| 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 'SlopeHeuristic', 'BIC', '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,selecMod,gamma = 1,mini = 10, |
| 19 | maxi = 100,eps = 1e-4,kmin = 2,kmax = 10, |
| 20 | rang.min = 1,rang.max = 10) { |
| 21 | ################################## |
| 22 | #core workflow: compute all models |
| 23 | ################################## |
| 24 | |
| 25 | p = dim(phiInit)[1] |
| 26 | m = dim(phiInit)[2] |
| 27 | |
| 28 | print("main loop: over all k and all lambda") |
| 29 | for (k in kmin:kmax) |
| 30 | { |
| 31 | print(k) |
| 32 | |
| 33 | print("Parameters initialization") |
| 34 | #smallEM initializes parameters by k-means and regression model in each component, |
| 35 | #doing this 20 times, and keeping the values maximizing the likelihood after 10 |
| 36 | #iterations of the EM algorithm. |
| 37 | init = initSmallEM(k, X, Y) |
| 38 | phiInit <<- init$phiInit |
| 39 | rhoInit <<- init$rhoInit |
| 40 | piInit <<- init$piInit |
| 41 | gamInit <<- init$gamInit |
| 42 | |
| 43 | gridLambda <<- gridLambda(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, maxi, eps) |
| 44 | |
| 45 | print("Compute relevant parameters") |
| 46 | #select variables according to each regularization parameter |
| 47 | #from the grid: A1 corresponding to selected variables, and |
| 48 | #A2 corresponding to unselected variables. |
| 49 | params = selectiontotale(phiInit,rhoInit,piInit,gamInit,mini,maxi,gamma,gridLambda,X,Y,1e-8,eps) |
| 50 | A1 <<- params$A1 |
| 51 | A2 <<- params$A2 |
| 52 | Rho <<- params$Rho |
| 53 | Pi <<- params$Pi |
| 54 | |
| 55 | if (procedure == 'LassoMLE') { |
| 56 | print('run the procedure Lasso-MLE') |
| 57 | #compute parameter estimations, with the Maximum Likelihood |
| 58 | #Estimator, restricted on selected variables. |
| 59 | model = constructionModelesLassoMLE( |
| 60 | phiInit, rhoInit,piInit,tauInit,mini,maxi, |
| 61 | gamma,gridLambda,X,Y,thresh,eps,A1,A2) |
| 62 | ################################################ |
| 63 | ### Regarder la SUITE |
| 64 | r1 = runProcedure1() |
| 65 | Phi2 = Phi |
| 66 | Rho2 = Rho |
| 67 | Pi2 = Pi |
| 68 | |
| 69 | if (is.null(dim(Phi2))) |
| 70 | #test was: size(Phi2) == 0 |
| 71 | { |
| 72 | Phi[, , 1:k] <<- r1$phi |
| 73 | Rho[, , 1:k] <<- r1$rho |
| 74 | Pi[1:k,] <<- r1$pi |
| 75 | } else |
| 76 | { |
| 77 | Phi <<- |
| 78 | array(0., dim = c(p, m, kmax, dim(Phi2)[4] + dim(r1$phi)[4])) |
| 79 | Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2 |
| 80 | Phi[, , 1:k, dim(Phi2)[4] + 1] <<- r1$phi |
| 81 | Rho <<- |
| 82 | array(0., dim = c(m, m, kmax, dim(Rho2)[4] + dim(r1$rho)[4])) |
| 83 | Rho[, , 1:(dim(Rho2)[3]), 1:(dim(Rho2)[4])] <<- Rho2 |
| 84 | Rho[, , 1:k, dim(Rho2)[4] + 1] <<- r1$rho |
| 85 | Pi <<- array(0., dim = c(kmax, dim(Pi2)[2] + dim(r1$pi)[2])) |
| 86 | Pi[1:nrow(Pi2), 1:ncol(Pi2)] <<- Pi2 |
| 87 | Pi[1:k, ncol(Pi2) + 1] <<- r1$pi |
| 88 | } |
| 89 | } else { |
| 90 | print('run the procedure Lasso-Rank') |
| 91 | #compute parameter estimations, with the Low Rank |
| 92 | #Estimator, restricted on selected variables. |
| 93 | model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps, |
| 94 | A1, rank.min, rank.max) |
| 95 | |
| 96 | ################################################ |
| 97 | ### Regarder la SUITE |
| 98 | phi = runProcedure2()$phi |
| 99 | Phi2 = Phi |
| 100 | if (dim(Phi2)[1] == 0) |
| 101 | { |
| 102 | Phi[, , 1:k,] <<- phi |
| 103 | } else |
| 104 | { |
| 105 | Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4])) |
| 106 | Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2 |
| 107 | Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi |
| 108 | } |
| 109 | } |
| 110 | } |
| 111 | print('Model selection') |
| 112 | if (selecMod == 'SlopeHeuristic') { |
| 113 | |
| 114 | } else if (selecMod == 'BIC') { |
| 115 | |
| 116 | } else if (selecMod == 'AIC') { |
| 117 | |
| 118 | } |
| 119 | } |