X-Git-Url: https://git.auder.net/?a=blobdiff_plain;f=pkg%2FR%2Fvalse.R;fp=pkg%2FR%2Fvalse.R;h=0000000000000000000000000000000000000000;hb=086ca318ed5580e961ceda3f1e122a2da58e4427;hp=d5d10ced034dd9d43b7e39794926864423bb6528;hpb=4e8267487c83c27273305b1379e44bc7abebf4b5;p=valse.git

diff --git a/pkg/R/valse.R b/pkg/R/valse.R
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-#' Main function
-#'
-#' @param X matrix of covariates (of size n*p)
-#' @param Y matrix of responses (of size n*m)
-#' @param procedure among 'LassoMLE' or 'LassoRank'
-#' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
-#' @param gamma integer for the power in the penaly, by default = 1
-#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
-#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
-#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
-#' @param kmin integer, minimum number of clusters, by default = 2
-#' @param kmax integer, maximum number of clusters, by default = 10
-#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
-#' @param rang.max integer, maximum rank in the
-#' @return a list with estimators of parameters
-#' @export
-#-----------------------------------------------------------------------
-valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
-                 maxi = 50,eps = 1e-4,kmin = 2,kmax = 2,
-                 rang.min = 1,rang.max = 10) {
-  ##################################
-  #core workflow: compute all models
-  ##################################
-  
-  p = dim(X)[2]
-  m = dim(Y)[2]
-  n = dim(X)[1]
-  
-  model = list()
-  tableauRecap = array(0, dim=c(1000,4))
-  cpt = 0
-  print("main loop: over all k and all lambda")
-  
-  for (k in kmin:kmax){
-    print(k)
-    print("Parameters initialization")
-    #smallEM initializes parameters by k-means and regression model in each component,
-    #doing this 20 times, and keeping the values maximizing the likelihood after 10
-    #iterations of the EM algorithm.
-    init = initSmallEM(k, X, Y)
-    phiInit <<- init$phiInit
-    rhoInit <<- init$rhoInit
-    piInit	<<- init$piInit
-    gamInit <<- init$gamInit
-    grid_lambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
-    
-    if (length(grid_lambda)>100){
-      grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
-    }
-    print("Compute relevant parameters")
-    #select variables according to each regularization parameter
-    #from the grid: A1 corresponding to selected variables, and
-    #A2 corresponding to unselected variables.
-    
-    params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda,X,Y,1e-8,eps)
-    #params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda[seq(1,length(grid_lambda), by=3)],X,Y,1e-8,eps)
-    ## etrange : params et params 2 sont différents ...
-    selected <<- params$selected
-    Rho <<- params$Rho
-    Pi <<- params$Pi
-    
-    if (procedure == 'LassoMLE') {
-      print('run the procedure Lasso-MLE')
-      #compute parameter estimations, with the Maximum Likelihood
-      #Estimator, restricted on selected variables.
-      model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
-      llh = matrix(ncol = 2)
-      for (l in seq_along(model[[k]])){
-        llh = rbind(llh, model[[k]][[l]]$llh)
-      }
-      LLH = llh[-1,1]
-      D = llh[-1,2]
-    } else {
-      print('run the procedure Lasso-Rank')
-      #compute parameter estimations, with the Low Rank
-      #Estimator, restricted on selected variables.
-      model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
-                                           A1, rank.min, rank.max)
-      
-      ################################################
-      ### Regarder la SUITE  
-      phi = runProcedure2()$phi
-      Phi2 = Phi
-      if (dim(Phi2)[1] == 0)
-      {
-        Phi[, , 1:k,] <<- phi
-      } else
-      {
-        Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
-        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
-        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
-      }
-    }
-    tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
-    cpt = cpt+length(model[[k]])
-  }
-  print('Model selection')
-  tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
-  tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
-  data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
-  require(capushe)
-  modSel = capushe(data, n)
-  if (selecMod == 'DDSE') {
-    indModSel = as.numeric(modSel@DDSE@model)
-  } else if (selecMod == 'Djump') {
-    indModSel = as.numeric(modSel@Djump@model)
-  } else if (selecMod == 'BIC') {
-    indModSel = modSel@BIC_capushe$model
-  } else if (selecMod == 'AIC') {
-    indModSel = modSel@AIC_capushe$model
-  }
-  return(model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]])
-}