valse-package: Variable Selection with Mixture of Models

In valse: Variable Selection with Mixture of Models

Description

Two methods are implemented to cluster data with finite mixture regression models. Those procedures deal with high-dimensional covariates and responses through a variable selection procedure based on the Lasso estimator. A low-rank constraint could be added, computed for the Lasso-Rank procedure. A collection of models is constructed, varying the level of sparsity and the number of clusters, and a model is selected using a model selection criterion (slope heuristic, BIC or AIC). Details of the procedure are provided in "Model-based clustering for high-dimensional data. Application to functional data" by Emilie Devijver (2016) <arXiv:1409.1333v2>, published in Advances in Data Analysis and Clustering.

Details

Two methods are implemented to cluster data with finite mixture regression models. Those procedures deal with high-dimensional covariates and responses through a variable selection procedure based on the Lasso estimator.

The main function is runValse(), which calls all other functions. See also plot_valse() which plots the relevant parameters after a run.

Author(s)

Benjamin Auder <[e-mail]> [aut,cre], Emilie Devijver <[e-mail]> [aut], Benjamin Goehry <[e-mail]> [ctb]

Maintainer: Benjamin Auder <[e-mail]>

valse documentation built on May 31, 2021, 9:10 a.m.