From f16e242d9a0d6105a6168c1d1a3ad432a503ff17 Mon Sep 17 00:00:00 2001
From: Francesco Sabatini <francesco.sabatini@idiv.de>
Date: Wed, 22 Jul 2020 15:33:44 +0200
Subject: [PATCH] Cleaned the house
---
00_testing.R | 401 --------------------------------------
98_SummarizeSimulations.R | 144 --------------
2 files changed, 545 deletions(-)
delete mode 100644 00_testing.R
delete mode 100644 98_SummarizeSimulations.R
diff --git a/00_testing.R b/00_testing.R
deleted file mode 100644
index 490bd32..0000000
--- a/00_testing.R
+++ /dev/null
@@ -1,401 +0,0 @@
-setwd("/data/sPlot/users/Helge/HIDDEN")
-library(tidyverse)
-library(SYNCSA)
-library(vegan)
-library(abind)
-library(ade4)
-source("99_HIDDEN_functions.R")
-
-#library(MatrixCorrelation)
-
-### Import data
-#Bi <- read_delim("_data/B_Simulated.txt", n_max = 50, delim="\t", col_names = F)[,1:2]
-#Bi <- read_delim("_data/Two_Simulated_Datasets/B_Simulated.txt", n_max = 50, delim="\t", col_names = F)[,1:2]
-#Bi <- read_delim("_data/SimulatedData_Neutral/B_Simulated.txt", n_max = 50, delim="\t", col_names = F)[,1:2]
-Bi <- read_delim("_data/SimulatedData_PartialFiltering_Feedback/B_Simulated.txt", n_max = 50, delim="\t", col_names = F)[,1:5]
-Bi <- Bi %>%
- #dplyr::rename(trait1=X1, trait2=X2) %>%
- mutate(rown = paste0("Sp", 1:50)) %>%
- column_to_rownames("rown")
-colnames(Bi) <- paste0("traits", 1:ncol(Bi))
-Bi <- as.matrix(Bi)
-
-#Wi <- read_delim("_data/W_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:50]
-#Wi <- read_delim("_data/Two_Simulated_Datasets/W_Simulated_B.txt", n_max = 200, delim="\t", col_names = F)[,1:50]
-#Wi <- read_delim("_data/SimulatedData_Neutral/W_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:50]
-Wi <- read_delim("_data/SimulatedData_PartialFiltering_Feedback/W_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:50]
-Wi <- Wi %>%
- mutate(rown = paste0("Site", 1:200)) %>%
- column_to_rownames("rown")
-colnames(Wi) <- paste0("Sp", 1:50)
-Wi <- as.matrix(Wi)
-
-#Ei <- read_delim("_data/E_Simulated.txt", n_max = 2, delim="\t", col_names = F)[,1:200]
-#Ei <- read_delim("_data/Two_Simulated_Datasets/E_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:2]
-#Ei <- read_delim("_data/SimulatedData_Neutral/E_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:2]
-Ei <- read_delim("_data/SimulatedData_PartialFiltering_Feedback/E_Simulated.txt", n_max = 200, delim="\t", col_names = F)[,1:2]
-Ei <- as.data.frame(Ei)
-rownames(Ei) <- paste0("Site", 1:200)
-colnames(Ei) <- paste0("Env", 1:2)
-Ei$Env3 <- Ei$Env1*Ei$Env2
-Ei <- (as.matrix(Ei))
-
-
-
-
-## create list of indices for each combination of traits up to a max number of interactions
-n.traits <- ncol(Bi)
-max.inter.t <- 1 #ncol(Bi)
-allcomb.t <- NULL
-for(n.inter in 1:max.inter.t){
- allcomb.t <- c(allcomb.t, combn(1:n.traits, n.inter, simplify=F))
-}
-## same for environmental variables
-n.env <- ncol(Ei)
-max.inter.env <- ncol(Ei)
-allcomb.env <- NULL
-for(n.inter in 1:max.inter.env){
- allcomb.env <- c(allcomb.env, combn(1:n.env, n.inter, simplify=F))
-}
-
-names.t <- unlist(lapply(allcomb.t, paste, collapse="_"))
-names.env <- unlist(lapply(allcomb.env, paste, collapse="_"))
-comb.list <- expand.grid(names.t, names.env)
