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MLCIRTwithin (version 1.1)

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

Description

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.

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Version

Install

install.packages('MLCIRTwithin')

Monthly Downloads

217

Version

1.1

License

GPL (>= 2)

Maintainer

Francesco Bartolucci

Last Published

February 3rd, 2016

Functions in MLCIRTwithin (1.1)

search.model_within

Search for the global maximum of the log-likelihood of within-item muldimensional models
prob_multi_glob_gen

Global probabilities
MLCIRTwithin-package

Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality
SF12_nomiss

SF12 dataset without missing responses
summary.est_multi_poly_within

Print the output of est_multi_poly_within object
lk_obs_score_within

Compute observed log-likelihood and score
est_multi_glob_gen

Fit marginal regression models for categorical responses
print.est_multi_poly_within

Print the call of est_multi_poly_within object
est_multi_poly_between

Estimate latent class item response theory (LC-IRT) models for dichotomous and polytomous responses under between-item multidimensionality
inv_glob

Invert marginal logits
summary.est_multi_poly_between

Print the output of est_multi_poly_between object
est_multi_poly_within

Estimate latent class item response theory (LC-IRT) models for dichotomous and polytomous responses under within-item multidimensionality
RLMS

RLMS dataset
print.est_multi_poly_between

Print the output of est_multi_poly_between object
lk_obs_score_between

Compute observed log-likelihood and score
search.model_between

Search for the global maximum of the log-likelihood of between-item muldimensional models
SF12

SF12 dataset