#' @export
#'
selectVariables = function(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda,
- X,Y,thresh,tau, ncores=3, fast=TRUE)
+ X,Y,thresh,tau, 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","tau"),
- envir=environment())
- }
-
- # Calcul pour un lambda
- computeCoefs <- function(lambda)
- {
- params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau,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$pi)
- }
-
- # Pour chaque lambda de la grille, on calcule les coefficients
- out <-
- if (ncores > 1)
- parLapply(cl, glambda, computeCoefs)
- else
- lapply(glambda, computeCoefs)
- if (ncores > 1)
- parallel::stopCluster(cl)
-
- # Suppression doublons
- sha1_array <- lapply(out, digest::sha1)
- out[ !duplicated(sha1_array) ]
-
- out
+ if (ncores > 1)
+ {
+ cl = parallel::makeCluster(ncores, outfile='')
+ parallel::clusterExport(cl=cl,
+ varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","thresh","tau"),
+ envir=environment())
+ }
+
+ # Computation for a fixed lambda
+ computeCoefs <- function(lambda)
+ {
+ params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,lambda,X,Y,tau,fast)
+
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+
+ #selectedVariables: list where element j contains vector of selected variables in [1,m]
+ selectedVariables = lapply(1:p, function(j) {
+ #from boolean matrix mxk of selected variables obtain the corresponding boolean m-vector,
+ #and finally return the corresponding indices
+ seq_len(m)[ apply( abs(params$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
}