Created ROSALI screening version

.gitignore

@@ -9,3 +9,5 @@
 !res_dat_dif_rosali.csv
 !res_dat_dif_resali.csv
 *.dcf
+*.pdf
+*.png

DATA_LICENSE

@@ -0,0 +1,232 @@
+Preamble
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LICENSE

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+GNU AFFERO GENERAL PUBLIC LICENSE
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README.md

@@ -1,6 +1,6 @@
 # Simulations
 
-This repository contains all code files related to our ROSALI/Resiuals RCT simulation project. In order to save disk space, data files are not stored on this server and are instead available on https://osf.io.
+This repository contains all code files related to our ROSALI/Resiuals RCT simulation project. In order to save disk space, data files are not stored on this server and are instead available on https://osf.io/zjtfe/
 
 ## File Structure
 

RProject/Scripts/Analysis/aggregation.R

@@ -17,7 +17,7 @@ lastChar <- function(str){
   substr(str, nchar(str), nchar(str))
 }
 
-source(paste0(getwd(),"/functions/resali.R"))
+source(paste0(getwd(),"/Scripts/Analysis/functions/resali.R"))
 
 ##############################################################################
 #----------------------------------------------------------------------------#
@@ -147,13 +147,23 @@ replicate_pcm_analysis<- function(df=NULL,treatment='TT',irtmodel='PCM2',method=
 
 #### 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'))))
+#results <- c(sapply(c(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
+#results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
+#results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
 
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
+#results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
+
+#results <- c(results,results2)
+
+results <- c(sapply(c(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results <- c(sapply(c("050",100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c("050",100,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 
@@ -161,6 +171,11 @@ results2 <- sort(results2)
 
 results <- c(results,results2)
 
+results <- gsub('050',"50",results)
+
+# results <- c(sapply(1:16,function(x) c(results[x],results[x+16],results[x+32])),
+#             sapply(1:30,function(x) c(results[x+48],results[x+30+48],results[x+60+48]))
+#             )
 #### Compiler function
 
 compile_simulation <- function(scenario) {
@@ -216,6 +231,41 @@ compile_simulation <- function(scenario) {
   }
   N <- ifelse(substr(scenario,start=nchar(scenario)-1,stop=nchar(scenario))=="50","50",substr(scenario,start=nchar(scenario)-2,stop=nchar(scenario)))
   zz <- ifelse(N=="50",substr(scenario,start=0,stop=nchar(scenario)-3),substr(scenario,start=0,stop=nchar(scenario)-4)) 
+  zz.type <- substr(zz,start=nchar(zz),stop=nchar(zz))
+  
+  if (unique(is.na(s$dif.size))) {
+    s$dif.size <- 0
+  }
+  if(zz=="10B") {
+    s$dif.size <- 0.3
+  }
+  if (substr(zz,1,1)%in%c("6","8")) {
+    s$nb.dif <- 1
+  }
+  if (substr(zz,1,2)%in%c("10","12","14",'16')) {
+    s$nb.dif <- 2
+  }
+  if (substr(zz,1,1)%in%c("18","20")) {
+    s$nb.dif <- 3
+  }
+  
+  if (zz.type!="A") {
+  if (zz.type=="B") {
+    s$eff.size <- 0.2
+  } else if (zz.type=="C" & unique(s$dif.size)==0) {
+    s$eff.size <- 0.4
+  } else if (zz.type=="C" & unique(s$dif.size)!=0) {
+    s$eff.size <- 0.2
+  } else if (zz.type=="D" & unique(s$dif.size)!=0) {
+    s$eff.size <- 0.4
+  } else if (zz.type=="E" & unique(s$dif.size)!=0) {
+    s$eff.size <- 0.4
+  }
+  } else {
+    s$eff.size <- 0
+    s$dif.size <- -1*s$dif.size
+  }
+  
   b <- data.frame(scenario=zz,
                   scenario.type=substr(zz,start=nchar(zz),stop=nchar(zz)),
                   N=N,
@@ -230,6 +280,7 @@ compile_simulation <- function(scenario) {
                   se.analytical.beta=mean(s$se.beta),
                   m.low.ci.beta=mean(s$low.ci.beta),
                   m.high.ci.beta=mean(s$high.ci.beta),
+                  bias=mean(s$beta-s$eff.size),
                   true.value.in.ci.p=mean(s$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),
@@ -241,37 +292,22 @@ compile_simulation <- function(scenario) {
 
 #### Compiled results
 
-res.dat <- compile_simulation('1A_100')
+res.dat <- compile_simulation('2A_50')
 
 for (x in results[seq(2,length(results))]) {
   y <- compile_simulation(x)
   res.dat <- bind_rows(res.dat,y)
 }
-res.dat[res.dat$scenario.type=='A','dif.size'] <- -res.dat[res.dat$scenario.type=='A','dif.size']
-res.dat[is.na(res.dat$dif.size),'dif.size'] <- 0
-res.dat[193:417,'nb.dif'] <- 2
-res.dat[417:528,'nb.dif'] <- 3
-res.dat[res.dat$scenario.type=="B",]$eff.size <- 0.2
-res.dat[res.dat$scenario.type=="C" & res.dat$dif.size==0,]$eff.size <- 0.4
-res.dat[res.dat$scenario.type=="C" & res.dat$dif.size!=0,]$eff.size <- 0.2
-res.dat[res.dat$scenario.type=="D" & res.dat$dif.size==0,]$eff.size <- -0.2
-res.dat[res.dat$scenario.type=="D" & res.dat$dif.size!=0,]$eff.size <- 0.4
-res.dat[res.dat$scenario.type=="E" & res.dat$dif.size==0,]$eff.size <- -0.4
-res.dat[res.dat$scenario.type=="E" & res.dat$dif.size!=0,]$eff.size <- 0.4
-res.dat[res.dat$scenario.type=="F",]$eff.size <- -0.2
-res.dat[res.dat$scenario.type=="G",]$eff.size <- -0.4
-View(res.dat)
 
-res.dat.simple <- res.dat[,c(1:8,13,16:18)]
+res.dat[res.dat$N==50,"dif.size"] <- res.dat[which(res.dat$N==50)+1,"dif.size"]
-res.dat.simple$m.beta <- round(res.dat.simple$m.beta,3)
-res.dat.simple
 
-is.nan.data.frame <- function(x)
+
+is.nan.data.frame <- function(x) {
   do.call(cbind, lapply(x, is.nan))
+}
 
 
 res.dat[is.nan(res.dat)] <- NA
-res.dat$bias <- res.dat$eff.size-res.dat$m.beta
 
 
 ##############################################################################
@@ -282,19 +318,21 @@ res.dat$bias <- res.dat$eff.size-res.dat$m.beta
 
 #### 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'))))
+results <- c(sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
+results <- c(sapply(c("050",100,300),function(x) paste0(results,'_',x)))
 
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
+results2 <- c(sapply(c("050",100,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 
 results2 <- sort(results2)
 
-results <- c(results,results2)[81:528]
+results <- c(results,results2)
+
+results <- gsub('050',"50",results)
 
 
 #### Compiler function
@@ -376,14 +414,23 @@ compile_simulation2 <- function(scenario) {
 
 #### Compiled results
 
-res.dat.dif <- compile_simulation2('5A_100')
+res.dat.dif <- compile_simulation2('6A_50')
 
 for (x in results[seq(2,length(results))]) {
   y <- compile_simulation2(x)
   res.dat.dif <- bind_rows(res.dat.dif,y)
 }
 
-res.dat$bias <- res.dat$eff.size-res.dat$m.beta
+res.dat.dif[is.na(res.dat.dif$dif.size),'dif.size'] <- 0
+res.dat.dif[substr(res.dat.dif$scenario,1,1)%in%c("6","8"),'nb.dif'] <- 1
+res.dat.dif[substr(res.dat.dif$scenario,1,2)%in%seq(10,16,2),'nb.dif'] <- 2
+res.dat.dif[substr(res.dat.dif$scenario,1,2)%in%seq(18,20,2),'nb.dif'] <- 3
+res.dat.dif[res.dat.dif$N==50,"dif.size"] <- res.dat.dif[which(res.dat.dif$N==50)+1,"dif.size"]
+res.dat.dif[res.dat.dif$scenario.type=="B",]$eff.size <- 0.2
+res.dat.dif[res.dat.dif$scenario.type=="C" & res.dat.dif$dif.size!=0,]$eff.size <- 0.2
+res.dat.dif[res.dat.dif$scenario.type=="D" & res.dat.dif$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif[res.dat.dif$scenario.type=="E" & res.dat.dif$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif[res.dat.dif$scenario=="10B",]$dif.size <- 0.3 
 res.dat.dif$bias <- res.dat.dif$eff.size-res.dat.dif$m.beta
 
 ##############################################################################
@@ -394,13 +441,14 @@ res.dat.dif$bias <- res.dat.dif$eff.size-res.dat.dif$m.beta
 
 #### 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(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
+results <- c(sapply(c("050",100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c("050",100,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 
@@ -408,6 +456,8 @@ results2 <- sort(results2)
 
 results <- c(results,results2)
 
+results <- gsub('050',"50",results)
+
 
 #### Compiler function
 
@@ -650,13 +700,33 @@ compile_simulation2_rosali <- function(scenario) {
 
 #### Compiled results
 
-res.dat.dif.rosali <- compile_simulation2_rosali('1A_100')
+res.dat.dif.rosali <- compile_simulation2_rosali('2A_50')
 
 for (x in results[seq(2,length(results))]) {
   y <- compile_simulation2_rosali(x)
   res.dat.dif.rosali <- bind_rows(res.dat.dif.rosali,y)
 }
 
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=='A','dif.size'] <- -res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=='A','dif.size']
+res.dat.dif.rosali[is.na(res.dat.dif.rosali$dif.size),'dif.size'] <- 0
+res.dat.dif.rosali[substr(res.dat.dif.rosali$scenario,1,1)%in%c("6","8"),'nb.dif'] <- 1
+res.dat.dif.rosali[substr(res.dat.dif.rosali$scenario,1,2)%in%seq(10,16,2),'nb.dif'] <- 2
+res.dat.dif.rosali[substr(res.dat.dif.rosali$scenario,1,2)%in%seq(18,20,2),'nb.dif'] <- 3
+res.dat.dif.rosali[res.dat.dif.rosali$N==50,"dif.size"] <- res.dat.dif.rosali[which(res.dat.dif.rosali$N==50)+1,"dif.size"]
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="B",]$eff.size <- 0.2
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="C" & res.dat.dif.rosali$dif.size==0,]$eff.size <- 0.4
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="C" & res.dat.dif.rosali$dif.size!=0,]$eff.size <- 0.2
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="D" & res.dat.dif.rosali$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif.rosali[res.dat.dif.rosali$scenario.type=="E" & res.dat.dif.rosali$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif.rosali[res.dat.dif.rosali$scenario=="10B",]$dif.size <- 0.3 
+
+is.nan.data.frame <- function(x) {
+  do.call(cbind, lapply(x, is.nan))
+}
+
+
+res.dat.dif.rosali[is.nan(res.dat.dif.rosali)] <- NA
+
 res.dat.dif.rosali$bias <- res.dat.dif.rosali$eff.size-res.dat.dif.rosali$m.beta
 
 
@@ -668,13 +738,29 @@ res.dat.dif.rosali$bias <- res.dat.dif.rosali$eff.size-res.dat.dif.rosali$m.beta
 
