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513 lines
21 KiB
R
513 lines
21 KiB
R
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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for (s in seq(1,max(df[,sequence]))) {
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for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
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}
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}
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returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
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pcm_analysis(dat1[dat1$replication==1,])
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pcm_analysis(dat1[dat1$replication==1,])$xsi
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pcm_analysis(dat1[dat1$replication==1,])$xsi$xsi
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pcm_analysis(dat1[dat1$replication==1,])
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u <- pcm_analysis(dat1[dat1$replication==1,])
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u$item_irt
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u$item
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-3.06+2.13
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########################################
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## LIBRARIES
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########################################
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library(TAM)
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library(doMC)
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library(parallel)
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library(pbmcapply)
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library(funprog)
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lastChar <- function(str){
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substr(str, nchar(str)-2, nchar(str))
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}
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#######################################
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## ANALYSIS FUNCTIONS
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#######################################
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pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
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}
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if (method=='JML') {
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tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
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}
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if (method!='MML' & method!='JML') {
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stop('Invalid method. Please choose among MML or JML')
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}
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return(tam1)
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}
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replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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for (s in seq(1,max(df[,sequence]))) {
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for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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}
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}
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returndat <- returndat[,sort_by(listitems, lastChar)]
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returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
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########################################
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## LIBRARIES
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########################################
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library(TAM)
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library(doMC)
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library(parallel)
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library(pbmcapply)
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library(funprog)
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lastChar <- function(str){
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substr(str, nchar(str)-2, nchar(str))
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}
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#######################################
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## ANALYSIS FUNCTIONS
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#######################################
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pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
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}
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if (method=='JML') {
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tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
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}
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if (method!='MML' & method!='JML') {
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stop('Invalid method. Please choose among MML or JML')
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}
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return(tam1)
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}
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replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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for (s in seq(1,max(df[,sequence]))) {
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for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']
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returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']
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}
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}
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returndat <- returndat[,sort_by(listitems, lastChar)]
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returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
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########################################
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## LIBRARIES
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########################################
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library(TAM)
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library(doMC)
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library(parallel)
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library(pbmcapply)
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library(funprog)
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lastChar <- function(str){
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substr(str, nchar(str)-2, nchar(str))
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}
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#######################################
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## ANALYSIS FUNCTIONS
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#######################################
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pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
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}
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if (method=='JML') {
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tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
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}
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if (method!='MML' & method!='JML') {
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stop('Invalid method. Please choose among MML or JML')
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}
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return(tam1)
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}
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replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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for (s in seq(1,max(df[,sequence]))) {
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for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
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}
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}
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returndat <- returndat[,sort_by(listitems, lastChar)]
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returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
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dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
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replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
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dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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replicate_pcm_analysis_m2(dat1[dat1$replication==1,])
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########################################
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## LIBRARIES
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########################################
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library(TAM)
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library(doMC)
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library(parallel)
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library(pbmcapply)
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library(funprog)
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lastChar <- function(str){
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substr(str, nchar(str)-2, nchar(str))
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}
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#######################################
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## ANALYSIS FUNCTIONS
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#######################################
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pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
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}
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if (method=='JML') {
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tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
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}
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if (method!='MML' & method!='JML') {
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stop('Invalid method. Please choose among MML or JML')
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}
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return(tam1)
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}
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replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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for (s in seq(1,max(df[,sequence]))) {
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for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
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returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
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}
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}
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returndat <- returndat[,sort_by(listitems, lastChar)]
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returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
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returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
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returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
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returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
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returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
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if (truebeta==0) {
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returndat$beta.same.sign.truebeta <- NA
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} else {
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returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
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}
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returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
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M=1+max(df$item1),
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N=nrow(df[df$replication==1,])/2,
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eff.size=eff.size,
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dif.size= difsize,
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|
nb.dif= nbdif
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)
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returndat <- cbind(returndat2,returndat)
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return(returndat)
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}
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replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
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resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
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if (method=='MML') {
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n <- max(df[,sequence])
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print(n)
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tam1 <- pbmclapply(seq(1,n),
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function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
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)
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}
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listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
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returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
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colnames(returndat) <- listitems
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|
for (s in seq(1,max(df[,sequence]))) {
|
|
for (k in seq(1,nbitems)) {
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returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
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}
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|
}
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|
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
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|
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)
|
|
}
|
|
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,])
|