-colnames(comb.list) <- c("trait", "env")
-
-
-
-##Run on simulated data
-corXY <- NULL
-for(i in 1:length(allcomb.t)){
- tt <- unlist(allcomb.t[i])
- corXY <- rbind(corXY,
- get.corXY(comm=Wi, trait=Bi, trait.sel=tt, stat="RV"))
- print(i)
-}
-corXY %>% arrange(Test, desc(Coef))
-
-
-corTE <- NULL
-for(i in 1:nrow(comb.list)){
- tt <- as.numeric(unlist(strsplit(as.character(comb.list[i, "trait"]), "_")))
- ee <- as.numeric(unlist(strsplit(as.character(comb.list[i, "env"]), "_")))
- corTE <- rbind(corTE,
- get.corTE(comm=Wi, trait=Bi, envir = Ei, trait.sel = tt, env.sel=ee, stat="RV"))
- print(i)
-}
-
-corTE %>% arrange(Test, desc(Coef))
-
-
-corXE <- NULL
-for(i in 1:nrow(comb.list)){
- tt <- as.numeric(unlist(strsplit(as.character(comb.list[i, "trait"]), "_")))
- ee <- as.numeric(unlist(strsplit(as.character(comb.list[i, "env"]), "_")))
- corXE <- rbind(corXE,
- get.corXE(comm=Wi, trait=Bi, envir = Ei, trait.sel = tt, env.sel=ee, stat="RV"))
- print(i)
-}
-
-corXE %>% arrange(Test, desc(Coef))
-
-
-
-### Create Simulated dataset and calculate corXY and corTE
-#create list of permuted trait matrices
-nperm <- 199
-Bi.perm <- lapply(1:nperm, FUN=function(x){
- tmp <- Bi[sample(1:nrow(Bi)),]
- rownames(tmp) <- rownames(Bi)
- return(tmp)
- }
- )
-
-
-
-## corXY on permuted
-corXY.perm <- matrix(NA, nrow=length(allcomb.t), ncol=nperm, dimnames = list(paste("t", names.t, sep=""),
- paste("p",1:nperm, sep="")))
-for(i in 1:length(allcomb.t)){
- tt <- unlist(allcomb.t[i])
- for(b in 1:nperm){
- corXY.perm[i,b] <- get.corXY(comm=Wi, trait=Bi.perm[[b]], trait.sel=tt, stat="RV")$Coef}
- print(i)
-}
-
-
-
-## transform obs to SES
-corXY.perm.df <- get.SES(corXY, corXY.perm, stat="RV") %>%
- rowwise() %>%
- mutate(nvar=length(unlist(str_split(Trait.comb, "_")))) #%>%
-# arrange(nvar) %>%
-# ungroup() %>%
-# filter(grepl(pattern = "1", x = Trait.comb)) %>%
-# group_by(nvar) %>%
-# arrange(nvar, desc(obs)) %>%
-# ungroup() %>%
-# mutate(seq=row_number())
-
-
-
-
-
-
-ggplot(data=corXY.perm.df) +
- #geom_segment(aes(x=conf.m, xend=conf.p, y=as.numeric(as.factor(Trait.comb)), yend=as.numeric(as.factor(Trait.comb)))) +
- geom_segment(aes(x=conf.m, xend=conf.p, y=seq, yend=seq)) +
- geom_point(aes(x=obs, y=seq), pch=15) +
- scale_y_continuous(breaks=corXY.perm.df$seq,
- labels=corXY.perm.df$Trait.comb)
-
-
-## corTE on permuted
-corTE.perm <- array(NA, dim = list(length(allcomb.t),
- nperm,
- length(allcomb.env)),
- dimnames = list(paste("t", names.t, sep=""),
- paste("p",1:nperm, sep=""),
- paste("e", names.env, sep="")))
-
-for(i in 1:length(allcomb.t)){
- tt <- unlist(allcomb.t[i])
- for(e in 1:length(allcomb.env)){
- ee <- unlist(allcomb.env[e])
- for(b in 1:nperm){
- corTE.perm[i,b,e] <- get.corTE(comm=Wi, trait=Bi.perm[[b]], envir = Ei, trait.sel=tt, env.sel=ee, stat="RV")$Coef
- }
- }
- print(i)
-}
-
-
-## transform obs to SES
-corTE.perm.df <- get.SES(corTE, corTE.perm, stat="RV") %>%
- rowwise() %>%
- mutate(nvar=length(unlist(str_split(Trait.comb, "_")))) %>%
- mutate(nenv=length(unlist(str_split(Env.comb, "_")))) %>%
- ungroup() %>%
- arrange(nvar) %>%
- mutate(Trait.comb=factor(Trait.comb, levels=paste("t", names.t, sep=""))) %>%
