prepare EMGLLF / EMGrank wrappers, simplify folder generateTestData

[valse.git] / R / gridLambda.R

diff --git a/R/gridLambda.R b/R/gridLambda.R
index 7b82f63..855b4a6 100644 (file)
--- a/R/gridLambda.R
+++ b/R/gridLambda.R
@@ -1,31 +1,31 @@
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #' @param phiInit value for phi
-#' @param rhoInt  value for rho
-#' @param piInit  value for pi
+#' @param rhoInt       value for rho
+#' @param piInit       value for pi
 #' @param gamInit value for gamma
-#' @param mini    minimum number of iterations in EM algorithm
-#' @param maxi    maximum number of iterations in EM algorithm
-#' @param tau    threshold to stop EM algorithm
+#' @param mini         minimum number of iterations in EM algorithm
+#' @param maxi         maximum number of iterations in EM algorithm
+#' @param tau          threshold to stop EM algorithm
 #' @return the grid of regularization parameters
 #' @export
 #-----------------------------------------------------------------------
 gridLambda = function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, tau)
 {
-  n = nrow(X)
-  p = dim(phiInit)[1]
-  m = dim(phiInit)[2]
-  k = dim(phiInit)[3]
-  
-  list_EMG = .Call("EMGLLF",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
-  
-  grid = array(0, dim=c(p,m,k))
-  for (i in 1:p)
-  {
-    for (j in 1:m)
-      grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
-  }
-  grid = unique(grid)
-  grid = grid[grid <=1]
-  
-  return(grid)
+       n = nrow(X)
+       p = dim(phiInit)[1]
+       m = dim(phiInit)[2]
+       k = dim(phiInit)[3]
+
+       list_EMG = .Call("EMGLLF_core",phiInit,rhoInit,piInit,gamInit,mini,maxi,1,0,X,Y,tau)
+
+       grid = array(0, dim=c(p,m,k))
+       for (i in 1:p)
+       {
+               for (j in 1:m)
+                       grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
+       }
+       grid = unique(grid)
+       grid = grid[grid <=1]
+
+       return(grid)
 }