- print(paste("Computations for lambda=",lambda))
-
- n = dim(X)[1]
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
- k = dim(phiInit)[3]
-
- sel.lambda = selected[[lambda]]
-# 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)
-
- # Eval dimension from the result + selected
- phiLambda2 = res_EM$phi
- rhoLambda = res_EM$rho
- piLambda = res_EM$pi
- phiLambda = array(0, dim = c(p,m,k))
- for (j in seq_along(col.sel))
- phiLambda[col.sel[j],,] = phiLambda2[j,,]
-
- dimension = 0
- for (j in 1:p)
- {
- b = setdiff(1:m, sel.lambda[,j])
- if (length(b) > 0)
- phiLambda[j,b,] = 0.0
- dimension = dimension + sum(sel.lambda[,j]!=0)
- }
-
- # on veut calculer la vraisemblance avec toutes nos estimations
- densite = vector("double",n)
- for (r in 1:k)
- {
- delta = Y%*%rhoLambda[,,r] - (X[, col.sel]%*%phiLambda[col.sel,,r])
- densite = densite + piLambda[r] *
- det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
- }
- llhLambda = c( sum(log(densite)), (dimension+m+1)*k-1 )
- list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
- }
-
- #Pour chaque lambda de la grille, on calcule les coefficients
+ print(paste("Computations for lambda=",lambda))
+
+ n = dim(X)[1]
+ p = dim(phiInit)[1]
+ m = dim(phiInit)[2]
+ k = dim(phiInit)[3]
+
+ sel.lambda = S[[lambda]]$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)} else {
+
+ # lambda == 0 because we compute the EMV: no penalization here
+ res_EM = EMGLLF(phiInit[col.sel,,],rhoInit,piInit,gamInit,mini,maxi,gamma,0,
+ X[,col.sel],Y,tau)
+
+ # Eval dimension from the result + selected
+ phiLambda2 = res_EM$phi
+ rhoLambda = res_EM$rho
+ piLambda = res_EM$pi
+ phiLambda = array(0, dim = c(p,m,k))
+ for (j in seq_along(col.sel))
+ phiLambda[col.sel[j],,] = phiLambda2[j,,]
+
+ dimension = 0
+ for (j in 1:p)
+ {
+ b = setdiff(1:m, sel.lambda[[j]])## je confonds un peu ligne et colonne : est-ce dans le bon sens ?
+ ## moi pour la dimension, j'aurai juste mis length(unlist(sel.lambda)) mais je sais pas si c'est rapide
+ if (length(b) > 0)
+ phiLambda[j,b,] = 0.0
+ dimension = dimension + sum(sel.lambda[[j]]!=0)
+ }
+
+ # 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))
+ densite = densite + piLambda[r] *
+ det(rhoLambda[,,r])/(sqrt(2*base::pi))^m * exp(-tcrossprod(delta)/2.0)
+ }
+ llhLambda = c( sum(artefact^2 * log(densite)), (dimension+m+1)*k-1 )
+ list("phi"= phiLambda, "rho"= rhoLambda, "pi"= piLambda, "llh" = llhLambda)
+ }
+ }
+
+ # For each lambda, computation of the parameters