Started PCM analysis using R TAM
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*.csv
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.Rproj.user
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RProject/.RData
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RProject/.RData
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}
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#######################################
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## SCENARIO ANALYSIS
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#######################################
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registerDoMC(4)
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######### Scenario 1: J=4 / M=2 / H_0 TRUE
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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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dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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replicate_pcm_analysis(dat1)
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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 <- 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(dat1)
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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(SimDesign)
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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 <- quiet(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 <- 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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#######################################
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## SCENARIO ANALYSIS
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#######################################
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registerDoMC(4)
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######### Scenario 1: J=4 / M=2 / H_0 TRUE
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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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dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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replicate_pcm_analysis(dat1)
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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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#######################################
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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 <- invisible(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 <- 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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#######################################
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## SCENARIO ANALYSIS
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#######################################
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registerDoMC(4)
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######### Scenario 1: J=4 / M=2 / H_0 TRUE
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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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dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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replicate_pcm_analysis(dat1)
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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(SimDesign)
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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 <- quiet(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 <- 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) quiet(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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#######################################
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## SCENARIO ANALYSIS
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#######################################
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registerDoMC(4)
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######### Scenario 1: J=4 / M=2 / H_0 TRUE
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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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dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
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replicate_pcm_analysis(dat1)
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View(tam.mml)
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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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foo <- deparse(tam.mml)
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tam.mml <- eval(parse(text=gsub("cat","#cat",foo)))
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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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oldcat <- cat
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cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
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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 <- quiet(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 <- 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)
|
||||
)
|
||||
}
|
||||
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)))
|
||||
colnames(returndat) <- listitems
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||||
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
|
||||
13
RProject/RProject.Rproj
Normal file
13
RProject/RProject.Rproj
Normal file
@ -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
|
||||
105
RProject/pcm.R
Normal file
105
RProject/pcm.R
Normal file
@ -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')
|
||||
@ -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)
|
||||
|
||||
Reference in New Issue
Block a user