 #### 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(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
+#results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
+#results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
+
+#results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
+
+#results <- c(results,results2)
+
+#results <- c(sapply(1:16,function(x) c(results[x],results[x+16],results[x+32])),
+#             sapply(1:30,function(x) c(results[x+48],results[x+30+48],results[x+60+48]))
+#)
+
+
+results <- c(sapply(c(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results <- c(sapply(c("050",100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c("050",100,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 
@@ -682,6 +768,8 @@ results2 <- sort(results2)
 
 results <- c(results,results2)
 
+results <- gsub('050',"50",results)
+
 
 #### Compiler function
 
@@ -920,13 +1008,34 @@ compile_simulation2_resali <- function(scenario) {
 
 #### Compiled results
 
-res.dat.dif.resali <- compile_simulation2_resali('1A_100')
+res.dat.dif.resali <- compile_simulation2_resali('2A_50')
 
 for (x in results[seq(2,length(results))]) {
   y <- compile_simulation2_resali(x)
   res.dat.dif.resali <- bind_rows(res.dat.dif.resali,y)
 }
 
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=='A','dif.size'] <- -res.dat.dif.resali[res.dat.dif.resali$scenario.type=='A','dif.size']
+res.dat.dif.resali[is.na(res.dat.dif.resali$dif.size),'dif.size'] <- 0
+res.dat.dif.resali[substr(res.dat.dif.resali$scenario,1,1)%in%c("6","8"),'nb.dif'] <- 1
+res.dat.dif.resali[substr(res.dat.dif.resali$scenario,1,2)%in%seq(10,16,2),'nb.dif'] <- 2
+res.dat.dif.resali[substr(res.dat.dif.resali$scenario,1,2)%in%seq(18,20,2),'nb.dif'] <- 3
+res.dat.dif.resali[res.dat.dif.resali$N==50,"dif.size"] <- res.dat.dif.resali[which(res.dat.dif.resali$N==50)+1,"dif.size"]
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=="B",]$eff.size <- 0.2
+res.dat.dif.resali[res.dat.dif.resali$scenario=="20E" & res.dat.dif.resali$N==50,]$dif.size <- -0.5
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=="C" & res.dat.dif.resali$dif.size==0,]$eff.size <- 0.4
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=="C" & res.dat.dif.resali$dif.size!=0,]$eff.size <- 0.2
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=="D" & res.dat.dif.resali$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif.resali[res.dat.dif.resali$scenario.type=="E" & res.dat.dif.resali$dif.size!=0,]$eff.size <- 0.4
+res.dat.dif.resali[res.dat.dif.resali$scenario=="10B",]$dif.size <- 0.3 
+
+is.nan.data.frame <- function(x) {
+  do.call(cbind, lapply(x, is.nan))
+}
+
+
+res.dat.dif.resali[is.nan(res.dat.dif.resali)] <- NA
+
 res.dat.dif.resali$bias <- res.dat.dif.resali$eff.size-res.dat.dif.resali$m.beta
 
 
@@ -943,20 +1052,6 @@ res.dat$theoretical.power <- 0
 
 ### Scénarios N=100
 
-## Scénarios J=4 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==100,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==100,]$theoretical.power <- 0.1543
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==100,]$theoretical.power <- 0.1543
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==100,]$theoretical.power <- 0.4627
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==100,]$theoretical.power <- 0.4627
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==100,]$theoretical.power <- 0.1543
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==100,]$theoretical.power <- 0.4627
-
-res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==100,]$theoretical.power <- 0.4627
-res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==100,]$theoretical.power <- 0.1543
-res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==100,]$theoretical.power <- 0.4627
-
 ## Scénarios J=4 / M=4
 
 res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==100,]$theoretical.power <- 0.05
@@ -964,26 +1059,8 @@ res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==100,]$theo
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==100,]$theoretical.power <- 0.2177
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==100,]$theoretical.power <- 0.6586
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==100,]$theoretical.power <- 0.6586
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==100,]$theoretical.power <- 0.2177
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==100,]$theoretical.power <- 0.6586
 
 res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==100,]$theoretical.power <- 0.6586
-res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==100,]$theoretical.power <- 0.2177
-res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==100,]$theoretical.power <- 0.6586
-
-## Scénarios J=7 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==100,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==100,]$theoretical.power <- 0.1870
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==100,]$theoretical.power <- 0.1870
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==100,]$theoretical.power <- 0.5666
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==100,]$theoretical.power <- 0.5666
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==100,]$theoretical.power <- 0.1870
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==100,]$theoretical.power <- 0.5666
-
-res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==100,]$theoretical.power <- 0.5666
-res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==100,]$theoretical.power <- 0.1870
-res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==100,]$theoretical.power <- 0.5666
 
 ## Scénarios J=7 / M=4
 
@@ -992,91 +1069,13 @@ res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==100,]$th
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==100,]$theoretical.power <- 0.2450
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==100,]$theoretical.power <- 0.7136
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==100,]$theoretical.power <- 0.7136
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==100,]$theoretical.power <- 0.2450
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==100,]$theoretical.power <- 0.7136
 
 res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==100,]$theoretical.power <- 0.7136
-res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==100,]$theoretical.power <- 0.2450
-res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==100,]$theoretical.power <- 0.7136
-
-
-### Scénarios N=200
-
-## Scénarios J=4 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==200,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==200,]$theoretical.power <- 0.2618
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==200,]$theoretical.power <- 0.2618
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==200,]$theoretical.power <- 0.7507
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==200,]$theoretical.power <- 0.7507
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==200,]$theoretical.power <- 0.2618
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==200,]$theoretical.power <- 0.7507
-
-res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==200,]$theoretical.power <- 0.7507
-res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==200,]$theoretical.power <- 0.2618
-res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==200,]$theoretical.power <- 0.7507
-
-## Scénarios J=4 / M=4
-
-res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==200,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==200,]$theoretical.power <- 0.3875
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==200,]$theoretical.power <- 0.3875
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==200,]$theoretical.power <- 0.9161
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==200,]$theoretical.power <- 0.9161
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==200,]$theoretical.power <- 0.3875
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==200,]$theoretical.power <- 0.9161
-
-res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==200,]$theoretical.power <- 0.9161
-res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==200,]$theoretical.power <- 0.3875
-res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==200,]$theoretical.power <- 0.9161
-
-## Scénarios J=7 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==200,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==200,]$theoretical.power <- 0.3258
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==200,]$theoretical.power <- 0.3258
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==200,]$theoretical.power <- 0.8538
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==200,]$theoretical.power <- 0.8538
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==200,]$theoretical.power <- 0.3258
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==200,]$theoretical.power <- 0.8538
-
-res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==200,]$theoretical.power <- 0.8538
-res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==200,]$theoretical.power <- 0.3258
-res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==200,]$theoretical.power <- 0.8538
-
-## Scénarios J=7 / M=4
-
-res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'A') & res.dat$N==200,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==200,]$theoretical.power <- 0.4321
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==200,]$theoretical.power <- 0.4321
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==200,]$theoretical.power <- 0.9471
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==200,]$theoretical.power <- 0.9471
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==200,]$theoretical.power <- 0.4321
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==200,]$theoretical.power <- 0.9471
-
-res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==200,]$theoretical.power <- 0.9471
-res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==200,]$theoretical.power <- 0.4321
-res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==200,]$theoretical.power <- 0.9471
-
 
 
 
 ### Scénarios N=300
 
-## Scénarios J=4 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==300,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==300,]$theoretical.power <- 0.3660
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==300,]$theoretical.power <- 0.3660
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==300,]$theoretical.power <- 0.8981
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==300,]$theoretical.power <- 0.8981
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==300,]$theoretical.power <- 0.3660
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==300,]$theoretical.power <- 0.8981
-
-res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==300,]$theoretical.power <- 0.8981
-res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==300,]$theoretical.power <- 0.3660
-res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==300,]$theoretical.power <- 0.8981
-
 ## Scénarios J=4 / M=4
 
 res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==300,]$theoretical.power <- 0.05
@@ -1084,26 +1083,8 @@ res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==300,]$theo
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==300,]$theoretical.power <- 0.5373
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==300,]$theoretical.power <- 0.9834
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==300,]$theoretical.power <- 0.9834
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==300,]$theoretical.power <- 0.5373
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==300,]$theoretical.power <- 0.9834
 
 res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==300,]$theoretical.power <- 0.9834
-res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==300,]$theoretical.power <- 0.5373
-res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==300,]$theoretical.power <- 0.9834
-
-## Scénarios J=7 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==300,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==300,]$theoretical.power <- 0.4550
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==300,]$theoretical.power <- 0.4550
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==300,]$theoretical.power <- 0.9584
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==300,]$theoretical.power <- 0.9584
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==300,]$theoretical.power <- 0.4550
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==300,]$theoretical.power <- 0.9584
-
-res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==300,]$theoretical.power <- 0.9584
-res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==300,]$theoretical.power <- 0.4550
-res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==300,]$theoretical.power <- 0.9584
 
 ## Scénarios J=7 / M=4
 
@@ -1112,30 +1093,12 @@ res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==300,]$th
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==300,]$theoretical.power <- 0.5907
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==300,]$theoretical.power <- 0.9919
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==300,]$theoretical.power <- 0.9919
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==300,]$theoretical.power <- 0.5907
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==300,]$theoretical.power <- 0.9919
 
 res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==300,]$theoretical.power <- 0.9919
-res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==300,]$theoretical.power <- 0.5907
-res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==300,]$theoretical.power <- 0.9919
 
 
 ### Scénarios N=50
 
-## Scénarios J=4 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'A') & res.dat$N==50,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(1,5,7,9,11),'B') & res.dat$N==50,]$theoretical.power <- 0.1013
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'C') & res.dat$N==50,]$theoretical.power <- 0.1013
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'D') & res.dat$N==50,]$theoretical.power <- 0.2615
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'E') & res.dat$N==50,]$theoretical.power <- 0.2615
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'F') & res.dat$N==50,]$theoretical.power <- 0.1013
-res.dat[res.dat$scenario %in% paste0(c(5,7,9,11),'G') & res.dat$N==50,]$theoretical.power <- 0.2615
-
-res.dat[res.dat$scenario %in% paste0(1,'C') & res.dat$N==50,]$theoretical.power <- 0.2615
-res.dat[res.dat$scenario %in% paste0(1,'D') & res.dat$N==50,]$theoretical.power <- 0.1013
-res.dat[res.dat$scenario %in% paste0(1,'E') & res.dat$N==50,]$theoretical.power <- 0.2615
-
 ## Scénarios J=4 / M=4
 
 res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'A') & res.dat$N==50,]$theoretical.power <- 0.05
@@ -1143,26 +1106,8 @@ res.dat[res.dat$scenario %in% paste0(c(2,6,8,10,12),'B') & res.dat$N==50,]$theor
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'C') & res.dat$N==50,]$theoretical.power <- 0.1339
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'D') & res.dat$N==50,]$theoretical.power <- 0.3863
 res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'E') & res.dat$N==50,]$theoretical.power <- 0.3863
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'F') & res.dat$N==50,]$theoretical.power <- 0.1339
-res.dat[res.dat$scenario %in% paste0(c(6,8,10,12),'G') & res.dat$N==50,]$theoretical.power <- 0.3863
 
 res.dat[res.dat$scenario %in% paste0(2,'C') & res.dat$N==50,]$theoretical.power <- 0.3863
-res.dat[res.dat$scenario %in% paste0(2,'D') & res.dat$N==50,]$theoretical.power <- 0.1339
-res.dat[res.dat$scenario %in% paste0(2,'E') & res.dat$N==50,]$theoretical.power <- 0.3863
-
-## Scénarios J=7 / M=2
-
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'A') & res.dat$N==50,]$theoretical.power <- 0.05
-res.dat[res.dat$scenario %in% paste0(c(3,13,15,17,19),'B') & res.dat$N==50,]$theoretical.power <- 0.1171
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'C') & res.dat$N==50,]$theoretical.power <- 0.1171
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'D') & res.dat$N==50,]$theoretical.power <- 0.3236
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'E') & res.dat$N==50,]$theoretical.power <- 0.3236
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'F') & res.dat$N==50,]$theoretical.power <- 0.1171
-res.dat[res.dat$scenario %in% paste0(c(13,15,17,19),'G') & res.dat$N==50,]$theoretical.power <- 0.3236
-
-res.dat[res.dat$scenario %in% paste0(3,'C') & res.dat$N==50,]$theoretical.power <- 0.3236
-res.dat[res.dat$scenario %in% paste0(3,'D') & res.dat$N==50,]$theoretical.power <- 0.1171
-res.dat[res.dat$scenario %in% paste0(3,'E') & res.dat$N==50,]$theoretical.power <- 0.3236
 
