few last things
[valse.git] / pkg / R / valse.R
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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#-----------------------------------------------------------------------
18valse = function(X,Y,procedure = 'LassoMLE',selecMod = 'DDSE',gamma = 1,mini = 10,
19 maxi = 50,eps = 1e-4,kmin = 2,kmax = 2,
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)>100){
49 grid_lambda = grid_lambda[seq(1, length(grid_lambda), length.out = 100)]
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 selected <<- params$selected
60 Rho <<- params$Rho
61 Pi <<- params$Pi
62
63 if (procedure == 'LassoMLE') {
64 print('run the procedure Lasso-MLE')
65 #compute parameter estimations, with the Maximum Likelihood
66 #Estimator, restricted on selected variables.
67 model[[k]] = constructionModelesLassoMLE(phiInit, rhoInit,piInit,gamInit,mini,maxi,gamma,X,Y,thresh,eps,selected)
68 llh = matrix(ncol = 2)
69 for (l in seq_along(model[[k]])){
70 llh = rbind(llh, model[[k]][[l]]$llh)
71 }
72 LLH = llh[-1,1]
73 D = llh[-1,2]
74 } else {
75 print('run the procedure Lasso-Rank')
76 #compute parameter estimations, with the Low Rank
77 #Estimator, restricted on selected variables.
78 model = constructionModelesLassoRank(Pi, Rho, mini, maxi, X, Y, eps,
79 A1, rank.min, rank.max)
80
81 ################################################
82 ### Regarder la SUITE
83 phi = runProcedure2()$phi
84 Phi2 = Phi
85 if (dim(Phi2)[1] == 0)
86 {
87 Phi[, , 1:k,] <<- phi
88 } else
89 {
90 Phi <<- array(0, dim = c(p, m, kmax, dim(Phi2)[4] + dim(phi)[4]))
91 Phi[, , 1:(dim(Phi2)[3]), 1:(dim(Phi2)[4])] <<- Phi2
92 Phi[, , 1:k,-(1:(dim(Phi2)[4]))] <<- phi
93 }
94 }
95 tableauRecap[(cpt+1):(cpt+length(model[[k]])), ] = matrix(c(LLH, D, rep(k, length(model[[k]])), 1:length(model[[k]])), ncol = 4)
96 cpt = cpt+length(model[[k]])
97 }
98 print('Model selection')
99 tableauRecap = tableauRecap[rowSums(tableauRecap[, 2:4])!=0,]
100 tableauRecap = tableauRecap[(tableauRecap[,1])!=Inf,]
101 data = cbind(1:dim(tableauRecap)[1], tableauRecap[,2], tableauRecap[,2], tableauRecap[,1])
102 require(capushe)
103 modSel = capushe(data, n)
104 if (selecMod == 'DDSE') {
105 indModSel = as.numeric(modSel@DDSE@model)
106 } else if (selecMod == 'Djump') {
107 indModSel = as.numeric(modSel@Djump@model)
108 } else if (selecMod == 'BIC') {
109 indModSel = modSel@BIC_capushe$model
110 } else if (selecMod == 'AIC') {
111 indModSel = modSel@AIC_capushe$model
112 }
113 return(model[[tableauRecap[indModSel,3]]][[tableauRecap[indModSel,4]]])
114}