+++ /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 thres threshold to consider a coefficient to be equal to 0
-#' @param tau threshold to say that EM algorithm has converged
-#'
-#' @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,seuil,tau)
-{
- #TODO: parameter ncores (chaque tâche peut aussi demander du parallélisme...)
- cl = parallel::makeCluster( parallel::detectCores() / 4 )
- parallel::clusterExport(cl=cl,
- varlist=c("phiInit","rhoInit","gamInit","mini","maxi","glambda","X","Y","seuil","tau"),
- envir=environment())
- #Pour chaque lambda de la grille, on calcule les coefficients
- out = parLapply( seq_along(glambda), function(lambdaindex)
- {
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
-
- params = EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,glambda[lambdaIndex],X,Y,tau)
-
- #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$phi[j,,]) > seuil, 1, any ) ]
- })
-
- list("selected"=selectedVariables,"Rho"=params$Rho,"Pi"=params$Pi)
- })
- parallel::stopCluster(cl)
- out
-}