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corrected rosali bug

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main
Corentin Choisy 8 months ago
parent cfcbc42b59
commit 65f2a62ba8
4 changed files with 265 additions and 151 deletions
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12
Modules/rosali_custom/rosali_original.ado
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@ -767,11 +767,12 @@ if "`group'"!="" & "`nodif'"=="" { // PARTIE 1 = Slmt si option group & pas de "
local nb_stepC = 0
qui lrtest modeldifA modeldifB
local diftestp=r(p)
local lrt_passed=0
if `diftestp'<0.05{ /*If pvalue(LRtest)<0.05 then step C*/
di
di as input "PROCESSING STEP C"
di
local lrt_passed=1
/*test DIF pour chaque item*/
local boucle = 1
local stop = 0
@ -1135,11 +1136,18 @@ else if "`group'" == "" {
di
}
matrix dif_detect = J(1,`nbitems',.)
matrix dif_detect = J(1,2*`nbitems'+1,.)
local numdif=1
matrix dif_detect[1,`nbitems'+`nbitems'+1]=`lrt_passed'
forvalues j=1/`nbitems' {
if dif_rc[`j',1] != . {
matrix dif_detect[1,`numdif']=`j'
if dif_rc[`j',2] == 0 {
matrix dif_detect[1,`nbitems'+`numdif']=0
}
if dif_rc[`j',2] != 0 {
matrix dif_detect[1,`nbitems'+`numdif']=1
}
local numdif = `numdif'+1
}
}

258
RProject/Scripts/aggregation.R
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@ -417,10 +417,25 @@ compile_simulation2_rosali <- function(scenario) {
J <- max(which(sapply(1:7,function(x) paste0('item',x) %in% colnames(s) | paste0('item',x,'_1') %in% colnames(s))))
M <- 1+sum(sapply(1:3,function(x) paste0('item1_',x) %in% colnames(s) ))
if (M==1) {M <- 2}
nb.dif <- max(which(sapply(1:3,function(x) paste0('dif',x) %in% colnames(s) | paste0('dif',x,'_1') %in% colnames(s))))
nb.dif.true <- ifelse(name<=4,0,ifelse(name<=8,1,ifelse(name<=16,2,3)))
if (name %in% c(3,4,13:20)) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,
ifelse(is.na(s$dif_5_1),4,
ifelse(is.na(s$dif_6_1),5,
ifelse(is.na(s$dif_7_1),6,7))))))))
}
if (!(name %in% c(3,4,13:20))) {
m.nb.dif.detect <- mean(ifelse(is.na(s$dif_1_1),0,
ifelse(is.na(s$dif_2_1),1,
ifelse(is.na(s$dif_3_1),2,
ifelse(is.na(s$dif_4_1),3,4)))))
}
if (J==4) {
if (M==2) {
a <- data.frame(m.item1=mean(s$item1),m.item2=mean(s$item2),m.item3=mean(s$item3),m.item4=mean(s$item4))
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1))
} else {
a <- data.frame(m.item1_1=mean(s$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$item2_3),
@ -430,8 +445,8 @@ compile_simulation2_rosali <- function(scenario) {
}
} else {
if (M==2) {
a <- data.frame(m.item1=mean(s$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))
a <- data.frame(m.item1=mean(s$item1_1),m.item2=mean(s$item2_1),m.item3=mean(s$item3_1),m.item4=mean(s$item4_1),
m.item5=mean(s$item5_1),m.item6=mean(s$item6_1),m.item7=mean(s$item7_1))
} else {
a <- data.frame(m.item1_1=mean(s$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$item2_3),
@ -453,12 +468,97 @@ compile_simulation2_rosali <- function(scenario) {
J=J,
M=M,
eff.size=eff.size,
nb.dif=nb.dif,
nb.dif=nb.dif.true,
m.nb.dif.detect=m.nb.dif.detect,
dif.size=dif.size
)
true.value.in.ci <- eff.size <= s$beta+1.96*s$se_beta & eff.size >= s$beta-1.96*s$se_beta
beta.same.sign.truebeta.p <- ifelse(rep(eff.size,nrow(s))==0,NA,(rep(eff.size,nrow(s))/s$beta)>0)
num.reject <- which((s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0)
dif.d <- mean(sapply(1:1000,function(x) any(!is.na(s[x,paste0("dif_",1:unique(b$J),"_1")]))))
if (nb.dif.true==0) {
prop.perfect <- NA
flexible.detect <- NA
moreflexible.detect <- NA
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,"dif_detect_unif_1"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==4) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,"dif_detect_unif_1"]==1 & s[x,"dif_detect_unif_2"]==1 & s[x,"dif_detect_unif_3"]==1 & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
flexible.detect <- mean(flexible.detect)
