diff --git a/RProject/.RData b/RProject/.RData
index ec0b6f7..cb9f20e 100644
Binary files a/RProject/.RData and b/RProject/.RData differ
diff --git a/RProject/.Rhistory b/RProject/.Rhistory
index 32ebca5..8d80ce6 100644
--- a/RProject/.Rhistory
+++ b/RProject/.Rhistory
@@ -1,28 +1,24 @@
+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))
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
-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)
+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 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -61,7 +57,15 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-replicate_pcm_analysis(dat1)
+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
 ########################################
@@ -69,7 +73,10 @@ library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
-library(SimDesign)
+library(funprog)
+lastChar <- function(str){
+substr(str, nchar(str)-2, nchar(str))
+}
 #######################################
 ## ANALYSIS FUNCTIONS
 #######################################
@@ -77,7 +84,7 @@ 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 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+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])
@@ -87,7 +94,7 @@ stop('Invalid method. Please choose among MML or JML')
 }
 return(tam1)
 }
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -97,14 +104,17 @@ 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))
+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)] <- tam1[[s]]$xsi$xsi[k]
+returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$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
@@ -126,15 +136,7 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
+replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
 ########################################
 ## LIBRARIES
 ########################################
@@ -142,6 +144,10 @@ library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
+library(funprog)
+lastChar <- function(str){
+substr(str, nchar(str)-2, nchar(str))
+}
 #######################################
 ## ANALYSIS FUNCTIONS
 #######################################
@@ -149,7 +155,7 @@ 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 <- invisible(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+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])
@@ -159,7 +165,7 @@ stop('Invalid method. Please choose among MML or JML')
 }
 return(tam1)
 }
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -169,14 +175,17 @@ 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))
+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)] <- tam1[[s]]$xsi$xsi[k]
+returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$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
@@ -198,15 +207,7 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
+replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
 ########################################
 ## LIBRARIES
 ########################################
@@ -214,7 +215,10 @@ library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
-library(SimDesign)
+library(funprog)
+lastChar <- function(str){
+substr(str, nchar(str)-2, nchar(str))
+}
 #######################################
 ## ANALYSIS FUNCTIONS
 #######################################
@@ -222,7 +226,7 @@ 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 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+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])
@@ -232,24 +236,27 @@ stop('Invalid method. Please choose among MML or JML')
 }
 return(tam1)
 }
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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) quiet(pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel))
+function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
 )
 }
-listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
+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)] <- tam1[[s]]$xsi$xsi[k]
+returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$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
@@ -271,28 +278,22 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
+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')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
-View(tam.mml)
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-foo <- deparse(tam.mml)
-tam.mml <- eval(parse(text=gsub("cat","#cat",foo)))
+replicate_pcm_analysis_m2(dat1[dat1$replication==1,])
+########################################
+## LIBRARIES
+########################################
 library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
-oldcat <- cat
-cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
+library(funprog)
+lastChar <- function(str){
+substr(str, nchar(str)-2, nchar(str))
+}
 #######################################
 ## ANALYSIS FUNCTIONS
 #######################################
@@ -300,7 +301,7 @@ 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 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+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])
@@ -310,7 +311,49 @@ stop('Invalid method. Please choose among MML or JML')
 }
 return(tam1)
 }
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -349,15 +392,8 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
+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
 ########################################
@@ -365,8 +401,10 @@ library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
-oldcat <- cat
-cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
+library(funprog)
+lastChar <- function(str){
+substr(str, nchar(str)-2, nchar(str))
+}
 #######################################
 ## ANALYSIS FUNCTIONS
 #######################################
@@ -374,7 +412,7 @@ 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 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+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])
@@ -384,7 +422,7 @@ stop('Invalid method. Please choose among MML or JML')
 }
 return(tam1)
 }
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -394,20 +432,23 @@ 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))
+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)] <- tam1[[s]]$xsi$xsi[k]
+returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$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$high.ci.beta)
+returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
 if (truebeta==0) {
 returndat$beta.same.sign.truebeta <- NA
 } else {
@@ -423,39 +464,7 @@ nb.dif= nbdif
 returndat <- cbind(returndat2,returndat)
 return(returndat)
 }
-#######################################
-## SCENARIO ANALYSIS
-#######################################
-registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-replicate_pcm_analysis(dat1)
-tam1 <- tam(dat1)
-tam1 <- tam(dat1[dat1$replication==1,])
-library(TAM)
-library(doMC)
-library(parallel)
-library(pbmcapply)
-#######################################
-## 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 <- quiet(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 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -478,7 +487,7 @@ returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.s
 returndat$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$high.ci.beta)
+returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
 if (truebeta==0) {
 returndat$beta.same.sign.truebeta <- NA
 } else {
@@ -498,15 +507,6 @@ return(returndat)
 ## SCENARIO ANALYSIS
 #######################################
 registerDoMC(4)
-######### Scenario 1: J=4 / M=2 / H_0 TRUE
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
-tam1 <- tam(dat1[dat1$replication==1,])
-pcm_analysis(dat1[dat1$replication==1])
-pcm_analysis(dat1[dat1$replication==1,])
-replicate_pcm_analysis(dat1)
-dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_3A_100.csv')
-pcm_analysis(dat1[dat1$replication==1,])
-tam.se(pcm_analysis(dat1[dat1$replication==1,]))
-1.413612*0.1225149
+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,])
diff --git a/RProject/pcm.R b/RProject/pcm.R
index e00da0b..ca5523d 100644
--- a/RProject/pcm.R
+++ b/RProject/pcm.R
@@ -6,6 +6,11 @@ library(TAM)
 library(doMC)
 library(parallel)
 library(pbmcapply)
+library(funprog)
+
+lastChar <- function(str){
+  substr(str, nchar(str)-2, nchar(str))
+}
 
