fix for m==1

[valse.git] / pkg / R / constructionModelesLassoRank.R

diff --git a/pkg/R/constructionModelesLassoRank.R b/pkg/R/constructionModelesLassoRank.R
index fe75d2c..dc88f67 100644 (file)
--- a/pkg/R/constructionModelesLassoRank.R
+++ b/pkg/R/constructionModelesLassoRank.R
@@ -1,7 +1,7 @@
 #' constructionModelesLassoRank
 #'
 #' Construct a collection of models with the Lasso-Rank procedure.
-#' 
+#'
 #' @param S output of selectVariables.R
 #' @param k number of components
 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
@@ -14,18 +14,18 @@
 #' @param ncores Number of cores, by default = 3
 #' @param fast TRUE to use compiled C code, FALSE for R code only
 #' @param verbose TRUE to show some execution traces
-#' 
+#'
 #' @return a list with several models, defined by phi, rho, pi, llh
 #'
 #' @export
 constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min, rank.max, 
-  ncores, fast = TRUE, verbose = FALSE)
-  {
-  n <- dim(X)[1]
-  p <- dim(X)[2]
-  m <- dim(Y)[2]
+  ncores, fast, verbose)
+{
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
   L <- length(S)
-  
+
   # Possible interesting ranks
   deltaRank <- rank.max - rank.min + 1
   Size <- deltaRank^k
@@ -42,7 +42,7 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       each = deltaRank^(k - r)), each = L)
   }
   RankLambda[, k + 1] <- rep(1:L, times = Size)
-  
+
   if (ncores > 1)
   {
     cl <- parallel::makeCluster(ncores, outfile = "")
@@ -50,23 +50,21 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       "Pi", "Rho", "mini", "maxi", "X", "Y", "eps", "Rank", "m", "phi", "ncores", 
       "verbose"))
   }
-  
+
   computeAtLambda <- function(index)
   {
     lambdaIndex <- RankLambda[index, k + 1]
     rankIndex <- RankLambda[index, 1:k]
     if (ncores > 1) 
       require("valse")  #workers start with an empty environment
-    
+
     # 'relevant' will be the set of relevant columns
     selected <- S[[lambdaIndex]]$selected
     relevant <- c()
     for (j in 1:p)
     {
       if (length(selected[[j]]) > 0)
-      {
         relevant <- c(relevant, j)
-      }
     }
     if (max(rankIndex) < length(relevant))
     {
@@ -75,21 +73,23 @@ constructionModelesLassoRank <- function(S, k, mini, maxi, X, Y, eps, rank.min,
       {
         res <- EMGrank(S[[lambdaIndex]]$Pi, S[[lambdaIndex]]$Rho, mini, maxi, 
           X[, relevant], Y, eps, rankIndex, fast)
-        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + 
-          m)))
+        llh <- c(res$LLF, sum(rankIndex * (length(relevant) - rankIndex + m)))
         phi[relevant, , ] <- res$phi
       }
       list(llh = llh, phi = phi, pi = S[[lambdaIndex]]$Pi, rho = S[[lambdaIndex]]$Rho)
-      
     }
   }
-  
+
   # For each lambda in the grid we compute the estimators
-  out <- if (ncores > 1) 
-    parLapply(cl, seq_len(length(S) * Size), computeAtLambda) else lapply(seq_len(length(S) * Size), computeAtLambda)
-  
+  out <-
+    if (ncores > 1) {
+      parLapply(cl, seq_len(length(S) * Size), computeAtLambda)
+    } else {
+      lapply(seq_len(length(S) * Size), computeAtLambda)
+    }
+
   if (ncores > 1) 
     parallel::stopCluster(cl)
-  
+
   out
 }