Started PCM analysis using R TAM

.gitignore

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 *.csv
+.Rproj.user

RProject/.Rhistory

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+}
+#######################################
+## 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)
+}
+replicate_pcm_analysis <- 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)
+}
+replicate_pcm_analysis(dat1)
+########################################
+## LIBRARIES
+########################################
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+library(SimDesign)
+#######################################
+## 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) {
+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)
+######### 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)
+########################################
+## LIBRARIES
+########################################
+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 <- invisible(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) {
+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)
+######### 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)
+########################################
+## LIBRARIES
+########################################
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+library(SimDesign)
+#######################################
+## 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) {
+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))
+)
+}
+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)
+######### 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)
+View(tam.mml)
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+foo <- deparse(tam.mml)
+tam.mml <- eval(parse(text=gsub("cat","#cat",foo)))
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+oldcat <- cat
+cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
+#######################################
+## 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) {
+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)
+######### 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)
+########################################
+## LIBRARIES
+########################################
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+oldcat <- cat
+cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
+#######################################
+## 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) {
+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)
+######### 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$replication==1,])
+library(TAM)
+library(doMC)
+library(parallel)
+library(pbmcapply)
+#######################################
+## ANALYSIS FUNCTIONS
+#######################################
+pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
+nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
+resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
+if (method=='MML') {
+tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
+}
+if (method=='JML') {
+tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
+}
+if (method!='MML' & method!='JML') {
+stop('Invalid method. Please choose among MML or JML')
+}
+return(tam1)
+}
+replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
+nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
+resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
+if (method=='MML') {
+n <- max(df[,sequence])
+print(n)
+tam1 <- pbmclapply(seq(1,n),
+function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
+)
+}
+listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
+returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
+colnames(returndat) <- listitems
+for (s in seq(1,max(df[,sequence]))) {
+for (k in seq(1,nbitems)) {
+returndat[s,paste0('item',k)] <- tam1[[s]]$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$replication==1,])/2,
+eff.size=eff.size,
+dif.size= difsize,
+nb.dif= nbdif
+)
+returndat <- cbind(returndat2,returndat)
+return(returndat)
+}
+#######################################
+## SCENARIO ANALYSIS
+#######################################
+registerDoMC(4)
+######### Scenario 1: J=4 / M=2 / H_0 TRUE
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
+dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
+dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
+tam1 <- tam(dat1[dat1$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

RProject/RProject.Rproj

@@ -0,0 +1,13 @@
+Version: 1.0
+
+RestoreWorkspace: Default
+SaveWorkspace: Default
+AlwaysSaveHistory: Default
+
+EnableCodeIndexing: Yes
+UseSpacesForTab: Yes
+NumSpacesForTab: 2
+Encoding: UTF-8
+
+RnwWeave: Sweave
+LaTeX: pdfLaTeX

RProject/pcm.R

@@ -0,0 +1,105 @@
+########################################
+## LIBRARIES
+########################################
+
+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 <-  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) {
+  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)
+
+######### Scenario 1: J=4 / M=2 
+
+#### A: H0 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/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)))
+
+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 3: J=7 / M=2 
+
+#### A: H0 TRUE
+
+dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_3A_100.csv')
+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)))
+
+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')

Scripts/Analysis/NoDIF/pcm_nodif_100.do

@@ -29,15 +29,17 @@ local Nn = 100
 * Scenario 1A : H_0 is TRUE
 
 clear
-import delim "`path_data'/scenario_1A_100.csv", encoding(ISO-8859-2)  case(preserve) clear
+import delim "`path_data'/scenario_3A_100.csv", encoding(ISO-8859-2)  case(preserve) clear
 rename TT tt
 
-preserve
+
 keep if replication==1
+di `k'
 gsem (1.item1<-THETA@1)///
      (1.item2<-THETA@1)///
      (1.item3<-THETA@1)///
      (1.item4<-THETA@1)///
+     (1.item5<-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)