Published by the Foundation for Open Access Statistics Editors-in-chief: Bettina Grün, Torsten Hothorn, Rebecca Killick, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
Authors: Drew A. Linzer, Jeffrey B. Lewis
Title: poLCA: An R Package for Polytomous Variable Latent Class Analysis
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.

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Paper: poLCA: An R Package for Polytomous Variable Latent Class Analysis     Download PDF (Downloads: 34031)
Supplements:
poLCA_1.3.1.tar.gz: R source package Download (Downloads: 1297; 350KB)
v42i10.R: R example code from the paper Download (Downloads: 1614; 4KB)
DOI: 10.18637/jss.v042.i10

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.