#' EMGLLF
#'
-#' Run a generalized EM algorithm developped for mixture of Gaussian regression
+#' Run a generalized EM algorithm developped for mixture of Gaussian regression
#' models with variable selection by an extension of the Lasso estimator (regularization parameter lambda).
#' Reparametrization is done to ensure invariance by homothetic transformation.
#' It returns a collection of models, varying the number of clusters and the sparsity in the regression mean.
#' @param X matrix of covariates (of size n*p)
#' @param Y matrix of responses (of size n*m)
#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
+#' @param fast boolean to enable or not the C function call
#'
#' @return A list (corresponding to the model collection) defined by (phi,rho,pi,LLF,S,affec):
#' phi : regression mean for each cluster
}
# Function in C
- n <- nrow(X) #nombre d'echantillons
- p <- ncol(X) #nombre de covariables
- m <- ncol(Y) #taille de Y (multivarie)
- k <- length(piInit) #nombre de composantes dans le melange
.Call("EMGLLF", phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
- X, Y, eps, phi = double(p * m * k), rho = double(m * m * k), pi = double(k),
- llh = double(1), S = double(p * m * k), affec = integer(n), n, p, m, k,
- PACKAGE = "valse")
+ X, Y, eps, PACKAGE = "valse")
}
# R version - slow but easy to read