- #filter(grepl(pattern = "1", x = Trait.comb) & nvar==1) %>%
- #filter(grepl(pattern = "1", x = Env.comb)) %>%
- filter(nenv==1, SES>1.96) %>%
- group_by(nvar) %>%
- arrange(nvar, desc(obs)) %>%
- ungroup() %>%
- mutate(seq=row_number()) %>%
- arrange(nvar, seq) %>%
- mutate(Env.comb=factor(Env.comb))
-
-
-# scatterplot with color coding
-ggplot(data=corTE.perm.df) +
- geom_point(aes(x=as.factor(Env.comb), y=Trait.comb, col=obs, size=obs), pch=16) +
- scale_color_viridis(option="magma", direction = -1) +
- #scale_x_continuous(name="Env",
- # breaks=(1:3),
- # labels=levels(corTE.perm.df$Env.comb), limits = c(0.5,3.5)) +
- theme_bw()
-
-# faceted spider plot
-ggplot(data=corTE.perm.df) +
- geom_segment(aes(x=conf.m, xend=conf.p, y=seq, yend=seq)) +
- geom_point(aes(x=obs, y=seq), pch=15) +
- scale_y_continuous(breaks=corTE.perm.df$seq,
- labels=corTE.perm.df$Trait.comb) +
- facet_wrap(factor(corTE.perm.df$Env.comb), nrow=2)
-
-
-ggplot(data=corTE.perm.df) +
- #geom_point(aes(x=as.factor(Env.comb), y=Trait.comb, col=obs, size=obs), pch=16) +
- geom_segment(aes(x=conf.m, xend=conf.p, y=Trait.comb, yend=Trait.comb)) +
- geom_point(aes(x=obs, y=Trait.comb), pch=17, cex=2) +
- #scale_color_distiller(type="seq", direction = -1) +
- #scale_color_viridis(option="magma", direction = -1) +
- #scale_x_continuous(name="Env",
- # breaks=(1:3),
- # labels=levels(corTE.perm.df$Env.comb), limits = c(0.5,3.5)) +
- theme_bw() +
- facet_grid(Env.comb~.)
-
-
-
-
-## corXE on permuted
-corXE.perm <- array(NA, dim = list(length(allcomb.t),
- nperm,
- length(allcomb.env)),
- dimnames = list(paste("t", names.t, sep=""),
- paste("p",1:nperm, sep=""),
- paste("e", names.env, sep="")))
-
-for(i in 1:length(allcomb.t)){
- tt <- unlist(allcomb.t[i])
- for(e in 1:length(allcomb.env)){
- ee <- unlist(allcomb.env[e])
- for(b in 1:nperm){
- corXE.perm[i,b,e] <- get.corXE(comm=Wi, trait=Bi.perm[[b]], envir = Ei, trait.sel=tt, env.sel=ee, stat="RV")$Coef
- }
- print(ee)
- }
- print(paste("i=",i, "tt=", paste(tt, collapse=" "), "done"))
-}
-
-## transform obs to SES
-corXE.perm.df <- get.SES(corXE, corXE.perm, stat="RV") %>%
- rowwise() %>%
- mutate(nvar=length(unlist(str_split(Trait.comb, "_")))) %>%
- mutate(nenv=length(unlist(str_split(Env.comb, "_")))) %>%
- ungroup() %>%
- arrange(nvar) %>%
- mutate(Trait.comb=factor(Trait.comb, levels=paste("t", names.t, sep=""))) %>%
- #filter(grepl(pattern = "1", x = Trait.comb) & nvar==1) %>%
- #filter(grepl(pattern = "1", x = Env.comb)) %>%
- filter(nenv==1, SES>1.96) %>%
- group_by(nvar) %>%
- arrange(nvar, desc(obs)) %>%
- ungroup() %>%
- mutate(seq=row_number()) %>%
- arrange(nvar, seq) %>%
- mutate(Env.comb=factor(Env.comb))
-
-ggplot(data=corXE.perm.df) +
- #geom_point(aes(x=as.factor(Env.comb), y=Trait.comb, col=obs, size=obs), pch=16) +
- geom_segment(aes(x=conf.m, xend=conf.p, y=Trait.comb, yend=Trait.comb)) +
- geom_point(aes(x=obs, y=Trait.comb), pch=17, cex=2) +
- #scale_color_distiller(type="seq", direction = -1) +
- #scale_color_viridis(option="magma", direction = -1) +
- #scale_x_continuous(name="Env",
- # breaks=(1:3),
- # labels=levels(corTE.perm.df$Env.comb), limits = c(0.5,3.5)) +
- theme_bw() +
- facet_grid(Env.comb~.)
