RSM {eRm}R Documentation

Estimation of rating scale models

Description

This function computes the parameter estimates of a rating scale model for polytomous item responses by using CML estimation.

Usage

RSM(X, W, se = TRUE, sum0 = TRUE, etaStart)

Arguments

X Input data matrix or data frame with item responses (starting from 0); rows represent individuals, columns represent items. Missing values are inserted as NA.
W Design matrix for the RSM. If omitted, the function will compute W automatically.
se If TRUE, the standard errors are computed.
sum0 If TRUE, the parameters are normed to sum-0 by specifying an appropriate W. If FALSE, the first parameter is restricted to 0.
etaStart A vector of starting values for the eta parameters can be specified. If missing, the 0-vector is used.

Details

The design matrix approach transforms the RSM into a partial credit model and estimates the corresponding basic parameters by using CML. Available methods for RSM-objects are print, coef, model.matrix, vcov, summary, logLik, person.parameters, plotICC, LRtest.

Value

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

loglik The log-likelihood.
iter Number of iterations.
etapar Estimated basic item parameters.
se.eta Standard errors of the estimated basic item parameters.
betapar Estimated item parameters.
hessian Hessian matrix if se = TRUE.
IC List of AIC, BIC, and cAIC.
W Design matrix.
X Data matrix.
X01 Dichotomized data matrix.

...

Note

Author(s)

Patrick Mair, Reinhold Hatzinger

References

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

Mair, P., and Hatzinger, R. (2007). Extended Rasch modeling: The eRm package for the application of IRT models in R. Journal of Statistical Software, 20(9), 1-20.

Mair, P., and Hatzinger, R. (2007). CML based estimation of extended Rasch models with the eRm package in R. Psychology Science, 49, 26-43.

See Also

RM,PCM,LRtest

Examples


##RSM with 10 subjects, 3 items
data(rsmdat)
res <- RSM(rsmdat)
print(res)  
summary(res)            

[Package eRm version 0.9.1.1 Index]