diff --git a/02_Figures.R b/02_Figures.R
deleted file mode 100644
index a70509f9c5a11ba3cfc9fa4686331aceb5716d23..0000000000000000000000000000000000000000
--- a/02_Figures.R
+++ /dev/null
@@ -1,216 +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)
-
-#### import function
-FormatData <- function(myfiles){
- 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(inter=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("TY", "XY", "XY.T", "XY.TR")) %>%
- group_by(main, inter, 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("TY", "XY", "XY.T", "XY.TR")) %>%
- group_by(main, inter, 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, inter, corr, trait, envir)
- return(outp.summary)
-}
-
-
-get.ntraits <- function(x){
- tmp <- str_split(x, pattern = " ")[[1]]
- return(length(tmp))
-}
-
-
-#### FIGURE 2 #####
-mypath <- "_data/Experiment_02Mar2020_FactorInteraction&TraitCorr"
-myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T)
-outp.summary <- FormatData(myfiles)
-
-
-#e41a1c ##red
-#377eb8 ##blue
-#ffffb2 ## yellow
-#4daf4a ##green
-#984ea3 ##violet
-#ff7f00 ##orange
-
-
-mypalette <- palette(c("#e41a1c", #1 - red)
- "#ff7f00", #12 - orange
- "#984ea3", #13 - violet
- "##ffed6f", #2 - yellow
- "#4daf4a", #23 - green
- "#377eb8")) #3 - blue
-
-ggplot(data=outp.summary %>%
- mutate(main=main/100) %>%
- mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
- mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- dplyr::filter(trait %in% c("1", "2", "1 2", "3", "1 3", "2 3")) %>%
- mutate(trait=factor(trait, levels=c("1", "2", "3", "1 2", "1 3", "2 3"),
- labels=c("t1", "t2", "t3", "t1 t2", "t1 t3", "t2 t3")))) + #%>%
- #dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- #mutate(inter=as.factor(inter))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- #scale_colour_brewer(palette = "Dark2") +
- scale_color_manual("Trait\ncomb.",
- values=c("#e41a1c", #1 - red)
- "#e6ab02", #2 - yellow
- "#377eb8", #3 - blue
- "#d95f02", #12 - orange
- "#984ea3", #13 - violet
- "#4daf4a" #23 - green
- )
- ) +
- facet_grid(corr~inter) +
- theme_bw() +
- scale_x_continuous(name="Effect of factor e1 -> trait t1") +
- scale_y_continuous(name="Power") +
- theme(panel.grid = element_blank())
-
-ggsave(filename="_pics/Fig2_CorrInte_02March.png", width=6, height=5, device="png", dpi = 300, last_plot())
-
-
-
-
-#### FIGURE 3 #####
-mypath <- "_data/Experiment_04Mar2020_TraitNumber"
-myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T)
-outp.summary <- FormatData(myfiles) %>%
- rename(ntraits=inter)
-
-## plotting power for XY with corr
-add.t.label <- function(x) {
- x <- gsub(pattern=" ", replacement=" t", x=x, perl=T)
- x <- paste0("t", x)
- return(x)
-}
-
-outp.summary2 <- outp.summary %>%
- ungroup() %>%
- mutate(main=main/10) %>%
- #mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
- mutate(ntraits.lab=factor(ntraits, levels=1:5, labels=paste0("n. traits = ", 1:5))) %>%
- rowwise() %>%