 ## Scénarios J=7 / M=4
 
@@ -1171,17 +1116,12 @@ res.dat[res.dat$scenario %in% paste0(c(4,14,16,18,20),'B') & res.dat$N==50,]$the
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'C') & res.dat$N==50,]$theoretical.power <- 0.1448
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'D') & res.dat$N==50,]$theoretical.power <- 0.4328
 res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'E') & res.dat$N==50,]$theoretical.power <- 0.4328
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'F') & res.dat$N==50,]$theoretical.power <- 0.1448
-res.dat[res.dat$scenario %in% paste0(c(14,16,18,20),'G') & res.dat$N==50,]$theoretical.power <- 0.4328
 
 res.dat[res.dat$scenario %in% paste0(4,'C') & res.dat$N==50,]$theoretical.power <- 0.4328
-res.dat[res.dat$scenario %in% paste0(4,'D') & res.dat$N==50,]$theoretical.power <- 0.1448
-res.dat[res.dat$scenario %in% paste0(4,'E') & res.dat$N==50,]$theoretical.power <- 0.4328
-
 
 ### DIF scenarios
 
-res.dat.dif$theoretical.power <- res.dat[81:nrow(res.dat),]$theoretical.power
+res.dat.dif$theoretical.power <- res.dat[res.dat$dif.size!=0,]$theoretical.power
 res.dat.dif.rosali$theoretical.power <- res.dat$theoretical.power
 res.dat.dif.resali$theoretical.power <- res.dat$theoretical.power
 
@@ -1200,37 +1140,180 @@ res.dat.dif.rosali$method <- "ROSALI"
 res.dat.dif.resali$method <- "RESIDUS"
 
 
-# Correction of N=50 scenarios
-
-res.dat[res.dat$N==50,]$dif.size <- sapply(which(res.dat$N==50),function(k) res.dat[k-1,]$dif.size)
-res.dat.dif[res.dat.dif$N==50,]$dif.size <- sapply(which(res.dat.dif$N==50),function(k) res.dat.dif[k-1,]$dif.size)
-res.dat.dif.rosali[res.dat.dif.rosali$N==50,]$dif.size <- sapply(which(res.dat.dif.rosali$N==50),function(k) res.dat.dif.rosali[k-1,]$dif.size)
-res.dat.dif.resali[res.dat.dif.resali$N==50,]$dif.size <- sapply(which(res.dat.dif.resali$N==50),function(k) res.dat.dif.resali[k-1,]$dif.size)
-res.dat[res.dat$dif.size!=0 & res.dat$nb.dif==0,]$nb.dif <- 1
-res.dat.dif <- res.dat.dif %>%
-  relocate(method, .after = theoretical.power)
-res.dat[res.dat$scenario=="10B",]$dif.size <- 0.3
-res.dat.dif[res.dat.dif$scenario=="10B",]$dif.size <- 0.3
-res.dat.dif.rosali[res.dat.dif.rosali$scenario=="10B",]$dif.size <- 0.3
-res.dat.dif.resali[res.dat.dif.resali$scenario=="10B",]$dif.size <- 0.3
-res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0,]$eff.size <- rep(c(0,0.2,0.2,0.4,0.4,-0.2,-0.4),16)
-res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="C",]$bias <- res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="C",]$bias -0.2
-res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="D",]$bias <- res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="D",]$bias +0.6
-res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="E",]$bias <- res.dat.dif[res.dat.dif$N=="50" & res.dat.dif$nb.dif>0 & res.dat.dif$scenario.type=="E",]$bias +0.8
-
-res.dat[res.dat$N=="50" & res.dat$nb.dif>0,]$eff.size <- rep(c(0,0.2,0.2,0.4,0.4,-0.2,-0.4),16)
-res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="C",]$bias <- res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="C",]$bias -0.2
-res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="D",]$bias <- res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="D",]$bias +0.6
-res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="E",]$bias <- res.dat[res.dat$N=="50" & res.dat$nb.dif>0 & res.dat$scenario.type=="E",]$bias +0.8
-
-res.dat.dicho <- res.dat[res.dat$M==2,]
-res.dat.dicho <- rbind(res.dat.dicho,res.dat.dif[res.dat.dif$M==2,])
-res.dat.dicho <- rbind.fill(res.dat.dicho,res.dat.dif.rosali[res.dat.dif.rosali$M==2,])
-res.dat.dicho <- rbind.fill(res.dat.dicho,res.dat.dif.resali[res.dat.dif.resali$M==2,])
-
 # Items polytomiques
 
