commit 30dc985ae3e2f705d714eed4a2d7b9e7dab63c5e Author: corentinchoisy Date: Tue Apr 29 17:59:26 2025 +0200 first commit diff --git a/DESCRIPTION b/DESCRIPTION new file mode 100644 index 0000000..3dbe7a4 --- /dev/null +++ b/DESCRIPTION @@ -0,0 +1,15 @@ +Package: SPT +Type: Package +Title: SPHERE Psychometrics Tools +Version: 1.0.0 +Author: Corentin Choisy +Maintainer: Corentin Choisy +Description: Various tools for psychometrics analyses +License: AGPLv3 +URL: https://git.corentinchoisy.xyz/corentinchoisy/SPT +Encoding: UTF-8 +LazyData: true +RoxygenNote: 7.3.2 +Imports: + vcrpart, + rjags diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..6bb5339 --- /dev/null +++ b/LICENSE @@ -0,0 +1,661 @@ +GNU AFFERO GENERAL PUBLIC LICENSE + Version 3, 19 November 2007 + +Copyright (C) 2007 Free Software Foundation, Inc. +Everyone is permitted to copy and distribute verbatim copies +of this license document, but changing it is not allowed. + + Preamble + +The GNU Affero General Public License is a free, copyleft license for +software and other kinds of works, specifically designed to ensure +cooperation with the community in the case of network server software. + +The licenses for most software and other practical works are designed +to take away your freedom to share and change the works. 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It is safest +to attach them to the start of each source file to most effectively +state the exclusion of warranty; and each file should have at least +the "copyright" line and a pointer to where the full notice is found. + + +Copyright (C) + +This program is free software: you can redistribute it and/or modify +it under the terms of the GNU Affero General Public License as published +by the Free Software Foundation, either version 3 of the License, or +(at your option) any later version. + +This program is distributed in the hope that it will be useful, +but WITHOUT ANY WARRANTY; without even the implied warranty of +MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +GNU Affero General Public License for more details. + +You should have received a copy of the GNU Affero General Public License +along with this program. If not, see . + +Also add information on how to contact you by electronic and paper mail. + +If your software can interact with users remotely through a computer +network, you should also make sure that it provides a way for users to +get its source. For example, if your program is a web application, its +interface could display a "Source" link that leads users to an archive +of the code. There are many ways you could offer source, and different +solutions will be better for different programs; see section 13 for the +specific requirements. + +You should also get your employer (if you work as a programmer) or school, +if any, to sign a "copyright disclaimer" for the program, if necessary. +For more information on this, and how to apply and follow the GNU AGPL, see +. diff --git a/NAMESPACE b/NAMESPACE new file mode 100644 index 0000000..914eeea --- /dev/null +++ b/NAMESPACE @@ -0,0 +1,8 @@ +# Generated by roxygen2: do not edit by hand + +export(bpcm) +export(pcm) +export(res_ij) +export(residif) +import(rjags) +import(vcrpart) diff --git a/R/bpcm.R b/R/bpcm.R new file mode 100644 index 0000000..8acca22 --- /dev/null +++ b/R/bpcm.R @@ -0,0 +1,416 @@ +## File Name: bpcm.R +## File version: 1.0 + + +#' Compute Bayesian Partial Credit Model (BPCM) for polytomous and dichotomous items +#' +#' This function computes a bayesian PCM, potentially accounting for DIF on specified items +#' +#' @param df data.frame containing the response data +#' @param grp vector containing the column where an optional group membership variable is stored in df +#' @param is.dif indicator vector containing 1 at the inded of each DIF item in df and 0 otherwise +#' @param is.unif indicator vector containing 1 at the inded of each uniform DIF item in df and 0 otherwise +#' @param prior prior function to be used +#' @param diag.plots boolean indicating whether the JAGS diagnosis plots should be displayed +#' @return A data.frame containing various model outputs +#' @import rjags +#' @export + +bpcm <- function(df=NULL,grp=NULL,is.dif=NULL,is.unif=NULL,priors=NULL,param=list(),verbose=T,diag.plots=F) { + + ##### Detecting errors + if ( (sum(is.dif)!