Nothing
difRaju<-function (Data, group, focal.name, model, c = NULL, engine = "ltm",
discr = 1, irtParam = NULL, same.scale = TRUE, anchor = NULL,
alpha = 0.05, signed = FALSE, purify = FALSE, nrIter = 10,
p.adjust.method = NULL, save.output = FALSE, output = c("out", "default"))
{
internalRaju <- function() {
if (!is.null(irtParam)) {
nrItems <- nrow(irtParam)/2
m0 <- irtParam[1:nrItems, ]
m1 <- irtParam[(nrItems + 1):(2 * nrItems), ]
dataName = rownames(m0)
if (!is.null(anchor) & !same.scale) {
dif.anchor <- anchor
if (is.numeric(anchor))
ANCHOR <- anchor
else {
ANCHOR <- NULL
for (i in 1:length(anchor)) ANCHOR[i] <- (1:length(dataName))[dataName ==
anchor[i]]
}
}
else {
ANCHOR <- 1:nrItems
dif.anchor <- NULL
}
if (same.scale)
m1p <- m1
else m1p <- itemRescale(m0, m1, items = ANCHOR)
mod <- as.character(ncol(irtParam))
model = switch(mod, `2` = "1PL", `5` = "2PL", `6` = "3PL")
if (ncol(irtParam) != 6)
Guess <- NULL
else {
Guess <- irtParam[1:nrItems, 6]
if (length(unique(round(Guess, 4))) == 1)
Guess <- unique(round(Guess, 4))
}
itemParInit <- irtParam
estPar <- FALSE
}
else {
if (length(group) == 1) {
if (is.numeric(group) == TRUE) {
gr <- Data[, group]
DATA <- Data[, (1:ncol(Data)) != group]
colnames(DATA) <- colnames(Data)[(1:ncol(Data)) !=
group]
}
else {
gr <- Data[, colnames(Data) == group]
DATA <- Data[, colnames(Data) != group]
colnames(DATA) <- colnames(Data)[colnames(Data) !=
group]
}
}
else {
gr <- group
DATA <- Data
}
Group <- rep(0, nrow(DATA))
Group[gr == focal.name] <- 1
nr1 <- sum(Group)
nr0 <- length(Group) - nr1
d0 <- matrix(NA, nr0, ncol(DATA))
d1 <- matrix(NA, nr1, ncol(DATA))
c0 <- c1 <- 0
for (i in 1:length(Group)) {
if (Group[i] == 0) {
c0 <- c0 + 1
d0[c0, ] <- as.numeric(DATA[i, ])
}
else {
c1 <- c1 + 1
d1[c1, ] <- as.numeric(DATA[i, ])
}
}
Guess <- c
if (is.null(Guess)) {
m0 <- switch(model, `1PL` = itemParEst(d0, model = "1PL",
engine = engine, discr = discr), `2PL` = itemParEst(d0,
model = "2PL"))
m1 <- switch(model, `1PL` = itemParEst(d1, model = "1PL",
engine = engine, discr = discr), `2PL` = itemParEst(d1,
model = "2PL"))
}
else {
m0 <- itemParEst(d0, model = "3PL", c = Guess)
m1 <- itemParEst(d1, model = "3PL", c = Guess)
}
nrItems <- ncol(DATA)
if (!is.null(anchor)) {
dif.anchor <- anchor
if (is.numeric(anchor))
ANCHOR <- anchor
else {
ANCHOR <- NULL
for (i in 1:length(anchor)) ANCHOR[i] <- (1:ncol(DATA))[colnames(DATA) ==
anchor[i]]
}
}
else {
ANCHOR <- 1:nrItems
dif.anchor <- NULL
}
m1p <- itemRescale(m0, m1, items = ANCHOR)
dataName <- colnames(DATA)
itemParInit <- rbind(m0, m1)
estPar <- TRUE
}
if (!purify | !is.null(anchor)) {
STATS <- RajuZ(m0, m1p, signed = signed)$res[, 3]
PVAL<-2*(1-pnorm(STATS))
if (max(abs(STATS)) <= qnorm(1 - alpha/2))
DIFitems <- "No DIF item detected"
else DIFitems <- (1:nrItems)[abs(STATS) > qnorm(1 -
alpha/2)]
RES <- list(RajuZ = STATS, p.value=PVAL, alpha = alpha, thr = qnorm(1 -
alpha/2), DIFitems = DIFitems, signed = signed,
p.adjust.method = p.adjust.method,
adjusted.p = NULL, purification = purify, model = model, c = Guess,
engine = engine, discr = discr, itemParInit = itemParInit,
estPar = estPar, names = dataName, anchor.names = dif.anchor,
save.output = save.output, output = output)
