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simul_these/RProject/Scripts/article.R

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##########################
# 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 DETECT
##########################
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$dif.size!=0,
c("scenario","N",
"dif.detected","prop.perfect","flexible.detect","moreflexible.detect"
)]
tab3.resali <-reshape(data = tab3.resali,direction = "wide", idvar = c("scenario"),timevar = "N")
tab3.rosali <- res.dat.dif.rosali[res.dat.dif.rosali$dif.size!=0,
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")
##########################
# 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)
tab4.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,
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")
##########################
# 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")
##########################
# 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)
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