 #######################################
 ## ANALYSIS FUNCTIONS
@@ -26,7 +31,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
   return(tam1)
 }
 
-replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+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') {
@@ -36,20 +41,23 @@ replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method
                    function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
                    )
   }
-  listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
+  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)] <- tam1[[s]]$xsi$xsi[k]
+    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)
+  returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
   if (truebeta==0) {
     returndat$beta.same.sign.truebeta <- NA
   } else {
@@ -61,7 +69,51 @@ replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method
                            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)
 }
@@ -80,14 +132,26 @@ dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Da
 dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_1A_200.csv')
 dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_1A_300.csv')
 
-res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))
+res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m2(get(x)))
 
 write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_1A_100.csv')
 write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_1A_200.csv')
 write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_1A_300.csv')
 
 
+######### Scenario 2: J=4 / M=4
 
+#### A: H0 TRUE
+
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv')
+dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_2A_200.csv')
+dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_2A_300.csv')
+
+res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m4(get(x)))
+
+write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_2A_100.csv')
+write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_2A_200.csv')
+write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_2A_300.csv')
 
 
 ######### Scenario 3: J=7 / M=2 
@@ -98,8 +162,24 @@ dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Da
 dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_3A_200.csv')
 dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_3A_300.csv')
 
-res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))
+res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m2(get(x)))
 
 write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_3A_100.csv')
 write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_3A_200.csv')
 write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_3A_300.csv')
+
+
+######### Scenario 4: J=7 / M=4
+
+#### A: H0 TRUE
+
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
+dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_4A_200.csv')
+dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_4A_300.csv')
+
+res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m4(get(x)))
+
+write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_4A_100.csv')
+write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_4A_200.csv')
+write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_4A_300.csv')
+
diff --git a/Scripts/Analysis/NoDIF/pcm_nodif_100.do b/Scripts/Analysis/NoDIF/pcm_nodif_100.do
index c176341..ef822df 100644
--- a/Scripts/Analysis/NoDIF/pcm_nodif_100.do
+++ b/Scripts/Analysis/NoDIF/pcm_nodif_100.do
@@ -29,10 +29,11 @@ local Nn = 100
 * Scenario 1A : H_0 is TRUE
 
 clear
-import delim "`path_data'/scenario_3A_100.csv", encoding(ISO-8859-2)  case(preserve) clear
+import delim "`path_data'/scenario_1A_100.csv", encoding(ISO-8859-2)  case(preserve) clear
 rename TT tt
 
 
+
 keep if replication==1
 di `k'
 gsem (1.item1<-THETA@1)///
@@ -43,3 +44,6 @@ gsem (1.item1<-THETA@1)///
      (1.item6<-THETA@1)///
      (1.item7<-THETA@1)///
      (THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent
+
+
+pcm item1 item2 item3 item4, categorical(tt)