if (!is.null(anchor) & (RES$estPar | (!RES$estPar &
!same.scale))) {
RES$RajuZ[ANCHOR] <- NA
for (i in 1:length(RES$DIFitems)) {
if (sum(RES$DIFitems[i] == ANCHOR) == 1)
RES$DIFitems[i] <- NA
}
RES$DIFitems <- RES$DIFitems[!is.na(RES$DIFitems)]
}
}
else {
nrPur <- 0
difPur <- NULL
noLoop <- FALSE
stats1 <- RajuZ(m0, m1p, signed = signed)$res[, 3]
if (max(abs(stats1)) <= qnorm(1 - alpha/2)) {
PVAL<-2*(1-pnorm(stats1))
DIFitems <- "No DIF item detected"
noLoop <- TRUE
itemParFinal = rbind(m0, m1p)
RES <- list(RajuZ = stats1, p.value=PVAL,alpha = alpha, thr = qnorm(1 -
alpha/2), DIFitems = DIFitems, signed = signed,
p.adjust.method = p.adjust.method,
adjusted.p = NULL, purification = purify, nrPur = nrPur, difPur = difPur,
convergence = noLoop, model = model, c = Guess,
engine = engine, discr = discr, itemParInit = itemParInit,
itemParFinal = itemParFinal, estPar = estPar,
names = dataName, anchor.names = NULL, save.output = save.output,
output = output)
}
else {
dif <- (1:nrItems)[abs(stats1) > qnorm(1 - alpha/2)]
difPur <- rep(0, length(stats1))
difPur[dif] <- 1
repeat {
if (nrPur >= nrIter) {
itemParFinal <- rbind(m0, itemRescale(m0,
m1, items = nodif))
break
}
else {
nrPur <- nrPur + 1
nodif <- NULL
if (is.null(dif))
nodif <- 1:nrItems
else {
for (i in 1:nrItems) {
if (sum(i == dif) == 0)
nodif <- c(nodif, i)
}
}
stats2 <- RajuZ(m0, itemRescale(m0, m1, items = nodif),
signed = signed)$res[, 3]
if (max(abs(stats2)) <= qnorm(1 - alpha/2))
dif2 <- NULL
else dif2 <- (1:nrItems)[abs(stats2) > qnorm(1 -
alpha/2)]
difPur <- rbind(difPur, rep(0, nrItems))
difPur[nrPur + 1, dif2] <- 1
if (length(dif) != length(dif2))
dif <- dif2
else {
dif <- sort(dif)
dif2 <- sort(dif2)
if (sum(dif == dif2) == length(dif)) {
noLoop <- TRUE
itemParFinal <- rbind(m0, itemRescale(m0,
m1, items = nodif))
break
}
else dif <- dif2
}
}
}
if (is.null(difPur) == FALSE) {
ro <- co <- NULL
for (ir in 1:nrow(difPur)) ro[ir] <- paste("Step",
ir - 1, sep = "")
for (ic in 1:ncol(difPur)) co[ic] <- paste("Item",
ic, sep = "")
rownames(difPur) <- ro
colnames(difPur) <- co
}
PVAL<-2*(1-pnorm(stats2))
RES <- list(RajuZ = stats2, p.value=PVAL,alpha = alpha, thr = qnorm(1 -
alpha/2), DIFitems = dif2, signed = signed,
p.adjust.method = p.adjust.method,
adjusted.p = NULL, purification = purify, nrPur = nrPur, difPur = difPur,
convergence = noLoop, model = model, c = Guess,
engine = engine, discr = discr, itemParInit = itemParInit,
itemParFinal = itemParFinal, estPar = estPar,
names = dataName, anchor.names = NULL, save.output = save.output,
output = output)
}
}
if (!is.null(p.adjust.method)) {
pval <- 2 * (1 - pnorm(abs(RES$RajuZ)))
RES$adjusted.p <- p.adjust(pval, method = p.adjust.method)
if (min(RES$adjusted.p, na.rm = TRUE) > alpha)
RES$DIFitems <- "No DIF item detected"
else RES$DIFitems <- which(RES$adjusted.p < alpha)
}
class(RES) <- "Raj"
return(RES)
}
resToReturn <- internalRaju()
if (save.output) {
if (output[2] == "default")
wd <- paste(getwd(), "/", sep = "")
else wd <- output[2]
fileName <- paste(wd, output[1], ".txt", sep = "")
capture.output(resToReturn, file = fileName)
}
return(resToReturn)
}
# METHODS
plot.Raj<-function(x,pch=8,number=TRUE,col="red", save.plot=FALSE,save.options=c("plot","default","pdf"),...)
