#' 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,thresh,tau, ncores=1) #ncores==1 ==> no // { if (ncores > 1) { cl = parallel::makeCluster(ncores) 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) 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, seq_along(glambda, computeCoefs) else lapply(seq_along(glambda), computeCoefs) if (ncores > 1) parallel::stopCluster(cl) out }