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simul_these/RProject/Scripts/Analysis/article.R

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source(paste0(getwd(),"/functions/resali.R"))
##########################
# IGNORING DIF
##########################
########### Power
# Prepare
res.dat$dif.agrees.tt <- ifelse(res.dat$eff.size!=0 & res.dat$dif.size!=0, res.dat$dif.size/res.dat$eff.size<0,NA)
res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0
& res.dat$dif.agrees.tt,"dif.power"] <- res.dat[res.dat$scenario.type!="A"
& res.dat$nb.dif>0 & res.dat$dif.agrees.tt,]$h0.rejected.p-res.dat[res.dat$scenario.type!="A"
& res.dat$nb.dif>0 & res.dat$dif.agrees.tt,]$theoretical.power
res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0
& !res.dat$dif.agrees.tt,"dif.power"] <- res.dat[res.dat$scenario.type!="A"
& res.dat$nb.dif>0 & !res.dat$dif.agrees.tt,]$h0.rejected.p-res.dat[res.dat$scenario.type!="A"
& res.dat$nb.dif>0 & !res.dat$dif.agrees.tt,]$theoretical.power
# Histo coloré par typo
par(mfrow=c(2,1))
hist(res.dat[abs(res.dat$dif.size)==0.3 & !res.dat$dif.agrees.tt,]$dif.power,breaks = seq(-0.7,0.6,0.05),freq=F,xlim = c(-0.7,0.7),ylim=c(0,4),col=rgb(1,0,0,1/4),
main="real power - theoretical power in scenarios with DIF size 0.3",xlab="Real power - theoretical power (raw % difference)")
hist(res.dat[abs(res.dat$dif.size)==0.3 & res.dat$dif.agrees.tt,]$dif.power,breaks = seq(-0.7,0.6,0.05),freq=F,xlim = c(-0.7,0.7),ylim=c(0,4),col=rgb(0,0,1,1/4),add=T)
abline(v=0,lty=2,col="black",lwd=2)
hist(res.dat[abs(res.dat$dif.size)==0.5 & !res.dat$dif.agrees.tt,]$dif.power,breaks = seq(-0.7,0.6,0.05),freq=F,xlim = c(-0.7,0.7),ylim=c(0,4),col=rgb(1,0,0,1/4),
main="real power - theoretical power in scenarios with DIF size 0.5",xlab="Real power - theoretical power (raw % difference)")
hist(res.dat[abs(res.dat$dif.size)==0.5 & res.dat$dif.agrees.tt,]$dif.power,breaks = seq(-0.7,0.6,0.05),freq=F,xlim = c(-0.7,0.7),ylim=c(0,4),col=rgb(0,0,1,1/4),add=T)
abline(v=0,lty=2,col="black",lwd=2)
par(xpd=NA)
legend(x = -0.825,y=6.25,fill = c(rgb(1,0,0,1/4),rgb(0,0,1,1/4)),c('DIF effect contradicts treatment effect',"DIF effect concurs with treatment effect"),ncol=2)
par(mfrow=c(1,1))
# DIF and treatment opposite signs
summary(res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0 & res.dat$dif.agrees.tt,c("dif.power")])
# DIF and treatment same signs
summary(res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0 & 1-res.dat$dif.agrees.tt,c("dif.power")])
# N=50 vs 300
summary(res.dat[res.dat$scenario.type!="A" & res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$dif.agrees.tt,c("dif.power")])
summary(res.dat[res.dat$scenario.type!="A" & res.dat$N==100 & res.dat$nb.dif>0 & res.dat$dif.agrees.tt,c("dif.power")])
summary(res.dat[res.dat$scenario.type!="A" & res.dat$N==200 & res.dat$nb.dif>0 & res.dat$dif.agrees.tt,c("dif.power")])
summary(res.dat[res.dat$scenario.type!="A" & res.dat$N==300 & res.dat$nb.dif>0 & res.dat$dif.agrees.tt,c("dif.power")])
########### Treatment effect estimation sign
# Overall
summary(res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0,c("beta.same.sign.truebeta.p")])
# Worst case scenario
summary(res.dat[res.dat$scenario.type!="A" & res.dat$nb.dif>0 & res.dat$dif.agrees.tt==FALSE & abs(res.dat$dif.size)>0.3 & abs(res.dat$eff.size)==0.2,c("beta.same.sign.truebeta.signif.p")])
########### Bias
summary(res.dat[res.dat$nb.dif>0,c("bias")])
########### true value in CI
summary(abs(res.dat[res.dat$nb.dif>0,c("true.value.in.ci.p")]))
summary(abs(res.dat[res.dat$N=="50" & res.dat$nb.dif>0,c("true.value.in.ci.p")]))
##########################
# DETECTION
##########################
# Which performed better
summary(res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect)
# ROSALI better more than 10% ?
res.dat.dif.rosali$better <- res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect>0.1
table(res.dat.dif.rosali[res.dat.dif.rosali$better & res.dat.dif.rosali$nb.dif!=0,"scenario.type"])