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==1 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==1, s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==4 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:4),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:4)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:4)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:4)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==2 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==2,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
if (nb.dif.true==3 & unique(b$J)==7 & unique(b$M)==2) {
perfect.detection <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))==3,s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[1])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[2])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")]) & s[x,paste0('dif_detect_',which(sapply(paste0("dif_detect_",1:7),function(y) !is.na(s[x,y])))[3])]%in%c(s[x,c("real_dif_1","real_dif_2","real_dif_3")])
,0) )
prop.perfect <- mean(perfect.detection)
flexible.detect <- prop.perfect
moreflexible.detect <- sapply(1:1000,function(x) ifelse(sum(!is.na(s[x,paste0("dif_detect_",1:7)]))!=0,s[x,"real_dif_1"]%in%c(s[x,paste0("dif_detect_",1:7)]) &
s[x,"real_dif_2"]%in%c(s[x,paste0("dif_detect_",1:7)]) & s[x,"real_dif_3"]%in%c(s[x,paste0("dif_detect_",1:7)]),0) )
moreflexible.detect <- mean(moreflexible.detect)
}
z <- data.frame(m.beta=mean(s$beta),
se.empirical.beta=sd(s$beta),
se.analytical.beta=mean(s$se_beta),
@ -467,7 +567,11 @@ compile_simulation2_rosali <- function(scenario) {
true.value.in.ci.p=mean(true.value.in.ci),
h0.rejected.p=mean( (s$beta-1.96*s$se_beta)>0 | (s$beta+1.96*s$se_beta)<0 ),
beta.same.sign.truebeta.p=mean(beta.same.sign.truebeta.p),
beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject])
beta.same.sign.truebeta.signif.p=mean(beta.same.sign.truebeta.p[num.reject]),
dif.detected=dif.d,
prop.perfect=prop.perfect,
flexible.detect=flexible.detect,
moreflexible.detect=moreflexible.detect
)
d <- cbind(b,a,z)
d$prop.
@ -487,148 +591,6 @@ for (x in results[seq(2,length(results))]) {
res.dat.dif.rosali$bias <- res.dat.dif.rosali$eff.size-res.dat.dif.rosali$m.beta
##############################################################################
#----------------------------------------------------------------------------#
################################### RESALI ###################################
#----------------------------------------------------------------------------#
##############################################################################
generate_resali <- function(scenario=NULL,grp=NULL) {
scen <- as.numeric(gsub("[A,B,C,D,E,F,G,_]","",substr(scenario,0,3)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50") {
N <- 50
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100") {
N <- 100
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200") {
N <- 200
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300") {
N <- 300
}
if (scen<5) {
dat <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N',N,'/scenario_',scenario,'.csv'))
}
if (scen>=5) {
dat <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N',N,'/scenario_',scenario,'.csv'))
}
if (scen%in%c(3,4,13:20)) {
res <- resali(df=dat[dat$replication==1,],items = seq(1,7),group=grp,verbose=FALSE)
df_res <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.5=ifelse(length(res$dif.items)>=5,res$dif.items[5],NA),
dif.detect.6=ifelse(length(res$dif.items)>=6,res$dif.items[6],NA),
dif.detect.7=ifelse(length(res$dif.items)>=7,res$dif.items[7],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
dif.detect.unif.5=ifelse(length(res$uniform)>=5,res$uniform[5],NA),
dif.detect.unif.6=ifelse(length(res$uniform)>=6,res$uniform[6],NA),
dif.detect.unif.7=ifelse(length(res$uniform)>=7,res$uniform[7],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=16,2,3)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif1)),
