plot fonctionne

[valse.git] / pkg / R / main.R

diff --git a/pkg/R/main.R b/pkg/R/main.R
index 8649342..129aa25 100644 (file)
--- a/pkg/R/main.R
+++ b/pkg/R/main.R
@@ -1,4 +1,4 @@
-#' valse
+#' runValse
 #'
 #' Main function
 #'
@@ -18,18 +18,32 @@
 #' @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)
+#' 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)
@@ -123,30 +137,31 @@ valse <- function(X, Y, procedure = "LassoMLE", selecMod = "DDSE", gamma = 1, mi
       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
 }