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R script for analysis 1-4A

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Corentin Choisy 11 months ago
parent c86b9b271f
commit 30d95f324c
4 changed files with 230 additions and 146 deletions
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@ -1,28 +1,24 @@
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -61,7 +57,15 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis(dat1)
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
pcm_analysis(dat1[dat1$replication==1,])
pcm_analysis(dat1[dat1$replication==1,])$xsi
pcm_analysis(dat1[dat1$replication==1,])$xsi$xsi
pcm_analysis(dat1[dat1$replication==1,])
u <- pcm_analysis(dat1[dat1$replication==1,])
u$item_irt
u$item
-3.06+2.13
########################################
## LIBRARIES
########################################
@ -69,7 +73,10 @@ library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(SimDesign)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
@ -77,7 +84,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
@ -87,7 +94,7 @@ stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -97,14 +104,17 @@ tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
@ -126,15 +136,7 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
########################################
## LIBRARIES
########################################
@ -142,6 +144,10 @@ library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
@ -149,7 +155,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- invisible(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
@ -159,7 +165,7 @@ stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -169,14 +175,17 @@ tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
@ -198,15 +207,7 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
########################################
## LIBRARIES
########################################
@ -214,7 +215,10 @@ library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(SimDesign)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
@ -222,7 +226,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
@ -232,24 +236,27 @@ stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) quiet(pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel))
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
@ -271,28 +278,22 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
View(tam.mml)
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
foo <- deparse(tam.mml)
tam.mml <- eval(parse(text=gsub("cat","#cat",foo)))
replicate_pcm_analysis_m2(dat1[dat1$replication==1,])
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
oldcat <- cat
cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
@ -300,7 +301,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
@ -310,7 +311,49 @@ stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -349,15 +392,8 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv')
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
########################################
## LIBRARIES
########################################
@ -365,8 +401,10 @@ library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
oldcat <- cat
cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
@ -374,7 +412,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
tam1 <- tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
@ -384,7 +422,7 @@ stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -394,20 +432,23 @@ tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat2']-tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$item[k,'AXsi_.Cat3']-tam1[[s]]$item[k,'AXsi_.Cat2']
}
}
returndat <- returndat[,sort_by(listitems, lastChar)]
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
@ -423,39 +464,7 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
tam1 <- tam(dat1)
tam1 <- tam(dat1[dat1$replication==1,])
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -478,7 +487,7 @@ returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.s
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
@ -498,15 +507,6 @@ return(returndat)
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
tam1 <- tam(dat1[dat1$replication==1,])
pcm_analysis(dat1[dat1$replication==1])
pcm_analysis(dat1[dat1$replication==1,])
replicate_pcm_analysis(dat1)
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_3A_100.csv')
pcm_analysis(dat1[dat1$replication==1,])
tam.se(pcm_analysis(dat1[dat1$replication==1,]))
1.413612*0.1225149
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
replicate_pcm_analysis_m4(dat1[dat1$replication==1,])

88
RProject/pcm.R
Unescape Escape View File

@ -6,6 +6,11 @@ library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
@ -26,7 +31,54 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
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$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
@ -49,7 +101,7 @@ replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0<returndat$low.ci.beta | 0>returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
@ -80,14 +132,26 @@ dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Da
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_1A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_1A_300.csv')
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m2(get(x)))
write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_1A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_1A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_1A_300.csv')
######### Scenario 2: J=4 / M=4
#### A: H0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_2A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_2A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_2A_300.csv')
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m4(get(x)))
write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_2A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_2A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_2A_300.csv')
######### Scenario 3: J=7 / M=2
@ -98,8 +162,24 @@ dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Da
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_3A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_3A_300.csv')
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis(get(x)))
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m2(get(x)))
write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_3A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_3A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_3A_300.csv')
######### Scenario 4: J=7 / M=4
#### A: H0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_4A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N200/scenario_4A_200.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N300/scenario_4A_300.csv')
res <- pbmclapply(c('dat1','dat2','dat3'),function(x) replicate_pcm_analysis_m4(get(x)))
write.csv(res[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N100/scenario_4A_100.csv')
write.csv(res[[2]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N200/scenario_4A_200.csv')
write.csv(res[[3]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/NoDIF/N300/scenario_4A_300.csv')

6
Scripts/Analysis/NoDIF/pcm_nodif_100.do
Unescape Escape View File

@ -29,10 +29,11 @@ local Nn = 100
* Scenario 1A : H_0 is TRUE
clear
import delim "`path_data'/scenario_3A_100.csv", encoding(ISO-8859-2) case(preserve) clear
import delim "`path_data'/scenario_1A_100.csv", encoding(ISO-8859-2) case(preserve) clear
rename TT tt
keep if replication==1
di `k'
gsem (1.item1<-THETA@1)///
@ -43,3 +44,6 @@ gsem (1.item1<-THETA@1)///
(1.item6<-THETA@1)///
(1.item7<-THETA@1)///
(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent
pcm item1 item2 item3 item4, categorical(tt)

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