poLCA: An R Package for Polytomous Variable Latent Class Analysis

Drew A. Linzer and Jeffrey B. Lewis

Journal of Statistical Software, 2011, vol. 042, issue i10

Abstract: poLCA 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. poLCA uses expectation-maximization and Newton-Raphson algorithms to find maximum likelihood estimates of the model parameters.

Date: 2011-06-14
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Persistent link: https://EconPapers.repec.org/RePEc:jss:jstsof:v:042:i10

DOI: 10.18637/jss.v042.i10

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