eRm (version 0.3.1)

PCM: Estimation of partial credit models

Description

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

Usage

PCM(X, W)

Arguments

X

Input data matrix or data frame with item responses (starting from 0); rows represent individuals, columns represent items.

W

Design matrix for the PCM. If omitted, the function will compute W automatically.

Value

• Returns an object of class Rm and contains the log-likelihood value, the parameter estimates and their standard errors.
• model
  Type of model.
• loglik
  The log-likelihood.
• df
  Degrees of freedom.
• iter
  Number of iterations required.
• etapar
  Estimated basic item parameters.
• se_eta
  Standard errors of the estimated basic item parameters.
• hessian
  Hessian matrix.
• betapar
  Estimated item parameters.
• LR
  The log-likelihood test statistic for the model.
• W
  Design matrix.
• etaparG1
  Parameters for first LR-group.
• etaparG2
  Parameters for second LR-group.

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.

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,plot.Rm, summary.eRm

Examples

#PCM with 10 subjects, 3 items

data(pcmdat)
res <- PCM(pcmdat)
res