diff --git a/RProject/Scripts/aggregation.R b/RProject/Scripts/aggregation.R index 5aae908..2339800 100644 --- a/RProject/Scripts/aggregation.R +++ b/RProject/Scripts/aggregation.R @@ -14,7 +14,7 @@ library(dplyr) library(readxl) lastChar <- function(str){ - substr(str, nchar(str)-2, nchar(str)) + substr(str, nchar(str), nchar(str)) } ############################################################################## diff --git a/RProject/Scripts/article.R b/RProject/Scripts/article.R index 769872f..b396463 100644 --- a/RProject/Scripts/article.R +++ b/RProject/Scripts/article.R @@ -110,31 +110,92 @@ summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"true.value.in.ci.p"]) summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"true.value.in.ci.p"]) +#################################################### +# TABLES +#################################################### + + +########################## +# TABLES NO DIF RECOVERY +########################## +res.dat$dif.dir <- sign(res.dat$dif.size) +res.dat.dif$dif.dir <- sign(res.dat.dif$dif.size) + +tabs1 <- res.dat[res.dat$dif.size==0, + c("scenario","N","J","M","eff.size", + "h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" + )] +tabs1 <-reshape(data = tabs1,direction = "wide", idvar = c("scenario","J","M","eff.size"),timevar = "N") +write.csv(tabs1,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs1.csv") + + +tabs2 <- res.dat[res.dat$dif.size!=0, + c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif", + "h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" + )] +tabs2 <-reshape(data = tabs2,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") +tabs2$dif.size <- abs(tabs2$dif.size) +write.csv(tabs2,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs2.csv") + +########################## +# TABLES PERF DIF RECOVERY +########################## +tabs3 <- res.dat.dif[res.dat.dif$dif.size!=0, + c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif", + "h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" + )] +tabs3 <-reshape(data = tabs3,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") +tabs3$dif.size <- abs(tabs3$dif.size) +write.csv(tabs3,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs3.csv") + + + + + + + + ########################## # TABLES DETECT ########################## + +# false dif detection + +tab3.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size==0, + c("scenario","N", + "dif.detected" + )] +tab3.resali <-reshape(data = tab3.resali,direction = "wide", idvar = c("scenario"),timevar = "N") +tab3.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size==0, + c("scenario","N","J","M","eff.size", + "dif.detected" + )] +tab3.rosali <-reshape(data = tab3.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size"),timevar = "N") +tab3 <- merge(tab3.rosali,tab3.resali,by="scenario",suffixes = c(".rosali",".residuals")) +write.csv(tab3,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tab3.csv") + + +# dif detection + res.dat.dif.rosali$dif.agrees.tt <- ifelse(res.dat.dif.rosali$eff.size!=0 & res.dat.dif.rosali$dif.size!=0, res.dat.dif.rosali$dif.size/res.dat.dif.rosali$eff.size<0,NA) res.dat.dif.resali$dif.agrees.tt <- ifelse(res.dat.dif.resali$eff.size!=0 & res.dat.dif.resali$dif.size!=0, res.dat.dif.resali$dif.size/res.dat.dif.resali$eff.size<0,NA) res.dat.dif.rosali$dif.dir <- sign(res.dat.dif.rosali$dif.size) res.dat.dif.resali$dif.dir <- sign(res.dat.dif.resali$dif.size) -tab3.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size!=0, +tabs4.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size!=0, c("scenario","N", "dif.detected","prop.perfect","flexible.detect","moreflexible.detect" )] -tab3.resali <-reshape(data = tab3.resali,direction = "wide", idvar = c("scenario"),timevar = "N") -tab3.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0, +tabs4.resali <-reshape(data = tabs4.resali,direction = "wide", idvar = c("scenario"),timevar = "N") +tabs4.