PCM: Estimation of partial credit models

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

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

Usage

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PCM(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 PCM. 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

Through specification in W, the parameters of the categories with 0 responses are set to 0 as well as the first category of the first item. Available methods for PCM-objects are:
print, coef, model.matrix, vcov, plot, summary, logLik, person.parameters, plotICC, LRtest.

Value

Returns an object of class Rm, eRm containing.

loglik

Conditional log-likelihood.

iter

Number of iterations.

npar

Number of parameters.

convergence

See code output in nlm.

etapar

Estimated basic item difficulty parameters.

se.eta

Standard errors of the estimated basic item parameters.

betapar

Estimated item-category (easiness) parameters.

se.beta

Standard errors of item parameters.

hessian

Hessian matrix if se = TRUE.

W

Design matrix.

X

Data matrix.

X01

Dichotomized data matrix.

call

The matched call.

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,RSM,LRtest

Examples

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##PCM with 10 subjects, 3 items
res <- PCM(pcmdat)
res
summary(res)                #eta and beta parameters with CI
thresholds(res)             #threshold parameters

eRm documentation built on May 26, 2018, 5:03 p.m.

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