msq.itemfit( object, fitindices=NULL)

## S3 method for class 'msq.itemfit'
summary(object, file=NULL, ... )

msq.itemfitWLE( tamobj, fitindices=NULL, ... )

## S3 method for class 'msq.itemfitWLE'
summary(object, file=NULL, ... )

#############################################################################
# EXAMPLE 1: Simulated data Rasch model
#############################################################################

#*** simulate data
library(sirt)
set.seed(9875)
N <- 2000
I <- 20
b <- sample( seq( -2, 2, length=I ) )
a <- rep( 1, I )
# create some misfitting items
a[c(1,3)] <- c(.5, 1.5 )
# simulate data
dat <- sirt::sim.raschtype( rnorm(N), b=b, fixed.a=a )
#*** estimate Rasch model
mod1 <- TAM::tam.mml(resp=dat)
# compute WLEs
wmod1 <- TAM::tam.wle(mod1)$theta

#--- item fit from "msq.itemfit" function
fit1 <- TAM::msq.itemfit(mod1)
summary( fit1 )

## Not run: 
#--- item fit using simulation in "tam.fit"
fit0 <- TAM::tam.fit( mod1 )
summary(fit0)

#--- item fit based on WLEs
fit2a <- TAM::msq.itemfitWLE( mod1 )
summary(fit2a)

#++ fit assessment in mirt package
library(mirt)
mod1b <- mirt::mirt( dat, model=1, itemtype="Rasch", verbose=TRUE )
print(mod1b)
sirt::mirt.wrapper.coef(mod1b)
fmod1b <- mirt::itemfit(mod1b, Theta=as.matrix(wmod1,ncol=1),
                 Zh=TRUE, X2=FALSE, S_X2=FALSE )
cbind( fit2a$fit_data, fmod1b )

#++ fit assessment in eRm package
library(eRm)
mod1c <- eRm::RM( dat )
summary(mod1c)
eRm::plotPImap(mod1c)    # person-item map
pmod1c <- eRm::person.parameter(mod1c)
fmod1c <- eRm::itemfit(pmod1c)
print(fmod1c)
plot(fmod1c)

#--- define some item groups for fit assessment

# bases on evaluating the posterior
fitindices <- rep( paste0("IG",c(1,2)), each=10)
fit2 <- TAM::msq.itemfit( mod1, fitindices )
summary(fit2)

# using WLEs
fit2b <- TAM::msq.itemfitWLE( mod1, fitindices )
summary(fit2b)

## End(Not run)

#############################################################################
# EXAMPLE 2: data.read | fit statistics assessed for testlets
#############################################################################

library(sirt)
data(data.read,package="sirt")
dat <- data.read

# fit Rasch model
mod <- TAM::tam.mml( dat )

#***** item fit for each item
# based on posterior
res1 <- TAM::msq.itemfit( mod  )
summary(res1)
# based on WLEs
res2 <- TAM::msq.itemfitWLE( mod  )
summary(res2)

#***** item fit for item groups
# define item groups
fitindices <- substring( colnames(dat), 1, 1 )
# based on posterior
res1 <- TAM::msq.itemfit( mod, fitindices )
summary(res1)
# based on WLEs
res2 <- TAM::msq.itemfitWLE( mod, fitindices )
summary(res2)

## Not run: 
#############################################################################
# EXAMPLE 3: Fit statistics for rater models
#############################################################################

library(sirt)
data(data.ratings2, package="sirt")
dat <- data.ratings2

# fit rater model "~ item*step + rater"
mod <- TAM::tam.mml.mfr( resp=dat[, paste0( "k",1:5) ],
            facets=dat[, "rater", drop=FALSE],
            pid=dat$pid, formulaA=~ item*step + rater )

# fit for parameter with "tam.fit" function
fmod1a <- TAM::tam.fit( mod )
fmod1b <- TAM::msq.itemfit( mod )
summary(fmod1a)
summary(fmod1b)

# define item groups using pseudo items from object "mod"
pseudo_items <- colnames(mod$resp)
pss <- strsplit( pseudo_items, split="-" )
item_parm <- unlist( lapply( pss, FUN=function(ll){ ll[1] } ) )
rater_parm <- unlist( lapply( pss, FUN=function(ll){ ll[2] } ) )

# fit for items with "msq.itemfit" functions
res2a <- TAM::msq.itemfit( mod, item_parm )
res2b <- TAM::msq.itemfitWLE( mod, item_parm )
summary(res2a)
summary(res2b)

# fit for raters
res3a <- TAM::msq.itemfit( mod, rater_parm )
res3b <- TAM::msq.itemfitWLE( mod, rater_parm )
summary(res3a)
summary(res3b)

## End(Not run)