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

In clere: 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).

See Also

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

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)

clere documentation built on Feb. 7, 2020, 1:06 a.m.