pch, col = colore)
} # rounds to nearest integer (year)
##### F07 Segmented Regression #####
#####
### RWI: F07Dec ###
# rwi.F07Dec = add_rownames(rwl.F07Dec.mne.crn, var = "year")
# rwi.F07Dec$year = as.numeric(rwi.F07Dec$year) # Years column in chr by default
#
# # Build and Plot Simple Linear Model #
# lm.F07Dec = lm(xxxstd ~ year, data = rwi.F07Dec)
# summary(lm.F07Dec) # Linear model
#
# plot(lm.F07Dec) # Diagnostic plots
# # Looks good
#
# plot(rwi.F07Dec$year, rwi.F07Dec$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
# lines(rwi.F07Dec$year, fitted(lm.F07Dec), lwd = 3, col = "gray48")
# # Not good fit
# # Est. breakpoints: 1965 (maybe), 1995
#
# # Select Breakpoints #
# selgmented(lm.F07Dec, type = "bic", Kmax = 10) # Experimental, uses pscore / davies
# # Select number (max = 10) and estimated year of breakpoints
# # Using BIC allows to compare models with more than 2 breakpoints
# # Selected 1 breakpoint at 1994
#
# davies.test(lm.F07Dec, k = round(nrow(rwi.F07Dec) / 5)) # Default k = 10
# # Estimate first break point. Check 20% of years
# # Best at 1996, significant
#
# pscore.test(lm.F07Dec, k = round(nrow(rwi.F07Dec) / 5))
# # Tests for the existence of one breakpoint
# # Significant for 1 breakpoint
#
# # Build Segmented Model #
# # Run preliminary automatic segmentation and plot results
# #slm.F07Dec = segmented.lm(lm.F07Dec, psi = NA,
# # control = seg.control(n.boot = 0, fix.npsi = FALSE,
# # display = TRUE, it.max = 100))
# #summary(slm.F07Dec) # Expected to overestimate number of breakpoints
#
# slm.F07Dec = segmented.lm(lm.F07Dec, psi = c(1994), # psi based on selgmented
# control = seg.control(display = TRUE, n.boot = 50)) # n.boot 0
# summary(slm.F07Dec)
#
# davies.test(slm.F07Dec, k = round(nrow(rwi.F07Dec) / 5))
# # Estimate possible additional breakpoint
# # Not significant (1964, p = 0.55)
#
# pscore.test(slm.F07Dec, k = round(nrow(rwi.F07Dec) / 5), more.break = TRUE)
# # Test for an additional breakpoint
# # Not significant
#
# draw.history(slm.F07Dec) # Changes if n.boot = 0
# dev.off()
#
# plot(slm.F07Dec) # Preliminary plot
#
# confint(slm.F07Dec, digits = 4) # 1.96 x SE at 95% CI
#
# plot(rwi.F07Dec$year, rwi.F07Dec$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
# plot(slm.F07Dec, add = TRUE, res = FALSE, lwd = 3, lty = 2) # Segmented fit
# my.seglines(slm.F07Dec, col = "blue", bottom = TRUE) # Rounded breakpoint CIs
# abline(v = round(confint(slm.F07Dec)[1]), lty = 2, col = "blue") # Round vertical break
#
# # MK Significant and TS Slope for Segments #
# brks = slm.F07Dec$psi
# brk1 = round(brks[1,2])
# brk1
#
# seg1 = subset(rwi.F07Dec, year <= brk1)
# seg1$year
# seg2 = subset(rwi.F07Dec, year >= brk1)
# seg2$year
#
# # mk.test(seg1$xxxstd) # Adjust based on breakpoint years from above
# # Mann-Kendall test for significant slope, no correction for autocorrelation
# sens.slope(seg1$xxxstd) # Includes mk.test p-value
# mmkh(seg1$xxxstd)
# #Mann-Kendal modified for autocorrelated data, shows old results too + Sen's slope
#
# sens.slope(seg2$xxxstd)
# mmkh(seg2$xxxstd)
#
# # Plot Sens splope using zyp method
# seg1.lm = zyp.sen(xxxstd ~ year, seg1)
# seg2.lm = zyp.sen(xxxstd ~ year, seg2)
# # Gives Sen slope and median intercept
#
# seg1$slope = seg1.lm$coefficients[2] * seg1$year + seg1.lm$coefficients[1]
# seg2$slope = seg2.lm$coefficients[2] * seg2$year + seg2.lm$coefficients[1]
#
# # Update Plot
# lines(seg1$year, seg1$slope, lwd = 3, col = "gray48") # Color based on sig
# lines(seg2$year, seg2$slope, lwd = 3, col = "red3") # Color based on sig
#bbsmk(rwi.F07Dec$xxxstd[51:77]) # Nonparametric block bootstrapped Mann-Kendal trend test
