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Corrected pcmdif.do error

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Corentin Choisy 11 months ago
parent ef1fbfbd4f
commit d5baf77bc1
5 changed files with 753 additions and 745 deletions
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res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
points(y=res.null0$h0.rejected.p,x=rep(1,nrow(res.null0)),col='gray',pch=3)
res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(3,nrow(res.null5)),col='#053305',pch=3)
## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size (1 item)
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
nrow(res.null)
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
nrow(res.null)
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
nrow(res.null)
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2,3),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(2,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
# Overall
boxplot(beta.same.sign.truebeta.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of estimates with same sign as true value in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)
#### Create data.frame
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
#### Compiler function
compile_simulation <- function(scenario) {
name <- as.numeric(gsub("[^0-9.-]", "", substr(scenario,start=0,stop=2)))
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'.csv'))
muB=10
kappa=1
sd=10
alpha=0.05
beta=0.20
(nB=(1+1/kappa)*(sd*(qnorm(1-alpha/2)+qnorm(1-beta))/(muA-muB))^2)
ceiling(nB) # 63
z=(muA-muB)/(sd*sqrt((1+1/kappa)/nB))
(Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2)))
muA=5
muB=10
kappa=1
sd=10
alpha=0.05
beta=0.20
(nB=(1+1/kappa)*(sd*(qnorm(1-alpha/2)+qnorm(1-beta))/(muA-muB))^2)
ceiling(nB) # 63
z=(muA-muB)/(sd*sqrt((1+1/kappa)/nB))
(Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2)))
-1*(-0.49-0.47)
-1*(-1.69-0.95)+(-0.49-0.47)
-1*(-0.49-0.47)-0.49
-1*(-0.49-0.47)-1.69
-1*(-0.49-0.47)-(1.69-0.49)
3.35-1.69
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
library(dplyr)
lastChar <- function(str){
substr(str, nchar(str)-2, nchar(str))
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_',scenario,'_nodif.csv'))
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 (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'.csv'))
if (method=='JML') {
tam1 <- tam.jml(resp=resp,group=1+df[,treatment])
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N200/scenario_',scenario,'_nodif.csv'))
if (method!='MML' & method!='JML') {
stop('Invalid method. Please choose among MML or JML')
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name<=4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'.csv'))
return(tam1)
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300" & name>4) {
s <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N300/scenario_',scenario,'_nodif.csv'))
replicate_pcm_analysis_m4 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',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))]
truebeta <- eff.size
if (method=='MML') {
n <- max(df[,sequence])
print(n)
tam1 <- lapply(seq(1,n),
function(x) pcm_analysis(df=df[df[,sequence]==x,],treatment=treatment,irtmodel=irtmodel)
)
}
if (unique(s$J)==4) {
if (unique(s$M)==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
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$beta.same.sign.truebeta <- NA
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3)
returndat$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$replication==1,])/2,
eff.size=truebeta,
dif.size= difsize,
nb.dif= nbdif
)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
} else {
if (unique(s$M)==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4),
m.item5=mean(s$item5),m.item6=mean(s$item6),m.item7=mean(s$item7))
} else {
a <- data.frame(m.item1_1=mean(s$item1_1),m.item1_2=mean(s$item1_2),m.item1_3=mean(s$item1_3),
m.item2_1=mean(s$item2_1),m.item2_2=mean(s$item2_2),m.item2_3=mean(s$item2_3),
m.item3_1=mean(s$item3_1),m.item3_2=mean(s$item3_2),m.item3_3=mean(s$item3_3),
m.item4_1=mean(s$item4_1),m.item4_2=mean(s$item4_2),m.item4_3=mean(s$item4_3),
m.item5_1=mean(s$item5_1),m.item5_2=mean(s$item5_2),m.item5_3=mean(s$item5_3),
m.item6_1=mean(s$item6_1),m.item6_2=mean(s$item6_2),m.item6_3=mean(s$item6_3),
m.item7_1=mean(s$item7_1),m.item7_2=mean(s$item7_2),m.item7_3=mean(s$item7_3)
replicate_pcm_analysis_m2 <- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',eff.size=0,difsize=NA,nbdif=0) {
truebeta <- eff.size
