期刊
JOURNAL OF STATISTICAL SOFTWARE
卷 42, 期 10, 页码 1-29出版社
JOURNAL STATISTICAL SOFTWARE
DOI: 10.18637/jss.v042.i10
关键词
latent class analysis; latent class regression; polytomous; categorical; concomitant
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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