# ROSALI worse more than 10% ?
res.dat.dif.rosali$worse <- res.dat.dif.rosali$prop.perfect-res.dat.dif.resali$prop.perfect< -0.1
res.dat.dif.rosali[res.dat.dif.rosali$worse & res.dat.dif.rosali$nb.dif!=0,]
# ROSALI perf per subsc
summary(res.dat.dif.rosali[ res.dat.dif.rosali$N==300 & res.dat.dif.rosali$nb.dif>0 & res.dat.dif.rosali$scenario.type%in%c("C","E"),]$prop.perfect)
summary(res.dat.dif.rosali[ res.dat.dif.rosali$N==300 & res.dat.dif.rosali$nb.dif>0 & res.dat.dif.rosali$scenario.type%in%c("B","D","F","G"),]$prop.perfect)
# AHRM perf per subsc
summary(res.dat.dif.resali[ res.dat.dif.resali$N==300 & res.dat.dif.resali$nb.dif>0 & res.dat.dif.resali$scenario.type%in%c("C","E"),]$prop.perfect)
summary(res.dat.dif.resali[ res.dat.dif.resali$N==300 & res.dat.dif.resali$nb.dif>0 & res.dat.dif.resali$scenario.type%in%c("B","D","F","G"),]$prop.perfect)
# False DIF detect
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"dif.detected"])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"dif.detected"])
# Causal inference NO DIF
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0,"bias"])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0,"bias"])
summary(res.dat.dif.rosali[res.dat.dif.rosali$nb.dif==0 & res.dat.dif.rosali$eff.size==0,"h0.rejected.p"])
summary(res.dat.dif.resali[res.dat.dif.resali$nb.dif==0 & res.dat.dif.resali$eff.size==0,"h0.rejected.p"])
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 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$dif.size)
tabs4.resali <- res.dat.dif.resali[res.dat.dif.resali$dif.size!=0,
c("scenario","N",
"dif.detected","prop.perfect","flexible.detect","moreflexible.detect"
)]
tabs4.resali <-reshape(data = tabs4.resali,direction = "wide", idvar = c("scenario"),timevar = "N")
tabs4.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0,
c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif",
"dif.detected","prop.perfect","flexible.detect","moreflexible.detect"
)]
tabs4.rosali <-reshape(data = tabs4.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N")
tabs4 <- merge(tabs4.rosali,tabs4.resali,by="scenario",suffixes = c(".rosali",".residuals"))
tabs4 <- rbind(tabs4[78:112,],tabs4[1:77,])
tabs4$dif.size <- abs(tabs4$dif.size)
write.csv(tabs4,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs4.csv")
##########################
# TABLES CAUSAL
##########################
res.dat.dif.rosali$dif.power <- res.dat.dif.rosali$h0.rejected.p-res.dat.dif.rosali$theoretical.power
res.dat.dif.resali$dif.power <- res.dat.dif.resali$h0.rejected.p-res.dat.dif.resali$theoretical.power
res.dat.dif.rosali$typeI.error <- ifelse(res.dat.dif.rosali$scenario.type=="A",res.dat.dif.rosali$h0.rejected.p,NA)
res.dat.dif.rosali$diff.power <- ifelse(res.dat.dif.rosali$scenario.type!="A",res.dat.dif.rosali$dif.power,NA)
res.dat.dif.resali$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$dif.power,NA)
tabs5.resali <- res.dat.dif.resali[res.dat.dif.resali$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"
)]
tabs5.resali <-reshape(data = tabs5.resali,direction = "wide", idvar = c("scenario"),timevar = "N")
tabs5.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0,
c("scenario","N","J","M","eff.size","dif.size","dif.dir","nb.dif",
"h0.rejected.p","theoretical.power","true.value.in.ci.p","beta.same.sign.truebeta.p","beta.same.sign.truebeta.signif.p","bias"
)]
tabs5.rosali <-reshape(data = tabs5.rosali,direction = "wide", idvar = c("scenario","J","M","eff.size","dif.size","dif.dir","nb.dif"),timevar = "N")
tabs5 <- merge(tabs5.rosali,tabs5.resali,by="scenario",suffixes = c(".rosali",".residuals"))
tabs5 <- rbind(tabs5[78:112,],tabs5[1:77,])
tabs5$dif.size <- abs(tabs5$dif.size)
write.csv(tabs5,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Figures/tabs5.csv")
##########################
# STATS DIF DETECTION
##########################
# sample size
summary(tab3$moreflexible.detect.50.rosali)
summary(tab3$moreflexible.detect.50.residuals)
tab3[which.max(tab3$prop.perfect.50.residuals),]
summary(tab3$moreflexible.detect.100.rosali)
summary(tab3$moreflexible.detect.100.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.100.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.100.rosali)
summary(tab3$moreflexible.detect.200.rosali)
summary(tab3$moreflexible.detect.200.residuals)
summary(tab3$moreflexible.detect.300.rosali)
summary(tab3$moreflexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.300.rosali)
summary(tab3[tab3$nb.dif==1,]$moreflexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==3 & tab3$J==7,]$flexible.detect.300.rosali)
summary(tab3[tab3$nb.dif==3 & tab3$J==7,]$flexible.detect.300.residuals)
summary(tab3[tab3$nb.dif==2 & tab3$J==4,]$prop.perfect.300.rosali)
summary(tab3[tab3$nb.dif==2 & tab3$J==4,]$prop.perfect.300.residuals)
summary(tab3[tab3$nb.dif==2 & tab3$J==7,]$prop.perfect.300.rosali)
summary(tab3[tab3$nb.dif==2 & tab3$J==7,]$prop.perfect.300.residuals)
nrow(tab3[tab3$nb.dif==2 & tab3$prop.perfect.300.residuals>0.5,])
nrow(tab3[tab3$nb.dif==2 & tab3$prop.perfect.300.rosali>0.5,])
summary(tab3[tab3$M==2,]$prop.perfect.300.rosali)
summary(tab3[tab3$M==2,]$prop.perfect.300.residuals)
summary(tab3[tab3$M==4,]$prop.perfect.300.rosali)
summary(tab3[tab3$M==4,]$prop.perfect.300.residuals)
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))
###
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