Title: Variable Selection With Mixture Of Models
Date: 2016-12-01
Version: 0.1-0
-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, published in
- Advances in Data Analysis and Clustering (2016).
+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, published in Advances in Data Analysis and Clustering (2016).
Author: Benjamin Auder <Benjamin.Auder@math.u-psud.fr> [aut,cre],
Emilie Devijver <Emilie.Devijver@kuleuven.be> [aut],
Benjamin Goehry <Benjamin.Goehry@math.u-psud.fr> [aut]
URL: http://git.auder.net/?p=valse.git
License: MIT + file LICENSE
VignetteBuilder: knitr
-RoxygenNote: 6.0.1
+RoxygenNote: 5.0.1
-EMGLLF = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
+EMGLLF_R = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau)
{
#matrix dimensions
n = dim(X)[1]