=0 | sum(is.unif)!=0) & any(is.null(grp))) { + stop('ERROR: no group variable provided, but is.dif or is.unif are not NULL') + } + if ( sum(is.dif)==0 & sum(is.unif)!=0) { + warning('WARNING: no DIF item specified, but is.unif is not NULL. Ignoring is.unif') + } + if ( sum(is.dif)!=0 ) { + if (length(is.dif)!=ncol(df)) { + stop('ERROR: is.dif must have one element per item in df') + } + if (length(is.unif)!=ncol(df)) { + stop('ERROR: is.unif must have one element per item in df') + } + if ( !("m.gamma"%in%names(priors)) ) { + stop('ERROR: DIF requested. Please provide mean of prior gamma distribution as priors$m.gamma') + } + if ( !("s.gamma"%in%names(priors)) ) { + stop('ERROR: DIF requested. Please provide SD of prior gamma distribution as priors$s.gamma') + } + } + if ( !any(is.null(grp)) ) { + if ( !("m.beta"%in%names(priors)) ) { + stop('ERROR: Group effect requested. Please provide mean of prior beta distribution as priors$m.beta') + } + if ( !("s.gamma"%in%names(priors)) ) { + stop('ERROR: Group effect requested. Please provide SD of prior beta distribution as priors$s.beta') + } + if (nrow(df)!=length(grp)) { + stop('ERROR: grp must be of length nrow(df)') + } + } + if ( !("m.delta"%in%names(priors)) ) { + stop('ERROR: Please provide mean of prior delta distribution as priors$m.delta') + } + if ( !("s.delta"%in%names(priors)) ) { + stop('ERROR: Please provide SD of prior delta distribution as priors$s.delta') + } + + + if (verbose) { + cat('\n') + cat("#################################################################################################\n") + cat("######################################### FITTING MODEL #########################################\n") + cat("#################################################################################################\n") + } + + ############################################### + ############### NO GROUP EFFECT ############### + ############################################### + if (is.null(grp)) { + nam <- colnames(df) + nam_o <- nam + Y <- matrix(unlist(df),nrow=nrow(df))+1 + n <- nrow(Y) + p <- ncol(Y) + K <- apply(Y,2,max,na.rm=TRUE) + m.delta <- priors$m.delta + s.delta <- priors$s.delta + + namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]")))) + if (sum(is.dif)!=0) { + namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta") + } else { + if (!is.null(grp)) { + namlist <- c(namlist,"beta") + } + } + + data <- list(Y=Y,n=n,p=p,K=K,m.delta=m.delta,s.delta=s.delta) + params <- c("delta") + + + if ("n.burn"%in%names(param)) { + n.burn <- param$n.burn + } else { + n.burn <- 4000 + } + if ("n.thin"%in%names(param)) { + n.thin <- param$n.thin + } else { + n.thin <- 20 + } + if ("n.chains"%in%names(param)) { + n.chains <- param$n.chains + } else { + n.chains <- 2 + } + if ("n.sim"%in%names(param)) { + n.sim <- param$n.sim + } else { + n.sim <- 20000 + } + if ("nb.cores"%in%names(param)) { + nb.cores <- param$nb.cores + } else { + nb.cores <- 6 + } + cl <- parallel::makeForkCluster(nb.cores) + + if (verbose) { + cat('Initializing Chains...') + } + dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm",n.chains=n.chains) + if (verbose) { + cat('DONE \n') + cat('Applying burn-in iterations...') + } + dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn) + if (verbose) { + cat('DONE \n') + cat('Running Markov Chains...') + } + mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin) + if (verbose) { + cat('DONE \n') + } + parallel::stopCluster(cl) + if (diag.plots) { + cat("Displaying traceplots...") + traceplot(mod.samples) + readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue") + cat("DONE\n") + cat("Displaying autocorrelation plots...") + autocorr.plot(mod.samples,ask=F) + readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue") + cat("DONE\n ") + } + res <- mod.samples[[1]] + res <- as.data.frame(res)[,namlist] + namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1)))) + if (sum(is.dif)!=0) { + namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta") + } else { + if (!is.null(grp)) { + namlist2 <- c(namlist2,"beta") + } + } + colnames(res) <- namlist2 + res <- res[,namlist2] + res <- res[,apply(res,2,function(x) all(x==0))==0] + xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) ) + rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high") + xsi <- round(t(xsi),4) + if ("beta" %in% rownames(xsi)) { + beta <- xsi[rownames(xsi)=="beta",] + xsi <- xsi[rownames(xsi)!