-
-
-
-break()
-
-
-
-
-
-
-
-### parallel alternative to above
-ncores=4
-library(parallel)
-library(doParallel)
-cl <- makeForkCluster(ncores, outfile="" )
-registerDoParallel(cl)
-
-mybind.array <- function(x, y){
- require(abind)
- return(abind(x,y, along=3))
-}
-
-
-#foreach(i=1:length(comb.list), .combine=mybind.array ) %do% {
-myarray <- foreach(i=1:5, .combine=mybind.array ) %do% {
- tt <- as.numeric(unlist(strsplit(as.character(comb.list[i, "trait"]), split="_")))
- ee <- as.numeric(unlist(strsplit(as.character(comb.list[i, "env"]), split="_")))
- corTE.tmp <- matrix(NA, nrow=nperm, ncol=1)
- for(b in 1:nperm){
- corTE.tmp[b,1] <- get.corTE(comm=Wi, trait=Bi.perm[[b]], envir = Ei, trait.sel=tt, env.sel=ee, stat="RV")$Coef
- }
- print(i)
-}
-
-stopCluster(cl)
-
-
-
-
-
-#### deprecated
-#### Observed
-corXY <- NULL #data.frame(Trait.comb=NULL, Env.comb=NULL, Test=NULL, Coef=NULL, pvalue=NULL)
-corTE <- NULL
-for(i in 1:length(allcomb.t)) {
- ii <- unlist(allcomb.t[i])
- lab.tmp <- paste(ii, collapse="_")
- syn.out.tmp <- syncsa(Wi, traits = Bi[,ii, drop=F], envir = Ei,
- checkdata = TRUE, ro.method = "mantel", method = "pearson", dist = "euclidean", scale = TRUE,
- scale.envir = F, ranks = TRUE, put.together = NULL, na.rm = FALSE, strata = NULL,
- permutations = 1, parallel = NULL, notification = TRUE)
- mantel.tmp <- mantel(W.beals.d, dist(syn.out.tmp$matrices$X))
- RV.tmp <- RV.rtest(W.beals, as.data.frame(syn.out.tmp$matrices$X))
- prot.tmp <- protest(W.beals, syn.out.tmp$matrices$X)
-
- corXY <- rbind(corXY,
- data.frame(Trait.comb=lab.tmp, Test="Mantel", Coef=mantel.tmp$statistic, pvalue=mantel.tmp$signif),
- data.frame(Trait.comb=lab.tmp, Test="RV", Coef=RV.tmp$obs, pvalue=RV.tmp$pvalue),
- data.frame(Trait.comb=lab.tmp, Test="Procrustes", Coef=prot.tmp$t0, pvalue=prot.tmp$signif))
- print(paste("Trait=", paste(ii, collapse="_")))
- for(e in 1:length(allcomb.env)){
- ee <- unlist(allcomb.env[e])
- lab.env <- paste(ee, collapse="_")
- mantel.tmp <- mantel(dist(syn.out.tmp$matrices$E[,ee, drop=F]), dist(syn.out.tmp$matrices$T))
- RV.tmp <- RV.rtest(as.data.frame(syn.out.tmp$matrices$E[,ee, drop=F]), as.data.frame(syn.out.tmp$matrices$T))
- #prot.tmp <- protest(syn.out.tmp$matrices$E[,ee, drop=F], syn.out.tmp$matrices$T)
-
- corTE <- rbind(corTE,
- data.frame(Trait.comb=lab.tmp, Env.comb=lab.env, Test="Mantel", Coef=mantel.tmp$statistic, pvalue=mantel.tmp$signif),
- data.frame(Trait.comb=lab.tmp, Env.comb=lab.env, Test="RV", Coef=RV.tmp$obs, pvalue=RV.tmp$pvalue)#,
- #data.frame(Trait.comb=lab.tmp, Env.comb=lab.env, Test="Procrustes", Coef=prot.tmp$t0, pvalue=prot.tmp$signif)
- )
- print(paste("Env=", paste(ee, collapse="_")))
- }
-}
-
-####
-### From Zoltan Botta-Dukat
-PValue<-function(x,stat,k=99,lower=F)
- # Calculation of p-values by algorithm proposed by
- # Knijnenburg, T. A., L. F. A. Wessels, M. J. T. Reinders, and I. Shmulevich. 2009.
