pkg/R/constructionModelesLassoMLE.R

   1 #' constructionModelesLassoMLE
   2 #'
   3 #' Construct a collection of models with the Lasso-MLE procedure.
   4 #'
   5 #' @param phiInit an initialization for phi, get by initSmallEM.R
   6 #' @param rhoInit an initialization for rho, get by initSmallEM.R
   7 #' @param piInit an initialization for pi, get by initSmallEM.R
   8 #' @param gamInit an initialization for gam, get by initSmallEM.R
   9 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
  10 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
  11 #' @param gamma integer for the power in the penaly, by default = 1
  12 #' @param X matrix of covariates (of size n*p)
  13 #' @param Y matrix of responses (of size n*m)
  14 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
  15 #' @param S output of selectVariables.R
  16 #' @param ncores Number of cores, by default = 3
  17 #' @param fast TRUE to use compiled C code, FALSE for R code only
  18 #' @param verbose TRUE to show some execution traces
  19 #'
  20 #' @return a list with several models, defined by phi, rho, pi, llh
  21 #'
  22 #' @export
  23 constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
  24   maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE)
  25   {
  26   if (ncores > 1)
  27   {
  28     cl <- parallel::makeCluster(ncores, outfile = "")
  29     parallel::clusterExport(cl, envir = environment(), varlist = c("phiInit",
  30       "rhoInit", "gamInit", "mini", "maxi", "gamma", "X", "Y", "eps", "S",
  31       "ncores", "fast", "verbose"))
  32   }
  33
  34   # Individual model computation
  35   computeAtLambda <- function(lambda)
  36   {
  37     if (ncores > 1)
  38       require("valse")  #nodes start with an empty environment
  39
  40     if (verbose)
  41       print(paste("Computations for lambda=", lambda))
  42
  43     n <- dim(X)[1]
  44     p <- dim(phiInit)[1]
  45     m <- dim(phiInit)[2]
  46     k <- dim(phiInit)[3]
  47     sel.lambda <- S[[lambda]]$selected
  48     # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
  49     col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
  50     if (length(col.sel) == 0)
  51       return(NULL)
  52
  53     # lambda == 0 because we compute the EMV: no penalization here
  54     res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi,
  55       gamma, 0, X[, col.sel], Y, eps, fast)
  56
  57     # Eval dimension from the result + selected
  58     phiLambda2 <- res$phi
  59     rhoLambda <- res$rho
  60     piLambda <- res$pi
  61     phiLambda <- array(0, dim = c(p, m, k))
  62     for (j in seq_along(col.sel)) phiLambda[col.sel[j], sel.lambda[[j]], ] <- phiLambda2[j,
  63       sel.lambda[[j]], ]
  64     dimension <- length(unlist(sel.lambda))
  65
  66     # Computation of the loglikelihood
  67     densite <- vector("double", n)
  68     for (r in 1:k)
  69     {
  70       if (length(col.sel) == 1)
  71       {
  72         delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% t(phiLambda[col.sel,
  73           , r])))
  74       } else delta <- (Y %*% rhoLambda[, , r] - (X[, col.sel] %*% phiLambda[col.sel,
  75         , r]))
  76       densite <- densite + piLambda[r] * det(rhoLambda[, , r])/(sqrt(2 * base::pi))^m *
  77         exp(-diag(tcrossprod(delta))/2)
  78     }
  79     llhLambda <- c(sum(log(densite)), (dimension + m + 1) * k - 1)
  80     list(phi = phiLambda, rho = rhoLambda, pi = piLambda, llh = llhLambda)
  81   }
  82
  83   # For each lambda, computation of the parameters
  84   out <- if (ncores > 1)
  85     parLapply(cl, 1:length(S), computeAtLambda) else lapply(1:length(S), computeAtLambda)
  86
  87   if (ncores > 1)
  88     parallel::stopCluster(cl)
  89
  90   out
  91 }