| PCM {eRm} | R Documentation |
This function computes the parameter estimates of a partial credit model for polytomous item responses by using CML estimation.
PCM(X, W)
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. |
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.
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. |
Missing Values are not allowed in X, the category coding must start with 0 (lowest category).
Patrick Mair, Reinhold Hatzinger
Fischer, G. H., and Molenaar, I. (1995). Rasch Models - Foundations, Recent Developements, and Applications. Springer.
print.eRm,coef.eRm,vcov.eRm,model.matrix.eRm,plot.Rm,
summary.eRm
#PCM with 10 subjects, 3 items data(pcmdat) res <- PCM(pcmdat) res