true.dif.2=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif2)),
true.dif.3=ifelse(scen<=16,NA,unique(dat[dat$replication==1,]$dif3))
)
for (k in 2:1000) {
if (k%%100==0) {
cat(paste0('N=',k,'/1000\n'))
}
res <- resali(df=dat[dat$replication==k,],items = seq(1,7),group=grp,verbose=FALSE)
df_res2 <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.5=ifelse(length(res$dif.items)>=5,res$dif.items[5],NA),
dif.detect.6=ifelse(length(res$dif.items)>=6,res$dif.items[6],NA),
dif.detect.7=ifelse(length(res$dif.items)>=7,res$dif.items[7],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
dif.detect.unif.5=ifelse(length(res$uniform)>=5,res$uniform[5],NA),
dif.detect.unif.6=ifelse(length(res$uniform)>=6,res$uniform[6],NA),
dif.detect.unif.7=ifelse(length(res$uniform)>=7,res$uniform[7],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=16,2,3)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif1)),
true.dif.2=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif2)),
true.dif.3=ifelse(scen<=16,NA,unique(dat[dat$replication==k,]$dif3)))
df_res <- rbind(df_res,df_res2)
}
}
else if (scen%in%c(1,2,5:12)) {
res <- resali(df=dat[dat$replication==1,],items = seq(1,4),group=grp,verbose=FALSE)
df_res <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=8,1,2)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif1)),
true.dif.2=ifelse(scen<=8,NA,unique(dat[dat$replication==1,]$dif2))
)
for (k in 2:1000) {
if (k%%100==0) {
cat(paste0('N=',k,'/1000\n'))
}
res <- resali(df=dat[dat$replication==k,],items = seq(1,4),group=grp,verbose=FALSE)
df_res2 <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=8,1,2)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif1)),
true.dif.2=ifelse(scen<=8,NA,unique(dat[dat$replication==k,]$dif2)))
df_res <- rbind(df_res,df_res2)
}
}
return(df_res)
}
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(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
for (r in results) {
cat(paste0(r,"\n"))
cat(paste0("-------------------------------------------","\n"))
write.csv(generate_resali(r,"TT"),paste0("/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection/",r,".csv"))
cat(paste0("-------------------------------------------","\n"))
}
##############################################################################
#----------------------------------------------------------------------------#
####################### AGGREGATION DIF MATRICES RESALI ######################

144
RProject/Scripts/resali_generate_newdata.R
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@ -134,3 +134,147 @@ for (r in results) {
analyse <- analyse[,!names(analyse) %in% c("X","X.1","X.2")]
write.csv(analyse,paste0("/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data/",r,".csv"))
}
##############################################################################
#----------------------------------------------------------------------------#
################################### RESALI ###################################
#----------------------------------------------------------------------------#
##############################################################################
generate_resali <- function(scenario=NULL,grp=NULL) {
scen <- as.numeric(gsub("[A,B,C,D,E,F,G,_]","",substr(scenario,0,3)))
if (substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50") {
N <- 50
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="100") {
N <- 100
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="200") {
N <- 200
}
if (substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario))=="300") {
N <- 300
}
if (scen<5) {
dat <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N',N,'/scenario_',scenario,'.csv'))
}
if (scen>=5) {
dat <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N',N,'/scenario_',scenario,'.csv'))
}
if (scen%in%c(3,4,13:20)) {
res <- resali(df=dat[dat$replication==1,],items = seq(1,7),group=grp,verbose=FALSE)
