IRT.informationCurves: Item and Test Information Curve

In TAM: Test Analysis Modules

Item and Test Information Curve

Description

An S3 method which computes item and test information curves, see Muraki (1993).

Usage

IRT.informationCurves(object, ...)

## S3 method for class 'tam.mml'
IRT.informationCurves( object, h=.0001, iIndex=NULL,
          theta=NULL, ... )

## S3 method for class 'tam.mml.2pl'
IRT.informationCurves( object, h=.0001, iIndex=NULL,
          theta=NULL, ... )

## S3 method for class 'tam.mml.mfr'
IRT.informationCurves( object, h=.0001, iIndex=NULL,
          theta=NULL, ... )

## S3 method for class 'tam.mml.3pl'
IRT.informationCurves( object, h=.0001, iIndex=NULL,
          theta=NULL, ... )

## S3 method for class 'IRT.informationCurves'
plot(x, curve_type="test", ...)

Arguments

object	Object of class tam.mml, tam.mml.2pl, tam.mml.mfr or tam.mml.3pl.
...	Further arguments to be passed
h	Numerical differentiation parameter
iIndex	Indices of items for which test information should be computed. The default is to use all items.
theta	Optional vector of \theta for which information curves should be computed.
curve_type	Type of information to be plotted. It can be "test" for the test information curve and "se" for the standard error curve.
x	Object of class tam.mml, tam.mml.2pl, tam.mml.mfr or tam.mml.3pl.

Value

List with following entries

se_curve	Standard error curves
test_info_curve	Test information curve
info_curves_item	Item information curves
info_curves_categories	Item-category information curves
theta	Used \theta grid

References

Muraki, E. (1993). Information functions of the generalized partial credit model. Applied Psychological Measurement, 17(4), 351-363. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/014662169301700403")}

Examples

## Not run: 
#############################################################################
# EXAMPLE 1: Dichotomous data | data.read
#############################################################################

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

# fit 2PL model
mod1 <- TAM::tam.mml.2pl( dat )
summary(mod1)

# compute information curves at grid seq(-5,5,length=100)
imod1 <- TAM::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::tam.mml( resp )
summary(mod1)
# information curves
imod1 <- TAM::IRT.informationCurves( mod1, theta=seq(-3,3,len=20)  )

#*** Model 2: Generalized partial credit model
mod2 <- TAM::tam.mml.2pl( resp, irtmodel="GPCM")
summary(mod2)
imod2 <- TAM::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::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 <- TAM::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)

TAM documentation built on May 29, 2024, 2:20 a.m.