FlexMix A general framework for finite.pdf

FlexMix: A general framework for finite mixture models and latent class regression in R Friedrich Leisch Report No. 86 December 2003 December 2003 SFB ‘Adaptive Information Systems and Modelling in Economics and Management Science’ Vienna University of Economics and Business Administration Augasse 2–6, 1090 Wien, Austria in cooperation with University of Vienna Vienna University of Technology http://www.wu-wien.ac.at/am This piece of research was supported by the Austrian Science Foundation (FWF) under grant SFB#010 (‘Adaptive Information Systems and Modelling in Economics and Management Science’). FlexMix: A general framework for finite mixture models and latent class regression in R Friedrich Leisch Institut fu?r Statistik und Wahrscheinlichkeitstheorie Technische Universita?t Wien Wiedner Hauptstra?e 8-10/107, 1040 Wien, Austria 〈Friedrich.Leisch@ci.tuwien.ac.at〉 Abstract 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. 1 Introduction Finite mixture models have been used for more than 100 years, but have seen a real boost in popularity over the last decade due to the tremendous increase in available computing power. The areas of application of mixture models range from biology and medicine to physics, eco- nomics and marketing. On