-res.dat.poly <- res.dat[res.dat$M==4,]
+res.dat.full <- res.dat[res.dat$M==4,]
-res.dat.poly <- rbind(res.dat.poly,res.dat.dif[res.dat.dif$M==4,])
+res.dat.full <- rbind(res.dat.full,res.dat.dif[res.dat.dif$M==4,])
-res.dat.poly <- rbind.fill(res.dat.poly,res.dat.dif.rosali[res.dat.dif.rosali$M==4,])
+res.dat.full <- rbind.fill(res.dat.full,res.dat.dif.rosali[res.dat.dif.rosali$M==4,])
-res.dat.poly <- rbind.fill(res.dat.poly,res.dat.dif.resali[res.dat.dif.resali$M==4,])
+res.dat.full <- rbind.fill(res.dat.full,res.dat.dif.resali[res.dat.dif.resali$M==4,])
+
+##############################################################################
+#----------------------------------------------------------------------------#
+############################ ARTICLE TABLE OUTPUT ############################
+#----------------------------------------------------------------------------#
+##############################################################################
+
+# STRATEGY 1 - IGNORE DIF
+
+res.dat.article <- res.dat[,c("N","J","eff.size","nb.dif","dif.size",
+                              "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                              "theoretical.power")]
+colnames(res.dat.article)[3] <- "true.beta"
+colnames(res.dat.article)[5] <- "true.gamma"
+colnames(res.dat.article)[6] <- "betahat"
+colnames(res.dat.article)[8] <- "coverage"
+colnames(res.dat.article)[9] <- "power"
+res.dat.article[,6:10] <- round(res.dat.article[,6:10],2)
+res.dat.article[res.dat.article$true.beta==0,"typeIerror"] <- res.dat.article[res.dat.article$true.beta==0,"power"]
+res.dat.article[res.dat.article$true.beta==0,"power"] <- NA
+res.dat.article <- res.dat.article[,c(1:7,11,9:10,8)]
+res.dat.article[res.dat.article$nb.dif==0,"true.gamma"] <- NA
+res.dat.article[is.na(res.dat.article)] <- " "
+res.dat.article$bias <- -1*res.dat.article$bias
+res.dat.article.ignore <- reshape(res.dat.article[res.dat.article$nb.dif>0,],
+                                  direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N" )
+write.csv(res.dat.article.ignore,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_ignoreDIF.csv")
+res.dat.article.nodif.long <- res.dat.article[res.dat.article$nb.dif==0,]
+res.dat.article.nodif <- reshape(res.dat.article[res.dat.article$nb.dif==0,],
+                                  direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N" )
+write.csv(res.dat.article.nodif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_noDIF.csv")
+res.dat.article <- reshape(res.dat.article[res.dat.article$nb.dif==0,],
+                                 direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N" )
+write.csv(res.dat.article.nodif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_noDIF.csv")
+
+
+
+res.dat.article.2 <- res.dat[,c("N","J","eff.size","nb.dif","dif.size",
+                              "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                              "theoretical.power")]
+colnames(res.dat.article.2)[3] <- "true.beta"
+colnames(res.dat.article.2)[5] <- "true.gamma"
+colnames(res.dat.article.2)[6] <- "betahat"
+colnames(res.dat.article.2)[8] <- "coverage"
+colnames(res.dat.article.2)[9] <- "power"
+res.dat.article.2[,6:10] <- round(res.dat.article.2[,6:10],2)
+res.dat.article.2[res.dat.article.2$true.beta==0,"typeIerror"] <- res.dat.article.2[res.dat.article.2$true.beta==0,"power"]
+res.dat.article.2[res.dat.article.2$true.beta==0,"power"] <- NA
+res.dat.article.2 <- res.dat.article.2[,c(1:7,11,9:10,8)]
+res.dat.article.2[res.dat.article.2$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.2[is.na(res.dat.article.2)] <- " "
+res.dat.article.2$bias <- -1*res.dat.article.2$bias
+res.dat.article.nodif.2 <- res.dat.article.2[res.dat.article.2$nb.dif==0,]
+  
+# STRATEGY 2 - ROSALI
+
+res.dat.article.rosali <- res.dat.dif.rosali[,c("N","J","eff.size","nb.dif","dif.size",
+                              "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                              "theoretical.power")]
+colnames(res.dat.article.rosali)[3] <- "true.beta"
+colnames(res.dat.article.rosali)[5] <- "true.gamma"
+colnames(res.dat.article.rosali)[6] <- "betahat"
+colnames(res.dat.article.rosali)[8] <- "coverage"
+colnames(res.dat.article.rosali)[9] <- "power"
+res.dat.article.rosali[res.dat.article.rosali$true.beta==0,"typeIerror"] <- res.dat.article.rosali[res.dat.article.rosali$true.beta==0,"power"]
+res.dat.article.rosali[res.dat.article.rosali$true.beta==0,"power"] <- NA
+res.dat.article.rosali <- res.dat.article.rosali[,c(1:7,11,9:10,8)]
+res.dat.article.rosali[res.dat.article.rosali$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.rosali[is.na(res.dat.article.rosali)] <- " "
+res.dat.article.rosali$bias <- -1*res.dat.article.rosali$bias
+res.dat.article.rosali <- reshape(res.dat.article.rosali,
+                                   direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N" )
+res.dat.article.rosali.dif <- res.dat.article.rosali[res.dat.article.rosali$nb.dif>0,]
+write.csv(res.dat.article.rosali.dif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_ROSALI_DIF.csv")
+res.dat.article.rosali.nodif <- res.dat.article.rosali[res.dat.article.rosali$nb.dif==0,]
+write.csv(res.dat.article.rosali.nodif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_ROSALI_noDIF.csv")
+
+
+res.dat.article.rosali.2 <- res.dat.dif.rosali[,c("N","J","eff.size","nb.dif","dif.size",
+                                                "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                                                "theoretical.power")]
+colnames(res.dat.article.rosali.2)[3] <- "true.beta"
+colnames(res.dat.article.rosali.2)[5] <- "true.gamma"
+colnames(res.dat.article.rosali.2)[6] <- "betahat"
+colnames(res.dat.article.rosali.2)[8] <- "coverage"
+colnames(res.dat.article.rosali.2)[9] <- "power"
+res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta==0,"typeIerror"] <- res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta==0,"power"]
+res.dat.article.rosali.2[res.dat.article.rosali.2$true.beta==0,"power"] <- NA
+res.dat.article.rosali.2 <- res.dat.article.rosali.2[,c(1:7,11,9:10,8)]
+res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.rosali.2[is.na(res.dat.article.rosali.2)] <- " "
+res.dat.article.rosali.2$bias <- -1*res.dat.article.rosali.2$bias
+res.dat.article.rosali.2.nodif <- res.dat.article.rosali.2[res.dat.article.rosali.2$nb.dif==0,]
+
+# STRATEGY 3 - RESIDIF
+res.dat.dif.resali[1,"N"] <- 50
+res.dat.dif.resali$dif.size <- res.dat.dif.rosali$dif.size
+res.dat.article.residif <- res.dat.dif.resali[,c("N","J","eff.size","nb.dif","dif.size",
+                                         "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                                         "theoretical.power")]
+colnames(res.dat.article.residif)[3] <- "true.beta"
+colnames(res.dat.article.residif)[5] <- "true.gamma"
+colnames(res.dat.article.residif)[6] <- "betahat"
+colnames(res.dat.article.residif)[8] <- "coverage"
+colnames(res.dat.article.residif)[9] <- "power"
+res.dat.article.residif[res.dat.article.residif$true.beta==0,"typeIerror"] <- res.dat.article.residif[res.dat.article.residif$true.beta==0,"power"]
+res.dat.article.residif[res.dat.article.residif$true.beta==0,"power"] <- NA
+res.dat.article.residif <- res.dat.article.residif[,c(1:7,11,9:10,8)]
+res.dat.article.residif[res.dat.article.residif$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.residif[is.na(res.dat.article.residif)] <- " "
+res.dat.article.residif$bias <- -1*res.dat.article.residif$bias
+res.dat.article.residif <- reshape(res.dat.article.residif,
+                               direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N")
+res.dat.article.residif.dif <- res.dat.article.residif[res.dat.article.residif$nb.dif>0,]
+write.csv(res.dat.article.residif.dif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_RESIDIF_DIF.csv")
+res.dat.article.residif.nodif <- res.dat.article.residif[res.dat.article.residif$nb.dif==0,]
+write.csv(res.dat.article.residif.nodif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_RESIDIF_noDIF.csv")
+
+res.dat.article.residif.2 <- res.dat.dif.resali[,c("N","J","eff.size","nb.dif","dif.size",
+                                                 "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                                                 "theoretical.power")]
+colnames(res.dat.article.residif.2)[3] <- "true.beta"
+colnames(res.dat.article.residif.2)[5] <- "true.gamma"
+colnames(res.dat.article.residif.2)[6] <- "betahat"
+colnames(res.dat.article.residif.2)[8] <- "coverage"
+colnames(res.dat.article.residif.2)[9] <- "power"
+res.dat.article.residif.2[res.dat.article.residif.2$true.beta==0,"typeIerror"] <- res.dat.article.residif.2[res.dat.article.residif.2$true.beta==0,"power"]
+res.dat.article.residif.2[res.dat.article.residif.2$true.beta==0,"power"] <- NA
+res.dat.article.residif.2 <- res.dat.article.residif.2[,c(1:7,11,9:10,8)]
+res.dat.article.residif.2[res.dat.article.residif.2$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.residif.2[is.na(res.dat.article.residif.2)] <- " "
+res.dat.article.residif.2$bias <- -1*res.dat.article.residif.2$bias
+res.dat.article.residif.2.nodif <- res.dat.article.residif.2[res.dat.article.residif.2$nb.dif==0,]
+res.dat.article.residif.dif
+
+# STRATEGY 4 - PERFECT-DIF
+
+res.dat.article.dif <- res.dat.dif[,c("N","J","eff.size","nb.dif","dif.size",
+                                         "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                                         "theoretical.power")]
+colnames(res.dat.article.dif)[3] <- "true.beta"
+colnames(res.dat.article.dif)[5] <- "true.gamma"
+colnames(res.dat.article.dif)[6] <- "betahat"
+colnames(res.dat.article.dif)[8] <- "coverage"
+colnames(res.dat.article.dif)[9] <- "power"
+res.dat.article.dif[res.dat.article.dif$true.beta==0,"typeIerror"] <- res.dat.article.dif[res.dat.article.dif$true.beta==0,"power"]
+res.dat.article.dif[res.dat.article.dif$true.beta==0,"power"] <- NA
+res.dat.article.dif <- res.dat.article.dif[,c(1:7,11,9:10,8)]
+res.dat.article.dif[res.dat.article.dif$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.dif[is.na(res.dat.article.dif)] <- " "
+res.dat.article.dif$bias <- -1*res.dat.article.dif$bias
+write.csv(res.dat.article.dif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_perfect.csv")
+res.dat.article.dif <- reshape(res.dat.article.dif,
+                                  direction = "wide", idvar = c("J","true.beta","nb.dif",'true.gamma'),timevar = "N" )
+write.csv(res.dat.article.dif,"/home/corentin/Documents/These/Valorisation/Articles/Simulations 1/Tables/res_perfect.csv")
+
+
+res.dat.article.dif.2 <- res.dat.dif[,c("N","J","eff.size","nb.dif","dif.size",
+                                      "m.beta","bias","true.value.in.ci.p","h0.rejected.p",
+                                      "theoretical.power")]
+colnames(res.dat.article.dif.2)[3] <- "true.beta"
+colnames(res.dat.article.dif.2)[5] <- "true.gamma"
+colnames(res.dat.article.dif.2)[6] <- "betahat"
+colnames(res.dat.article.dif.2)[8] <- "coverage"
+colnames(res.dat.article.dif.2)[9] <- "power"
+res.dat.article.dif.2[res.dat.article.dif.2$true.beta==0,"typeIerror"] <- res.dat.article.dif.2[res.dat.article.dif.2$true.beta==0,"power"]
+res.dat.article.dif.2[res.dat.article.dif.2$true.beta==0,"power"] <- NA
+res.dat.article.dif.2 <- res.dat.article.dif.2[,c(1:7,11,9:10,8)]
+res.dat.article.dif.2[res.dat.article.dif.2$nb.dif==0,"true.gamma"] <- NA
+res.dat.article.dif.2[is.na(res.dat.article.dif.2)] <- " "
+res.dat.article.dif.2$bias <- -1*res.dat.article.dif.2$bias

RProject/Scripts/Analysis/functions/resali.R

@@ -27,8 +27,8 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
   for (i in items) {
     dat[,paste0('res_',i)] <- IRT.residuals(pcm_initial)$stand_residuals[,i]
     res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
-    pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+    pval[c(i,i+nbitems)] <- c(res.anova[[i]][1,"Pr(>F)"],res.anova[[i]][3,"Pr(>F)"])
-    fval[i] <- res.anova[[i]][1,'F value']
+    fval[c(i,i+nbitems)] <- c(res.anova[[i]][1,'F value'],res.anova[[i]][3,"F value"])
   }
   if (verbose) {
     cat('DONE\n')
@@ -43,9 +43,10 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
       cat(paste('COMPUTING STEP',k,'\n'))
       cat('-----------------------------------------------------------\n')
     }
-    res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
+    numitem <- ifelse(which.max(fval)%%(length(fval)/2)!=0,which.max(fval)%%(length(fval)/2),length(fval)/2)
+    res.item <- gsub("[a-z]", "",colnames(resp)[numitem])
     res.items <- c(res.items,res.item)
-    res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
+    res.uni <- res.anova[[numitem]][3,"Pr(>F)"]>0.05
     res.uniform <- c(res.uniform,res.uni)
     items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
     dat[dat$TT==1,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
@@ -53,24 +54,19 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
     resp <- dat[,items_n]
     grp <- dat[,group]
     pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
-    nbitems <- length(items_n)
+    nbitems <- length(items_n)-2*length(res.items)
     res.anova <- rep(NA,nbitems)
-    pval <- rep(NA,nbitems)
+    pval <- rep(NA,2*nbitems)
-    fval <- rep(NA,nbitems)
+    fval <- rep(NA,2*nbitems)
     for (i in 1:nbitems) {
       dat[,paste0('res_',i)] <- IRT.residuals(pcm_while)$stand_residuals[,i]
       res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
       pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+      pval[i+nbitems] <- res.anova[[i]][3,"Pr(>F)"]
       fval[i] <- res.anova[[i]][1,'F value']
+      fval[i+nbitems] <- res.anova[[i]][3,"F value"]
     }
     zz <- 0
-    for (name_i in items_n) {
-      zz <- zz+1
-      if (grepl("TT",name_i)) {
-        pval[zz] <- 1
-        fval[zz] <- 0
-      }
-    }
     if (verbose) {
       cat('DONE\n')
       cat('-----------------------------------------------------------\n')

