+++ /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 an initial estimator for pi (size : k)
-#' @param gamInit an initial estimator for gamma
-#' @param mini minimum number of iterations in EM algorithm
-#' @param maxi maximum number of iterations in EM algorithm
-#' @param gamma power in the penalty
-#' @param glambda grid of regularization parameters
-#' @param X matrix of regressors
-#' @param Y matrix of responses
-#' @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)
-#'
-#' @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-8,eps, ncores=3, fast=TRUE)
-{
- 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 = dim(phiInit)[1]
- m = dim(phiInit)[2]
-
- #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
- seq_len(m)[ apply( abs(params$phi[j,,]) > thresh, 1, any ) ]
- })
-
- list("selected"=selectedVariables,"Rho"=params$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
-}