findmodel {PP} | R Documentation |
This is a small helper function which creates a vector template quick and easily for the PPall()
function. Modify this template as you like.
findmodel(thres)
thres |
A numeric matrix which contains the threshold parameter for each item. NA is allowed - in fact expected! |
This function tries to guess the model which was applied to each item by using the matrix of threshold parameters. It only discriminates between GPCM and 4-PL model, and returns a character vector of length equal to the number of items, that contains "GPCM"
or "4PL"
entries depending on the structure of the thres matrix.
Manuel Reif
################# GPCM and 4PL mixed ######################################### # some threshold parameters THRES <- matrix(c(-2,-1.23,1.11,3.48,1 ,2,-1,-0.2,0.5,1.3,-0.8,1.5),nrow=2) # slopes sl <- c(0.5,1,1.5,1.1,1,0.98) THRESx <- THRES THRESx[2,1:3] <- NA # for the 4PL item the estimated parameters are submitted, # for the GPCM items the lower asymptote = 0 # and the upper asymptote = 1. la <- c(0.02,0.1,0,0,0,0) ua <- c(0.97,0.91,1,1,1,1) awmatrix <- matrix(c(1,0,1,0,1,1,1,0,0,1 ,2,0,0,0,0,0,0,0,0,1 ,1,2,2,1,1,1,1,0,0,1),byrow=TRUE,nrow=5) # create model2est # this function tries to help finding the appropriate # model by inspecting the THRESx. model2est <- findmodel(THRESx) # MLE respmixed_mle <- PPall(respm = awmatrix,thres = THRESx, slopes = sl,lowerA = la, upperA=ua,type = "mle", model2est=model2est) # WLE respmixed_wle <- PPall(respm = awmatrix,thres = THRESx, slopes = sl,lowerA = la, upperA=ua,type = "wle", model2est=model2est) # MAP estimation respmixed_map <- PPall(respm = awmatrix,thres = THRESx, slopes = sl,lowerA = la, upperA=ua, type = "map", model2est=model2est) # EAP estimation respmixed_eap <- PPall(respm = awmatrix,thres = THRESx, slopes = sl,lowerA = la, upperA=ua, type = "eap", model2est=model2est) # Robust estimation respmixed_rob <- PPall(respm = awmatrix,thres = THRESx, slopes = sl,lowerA = la, upperA=ua, type = "robust", model2est=model2est) # summary to summarize the results summary(respmixed_mle) summary(respmixed_wle) summary(respmixed_map) summary(respmixed_eap) summary(respmixed_rob)