-#' valse
+#' runValse
#'
#' Main function
#'
#' @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 'DDSE' or 'DJump' is used to select a model and the collection of models
+#' has less than 11 models, the function returns the collection as it can not select one - in that case,
+#' it is adviced to use 'AIC' or 'BIC' to select a model)
#'
#' @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)
+#' X <- matrix(runif(100), nrow=50)
+#' Y <- matrix(runif(100), nrow=50)
+#' res = runValse(X, Y)
+#'
#' @export
-valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mini = 10,
+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)
complexity = sumPen, contrast = -LLH)
}))
tableauRecap <- tableauRecap[which(tableauRecap[, 4] != Inf), ]
-
- if (verbose == TRUE)
+ if (verbose)
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)
+ print(plot_valse(X, Y, modelSel))
+ return(modelSel)
+ }
+ tableauRecap
}