diff --git a/RProject/.RData b/RProject/.RData
index cb9f20e..5faaf71 100644
Binary files a/RProject/.RData and b/RProject/.RData differ
diff --git a/RProject/.Rhistory b/RProject/.Rhistory
index 8d80ce6..7d87e1c 100644
--- a/RProject/.Rhistory
+++ b/RProject/.Rhistory
@@ -1,512 +1,512 @@
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
+View(res.dat)
+res.dat.simple <- res.dat[,c(1:7,12,15:17)]
+res.dat.simple
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+compile_simulation <- function(scenario) {
+name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
+}
+if (unique(s$J)==4) {
+if (unique(s$M)==2) {
+a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
-)
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
+a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
 )
 }
-listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
-}
-}
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
-)
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-pcm_analysis(dat1[dat1$replication==1,])
-pcm_analysis(dat1[dat1$replication==1,])$xsi
-pcm_analysis(dat1[dat1$replication==1,])$xsi$xsi
-pcm_analysis(dat1[dat1$replication==1,])
-u <- pcm_analysis(dat1[dat1$replication==1,])
-u$item_irt
-u$item
--3.06+2.13
-########################################
-## LIBRARIES
-########################################
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-library(funprog)
-lastChar <- function(str){
-substr(str, nchar(str)-2, nchar(str))
-}
-#######################################
-## ANALYSIS FUNCTIONS
-#######################################
-pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
-}
-if (method=='JML') {
-tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
-}
-if (method!='MML' & method!='JML') {
-stop('Invalid method. Please choose among MML or JML')
-}
-return(tam1)
-}
-replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-}
-}
-returndat <- returndat[,sort_by(listitems, lastChar)]
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
+if (unique(s$M)==2) {
+a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
+m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
+a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
+m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
+m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
+m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
 )
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-########################################
-## LIBRARIES
-########################################
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-library(funprog)
-lastChar <- function(str){
-substr(str, nchar(str)-2, nchar(str))
 }
-#######################################
-## ANALYSIS FUNCTIONS
-#######################################
-pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
 }
-if (method=='JML') {
-tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
-}
-if (method!='MML' & method!='JML') {
-stop('Invalid method. Please choose among MML or JML')
-}
-return(tam1)
-}
-replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
+zz <- substr(scenario,start=0,stop=nchar(scenario)-4)
+b <- data.frame(scenario=zz,
+scenario.type=substr(zz,start=0,stop=nchar(zz)-1),
+N=substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)),
+J=unique(s$J),
+M=unique(s$M),
+eff.size=unique(s$eff.size),
+nb.dif=unique(s$nb.dif),
+dif.size=unique(s$dif.size)
 )
-}
-listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']
-returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']
-}
-}
-returndat <- returndat[,sort_by(listitems, lastChar)]
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
+z <- data.frame(m.beta=mean(s$beta),
+m.low.ci.beta=mean(s$low.ci.beta),
+m.high.ci.beta=mean(s$high.ci.beta),
+true.value.in.ci.p=mean(s$true.value.in.ci),
+h0.rejected.p=mean(s$h0.rejected),
+beta.same.sign.truebeta.p=mean(s$beta.same.sign.truebeta,na.rm=T))
+d <- cbind(b,a,z)
+d$prop.
+return(d)
+}
+res.dat <- compile_simulation('1A_100')
+for (x in results[seq(2,length(results))]) {
+y <- compile_simulation(x)
+res.dat <- bind_rows(res.dat,y)
+}
+View(res.dat)
+res.dat.simple <- res.dat[,c(1:7,12,15:17)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+compile_simulation <- function(scenario) {
+name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
+}
+if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
+s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
+}
+if (unique(s$J)==4) {
+if (unique(s$M)==2) {
+a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
+a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
 )
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-########################################
-## LIBRARIES
-########################################
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-library(funprog)
-lastChar <- function(str){
-substr(str, nchar(str)-2, nchar(str))
-}
-#######################################
-## ANALYSIS FUNCTIONS
-#######################################
-pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
-}
-if (method=='JML') {
-tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
 }
-if (method!='MML' & method!='JML') {
-stop('Invalid method. Please choose among MML or JML')
-}
-return(tam1)
-}
-replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
-}
-}
-returndat <- returndat[,sort_by(listitems, lastChar)]
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
-)
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis_m2(dat1[dat1$replication==1,])
-########################################
-## LIBRARIES
-########################################
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-library(funprog)
-lastChar <- function(str){
-substr(str, nchar(str)-2, nchar(str))
-}
-#######################################
-## ANALYSIS FUNCTIONS
-#######################################
-pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
-}
-if (method=='JML') {
-tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
-}
-if (method!='MML' & method!='JML') {
-stop('Invalid method. Please choose among MML or JML')
-}
-return(tam1)
-}
-replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
-}
-}
-returndat <- returndat[,sort_by(listitems, lastChar)]
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
+if (unique(s$M)==2) {
+a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
+m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
 } else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
+a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
+m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
+m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
+m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
 )
-returndat <- cbind(returndat2,returndat)
-return(returndat)
 }
-replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
-}
-}
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
-} else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
 }
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
+zz <- substr(scenario,start=0,stop=nchar(scenario)-4)
+b <- data.frame(scenario=zz,
+scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
+N=substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)),
+J=unique(s$J),
+M=unique(s$M),
+eff.size=unique(s$eff.size),
+nb.dif=unique(s$nb.dif),
+dif.size=unique(s$dif.size)
 )
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv')
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-########################################
-## LIBRARIES
-########################################
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-library(funprog)
-lastChar <- function(str){
-substr(str, nchar(str)-2, nchar(str))
-}
-#######################################
-## ANALYSIS FUNCTIONS
-#######################################
-pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
-}
-if (method=='JML') {
-tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
-}
-if (method!='MML' & method!='JML') {
-stop('Invalid method. Please choose among MML or JML')
-}
-return(tam1)
-}
-replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
-returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
-}
-}
-returndat <- returndat[,sort_by(listitems, lastChar)]
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
-} else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
-)
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
-nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
-resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
-if (method=='MML') {
-n <- max(df[,sequence])
-print(n)
-tam1 <- pbmclapply(seq(1,n),
-function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
-)
-}
-listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
-returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
-colnames(returndat) <- listitems
-for (s in seq(1,max(df[,sequence]))) {
-for (k in seq(1,nbitems)) {
-returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
-}
-}
-returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
-returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
-returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
-returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
-returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
-returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
-if (truebeta==0) {
-returndat$beta.same.sign.truebeta <- NA
-} else {
-returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
-}
-returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
-M=1+max(df$item1),
-N=nrow(df[df$replication==1,])/2,
-eff.size=eff.size,
-dif.size= difsize,
-nb.dif= nbdif
-)
-returndat <- cbind(returndat2,returndat)
-return(returndat)
-}
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
-replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
+z <- data.frame(m.beta=mean(s$beta),
+m.low.ci.beta=mean(s$low.ci.beta),
+m.high.ci.beta=mean(s$high.ci.beta),
+true.value.in.ci.p=mean(s$true.value.in.ci),
+h0.rejected.p=mean(s$h0.rejected),
+beta.same.sign.truebeta.p=mean(s$beta.same.sign.truebeta,na.rm=T))
+d <- cbind(b,a,z)
+d$prop.
+return(d)
+}
+res.dat <- compile_simulation('1A_100')
+for (x in results[seq(2,length(results))]) {
+y <- compile_simulation(x)
+res.dat <- bind_rows(res.dat,y)
+}
+View(res.dat)
+res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+res.dat[is.na(res.dat$dif.size),'dif.size'] <- 0
+res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+plot(h0.rejected.p~dif.size,data=res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null)+points(h0.rejected.p~dif.size,data=res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(h0.rejected.p~dif.size,data=res.null)
+points(h0.rejected.p~1.dif.size,data=res.null)
+points(h0.rejected.p~1+dif.size,data=res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(h0.rejected.p~1+dif.size,data=res.null)
+points(y=h0.rejected.p,x=0,data=res.null)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(y=h0.rejected.p,x=0,data=res.null)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(y=res.null$h0.rejected.p,x=0)
+points(y=res.null$h0.rejected.p,x=rep(0,nrow(res.null)))
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(y=res.null$h0.rejected.p,x=rep(0,nrow(res.null)))
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)))
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null)
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)))
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)))
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)))
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)))
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)))
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col=2)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col=3)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)))
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col=2)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col=3)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),'gray')
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray')
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='darkred')
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='darkgreen')
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pty=2)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pty=2)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='darkred',pty=2)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='darkgreen',pty=2)
+res.null <- res.dat[res.dat$eff.size==0,]
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='darkred',pch=3)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='darkgreen',pch=3)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3))
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(2,2))
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item')
+res.null0 <- res.null[res.null$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items')
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+res.null
+par(mfrow=c(2,2))
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item')
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(2,2))
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item')
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF')
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null
+res.dat$nb.dif
+table(res.dat$scenario)
+cumsum(table(res.dat$scenario))
+table(res.dat[1:132]$scenario)
+table(res.dat[1:132,]$scenario)
+nrow(res.dat)
+res.dat[132:300,'nb.dif'] <- 2
+res.dat[300:396,'nb.dif'] <- 3
+View(res.dat)
+res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+#### plots
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size (1 item)
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
+nrow(res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
+nrow(res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
+par(mfrow=c(2,2))
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
+nrow(res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+par(mfrow=c(1,1))
diff --git a/RProject/pcm.R b/RProject/pcm.R
index 7fcd065..079695e 100644
--- a/RProject/pcm.R
+++ b/RProject/pcm.R
@@ -9,6 +9,7 @@ library(doMC)
 library(parallel)
 library(pbmcapply)
 library(funprog)
+library(dplyr)
 
