X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=R%2FmodelSelection.R;h=86e2efd563829e9474b6769a03a76803b907be16;hb=31ef8a5c9bbaefcf40d2c2bad43b27d469c28c34;hp=5a79bb6326299e8d852aef47566288f24a2bbb25;hpb=f2a9120810d7e1e423c7b5c2c4320f4e27221f50;p=valse.git

diff --git a/R/modelSelection.R b/R/modelSelection.R
index 5a79bb6..86e2efd 100644
--- a/R/modelSelection.R
+++ b/R/modelSelection.R
@@ -1,36 +1,40 @@
 #' Among a collection of models, this function constructs a subcollection of models with
-#' models having strictly different dimensions, keeping the model which minimizes 
+#' models having strictly different dimensions, keeping the model which minimizes
 #' the likelihood if there were several with the same dimension
 #'
 #' @param LLF a matrix, the first column corresponds to likelihoods for several models
-#'        the second column corresponds to the dimensions of the corresponding models.
+#'				the second column corresponds to the dimensions of the corresponding models.
 #'
-#' @return a list with indices, a vector of indices selected models, 
-#'         and D1, a vector of corresponding dimensions
+#' @return a list with indices, a vector of indices selected models,
+#'				 and D1, a vector of corresponding dimensions
 #' @export
 #'
 modelSelection = function(LLF)
 {
-  D = LLF[,2]
-  D1 = unique(D)
-  
-  indices = rep(1, length(D1))
-  #select argmax MLE
-  if (length(D1)>2)
-  {
-    for (i in 1:length(D1))
-    {
-      A = c()
-      for (j in 1:length(D))
-      {
-        if(D[[j]]==D1[[i]])
-          a = c(a, LLF[j,1])
-      }
-      b = max(a)
-      #indices[i] : first indices of the binary vector where u_i ==1
-      indices[i] = which.max(vec_bin(LLF,b)[[1]])
-    }
-  }
-  
-  return (list(indices=indices,D1=D1))
+	D = LLF[,2]
+	D1 = unique(D)
+
+	indices = rep(1, length(D1))
+	#select argmax MLE
+	if (length(D1)>2)
+	{
+		for (i in 1:length(D1))
+		{
+			A = c()
+			for (j in 1:length(D))
+			{
+				if(D[[j]]==D1[[i]])
+					a = c(a, LLF[j,1])
+			}
+			b = max(a)
+			#indices[i] : first indices of the binary vector where u_i ==1
+			indices[i] = which.max(LLF == b)
+		}
+	}
+
+	return (list(indices=indices,D1=D1))
 }
+
+#TODO:
+## Programme qui sélectionne un modèle
+## proposer à l'utilisation différents critères (BIC, AIC, slope heuristic)