Adjustments for CRAN upload

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

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index 597d5c8..f4073d0 100644 (file)
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -1,4 +1,4 @@
-#' computeGridLambda 
+#' computeGridLambda
 #'
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
@@ -11,21 +11,24 @@
 #' @param gamma power of weights in the penalty
 #' @param mini minimum number of iterations in EM algorithm
 #' @param maxi maximum number of iterations in EM algorithm
-#' @param tau threshold to stop EM algorithm
+#' @param eps threshold to stop EM algorithm
+#' @param fast boolean to enable or not the C function call
 #'
-#' @return the grid of regularization parameters
+#' @return the grid of regularization parameters for the Lasso estimator. The output is a vector with nonnegative values that are relevant
+#' to be considered as regularization parameter as they are equivalent to a 0 in the regression parameter.
 #'
 #' @export
-computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini, 
-  maxi, tau, fast)
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
+  maxi, eps, fast)
 {
   n <- nrow(X)
-  p <- dim(phiInit)[1]
-  m <- dim(phiInit)[2]
-  k <- dim(phiInit)[3]
+  p <- ncol(X)
+  m <- ncol(Y)
+  k <- length(piInit)
+
+  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
+    X, Y, eps, fast)
 
-  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0, 
-    X, Y, tau, fast)
   grid <- array(0, dim = c(p, m, k))
   for (j in 1:p)
   {