clere (version 1.2.0)

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).

See Also

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

Examples

# NOT RUN {
 
 # 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)

# }