- mutate(sel.ntraits=factor(get.ntraits(trait))) %>%
- mutate(sel.ntraits.lab=factor(sel.ntraits, levels=levels(sel.ntraits), labels=paste0("Comb. tier = ", levels(sel.ntraits)))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- mutate(trait=factor(trait)) %>%
- mutate(trait=factor(trait, levels=levels(trait),
- labels=add.t.label(levels(trait))))
-
-### rename null trait to "tn"
-outp.summary2 <- outp.summary2 %>%
- mutate(tn.name=paste0("t", ntraits)) %>%
- mutate(trait=str_replace(trait, pattern=tn.name, replacement="tn"))
-
-#reorder factors
-outp.summary2$trait <- factor(outp.summary2$trait, levels= c('t1', 't2','t3', 't4', 'tn',
- 't1 t2', 't1 t3','t1 t4','t1 tn','t2 t3','t2 t4','t2 tn','t3 t4','t3 tn', 't4 tn',
- 't1 t2 t3', 't1 t2 t4','t1 t2 tn','t1 t3 t4', 't1 t3 tn','t1 t4 tn','t2 t3 t4','t2 t3 tn', 't2 t4 tn','t3 t4 tn',
- 't1 t2 t3 t4', 't1 t2 t3 tn','t1 t2 t4 tn','t1 t3 t4 tn','t2 t3 t4 tn',
- 't1 t2 t3 t4 tn'))
-
-
-
-hugepalette0 <- c(RColorBrewer::brewer.pal(4, "Dark2"),
- gray(0.2),
- RColorBrewer::brewer.pal(10, "Paired"),
- RColorBrewer::brewer.pal(10, "Set3"),
- RColorBrewer::brewer.pal(5, "Pastel1"), "brown")
-#change tone of yellow of t1-t2-t4
-hugepalette0[17] <- "#ccebc5"
-
-hugepalette <- data.frame(trait=c('t1', 't2','t3', 't4', 'tn',
- 't1 t2', 't1 t3','t1 t4','t1 tn','t2 t3','t2 t4','t2 tn','t3 t4','t3 tn', 't4 tn',
- 't1 t2 t3', 't1 t2 t4','t1 t2 tn','t1 t3 t4', 't1 t3 tn','t1 t4 tn','t2 t3 t4','t2 t3 tn', 't2 t4 tn','t3 t4 tn',
- 't1 t2 t3 t4', 't1 t2 t3 tn','t1 t2 t4 tn','t1 t3 t4 tn','t2 t3 t4 tn',
- 't1 t2 t3 t4 tn'),
- trait.col=factor(hugepalette0, levels=hugepalette0))
-
-outp.summary2 <- outp.summary2 %>%
- left_join(hugepalette, by="trait")
-
-fig3 <- ggplot(data=outp.summary2) +
- geom_line(aes(x=main, y=power, group=trait, col=trait.col)) +
- scale_x_continuous(name="Effect of factor e1 -> trait t1", n.breaks = 6) +
- scale_y_continuous(name="Power") +
- facet_grid(sel.ntraits.lab~ntraits.lab) +
- scale_color_identity(guide = "legend",
- labels= hugepalette$trait) +
- theme_bw() +
- theme(panel.grid = element_blank())
-
-
-
-ncols <- c(2,4,4,3,1)
-
-
-leg.list <- list()
-col.used <- 1
-for(tier in 1:5){
- outp.summary.tier <- outp.summary2 %>% filter(sel.ntraits==tier)
- ncombinations <- length(levels(factor(outp.summary.tier$trait)))
- leg.list[[tier+1]] <- cowplot::get_legend(fig3 %+% outp.summary.tier +
- guides(col=guide_legend(ncol=ncols[tier], byrow=TRUE))+
- scale_color_identity(name=ifelse(tier==1, "Trait combination - tier 1",paste0("tier - ", tier)),
- guide = "legend",
- labels= hugepalette$trait[col.used:(col.used+ncombinations-1)])
- )
-col.used <- col.used + ncombinations
-}
-leg.list[[tier+2]] <- NULL
-
-fig3.panel <- cowplot::plot_grid(fig3 + theme(legend.position = "none"),
- cowplot::plot_grid(plotlist = leg.list, nrow = 7,
- rel_heights = c(0.05, .2,.2,.2,.2,.2, 0.15), align="hv"),
- nrow=1, rel_widths = c(0.6,.4))
-
-