="beta",] + } + out <- list(mcmc.res=res, + dif.items=nam_o[which(is.dif==1)], + beta=beta, + thresholds=xsi) + + + ##################################################### + ############### GROUP EFFECT / NO DIF ############### + ##################################################### + } else if (is.null(is.dif) | sum(is.dif)==0) { + nam <- colnames(df) + nam_o <- nam + Y <- matrix(unlist(df),nrow=nrow(df))+1 + Z <- matrix(unlist(grp),nrow=length(grp)) + n <- nrow(Y) + p <- ncol(Y) + K <- apply(Y,2,max,na.rm=TRUE) + m.delta <- priors$m.delta + s.delta <- priors$s.delta + m.beta <- priors$m.beta + s.beta <- priors$s.beta + + namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]")))) + if (sum(is.dif)!=0) { + namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta") + } else { + if (!is.null(grp)) { + namlist <- c(namlist,"beta") + } + } + + data <- list(Y=Y,Z=Z,n=n,p=p,K=K,m.beta=m.beta,s.beta=s.beta,m.delta=m.delta,s.delta=s.delta) + params <- c("delta","beta") + + + if ("n.burn"%in%names(param)) { + n.burn <- param$n.burn + } else { + n.burn <- 4000 + } + if ("n.thin"%in%names(param)) { + n.thin <- param$n.thin + } else { + n.thin <- 20 + } + if ("n.chains"%in%names(param)) { + n.chains <- param$n.chains + } else { + n.chains <- 2 + } + if ("n.sim"%in%names(param)) { + n.sim <- param$n.sim + } else { + n.sim <- 20000 + } + if ("nb.cores"%in%names(param)) { + nb.cores <- param$nb.cores + } else { + nb.cores <- 6 + } + cl <- parallel::makeForkCluster(nb.cores) + + if (verbose) { + cat('Initializing Chains...') + } + dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm_beta",n.chains=n.chains) + if (verbose) { + cat('DONE \n') + cat('Applying burn-in iterations...') + } + dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn) + if (verbose) { + cat('DONE \n') + cat('Running Markov Chains...') + } + mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin) + if (verbose) { + cat('DONE \n') + } + parallel::stopCluster(cl) + if (diag.plots) { + cat("Displaying traceplots...") + traceplot(mod.samples) + readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue") + cat("DONE\n") + cat("Displaying autocorrelation plots...") + autocorr.plot(mod.samples,ask=F) + readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue") + cat("DONE\n ") + } + res <- mod.samples[[1]] + res <- as.data.frame(res)[,namlist] + namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1)))) + if (sum(is.dif)!=0) { + namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta") + } else { + if (!is.null(grp)) { + namlist2 <- c(namlist2,"beta") + } + } + colnames(res) <- namlist2 + res <- res[,namlist2] + res <- res[,apply(res,2,function(x) all(x==0))==0] + xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) ) + rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high") + xsi <- round(t(xsi),4) + if ("beta" %in% rownames(xsi)) { + beta <- xsi[rownames(xsi)=="beta",] + xsi <- xsi[rownames(xsi)!="beta",] + } + out <- list(mcmc.res=res, + dif.items=nam_o[which(is.dif==1)], + beta=beta, + thresholds=xsi) + + ##################################################### + ############### GROUP EFFECT / NO DIF ############### + ##################################################### + } else { + + + nam <- colnames(df) + nam_o <- nam + Y <- matrix(unlist(df),nrow=nrow(df))+1 + Z <- matrix(unlist(grp),nrow=length(grp)) + n <- nrow(Y) + p <- ncol(Y) + + pnodif <- p-sum(is.dif) + pnodif1 <- p-sum(is.dif)+1 + pdif <- sum(is.dif) + pnounif <- pnodif+pdif-sum(is.unif) + pnounif1 <- pnodif+pdif-sum(is.unif)+1 + + K <- apply(Y,2,max,na.rm=TRUE) + m.delta <- priors$m.delta + s.delta <- priors$s.delta + m.beta <- priors$m.beta + s.beta <- priors$s.beta + + m.gamma <- priors$m.gamma + s.gamma <- priors$s.gamma + + namlist <- c(sapply(1:p, function(x) paste0("delta[",x,',',paste0(1:K[p],"]")))) + if (sum(is.dif)!