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 <- lapply(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]
}
zz <- substr(scenario,start=0,stop=nchar(scenario)-4)
b <- data.frame(scenario=zz,
scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
N=substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)),
J=unique(s$J),
M=unique(s$M),
eff.size=unique(s$eff.size),
nb.dif=unique(s$nb.dif),
dif.size=unique(s$dif.size)
}
returndat$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$replication==1,])/2,
eff.size=truebeta,
dif.size= difsize,
nb.dif= nbdif
)
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se.beta),
m.low.ci.beta=mean(s$low.ci.beta),
m.high.ci.beta=mean(s$high.ci.beta),
true.value.in.ci.p=mean(s$true.value.in.ci),
h0.rejected.p=mean(s$h0.rejected),
beta.same.sign.truebeta.p=mean(s$beta.same.sign.truebeta,na.rm=T),
beta.same.sign.truebeta.signif.p=mean(s[s$h0.rejected==1,]$beta.same.sign.truebeta,na.rm=T))
d <- cbind(b,a,z)
d$prop.
return(d)
returndat <- cbind(returndat2,returndat)
return(returndat)
}
#### Compiled results
res.dat <- compile_simulation('1A_100')
for (x in results[seq(2,length(results))]) {
y <- compile_simulation(x)
res.dat <- bind_rows(res.dat,y)
replicate_pcm_analysis<- function(df=NULL,treatment='TT',irtmodel='PCM2',method='MML',sequence='replication',eff.size=0,difsize=NA,nbdif=0) {
j <- max(df$item1)
if(j==1) {
return(replicate_pcm_analysis_m2(df=df,treatment=treatment,irtmodel=irtmodel,method=method,sequence=sequence,eff.size=eff.size,difsize=difsize,nbdif=nbdif))
} else {
return(replicate_pcm_analysis_m4(df=df,treatment=treatment,irtmodel=irtmodel,method=method,sequence=sequence,eff.size=eff.size,difsize=difsize,nbdif=nbdif))
}
library(TAM)
library(doMC)
library(parallel)
library(pbmcapply)
library(funprog)
library(dplyr)
#### Create data.frame
}
registerDoMC(4)
dat1 <- read.csv(file = '/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N100/scenario_20A_100.csv')
res_nodif <- pbmclapply(c('dat1'),function(x) replicate_pcm_analysis(get(x),nbdif = 1,difsize = 0.5))
write.csv(res_nodif[[1]],'/home/corentin/Documents/These/Recherche/Simulation/Analysis/DIF/N100/scenario_20A_100_nodif.csv')
res_nodif[[1]]
write.csv(res_nodif[[1]],'/home/corentin/Documents/These/Recherche/Simulationss/Analysis/NoDIF/N100/scenario_20A_100_nodif.csv')
write.csv(res_nodif[[1]],'/home/corentin/Documents/These/Recherche/Simulations/Analysis/NoDIF/N100/scenario_20A_100_nodif.csv')
results <- c(sapply(1:4,function(x) paste0(x,c('A','B','C','D','E'))),sapply(5:9,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
results <- c(sapply(c(100,200,300),function(x) paste0(results,'_',x)))
@ -346,63 +233,188 @@ res.dat[300:396,'nb.dif'] <- 3
res.dat.simple <- res.dat[,c(1:8,13,16:18)]
res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
res.dat.simple
boxplot(beta.same.sign.truebeta.signif.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of estimates with same sign as true value in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)
boxplot(beta.same.sign.truebeta.signif.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of estimates with same sign as true value in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)
## Proportion of rejected h0 per dif value in h0 scenarios (A) by DIF size
res.null <- res.dat[res.dat$eff.size!=0,]
res.null <- res.dat[res.dat$eff.size==0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,1),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size!=0 & res.dat$dif.size==0,]
res.null0 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==0,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='gray',pch=3)
res.null3 <- res.dat[res.dat$eff.size!=0 & res.dat$dif.size==-0.3,]
res.null3 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size!=0 & res.dat$dif.size==-0.5,]
res.null5 <- res.dat[res.dat$eff.size==0 & res.dat$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size>0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,1),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='gray',pch=3)
res.null3 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.3,]
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.5,]
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size>0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='gray',pch=3)
res.null3 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.3,]
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.5,]
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size>0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='darkblue',pch=3)