{
internalRaju<-function(){
res <- x
title<-paste("Raju's method (",res$model,")",sep="")
if (!number) {
plot(res$RajuZ,xlab="Item",ylab="Raju's statistic",ylim=c(min(c(res$RajuZ,-res$thr)-1,na.rm=TRUE),max(c(res$RajuZ,res$thr)+1,na.rm=TRUE)),pch=pch,main=title)
if (!is.character(res$DIFitems)) points(res$DIFitems,res$RajuZ[res$DIFitems],pch=pch,col=col)
}
else {
plot(res$RajuZ,xlab="Item",ylab="Raju's statistic",ylim=c(min(c(res$RajuZ,-res$thr)-1,na.rm=TRUE),max(c(res$RajuZ,res$thr)+1,na.rm=TRUE)),col="white",main=title)
text(1:length(res$RajuZ),res$RajuZ,1:length(res$RajuZ))
if (!is.character(res$DIFitems)) text(res$DIFitems,res$RajuZ[res$DIFitems],res$DIFitems,col=col)
}
abline(h=res$thr)
abline(h=-res$thr)
abline(h=0,lty=2)
}
internalRaju()
if (save.plot){
plotype<-NULL
if (save.options[3]=="pdf") plotype<-1
if (save.options[3]=="jpeg") plotype<-2
if (is.null(plotype)) cat("Invalid plot type (should be either 'pdf' or 'jpeg').","\n","The plot was not captured!","\n")
else {
if (save.options[2]=="default") wd<-paste(getwd(),"/",sep="")
else wd<-save.options[2]
fileName<-paste(wd,save.options[1],switch(plotype,'1'=".pdf",'2'=".jpg"),sep="")
if (plotype==1){
{
pdf(file=fileName)
internalRaju()
}
dev.off()
}
if (plotype==2){
{
jpeg(filename=fileName)
internalRaju()
}
dev.off()
}
cat("The plot was captured and saved into","\n"," '",fileName,"'","\n","\n",sep="")
}
}
else cat("The plot was not captured!","\n",sep="")
}
print.Raj<-function (x, ...)
{
res <- x
if (is.character(res))
cat("Error: unappropriate model specification!", "\n")
else {
cat("\n")
cat("Detection of Differential Item Functioning using Raju's method",
"\n")
if (res$purification & is.null(res$anchor.names))
pur <- "with "
else pur <- "without "
cat("with ", res$model, " model and ", pur, "item purification",
"\n", "\n", sep = "")
if (res$signed)
cat("Type of Raju's Z statistic: based on signed area",
"\n", "\n")
else cat("Type of Raju's Z statistic: based on unsigned area",
"\n", "\n")
if (res$estPar) {
if (res$model != "1PL" | res$engine == "ltm")
cat("Engine 'ltm' for item parameter estimation",
"\n", "\n")
else cat("Engine 'lme4' for item parameter estimation",
"\n", "\n")
}
if (res$model == "1PL" & res$engine == "ltm") {
if (is.null(res$discr))
cat("Common discrimination parameter: estimated from 'ltm'",
"\n", "\n")
else cat("Common discrimination parameter: fixed to ",
res$discr, "\n", "\n", sep = "")
}
if (!is.null(res$c)) {
if (length(res$c) == 1)
cat("Common pseudo-guessing value:", res$c, "\n",
"\n")
else {
mat <- cbind(res$c)
if (!is.null(res$names))
rownames(mat) <- res$names
else {
rn <- NULL
for (i in 1:nrow(mat)) rn[i] <- paste("Item",
i, sep = "")
rownames(mat) <- rn
}
colnames(mat) <- "c"
cat("Common pseudo-guessing values:", "\n", "\n")
print(mat)
cat("\n")
}
}
if (res$purification & is.null(res$anchor.names)) {
if (res$nrPur <= 1)
word <- " iteration"
else word <- " iterations"
if (!res$convergence) {
cat("WARNING: no item purification convergence after ",
res$nrPur, word, "\n", sep = "")
loop <- NULL
for (i in 1:res$nrPur) loop[i] <- sum(res$difPur[1,
] == res$difPur[i + 1, ])
if (max(loop) != length(res$RajuZ))
cat("(Note: no loop detected in less than ",
res$nrPur, word, ")", "\n", sep = "")
else cat("(Note: loop of length ", min((1:res$nrPur)[loop ==
length(res$RajuZ)]), " in the item purification process)",
"\n", sep = "")
cat("WARNING: following results based on the last iteration of the purification",
"\n", "\n")
}
else cat("Convergence reached after ", res$nrPur,
word, "\n", "\n", sep = "")
}
if (is.null(res$anchor.names)) {
