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).
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# 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)
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