- # Fewer permutations, more accurate P-values. Bioinformatics 25:i161–i168.
- #
- # Parameters:
- # x = vector of test statistic generated by randomization algorithm
- # stat = observed value of test statistic
- # k = maximum number of most extreme values used for fitting generalized Pateto distribution
- # lower = if H1 is that stat is lower than expected
-{
- require(evd)
- require(DescTools)
- n<-length(x)
- if (n<k) stop("Error: Too few random values", "\n")
- if (lower)
- {
- m<-max(c(x,stat))
- x<-m-x
- stat<-m-stat
- }
- p<-(sum(stat<x)+sum(stat==x)/2)/n
- if (p<0.05)
- {
- x<-sort(x,decreasing=T)
- k<-99
- repeat
- {
- tresh<-(x[k]+x[k+1])/2
- M<-fpot(x, tresh,std.err=F)
- AD.test<-AndersonDarlingTest(x[1:k], null = "pgpd",loc=tresh,scale=M$estimate[1],shape=M$estimate[2])
- if (AD.test$p.value>0.05) break
- k<-k-1
- if (k==0) break
- }
- #if (k==0) stop("Error: Generalized Pareto distribution cannot be fitted", "\n")
- if (k==0) {
- p <- NA
- } else {
- p<-pgpd(tresh,scale=M$estimate[1],shape=M$estimate[2])
- }
- }
- return(p)
-}
-
-PValue(Coeff.var.random[,1,3],stat=coeff.var[1,4])
-x <- Coeff.var.random[,1,3]
-stat <- coeff.var[1,4]
-
diff --git a/98_SummarizeSimulations.R b/98_SummarizeSimulations.R
deleted file mode 100644
index 4b1c15e..0000000
--- a/98_SummarizeSimulations.R
+++ /dev/null
@@ -1,144 +0,0 @@
-## this script extracts all the summary.txt files from all subfolder
-## and summarizes the output for each run, trait x environment combination, and statistics
-## It then plots the summarized output
-library(tidyverse)
-
-mypath <- "_data/Experiment_04Mar2020"
-
-myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T)
-output <- NULL
-for(ff in myfiles){
- iter <- gsub(pattern="/Summary.txt$", replacement="", ff)
- iter <- strsplit(iter, split = "_")[[1]]
- iter <- as.integer(unlist(regmatches(iter, gregexpr("[[:digit:]]+", iter))))
- tmp <- read_delim(paste(mypath, ff, sep="/"), delim="\t", col_names = F) %>%
- dplyr::select(-X1, -X3, -X5, -X9, -X11, -X13) %>%
- rename(simulated=X2, trait=X4, envir=X6, stat.type=X7, stat.obs=X8, pvalue=X10, SES=X12, exp.med=X14 ) %>%
- mutate(stat.type=gsub(pattern = "^r\\(", replacement="", stat.type)) %>%
- mutate(stat.type=gsub(pattern = "\\)\\=$", replacement="", stat.type)) %>%
- mutate(stat.type=gsub(pattern = "_", replacement="\\.", stat.type)) %>%
- mutate(trait=gsub(pattern="[[:space:]]+$", replacement="", trait)) %>%
- mutate(envir=gsub(pattern="[[:space:]]+$", replacement="", envir)) %>%
- mutate(main=iter[[1]]) %>%
- mutate(ntraits=iter[[2]]) %>%
- mutate(corr=iter[[3]]) %>%
- dplyr::select(main:corr, everything())
- output <- bind_rows(output, tmp)
-}
-
-outp.summary <- output %>%
- dplyr::filter(!stat.type %in% c("XY", "XY.T", "XY.TR")) %>%
- group_by(main, ntraits, corr, trait, envir, stat.type) %>%
- summarize(stat.obs.med=median(stat.obs),
- power=mean(pvalue<=0.05),
- SES.med=median(SES),
- exp.med.med=median(exp.med),
- nsim=n()) %>%
- bind_rows(output %>%
- dplyr::filter(stat.type %in% c("XY", "XY.T", "XY.TR")) %>%
- group_by(main, ntraits, corr, trait, stat.type) %>%
- summarize(stat.obs.med=median(stat.obs),
