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poLCA: An R Package for Polytomous Variable Latent Class Analysis

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  • Linzer, Drew A.
  • Lewis, Jeffrey B.

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.

Suggested Citation

  • Linzer, Drew A. & Lewis, Jeffrey B., 2011. "poLCA: An R Package for Polytomous Variable Latent Class Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i10).
    • Handle: RePEc:jss:jstsof:v:042:i10
    DOI: http://hdl.handle.net/10.18637/jss.v042.i10
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    References listed on IDEAS

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    1. Linzer, Drew A., 2011. "Reliable Inference in Highly Stratified Contingency Tables: Using Latent Class Models as Density Estimators," Political Analysis, Cambridge University Press, vol. 19(2), pages 173-187, April.
    2. Bolck, Annabel & Croon, Marcel & Hagenaars, Jacques, 2004. "Estimating Latent Structure Models with Categorical Variables: One-Step Versus Three-Step Estimators," Political Analysis, Cambridge University Press, vol. 12(1), pages 3-27, January.
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