 lastChar <- function(str){
   substr(str, nchar(str)-2, nchar(str))
@@ -205,13 +206,6 @@ write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis
 write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_1E_300.csv')
 
 
-
-
-
-
-
-
-
 ###################################### Scenario 2: J=4 / M=4 
 
 #### A: H0 TRUE
@@ -2054,12 +2048,223 @@ write.csv(res_nodif[[2]],'/home/corentin/Documents/These/Recherche/Simulation/An
 write.csv(res_nodif[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/DIF/N300/scenario_20G_300_nodif.csv')
 
 
+##############################################################################
+#----------------------------------------------------------------------------#
+################################# AGGREGATION ################################
+#----------------------------------------------------------------------------#
+##############################################################################
 
+#### Create data.frame
 
+results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
 
+results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
 
+results <- c(sapply(c(100,200,300),function(x) paste0(results,'_',x)))
 
+results2 <- c(sapply(c(100,200,300),function(x) paste0(results2,'_',x)))
 
+results <- sort(results)
 
+results2 <- sort(results2)
 
+results <- c(results,results2)
 
+#### Compiler function
+
+compile_simulation <- function(scenario) {
+  name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
+  }
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
+  }
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
+  }
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
+  }
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
+  }
+  if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
+    s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
+  }
+  if (unique(s$J)==4) {
+    if (unique(s$M)==2) {
+      a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
+    } else {
+      a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+                      m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+                      m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+                      m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
+                      )
+    }
+  } else {
+    if (unique(s$M)==2) {
+      a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
+                      m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
+    } else {
+      a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
+                      m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
+                      m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
+                      m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
+                      m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
+                      m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
+                      m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
+      )
+    }
+  }
+  zz <- substr(scenario,start=0,stop=nchar(scenario)-4)
+  b <- data.frame(scenario=zz,
+                  scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
+                  N=substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)),
+                  J=unique(s$J),
+                  M=unique(s$M),
+                  eff.size=unique(s$eff.size),
+                  nb.dif=unique(s$nb.dif),
+                  dif.size=unique(s$dif.size)
+                  )
+  z <- data.frame(m.beta=mean(s$beta),
+                  m.low.ci.beta=mean(s$low.ci.beta),
+                  m.high.ci.beta=mean(s$high.ci.beta),
+                  true.value.in.ci.p=mean(s$true.value.in.ci),
+                  h0.rejected.p=mean(s$h0.rejected),
+                  beta.same.sign.truebeta.p=mean(s$beta.same.sign.truebeta,na.rm=T))
+  d <- cbind(b,a,z)
+  d$prop.
+  return(d)
+}
+
+#### Compiled results
+
+res.dat <- compile_simulation('1A_100')
+
+for (x in results[seq(2,length(results))]) {
+  y <- compile_simulation(x)
+  res.dat <- bind_rows(res.dat,y)
+}
+res.dat[is.na(res.dat$dif.size),'dif.size'] <- 0
+res.dat[132:300,'nb.dif'] <- 2
+res.dat[300:396,'nb.dif'] <- 3
+View(res.dat)
+
+res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
+res.dat.simple
+
+################ Boxplots of Rejected H0 in null scenarios
+
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size
+
+res.null <- res.dat[res.dat$eff.size==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(1,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
+res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
+points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
+res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
+
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size (1 item)
+
+par(mfrow=c(2,2))
+
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+par(mfrow=c(1,1))
+
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size (1 item) J=4
+
+par(mfrow=c(2,2))
+
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+
+# 1 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+par(mfrow=c(1,1))
+
+
+## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size (1 item) J=7
+
+par(mfrow=c(2,2))
+
+# 0 item
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
+nrow(res.null)
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
+points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
+
+# 2 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+# 3 items
+res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
+boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
+        ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
+res.null3 <- res.null[res.null$dif.size==0.3,]
+points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
+res.null5 <- res.null[res.null$dif.size==0.5,]
+points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
+
+par(mfrow=c(1,1))