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R script for analysis 1-4A

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Corentin Choisy
2024-01-26 00:09:35 +01:00
parent c86b9b271f
commit 30d95f324c
4 changed files with 562 additions and 478 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))
} }
####################################### returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
## SCENARIO ANALYSIS M=1+max(df$item1),
####################################### N=nrow(df[df$replication==1,])/2,
registerDoMC(4) eff.size=eff.size,
######### Scenario 1: J=4 / M=2 / H_0 TRUE dif.size= difsize,
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv') nb.dif= nbdif
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') returndat <- cbind(returndat2,returndat)
replicate_pcm_analysis(dat1) return(returndat)
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') { replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
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)) nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))] resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') { if (method=='MML') {
@ -61,452 +57,456 @@ nb.dif= nbdif
returndat <- cbind(returndat2,returndat) returndat <- cbind(returndat2,returndat)
return(returndat) return(returndat)
} }
replicate_pcm_analysis(dat1) replicate_pcm_analysis_m4(dat1[dat1$replication==1,])
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(SimDesign)
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- invisible(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(SimDesign)
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) quiet(pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel))
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
View(tam.mml)
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
foo <- deparse(tam.mml)
tam.mml <- eval(parse(text=gsub("cat","#cat",foo)))
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
oldcat <- cat
cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
########################################
## LIBRARIES
########################################
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
oldcat <- cat
cat <- function( ..., file="", sep=" ", fill=F, labels=NULL, append=F ) {}
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
replicate_pcm_analysis(dat1)
tam1 <- tam(dat1)
tam1 <- tam(dat1[dat1$replication==1,])
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
#######################################
## ANALYSIS FUNCTIONS
#######################################
pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
tam1 <- quiet(tam.mml(resp=resp,Y=df[,treatment],irtmodel = irtmodel,est.variance = T,verbose=F))
}
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
return(tam1)
}
replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$xsi$xsi[k]
}
}
returndat$beta <- sapply(seq(1,max(df[,sequence])),function(k) tam1[[k]]$beta[2])
returndat$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.ci.beta <- returndat$beta+1.96*returndat$se.beta
returndat$true.value.in.ci <- 1*(truebeta>returndat$low.ci.beta & truebeta<returndat$high.ci.beta)
returndat$h0.rejected <- 1*(0>returndat$low.ci.beta & 0<returndat$high.ci.beta)
if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA
} else {
returndat$beta.same.sign.truebeta <- 1*(sign(truebeta)==sign(returndat$beta))
}
returndat2 <- data.frame(J=rep(nbitems,max(df[,sequence])),
M=1+max(df$item1),
N=nrow(df[df$replication==1,])/2,
eff.size=eff.size,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#######################################
## SCENARIO ANALYSIS
#######################################
registerDoMC(4)
######### Scenario 1: J=4 / M=2 / H_0 TRUE
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat2 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
dat3 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_1A_100.csv')
tam1 <- tam(dat1[dat1$replication==1,])
pcm_analysis(dat1[dat1$replication==1])
pcm_analysis(dat1[dat1$replication==1,]) pcm_analysis(dat1[dat1$replication==1,])
replicate_pcm_analysis(dat1) pcm_analysis(dat1[dat1$replication==1,])$xsi
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N100/scenario_3A_100.csv') pcm_analysis(dat1[dat1$replication==1,])$xsi$xsi
pcm_analysis(dat1[dat1$replication==1,]) pcm_analysis(dat1[dat1$replication==1,])
tam.se(pcm_analysis(dat1[dat1$replication==1,])) u <- pcm_analysis(dat1[dat1$replication==1,])
1.413612*0.1225149 u$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,])

94
RProject/pcm.R
View File

@ -6,6 +6,11 @@ library(TAM)
library(doMC) library(doMC)
library(parallel) library(parallel)
library(pbmcapply) library(pbmcapply)
library(funprog)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
####################################### #######################################
## ANALYSIS FUNCTIONS ## ANALYSIS FUNCTIONS
@ -26,7 +31,7 @@ pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML') {
return(tam1) 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)) nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))] resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') { if (method=='MML') {
@ -36,20 +41,23 @@ replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method
function(x) 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))) returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) { for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) { 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]]$beta[2]) returndat$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$se.beta <- 1.413612*sapply(seq(1,max(df[,sequence])),function(k) tam.se(tam1[[k]])$beta$se.Dim1[2] )
returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta returndat$low.ci.beta <- returndat$beta-1.96*returndat$se.beta
returndat$high.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$true.value.in.ci <- 1*(truebeta>returndat$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$high.ci.beta)
if (truebeta==0) { if (truebeta==0) {
returndat$beta.same.sign.truebeta <- NA returndat$beta.same.sign.truebeta <- NA
} else { } else {
@ -61,7 +69,51 @@ replicate_pcm_analysis <- function(df=NULL,treatment='TT',irtmodel='PCM2',method
eff.size=eff.size, eff.size=eff.size,
dif.size= difsize, dif.size= difsize,
nb.dif= nbdif nb.dif= nbdif
) )
returndat <- cbind(returndat2,returndat)
return(returndat)
}
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',truebeta=0,eff.size=0,difsize=NA,nbdif=0) {
nbitems <- sum(sapply(1:20,function(x) paste0('item',x)) %in% colnames(df))
resp <- df[,sapply(seq(1,nbitems),function(x) paste0('item',x))]
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- pbmclapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
listitems <- sapply(seq(1,nbitems),function(x) paste0('item',x))
returndat <- data.frame(matrix(nrow=max(df[,sequence]),ncol=length(listitems)))
colnames(returndat) <- listitems
for (s in seq(1,max(df[,sequence]))) {
for (k in seq(1,nbitems)) {
returndat[s,paste0('item',k)] <- tam1[[s]]$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) returndat <- cbind(returndat2,returndat)
return(returndat) return(returndat)
} }
@ -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') 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') 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[[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[[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') 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 ######### 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') 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') 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[[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[[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') 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
View File

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