df_res <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.5=ifelse(length(res$dif.items)>=5,res$dif.items[5],NA),
dif.detect.6=ifelse(length(res$dif.items)>=6,res$dif.items[6],NA),
dif.detect.7=ifelse(length(res$dif.items)>=7,res$dif.items[7],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
dif.detect.unif.5=ifelse(length(res$uniform)>=5,res$uniform[5],NA),
dif.detect.unif.6=ifelse(length(res$uniform)>=6,res$uniform[6],NA),
dif.detect.unif.7=ifelse(length(res$uniform)>=7,res$uniform[7],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=16,2,3)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif1)),
true.dif.2=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif2)),
true.dif.3=ifelse(scen<=16,NA,unique(dat[dat$replication==1,]$dif3))
)
for (k in 2:1000) {
if (k%%100==0) {
cat(paste0('N=',k,'/1000\n'))
}
res <- resali(df=dat[dat$replication==k,],items = seq(1,7),group=grp,verbose=FALSE)
df_res2 <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.5=ifelse(length(res$dif.items)>=5,res$dif.items[5],NA),
dif.detect.6=ifelse(length(res$dif.items)>=6,res$dif.items[6],NA),
dif.detect.7=ifelse(length(res$dif.items)>=7,res$dif.items[7],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
dif.detect.unif.5=ifelse(length(res$uniform)>=5,res$uniform[5],NA),
dif.detect.unif.6=ifelse(length(res$uniform)>=6,res$uniform[6],NA),
dif.detect.unif.7=ifelse(length(res$uniform)>=7,res$uniform[7],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=16,2,3)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif1)),
true.dif.2=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif2)),
true.dif.3=ifelse(scen<=16,NA,unique(dat[dat$replication==k,]$dif3)))
df_res <- rbind(df_res,df_res2)
}
}
else if (scen%in%c(1,2,5:12)) {
res <- resali(df=dat[dat$replication==1,],items = seq(1,4),group=grp,verbose=FALSE)
df_res <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=8,1,2)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==1,]$dif1)),
true.dif.2=ifelse(scen<=8,NA,unique(dat[dat$replication==1,]$dif2))
)
for (k in 2:1000) {
if (k%%100==0) {
cat(paste0('N=',k,'/1000\n'))
}
res <- resali(df=dat[dat$replication==k,],items = seq(1,4),group=grp,verbose=FALSE)
df_res2 <- data.frame(dif.detect.1=ifelse(length(res$dif.items)>=1,res$dif.items[1],NA),
dif.detect.2=ifelse(length(res$dif.items)>=2,res$dif.items[2],NA),
dif.detect.3=ifelse(length(res$dif.items)>=3,res$dif.items[3],NA),
dif.detect.4=ifelse(length(res$dif.items)>=4,res$dif.items[4],NA),
dif.detect.unif.1=ifelse(length(res$uniform)>=1,res$uniform[1],NA),
dif.detect.unif.2=ifelse(length(res$uniform)>=2,res$uniform[2],NA),
dif.detect.unif.3=ifelse(length(res$uniform)>=3,res$uniform[3],NA),
dif.detect.unif.4=ifelse(length(res$uniform)>=4,res$uniform[4],NA),
N=N,
nbdif=ifelse(scen<=4,0,ifelse(scen<=8,1,2)),
true.dif.1=ifelse(scen<=4,NA,unique(dat[dat$replication==k,]$dif1)),
true.dif.2=ifelse(scen<=8,NA,unique(dat[dat$replication==k,]$dif2)))
df_res <- rbind(df_res,df_res2)
}
}
return(df_res)
}
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(50,100,200,300),function(x) paste0(results,'_',x)))
results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
results <- sort(results)
results2 <- sort(results2)
results <- c(results,results2)
for (r in results) {
cat(paste0(r,"\n"))
cat(paste0("-------------------------------------------","\n"))
write.csv(generate_resali(r,"TT"),paste0("/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection/",r,".csv"))
cat(paste0("-------------------------------------------","\n"))
}

2
Scripts/Analysis/DIF-RESIDUS/pcm_dif_residus.do
Unescape Escape View File

@ -12,7 +12,7 @@ adopath+"/home/corentin/Documents/These/Recherche/ROSALI-SIM/Modules/rosali_cust
local N = "50 100 200 300"
local ss = "1 2 3 4 5 6 7 8 9 10 11 1 13 14 15 16 17 18 19 20"
local ss = "1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20"
foreach s in `ss' {
foreach Nnn in `N' {
local Nn = `Nnn'

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