IRT.jackknife
IRT.repDesign.Rd
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.
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)
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.
|
jkzone | Variable name for jackknife zones.
If |
jkrep | Variable name containing Jackknife replicates |
jkfac | Factor for multiplying jackknife replicate weights.
If |
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 |
A list with following entries
Vector with weights
Matrix containing the replicate design
Fay factor needed for Jackknife calculations
See IRT.jackknife
for further examples.
See the BIFIE.data.jack
function in the BIFIEsurvey package.
if (FALSE) { # 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 ) }