res.null3 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.3,]
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==-0.5,]
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==4,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$J==7,]
nrow(res.null)
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$J==7,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
####### N=200
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$N==200,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$N==200,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$N==200,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$N==200,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
####### N=300
par(mfrow=c(2,2))
# 0 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==0 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='No DIF',ylim=c(0,1))
points(y=res.null$h0.rejected.p,x=rep(1,nrow(res.null)),col='#590b0c',pch=3)
# 1 item
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==1 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 1 item',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 2 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==2 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 2 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
# 3 items
res.null <- res.dat[res.dat$eff.size==0 & res.dat$nb.dif==3 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2),xlab='DIF size',
ylab='H0 rejection proportion in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.null[res.null$dif.size==-0.3,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.null[res.null$dif.size==-0.5,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
par(mfrow=c(1,1))
res.null <- res.dat[res.dat$eff.size>0,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0,]
@ -415,6 +427,7 @@ res.null3 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0.3,]
points(y=res.null3$h0.rejected.p,x=rep(4,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0.5,]
points(y=res.null5$h0.rejected.p,x=rep(5,nrow(res.null5)),col='#053305',pch=3)
####### N=100
res.null <- res.dat[res.dat$eff.size>0 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0 & res.dat$N==100,]
@ -427,6 +440,7 @@ res.null3 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0.3 & res.dat$N==100
points(y=res.null3$h0.rejected.p,x=rep(4,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0.5 & res.dat$N==100,]
points(y=res.null5$h0.rejected.p,x=rep(5,nrow(res.null5)),col='#053305',pch=3)
####### N=300 // DIF à 0.5 - QUELS SONT LES SCENARIOS EN HAUT
res.null <- res.dat[res.dat$eff.size>0 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size>0 & res.dat$dif.size==0 & res.dat$N==300,]
@ -449,10 +463,6 @@ res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.3 & res.dat$N==10
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.5 & res.dat$N==100,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0.3 & res.dat$N==100,]
points(y=res.null3$h0.rejected.p,x=rep(4,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0.5 & res.dat$N==100,]
points(y=res.null5$h0.rejected.p,x=rep(5,nrow(res.null5)),col='#053305',pch=3)
####### N=300 // DIF à 0.5 - QUELS SONT LES SCENARIOS EN HAUT
res.null <- res.dat[res.dat$eff.size<0 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
@ -462,51 +472,41 @@ res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.3 & res.dat$N==30
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.5 & res.dat$N==300,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0.3 & res.dat$N==300,]
points(y=res.null3$h0.rejected.p,x=rep(4,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0.5 & res.dat$N==300,]
points(y=res.null5$h0.rejected.p,x=rep(5,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size<0 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0 & res.dat$N==100,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='darkblue',pch=3)
res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.3 & res.dat$N==100,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.5 & res.dat$N==100,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size<0 & res.dat$N==300,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0 & res.dat$N==300,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='darkblue',pch=3)
res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.3 & res.dat$N==300,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.5 & res.dat$N==300,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
res.null <- res.dat[res.dat$eff.size<0 & res.dat$N==100,]
