X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2FconstructionModelesLassoMLE.R;h=1275ca3cc5d94c754ea3c741f605e78ac939e23a;hp=760da40be6952bad6312c36636d99df410009583;hb=ea5860f1b4fc91f06e371a0b26915198474a849d;hpb=ffdf94474d96cdd3e9d304ce809df7e62aa957ed

diff --git a/pkg/R/constructionModelesLassoMLE.R b/pkg/R/constructionModelesLassoMLE.R
index 760da40..1275ca3 100644
--- a/pkg/R/constructionModelesLassoMLE.R
+++ b/pkg/R/constructionModelesLassoMLE.R
@@ -21,8 +21,8 @@
 #'
 #' @export
 constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini, 
-  maxi, gamma, X, Y, eps, S, ncores = 3, fast = TRUE, verbose = FALSE)
-  {
+  maxi, gamma, X, Y, eps, S, ncores = 3, fast, verbose)
+{
   if (ncores > 1)
   {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -30,62 +30,64 @@ constructionModelesLassoMLE <- function(phiInit, rhoInit, piInit, gamInit, mini,
       "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]
+
+    n <- nrow(X)
+    p <- ncol(X)
+    m <- ncol(Y)
+    k <- length(piInit)
     sel.lambda <- S[[lambda]]$selected
     # col.sel = which(colSums(sel.lambda)!=0) #if boolean matrix
     col.sel <- which(sapply(sel.lambda, length) > 0)  #if list of selected vars
     if (length(col.sel) == 0) 
       return(NULL)
-    
+
     # lambda == 0 because we compute the EMV: no penalization here
-    res <- EMGLLF(phiInit[col.sel, , ], rhoInit, piInit, gamInit, mini, maxi, 
-      gamma, 0, X[, col.sel], Y, eps, fast)
-    
+    res <- EMGLLF(array(phiInit,dim=c(p,m,k))[col.sel, , ], rhoInit, piInit, gamInit,
+      mini, maxi, gamma, 0, as.matrix(X[, col.sel]), Y, eps, fast)
+
     # Eval dimension from the result + selected
     phiLambda2 <- res$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]], ]
+    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 * 
+        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] * gdet(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)
-  
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, 1:length(S), computeAtLambda)
+    } else {
+      lapply(1:length(S), computeAtLambda)
+    }
+
   if (ncores > 1) 
     parallel::stopCluster(cl)
-  
+
   out
 }