clere (version 1.1.2)

clere-package: CLERE methodology for simultaneous variables clustering and regression

Description

The methodology consists in creating clusters of variables involved in a high dimensional linear regression model so as to reduce the dimensionality. A model-based approach is proposed and fitted using a Stochastic EM-Gibbs algorithm (SEM-Gibbs).

Details

ll{ Package: clere Title: CLERE methodology for simultaneous variables clustering and regression Version: 1.1.2 Date: 2015-10-02 Author: Loic Yengo Contributor: Mickael Canouil Maintainer: Loic Yengo License: GPL (>= 3) Depends: methods, parallel Imports: Rcpp LinkingTo: Rcpp, RcppEigen } The package implements mainly the fitClere function (an example is given below) for fitting the model from a matrix of covariates and a vector of response. The package also implements a summary method and graphical summary plot which represents the course of each parameters at each step of the SEM-Gibbs and a predict method for making prediction from a new design matrix.

References

Yengo L., Jacques J. and Biernacki C. Variable clustering in high dimensional linear regression, Journal de la Societe Francaise de Statistique (2013).

See Also

Overview : clere-package Classes : Clere Methods : show, plot, clusters, predict, summary Functions : fitClere Datasets : numExpRealData, numExpSimData

Examples

# Simple example using simulated data
 # to see how to you the main function clere
 # library(clere)
 x     <- matrix(rnorm(50 * 100), nrow = 50, ncol = 100)
 y     <- rnorm(50)
 model <- fitClere(y = y, x = x, g = 2, plotit = FALSE)
 plot(model) 
 clus <- clusters(model, threshold = NULL)
 predict(model, newx = x+1)
 summary(model)