R/modelSelection.R

   1 #' Among a collection of models, this function constructs a subcollection of models with
   2 #' models having strictly different dimensions, keeping the model which minimizes
   3 #' the likelihood if there were several with the same dimension
   4 #'
   5 #' @param LLF a matrix, the first column corresponds to likelihoods for several models
   6 #'              the second column corresponds to the dimensions of the corresponding models.
   7 #'
   8 #' @return a list with indices, a vector of indices selected models,
   9 #'               and D1, a vector of corresponding dimensions
  10 #' @export
  11 #'
  12 modelSelection = function(LLF)
  13 {
  14     D = LLF[,2]
  15     D1 = unique(D)
  16
  17     indices = rep(1, length(D1))
  18     #select argmax MLE
  19     if (length(D1)>2)
  20     {
  21         for (i in 1:length(D1))
  22         {
  23             A = c()
  24             for (j in 1:length(D))
  25             {
  26                 if(D[[j]]==D1[[i]])
  27                     a = c(a, LLF[j,1])
  28             }
  29             b = max(a)
  30             #indices[i] : first indices of the binary vector where u_i ==1
  31             indices[i] = which.max(LLF == b)
  32         }
  33     }
  34
  35     return (list(indices=indices,D1=D1))
  36 }
  37
  38 #TODO:
  39 ## Programme qui sélectionne un modèle
  40 ## proposer à l'utilisation différents critères (BIC, AIC, slope heuristic)