LPCM {eRm}R Documentation

Estimation of linear partial credit models

Description

This function computes the parameter estimates of a linear partial credit model (LRSM) for polytomuous item responses by using CML estimation.

Usage

LPCM(X, W , mpoints = 1, Groups = 1)

Arguments

X Input data matrix or data frame; rows represent individuals (N in total), columns represent items.
W Design matrix for the LPCM. If omitted, the function will compute W automatically.
mpoints Number of measurement points.
Groups Vector of length N which determines the group membership of each subject, starting from 1

Details

Through appropriate definition of W the LPCM can be viewed as a more parsimonous PCM, on the one hand, e.g. by imposing some cognitive base operations to solve the items. One the other hand, linear extensions of the Rasch model such as group comparisons and repeated measurement designs can be computed. If more than one measurement point is examined, the item responses for the 2nd, 3rd, etc. measurement point are added column-wise in X. Available methods for LPCM-objects are print, coef, model.matrix, vcov.

Value

Returns on object of class eRm and contains the log-likelihood value, the parameter estimates and their standard errors.

loglik The log-likelihood.
iter Number of iterations required.
etapar Estimated basic item parameters.
se_eta Standard errors of the estimated basic item parameters.
betapar Estimated item parameters.

Note

NA's are not allowed in X.

Author(s)

Patrick Mair, Reinhold Hatzinger

References

Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.

See Also

print.eRm,coef.eRm,vcov.eRm,model.matrix.eRm

Examples


#LPCM for two measurement points and two subject groups
#20 subjects, 2*3 items

data(X_lpcm)
data(W_lpcm)
G <- c(rep(1,10),rep(2,10))             
res <- LPCM(X_lpcm, W_lpcm, mpoints = 2, Groups = G)
res

[Package eRm version 0.3 Index]