correction typo
[valse.git] / R / selectVariables.R
... / ...
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1#' selectVaribles
2#' It is a function which construct, for a given lambda, the sets of
3#' relevant variables and irrelevant variables.
4#'
5#' @param phiInit an initial estimator for phi (size: p*m*k)
6#' @param rhoInit an initial estimator for rho (size: m*m*k)
7#' @param piInit an initial estimator for pi (size : k)
8#' @param gamInit an initial estimator for gamma
9#' @param mini minimum number of iterations in EM algorithm
10#' @param maxi maximum number of iterations in EM algorithm
11#' @param gamma power in the penalty
12#' @param glambda grid of regularization parameters
13#' @param X matrix of regressors
14#' @param Y matrix of responses
15#' @param thres threshold to consider a coefficient to be equal to 0
16#' @param tau threshold to say that EM algorithm has converged
17#'
18#' @return
19#' @export
20#'
21#' @examples
22selectVariables <- function(phiInit,rhoInit,piInit,gamInit,
23 mini,maxi,gamma,glambda,X,Y,thres,tau)
24{
25 dimphi <- dim(phiInit)
26 p <- dimPhi[1]
27 m <- dimPhi[2]
28 k <- dimPhi[3]
29 L <- length(glambda);
30 A1 <- array(0, dim <- c(p,m+1,L))
31 A2 <- array(0, dim <- c(p,m+1,L))
32 Rho <- array(0, dim <- c(m,m,k,L))
33 Pi <- array(0, dim <- c(k,L));
34
35 # For every lambda in gridLambda, comutation of the coefficients
36 for (lambdaIndex in c(1:L))
37 {
38 Res <- EMGLLF(phiInit,rhoInit,piInit,gamInit,mini,maxi,
39 gamma,glambda[lambdaIndex],X,Y,tau);
40 phi <- Res$phi
41 rho <- Res$rho
42 pi <- Res$pi
43
44 # If a coefficient is larger than the threshold, we keep it
45 selectedVariables <- array(0, dim = c(p,m))
46 discardedVariables <- array(0, dim = c(p,m))
47 atLeastOneSelectedVariable <- false
48 for (j in c(1:p))
49 {
50 cpt <- 1
51 cpt2 <-1
52 for (mm in c(1:m))
53 {
54 if (max(abs(phi[j,mm,])) > thres)
55 {
56 selectedVariables[j,cpt] <- mm
57 cpt <- cpt+1
58 atLeastOneSelectedVariable <- true
59 } else
60 {
61 discardedVariables[j,cpt2] <- mm
62 cpt2 <- cpt2+1
63 }
64 }
65 }
66
67 # If no coefficients have been selected, we provide the zero matrix
68 # We delete zero coefficients: vec = indices of zero values
69 if (atLeastOneSelectedVariable)
70 {
71 vec <- c()
72 for (j in c(1:p))
73 {
74 if (selectedVariables(j,1) != 0)
75 vec <- c(vec,j)
76 # Else ( NOTE: [auder] else ?! TODO: explain? )
77 # we provide the indices of relevant coefficients
78 A1[,1,lambdaIndex] <- c(vec,rep(0,p-length(vec)))
79 A1[1:length(vec),2:(m+1),lambdaIndex] <- selectedVariables[vec,]
80 A2[,1,lambdaIndex] <- 1:p
81 A2[,2:(m+1),lambdaIndex] <- discardedVariables
82 Rho[,,,lambdaIndex] <- rho
83 Pi[,lambdaIndex] <- pi
84 }
85 }
86 }
87
88 return(res = list(A1 = A1, A2 = A2 , Rho = Rho, Pi = Pi))
89}