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)
}
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$item
-3.06+2.13
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <-  tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F)
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- c(sapply(c('_1','_2','_3'),function(x) paste0(sapply(seq(1,nbitems),function(x) paste0('item',x)),x)))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k,'_1')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_2')] <- tam1[[s]]$item[k,'AXsi_.Cat1']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$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
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_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]]$item[k,'AXsi_.Cat2']
returndat[s,paste0('item',k,'_3')] <- tam1[[s]]$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
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_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_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')
replicate_pcm_analysis_m2(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)
}
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,])