文章基本信息

标题：BayesLCA: An R Package for Bayesian Latent Class Analysis
本地全文：下载
作者：Arthur White ; Thomas Brendan Murphy
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2014
卷号：61
期号：1
页码：1-28
语种：English
出版社：University of California, Los Angeles
摘要：The BayesLCA package for R provides tools for performing latent class analysis within a Bayesian setting. Three methods for fitting the model are provided, incorporating an expectation-maximization algorithm, Gibbs sampling and a variational Bayes approximation. The article briefly outlines the methodology behind each of these techniques and discusses some of the technical difficulties associated with them. Methods to remedy these problems are also described. Visualization methods for each of these techniques are included, as well as criteria to aid model selection.