-ggsave(filename="_pics/Fig3_TraitNumber.png",
- width=9, height=7, device="png", dpi = 300, fig3.panel)
-
-
-
-
-
-
-
-
-
-
diff --git a/03_Extra_Figures_Simulations.R b/03_Extra_Figures_Simulations.R
deleted file mode 100644
index fd6fcf77039688c084e386998f0eff1acbdb5fb4..0000000000000000000000000000000000000000
--- a/03_Extra_Figures_Simulations.R
+++ /dev/null
@@ -1,113 +0,0 @@
-## this script extracts all the summary.txt files from all subfolder
-## and summarizes the output for each run, trait x environment combination, and statistics
-## It then plots the summarized output
-library(tidyverse)
-
-#### import function
-FormatData <- function(myfiles){
- output <- NULL
- for(ff in myfiles){
- iter <- gsub(pattern="/Summary.txt$", 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(inter=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("TY", "XY", "XY.T", "XY.TR")) %>%
- group_by(main, inter, 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("TY", "XY", "XY.T", "XY.TR")) %>%
- group_by(main, inter, 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, inter, corr, trait, envir)
- return(outp.summary)
-}
-
-
-get.ntraits <- function(x){
- tmp <- str_split(x, pattern = " ")[[1]]
- return(length(tmp))
-}
-
-
-#### FIGURE S4 #####
-mypath <- "_data/Experiment_08Jul2020_TraitSuppression&TraitCorr"
-myfiles <- list.files(path=mypath, pattern = "Summary.txt", recursive = T)
-outp.summary <- FormatData(myfiles)
-
-
-#e41a1c ##red
-#377eb8 ##blue
-#ffffb2 ## yellow
-#4daf4a ##green
-#984ea3 ##violet
-#ff7f00 ##orange
-
-
-mypalette <- palette(c("#e41a1c", #1 - red)
- "#ff7f00", #12 - orange
- "#984ea3", #13 - violet
- "##ffed6f", #2 - yellow
- "#4daf4a", #23 - green
- "#377eb8")) #3 - blue
-
-ggplot(data=outp.summary %>%
- mutate(main=main/100) %>%
- mutate(corr=factor(corr/10, levels=c(0, 0.4, 0.8), labels=paste0("Correlation = ", c(0, 0.4, 0.8)))) %>%
- mutate(inter=factor(inter/10, levels=c(0, 0.3, 0.5), labels=paste0("Interaction = ", c(0, 0.3, 0.5)))) %>%
- ungroup() %>%
- dplyr::filter(stat.type=="XY") %>%
- dplyr::filter(trait %in% c("1", "2", "3", "4")) %>%
- mutate(trait=factor(trait, levels=c("1", "2", "3", "4"),
- labels=c("t1", "t2", "t3","tn")))) + #%>%
- #dplyr::filter(trait %in% c("1", "2", "1 2", "3")) %>%
- #mutate(inter=as.factor(inter))) +
- geom_line(aes(x=main, y=power, group=trait, col=trait)) +
- #scale_colour_brewer(palette = "Dark2") +
- #scale_color_manual("Trait\ncomb.",
- scale_color_manual("Trait",
- values=c("#e41a1c", #1 - red)
- "#e6ab02", #2 - yellow
- "#4daf4a", #23 - green
- "#377eb8", #3 - blue
- "#d95f02", #12 - orange
- "#984ea3" #13 - violet
- )
- ) +
- facet_grid(corr~inter) +
- theme_bw() +
- scale_x_continuous(name="Effect of factor e1 -> trait t1") +
- scale_y_continuous(name="Power") +
- theme(panel.grid = element_blank())
-
-ggsave(filename="_pics/Fig2_Extra_CorrInte_08Jul20.png", width=6, height=5, device="png", dpi = 300, last_plot())
-
-
-
-
-
-
-