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   3     "contents" : "#' Main function\n#'\n#' @param X matrix of covariates (of size n*p)\n#' @param Y matrix of responses (of size n*m)\n#' @param procedure among 'LassoMLE' or 'LassoRank'\n#' @param selecMod method to select a model among 'SlopeHeuristic', 'BIC', 'AIC'\n#' @param gamma integer for the power in the penaly, by default = 1\n#' @param mini integer, minimum number of iterations in the EM algorithm, by default = 10\n#' @param maxi integer, maximum number of iterations in the EM algorithm, by default = 100\n#' @param eps real, threshold to say the EM algorithm converges, by default = 1e-4\n#' @param kmin integer, minimum number of clusters, by default = 2\n#' @param kmax integer, maximum number of clusters, by default = 10\n#' @param rang.min integer, minimum rank in the low rank procedure, by default = 1\n#' @param rang.max integer, maximum rank in the\n#' @return a list with estimators of parameters\n#' @export\n#-----------------------------------------------------------------------\nvalse = function(X,Y,procedure,selecMod,gamma = 1,mini = 10,\n                 maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,\n                 rang.min = 1,rang.max = 10) {\n  ##################################\n  #core workflow: compute all models\n  ##################################\n  \n  p = dim(phiInit)[1]\n  m = dim(phiInit)[2]\n  \n  print(\"main loop: over all k and all lambda\")\n  for (k in kmin:kmax)\n  {\n    print(k)\n    \n    print(\"Parameters initialization\")\n    #smallEM initializes parameters by k-means and regression model in each component,\n    #doing this 20 times, and keeping the values maximizing the likelihood after 10\n    #iterations of the EM algorithm.\n    init = initSmallEM(k, X, Y)\n    phiInit <<- init$phiInit\n    rhoInit <<- init$rhoInit\n    piInit\t<<- init$piInit\n    gamInit <<- init$gamInit\n    \n    gridLambda <<- gridLambda(phiInit, rhoInit, piInit, tauInit, X, Y, gamma, mini, maxi, eps)\n    \n    print(\"Compute relevant parameters\")\n    #select variables according to each regularization parameter\n    #from the grid: A1 corresponding to selected variables, and\n    #A2 corresponding to unselected variables.\n    params = selectiontotale(phiInit,rhoInit,piInit,tauInit,\n                             mini,maxi,gamma,gridLambda,\n                             X,Y,thresh,eps)\n    A1 <<- params$A1\n    A2 <<- params$A2\n    Rho <<- params$Rho\n    Pi <<- params$Pi\n    \n    if (procedure == 'LassoMLE') {\n      print('run the procedure Lasso-MLE')\n      #compute parameter estimations, with the Maximum Likelihood\n      #Estimator, restricted on selected variables.\n      model = constructionModelesLassoMLE(\n        phiInit, rhoInit,piInit,tauInit,mini,maxi,\n        gamma,gridLambda,X,Y,thresh,eps,A1,A2)\n      ################################################\n      ### Regarder la SUITE\n      r1 = runProcedure1()\n      Phi2 = Phi\n      Rho2 = Rho\n      Pi2 = Pi\n      \n      if (is.null(dim(Phi2)))\n        #test was: size(Phi2) == 0\n      {\n        Phi[, , 1:k] <<- r1$phi\n        Rho[, , 1:k] <<- r1$rho\n        Pi[1:k,] <<- r1$pi\n      } else\n      {\n        Phi <<-\n          array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))\n        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2\n        Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi\n        Rho <<-\n          array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))\n        Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2\n        Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho\n        Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))\n        Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2\n        Pi[1:k, ncol(Pi2) + 1] <<- r1$pi\n      }\n    } else {\n      print('run the procedure Lasso-Rank')\n      #compute parameter estimations, with the Low Rank\n      #Estimator, restricted on selected variables.\n      model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,\n                                           A1, rank.min, rank.max)\n      \n      ################################################\n      ### Regarder la SUITE  \n      phi = runProcedure2()$phi\n      Phi2 = Phi\n      if (dim(Phi2)[1] == 0)\n      {\n        Phi[, , 1:k,] <<- phi\n      } else\n      {\n        Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))\n        Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2\n        Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi\n      }\n    }\n  }\n  print('Model selection')\n  if (selecMod == 'SlopeHeuristic') {\n    \n  } else if (selecMod == 'BIC') {\n    \n  } else if (selecMod == 'AIC') {\n    \n  }\n}\n",
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