prob_multi_glob_gen

It provides matrix of probabilities under different parametrizations and for the case of response variables having a different number of categories.

Framework for the Item Response Theory analysis of dichotomous and ordinal polytomous outcomes under the assumption of within-item multidimensionality and discreteness of the latent traits. The fitting algorithms allow for missing responses and for different item parametrizations and are based on the Expectation-Maximization paradigm. Individual covariates affecting the class weights may be included in the new version together with possibility of constraints on all model parameters.

Francesco Bartolucci

MLCIRTwithin

Latent Class Item Response Theory (LC-IRT) Models under
Within-Item Multidimensionality

Silvia Bacci

prob_multi_glob_gen function

<dl> <dt>X</dt>
<dd>array of all distinct covariate configurations</dd> <dt>model</dt>
<dd>type of logit (g = global, l = local, m = multinomial)</dd> <dt>be</dt>
<dd>initial vector of regression coefficients</dd> <dt>ind</dt>
<dd>vector to link responses to covariates</dd></dl>

Arguments

Francesco Bartolucci - University of Perugia (IT)

Author

Global probabilities — prob_multi_glob_gen

<dl>

 <dt>X</dt>
<dd>array of all distinct covariate configurations</dd>

 <dt>model</dt>
<dd>type of logit (g = global, l = local, m = multinomial)</dd>

 <dt>be</dt>
<dd>initial vector of regression coefficients</dd>

 <dt>ind</dt>
<dd>vector to link responses to covariates</dd>

</dl>

prob_multi_glob_gen: Global probabilities

Description

Usage

Value

Arguments

Author

References