=0) { + namlist <- c(namlist,sapply(1:p, function(x) paste0("gamma[",x,',',paste0(1:K[p],"]"))),"beta") + } else { + if (!is.null(grp)) { + namlist <- c(namlist,"beta") + } + } + + Y <- Y[,c(which(is.dif+is.unif==0),which(is.dif+is.unif==1),which(is.dif+is.unif==2))] + nam <- nam[c(which(is.dif+is.unif==0),which(is.dif+is.unif==1),which(is.dif+is.unif==2))] + + data <- list(Y=Y,Z=Z,n=n,p=p,pnounif=pnounif,pnounif1=pnounif1,pdif=pdif,pnodif1=pnodif1,pnodif=pnodif,K=K,m.beta=m.beta,s.beta=s.beta,m.gamma=m.gamma,s.gamma=s.gamma,m.delta=m.delta,s.delta=s.delta,difff=as.factor(is.dif),unif=as.factor(is.unif)) + params <- c("delta","gamma","beta") + + + if ("n.burn"%in%names(param)) { + n.burn <- param$n.burn + } else { + n.burn <- 4000 + } + if ("n.thin"%in%names(param)) { + n.thin <- param$n.thin + } else { + n.thin <- 20 + } + if ("n.chains"%in%names(param)) { + n.chains <- param$n.chains + } else { + n.chains <- 2 + } + if ("n.sim"%in%names(param)) { + n.sim <- param$n.sim + } else { + n.sim <- 20000 + } + if ("nb.cores"%in%names(param)) { + nb.cores <- param$nb.cores + } else { + nb.cores <- 6 + } + cl <- parallel::makeForkCluster(nb.cores) + + + if (verbose) { + cat('Initializing Chains...') + } + dclone::parJagsModel(cl,name="mod.jags",data = data,inits = NULL,file = "/home/corentin/Documents/These/Recherche/SPT/src/bpcm_dif",n.chains=n.chains) + if (verbose) { + cat('DONE \n') + cat('Applying burn-in iterations...') + } + dclone::parUpdate(cl,object="mod.jags",n.burn=n.burn) + if (verbose) { + cat('DONE \n') + cat('Running Markov Chains...') + } + mod.samples <- dclone::parCodaSamples(cl,model="mod.jags",variable.names=params,n.iter = n.sim,thin = n.thin) + if (verbose) { + cat('DONE \n') + } + parallel::stopCluster(cl) + if (diag.plots) { + cat("Displaying traceplots...") + traceplot(mod.samples) + readline(prompt="Use the arrows to navigate between traceplots. Press [enter] to continue") + cat("DONE\n") + cat("Displaying autocorrelation plots...") + autocorr.plot(mod.samples,ask=F) + readline(prompt="Use the arrows to navigate between autocorr plots. Press [enter] to continue") + cat("DONE\n ") + } + res <- mod.samples[[1]] + res <- as.data.frame(res)[,namlist] + namlist2 <- unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1)))) + if (sum(is.dif)!=0) { + namlist2 <- c(namlist2,unlist(c(sapply(nam,function(x) paste0(x,"_",1:K[which(nam_o==x)]-1,":grp")))),"beta") + } else { + if (!is.null(grp)) { + namlist2 <- c(namlist2,"beta") + } + } + colnames(res) <- namlist2 + res <- res[,namlist2] + res <- res[,apply(res,2,function(x) all(x==0))==0] + xsi <- apply(res,2,function(x) c(mean(x),sd(x),quantile(x,0.05),quantile(x,0.95)) ) + rownames(xsi) <- c("post.mean","post.sd","post.90.cred.low","post.90.cred.high") + xsi <- round(t(xsi),4) + if ("beta" %in% rownames(xsi)) { + beta <- xsi[rownames(xsi)=="beta",] + if (is.null(grp)) { + beta <- NA + } + xsi <- xsi[rownames(xsi)!="beta",] + } + out <- list(mcmc.res=res, + dif.items=nam_o[which(is.dif==1)], + beta=beta, + thresholds=xsi) + } + if (is.null(dim(out$beta))) { + out$beta <- NA + } + return(out) +} diff --git a/R/pcm.R b/R/pcm.R new file mode 100644 index 0000000..bb3ebc0 --- /dev/null +++ b/R/pcm.R @@ -0,0 +1,236 @@ +## File Name: pcm.R +## File version: 1.0 + +#' Compute Partial Credit Model (PCM) for polytomous and dichotomous items +#' +#' This function computes a frequentist PCM, potentially accounting for DIF on specified items +#' +#' @param df data.frame containing the data +#' @param items vector containing the names of columns where item responses are stored in df +#' @param grp string containing the name of the column where an optional group membership variable is stored in df +#' @param dif.items vector containing the list of indexes in "items" corresponding to dif items +#' @param type.dif vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF +#' @return A data.frame containing various model outputs +#' @import vcrpart +#' @export + +pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,verbose=T,fit="ucminf") { + ##### Detecting errors + + if (any(!(items %in% colnames(df)))) { + stop("ERROR: provided item name does not exist in df") + } + if (any(!(grp %in% colnames(df)))) { + stop("ERROR: provided group variable name does not exist in df") + } + if (any(!is.null(grp))) { + if (any(!(grp%in%colnames(df)))) { + stop("ERROR: group name does not exist in df") + } + } + if (!is.null(dif.items) & length(dif.items)!=length(type.dif)) { + stop('ERROR: type.dif is not the same length as dif.items') + } + if (!is.null(dif.items) & is.null(type.dif)) { + warning("WARNING: no type.dif provided, assuming non-homogeneous DIF on all items") + } + if (!