Correted pcm weights
This commit is contained in:
168
R/pcm.R
168
R/pcm.R
@ -1,5 +1,5 @@
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## File Name: pcm.R
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## File version: 1.0
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## File version: 1.1
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#' Compute Partial Credit Model (PCM) for polytomous and dichotomous items
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#'
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@ -10,7 +10,7 @@
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#' @param grp string containing the name of the column where an optional group membership variable is stored in df
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#' @param dif.items vector containing the list of indexes in "items" corresponding to dif items
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#' @param type.dif vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF
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#' @param weights string containing the name of the column where optional weights are stored in df
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#' @param weights string containing the name of the column where an optional variable containing weights is stored in df
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#' @param verbose set to TRUE to print a detailed output, FALSE otherwise
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#' @param fit string determining the optimization algorithm. Values "ucminf" or "nlminb" ar recommended
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#' @param method.theta string determining the estimation method for individual latent variable values. Either "eap", "mle" or "wle"
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@ -19,6 +19,7 @@
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#' @import PP
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#' @export
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pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights=NULL,verbose=T,fit="ucminf",method.theta="eap") {
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##### Detecting errors
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@ -64,39 +65,42 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights
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cat("######################################### FITTING MODEL #########################################\n")
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cat("#################################################################################################\n")
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}
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grp <- NULL
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# prepare data
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if (is.null(weights)) {
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df <- df[,c('id',items)]
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} else {
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df <- df[,c('id',items,weights)]
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}
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print(df)
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grp <- NULL
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# prepare data
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if (is.null(weights)) {
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df <- df[,c('id',items)]
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colnames(df)[2:(length(colnames(df)))] <- paste0("item",seq(1,length(colnames(df))-1))
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df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
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if (is.null(weights)) {
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colnames(df.long) <- c("id","item","resp")
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} else {
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colnames(df.long) <- c("id","item","resp","weights")
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}
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} else {
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df <- df[,c('id',items,weights)]
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colnames(df)[2:(length(colnames(df)-1))] <- paste0("item",seq(1,length(colnames(df))-1))
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}
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df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
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if (is.null(weights)) {
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colnames(df.long) <- c("id","item","resp")
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nbitems <- length(2:(length(colnames(df))))
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maxmod <- max(df[,2:(length(colnames(df)))])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-1),ordered = F)
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df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
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df.long$id <- factor(df.long$id)
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# fit pcm
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if (is.null(weights)) {
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mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"))
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} else {
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mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),weights = df.long$weights)
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}
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comod <- coef(mod)
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# output results
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restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-1,nbitems)]))
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rownames(restab) <- paste0("item",1:nbitems)
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colnames(restab) <- paste0("delta_",1:maxmod)
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restab.dif <- NULL
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beta <- NULL
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} else {
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colnames(df.long) <- c("id","weights","item","resp")
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nbitems <- length(2:(length(colnames(df))-1))
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maxmod <- max(df[,2:(length(colnames(df))-1)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
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}
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df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
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df.long$id <- factor(df.long$id)
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# fit pcm
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if (is.null(weights)) {
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mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"))
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} else {
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mod <- olmm(resp ~ 0 + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),weights = df.long$weights)
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}
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comod <- coef(mod)
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# output results
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restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-1,nbitems)]))
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rownames(restab) <- paste0("item",1:nbitems)
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colnames(restab) <- paste0("delta_",1:maxmod)
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restab.dif <- NULL
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beta <- NULL
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}
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# If group
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else {
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@ -114,19 +118,23 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights
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# prepare data
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if (is.null(weights)) {
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df <- df[,c('id',items,"grp")]
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colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
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} else {
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df <- df[,c('id',items,"grp",weights)]
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colnames(df)[2:(length(colnames(df))-2)] <- paste0("item",seq(1,length(colnames(df))-3))
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}
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colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
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df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
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if (is.null(weights)) {
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colnames(df.long) <- c("id","grp","item","resp")
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nbitems <- length(2:(length(colnames(df))-1))
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maxmod <- max(df[,2:(length(colnames(df))-1)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
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} else {
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colnames(df.long) <- c("id","grp","item","resp","weights")
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colnames(df.long) <- c("id","grp","weights","item","resp")
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nbitems <- length(2:(length(colnames(df))-2))
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maxmod <- max(df[,2:(length(colnames(df))-2)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-3),ordered = F)
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}
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nbitems <- length(2:(length(colnames(df))-1))
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maxmod <- max(df[,2:(length(colnames(df))-1)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