- power=mean(pvalue<=0.05),
- SES.med=median(SES),
- exp.med.med=median(exp.med),
- nsim=n())) %>%
- dplyr::select(main:stat.type, nsim, stat.obs.med:exp.med.med) %>%
- arrange(stat.type, main, ntraits, corr, trait, envir)
-
-
-outp.summary
-
-
-get.ntraits <- function(x){
- tmp <- str_split(x, pattern = " ")[[1]]
- return(length(tmp))
-}
-
-
-
-## plotting power for XY with corr
-
-ggplot(data=outp.summary %>%
- ungroup() %>%
- rowwise() %>%
- mutate(sel.ntraits=factor(get.ntraits(trait))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- #filter(ntraits==3) %>%
- #dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- #dplyr::filter(trait %in% c("1", "2", "3")) %>%
- #dplyr::filter(trait %in% c("1", "2", "3", "1 2", "1 2 3")) %>%
- mutate(ntraits=as.factor(ntraits))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- #scale_colour_brewer(palette = "Dark2") +
- facet_grid(sel.ntraits~ntraits) +
- theme_bw() +
- theme(panel.grid = element_blank())
-
-ggsave(filename="_data/Experiment_04Mar2020/corXY_obs_Exp04March2020.png", width=6, height=5, device="png", dpi = 300, last_plot())
-
-## plotting non-parametric SES for XY with corr
-
-ggplot(data=outp.summary %>%
- ungroup() %>%
- rowwise() %>%
- mutate(sel.ntraits=factor(get.ntraits(trait))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- filter(ntraits==3) %>%
-
- #dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- #dplyr::filter(trait %in% c("1", "2", "3")) %>%
- #dplyr::filter(trait %in% c("1", "2", "3", "1 2", "1 2 3")) %>%
- mutate(ntraits=as.factor(ntraits))) +
- geom_line(aes(x=main, y=SES.med, group=trait, col=trait)) +
- #scale_colour_brewer(palette = "Dark2") +
- #facet_grid(sel.ntraits~ntraits, scales = "free") +
- theme_bw() +
- theme(panel.grid = element_blank())
-
-ggsave(filename="_data/Experiment_04Mar2020/corXY_SES_Exp04March2020.png", width=6, height=5, device="png", dpi = 300, last_plot())
-
-## plotting XE
-
-ggplot(data=outp.summary %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XE") %>%
- #dplyr::filter(envir=="1") %>%
- filter(corr==0) %>%
- filter(trait %in% c("1","2","3", "1 2")) %>%
- mutate(ntraits=as.factor(ntraits))
- #mutate(group0=paste("t", trait, " - e", envir))
-) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- scale_colour_brewer(palette = "Dark2") +
- facet_grid(envir~ntraits) +
- theme_bw() +
- theme(panel.grid = element_blank())
-
-## plotting XY.T
-
-ggplot(data=outp.summary %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY.T") %>%
- dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- mutate(ntraits=as.factor(ntraits))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- scale_colour_brewer(palette = "Dark2") +
- facet_grid(corr~ntraits) +
- theme_bw() +
- theme(panel.grid = element_blank())
-
-## plotting XY.TR
-
-ggplot(data=outp.summary %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY.TR") %>%
- dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- mutate(ntraits=as.factor(ntraits))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- scale_colour_brewer(palette = "Dark2") +
- facet_grid(corr~ntraits) +
- theme_bw() +
- theme(panel.grid = element_blank())
-
--
GitLab