RProject/Scripts/Analysis/functions/resali_v2.R

@@ -0,0 +1,167 @@
+library(TAM)
+
+resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
+  if (verbose) {
+    cat('-----------------------------------------------------------\n')
+    cat('COMPUTING INITIAL PCM\n')
+    cat('-----------------------------------------------------------\n')
+  }
+  nbitems <- length(items)
+  nbitems_o <- nbitems
+  items_n <- paste0('item',items)
+  resp <- df[,items_n]
+  grp <- df[,group]
+  pcm_initial <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
+  dat <- df
+  dat$score <- rowSums(dat[,items_n])
+  nqt <- ifelse(length(unique(quantile(dat$score,seq(0,1,0.2))))==6,5,length(unique(quantile(dat$score,seq(0,1,0.2))))-1)
+  while (length(unique(quantile(dat$score,seq(0,1,1/nqt))))!=nqt+1) {
+    nqt <- nqt-1
+  }
+  # ITEM POLYTOMIQUE
+  if (max(resp)>1) {
+    dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
+    res.anova <- rep(NA,nbitems)
+    pval <- rep(NA,nbitems)
+    fval <- rep(NA,nbitems)
+    for (i in items) {
+      dat[,paste0('res_',i)] <- IRT.residuals(pcm_initial)$stand_residuals[,i]
+      res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
+      pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+      fval[i] <- res.anova[[i]][1,'F value']
+    }
+    print(res.anova)
+    if (verbose) {
+      cat('DONE\n')
+      cat('-----------------------------------------------------------\n')
+    }
+    res.items <- c()
+    res.uniform <- c()
+    k <- 1
+    while(any(pval<0.05/(nbitems_o*3))) {
+      k <- k+1
+      if (verbose) {
+        cat(paste('COMPUTING STEP',k,'\n'))
+        cat('-----------------------------------------------------------\n')
+      }
+      res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
+      res.items <- c(res.items,res.item)
+      res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
+      res.uniform <- c(res.uniform,res.uni)
+      items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
+      dat[dat$TT==1,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
+      dat[dat$TT==0,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
+      resp <- dat[,items_n]
+      grp <- dat[,group]
+      pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
+      nbitems <- length(items_n)
+      res.anova <- rep(NA,nbitems)
+      pval <- rep(NA,nbitems)
+      fval <- rep(NA,nbitems)
+      for (i in 1:nbitems) {
+        dat[,paste0('res_',i)] <- IRT.residuals(pcm_while)$stand_residuals[,i]
+        res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
+        pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+        fval[i] <- res.anova[[i]][1,'F value']
+      }
+      zz <- 0
+      for (name_i in items_n) {
+        zz <- zz+1
+        if (grepl("TT",name_i)) {
+          pval[zz] <- 1
+          fval[zz] <- 0
+        }
+      }
+      if (verbose) {
+        cat('DONE\n')
+        cat('-----------------------------------------------------------\n')
+      }
+    }
+    if (verbose) {
+      cat("DETECTED DIF ITEMS\n")
+      cat('-----------------------------------------------------------\n')
+    }
+    if (length(res.items>0)) {
+      results <- data.frame(dif.items=res.items,
+                            uniform=1*res.uniform)
+      return(results)
+    }
+    else {
+      if (verbose) {
+        cat("No DIF was detected\n")
+      }
+      return(NULL)
+    }
+    # ITEM DICHOTOMIQUE
+  } else {
+    
+    res.anova <- rep(NA,nbitems)
+    pval <- rep(NA,nbitems)
+    fval <- rep(NA,nbitems)
+    for (i in items) {
+      dat[,paste0('res_',i)] <- IRT.residuals(pcm_initial)$stand_residuals[,i]
+      res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT,data=dat))
+      pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+      fval[i] <- res.anova[[i]][1,'F value']
+    }
+    if (verbose) {
+      cat('DONE\n')
+      cat('-----------------------------------------------------------\n')
+    }
+    res.items <- c()
+    k <- 1
+    while(any(pval<0.05/(nbitems_o))) {
+      k <- k+1
+      if (verbose) {
+        cat(paste('COMPUTING STEP',k,'\n'))
+        cat('-----------------------------------------------------------\n')
+      }
+      res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
+      res.items <- c(res.items,res.item)
+      items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
+      dat[dat$TT==1,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
+      dat[dat$TT==0,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
+      resp <- dat[,items_n]
+      grp <- dat[,group]
+      pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
+      nbitems <- length(items_n)
+      res.anova <- rep(NA,nbitems)
+      pval <- rep(NA,nbitems)
+      fval <- rep(NA,nbitems)
+      for (i in 1:nbitems) {
+        dat[,paste0('res_',i)] <- IRT.residuals(pcm_while)$stand_residuals[,i]
+        res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT,data=dat))
+        pval[i] <- res.anova[[i]][1,"Pr(>F)"]
+        fval[i] <- res.anova[[i]][1,'F value']
+      }
+      zz <- 0
+      for (name_i in items_n) {
+        zz <- zz+1
+        if (grepl("TT",name_i)) {
+          pval[zz] <- 1
+          fval[zz] <- 0
+        }
+      }
+      if (verbose) {
+        cat('DONE\n')
+        cat('-----------------------------------------------------------\n')
+      }
+    }
+    if (verbose) {
+      cat("DETECTED DIF ITEMS\n")
+      cat('-----------------------------------------------------------\n')
+    }
+    if (length(res.items>0)) {
+      results <- data.frame(dif.items=res.items,
+                            uniform=rep(1,length(res.items)))
+      return(results)
+    }
+    else {
+      if (verbose) {
+        cat("No DIF was detected\n")
+      }
+      return(NULL)
+    }
+    
+  }
+}

RProject/Scripts/Analysis/resali_analysis.R

@@ -4,7 +4,7 @@
 #----------------------------------------------------------------------------#
 ##############################################################################
 
-source(paste0(getwd(),"/functions/resali.R"))
+source("/home/corentin/Documents/These/Recherche/Simulations/RProject/Scripts/Analysis/functions/resali.R")
 
 generate_resali <- function(scenario=NULL,grp=NULL) {
   scen <- as.numeric(gsub("[A,B,C,D,E,F,G,_]","",substr(scenario,0,3)))
@@ -113,15 +113,18 @@ generate_resali <- function(scenario=NULL,grp=NULL) {
   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 <- c(sapply(c(2:4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
 
-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(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
 
-results2 <- c(sapply(10:20,function(x) paste0(x,c('A','B','C','D','E','F','G'))))
+results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
 
-results <- c(sapply(c(50,100,200,300),function(x) paste0(results,'_',x)))
+results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
-
-results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 
@@ -144,13 +147,27 @@ for (r in results) {
 
 ## Liste des scenarios
 
-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'))))
+#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'))))
+#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)))
+#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)))
+#results2 <- c(sapply(c(50,100,200,300),function(x) paste0(results2,'_',x)))
+
+#results <- sort(results)
+
+#results2 <- sort(results2)
+
+#results <- c(results,results2)
+
+results <- c(sapply(c(2:4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
 
 results <- sort(results)
 

RProject/Scripts/Analysis/residif_analysis.R

@@ -0,0 +1,279 @@
+##############################################################################
+#----------------------------------------------------------------------------#
+################################### RESIDIF ##################################
+#----------------------------------------------------------------------------#
