#' constructionModelesLassoMLE
#'
-#' TODO: description
+#' Construct a collection of models with the Lasso-MLE procedure.
+#'
#'
#' @param ...
#'
#'
#' export
constructionModelesLassoMLE = function(phiInit, rhoInit, piInit, gamInit, mini, maxi,
- gamma, X, Y, thresh, tau, S, ncores=3, artefact = 1e3, verbose=FALSE)
+ gamma, X, Y, thresh, tau, S, ncores=3, fast=TRUE, verbose=FALSE)
{
if (ncores > 1)
{
p = dim(phiInit)[1]
m = dim(phiInit)[2]
k = dim(phiInit)[3]
-
sel.lambda = S[[lambda]]$selected
# col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
col.sel <- which( sapply(sel.lambda,length) > 0 ) #if list of selected vars
-
if (length(col.sel) == 0)
return (NULL)
# lambda == 0 because we compute the EMV: no penalization here
res = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
- X[,col.sel],Y,tau)
+ X[,col.sel], Y, tau, fast)
# Eval dimension from the result + selected
phiLambda2 = res$phi
piLambda = res$pi
phiLambda = array(0, dim = c(p,m,k))
for (j in seq_along(col.sel))
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
+ 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)
{
- delta = (Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r]))/artefact
- print(max(delta))
+ 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(-tcrossprod(delta)/2.0)
+ det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-diag(tcrossprod(delta))/2.0)
}
- llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
+ llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
}