-#' selectVariables
+#' selectVariables
#'
-#' It is a function which construct, for a given lambda, the sets of relevant variables.
+#' It is a function which constructs, for a given lambda, the sets for each cluster of relevant variables.
#'
#' @param phiInit an initial estimator for phi (size: p*m*k)
#' @param rhoInit an initial estimator for rho (size: m*m*k)
#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
#' @param eps threshold to say that EM algorithm has converged
#' @param ncores Number or cores for parallel execution (1 to disable)
+#' @param fast boolean to enable or not the C function call
#'
#' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
#'
-#' @examples TODO
-#'
#' @export
-#'
-selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
+selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
{
if (ncores > 1) {
cl <- parallel::makeCluster(ncores, outfile = "")
- parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
+ parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
"mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
}
# Computation for a fixed lambda
computeCoefs <- function(lambda)
{
- params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
+ params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
X, Y, eps, fast)
p <- ncol(X)
} else {
lapply(glambda, computeCoefs)
}
- if (ncores > 1)
+ if (ncores > 1)
parallel::stopCluster(cl)
-
+
print(out)
- # Suppress models which are computed twice En fait, ca ca fait la comparaison de
- # tous les parametres On veut juste supprimer ceux qui ont les memes variables
- # sélectionnées
+ # Suppress models which are computed twice
# sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
selec <- lapply(out, function(model) model$selected)
ind_dup <- duplicated(selec)