+##############################################################################
+
+generate_residif <- 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 <- residif(df=dat[dat$replication==1,],items = paste0("item",1:7),grp="TT",verbose=FALSE)
+    df_res <- data.frame(dif.detect.1=ifelse(length(res$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,]$dif3))
+    )
+    for (k in 2:1000) {
+      if (k%%100==0) {
+        cat(paste0('N=',k,'/1000\n'))
+      }
+      res <- residif(df=dat[dat$replication==k,],items = paste0("item",1:7),grp="TT",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 <- residif(df=dat[dat$replication==1,],items = paste0("item",1:4),grp="TT",verbose=FALSE)
+    df_res <- data.frame(dif.detect.1=ifelse(length(res$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,]$dif2))
+    )
+    for (k in 2:1000) {
+      if (k%%100==0) {
+        cat(paste0('N=',k,'/1000\n'))
+      }
+      res <- residif(df=dat[dat$replication==k,],items = paste0("item",1:4),grp="TT",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(c(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
+
+results <- sort(results)
+
+results2 <- sort(results2)
+
+results <- c(results,results2)
+
+for (r in c(results[73:138])) {
+  cat(paste0(r,"\n"))
+  cat(paste0("-------------------------------------------","\n"))
+  write.csv(generate_residif(r,"TT"),paste0("/home/corentin/Documents/These/Recherche/residif/detection/",r,".csv"))
+  cat(paste0("-------------------------------------------","\n"))
+}
+
+##############################################################################
+#----------------------------------------------------------------------------#
+################################### NEWDATA ##################################
+#----------------------------------------------------------------------------#
+##############################################################################
+
+## Liste des scenarios
+
+results <- c(sapply(c(2,4),function(x) paste0(x,c('A','B','C'))),sapply(c(6,8),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results2 <- c(sapply(seq(10,20,2),function(x) paste0(x,c('A','B','C','D','E'))))
+
+results <- c(sapply(c(50,100,300),function(x) paste0(results,'_',x)))
+
+results2 <- c(sapply(c(50,100,300),function(x) paste0(results2,'_',x)))
+
+results <- sort(results)
+
+results2 <- sort(results2)
+
+results <- c(results,results2)
+
+## Importer l'analyse resali pour chaque scenario
+
+for (r in results) {
+  cat('--------------------------------------------------------------------------\n')
+  cat(paste0(r,"\n"))
+  cat('--------------------------------------------------------------------------\n')
+  #### Importer les datas
+  scen <- as.numeric(gsub("[A,B,C,D,E,F,G,_]","",substr(r,0,3)))
+  if (substr(r,start=nchar(r)-1,stop=nchar(r))=="50") {
+    N <- 50
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="100") {
+    N <- 100
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="200") {
+    N <- 200
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="300") {
+    N <- 300
+  }
+  if (scen<5) {
+    datt <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N',N,'/scenario_',r,'.csv'))
+  }
+  if (scen>=5) {
+    datt <- read.csv(paste0('/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N',N,'/scenario_',r,'.csv'))
+  }
+  #### Importer l'analyse
+  analyse <- read.csv(paste0('/home/corentin/Documents/These/Recherche/residif/detection/',r,".csv"))
+  #### Pour chaque replication
+  for (k in 1:1000) {
+    if (k%%100==0) {
+      cat(paste0("N = ",k," / 1000\n"))
+    }
+    datt[datt$replication==k,"dif.detect.1"] <- analyse[analyse$X==k,"dif.detect.1"]
+    datt[datt$replication==k,"dif.detect.2"] <- analyse[analyse$X==k,"dif.detect.2"]
+    datt[datt$replication==k,"dif.detect.3"] <- analyse[analyse$X==k,"dif.detect.3"]
+    datt[datt$replication==k,"dif.detect.4"] <- analyse[analyse$X==k,"dif.detect.4"]
+    datt[datt$replication==k,"dif.detect.unif.1"] <- analyse[analyse$X==k,"dif.detect.unif.1"]
+    datt[datt$replication==k,"dif.detect.unif.2"] <- analyse[analyse$X==k,"dif.detect.unif.2"]
+    datt[datt$replication==k,"dif.detect.unif.3"] <- analyse[analyse$X==k,"dif.detect.unif.3"]
+    datt[datt$replication==k,"dif.detect.unif.4"] <- analyse[analyse$X==k,"dif.detect.unif.4"]
+    if (scen==3 | scen==4 | scen>=13) {
+      datt[datt$replication==k,"dif.detect.5"] <- analyse[analyse$X==k,"dif.detect.5"]
+      datt[datt$replication==k,"dif.detect.6"] <- analyse[analyse$X==k,"dif.detect.6"]
+      datt[datt$replication==k,"dif.detect.7"] <- analyse[analyse$X==k,"dif.detect.7"]
+      datt[datt$replication==k,"dif.detect.unif.5"] <- analyse[analyse$X==k,"dif.detect.unif.5"]
+      datt[datt$replication==k,"dif.detect.unif.6"] <- analyse[analyse$X==k,"dif.detect.unif.6"]
+      datt[datt$replication==k,"dif.detect.unif.7"] <- analyse[analyse$X==k,"dif.detect.unif.7"]
+    }
+  }
+  datt[is.na(datt$dif.detect.1),"dif.detect.1"] <- ""
+  datt[is.na(datt$dif.detect.2),"dif.detect.2"] <- ""
+  datt[is.na(datt$dif.detect.3),"dif.detect.3"] <- ""
+  datt[is.na(datt$dif.detect.4),"dif.detect.4"] <- ""
+  datt[is.na(datt$dif.detect.unif.1),"dif.detect.unif.1"] <- ""
+  datt[is.na(datt$dif.detect.unif.2),"dif.detect.unif.2"] <- ""
+  datt[is.na(datt$dif.detect.unif.3),"dif.detect.unif.3"] <- ""
+  datt[is.na(datt$dif.detect.unif.4),"dif.detect.unif.4"] <- ""
+  if (scen==3 | scen==4 | scen>=13) {
+    datt[is.na(datt$dif.detect.5),"dif.detect.5"] <- ""
+    datt[is.na(datt$dif.detect.6),"dif.detect.6"] <- ""
+    datt[is.na(datt$dif.detect.7),"dif.detect.7"] <- ""
+    datt[is.na(datt$dif.detect.unif.5),"dif.detect.unif.5"] <- ""
+    datt[is.na(datt$dif.detect.unif.6),"dif.detect.unif.6"] <- ""
+    datt[is.na(datt$dif.detect.unif.7),"dif.detect.unif.7"] <- ""
+  }
+  write.csv(datt,paste0("/home/corentin/Documents/These/Recherche/residif/detection_data/",r,".csv"))
+}
+
+
+
+
+
+
+
+
+
+for (r in results) {
+  cat('--------------------------------------------------------------------------\n')
+  cat(paste0(r,"\n"))
+  cat('--------------------------------------------------------------------------\n')
+  #### Importer les datas
+  scen <- as.numeric(gsub("[A,B,C,D,E,F,G,_]","",substr(r,0,3)))
+  if (substr(r,start=nchar(r)-1,stop=nchar(r))=="50") {
+    N <- 50
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="100") {
+    N <- 100
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="200") {
+    N <- 200
+  }
+  if (substr(r,start=nchar(r)-2,stop=nchar(r))=="300") {
+    N <- 300
+  }
+  #### Importer l'analyse
+  analyse <- read.csv(paste0("/home/corentin/Documents/These/Recherche/residif/detection_data/",r,".csv"))
+  analyse[is.na(analyse)] <- ""
+  names(analyse)[names(analyse)=="dif.detect.1"] <- "dif_detect_1"
+  names(analyse)[names(analyse)=="dif.detect.2"] <- "dif_detect_2"
+  names(analyse)[names(analyse)=="dif.detect.3"] <- "dif_detect_3"
+  names(analyse)[names(analyse)=="dif.detect.4"] <- "dif_detect_4"
+  names(analyse)[names(analyse)=="dif.detect.unif.1"] <- "dif_detect_unif_1"
+  names(analyse)[names(analyse)=="dif.detect.unif.2"] <- "dif_detect_unif_2"
+  names(analyse)[names(analyse)=="dif.detect.unif.3"] <- "dif_detect_unif_3"
+  names(analyse)[names(analyse)=="dif.detect.unif.4"] <- "dif_detect_unif_4"
+  
+  
+  if (scen==3 | scen==4 | scen>=13) {
+    names(analyse)[names(analyse)=="dif.detect.5"] <- "dif_detect_5"
+    names(analyse)[names(analyse)=="dif.detect.6"] <- "dif_detect_6"
+    names(analyse)[names(analyse)=="dif.detect.7"] <- "dif_detect_7"
+    names(analyse)[names(analyse)=="dif.detect.unif.5"] <- "dif_detect_unif_5"
+    names(analyse)[names(analyse)=="dif.detect.unif.6"] <- "dif_detect_unif_6"
+    names(analyse)[names(analyse)=="dif.detect.unif.7"] <- "dif_detect_unif_7"
+    
+  }
+  analyse <- analyse[,!names(analyse) %in% c("X","X.1","X.2")]
+  write.csv(analyse,paste0("/home/corentin/Documents/These/Recherche/residif/detection_data/",r,".csv"))
+}
+

