Abstract #301617

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JSM 2003 Abstract #301617
Activity Number: 446
Type: Contributed
Date/Time: Thursday, August 7, 2003 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistical Computing
Abstract - #301617
Title: FlexMix: Discrete Mixtures of General Regression Models in R
Author(s): Friedrich Leisch*+
Companies: Vienna University of Technology
Address: Department of Statistics, Vienna, , 1040, Austria
Keywords: R ; mixture models ; EM algorithm ; multinomial logit ; generalized linear models
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, and the usual formula interface of the S language is used for convenient model specification. A modular concept of driver functions allows to interface many different types of regression models, like standard linear models, generalized linear models, or multinomial logit models. Flexmix provides the E-step and all data handling, while the M-step can be supplied by the user to define new models. We discuss theoretical and computational aspects of fitting mixtures of regression models with the EM algorithm and the corresponding R classes and methods. As an example application, we demonstrate a multinomial choice model for market segmentation.

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