pkg/R/valse.R

   1 #' Main function
   2 #'
   3 #' @param X matrix of covariates (of size n*p)
   4 #' @param Y matrix of responses (of size n*m)
   5 #' @param procedure among 'LassoMLE' or 'LassoRank'
   6 #' @param selecMod method to select a model among 'DDSE', 'DJump', 'BIC' or 'AIC'
   7 #' @param gamma integer for the power in the penaly, by default = 1
   8 #' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10
   9 #' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100
  10 #' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4
  11 #' @param kmin integer, minimum number of clusters, by default = 2
  12 #' @param kmax integer, maximum number of clusters, by default = 10
  13 #' @param rang.min integer, minimum rank in the low rank procedure, by default = 1
  14 #' @param rang.max integer, maximum rank in the
  15 #' @return a list with estimators of parameters
  16 #' @export
  17 #-----------------------------------------------------------------------
  18 valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
  19                  maxi = 50,eps = 1e-4,kmin = 2,kmax = 3,
  20                  rang.min = 1,rang.max = 10) {
  21   ##################################
  22   #core workflow: compute all models
  23   ##################################
  24
  25   p = dim(X)[2]
  26   m = dim(Y)[2]
  27   n = dim(X)[1]
  28
  29   model = list()
  30   tableauRecap = array(0, dim=c(1000,4))
  31   cpt = 0
  32   print("main loop: over all k and all lambda")
  33
  34   for (k in kmin:kmax){
  35     print(k)
  36     print("Parameters initialization")
  37     #smallEM initializes parameters by k-means and regression model in each component,
  38     #doing this 20 times, and keeping the values maximizing the likelihood after 10
  39     #iterations of the EM algorithm.
  40     init = initSmallEM(k, X, Y)
  41     phiInit <<- init$phiInit
  42     rhoInit <<- init$rhoInit
  43     piInit  <<- init$piInit
  44     gamInit <<- init$gamInit
  45     source('~/valse/pkg/R/gridLambda.R')
  46     grid_lambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
  47
  48     # if (length(grid_lambda)>50){
  49     #   grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 50)]
  50     # }
  51     print("Compute relevant parameters")
  52     #select variables according to each regularization parameter
  53     #from the grid: A1 corresponding to selected variables, and
  54     #A2 corresponding to unselected variables.
  55
  56     params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda,X,Y,1e-8,eps)
  57     #params2 = selectVariables(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,grid_lambda[seq(1,length(grid_lambda), by=3)],X,Y,1e-8,eps)
  58     ## etrange : params et params 2 sont différents ...
  59
  60     selected <<- params$selected
  61     Rho <<- params$Rho
  62     Pi <<- params$Pi
  63
  64     if (procedure == 'LassoMLE') {
  65       print('run the procedure Lasso-MLE')
  66       #compute parameter estimations, with the Maximum Likelihood
  67       #Estimator, restricted on selected variables.
  68       model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
  69       llh = matrix(ncol = 2)
  70       for (l in seq_along(model[[k]])){
  71         llh = rbind(llh, model[[k]][[l]]$llh)
  72       }
  73       LLH = llh[-1,1]
  74       D = llh[-1,2]
  75     } else {
  76       print('run the procedure Lasso-Rank')
  77       #compute parameter estimations, with the Low Rank
  78       #Estimator, restricted on selected variables.
  79       model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
  80                                            A1, rank.min, rank.max)
  81
  82       ################################################
  83       ### Regarder la SUITE
  84       phi = runProcedure2()$phi
  85       Phi2 = Phi
  86       if (dim(Phi2)[1] == 0)
  87       {
  88         Phi[, , 1:k,] <<- phi
  89       } else
  90       {
  91         Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
  92         Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
  93         Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
  94       }
  95     }
  96     tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
  97     cpt = cpt+length(model[[k]])
  98   }
  99   print('Model selection')
 100   tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
 101   tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
 102   data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
 103   require(capushe)
 104   modSel = capushe(data, n)
 105   if (selecMod == 'DDSE') {
 106     indModSel = as.numeric(modSel@DDSE@model)
 107   } else if (selecMod == 'Djump') {
 108     indModSel = as.numeric(modSel@Djump@model)
 109   } else if (selecMod == 'BIC') {
 110     indModSel = modSel@BIC_capushe$model
 111   } else if (selecMod == 'AIC') {
 112     indModSel = modSel@AIC_capushe$model
 113   }
 114   return(model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]])
 115 }