RProject/Scripts/Tools/generate_item_difficulties.R

@@ -15,7 +15,7 @@ generate_diff_irt <- function(J=7,M=4) {
   }
   difficulties = matrix(c(0), J,M-1)
   rownames(difficulties)=paste("item",1:J)
-  colnames(difficulties)=paste("Moda", 1:(M-1))
+  colnames(difficulties)=paste("delta", 1:(M-1))
   for (j in 1:J){
     difficulties[j,1] = qnorm(j/(J+1))
   }

Scripts/Analysis/DIF-RESIDUALS/pcm_dif_residus2.do

@@ -0,0 +1,375 @@
+*=================================================================================================================================================
+* Date : 2024-01-23
+* Stata version : Stata 18 SE
+*
+* This program analyses simulated data accounting for DIF through a partial credit model
+*
+* ado-files needed : - pcm, rosali (version 5.5 October 25, 2023, available on gitea)
+*
+*
+*================================================================================================================================================
+adopath+"/home/corentin/Documents/These/Recherche/ROSALI-SIM/Modules/rosali_custom"
+
+
+local N = "50 100 300"
+ local ss = "18 20"
+ foreach s in `ss' {
+ foreach Nnn in `N' {
+ local Nn =  `Nnn'
+ local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Detection_data"
+ local path_res = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/RESALI/Results/N`Nn'"
+ local scenarios = "A B C D E"
+ if (`s' <= 4) {
+   local scenarios = "A B C"
+ }
+ foreach scen in `scenarios' {
+ clear
+ import delim "`path_data'/`s'`scen'_`Nn'.csv", encoding(ISO-8859-2)  case(preserve) clear
+ rename TT tt
+
+  if (`s'<=2) {
+    local nbitems=4
+  }
+  else if (`s'<=4) {
+    local nbitems=7
+  }
+  else if (`s'<=12) {
+    local nbitems=4
+  }
+  else {
+    local nbitems=7
+  }
+
+ if (mod(`s',2)==0) {
+   local nbmoda=3
+ }
+ else {
+   local nbmoda=1
+ }
+
+  if (`s'<=4) {
+    local nbdif=0
+  }
+  else if (`s'<=8) {
+    local nbdif=1
+  }
+  else if (`s'<=16) {
+    local nbdif=2
+  }
+  else {
+    local nbdif=3
+  }
+  * taillemat = Maximum J*M cases pour les items par et J*M cases pour les dif par + J cases pour les DIF detect + nbdif cases pour dif réel
+  local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbdif'+2
+  if (mod(`s',2)==0) {
+    local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbitems'+`nbdif'+2
+  }
+  local colna=""
+  forvalues i=1/`nbitems' {
+    forvalues z=1/`nbmoda' {
+      local colna = "`colna'"+"item`i'_`z' "
+      local colna = "`colna'"+"dif_`i'_`z' "
+    }
+  }
+  forvalues i=1/`nbitems' {
+    if (mod(`s',2)==1) {
+      local colna = "`colna'"+"dif_detect_`i' "
+    }
+    if (mod(`s',2)==0) {
+      local colna = "`colna'"+"dif_detect_`i' "+"dif_detect_unif_`i' "
+    }
+  }
+
+  forvalues i=1/`nbdif' {
+    local colna = "`colna'"+"real_dif_`i' "
+  }
+ local colna = "`colna'" + "beta " + "se_beta"
+  mat outmat = J(1000,`taillemat',.)
+  mat colnames outmat= `colna'
+  di "Scenario `s'`scen' / N=`Nnn'"
+  forvalues k=1/1000 {
+    if (mod(`k',100)==0) {
+      di "`k'/1000"
+    }
+    preserve
+    qui keep if replication==`k'
+
+
+    * MERGE des modalités si non représentées
+
+    if (`nbmoda'>1 & `Nn'==50) {
+          local com_z = 0
+          qui gen comz = 0
+          forvalues j = 1 / `nbitems' {
+          local recoda_`j' = 0
+          qui tab item`j' if tt == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')
+          local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+          local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+
+          qui tab item`j' if tt == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+          local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+          local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+          local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t1_g1')
+          local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t1_g1')
+          local nbm_`j' =  `=`maxm_`j''-`minm_`j'''
+
+          if `minm_`j'' != 0 & `com_z' == 0 {
+            local com_z = 1
+          }
+
+          qui count if item`j' == 3 & tt == 0
+          local mod3plac = r(N)
+          qui count if item`j' == 3 & tt == 1
+          local mod3tt = r(N)
+          local nb_rn3 = min(`mod3plac',`mod3tt')
+          if `nb_rn3'==0 {
+            qui replace comz = 1
+          }
+
+          forvalues m = 0/`=`nbm_`j''-1' {
+            qui count if item`j' == `m' & tt == 0
+            local nb_rn1_g0 = r(N)
+            qui count if item`j' == `m' & tt == 1
+            local nb_rn1_g1 = r(N)
+            local nb_rn = min(`nb_rn1_g0',`nb_rn1_g1')
+            if `nb_rn' == 0 {
+              qui replace comz = 1
+              local recoda_`j' = 1
+              if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t1_g1' {
+                local stop = 1
+                forvalues kk = 1/`=`nbm_`j''-`m'' {
+                  qui count if item`j' == `=`m' + `kk'' & tt == 0
+                  local v`kk'1_0 = r(N)
+                  qui count if item`j' == `=`m' + `kk'' & tt == 1
+                  local v`kk'1_1 = r(N)
+                  if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                    qui replace item`j'= `=`m'+`kk'' if item`j'==`m'
+                    local zzz=`j'+`nbitems'
+                    *qui replace item`zzz'=`=`m'+`kk'' if item``=`j'+`nbitems'''==`m'
+                    *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                    local stop = 0
+                  }
+                }
+              }
+              else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t1_g1' {
+                local stop = 1
+                forvalues kk = 1/`=`m'' {
+                  qui count if item`j' == `=`m' - `kk'' & tt == 0
+                  local v`kk'1_0 = r(N)
+                  qui count if item`j' == `=`m' - `kk'' & tt == 1
+                  local v`kk'1_1 = r(N)
+                  if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                    qui replace item`j'= `=`m' - `kk'' if item`j'==`m'
+                    local zzz=`j'+`nbitems'
+                    *qui replace item`zzz'= `=`m' - `kk'' if item`zzz'==`m'
+                    *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                    local stop = 0
+                  }
+                }
+              }
+              else {
+                if runiform()>0.5{
+                  local stop = 1
+                  forvalues kk = 1/`m' {
+                    qui count if item`j' == `=`m' - `kk'' & tt == 0
+                    local v`kk'1_0 = r(N)
+                    qui count if item`j' == `=`m' - `kk'' & tt == 1
+                    local v`kk'1_1 = r(N)
+                    if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                      qui replace item`j'= `=`m'-`kk'' if item`j'==`m'
+                      local zzz=`j'+`nbitems'
+                      *qui replace item`zzz'=`=`m'-`kk'' if item``=`j'+`nbitems'''==`m'
+                      *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                      local stop = 0
+                    }
+                  }
+                }
+                else {
+                  local stop = 1
+                  forvalues kk = 1/`=`nbm_`j''-`m'' {
+                    qui count if item`j' == `=`m' + `kk'' & tt == 0
+                    local v`kk'1_0 = r(N)
+                    qui count if item`j' == `=`m' + `kk'' & tt == 1
+                    local v`kk'1_1 = r(N)
+                    if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0{
+                      qui replace item`j'=`=`m' + `kk'' if item`j'==`m'
+                      local zzz=`j'+`nbitems'
+                      *qui replace item`zzz'=`=`m' + `kk'' if item``=`j'+`nbitems'''==`m'
+                      *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                      local stop = 0
+                    }
+                    else {
+                      if `stop' != 0 {
+                        qui replace item`j'= `nbm_`j'' if item`j'==`m'
+                        local zzz=`j'+`nbitems'
+                        *qui replace item`zzz'= `nbm_`j'' if item``=`j'+`nbitems'''==`m'
+                        *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                        local stop = 0
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+          qui levelsof item`j'
+          local val = r(levels)
+          local checker: word 1 of `val'
+          local checker2: word 2 of `val'
+          local checker3: word 3 of `val'
+          local nummoda=r(r)
+          local nbmoda_`j'=`nummoda'-1
+          if (`nummoda'==2) {
+            qui recode item`j' (`checker'=0) (`checker2'=1)
+          }
+          if (`nummoda'==3) {
+          if (`checker'!=0) {
+            qui recode item`j' (`checker'=0) (`checker2'=1) (`checker3'=2)
+          }
+          else if (`checker2'!=1) {
+            qui recode item`j' (`checker2'=1) (`checker3'=2)
+          }
+          else if (`checker3'!=2) {
+            qui recode item`j' (`checker3'=2)
+          }
+        }
+      }
+
+      qui valuesof comz
+      local val = r(values)
+      local checker: word 1 of `val'
+    }
+    else {
+      forvalues jj=1/`nbitems' {
+        local nbmoda_`jj'=`nbmoda'
+      }
+    }
+    local nbitems2 = 2*`nbitems'
+
+    * Calculer le nbre d'items détectés
+    local nbdetect = 0
+    local stop = 0
+    forvalues jj=1/`nbitems' {
+        qui levelsof dif_detect_`jj'
+        local detected=r(levels)
+        if (`stop'==0) {
+          mat testm=J(1,1,.)
+          if (`detected'==testm[1,1]) {
+            local stop = 1
+            local nbdetect = `jj'-1
+          }
+        }
+    }
+    * Stocker les items détectés +
+    * Définition des contraintes
+    local csrt=0
+    mat testm=J(1,1,0)
+    forvalues u=1/`nbdetect' {
+      qui levelsof dif_detect_`u'
+      local detected=r(levels)
+       local difitems`u'=`detected'
+        local i=`difitems`u''
+        qui levelsof dif_detect_unif_`u'
+        local detected_unif=r(levels)
+         if (`nbmoda_`i''==3 & `detected_unif'!=testm[1,1]){
+          local constrnt`u' = "constraint `u' 2*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([2.item`i']_cons-([2.item`i']_cons+[2.item`i'] tt))"
+          qui `constrnt`u''
+          local v=`u'+100
+          local constrnt`u'_2 = "constraint `v' 3*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([3.item`i']_cons-([3.item`i']_cons+[3.item`i'] tt))"
+          qui `constrnt`u'_2'
+         }
+         if (`nbmoda_`i''==2 & `detected_unif'!=testm[1,1]){
+          local constrnt`u' = "constraint `u' 2*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([2.item`i']_cons-([2.item`i']_cons+[2.item`i'] tt))"
+          qui `constrnt`u''
+         }
+        }
+    * Définition du modèle
+    local mod "gsem "
+    local conformula = ""
+    forvalues i=1/`nbitems' {
+        local mod = "`mod'"+"(1.item`i'<-THETA@1)"
+        if (`nbmoda_`i''==3) {
+          local mod = "`mod'"+"(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
+        }
+        else if (`nbmoda_`i''==2) {
+          local mod = "`mod'"+"(2.item`i'<-THETA@2)"
+        }
+    }
+    forvalues u=1/`nbdetect' {
+        local v=`difitems`u''
+        local mod = "`mod'"+"(1.item`v'<-THETA@1 tt)"
+        if (`nbmoda_`v''==3) {
+          local mod = "`mod'"+"(2.item`v'<-THETA@2 tt)(3.item`v'<-THETA@3 tt)"
+        }
+        else if (`nbmoda_`v''==2) {
+          local mod = "`mod'"+"(2.item`v'<-THETA@2 tt)"
+        }
+        local w= 100+`u'
+        qui levelsof dif_detect_unif_`u'
+        local detected=r(levels)
+        local unif_`u'=r(levels)
+        if (`detected'!=testm[1,1] & `nbmoda_`v''==3) {
+          local conformula = "`conformula'" + "`u' " + "`w' "
+        }
+        else if (`detected'!=testm[1,1] & `nbmoda_`v''==2) {
+          local conformula = "`conformula'" + "`u' "
+        }
+    }
+    if ("`conformula'" != "") {
+      local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(`conformula')"
+    }
+    else {
+      local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent"
+    }
+    *calcul du modèle
+    qui `mod'
+    mat V=r(table)
+    mat W=V[1..2,1...]
+
+    * compilation
+    forvalues j=1/`nbitems' {
+      forvalues z=1/`nbmoda_`j'' {
+        mat outmat[`k',colnumb(outmat,"item`j'_`z'")] = W[1,colnumb(W,"`z'.item`j':_cons")]
+      }
+    }
+
+    * compilation DIF
+    forvalues u=1/`nbdetect' {
+      local j=`difitems`u''
+        forvalues z=1/`nbmoda_`j'' {
+          mat outmat[`k',colnumb(outmat,"dif_`u'_`z'")] = W[1,colnumb(W,"`z'.item`j':tt")]
+        }
+        mat outmat[`k',colnumb(outmat,"dif_detect_`u'")] = `j'
+        if (mod(`s',2)==0) {
+          mat outmat[`k',colnumb(outmat,"dif_detect_unif_`u'")] = `unif_`u''
+        }
+      }
+    * Stocker les items de DIF originaux
+    if (`nbdif' > 0) {
+      qui levelsof dif1
+      local ldif1 = r(levels)
+      local diff1: word 1 of `ldif1'
+      qui mat outmat[`k',colnumb(outmat,"real_dif_1")]=`diff1'
+      if (`nbdif' > 1) {
+        qui levelsof dif2
+        local ldif2 = r(levels)
+        local diff2: word 1 of `ldif2'
+        qui mat outmat[`k',colnumb(outmat,"real_dif_2")]=`diff2'
+        if (`nbdif' > 2) {
+          qui levelsof dif3
+          local ldif3 = r(levels)
+          local diff3: word 1 of `ldif3'
+          qui mat outmat[`k',colnumb(outmat,"real_dif_3")]=`diff3'
+        }
+      }
+    }
+    qui mat outmat[`k',colnumb(outmat,"beta")]=W[1,colnumb(W,"THETA:tt")]
+    qui mat outmat[`k',colnumb(outmat,"se_beta")]=W[2,colnumb(W,"THETA:tt")]
+    restore
+  }
+  putexcel set "`path_res'/`s'`scen'_`Nn'_original.xls", sheet("outmat") replace
+  putexcel A1=matrix(outmat), colnames
+ }
+ }
+ }

