#' 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)
+#' res = runValse(X, Y, kmax = 5, plot=FALSE)
#' X <- matrix(runif(100), nrow=50)
#' Y <- matrix(runif(100), nrow=50)
-#' res = runValse(X, Y)
+#' res = runValse(X, Y, plot=FALSE)
#'
#' @export
runValse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
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) print(tableauRecap)
if (nrow(tableauRecap) > 10) {
modSel <- capushe::capushe(tableauRecap, n)
- indModSel <- if (selecMod == "DDSE")
- {
+ indModSel <- if (selecMod == "DDSE") {
as.numeric(modSel@DDSE@model)
- } else if (selecMod == "Djump")
- {
+ } else if (selecMod == "Djump") {
as.numeric(modSel@Djump@model)
- } else if (selecMod == "BIC")
- {
+ } else if (selecMod == "BIC") {
modSel@BIC_capushe$model
- } else if (selecMod == "AIC")
- {
+ } else if (selecMod == "AIC") {
modSel@AIC_capushe$model
}
listMod <- as.integer(unlist(strsplit(as.character(indModSel), "[.]")))
modelSel <- models_list[[listMod[1]]][[listMod[2]]]
modelSel$models <- tableauRecap
- if (plot)
- print(plot_valse(X, Y, modelSel))
+ if (plot) plot_valse(X, Y, modelSel)
return(modelSel)
}
tableauRecap