IRT.repDesign {CDM}

Generation of a Replicate Design for IRT.jackknife

Description

This function generates a Jackknife replicate design which is necessary to use the IRT.jackknife function. The function is a wrapper to BIFIE.data.jack in the BIFIEsurvey package.

Usage

IRT.repDesign(data, wgt=NULL, jktype="JK_TIMSS", jkzone=NULL, jkrep=NULL,
   jkfac=NULL, fayfac=1, wgtrep="W_FSTR", ngr=100, Nboot=200, seed=.Random.seed)

Arguments

data

Dataset which must contain weights and item responses

wgt

Vector with sample weights

jktype

Type of jackknife procedure for creating the BIFIE.data object. jktype="JK_TIMSS" refers to TIMSS/PIRLS datasets. The type "JK_GROUP" creates jackknife weights based on a user defined grouping, the type "JK_RANDOM" creates random groups. The number of random groups can be defined in ngr. The argument type="RW_PISA" extracts the replicated design with balanced repeated replicate weights from PISA datasets into objects of class IRT.repDesign. Bootstrap samples can be obtained by type="BOOT".

jkzone

Variable name for jackknife zones. If jktype="JK_TIMSS", then jkzone="JKZONE". However, this default can be overwritten.

jkrep

Variable name containing Jackknife replicates

jkfac

Factor for multiplying jackknife replicate weights. If jktype="JK_TIMSS", then jkfac=2.

fayfac

Fay factor. For Jackknife, the default is 1. For a Bootstrap with R samples with replacement, the Fay factor is 1/R.

wgtrep

Already available replicate design

ngr

Number of groups

Nboot

Number of bootstrap samples

seed

Random seed

Value

A list with following entries

wgt

Vector with weights

wgtrep

Matrix containing the replicate design

fayfac

Fay factor needed for Jackknife calculations

See Also

See IRT.jackknife for further examples.

See the BIFIE.data.jack function in the BIFIEsurvey package.

Examples

## Not run: 
# load the BIFIEsurvey package
library(BIFIEsurvey)

#############################################################################
# EXAMPLE 1: Design with Jackknife replicate weights in TIMSS
#############################################################################

data(data.timss11.G4.AUT, package="CDM")
dat <- CDM::data.timss11.G4.AUT$data
# generate design
rdes <- CDM::IRT.repDesign( data=dat,  wgt="TOTWGT", jktype="JK_TIMSS",
             jkzone="JKCZONE", jkrep="JKCREP" )
str(rdes)

#############################################################################
# EXAMPLE 2: Bootstrap resampling
#############################################################################

data(sim.qmatrix, package="CDM")
q.matrix <- CDM::sim.qmatrix

# simulate data according to the DINA model
dat <- CDM::sim.din(N=2000, q.matrix=q.matrix )$dat

# bootstrap with 300 random samples
rdes <- CDM::IRT.repDesign( data=dat, jktype="BOOT", Nboot=300 )

## End(Not run)