Scripts/Analysis/DIF-ROSALI/pcm_dif_rosali_nolrt.do

@@ -0,0 +1,388 @@
+*=================================================================================================================================================
+* Date : 2024-01-23
+* Stata version : Stata 18 SE
+*
+* This program analyses simulated data accounting for DIF through a partial credit model
+*
+* ado-files needed : - pcm, rosali (version 5.5 October 25, 2023, available on gitea)
+*
+*
+*================================================================================================================================================
+adopath+"/home/corentin/Documents/These/Recherche/Simulations/Modules/rosali_custom"
+
+
+local N = "50 100 200 300"
+ local ss = "2 4 8 12 16 20"
+ foreach s in `ss' {
+ foreach Nnn in `N' {
+ local Nn =  `Nnn'
+ local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Data/DIF/N`Nn'"
+ if (`s'<=4) {
+   local path_data = "/home/corentin/Documents/These/Recherche/Simulations/Data/NoDIF/N`Nn'"
+ }
+ local path_res = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF-IMPROVED/MIMIC-NOLRT/N`Nn'"
+ local path_log = "/home/corentin/Documents/These/Recherche/Simulations/Analysis/ROSALI-DIF/log/"
+ local scenarios = "A B C D E"
+ if (`s' <= 4) {
+   local scenarios = "A B C"
+ }
+ foreach scen in `scenarios' {
+ clear
+ import delim "`path_data'/scenario_`s'`scen'_`Nn'.csv", encoding(ISO-8859-2)  case(preserve) clear
+ rename TT tt
+ log using "`path_log'/log_`s'`scen'_`Nn'.log", replace
+  if (`s'<=2) {
+    local nbitems=4
+  }
+  else if (`s'<=4) {
+    local nbitems=7
+  }
+  else if (`s'<=12) {
+    local nbitems=4
+  }
+  else {
+    local nbitems=7
+  }
+
+ if (mod(`s',2)==0) {
+   local nbmoda=3
+ }
+ else {
+   local nbmoda=1
+ }
+
+  if (`s'<=4) {
+    local nbdif=0
+  }
+  else if (`s'<=8) {
+    local nbdif=1
+  }
+  else if (`s'<=16) {
+    local nbdif=2
+  }
+  else {
+    local nbdif=3
+  }
+  * taillemat = Maximum J*M cases pour les items par et J*M cases pour les dif par + J cases pour les DIF detect + nbdif cases pour dif réel
+  local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbdif'+2+1
+  if (mod(`s',2)==0) {
+    local taillemat=2*`nbitems'*`nbmoda'+`nbitems'+`nbitems'+`nbdif'+2+1
+  }
+  local colna=""
+  forvalues i=1/`nbitems' {
+    forvalues z=1/`nbmoda' {
+      local colna = "`colna'"+"item`i'_`z' "
+      local colna = "`colna'"+"dif_`i'_`z' "
+    }
+  }
+  forvalues i=1/`nbitems' {
+    if (mod(`s',2)==1) {
+      local colna = "`colna'"+"dif_detect_`i' "
+    }
+    if (mod(`s',2)==0) {
+      local colna = "`colna'"+"dif_detect_`i' "+"dif_detect_unif_`i' "
+    }
+  }
+
+  forvalues i=1/`nbdif' {
+    local colna = "`colna'"+"real_dif_`i' "
+  }
+ local colna = "`colna'" + "beta " + "se_beta " + "lrt_passed"
+
+  mat outmat = J(1000,`taillemat',.)
+  mat colnames outmat= `colna'
+  di "Scenario `s'`scen' / N=`Nnn'"
+  forvalues k=1/1000 {
+    di "###################################################################################"
+    di "###################################################################################"
+    di "###################################################################################"
+    di "Scenario `s'`scen' N=`Nn' ########## `k'/1000"
+    di "###################################################################################"
+    di "###################################################################################"
+    di "###################################################################################"
+
+    preserve
+    qui keep if replication==`k'
+
+
+    * MERGE des modalités si non représentées
+
+    if (`nbmoda'>1 & `Nn'==50) {
+          local com_z = 0
+          qui gen comz = 0
+          forvalues j = 1 / `nbitems' {
+          local recoda_`j' = 0
+          qui tab item`j' if tt == 0, matrow(rect1_g0_`j') matcell(nbrt1_g0_`j')
+          local maxm`j'_t1_g0 = rect1_g0_`j'[r(r),1]
+          local minm`j'_t1_g0 = rect1_g0_`j'[1,1]
+
+          qui tab item`j' if tt == 1, matrow(rect1_g1_`j') matcell(nbrt1_g1_`j')
+          local minm`j'_t1_g1 = rect1_g1_`j'[1,1]
+          local maxm`j'_t1_g1 = rect1_g1_`j'[r(r),1]
+
+          local minm_`j' = min(`minm`j'_t1_g0',`minm`j'_t1_g1')
+          local maxm_`j' = max(`maxm`j'_t1_g0',`maxm`j'_t1_g1')
+          local nbm_`j' =  `=`maxm_`j''-`minm_`j'''
+
+          if `minm_`j'' != 0 & `com_z' == 0 {
+            local com_z = 1
+          }
+
+          qui count if item`j' == 3 & tt == 0
+          local mod3plac = r(N)
+          qui count if item`j' == 3 & tt == 1
+          local mod3tt = r(N)
+          local nb_rn3 = min(`mod3plac',`mod3tt')
+          if `nb_rn3'==0 {
+            qui replace comz = 1
+          }
+
+          forvalues m = 0/`=`nbm_`j''-1' {
+            qui count if item`j' == `m' & tt == 0
+            local nb_rn1_g0 = r(N)
+            qui count if item`j' == `m' & tt == 1
+            local nb_rn1_g1 = r(N)
+            local nb_rn = min(`nb_rn1_g0',`nb_rn1_g1')
+            if `nb_rn' == 0 {
+              qui replace comz = 1
+              local recoda_`j' = 1
+              if  `m' == 0 | `m' < `minm`j'_t1_g0' | `m' < `minm`j'_t1_g1' {
+                local stop = 1
+                forvalues kk = 1/`=`nbm_`j''-`m'' {
+                  qui count if item`j' == `=`m' + `kk'' & tt == 0
+                  local v`kk'1_0 = r(N)
+                  qui count if item`j' == `=`m' + `kk'' & tt == 1
+                  local v`kk'1_1 = r(N)
+                  if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                    qui replace item`j'= `=`m'+`kk'' if item`j'==`m'
+                    local zzz=`j'+`nbitems'
+                    *qui replace item`zzz'=`=`m'+`kk'' if item``=`j'+`nbitems'''==`m'
+                    *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                    local stop = 0
+                  }
+                }
+              }
+              else if `m' == `=`nbm_`j''-1' | `m' >= `maxm`j'_t1_g1' {
+                local stop = 1
+                forvalues kk = 1/`=`m'' {
+                  qui count if item`j' == `=`m' - `kk'' & tt == 0
+                  local v`kk'1_0 = r(N)
+                  qui count if item`j' == `=`m' - `kk'' & tt == 1
+                  local v`kk'1_1 = r(N)
+                  if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                    qui replace item`j'= `=`m' - `kk'' if item`j'==`m'
+                    local zzz=`j'+`nbitems'
+                    *qui replace item`zzz'= `=`m' - `kk'' if item`zzz'==`m'
+                    *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                    local stop = 0
+                  }
+                }
+              }
+              else {
+                if runiform()>0.5{
+                  local stop = 1
+                  forvalues kk = 1/`m' {
+                    qui count if item`j' == `=`m' - `kk'' & tt == 0
+                    local v`kk'1_0 = r(N)
+                    qui count if item`j' == `=`m' - `kk'' & tt == 1
+                    local v`kk'1_1 = r(N)
+                    if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0 {
+                      qui replace item`j'= `=`m'-`kk'' if item`j'==`m'
+                      local zzz=`j'+`nbitems'
+                      *qui replace item`zzz'=`=`m'-`kk'' if item``=`j'+`nbitems'''==`m'
+                      *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                      local stop = 0
+                    }
+                  }
+                }
+                else {
+                  local stop = 1
+                  forvalues kk = 1/`=`nbm_`j''-`m'' {
+                    qui count if item`j' == `=`m' + `kk'' & tt == 0
+                    local v`kk'1_0 = r(N)
+                    qui count if item`j' == `=`m' + `kk'' & tt == 1
+                    local v`kk'1_1 = r(N)
+                    if (`v`kk'1_0' != 0 | `v`kk'1_1' != 0) & `stop' != 0{
+                      qui replace item`j'=`=`m' + `kk'' if item`j'==`m'
+                      local zzz=`j'+`nbitems'
+                      *qui replace item`zzz'=`=`m' + `kk'' if item``=`j'+`nbitems'''==`m'
+                      *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                      local stop = 0
+                    }
+                    else {
+                      if `stop' != 0 {
+                        qui replace item`j'= `nbm_`j'' if item`j'==`m'
+                        local zzz=`j'+`nbitems'
+                        *qui replace item`zzz'= `nbm_`j'' if item``=`j'+`nbitems'''==`m'
+                        *di  "WARNING SCENARIO `k': items `j': answers `m' and `=`m'+`kk'' merged"
+                        local stop = 0
+                      }
+                    }
+                  }
+                }
+              }
+            }
+          }
+          qui levelsof item`j'
+          local val = r(levels)
+          local checker: word 1 of `val'
+          local checker2: word 2 of `val'
+          local checker3: word 3 of `val'
+          local nummoda=r(r)
+          local nbmoda_`j'=`nummoda'-1
+          if (`nummoda'==2) {
+            qui recode item`j' (`checker'=0) (`checker2'=1)
+          }
+          if (`nummoda'==3) {
+          if (`checker'!=0) {
+            qui recode item`j' (`checker'=0) (`checker2'=1) (`checker3'=2)
+          }
+          else if (`checker2'!=1) {
+            qui recode item`j' (`checker2'=1) (`checker3'=2)
+          }
+          else if (`checker3'!=2) {
+            qui recode item`j' (`checker3'=2)
+          }
+        }
+      }
+
+      qui valuesof comz
+      local val = r(values)
+      local checker: word 1 of `val'
+    }
+    else {
+      forvalues jj=1/`nbitems' {
+        local nbmoda_`jj'=`nbmoda'
+      }
+    }
+
+
+    * ROSALI
+    rosali_nolrt_screening item1-item`nbitems' item1-item`nbitems', group(tt)
+    qui mat resmat=r(difitems)
+    local nbitems2 = 2*`nbitems'
+    mat lrt_passed = resmat[1,`nbitems2'+1]
+    * Calculer le nbre d'items détectés
+    local nbdetect = 0
+    local stop = 0
+    forvalues jj=1/`nbitems' {
+        if (`stop'==0) {
+          mat testm=J(1,1,.)
+          if (resmat[1,`jj']==testm[1,1]) {
+            local stop = 1
+            local nbdetect = `jj'-1
+          }
+        }
+    }
+
+    * Stocker les items détectés +
+    * Définition des contraintes
+    local csrt=0
+    mat testm=J(1,1,0)
+    forvalues u=1/`nbdetect' {
+       local difitems`u'=resmat[1,`u']
+        local i=`difitems`u''
+         if (`nbmoda_`i''==3 & resmat[1,`nbitems'+`i']!=testm[1,1]){
+          local constrnt`u' = "constraint `u' 2*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([2.item`i']_cons-([2.item`i']_cons+[2.item`i'] tt))"
+          qui `constrnt`u''
+          local v=`u'+100
+          local constrnt`u'_2 = "constraint `v' 3*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([3.item`i']_cons-([3.item`i']_cons+[3.item`i'] tt))"
+          qui `constrnt`u'_2'
+         }
+         if (`nbmoda_`i''==2 & resmat[1,`nbitems'+`i']!=testm[1,1]){
+          local constrnt`u' = "constraint `u' 2*([1.item`i']_cons-([1.item`i']_cons+[1.item`i'] tt))=([2.item`i']_cons-([2.item`i']_cons+[2.item`i'] tt))"
+          qui `constrnt`u''
+         }
+        }
+
+    * Définition du modèle
+    local mod "gsem "
+    local conformula = ""
+    forvalues i=1/`nbitems' {
+        local mod = "`mod'"+"(1.item`i'<-THETA@1)"
+        if (`nbmoda_`i''==3) {
+          local mod = "`mod'"+"(2.item`i'<-THETA@2)(3.item`i'<-THETA@3)"
+        }
+        else if (`nbmoda_`i''==2) {
+          local mod = "`mod'"+"(2.item`i'<-THETA@2)"
+        }
+    }
+    forvalues u=1/`nbdetect' {
+        local v=`difitems`u''
+        local mod = "`mod'"+"(1.item`v'<-THETA@1 tt)"
+        if (`nbmoda_`v''==3) {
+          local mod = "`mod'"+"(2.item`v'<-THETA@2 tt)(3.item`v'<-THETA@3 tt)"
+        }
+        else if (`nbmoda_`v''==2) {
+          local mod = "`mod'"+"(2.item`v'<-THETA@2 tt)"
+        }
+        local w= 100+`u'
+        local unif_`u'=0
+        if (resmat[1,`nbitems'+`v']!=testm[1,1] & `nbmoda_`v''==3) {
+          local conformula = "`conformula'" + "`u' " + "`w' "
+          local unif_`u'=1
+        }
+        else if (resmat[1,`nbitems'+`v']!=testm[1,1] & `nbmoda_`v''==2) {
+          local conformula = "`conformula'" + "`u' "
+          local unif_`u'=1
+        }
+    }
+    if ("`conformula'" != "") {
+      local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent constraint(`conformula')"
+    }
+    else {
+      local mod = "`mod'" + "(THETA<-tt), mlogit tol(0.01) iterate(500) latent(THETA) nocapslatent"
+    }
+    *calcul du modèle
+    `mod'
+    mat V=r(table)
+    mat W=V[1..2,1...]
+
+    * compilation
+    forvalues j=1/`nbitems' {
+      forvalues z=1/`nbmoda_`j'' {
+        mat outmat[`k',colnumb(outmat,"item`j'_`z'")] = W[1,colnumb(W,"`z'.item`j':_cons")]
+      }
+    }
+    * compilation DIF
+    forvalues u=1/`nbdetect' {
+      local j=`difitems`u''
+        forvalues z=1/`nbmoda_`j'' {
+          mat outmat[`k',colnumb(outmat,"dif_`u'_`z'")] = W[1,colnumb(W,"`z'.item`j':tt")]
+        }
+        mat outmat[`k',colnumb(outmat,"dif_detect_`u'")] = `j'
+        if (mod(`s',2)==0) {
+          mat outmat[`k',colnumb(outmat,"dif_detect_unif_`u'")] = `unif_`u''
+        }
+      }
+
+    * Stocker les items de DIF originaux
+    if (`nbdif' > 0) {
+      qui levelsof dif1
+      local ldif1 = r(levels)
+      local diff1: word 1 of `ldif1'
+      qui mat outmat[`k',colnumb(outmat,"real_dif_1")]=`diff1'
+      if (`nbdif' > 1) {
+        qui levelsof dif2
+        local ldif2 = r(levels)
+        local diff2: word 1 of `ldif2'
+        qui mat outmat[`k',colnumb(outmat,"real_dif_2")]=`diff2'
+        if (`nbdif' > 2) {
+          qui levelsof dif3
+          local ldif3 = r(levels)
+          local diff3: word 1 of `ldif3'
+          qui mat outmat[`k',colnumb(outmat,"real_dif_3")]=`diff3'
+        }
+      }
+    }
+    qui mat outmat[`k',colnumb(outmat,"beta")]=W[1,colnumb(W,"THETA:tt")]
+    qui mat outmat[`k',colnumb(outmat,"se_beta")]=W[2,colnumb(W,"THETA:tt")]
+    qui mat outmat[`k',colnumb(outmat,"lrt_passed")]=lrt_passed[1,1]
+    restore
+  }
+  log close
+  putexcel set "`path_res'/`s'`scen'_`Nn'_original.xls", sheet("outmat") replace
+  putexcel A1=matrix(outmat), colnames
+ }
+ }
+ }