if (FALSE) {
#############################################################################
# EXAMPLE 1: Simulated data from the Rasch model
#############################################################################
set.seed(7655)
N <- 5000 # number of persons
I <- 11 # number of items
b <- seq( -2, 2, length=I )
dat <- sirt::sim.raschtype( rnorm( N ), b )
colnames(dat) <- paste( "I", 1:I, sep="")
# estimate the Rasch model with JML
mod <- sirt::rasch.jml(dat)
summary(mod)
# re-estimate the Rasch model using Jackknife
mod2 <- sirt::rasch.jml.jackknife1( mod )
##
## Joint Maximum Likelihood Estimation
## Jackknife Estimation
## 11 Jackknife Units are used
## |--------------------PROGRESS--------------------|
## |------------------------------------------------|
##
## N p b.JML b.JMLcorr b.jack b.jackse b.JMLse
## I1 4929 0.853 -2.345 -2.131 -2.078 0.079 0.045
## I2 4929 0.786 -1.749 -1.590 -1.541 0.075 0.039
## I3 4929 0.723 -1.298 -1.180 -1.144 0.065 0.036
## I4 4929 0.657 -0.887 -0.806 -0.782 0.059 0.035
## I5 4929 0.576 -0.420 -0.382 -0.367 0.055 0.033
## I6 4929 0.492 0.041 0.038 0.043 0.054 0.033
## I7 4929 0.409 0.502 0.457 0.447 0.056 0.034
## I8 4929 0.333 0.939 0.854 0.842 0.058 0.035
## I9 4929 0.264 1.383 1.257 1.229 0.065 0.037
## I10 4929 0.210 1.778 1.617 1.578 0.071 0.040
## I11 4929 0.154 2.266 2.060 2.011 0.077 0.044
#-> Item parameters obtained by jackknife seem to be acceptable.
}
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