#bbssr(rwi.F07Dec$xxxstd[51:77]) # Nonparametric block bootstrapped Spearmans Rank Correlation trend test
#mkttest(rwi.F07Dec$xxxstd[51:77]) # Same as mk.test, but less readible
#mmky(rwi.F07Dec$xxxstd[1:25]) # Other modified MK, seems to lower p-value
#tfpwmk(rwi.F07Dec$xxxstd[51:77]) #MK pre-whitened, includes Sen's slope
#ww.test(rwi.F07Dec$xxxstd[1:25]) # Wald-Wolf test for independence and stationarity
### RWI: F07Ace ###
rwi.F07Ace = add_rownames(rwl.F07Ace.mne.crn, var = "year")
rwi.F07Ace$year = as.numeric(rwi.F07Ace$year) # Years column in chr by default
# Build and Plot Simple Linear Model #
lm.F07Ace = lm(xxxstd ~ year, data = rwi.F07Ace)
summary(lm.F07Ace) # Linear model
#lm.F07Ace = lm(as.matrix(rwi.F07Ace[2]) ~ as.matrix(rwi.F07Ace[1]))
#summary(lm.F07Ace)
plot(lm.F07Ace) # Diagnostic plots
# Looks good
plot(rwi.F07Ace$year, rwi.F07Ace$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
lines(rwi.F07Ace$year, fitted(lm.F07Ace), lwd = 3, col = "gray48")
# Not good fit
# Est. breakpoints: 1965 (maybe), 1990
# Select Breakpoints #
selgmented(lm.F07Ace, type = "bic", Kmax = 10) # Experimental, uses pscore / davies
# Select number (max = 10) and estimated year of breakpoints
# Using BIC allows to compare models with more than 2 breakpoints
# Selected 1 breakpoint at 1993
davies.test(lm.F07Ace, k = round(nrow(rwi.F07Ace) / 5)) # Default k = 10
# Estimate first break point. Check 20% of years
# Best at 1991, significant
pscore.test(lm.F07Ace, k = round(nrow(rwi.F07Ace) / 5))
# Tests for the existence of one breakpoint
# Significant for 1 breakpoint
# Build Segmented Model #
slm.F07Ace = segmented.lm(lm.F07Ace, psi = c(1993), # psi based on selgmented
control = seg.control(display = TRUE, n.boot = 50)) # n.boot 0
summary(slm.F07Ace)
davies.test(slm.F07Ace, k = round(nrow(rwi.F07Ace) / 5))
# Estimate possible additional breakpoint
# Not significant (2007, p = 0.92)
pscore.test(slm.F07Ace, k = round(nrow(rwi.F07Ace) / 5), more.break = TRUE)
# Test for an additional breakpoint
# Not significant
draw.history(slm.F07Ace) # Changes if n.boot = 0
dev.off()
plot(slm.F07Ace) # Preliminary plot
confint(slm.F07Ace, digits = 4) # 1.96 x SE at 95% CI
plot(rwi.F07Ace$year, rwi.F07Ace$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
plot(slm.F07Ace, add = TRUE, res = FALSE, lwd = 3, lty = 2) # Segmented fit
my.seglines(slm.F07Ace, col = "blue", bottom = TRUE) # Rounded breakpoint CIs
abline(v = round(confint(slm.F07Ace)[1]), lty = 2, col = "blue") # Round vertical break
# MK Significant and TS Slope for Segments #
brks = slm.F07Ace$psi
brk1 = round(brks[1,2])
brk1
seg1 = subset(rwi.F07Ace, year <= brk1)
seg1$year
seg2 = subset(rwi.F07Ace, year >= brk1)
seg2$year
sens.slope(seg1$xxxstd) # Theil-Sen slope (outlier resistant) + Mann-Kendall p-value
mmkh(seg1$xxxstd) # Mann-Kendall modified for autocorrelated data (Neff)
sens.slope(seg2$xxxstd)
mmkh(seg2$xxxstd)
seg1.lm = zyp.sen(xxxstd ~ year, seg1) # Plot T-S slope using zyp method
seg2.lm = zyp.sen(xxxstd ~ year, seg2) # Gives Sen slope and median intercept
seg1$slope = seg1.lm$coefficients[2] * seg1$year + seg1.lm$coefficients[1] # Eq
seg2$slope = seg2.lm$coefficients[2] * seg2$year + seg2.lm$coefficients[1] # Eq
# Update Plot
lines(seg1$year, seg1$slope, lwd = 3, col = "darkgreen") # Color based on sig
