rasch.jml.biascorr.Rd
This function computes an analytical bias correction for the Rasch model according to the method of Arellano and Hahn (2007).
rasch.jml.biascorr(jmlobj,itemfac=NULL)
jmlobj | An object which is the output of the |
---|---|
itemfac | Number of items which are used for bias correction. By default it is the average number of item responses per person. |
A list with following entries
Matrix of item difficulty estimates. The column
b.analytcorr1
contains item difficulties by analytical bias
correction of Method 1 in Arellano and Hahn (2007) whereas b.analytcorr2
corresponds to Method 2.
Estimated bias by Method 1
Estimated bias by Method 2
Number of items which are used as the factor for bias correction
Arellano, M., & Hahn, J. (2007). Understanding bias in nonlinear panel models: Some recent developments. In R. Blundell, W. Newey & T. Persson (Eds.): Advances in Economics and Econometrics, Ninth World Congress, Cambridge University Press.
See rasch.jml.jackknife1
for bias correction based on
Jackknife.
See also the bife R package for analytical bias corrections.
############################################################################# # EXAMPLE 1: Dataset Reading ############################################################################# data(data.read) dat <- data( data.read ) # estimate Rasch model mod <- sirt::rasch.jml( data.read ) # JML with analytical bias correction res1 <- sirt::rasch.jml.biascorr( jmlobj=mod ) print( res1$b.biascorr, digits=3 ) ## b.JML b.JMLcorr b.analytcorr1 b.analytcorr2 ## 1 -2.0086 -1.8412 -1.908 -1.922 ## 2 -1.1121 -1.0194 -1.078 -1.088 ## 3 -0.0718 -0.0658 -0.150 -0.127 ## 4 0.5457 0.5002 0.393 0.431 ## 5 -0.9504 -0.8712 -0.937 -0.936 ## [...]