boxplot(h0.rejected.p~dif.size,data=res.null,col=c(3,2,4,2,3),xlab='DIF size',ylab='H0 rejection proportion in target scenario')
res.null0 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==0 & res.dat$N==100,]
points(y=res.null0$h0.rejected.p,x=rep(3,nrow(res.null0)),col='darkblue',pch=3)
res.null3 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.3 & res.dat$N==100,]
points(y=res.null3$h0.rejected.p,x=rep(2,nrow(res.null3)),col='#590b0c',pch=3)
res.null5 <- res.dat[res.dat$eff.size<0 & res.dat$dif.size==-0.5 & res.dat$N==100,]
points(y=res.null5$h0.rejected.p,x=rep(1,nrow(res.null5)),col='#053305',pch=3)
muA=5
muB=10
kappa=1
sd=10
alpha=0.05
beta=0.20
(nB=(1+1/kappa)*(sd*(qnorm(1-alpha/2)+qnorm(1-beta))/(muA-muB))^2)
ceiling(nB) # 63
z=(muA-muB)/(sd*sqrt((1+1/kappa)/nB))
(Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2)))
muA=5
muB=10
kappa=1
sd=10
alpha=0.05
beta=0.20
(nB=(1+1/kappa)*(sd*(qnorm(1-alpha/2)+qnorm(1-beta))/(muA-muB))^2)
ceiling(nB) # 63
z=(muA-muB)/(sd*sqrt((1+1/kappa)/nB))
(Power=pnorm(z-qnorm(1-alpha/2))+pnorm(-z-qnorm(1-alpha/2)))
boxplot(true.value.in.ci.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of true beta value in CI in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$true.value.in.ci.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$true.value.in.ci.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$true.value.in.ci.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$true.value.in.ci.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$true.value.in.ci.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)
# Overall
boxplot(beta.same.sign.truebeta.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of estimates with same sign as true value in target scenario',main='DIF on 3 items',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$beta.same.sign.truebeta.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)
# Overall // H0 rejected
boxplot(beta.same.sign.truebeta.signif.p~dif.size,data=res.dat,col=c(2,3),xlab='DIF size',
ylab='Proportion of estimates with same sign as true value in target scenario',main='When H0 rejected',ylim=c(0,1))
res.null3 <- res.dat[res.dat$dif.size==-0.5,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(1,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.5,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(5,nrow(res.null3)),col='#590b0c',pch=3)
res.null3 <- res.dat[res.dat$dif.size==-0.3,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(2,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0.3,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(4,nrow(res.null3)),col='#053305',pch=3)
res.null3 <- res.dat[res.dat$dif.size==0,]
points(y=res.null3$beta.same.sign.truebeta.signif.p,x=rep(3,nrow(res.null3)),col='gray',pch=3)

792
RProject/pcm.R
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File diff suppressed because it is too large Load Diff

20
Scripts/Analysis/DIF/pcm_dif.do
Unescape Escape View File

@ -296,6 +296,8 @@ putexcel A1=matrix(outmat), colnames
****** Scenarios with DIF on 2 items
* Load pcm.ado
adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/"
** Scenario 9: J = 4 items / M = 2 modalities / DIF size 0.3 x2
local N = "100 200 300"
@ -427,7 +429,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'
@ -611,7 +613,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'
@ -666,12 +668,14 @@ putexcel A1=matrix(outmat), colnames
*==========================
* Scenarios with : J=4
* Scenarios with : J=7
*==========================
****** Scenarios with DIF on 2 items
* Load pcm.ado
adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/"
** Scenario 13: J = 7 items / M = 2 modalities / DIF size 0.3 x2
local N = "100 200 300"
@ -803,7 +807,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'
@ -987,7 +991,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'
@ -1041,6 +1045,8 @@ putexcel A1=matrix(outmat), colnames
***** Scenarios with DIF on 3 items
* Load pcm.ado
adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/"
** Scenario 17: J = 7 items / M = 2 modalities / DIF size 0.3 x3
local N = "100 200 300"
@ -1207,7 +1213,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4 5 6)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'
@ -1440,7 +1446,7 @@ forvalues k=1/1000 {
local mod = "`mod'"+"(1.item`i'<-THETA@1)(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
}
}
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2)"
local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(1 2 3 4 5 6)"
qui `constrnt'
qui `constrnt2'
qui `constrnt3'

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