FlexMix: A General Framework for Finite Mixture Models and Latent Class Regression in R
AbstractFlexMix 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by American Statistical Association in its journal Journal of Statistical Software.
Volume (Year): 11 ()
Issue (Month): i08 ()
Contact details of provider:
Web page: http://www.jstatsoft.org/
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer, vol. 12(1), pages 21-55, March.
- Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer, vol. 5(2), pages 249-282, September.
- 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.
- Simon Jackman & Christian Kleiber & Achim Zeileis, 2007.
"Regression Models for Count Data in R,"
2007/24, Faculty of Business and Economics - University of Basel.
- Thomas De Graaff & Jaap Boter & Jan Rouwendal, 2006. "Do Dutch Musea Compete Or Cooperate?," ERSA conference papers ersa06p387, European Regional Science Association.
- 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.
- 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.
- 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.
- 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.
- Rainer Schlittgen, 2011. "A weighted least-squares approach to clusterwise regression," AStA Advances in Statistical Analysis, Springer, vol. 95(2), pages 205-217, June.
- 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.
- 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.
- Salvatore Ingrassia & Simona Caterina Minotti & Giorgio Vittadini, 2011.
"Local Statistical Modeling via Cluster-Weighted Approach with Elliptical Distributions,"
20111001, Università degli Studi di Milano-Bicocca, Dipartimento di Statistica.
- 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.
- 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.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum).
If references are entirely missing, you can add them using this form.