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0, c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif", "dif.detected","prop.perfect","flexible.detect","moreflexible.detect" )] -tab3.rosali <-reshape(data = tab3.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") -tab3 <- merge(tab3.rosali,tab3.resali,by="scenario",suffixes = c(".rosali",".residuals")) -tab3 <- rbind(tab3[78:112,],tab3[1:77,]) -tab3$dif.size <- abs(tab3$dif.size) -write.csv(tab3,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tab3.csv") - - - +tabs4.rosali <-reshape(data = tabs4.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") +tabs4 <- merge(tabs4.rosali,tabs4.resali,by="scenario",suffixes = c(".rosali",".residuals")) +tabs4 <- rbind(tabs4[78:112,],tabs4[1:77,]) +tabs4$dif.size <- abs(tabs4$dif.size) +write.csv(tabs4,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs4.csv") ########################## # TABLES CAUSAL @@ -147,37 +208,22 @@ res.dat.dif.rosali$diff.power <- ifelse(res.dat.dif.rosali$scenario.type!="A",re res.dat.dif.resali$typeI.error <- ifelse(res.dat.dif.resali$scenario.type=="A",res.dat.dif.resali$h0.rejected.p,NA) res.dat.dif.resali$diff.power <- ifelse(res.dat.dif.resali$scenario.type!="A",res.dat.dif.resali$dif.power,NA) -tab4.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size!=0, +tabs5.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size!=0, c("scenario","N", "h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" )] -tab4.resali <-reshape(data = tab4.resali,direction = "wide", idvar = c("scenario"),timevar = "N") -tab4.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0, +tabs5.resali <-reshape(data = tabs5.resali,direction = "wide", idvar = c("scenario"),timevar = "N") +tabs5.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0, c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif", "h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" )] -tab4.rosali <-reshape(data = tab4.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") -tab4 <- merge(tab4.rosali,tab4.resali,by="scenario",suffixes = c(".rosali",".residuals")) -tab4 <- rbind(tab4[78:112,],tab4[1:77,]) -tab4$dif.size <- abs(tab4$dif.size) -write.csv(tab4,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tab4.csv") +tabs5.rosali <-reshape(data = tabs5.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N") +tabs5 <- merge(tabs5.rosali,tabs5.resali,by="scenario",suffixes = c(".rosali",".residuals")) +tabs5 <- rbind(tabs5[78:112,],tabs5[1:77,]) +tabs5$dif.size <- abs(tabs5$dif.size) +write.csv(tabs5,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs5.csv") -########################## -# TABLES NODIF -########################## -tabs2.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size==0, - c("scenario","N", - "dif.detected","h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" - )] -tabs2.resali <-reshape(data = tabs2.resali,direction = "wide", idvar = c("scenario"),timevar = "N") -tabs2.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size==0, - c("scenario","N","J","M","eff.size", - "dif.detected","h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias" - )] -tabs2.rosali <-reshape(data = tabs2.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size"),timevar = "N") -tabs2 <- merge(tabs2.rosali,tabs2.resali,by="scenario",suffixes = c(".rosali",".residuals")) -write.csv(tabs2,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs2.csv") @@ -222,3 +268,172 @@ summary(tab3[tab3$M==2,]$prop.perfect.200.rosali) summary(tab3[tab3$M==2,]$prop.perfect.200.residuals) summary(tab3[tab3$M==4,]$prop.perfect.200.rosali) summary(tab3[tab3$M==4,]$prop.perfect.200.residuals) + +summary(tab3[tab3$dif.size==0.3,]$prop.perfect.300.rosali) +summary(tab3[tab3$dif.size==0.3,]$prop.perfect.300.residuals) + +summary(tab3[tab3$dif.size==0.5,]$prop.perfect.300.rosali) +summary(tab3[tab3$dif.size==0.5,]$prop.perfect.300.residuals) + +summary(tab3[tab3$dif.dir==sign(tab3$eff.size),]$prop.perfect.300.rosali) +summary(tab3[tab3$dif.dir!