("id"%in%colnames(df))) { + stop('ERROR: no column named id provided') + } + if ( any(apply(df[df[,grp]==0,items],2,max)0),"+",""),ifelse(length(difvar.unif>0),paste0(difvar.unif,":grp",collapse="+"),""),")+ce(item",ifelse(length(difvar.nonunif>0),"+",""),ifelse(length(difvar.nonunif)>0,paste0(difvar.nonunif,":grp",collapse="+"),""),")+re(0|id)") + formudif <- as.formula(formudif) + mod <- olmm(formudif,data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit)) + comod <- coef(mod) + # output results + nbcoef <- nbitems+length(difvar.nonunif) + restab <- t(sapply(1:nbcoef,function(x) comod[seq(x,length(comod)-2-length(difvar.unif),nbitems+length(difvar.nonunif))])) + difcoef.unif <- NULL + if (length(difvar.unif)>0) { + difcoef.unif <- comod[(length(comod)-length(difvar.unif)):(length(comod)-1)] + if (length(difvar.unif)!=1) { + difcoef.unif <- as.matrix(difcoef.unif) + } else { + difcoef.unif <- t(as.matrix(difcoef.unif)) + } + rname <- paste0("item",dif.items[type.dif==1]) + rownames(difcoef.unif) <- paste0("dif.",items_o[which(items%in%rname)]) + colnames(difcoef.unif) <- "gamma" + difcoef.unif <- as.data.frame(difcoef.unif) + for (k in 1:maxmod) { + difcoef.unif[,paste0("gamma_",k)] <- difcoef.unif[,"gamma"] + } + difcoef.unif <- as.matrix(difcoef.unif[,2:ncol(difcoef.unif)]) + } + difcoef.nonunif <- NULL + if (length(difvar.nonunif)>0) { + difcoef.nonunif <- restab[nbitems+c(1:length(difvar.nonunif)),] + if (length(difvar.nonunif)==1) { + difcoef.nonunif <- t(as.matrix(difcoef.nonunif)) + } else { + difcoef.nonunif <- as.matrix(difcoef.nonunif) + } + rname <- paste0("item",dif.items[type.dif==0]) + rownames(difcoef.nonunif) <- paste0("dif.",items_o[which(items%in%rname)]) + colnames(difcoef.nonunif) <- paste0("gamma_",1:maxmod) + } + restab <- restab[1:nbitems,] + rownames(restab) <- items_o + colnames(restab) <- paste0("delta_",1:maxmod) + restab.dif <- rbind(difcoef.nonunif,difcoef.unif) + restab.diftype <- matrix(ifelse(type.dif==1,"HOMOGENEOUS","NON-HOMOGENEOUS")) + restab.diftype <- noquote(restab.diftype) + rownames(restab.diftype) <- rownames(restab.dif) + colnames(restab.diftype) <- "dif.type" + beta <- comod["grp"] + se.beta <- (confint(mod)["grp",2]-beta)/1.96 + beta.ci <- confint(mod)["grp",] + beta.p <- 2*pnorm(-abs(beta/se.beta)) + beta <- as.numeric(beta) + se.beta <- as.numeric(se.beta) + beta.p <- as.numeric(beta.p) + beta <- -1*beta + beta.ci <- -1*c(beta.ci[2],beta.ci[1]) + + } else { + # If group no DIF + if (verbose) { + cat('\n') + cat("#################################################################################################\n") + cat("######################################### FITTING MODEL #########################################\n") + cat("#################################################################################################\n") + } + # prepare data + df <- df[,c('id',items,"grp")] + colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2)) + df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items)) + colnames(df.long) <- c("id","grp","item","resp") + nbitems <- length(2:(length(colnames(df))-1)) + maxmod <- max(df[,2:(length(colnames(df))-1)]) + df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F) + df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T) + df.long$id <- factor(df.long$id) + # fit pcm + mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit)) + comod <- coef(mod) + # output results + restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-2,nbitems)])) + rownames(restab) <- items_o + colnames(restab) <- paste0("delta_",1:maxmod) + restab.dif <- NULL + beta <- comod[length(comod)-1] + se.beta <- (confint(mod)["grp",2]-beta)/1.96 + beta.ci <- confint(mod)["grp",] + beta.p <- 2*pnorm(-abs(beta/se.beta)) + beta <- as.numeric(beta) + se.beta <- as.numeric(se.beta) + beta.p <- as.numeric(beta.p) + beta <- -1*beta + beta.ci <- -1*c(beta.ci[2],beta.ci[1]) + } + + } + theta <- -1*ranef(mod,norm=F)+ifelse(grp==1,beta,0) + resid <- apply(matrix(1:nbitems,ncol=length(nbitems)),1, function(k) sapply(1:nrow(df), function(j) res_ij(theta[j],restab[k,],df[j,items[k]],beta=0))) + colnames(resid) <- items_o + + ##### Output + if (verbose) { + cat(paste0('Number of individuals: ',nrow(df),"\n")) + cat(paste0('Number of items: ',length(items),"\n")) + cat(paste0('Item Thresholds and DIF parameters: ',"\n")) + } + + + out <- list( + beta=beta, + beta.se=se.beta, + beta.ci=beta.ci, + beta.p=beta.p, + dif.items=dif.items, + dif.type=restab.diftype, + thresholds=restab, + dif.param=restab.dif, + theta=theta, + residuals=resid + ) + return(out) +} + + + diff --git a/R/res_ij.R b/R/res_ij.R new file mode 100644 index 0000000..67e6a64 --- /dev/null +++ b/R/res_ij.R @@ -0,0 +1,37 @@ +## File Name: res_ij.R +## File version: 1.0 + +#' Compute rasch person-item residuals +#' +#' This function computes person-item residuals for rasch family adjacent-categories models +#' like the Rasch model or the PCM +#' +#' @param thetahat_i Person parameter estimate for person i +#' @param delta_hat_j Item threshold parameter estimates for item j +#' @param res Response of person i to item j +#' @param beta Estimated latent mean difference between groups, can be ignored +#' @return A single scalar equal to the residual for person i on item j +#' @import vcrpart +#' @export + +res_ij <- function(thetahat_i,delta_hat_j,res,beta=0){ + var=0 + denomin=1 + for (i in 1:length(delta_hat_j)) { + + denomin=denomin+exp(i*(thetahat_i+beta)-sum(delta_hat_j[1:i])) + } + posterior=c(1) + for (i in 1:length(delta_hat_j)) { + posterior=c(posterior,exp(i*(thetahat_i+beta)-sum(delta_hat_j[1:i]))) + } + + posterior=posterior/denomin + + for(i in 0:length(delta_hat_j)){ + + var=var+((i-sum(posterior*0:length(delta_hat_j)))**2)*posterior[i+1] + } + return((res-sum(posterior*0:length(delta_hat_j)))/sqrt(var)) + +} diff --git a/R/residif.R b/R/residif.R new file mode 100644 index 0000000..418d558 --- /dev/null +++ b/R/residif.R @@ -0,0 +1,117 @@ +## File Name: residif.R +## File version: 1.0 + +#' RESIDIF procedure for DIF detection as per Andrich and Hagquist (2015) +#' +#' This function detects DIF on PCM items using ANOVA of person-item residuals +#' +#' @param df data.frame containing the data +#' @param items vector containing the names of columns where item responses are stored in df +#' @param grp vector containing the name of the column where an optional group membership variable is stored in df +#' @return A data.frame containing a column listing the detected DIF item and another listing detected DIF forms +#' @import vcrpart +#' @export + +residif <- function(df=NULL,items=NULL,grp=NULL,verbose=T) { + if (any(!(items %in% colnames(df)))) { + stop("ERROR: provided item name does not exist in df") + } + if (any(!(grp %in% colnames(df)))) { + stop("ERROR: provided grp variable name does not exist in df") + } + if (any(is.null(grp))) { + stop("ERROR: no grp variable provided") + } + if (any(is.null(items))) { + stop("ERROR: no items provided") + } + + maxcat <- max(df[,items]) + nbitems <- length(items) + nbitems_o <- nbitems + + if (verbose) { + cat('\n') + cat("#################################################################################################\n") + cat("##################################### COMPUTING INITIAL PCM #####################################\n") + cat("#################################################################################################\n") + } + startt <- Sys.time() + pcm_initial <- pcm(df = df,items = items,grp = grp,verbose=F) + dat <- df + dat$score <- rowSums(dat[,items]) + 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) + fval <- rep(NA,nbitems) + for (i in 1:nbitems) { + dat[,paste0('res_',i)] <- pcm_initial$residuals[,i] + res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~get(grp)*score_q5,data=dat)) + pval[c(i,i+nbitems)] <- c(res.anova[[i]][1,"Pr(>F)"],res.anova[[i]][3,"Pr(>F)"]) + fval[c(i,i+nbitems)] <- c(res.anova[[i]][1,'F value'],res.anova[[i]][3,"F value"]) + } + if (verbose) { + cat('DONE\n') + cat('#################################################################################################\n') + } + res.items <- c() + res.uniform <- c() + resp <- df[,items] + k <- 1 + while(any(pval<0.05/(nbitems_o*3))) { + k <- k+1 + if (verbose) { + cat(paste("######################################## COMPUTING STEP",k,"#######################################\n")) + cat("#################################################################################################\n") + } + numitem <- ifelse(which.max(fval)%%(length(fval)/2)!