Nothing
irf<-function(data, bParams, item, plotERF=TRUE,
thetaEAP = NULL,
minCut = -3, maxCut = 3, NCuts = 9){
## irf can be used to plot model-implied and empirical item response functions for item sets that fit
## different polynomial IRT models
if(is.data.frame(bParams)) bParams <- as.matrix(bParams)
if(ncol(bParams)!=9) stop("\n\nbParams should have 9 columns")
x <- seq(-4,4,by=.01)
Numx <- length(x)
xpoly <- matrix(cbind(1,x, x^2, x^3, x^4, x^5, x^6, x^7),Numx, 8)
# Prob of a keyed response 2PL
P <- function(m){
1/(1+exp(-m))
}
# plot k=1 ICC
plot(x,P(xpoly %*% bParams[item,1:8]),
typ="l",
lwd=2,
xlim=c(-4,4), ylim=c(0,1),
main=paste("Item ",item, " k = ", bParams[item,9], sep="" ),
xlab=expression(theta), cex.lab=1.3,
ylab="Probability")
if(!is.null(thetaEAP)){
if(plotERF){
erfOUT<- erf(thetaEAP, data, whichItem=item, min=minCut, max=maxCut,Ncuts=NCuts)
points(erfOUT$centers, erfOUT$probs, pch=16, col="red", cex=1)
}
}
}#END irf
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