This function computes the parameter estimates of a partial credit model for polytomous item responses by using CML estimation.
PCM(X, W, se = TRUE, sum0 = TRUE, etaStart)Returns an object of class Rm, eRm containing.
Conditional log-likelihood.
Number of iterations.
Number of parameters.
See code output in nlm.
Estimated basic item difficulty parameters.
Standard errors of the estimated basic item parameters.
Estimated item-category (easiness) parameters.
Standard errors of item parameters.
Hessian matrix if se = TRUE.
Design matrix.
Data matrix.
Dichotomized data matrix.
The matched call.
Input data matrix or data frame with item responses (starting from 0); rows represent individuals, columns represent items. Missing values are inserted as NA.
Design matrix for the PCM. If omitted, the function will compute W automatically.
If TRUE, the standard errors are computed.
If TRUE, the parameters are normed to sum-0 by specifying
an appropriate W. If FALSE, the first parameter is restricted to 0.
A vector of starting values for the eta parameters can be specified. If missing, the 0-vector is used.
Patrick Mair, Reinhold Hatzinger
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.
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.
RM,RSM,LRtest
##PCM with 10 subjects, 3 items
res <- PCM(pcmdat)
res
summary(res) #eta and beta parameters with CI
thresholds(res) #threshold parameters
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