FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R

作者：Friedrich Leisch · 发表于：Journal of Statistical Software · 年份：2004 · DOI：10.18637/jss.v011.i08 · 被引用次数：697 · 研究领域：Bayesian Methods and Mixture Models、Statistical Methods and Inference、Statistical Methods and Bayesian Inference

FlexMix implements a general framework for fitting discrete mixtures of regression models in the R statistical computing environment: three variants of the EM algorithm can be used for parameter estimation, regressors and responses may be multivariate with arbitrary dimension, data may be grouped, e.g., to account for multiple observations per individual, the usual formula interface of the S language is used for convenient model specification, and a modular concept of driver functions allows to interface many different types of regression models. Existing drivers implement mixtures of standard linear models, generalized linear models and model-based clustering. FlexMix provides the E-step and all data handling, while the M-step can be supplied by the user to easily define new models.