itk <- 1:length(res$RajuZ)
cat("No set of anchor items was provided", "\n",
"\n")
}
else {
itk <- (1:length(res$RajuZ))[!is.na(res$RajuZ)]
cat("Anchor items (provided by the user):", "\n")
if (is.numeric(res$anchor.names))
mm <- res$names[res$anchor.names]
else mm <- res$anchor.names
mm <- cbind(mm)
rownames(mm) <- rep("", nrow(mm))
colnames(mm) <- ""
print(mm, quote = FALSE)
cat("\n", "\n")
}
if (is.null(res$p.adjust.method))
cat("No p-value adjustment for multiple comparisons",
"\n", "\n")
else {
pAdjMeth <- switch(res$p.adjust.method, bonferroni = "Bonferroni",
holm = "Holm", hochberg = "Hochberg", hommel = "Hommel",
BH = "Benjamini-Hochberg", BY = "Benjamini-Yekutieli")
cat("Multiple comparisons made with", pAdjMeth, "adjustement of p-values",
"\n", "\n")
}
cat("Raju's statistic:", "\n", "\n")
pval <- round(2 * (1 - pnorm(abs(res$RajuZ))), 4)
if (is.null(res$p.adjust.method)) symb <- symnum(pval, c(0, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("***", "**", "*", ".", ""))
else symb <- symnum(res$adjusted.p, c(0, 0.001, 0.01, 0.05, 0.1, 1),
symbols = c("***", "**", "*", ".", ""))
m1 <- cbind(round(res$RajuZ[itk], 4), pval[itk])
if (!is.null(res$p.adjust.method)) m1<-cbind(m1,round(res$adjusted.p[itk],4))
m1 <- noquote(cbind(format(m1, justify = "right"), symb[itk]))
if (!is.null(res$names))
rownames(m1) <- res$names[itk]
else {
rn <- NULL
for (i in 1:nrow(m1)) rn[i] <- paste("Item", i, sep = "")
rownames(m1) <- rn[itk]
}
con <- c("Stat.", "P-value")
if (!is.null(res$p.adjust.method))
con <- c(con, "Adj. P", "")
else con <- c(con, "")
colnames(m1) <- con
print(m1)
cat("\n")
cat("Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1 ",
"\n")
cat("\n", "Detection thresholds: ", -round(res$thr, 4),
" and ", round(res$thr, 4), " (significance level: ",
res$alpha, ")", "\n", "\n", sep = "")
if (is.character(res$DIFitems))
cat("Items detected as DIF items:", res$DIFitems,
"\n", "\n")
else {
cat("Items detected as DIF items:", "\n")
if (!is.null(res$names))
m2 <- res$names
else {
rn <- NULL
for (i in 1:length(res$RajuZ)) rn[i] <- paste("Item",
i, sep = "")
m2 <- rn
}
m2 <- cbind(m2[res$DIFitems])
rownames(m2) <- rep("", nrow(m2))
colnames(m2) <- ""
print(m2, quote = FALSE)
cat("\n")
}
}
if (res$model == "1PL") {
cat("Effect size (ETS Delta scale):", "\n", "\n")
cat("Effect size code:", "\n")
cat(" 'A': negligible effect", "\n")
cat(" 'B': moderate effect", "\n")
cat(" 'C': large effect", "\n", "\n")
if (res$purification & is.null(res$anchor.names))
pars <- res$itemParFinal
else pars <- res$itemParInit
J <- nrow(pars)/2
mR <- pars[1:J, 1]
mF <- itemRescale(pars[1:J, ], pars[(J + 1):(2 * J),
])[, 1]
rr1 <- round(mF - mR, 4)
rr2 <- round(-2.35 * rr1, 4)
symb1 <- symnum(abs(rr2), c(0, 1, 1.5, Inf), symbols = c("A",
"B", "C"))
matR2 <- cbind(rr1, rr2)[itk, ]
matR2 <- noquote(cbind(format(matR2, justify = "right"),
symb1[itk]))
if (!is.null(res$names))
rownames(matR2) <- res$names[itk]
else {
rn <- NULL
for (i in 1:nrow(matR2)) rn[i] <- paste("Item", i,
sep = "")
rownames(matR2) <- rn[itk]
}
colnames(matR2) <- c("mF-mR", "deltaRaju", "")
print(matR2)
cat("\n")
cat("Effect size codes: 0 'A' 1.0 'B' 1.5 'C'", "\n")
cat(" (for absolute values of 'deltaRaju')", "\n", "\n")
}
if (!x$save.output)
cat("Output was not captured!", "\n")
else {
if (x$output[2] == "default")
wd <- paste(getwd(), "/", sep = "")
else wd <- x$output[2]
fileName <- paste(wd, x$output[1], ".txt", sep = "")
cat("Output was captured and saved into file", "\n",
" '", fileName, "'", "\n", "\n", sep = "")
}
}
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