#' 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 }