IRT.repDesign: Generation of a Replicate Design for 'IRT.jackknife'

In CDM: Cognitive Diagnosis Modeling

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)

CDM documentation built on Aug. 25, 2022, 5:08 p.m.