=sign(tab3$eff.size),]$prop.perfect.300.rosali) + +summary(tab3[tab3$dif.dir==sign(tab3$eff.size),]$prop.perfect.300.residuals) +summary(tab3[tab3$dif.dir==-sign(tab3$eff.size),]$prop.perfect.300.residuals) + +summary(tab3$moreflexible.detect.300.rosali-tab3$flexible.detect.300.rosali) +summary(tab3$moreflexible.detect.300.residuals-tab3$flexible.detect.300.residuals) + + +res.dat[res.dat$N=="300" & res.dat$scenario.type=="A" & abs(res.dat$dif.size)==0.5 & + res.dat$nb.dif==2 & res.dat$J==4,] +summary(tab3[tab3$eff.size==0,]$prop.perfect.300.residuals) +summary(tab3[tab3$eff.size==0,]$prop.perfect.300.rosali) + + + +length(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0 & + res.dat.dif.rosali$prop.perfect>0.3,]$prop.perfect)/nrow(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0,]) + +summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif>0 & + res.dat.dif.rosali$prop.perfect>0.3,]$prop.perfect) + +length(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0 & + res.dat.dif.resali$prop.perfect>0.3,]$prop.perfect)/nrow(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0,]) + +summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif>0 & + res.dat.dif.resali$prop.perfect>0.3,]$prop.perfect) + + + + + + + + +########################## +# PLOTS CAUSAL +########################## + + + +### + +# Plot bias vs perf detect + +plot(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.rosali[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$bias),na.rm = T)), + type="l",ylim=c(0,0.2),lwd=2,col="red",xaxs = "i",yaxs="i",xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.resali[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$bias),na.rm = T)), + type="l",lwd=2,col="blue",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +#title("Average absolute value bias in scenarios at given perfect detection rate") + +# Plot true bias vs perf detect + +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$bias),na.rm = T)), + type="l",ylim=c(0,0.2),lwd=2,col="pink",xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$bias),na.rm = T)), + type="l",lwd=2,col="#8193f1",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +legend(x=0.535,y=0.195,legend=c('ROSALI - accounting for DIF','Residuals - accounting for DIF', + 'ROSALI - not accounting for DIF','Residuals - not accounting for DIF'), + col=c("red","blue","pink","#8193f1"),lty=c(1,2,1,2),lwd=c(2,2,2,2)) +### + + +### + +# Plot alpha vs. perfect + +plot(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="A" & res.dat.dif.rosali$prop.perfect>=x-0.1 & res.dat.dif.rosali$prop.perfect<=x+0.1,]$h0.rejected.p),na.rm = T)), + type="l",ylim=c(0,1),lwd=2,col="red",xaxs = "i",yaxs="i",xlab = "Perfect detection rate",ylab="Type-I error rate") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.resali[res.dat.dif.resali$scenario.type=="A" & res.dat.dif.resali$prop.perfect>=x-0.1 & res.dat.dif.resali$prop.perfect<=x+0.1,]$h0.rejected.p),na.rm = T)), + type="l",lwd=2,col="blue",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.rosali$scenario.type=="A" & res.dat.dif.rosali$prop.perfect>=x-0.1 & res.dat.dif.rosali$prop.perfect<=x+0.1,]$h0.rejected.p),na.rm = T)), + type="l",ylim=c(0,1),lwd=2,col="pink",xlab = "Perfect detection rate",ylab="Type-I error rate") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.resali$scenario.type=="A" & res.dat.dif.resali$prop.perfect>=x-0.1 & res.dat.dif.resali$prop.perfect<=x+0.1,]$h0.rejected.p),na.rm = T)), + type="l",lwd=2,col="#8193f1",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +#title("Average type-I error rate in scenarios at given perfect detection rate") +legend(x=0.535,y=0.98,legend=c('ROSALI - accounting for DIF','Residuals - accounting for DIF', + 'ROSALI - not accounting for DIF','Residuals - not accounting for DIF'), + col=c("red","blue","pink","#8193f1"),lty=c(1,2,1,2),lwd=c(2,2,2,2)) +abline(h=0.05,lty=3) +### + + + +### + +# Plot truevalueinci vs. perfect + +plot(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.rosali[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$true.value.in.ci.p),na.rm = T)), + type="l",ylim=c(0.5,1),lwd=2,col="red",xaxs = "i",yaxs="i",xlab = "Perfect detection rate",ylab="Average proportion of true effect in estimate CI") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.resali[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$true.value.in.ci.p),na.rm = T)), + type="l",lwd=2,col="blue",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$true.value.in.ci.p),na.rm = T)), + type="l",ylim=c(0,0.2),lwd=2,col="pink",xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$true.value.in.ci.p),na.rm = T)), + type="l",lwd=2,col="#8193f1",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") + +#title("Average proportion of true effect in estimate CI in scenarios at given perfect detection rate") +legend(x=0.535,y=0.6,legend=c('ROSALI - accounting for DIF','Residuals - accounting for DIF', + 'ROSALI - not accounting for DIF','Residuals - not accounting for DIF'), + col=c("red","blue","pink","#8193f1"),lty=c(1,2,1,2),lwd=c(2,2,2,2)) +### + + +### + +# Plot powerdif vs. perfect + +plot(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(res.dat.dif.rosali[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$diff.power,na.rm = T)), + type="l",col="red",xaxs = "i",yaxs="i",lwd=2,ylim=c(-0.5,0.5),xlab = "Perfect detection rate",ylab="Observed power - theoretical power (average)") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(res.dat.dif.resali[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$diff.power,na.rm = T)), + type="l",col="blue",lwd=2,lty=2,ylim=c(-0.2,0),xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(res.dat[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$dif.power,na.rm = T)), + type="l",col="pink",lwd=2,ylim=c(-0.2,0.1),xlab = "Perfect detection rate",ylab="Observed power - theoretical power (average)") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(res.dat[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$dif.power,na.rm = T)), + type="l",col="#8193f1",lwd=2,lty=2,ylim=c(-0.2,0),xlab = "Perfect detection rate",ylab="Average absolute value bias") +#title("Average difference with theoretical power in scenarios at given perfect detection rate") +legend(x=0.54,y=0.48,legend=c('ROSALI - accounting for DIF','Residuals - accounting for DIF', + 'ROSALI - not accounting for DIF','Residuals - not accounting for DIF'), + col=c("red","blue","pink","#8193f1"),lty=c(1,2,1,2),lwd=c(2,2,2,2)) +### + + +### + +# Plot betasame vs. perfect + +plot(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.rosali[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$beta.same.sign.truebeta.p),na.rm = T)), + type="l",ylim=c(0.5,1),lwd=2,col="red",xaxs = "i",yaxs="i",xlab = "Perfect detection rate",ylab="Average proportion of true effect of same sign as estimate") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat.dif.resali[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$beta.same.sign.truebeta.p),na.rm = T)), + type="l",lwd=2,col="blue",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.rosali$prop.perfect>=x-0.05 & res.dat.dif.rosali$prop.perfect<=x+0.05,]$beta.same.sign.truebeta.p),na.rm = T)), + type="l",ylim=c(0,0.2),lwd=2,col="pink",xlab = "Perfect detection rate",ylab="Average absolute value bias") +lines(seq(0,0.85,0.001),sapply(seq(0,0.85,0.001), + function(x) mean(abs(res.dat[res.dat.dif.resali$prop.perfect>=x-0.05 & res.dat.dif.resali$prop.perfect<=x+0.05,]$beta.same.sign.truebeta.p),na.rm = T)), + type="l",lwd=2,col="#8193f1",lty=2,xlab = "Perfect detection rate",ylab="Average absolute value bias") + +#title("Average proportion of true effect in estimate CI in scenarios at given perfect detection rate") +legend(x=0.535,y=0.6,legend=c('ROSALI - accounting for DIF','Residuals - accounting for DIF', + 'ROSALI - not accounting for DIF','Residuals - not accounting for DIF'), + col=c("red","blue","pink","#8193f1"),lty=c(1,2,1,2),lwd=c(2,2,2,2)) +###