## Not run:
# #############################################################################
# # EXAMPLE 1: Dichotomous data | data.read
# #############################################################################
#
# data(data.read , package="sirt")
# dat <- data.read
#
# # fit 2PL model
# mod1 <- tam.mml.2pl( dat )
# summary(mod1)
#
# # compute information curves at grid seq(-5,5,length=100)
# imod1 <- IRT.informationCurves( mod1 , theta= seq(-5,5,len=100) )
# str(imod1)
# # plot test information
# plot( imod1 )
# # plot standard error curve
# plot( imod1 , curve_type = "se" , xlim=c(-3,2) )
# # cutomized plot
# plot( imod1 , curve_type = "se" , xlim=c(-3,2) , ylim = c(0,2) , lwd=2 , lty=3)
#
# #############################################################################
# # EXAMPLE 2: Mixed dichotomous and polytomous data
# #############################################################################
#
# data(data.timssAusTwn.scored, package="TAM")
# dat <- data.timssAusTwn.scored
# # select item response data
# items <- grep( "M0" , colnames(dat) , value=TRUE )
# resp <- dat[, items ]
#
# #*** Model 1: Partial credit model
# mod1 <- tam.mml( resp )
# summary(mod1)
# # information curves
# imod1 <- IRT.informationCurves( mod1 , theta= seq(-3,3,len=20) )
#
# #*** Model 2: Generalized partial credit model
# mod2 <- tam.mml.2pl( resp , irtmodel="GPCM")
# summary(mod2)
# imod2 <- IRT.informationCurves( mod2 )
#
# #*** Model 3: Mixed 3PL and generalized partial credit model
# psych::describe(resp)
# maxK <- apply( resp , 2 , max , na.rm=TRUE )
# I <- ncol(resp)
# # specify guessing parameters, including a prior distribution
# est.guess <- 1:I
# est.guess[ maxK > 1 ] <- 0
# guess <- .2*(est.guess >0)
# guess.prior <- matrix( 0 , nrow=I , ncol=2 )
# guess.prior[ est.guess > 0 , 1] <- 5
# guess.prior[ est.guess > 0 , 2] <- 17
#
# # fit model
# mod3 <- tam.mml.3pl( resp , gammaslope.des = "2PL" , est.guess=est.guess , guess=guess ,
# guess.prior = guess.prior ,
# control=list( maxiter=100 , Msteps=10 , fac.oldxsi=0.1 ,
# nodes = seq(-8,8,len=41) ) , est.variance=FALSE )
# summary(mod3)
#
# # information curves
# imod3 <- IRT.informationCurves( mod3 )
# imod3
#
# #*** estimate model in mirt package
# library(mirt)
# itemtype <- rep("gpcm" , I)
# itemtype[ maxK==1] <- "3PL"
# mod3b <- mirt::mirt(resp , 1 , itemtype=itemtype , verbose=TRUE )
# print(mod3b)
# ## End(Not run)
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