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, maxi,gamma, X, Y,
  24      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(),
  30             varlist=c("phiInit","rhoInit","gamInit","mini","maxi","gamma","X","Y","eps",
  31             "S","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]]$selected
  48 #       col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
  49         col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
  50         if (length(col.sel) == 0)
  51             return (NULL)
  52
  53         # lambda == 0 because we compute the EMV: no penalization here
  54         res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
  55             X[,col.sel], Y, eps, fast)
  56
  57         # Eval dimension from the result + selected
  58         phiLambda2 = res$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))
  63             phiLambda[col.sel[j],sel.lambda[[j]],] = phiLambda2[j,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             delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%t(phiLambda[col.sel,,r])))
  72           } else delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))
  73             densite = densite + piLambda[r] *
  74                 det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
  75         }
  76         llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
  77         list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
  78     }
  79
  80     # For each lambda, computation of the parameters
  81     out =
  82         if (ncores > 1)
  83             parLapply(cl, 1:length(S), computeAtLambda)
  84         else
  85             lapply(1:length(S), computeAtLambda)
  86
  87     if (ncores > 1)
  88         parallel::stopCluster(cl)
  89
  90     out
  91 }