=0,which.max(fval)%%(length(fval)/2),length(fval)/2) + res.item <- gsub("[a-z]", "",colnames(resp)[numitem]) + res.items <- c(res.items,res.item) + res.uni <- res.anova[[numitem]][3,"Pr(>F)"]>0.05 + res.uniform <- c(res.uniform,res.uni) + pcm_while <- pcm(df = df,items = items,grp = grp,dif.items = res.items,type.dif = res.uniform,verbose=F) + res.anova <- rep(NA,nbitems) + pval <- rep(NA,nbitems_o) + fval <- rep(NA,nbitems_o) + numitems <- 1:nbitems_o + numitems <- numitems[-which(numitems%in%res.items)] + for (i in numitems) { + dat[,paste0('res_',i)] <- pcm_while$residuals[,i] + res.anova[i] <- summary(aov(dat[,paste0('res_',i)]~dat[,grp]*score_q5,data=dat)) + pval[c(i,i+nbitems)] <- c(res.anova[[i]][1,"Pr(>F)"],res.anova[[i]][3,"Pr(>F)"]) + fval[c(i,i+nbitems)] <- c(res.anova[[i]][1,'F value'],res.anova[[i]][3,"F value"]) + } + for (i in 1:nbitems_o) { + pval[i] <- ifelse(is.na(pval[i]),999,pval[i]) + fval[i] <- ifelse(is.na(fval[i]),-999,fval[i]) + } + if (verbose) { + cat('DONE\n') + if (any(pval<0.05/(nbitems_o*3))) { + cat('#################################################################################################\n') + } + } + } + endt <- Sys.time() + cat(paste(c('Algorithm ran for',round(endt-startt,4),"seconds\n"))) + if (verbose) { + cat('#################################################################################################\n') + cat("###################################### DETECTED DIF ITEMS #######################################\n") + cat("#################################################################################################\n") + } + if (length(res.items>0)) { + results <- data.frame(dif.items=res.items, + uniform=ifelse(res.uniform==1,TRUE,FALSE)) + return(results) + } + else { + if (verbose) { + cat("No DIF was detected\n") + } + return(NULL) + } +} diff --git a/SPT.Rproj b/SPT.Rproj new file mode 100644 index 0000000..497f8bf --- /dev/null +++ b/SPT.Rproj @@ -0,0 +1,20 @@ +Version: 1.0 + +RestoreWorkspace: Default +SaveWorkspace: Default +AlwaysSaveHistory: Default + +EnableCodeIndexing: Yes +UseSpacesForTab: Yes +NumSpacesForTab: 2 +Encoding: UTF-8 + +RnwWeave: Sweave +LaTeX: pdfLaTeX + +AutoAppendNewline: Yes +StripTrailingWhitespace: Yes + +BuildType: Package +PackageUseDevtools: Yes +PackageInstallArgs: --no-multiarch --with-keep.source diff --git a/man/bpcm.Rd b/man/bpcm.Rd new file mode 100644 index 0000000..21e4cc2 --- /dev/null +++ b/man/bpcm.Rd @@ -0,0 +1,36 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/bpcm.R +\name{bpcm} +\alias{bpcm} +\title{Compute Bayesian Partial Credit Model (BPCM) for polytomous and dichotomous items} +\usage{ +bpcm( + df = NULL, + grp = NULL, + is.dif = NULL, + is.unif = NULL, + priors = NULL, + param = list(), + verbose = T, + diag.plots = F +) +} +\arguments{ +\item{df}{data.frame containing the response data} + +\item{grp}{vector containing the column where an optional group membership variable is stored in df} + +\item{is.dif}{indicator vector containing 1 at the inded of each DIF item in df and 0 otherwise} + +\item{is.unif}{indicator vector containing 1 at the inded of each uniform DIF item in df and 0 otherwise} + +\item{diag.plots}{boolean indicating whether the JAGS diagnosis plots should be displayed} + +\item{prior}{prior function to be used} +} +\value{ +A data.frame containing various model outputs +} +\description{ +This function computes a bayesian PCM, potentially accounting for DIF on specified items +} diff --git a/man/pcm.Rd b/man/pcm.Rd new file mode 100644 index 0000000..21cae17 --- /dev/null +++ b/man/pcm.Rd @@ -0,0 +1,33 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/pcm.R +\name{pcm} +\alias{pcm} +\title{Compute Partial Credit Model (PCM) for polytomous and dichotomous items} +\usage{ +pcm( + df = NULL, + items = NULL, + grp = NULL, + dif.items = NULL, + type.dif = NULL, + verbose = T, + fit = "ucminf" +) +} +\arguments{ +\item{df}{data.frame containing the data} + +\item{items}{vector containing the names of columns where item responses are stored in df} + +\item{grp}{string containing the name of the column where an optional group membership variable is stored in df} + +\item{dif.items}{vector containing the list of indexes in "items" corresponding to dif items} + +\item{type.dif}{vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF} +} +\value{ +A data.frame containing various model outputs +} +\description{ +This function computes a frequentist PCM, potentially