Updated RESALI with new bonferroni correction
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6c3e75fc42
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library(TAM)
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resali <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
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if (verbose) {
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cat('-----------------------------------------------------------\n')
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cat('COMPUTING INITIAL PCM\n')
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cat('-----------------------------------------------------------\n')
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}
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nbitems <- length(items)
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nbitems_o <- nbitems
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items_n <- paste0('item',items)
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resp <- df[,items_n]
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grp <- df[,group]
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pcm_initial <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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dat <- df
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dat$score <- rowSums(dat[,items_n])
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nqt <- ifelse(length(unique(quantile(dat$score,seq(0,1,0.2))))==6,5,length(unique(quantile(dat$score,seq(0,1,0.2))))-1)
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dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in items) {
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dat[,paste0('res_',i)] <- IRT.residuals(pcm_initial)$stand_residuals[,i]
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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res.items <- c()
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res.uniform <- c()
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k <- 1
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while(any(pval<0.05/(nbitems_o*3))) {
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k <- k+1
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if (verbose) {
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cat(paste('COMPUTING STEP',k,'\n'))
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cat('-----------------------------------------------------------\n')
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}
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res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
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res.items <- c(res.items,res.item)
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res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
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res.uniform <- c(res.uniform,res.uni)
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items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
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dat[,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
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dat[,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
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resp <- dat[,items_n]
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grp <- dat[,group]
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pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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nbitems <- length(items_n)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in 1:nbitems) {
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dat[,paste0('res_',i)] <- IRT.residuals(pcm_while)$stand_residuals[,i]
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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}
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if (verbose) {
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cat("DETECTED DIF ITEMS\n")
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cat('-----------------------------------------------------------\n')
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}
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if (length(res.items>0)) {
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results <- data.frame(dif.items=res.items,
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uniform=1*res.uniform)
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return(results)
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}
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else {
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if (verbose) {
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cat("No DIF was detected\n")
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}
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return(NULL)
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}
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}
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resali_BF2 <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
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if (verbose) {
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cat('-----------------------------------------------------------\n')
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cat('COMPUTING INITIAL PCM\n')
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cat('-----------------------------------------------------------\n')
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}
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nbitems <- length(items)
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nbitems_o <- nbitems
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items_n <- paste0('item',items)
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resp <- df[,items_n]
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grp <- df[,group]
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pcm_initial <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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dat <- df
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dat$score <- rowSums(dat[,items_n])
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nqt <- ifelse(length(unique(quantile(dat$score,seq(0,1,0.2))))==6,5,length(unique(quantile(dat$score,seq(0,1,0.2))))-1)
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dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in items) {
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dat[,paste0('res_',i)] <- rowMeans(predict(pcm_initial)$stand.resid[,,i])
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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res.items <- c()
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res.uniform <- c()
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k <- 1
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while(any(pval<0.05/(nbitems_o-k+1))) {
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k <- k+1
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if (verbose) {
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cat(paste('COMPUTING STEP',k,'\n'))
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cat('-----------------------------------------------------------\n')
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}
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res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
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res.items <- c(res.items,res.item)
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res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
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res.uniform <- c(res.uniform,res.uni)
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items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
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dat[,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
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dat[,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
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resp <- dat[,items_n]
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grp <- dat[,group]
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pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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nbitems <- length(items_n)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in 1:nbitems) {
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dat[,paste0('res_',i)] <- rowMeans(predict(pcm_while)$stand.resid[,,i])
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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}
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if (verbose) {
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cat("DETECTED DIF ITEMS\n")
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cat('-----------------------------------------------------------\n')
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}
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if (length(res.items>0)) {
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results <- data.frame(dif.items=res.items,
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uniform=1*res.uniform)
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return(results)
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}
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else {
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if (verbose) {
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cat("No DIF was detected\n")
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}
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return(NULL)
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}
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}
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resali_LRT <- function(df=NULL,items=NULL,group=NULL,verbose=T) {
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if (verbose) {
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cat('-----------------------------------------------------------\n')
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cat('COMPUTING INITIAL PCM\n')
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cat('-----------------------------------------------------------\n')
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}
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nbitems <- length(items)
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nbitems_o <- nbitems
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items_n <- paste0('item',items)
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resp <- df[,items_n]
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grp <- df[,group]
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items_n_alt <- paste0(items_n,c("_TT","_noTT"))
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for (i in items_n) {
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df[df$TT==0,paste0(i,"_noTT")] <- df[df$TT==0,i]
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df[df$TT==1,paste0(i,"_TT")] <- df[df$TT==1,i]
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}
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resp_alt <- df[,items_n_alt]
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pcm_initial <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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pcm_alt <- TAM::tam.mml(resp=resp_alt,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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dat <- df
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dat$score <- rowSums(dat[,items_n])
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nqt <- ifelse(length(unique(quantile(dat$score,seq(0,1,0.2))))==6,5,length(unique(quantile(dat$score,seq(0,1,0.2))))-1)
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dat$score_q5 <- cut(dat$score,unique(quantile(dat$score,seq(0,1,1/nqt))),labels=1:nqt,include.lowest=T)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in items) {
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dat[,paste0('res_',i)] <- rowMeans(predict(pcm_initial)$stand.resid[,,i])
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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res.items <- c()
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res.uniform <- c()
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k <- 1
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if (anova(pcm_initial,pcm_alt)$p[1]<0.05) {
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while(any(pval<0.05/nbitems_o)) {
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k <- k+1
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if (verbose) {
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cat(paste('COMPUTING STEP',k,'\n'))
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cat('-----------------------------------------------------------\n')
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}
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res.item <- gsub("[a-z]", "",colnames(resp)[which.max(fval)])
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res.items <- c(res.items,res.item)
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res.uni <- res.anova[[which.max(fval)]][3,"Pr(>F)"]>0.05
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res.uniform <- c(res.uniform,res.uni)
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items_n <- c(items_n[items_n!=paste0('item',res.item)],paste0("item",res.item,c("noTT","TT")))
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dat[,paste0("item",res.item,'TT')] <- dat[dat$TT==1,paste0('item',res.item)]
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dat[,paste0("item",res.item,'noTT')] <- dat[dat$TT==0,paste0('item',res.item)]
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resp <- dat[,items_n]
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grp <- dat[,group]
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pcm_while <- TAM::tam.mml(resp=resp,Y=grp,irtmodel = "PCM",est.variance = T,verbose=F)
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nbitems <- length(items_n)
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res.anova <- rep(NA,nbitems)
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pval <- rep(NA,nbitems)
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fval <- rep(NA,nbitems)
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for (i in 1:nbitems) {
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dat[,paste0('res_',i)] <- rowMeans(predict(pcm_while)$stand.resid[,,i])
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res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~TT*score_q5,data=dat))
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pval[i] <- res.anova[[i]][1,"Pr(>F)"]
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fval[i] <- res.anova[[i]][1,'F value']
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}
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if (verbose) {
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cat('DONE\n')
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cat('-----------------------------------------------------------\n')
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}
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}
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if (verbose) {
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cat("DETECTED DIF ITEMS\n")
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cat('-----------------------------------------------------------\n')
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}
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if (length(res.items>0)) {
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results <- data.frame(dif.items=res.items,
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uniform=1*res.uniform)
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return(results)
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}
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else {
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if (verbose) {
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cat("No DIF was detected\n")
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}
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return(NULL)
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}
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}
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else {
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if (verbose) {
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cat("No DIF was detected\n")
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}
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return(NULL)
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}
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}
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