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


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  • Friedrich Leisch
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    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.

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    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of Statistical Software.

    Volume (Year): 11 ()
    Issue (Month): i08 ()

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    Handle: RePEc:jss:jstsof:11:i08

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    1. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer, vol. 12(1), pages 21-55, March.
    2. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer, vol. 5(2), pages 249-282, September.
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    Cited by:
    1. Maureen Lankhuizen & Thomas De Graaff & Henri De Groot, 2012. "Product Heterogeneity, Intangible Barriers & Distance Decay: The effect of multiple dimensions of distance on trade across different product categories," ERSA conference papers ersa12p151, European Regional Science Association.
    2. Sara Dolnicar & Friedrich Leisch, 2010. "Evaluation of structure and reproducibility of cluster solutions using the bootstrap," Marketing Letters, Springer, vol. 21(1), pages 83-101, March.
    3. Sanjeena Subedi & Antonio Punzo & Salvatore Ingrassia & Paul McNicholas, 2013. "Clustering and classification via cluster-weighted factor analyzers," Advances in Data Analysis and Classification, Springer, vol. 7(1), pages 5-40, March.
    4. Ingrassia, Salvatore & Minotti, Simona C. & Punzo, Antonio, 2014. "Model-based clustering via linear cluster-weighted models," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 159-182.
    5. Rainer Schlittgen, 2011. "A weighted least-squares approach to clusterwise regression," AStA Advances in Statistical Analysis, Springer, vol. 95(2), pages 205-217, June.
    6. David Plavcan & Georg J. Mayr & Achim Zeileis, 2013. "Automatic and Probabilistic Foehn Diagnosis with a Statistical Mixture Model," Working Papers 2013-22, Faculty of Economics and Statistics, University of Innsbruck.
    7. Chen, Cathy W.S. & Chan, Jennifer S.K. & So, Mike K.P. & Lee, Kevin K.M., 2011. "Classification in segmented regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2276-2287, July.
    8. Boris Branisa & Adriana Cardozo, 2009. "Revisiting the Regional Growth Convergence Debate in Colombia Using Income Indicators," Ibero America Institute for Econ. Research (IAI) Discussion Papers 194, Ibero-America Institute for Economic Research, revised 21 Aug 2009.
    9. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer, vol. 29(3), pages 363-401, October.
    10. Fernandez-Blanco, Victor & Orea, Luis & Prieto-Rodriguez, Juan, 2009. "Analyzing consumers heterogeneity and self-reported tastes: An approach consistent with the consumer's decision making process," Journal of Economic Psychology, Elsevier, vol. 30(4), pages 622-633, August.
    11. Achim Zeileis & Christian Kleiber & Simon Jackman, . "Regression Models for Count Data in R," Journal of Statistical Software, American Statistical Association, vol. 27(i08).
    12. Nicolas Städler & Peter Bühlmann & Sara Geer, 2010. "ℓ 1 -penalization for mixture regression models," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer, vol. 19(2), pages 209-256, August.
    13. Floh, Arne & Zauner, Alexander & Koller, Monika & Rusch, Thomas, 2014. "Customer segmentation using unobserved heterogeneity in the perceived-value–loyalty–intentions link," Journal of Business Research, Elsevier, vol. 67(5), pages 974-982.
    14. Luca Bagnato & Antonio Punzo, 2013. "Finite mixtures of unimodal beta and gamma densities and the $$k$$ -bumps algorithm," Computational Statistics, Springer, vol. 28(4), pages 1571-1597, August.
    15. Thomas De Graaff & Jaap Boter & Jan Rouwendal, 2006. "Do Dutch Musea Compete Or Cooperate?," ERSA conference papers ersa06p387, European Regional Science Association.
    16. Bettina Grün & Friedrich Leisch, 2008. "Identifiability of Finite Mixtures of Multinomial Logit Models with Varying and Fixed Effects," Journal of Classification, Springer, vol. 25(2), pages 225-247, November.


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