CDM: IRT.repDesign – R documentation

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IRT.repDesign

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

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

Cognitive Diagnosis Modeling

v7.5-15

GPL (>= 2)

Authors

Alexander Robitzsch [aut, cre], Thomas Kiefer [aut], Ann Cathrice George [aut], Ali Uenlue [aut]

Initial release

2020-03-10 14:19:21