#' @param ncores_inner Number of cores for the inner loop on lambda
#' @param thresh real, threshold to say a variable is relevant, by default = 1e-8
#' @param grid_lambda, a vector with regularization parameters if known, by default numeric(0)
-#' @param size_coll_mod (Maximum) size of a collection of models
+#' @param size_coll_mod (Maximum) size of a collection of models, by default 50
#' @param fast TRUE to use compiled C code, FALSE for R code only
#' @param verbose TRUE to show some execution traces
#' @param plot TRUE to plot the selected models after run
#'
-#' @return a list with estimators of parameters
+#' @return
+#' The selected model (except if the collection of models
+#' has less than 11 models, the function returns the collection as it can not select one using Capushe)
#'
#' @examples
-#' #TODO: a few examples
+#' n = 50; m = 10; p = 5
+#' beta = array(0, dim=c(p,m,2))
+#' beta[,,1] = 1
+#' beta[,,2] = 2
+#' data = generateXY(n, c(0.4,0.6), rep(0,p), beta, diag(0.5, p), diag(0.5, m))
+#' X = data$X
+#' Y = data$Y
+#' res = runValse(X, Y, kmax = 5, plot=FALSE)
+#' X <- matrix(runif(100), nrow=50)
+#' Y <- matrix(runif(100), nrow=50)
+#' res = runValse(X, Y, plot=FALSE)
#'
#' @export
runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
maxi = 50, eps = 1e-04, kmin = 2, kmax = 3, rank.min = 1, rank.max = 5, ncores_outer = 1,
- ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 10,
+ ncores_inner = 1, thresh = 1e-08, grid_lambda = numeric(0), size_coll_mod = 50,
fast = TRUE, verbose = FALSE, plot = TRUE)
{
n <- nrow(X)
p <- ncol(X)
m <- ncol(Y)
- if (verbose)
- print("main loop: over all k and all lambda")
+ if (verbose) print("main loop: over all k and all lambda")
if (ncores_outer > 1) {
cl <- parallel::makeCluster(ncores_outer, outfile = "")
}
# Compute models with k components
- computeModels <- function(k)
- {
+ computeModels <- function(k) {
if (ncores_outer > 1)
require("valse") #nodes start with an empty environment
# component, doing this 20 times, and keeping the values maximizing the
# likelihood after 10 iterations of the EM algorithm.
P <- initSmallEM(k, X, Y, fast)
- if (length(grid_lambda) == 0)
- {
+ if (length(grid_lambda) == 0) {
grid_lambda <- computeGridLambda(P$phiInit, P$rhoInit, P$piInit, P$gamInit,
X, Y, gamma, mini, maxi, eps, fast)
}
# List (index k) of lists (index lambda) of models
models_list <-
if (ncores_outer > 1) {
- parLapply(cl, kmin:kmax, computeModels)
+ parallel::parLapply(cl, kmin:kmax, computeModels)
} else {
lapply(kmin:kmax, computeModels)
}
- if (ncores_outer > 1)
- parallel::stopCluster(cl)
+ if (ncores_outer > 1) parallel::stopCluster(cl)
- if (!requireNamespace("capushe", quietly = TRUE))
- {
+ if (!requireNamespace("capushe", quietly = TRUE)) {
warning("'capushe' not available: returning all models")
return(models_list)
}
# Get summary 'tableauRecap' from models
- tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i)
- {
+ tableauRecap <- do.call(rbind, lapply(seq_along(models_list), function(i) {
models <- models_list[[i]]
# For a collection of models (same k, several lambda):
LLH <- sapply(models, function(model) model$llh[1])
k <- length(models[[1]]$pi)
- sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[,
- , 1] != 0) + 1) - 1)
- data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n,
- complexity = sumPen, contrast = -LLH)
+ sumPen <- sapply(models, function(model) k * (dim(model$rho)[1] + sum(model$phi[,,1] != 0) + 1) - 1)
+ data.frame(model = paste(i, ".", seq_along(models), sep = ""), pen = sumPen/n, complexity = sumPen, contrast = -LLH)
}))
tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
+ if (verbose) print(tableauRecap)
- if (verbose == TRUE)
- print(tableauRecap)
- modSel <- capushe::capushe(tableauRecap, n)
- indModSel <- if (selecMod == "DDSE")
- {
- as.numeric(modSel@DDSE@model)
- } else if (selecMod == "Djump")
- {
- as.numeric(modSel@Djump@model)
- } else if (selecMod == "BIC")
- {
- modSel@BIC_capushe$model
- } else if (selecMod == "AIC")
- {
- modSel@AIC_capushe$model
- }
-
- listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
- modelSel <- models_list[[listMod[1]]][[listMod[2]]]
- modelSel$tableau <- tableauRecap
-
- if (plot)
- print(plot_valse(X, Y, modelSel, n))
+ if (nrow(tableauRecap) > 10) {
+ modSel <- capushe::capushe(tableauRecap, n)
+ indModSel <- if (selecMod == "DDSE") {
+ as.numeric(modSel@DDSE@model)
+ } else if (selecMod == "Djump") {
+ as.numeric(modSel@Djump@model)
+ } else if (selecMod == "BIC") {
+ modSel@BIC_capushe$model
+ } else if (selecMod == "AIC") {
+ modSel@AIC_capushe$model
+ }
+ listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
+ modelSel <- models_list[[listMod[1]]][[listMod[2]]]
+ modelSel$models <- tableauRecap
- return(modelSel)
+ if (plot) plot_valse(X, Y, modelSel)
+ return(modelSel)
+ }
+ tableauRecap
}