Update to conform to CRAN rules (unfinished)
[valse.git] / pkg / R / selectVariables.R
1 #' selectVariables
2 #'
3 #' For a given lambda, construct the sets of relevant variables for each cluster.
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 thresh real, threshold to say a variable is relevant, by default = 1e-8
16 #' @param eps threshold to say that EM algorithm has converged
17 #' @param ncores Number or cores for parallel execution (1 to disable)
18 #' @param fast boolean to enable or not the C function call
19 #'
20 #' @return a list of outputs, for each lambda in grid: selected,Rho,Pi
21 #'
22 #' @export
23 selectVariables <- function(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma,
24 glambda, X, Y, thresh = 1e-08, eps, ncores = 3, fast)
25 {
26 if (ncores > 1) {
27 cl <- parallel::makeCluster(ncores, outfile = "")
28 parallel::clusterExport(cl = cl, varlist = c("phiInit", "rhoInit", "gamInit",
29 "mini", "maxi", "glambda", "X", "Y", "thresh", "eps"), envir = environment())
30 }
31
32 # Computation for a fixed lambda
33 computeCoefs <- function(lambda)
34 {
35 params <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda,
36 X, Y, eps, fast)
37
38 p <- ncol(X)
39 m <- ncol(Y)
40
41 # selectedVariables: list where element j contains vector of selected variables
42 # in [1,m]
43 selectedVariables <- lapply(1:p, function(j) {
44 # from boolean matrix mxk of selected variables obtain the corresponding boolean
45 # m-vector, and finally return the corresponding indices
46 if (m>1) {
47 seq_len(m)[apply(abs(params$phi[j, , ]) > thresh, 1, any)]
48 } else {
49 if (any(params$phi[j, , ] > thresh))
50 1
51 else
52 numeric(0)
53 }
54 })
55
56 list(selected = selectedVariables, Rho = params$rho, Pi = params$pi)
57 }
58
59 # For each lambda in the grid, we compute the coefficients
60 out <-
61 if (ncores > 1) {
62 parLapply(cl, glambda, computeCoefs)
63 } else {
64 lapply(glambda, computeCoefs)
65 }
66 if (ncores > 1)
67 parallel::stopCluster(cl)
68
69 # Suppress models which are computed twice
70 # sha1_array <- lapply(out, digest::sha1) out[ duplicated(sha1_array) ]
71 selec <- lapply(out, function(model) model$selected)
72 ind_dup <- duplicated(selec)
73 ind_uniq <- which(!ind_dup)
74 out2 <- list()
75 for (l in 1:length(ind_uniq))
76 out2[[l]] <- out[[ind_uniq[l]]]
77 out2
78 }