diff --git a/Rproject/resali.R b/Rproject/resali.R
index 304f32a..1cf0a42 100644
--- a/Rproject/resali.R
+++ b/Rproject/resali.R
@@ -2,9 +2,9 @@ library(TAM)
 
 resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
   if (verbose) {
-  cat('-----------------------------------------------------------\n')
-  cat('COMPUTING INITIAL PCM\n')
-  cat('-----------------------------------------------------------\n')
+    cat('-----------------------------------------------------------\n')
+    cat('COMPUTING INITIAL PCM\n')
+    cat('-----------------------------------------------------------\n')
   }
   nbitems <- length(items)
   nbitems_o <- nbitems
@@ -15,6 +15,9 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
   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
+  }
   dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
   res.anova <- rep(NA,nbitems)
   pval <- rep(NA,nbitems)
@@ -26,8 +29,8 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
     fval[i] <- res.anova[[i]][1,'F value']
   }
   if (verbose) {
-  cat('DONE\n')
-  cat('-----------------------------------------------------------\n')
+    cat('DONE\n')
+    cat('-----------------------------------------------------------\n')
   }
   res.items <- c()
   res.uniform <- c()
@@ -35,8 +38,8 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
   while(any(pval<0.05/(nbitems_o*3))) {
     k <- k+1
     if (verbose) {
-    cat(paste('COMPUTING STEP',k,'\n'))
-    cat('-----------------------------------------------------------\n')
+      cat(paste('COMPUTING STEP',k,'\n'))
+      cat('-----------------------------------------------------------\n')
     }
     res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
     res.items <- c(res.items,res.item)
@@ -59,13 +62,13 @@ resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
       fval[i] <- res.anova[[i]][1,'F value']
     }
     if (verbose) {
-    cat('DONE\n')
-    cat('-----------------------------------------------------------\n')
+      cat('DONE\n')
+      cat('-----------------------------------------------------------\n')
     }
   }
   if (verbose) {
-  cat("DETECTED DIF ITEMS\n")
-  cat('-----------------------------------------------------------\n')
+    cat("DETECTED DIF ITEMS\n")
+    cat('-----------------------------------------------------------\n')
   }
   if (length(res.items>0)) {
     results <- data.frame(dif.items=res.items,
@@ -211,52 +214,52 @@ resali_LRT <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
   res.uniform <- c()
   k <- 1
   if (anova(pcm_initial,pcm_alt)$p[1]<0.05) {
-  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)
-    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[,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
-    dat[,paste0("item",res.item,'noTT')] <- dat[dat$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)] <- rowMeans(predict(pcm_while)$stand.resid[,,i])
-      res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
-      pval[i] <- res.anova[[i]][1,"Pr(>F)"]
-      fval[i] <- res.anova[[i]][1,'F value']
+    while(any(pval<0.05/nbitems_o)) {
+      k <- k+1
+      if (verbose) {
+        cat(paste('COMPUTING STEP',k,'\n'))
+        cat('-----------------------------------------------------------\n')
+      }
+      res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
+      res.items <- c(res.items,res.item)
+      res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
+      res.uniform <- c(res.uniform,res.uni)
+      items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
+      dat[,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
+      dat[,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
+      resp <- dat[,items_n]
+      grp <- dat[,group]
+      pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
+      nbitems <- length(items_n)
+      res.anova <- rep(NA,nbitems)
+      pval <- rep(NA,nbitems)
+      fval <- rep(NA,nbitems)
+      for (i in 1:nbitems) {
+        dat[,paste0('res_',i)] <- rowMeans(predict(pcm_while)$stand.resid[,,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']
+      }
+      if (verbose) {
+        cat('DONE\n')
+        cat('-----------------------------------------------------------\n')
+      }
     }
     if (verbose) {
-      cat('DONE\n')
+      cat("DETECTED DIF ITEMS\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")
+    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)
     }
-    return(NULL)
-  }
   }
   else {
     if (verbose) {