Latent Class Item Response Theory (LC-IRT) Models under Within-Item Multidimensionality
Build block diagonal matrices
Display the estimated model parameters of est_multi_poly_between objec...
Display the estimated model parameters of est_multi_poly_within object
Display the estimated confidence intervals of the model parameters of ...
Display the estimated confidence intervals of the model parameters of ...
Fit marginal regression models for categorical responses
Estimate latent class item response theory (LC-IRT) models for dichoto...
Estimate latent class item response theory (LC-IRT) models for dichoto...
Compute observed log-likelihood and score
Compute observed log-likelihood and score
Display the log-likelihood at convergence of est_multi_poly_between ob...
Display the log-likelihood at convergence of est_multi_poly_within obj...
Latent Class Item Response Theory (LC-IRT) Models under Within-Item Mu...
Print the output of est_multi_poly_between object
Print the call of est_multi_poly_within object
Global probabilities
Search for the global maximum of the log-likelihood of between-item mu...
Search for the global maximum of the log-likelihood of within-item mul...
Print the output of est_multi_poly_between object
Print the output of est_multi_poly_within object
Display the estimated variance-and-covariance matrix of est_multi_poly...
Display the estimated variance-and-covariance matrix of est_multi_poly...
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.