+++ /dev/null
-#' 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 'SlopeHeuristic', 'BIC', '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,selecMod,gamma = 1,mini = 10,
- maxi = 100,eps = 1e-4,kmin = 2,kmax = 10,
- rang.min = 1,rang.max = 10) {
- ##################################
- #core workflow: compute all models
- ##################################
-
- p = dim(phiInit)[1]
- m = dim(phiInit)[2]
-
- 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
-
- gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps)
-
- 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,gridLambda,X,Y,1e-8,eps)
- A1 <<- params$A1
- A2 <<- params$A2
- 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 = constructionModelesLassoMLE(
- phiInit, rhoInit,piInit,tauInit,mini,maxi,
- gamma,gridLambda,X,Y,thresh,eps,A1,A2)
- ################################################
- ### Regarder la SUITE
- r1 = runProcedure1()
- Phi2 = Phi
- Rho2 = Rho
- Pi2 = Pi
-
- if (is.null(dim(Phi2)))
- #test was: size(Phi2) == 0
- {
- Phi[, , 1:k] <<- r1$phi
- Rho[, , 1:k] <<- r1$rho
- Pi[1:k,] <<- r1$pi
- } else
- {
- Phi <<-
- array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4]))
- Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
- Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi
- Rho <<-
- array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4]))
- Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2
- Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho
- Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2]))
- Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2
- Pi[1:k, ncol(Pi2) + 1] <<- r1$pi
- }
- } else {
- print('run the procedure Lasso-Rank')
- #compute parameter estimations, with the Low Rank
- #Estimator, restricted on selected variables.
- model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
- A1, rank.min, rank.max)
-
- ################################################
- ### Regarder la SUITE
- phi = runProcedure2()$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
- }
- }
- }
- print('Model selection')
- if (selecMod == 'SlopeHeuristic') {
-
- } else if (selecMod == 'BIC') {
-
- } else if (selecMod == 'AIC') {
-
- }
-}