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z`paK_UhnCCUD?^&eS0g{S3m1+-@f__zwfE9@&13S`hHdKKb1fFYIjfV;Fq1hdhXrH z_jbR&+V#86v%l=(H$T7fZdY=B-TznRe^st4>aX+cuYA)L^;i6NXGc%%>gxILI={Z! z(Un|R)Z0AJ9rf0}{>lS=^|!8w-+s_jy}h;LXZqd#&9D3Kt6Wd@^;S=JdVg2G>B_F2 z+Sh+m{@>+KeYNXn`rZD>Z#%o%r@Q@oJMWIKr|;F3-hV0|b*HDda{pAk^wytx%g--= z`(5|-GxfJ$b*HZ@zOLpyweNT5g$erdLI0y|CRlJ zHBP#c|5bi|Q*Zr~-|M|4e%0N+?$ezY!W<$7DUes#_->goM< zC(kcEy`}!nm;BDteYLl*_^7A!Pu){b*MH{gZT{jH@hg9H#n<2Y?^pHpHJD+MUof_0>T1Ah);jP6@PzO z{r4Bce}5Ug%gRd4$>#8`zvq@Vo#rAA=4!;Ff6oTl9N1+)bH~iiQ;p73&HtWibY|A* z%&gIwS+g^k$;J)IqkIHMcn0X2erQ>|E1|){vrIge+d4YUDR?X!EWV9@c%!@ fz`r=ei6QitS9Nr>jUqmN{5Ad$W*Z~7_wo7ww>90) diff --git a/RProject/.Rhistory b/RProject/.Rhistory index 688f60e..6e2f07c 100644 --- a/RProject/.Rhistory +++ b/RProject/.Rhistory @@ -1,84 +1,3 @@ -resp <- df[,c(dif.items.list)] -} -} -else { -resp <- df[,c(nodif.items.list)] -} -print(grp) -tam1 <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = model,est.variance = T,verbose=F) -} -} -return(summary(tam1)) -} -rosali(dat=dat,items=1:7,group="TT") -rosali <- function(dat=NULL,items=NULL,group=NULL) { -nbitems <- length(items) -items2 <- items -# Un seul groupe -if (length(group)!=1) { -stop('Only one variable can be used for the group option') -} -# Recoder groupe en 0/1 -dat[,group] <- as.factor(dat[,group]) -if (!(all(names(levels(dat[,group]))==c("0","1")))) { -levels(dat[,group]) <- c("0","1") -} -dat[,group] <- as.numeric(dat[,group]) -model_a <- pcm(df=dat,items=items,group = group,dif.items = items) -model_b <- pcm(df=dat,items=items,group = group,dif.items = NULL) -return(c(model_a,model_b)) -} -rosali(dat=dat,items=1:7,group="TT") -rosali <- function(dat=NULL,items=NULL,group=NULL) { -nbitems <- length(items) -items2 <- items -# Un seul groupe -if (length(group)!=1) { -stop('Only one variable can be used for the group option') -} -# Recoder groupe en 0/1 -dat[,group] <- as.factor(dat[,group]) -if (!(all(names(levels(dat[,group]))==c("0","1")))) { -levels(dat[,group]) <- c("0","1") -} -dat[,group] <- as.numeric(dat[,group])-1 -model_a <- pcm(df=dat,items=items,group = group,dif.items = items) -model_b <- pcm(df=dat,items=items,group = group,dif.items = NULL) -return(c(model_a,model_b)) -} -rosali(dat=dat,items=1:7,group="TT") -## File Name: pcm.R -## File version: 1.0 -#' @import TAM -#' @export -pcm <- function(df=NULL,items=NULL,group=NULL,model="PCM2",method="MML",dif.items=NULL) { -# Prepare analysis -if (is.null(items)) { -nbitems <- sum(sapply(1:100,function(x) paste0('item',x)) %in% colnames(df)) -items <- paste0('item',seq(1,nbitems)) -resp <- df[,items] -} -else { -nbitems <- length(items) -resp <- df[,paste0("item",items)] -} -if (is.null(group)) { -grp <- NULL -} -else { -grp <- df[,group] -df$grp <- grp -if (!is.null(dif.items)) { -for (i in dif.items) { -df[,paste0('item',i,'_noTT')] <- NA -df[,paste0('item',i,'_TT')] <- NA -df[df$grp==0,paste0('item',i,'_noTT')] <- df[df$grp==0,paste0('item',i)] -df[df$grp==1,paste0('item',i,'_TT')] <- df[df$grp==1,paste0('item',i)] -} -} -} -# Analyze -if (method=='MML') { if (is.null(dif.items)) { tam1 <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = model,est.variance = T,verbose=F) } @@ -510,3 +429,84 @@ return(difit) rosali(dat=dat,items=1:7,group="TT") pcm(dat,items=1:7,group="TT",dif.items=1:2) summary(pcm(dat,items=1:7,group="TT",dif.items=1:2)) +library(TAM) +resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) { +if (verbose) { +cat('-----------------------------------------------------------\n') +cat('COMPUTING INITIAL PCM\n') +cat('-----------------------------------------------------------\n') +} +nbitems <- length(items) +nbitems_o <- nbitems +items_n <- paste0('item',items) +resp <- df[,items_n] +grp <- df[,group] +pcm_initial <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F) +dat <- df +dat$score <- rowSums(dat[,items_n]) +dat$score_q5 <- cut(dat$score,quantile(dat$score,seq(0,1,0.2)),labels=1:5,include.lowest=T) +res.anova <- rep(NA,nbitems) +pval <- rep(NA,nbitems) +fval <- rep(NA,nbitems) +for (i in items) { +dat[,paste0('res_',i)] <- rowMeans(predict(pcm_initial)$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') +} +res.items <- c() +res.uniform <- 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) +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)$stand.resid[,,i]) +res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat)) +pval[i] <- res.anova[[i]][1,"Pr(>F)"] +fval[i] <- res.anova[[i]][1,'F value'] +} +if (verbose) { +cat('DONE\n') +cat('-----------------------------------------------------------\n') +} +} +if (verbose) { +cat("DETECTED DIF ITEMS\n") +cat('-----------------------------------------------------------\n') +} +if (length(res.items>0)) { +results <- data.frame(dif.items=res.items, +uniform=1*res.uniform) +return(results) +} +else { +cat("No DIF was detected") +return(NULL) +} +} +resali(df=dat,items=1:7,group="TT") +dat$dif1 +dat$dif2 +dat$dif3 diff --git a/RProject/resali.R b/RProject/resali.R new file mode 100644 index 0000000..bced126 --- /dev/null +++ b/RProject/resali.R @@ -0,0 +1,78 @@ +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$score <- rowSums(dat[,items_n]) + dat$score_q5 <- cut(dat$score,quantile(dat$score,seq(0,1,0.2)),labels=1:5,include.lowest=T) + res.anova <- rep(NA,nbitems) + pval <- rep(NA,nbitems) + fval <- rep(NA,nbitems) + for (i in items) { + dat[,paste0('res_',i)] <- rowMeans(predict(pcm_initial)$stand.resid[,,i]) + res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat)) + pval[i] <- res.anova[[i]][1,"Pr(>F)"] + fval[i] <- res.anova[[i]][1,'F value'] + } + if (verbose) { + cat('DONE\n') + cat('-----------------------------------------------------------\n') + } + res.items <- c() + res.uniform <- c() + k <- 1 + while(any(pval<0.05/nbitems_o)) { + k <- k+1 + if (verbose) { + cat(paste('COMPUTING STEP',k,'\n')) + cat('-----------------------------------------------------------\n') + } + res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)]) + res.items <- c(res.items,res.item) + res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05 + res.uniform <- c(res.uniform,res.uni) + items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT"))) + dat[,paste0("item",res.item,'TT')] <- dat[dat$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("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 { + cat("No DIF was detected") + return(NULL) + } +} \ No newline at end of file

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