lines(seg2$year, seg2$slope, lwd = 3, col = "red3") # Color based on sig
### LTS: F07 ###
lts.F07_1 = add_rownames(lts.F07, var = "year")
lts.F07_1$year = as.numeric(lts.F07_1$year) # Years column in chr by default
# Build and Plot Simple Linear Model #
lm.ltsF07 = lm(Avg_CC_Median ~ year, data = lts.F07_1)
summary(lm.ltsF07) # Linear model
plot(lm.ltsF07) # Diagnostic plots
# Residuals vs. fitted/leverage not normal
plot(lts.F07_1$year, lts.F07_1$Avg_CC_Median, type = "l", ylab = "RWI", xlab = "Year")
lines(lts.F07_1$year, fitted(lm.ltsF07), lwd = 3, col = "darkgreen")
# Significant, residuals not normal
# Est. breakpoints: 1980 (maybe), 2005
# Select Breakpoints #
selgmented(lm.ltsF07, type = "bic", Kmax = 10) # Experimental, uses pscore / davies
# Select number (max = 10) and estimated year of breakpoints
# Using BIC allows to compare models with more than 2 breakpoints
# Selected 3 breakpoints at 1981, 1984, 2005 (1 breakpoint also low)
davies.test(lm.ltsF07, k = 10) # 20% or 10 (whatever larger)
# Estimate first break point. Check 20% of years
# Best at 2002, significant
pscore.test(lm.ltsF07, k = 10) # 20% or 10
# Tests for the existence of one breakpoint
# Significant for 1 breakpoint
# Build Segmented Model #
slm.ltsF07 = segmented.lm(lm.ltsF07, psi = c(1981,1984,2005), # psi based on selgmented
control = seg.control(display = TRUE, n.boot = 50)) # n.boot 0
summary(slm.ltsF07)
plot(slm.ltsF07) # Preliminary plot
davies.test(slm.ltsF07, k = 10) # 20% or 10
# Estimate possible additional breakpoint
# Not significant (1993, p = 0.81)
pscore.test(slm.ltsF07, k = 10, more.break = TRUE) # 20% or 10
# Test for an additional breakpoint
# Not significant
draw.history(slm.ltsF07) # Changes if n.boot = 0
dev.off()
confint(slm.ltsF07, digits = 4) # 1.96 x SE at 95% CI
plot(lts.F07_1$year, lts.F07_1$Avg_CC_Median, type = "l", ylab = "%CC", xlab = "Year")
plot(slm.ltsF07, add = TRUE, res = FALSE, lwd = 3, lty = 2) # Segmented fit
my.seglines(slm.ltsF07, col = "blue", bottom = TRUE) # Breakpoint CIs
abline(v = round(confint(slm.ltsF07)[1:3]), lty = 2, col = "blue") # Breakpoint vertical
# MK Significant and TS Slope for Segments #
brks = slm.ltsF07$psi
brk1 = round(brks[1,2])
brk1
brk2 = round(brks[2,2])
brk2
brk3 = round(brks[3,2])
brk3
seg1 = subset(lts.F07_1, year <= brk1)
seg1$year
seg2 = subset(lts.F07_1, year >= brk1 & year <= brk2)
seg2$year
seg3 = subset(lts.F07_1, year >= brk2 & year <= brk3)
seg3$year
seg4 = subset(lts.F07_1, year >= brk3)
seg4$year
sens.slope(seg1$Avg_CC_Median) # Theil-Sen slope (outlier resistant) + Mann-Kendall p-value
mmkh(seg1$Avg_CC_Median) # Mann-Kendall modified for autocorrelated data (Neff)
sens.slope(seg2$Avg_CC_Median)
mmkh(seg2$Avg_CC_Median)
sens.slope(seg3$Avg_CC_Median)
mmkh(seg3$Avg_CC_Median)
sens.slope(seg4$Avg_CC_Median)
mmkh(seg4$Avg_CC_Median)
seg1.lm = zyp.sen(Avg_CC_Median ~ year, seg1) # Plot T-S slope using zyp method
seg2.lm = zyp.sen(Avg_CC_Median ~ year, seg2) # Gives Sen slope and median intercept
seg3.lm = zyp.sen(Avg_CC_Median ~ year, seg3)
seg4.lm = zyp.sen(Avg_CC_Median ~ year, seg4)
seg1$slope = seg1.lm$coefficients[2] * seg1$year + seg1.lm$coefficients[1] # Eq
seg2$slope = seg2.lm$coefficients[2] * seg2$year + seg2.lm$coefficients[1] # Eq
seg3$slope = seg3.lm$coefficients[2] * seg3$year + seg3.lm$coefficients[1] # Eq
seg4$slope = seg4.lm$coefficients[2] * seg4$year + seg4.lm$coefficients[1] # Eq
# Final Plot
lines(seg1$year, seg1$slope, lwd = 3, col = "gray48") # Color based on sig
