flexCWM : A Flexible Framework for Cluster-Weighted Models

• Published in: Journal of statistical software Vol. 86; no. 2; pp. 1 - 30
• Main Authors: Mazza, Angelo, Punzo, Antonio, Ingrassia, Salvatore
• Format: Journal Article
• Language: English
• Published: Foundation for Open Access Statistics 2018
• Subjects: cluster-weighted models; em algorithm; mixture models; model-based clustering; random covariates
• ISSN: 1548-7660; 1548-7660
• DOI: 10.18637/jss.v086.i02

Cluster-weighted models (CWMs) are mixtures of regression models with random covariates. However, besides having recently become rather popular in statistics and data mining, there is still a lack of support for CWMs within the most popular statistical suites. In this paper, we introduce flexCWM, an R package specifically conceived for fitting CWMs. The package supports modeling the conditioned response variable by means of the most common distributions of the exponential family and by the t distribution. Covariates are allowed to be of mixed-type and parsimonious modeling of multivariate normal covariates, based on the eigenvalue decomposition of the component covariance matrices, is supported. Furthermore, either the response or the covariates distributions can be omitted, yielding to mixtures of distributions and mixtures of regression models with fixed covariates, respectively. The expectation-maximization (EM) algorithm is used to obtain maximum-likelihood estimates of the parameters and likelihood-based information criteria are adopted to select the number of groups and/or a parsimonious model. For the component regression coefficients, standard errors and significance tests are also provided. Parallel computation can be used on multicore PCs and computer clusters, when several models have to be fitted. To exemplify the use of the package, applications to artificial and real datasets, included in the package, are presented.