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IRT.jackknife
IRT.jackknife
function. The function
is a wrapper to BIFIE.data.jack
in the 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)
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"
jktype="JK_TIMSS"
, then jkzone="JKZONE"
. However,
this default can be overwritten.jktype="JK_TIMSS"
, then jkfac=2
.IRT.jackknife
for further examples.
See the BIFIE.data.jack
function in the # load the BIFIEsurvey package
library(BIFIEsurvey)
#############################################################################
# EXAMPLE 1: Design with Jackknife replicate weights in TIMSS
#############################################################################
data(data.timss11.G4.AUT)
dat <- data.timss11.G4.AUT$data
# generate design
rdes <- IRT.repDesign( data= dat, wgt = "TOTWGT" , jktype="JK_TIMSS" ,
jkzone = "JKCZONE" , jkrep = "JKCREP" )
str(rdes)
#############################################################################
# SIMULATED EXAMPLE 2: Bootstrap resampling
#############################################################################
data(sim.qmatrix)
q.matrix <- sim.qmatrix
# simulate data according to the DINA model
dat <- sim.din(N=2000,q.matrix )$dat
# bootstrap with 300 random samples
rdes <- IRT.repDesign( data= dat , jktype="BOOT" , Nboot=300 )
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