lines(seg2$year, seg2$slope, lwd = 3, col = "gray48") # Color based on sig
lines(seg3$year, seg3$slope, lwd = 3, col = "darkgreen") # Color based on sig
lines(seg4$year, seg4$slope, lwd = 3, col = "red3") # Color based on sig
#####
##### F15 Segmented Regression #####
#####
### RWI: F15Dec ###
# rwi.F15Dec = add_rownames(rwl.F15Dec.mne.crn, var = "year")
# rwi.F15Dec$year = as.numeric(rwi.F15Dec$year) # Years column in chr by default
#
# # Build and Plot Simple Linear Model #
# lm.F15Dec = lm(xxxstd ~ year, data = rwi.F15Dec)
# summary(lm.F15Dec) # Linear model
#
# plot(lm.F15Dec) # Diagnostic plots
# # Looks good
#
# plot(rwi.F15Dec$year, rwi.F15Dec$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
# lines(rwi.F15Dec$year, fitted(lm.F15Dec), lwd = 3, col = "darkgreen")
# # Significant
# # Est. breakpoints: 1915, 1935, 1980, 2000, 2010
#
# # Select Breakpoints #
# selgmented(lm.F15Dec, type = "bic", Kmax = 10) # Experimental, uses pscore / davies
# # Select number (max = 10) and estimated year of breakpoints
# # Using BIC allows to compare models with more than 2 breakpoints
# # Selected 4 breakpoints at 1934, 1980, 1995, 2013
#
# davies.test(lm.F15Dec, k = round(nrow(rwi.F15Dec) / 5)) # Default k = 10
# # Estimate first break point. Check 20% of years
# # Best at 1929, significant
#
# pscore.test(lm.F15Dec, k = round(nrow(rwi.F15Dec) / 5))
# # Tests for the existence of one breakpoint
# # Significant for 1 breakpoint
#
# # Build Segmented Model #
# slm.F15Dec = segmented.lm(lm.F15Dec, psi = c(1934,1980,1995,2013), # psi based on selgmented
# control = seg.control(display = TRUE, n.boot = 50)) # n.boot 0
# summary(slm.F15Dec)
# plot(slm.F15Dec) # Preliminary plot
#
# davies.test(slm.F15Dec, k = round(nrow(rwi.F15Dec) / 5))
# # Estimate possible additional breakpoint
# # Not significant (1913, p = 1)
#
# pscore.test(slm.F15Dec, k = round(nrow(rwi.F15Dec) / 5), more.break = TRUE)
# # Test for an additional breakpoint
# # Not significant
#
# draw.history(slm.F15Dec) # Changes if n.boot = 0
# dev.off()
#
# confint(slm.F15Dec, digits = 4) # 1.96 x SE at 95% CI
#
# plot(rwi.F15Dec$year, rwi.F15Dec$xxxstd, type = "l", ylab = "RWI", xlab = "Year")
# plot(slm.F15Dec, add = TRUE, res = FALSE, lwd = 3, lty = 2) # Segmented fit
# my.seglines(slm.F15Dec, col = "blue", bottom = TRUE) # Rounded breakpoint CIs
# abline(v = round(confint(slm.F15Dec)[1:4]), lty = 2, col = "blue") # Round vertical break
#
# # MK Significant and TS Slope for Segments #
# brks = slm.F15Dec$psi
# brk1 = round(brks[1,2])
# brk1
# brk2 = round(brks[2,2])
# brk2
# brk3 = round(brks[3,2])
# brk3
# brk4 = round(brks[4,2])
# brk4
#
# seg1 = subset(rwi.F15Dec, year <= brk1)
# seg1$year
# seg2 = subset(rwi.F15Dec, year >= brk1 & year <= brk2)
# seg2$year
# seg3 = subset(rwi.F15Dec, year >= brk2 & year <= brk3)
# seg3$year
# seg4 = subset(rwi.F15Dec, year >= brk3 & year <= brk4)
# seg4$year
# seg5 = subset(rwi.F15Dec, year >= brk4)
# seg5$year
#
# sens.slope(seg1$xxxstd) # Theil-Sen slope (outlier resistant) + Mann-Kendall p-value
# mmkh(seg1$xxxstd) # Mann-Kendall modified for autocorrelated data (Neff)
#
# sens.slope(seg2$xxxstd)
# mmkh(seg2$xxxstd)
#
# sens.slope(seg3$xxxstd)
# mmkh(seg3$xxxstd)
#
# sens.slope(seg4$xxxstd)
# mmkh(seg4$xxxstd)
#
# sens.slope(seg5$xxxstd)
# mmkh(seg5$xxxstd)
#
# seg1.lm = zyp.sen(xxxstd ~ year, seg1) # Plot T-S slope using zyp method
# seg2.lm = zyp.sen(xxxstd ~ year, seg2) # Gives Sen slope and median intercept