accounting for DIF on specified items +} diff --git a/man/res_ij.Rd b/man/res_ij.Rd new file mode 100644 index 0000000..15f6f1a --- /dev/null +++ b/man/res_ij.Rd @@ -0,0 +1,24 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/res_ij.R +\name{res_ij} +\alias{res_ij} +\title{Compute rasch person-item residuals} +\usage{ +res_ij(thetahat_i, delta_hat_j, res, beta = 0) +} +\arguments{ +\item{thetahat_i}{Person parameter estimate for person i} + +\item{delta_hat_j}{Item threshold parameter estimates for item j} + +\item{res}{Response of person i to item j} + +\item{beta}{Estimated latent mean difference between groups, can be ignored} +} +\value{ +A single scalar equal to the residual for person i on item j +} +\description{ +This function computes person-item residuals for rasch family adjacent-categories models +like the Rasch model or the PCM +} diff --git a/man/residif.Rd b/man/residif.Rd new file mode 100644 index 0000000..3b4695e --- /dev/null +++ b/man/residif.Rd @@ -0,0 +1,21 @@ +% Generated by roxygen2: do not edit by hand +% Please edit documentation in R/residif.R +\name{residif} +\alias{residif} +\title{RESIDIF procedure for DIF detection as per Andrich and Hagquist (2015)} +\usage{ +residif(df = NULL, items = NULL, grp = NULL, verbose = T) +} +\arguments{ +\item{df}{data.frame containing the data} + +\item{items}{vector containing the names of columns where item responses are stored in df} + +\item{grp}{vector containing the name of the column where an optional group membership variable is stored in df} +} +\value{ +A data.frame containing a column listing the detected DIF item and another listing detected DIF forms +} +\description{ +This function detects DIF on PCM items using ANOVA of person-item residuals +} diff --git a/src/bpcm b/src/bpcm new file mode 100644 index 0000000..8706406 --- /dev/null +++ b/src/bpcm @@ -0,0 +1,23 @@ +model { + for (i in 1:n) { + for (j in 1:p) { + Y[i,j] ~ dcat(prob[i,j,1:K[j]]) + } + theta[i] ~ dnorm(0,1) + } + for (i in 1:n) { + for (j in 1:p) { + for (k in 1:K[j] ) { + eta[i,j,k] <- theta[i] - delta[j,k] + psum[i,j,k] <- sum(eta[i,j,1:k]) + exp.psum[i,j,k] <- exp(psum[i,j,k]) + prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]]) + } } } + for (j in 1:p) { + delta[j,1] <- 0.0 + for (k in 2:K[j]) { + delta[j,k] ~ dnorm(m.delta, pr.delta) + } + } + pr.delta <- pow(s.delta, -2) +} diff --git a/src/bpcm_beta b/src/bpcm_beta new file mode 100644 index 0000000..ffc441e --- /dev/null +++ b/src/bpcm_beta @@ -0,0 +1,25 @@ +model { + for (i in 1:n) { + for (j in 1:p) { + Y[i,j] ~ dcat(prob[i,j,1:K[j]]) + } + theta[i] ~ dnorm(0,1) + } + for (i in 1:n) { + for (j in 1:p) { + for (k in 1:K[j] ) { + eta[i,j,k] <- theta[i] + (beta * Z[i,1]) - delta[j,k] + psum[i,j,k] <- sum(eta[i,j,1:k]) + exp.psum[i,j,k] <- exp(psum[i,j,k]) + prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]]) + } } } + for (j in 1:p) { + delta[j,1] <- 0.0 + for (k in 2:K[j]) { + delta[j,k] ~ dnorm(m.delta, pr.delta) + } + } + beta ~ dnorm(m.beta,pr.beta) + pr.delta <- pow(s.delta, -2) + pr.beta <- pow(s.beta,-2) +} diff --git a/src/bpcm_dif b/src/bpcm_dif new file mode 100644 index 0000000..14a13c2 --- /dev/null +++ b/src/bpcm_dif @@ -0,0 +1,46 @@ +model { + for (i in 1:n) { + for (j in 1:p) { + Y[i,j] ~ dcat(prob[i,j,1:K[j]]) + } + theta[i] ~ dnorm(0, 1) + } + for (i in 1:n) { + for (j in 1:p) { + for (k in 1:K[j] ) { + eta[i,j,k] <- theta[i] + (beta * Z[i,1]) - ( delta[j,k] + Z[i,1]*ifelse(k==1,0,gamma[j,k]) ) + psum[i,j,k] <- sum(eta[i,j,1:k]) + exp.psum[i,j,k] <- exp(psum[i,j,k]) + prob[i,j,k] <- exp.psum[i,j,k] / sum(exp.psum[i,j,1:K[j]]) + } } } + for (j in 1:pnodif) { + delta[j,1] <- 0.0 + gamma[j,1] <- 0.0 + for (k in 2:K[j]) { + delta[j,k] ~ dnorm(m.delta, pr.delta) + gamma[j,k] <- 0.0 + } + } + for (j in pnodif1:pnounif) { + delta[j,1] <- 0.0 + gamma[j,1] <- 0.0 + for (k in 2:K[j]) { + delta[j,k] ~ dnorm(m.delta, pr.delta) + gamma[j,k] ~ dnorm(m.gamma,s.gamma) + } + } + for (j in pnounif1:p) { + delta[j,1] <- 0.0 + gamma[j,1] <- 0.0 + gamma[j,2] ~ dnorm(m.gamma,pr.gamma) + delta[j,2] ~ dnorm(m.delta, pr.delta) + for (k in 3:K[j]) { + delta[j,k] ~ dnorm(m.delta, pr.delta) + gamma[j,k] <- gamma[j,2] + } + } + beta ~ dnorm(m.beta,pr.beta) + pr.delta <- pow(s.delta, -2) + pr.beta <- pow(s.beta,-2) + pr.gamma <- pow(s.gamma,-2) +}