#' selectVariables #' #' For a given lambda, construct the sets of relevant variables for each cluster. #' #' @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) #' @param fast boolean to enable or not the C function call #' #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi #' #' @export selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast) { 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 <- ncol(X) m <- ncol(Y) # 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 if (m>1) { seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)] } else { if (any(params$phi[j, , ] > thresh)) 1 else numeric(0) } }) 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 # 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 }