X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2FEMGrank.R;h=5eea322f6c47c677d843cca24205f0afea64c24a;hb=9ccdd55a1f6e9d409e8ae43b878a5e89c42e20c7;hp=e30b605134d79b0ced43c3f80c2622e28b5a3aad;hpb=aa480ac1fef50618978307a4df2cf9da1e285abc;p=valse.git

diff --git a/pkg/R/EMGrank.R b/pkg/R/EMGrank.R
index e30b605..5eea322 100644
--- a/pkg/R/EMGrank.R
+++ b/pkg/R/EMGrank.R
@@ -2,7 +2,6 @@
 #'
 #' Description de EMGrank
 #'
-#' @param phiInit ...
 #' @param Pi Parametre de proportion
 #' @param Rho Parametre initial de variance renormalisé
 #' @param mini Nombre minimal d'itérations dans l'algorithme EM
@@ -22,7 +21,7 @@ EMGrank <- function(Pi, Rho, mini, maxi, X, Y, tau, rank, fast=TRUE)
 	if (!fast)
 	{
 		# Function in R
-		return (EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
+		return (.EMGrank_R(Pi, Rho, mini, maxi, X, Y, tau, rank))
 	}
 
 	# Function in C
@@ -47,7 +46,7 @@ matricize <- function(X)
 }
 
 # R version - slow but easy to read
-EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
+.EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
 {
   #matrix dimensions
   n = dim(X)[1]
@@ -70,14 +69,14 @@ EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
   ite = 1
   while (ite<=mini || (ite<=maxi && sumDeltaPhi>tau))
 	{
-    #M step: Mise à jour de Beta (et donc phi)
+    #M step: update for Beta ( and then phi)
     for(r in 1:k)
 		{
-      Z_indice = seq_len(n)[Z==r] #indices où Z == r
+      Z_indice = seq_len(n)[Z==r] #indices where Z == r
       if (length(Z_indice) == 0)
         next
       #U,S,V = SVD of (t(Xr)Xr)^{-1} * t(Xr) * Yr
-      s = svd( ginv(crossprod(matricize(X[Z_indice,]))) %*%
+      s = svd( MASS::ginv(crossprod(matricize(X[Z_indice,]))) %*%
 				crossprod(matricize(X[Z_indice,]),matricize(Y[Z_indice,])) )
       S = s$d
       #Set m-rank(r) singular values to zero, and recompose
@@ -87,7 +86,7 @@ EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
       phi[,,r] = s$u %*% diag(S) %*% t(s$v) %*% Rho[,,r]
     }
 
-		#Etape E et calcul de LLF
+		#Step E and computation of the loglikelihood
 		sumLogLLF2 = 0
 		for(i in seq_len(n))
 		{
@@ -107,7 +106,7 @@ EMGrank_R = function(Pi, Rho, mini, maxi, X, Y, tau, rank)
 			}
 			sumLogLLF2 = sumLogLLF2 + log(sumLLF1)
 		}
-  
+
 		LLF = -1/n * sumLogLLF2
 
 		#update distance parameter to check algorithm convergence (delta(phi, Phi))