#' constructionModelesLassoMLE #' #' Construct a collection of models with the Lasso-MLE procedure. #' #' @param phiInit an initialization for phi, get by initSmallEM.R #' @param rhoInit an initialization for rho, get by initSmallEM.R #' @param piInit an initialization for pi, get by initSmallEM.R #' @param gamInit an initialization for gam, get by initSmallEM.R #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100 #' @param gamma integer for the power in the penaly, by default = 1 #' @param X matrix of covariates (of size n*p) #' @param Y matrix of responses (of size n*m) #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4 #' @param S output of selectVariables.R #' @param ncores Number of cores, by default = 3 #' @param fast TRUE to use compiled C code, FALSE for R code only #' @param verbose TRUE to show some execution traces #' #' @return a list with several models, defined by phi, rho, pi, llh #' #' @export constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE) { if (ncores > 1) { cl <- parallel::makeCluster(ncores, outfile = "") parallel::clusterExport(cl, envir = environment(), varlist = c("phiInit", "rhoInit", "gamInit", "mini", "maxi", "gamma", "X", "Y", "eps", "S", "ncores", "fast", "verbose")) } # Individual model computation computeAtLambda <- function(lambda) { if (ncores > 1) require("valse") #nodes start with an empty environment if (verbose) print(paste("Computations for lambda=", lambda)) n <- dim(X)[1] p <- dim(phiInit)[1] m <- dim(phiInit)[2] k <- dim(phiInit)[3] sel.lambda <- S[[lambda]]$selected # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix col.sel <- which(sapply(sel.lambda, length) > 0) #if list of selected vars if (length(col.sel) == 0) return(NULL) # lambda == 0 because we compute the EMV: no penalization here res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi, gamma, 0, X[, col.sel], Y, eps, fast) # Eval dimension from the result + selected phiLambda2 <- res$phi rhoLambda <- res$rho piLambda <- res$pi phiLambda <- array(0, dim = c(p, m, k)) for (j in seq_along(col.sel)) phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j, sel.lambda[[j]], ] dimension <- length(unlist(sel.lambda)) # Computation of the loglikelihood densite <- vector("double", n) for (r in 1:k) { if (length(col.sel) == 1) { delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% t(phiLambda[col.sel, , r]))) } else delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% phiLambda[col.sel, , r])) densite <- densite + piLambda[r] * det(rhoLambda[, , r])/(sqrt(2 * base::pi))^m * exp(-diag(tcrossprod(delta))/2) } llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1) list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda) } # For each lambda, computation of the parameters out <- if (ncores > 1) { parLapply(cl, 1:length(S), computeAtLambda) } else { lapply(1:length(S), computeAtLambda) } if (ncores > 1) parallel::stopCluster(cl) out }