+++ /dev/null
-#' selectVariables
-#'
-#' It is a function which construct, for a given lambda, the sets 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 piInit\tan initial estimator for pi (size : k)
-#' @param gamInit an initial estimator for gamma
-#' @param mini\t\tminimum number of iterations in EM algorithm
-#' @param maxi\t\tmaximum number of iterations in EM algorithm
-#' @param gamma\t power in the penalty
-#' @param glambda grid of regularization parameters
-#' @param X\t\t\t matrix of regressors
-#' @param Y\t\t\t matrix of responses
-#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
-#' @param eps\t\t threshold to say that EM algorithm has converged
-#' @param ncores Number or cores for parallel execution (1 to disable)
-#'
-#' @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,
- 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",
- "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,
- X, Y, eps, fast)
-
- p <- ncol(X)
- m <- ncol(Y)
-
- # selectedVariables: list where element j contains vector of selected variables
- # in [1,m]
- selectedVariables <- lapply(1:p, function(j) {
- # from boolean matrix mxk of selected variables obtain the corresponding boolean
- # m-vector, and finally return the corresponding indices
- if (m>1) {
- seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
- } else {
- if (any(params$phi[j, , ] > thresh))
- 1
- else
- numeric(0)
- }
- })
-
- list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
- }
-
- # For each lambda in the grid, we compute the coefficients
- out <-
- if (ncores > 1) {
- parLapply(cl, glambda, computeCoefs)
- } else {
- lapply(glambda, computeCoefs)
- }
- if (ncores > 1)
- parallel::stopCluster(cl)
- # 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
- # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
- selec <- lapply(out, function(model) model$selected)
- ind_dup <- duplicated(selec)
- ind_uniq <- which(!ind_dup)
- out2 <- list()
- for (l in 1:length(ind_uniq))
- out2[[l]] <- out[[ind_uniq[l]]]
- out2
-}