Title: <b>poLCA</b>: An<i>R</i>Package for Polytomous Variable Latent Class Analysis
Abstract:<b>poLCA</b> is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both ...<b>poLCA</b> is a software package for the estimation of latent class and latent class regression models for polytomous outcome variables, implemented in the R statistical computing environment. Both models can be called using a single simple command line. The basic latent class model is a finite mixture model in which the component distributions are assumed to be multi-way cross-classification tables with all variables mutually independent. The latent class regression model further enables the researcher to estimate the effects of covariates on predicting latent class membership. <b>poLCA</b> uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.Read More