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df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
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df.long$id <- factor(df.long$id)
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@ -148,7 +156,11 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights
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comod <- coef(mod)
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# output results
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nbcoef <- nbitems+length(difvar.nonunif)
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restab <- t(sapply(1:nbcoef,function(x) comod[seq(x,length(comod)-2-length(difvar.unif),nbitems+length(difvar.nonunif))]))
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if (is.null(weights)) {
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restab <- t(sapply(1:nbcoef,function(x) comod[seq(x,length(comod)-2-length(difvar.unif),nbitems+length(difvar.nonunif))]))
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} else {
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restab <- t(sapply(1:nbcoef,function(x) comod[seq(x,length(comod)-2-length(difvar.unif),nbitems+length(difvar.nonunif))]))
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}
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difcoef.unif <- NULL
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if (length(difvar.unif)>0) {
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difcoef.unif <- comod[(length(comod)-length(difvar.unif)):(length(comod)-1)]
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@ -204,45 +216,54 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights
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cat("######################################### FITTING MODEL #########################################\n")
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cat("#################################################################################################\n")
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}
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# prepare data
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if (is.null(weights)) {
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df <- df[,c('id',items,"grp")]
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} else {
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df <- df[,c('id',items,"grp",weights)]
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}
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# prepare data
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if (is.null(weights)) {
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df <- df[,c('id',items,"grp")]
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colnames(df)[2:(length(colnames(df))-1)] <- paste0("item",seq(1,length(colnames(df))-2))
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df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
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if (is.null(weights)) {
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colnames(df.long) <- c("id","grp","item","resp")
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} else {
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colnames(df.long) <- c("id","grp","item","resp","weights")
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}
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} else {
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df <- df[,c('id',items,"grp",weights)]
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colnames(df)[2:(length(colnames(df))-2)] <- paste0("item",seq(1,length(colnames(df))-3))
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}
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df.long <- reshape(df,v.names=c("item"),direction="long",varying=c(items))
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if (is.null(weights)) {
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colnames(df.long) <- c("id","grp","item","resp")
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nbitems <- length(2:(length(colnames(df))-1))
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maxmod <- max(df[,2:(length(colnames(df))-1)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-2),ordered = F)
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df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
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df.long$id <- factor(df.long$id)
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# fit pcm
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if (is.null(weights)) {
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mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
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} else {
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mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit),weights=df.long$weights)
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}
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comod <- coef(mod)
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# output results
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} else {
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colnames(df.long) <- c("id","grp","weights","item","resp")
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nbitems <- length(2:(length(colnames(df))-2))
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maxmod <- max(df[,2:(length(colnames(df))-2)])
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df.long$item <- factor(df.long$item,levels=seq(1,length(colnames(df))-3),ordered = F)
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}
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df.long$resp <- factor(df.long$resp,0:maxmod,ordered=T)
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df.long$id <- factor(df.long$id)
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# fit pcm
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if (is.null(weights)) {
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mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit))
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} else {
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mod <- olmm(resp ~ 0 + ge(grp) + ce(item) + re(0|id),data=df.long,family = adjacent(link = "logit"),control=olmm_control(fit=fit),weights=df.long$weights)
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}
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comod <- coef(mod)
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# output results
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if (is.null(weights)) {
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restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-2,nbitems)]))
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rownames(restab) <- items_o
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colnames(restab) <- paste0("delta_",1:maxmod)
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restab.dif <- NULL
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beta <- comod[length(comod)-1]
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se.beta <- (confint(mod)["grp",2]-beta)/1.96
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beta.ci <- confint(mod)["grp",]
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beta.p <- 2*pnorm(-abs(beta/se.beta))
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beta <- as.numeric(beta)
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se.beta <- as.numeric(se.beta)
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beta.p <- as.numeric(beta.p)
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beta <- -1*beta
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beta.ci <- -1*c(beta.ci[2],beta.ci[1])
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} else {
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restab <- t(sapply(1:nbitems,function(x) comod[seq(x,length(comod)-2,nbitems)]))
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}
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rownames(restab) <- items_o
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colnames(restab) <- paste0("delta_",1:maxmod)
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restab.dif <- NULL
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beta <- comod[length(comod)-1]
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se.beta <- (confint(mod)["grp",2]-beta)/1.96
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beta.ci <- confint(mod)["grp",]
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beta.p <- 2*pnorm(-abs(beta/se.beta))
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beta <- as.numeric(beta)
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se.beta <- as.numeric(se.beta)
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beta.p <- as.numeric(beta.p)
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beta <- -1*beta
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beta.ci <- -1*c(beta.ci[2],beta.ci[1])
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}
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}
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@ -275,9 +296,6 @@ pcm <- function(df=NULL,items=NULL,grp=NULL,dif.items=NULL,type.dif=NULL,weights
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dif.param=restab.dif,
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theta=theta,
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residuals=resid
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)
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)
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return(out)
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}
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@ -27,7 +27,7 @@ pcm(
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\item{type.dif}{vector containing DIF form for each item specified in dif.items. 1 is homogeneous DIF, 0 is heterogeneous DIF}
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\item{weights}{string containing the name of the column where optional weights are stored in df}
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\item{weights}{string containing the name of the column where an optional variable containing weights is stored in df}
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\item{verbose}{set to TRUE to print a detailed output, FALSE otherwise}
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