X-Git-Url: https://git.auder.net/?p=valse.git;a=blobdiff_plain;f=pkg%2FR%2Fmain.R;h=129aa25720ece9d860dce72407f95d1f58767a71;hp=13df89fddad635010a1ab0128fd7c23e54df5efe;hb=206dfd5d377fac6cbb60f3d19e07521749d120e1;hpb=fb3557f39487d9631ffde30f20b70938d2a6ab0c

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 13df89f..129aa25 100644
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -18,14 +18,14 @@
 #' @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
-#' The selected model if enough data are available to estimate it,
-#' or a list of models otherwise.
+#' 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
 #' n = 50; m = 10; p = 5
@@ -35,7 +35,7 @@
 #' 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)
+#' res = runValse(X, Y, kmax = 5)
 #' X <- matrix(runif(100), nrow=50)
 #' Y <- matrix(runif(100), nrow=50)
 #' res = runValse(X, Y)
@@ -43,7 +43,7 @@
 #' @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)