Adjustments for CRAN upload

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

diff --git a/pkg/R/computeGridLambda.R b/pkg/R/computeGridLambda.R
index e2b6303..f4073d0 100644 (file)
--- a/pkg/R/computeGridLambda.R
+++ b/pkg/R/computeGridLambda.R
@@ -3,35 +3,37 @@
 #' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
 #'
 #' @param phiInit value for phi
-#' @param rhoInit      value for rho
-#' @param piInit       value for pi
+#' @param rhoInit  for rho
+#' @param piInit  for pi
 #' @param gamInit value for gamma
 #' @param X matrix of covariates (of size n*p)
 #' @param Y matrix of responses (of size n*m)
 #' @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)
+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]
+  n <- nrow(X)
+  p <- ncol(X)
+  m <- ncol(Y)
+  k <- length(piInit)
 
-       list_EMG = EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi,
-               gamma, lambda=0, X, Y, tau)
-       grid = array(0, dim=c(p,m,k))
-       for (i in 1:p)
-       {
-               for (j in 1:m)
-                       grid[i,j,] = abs(list_EMG$S[i,j,]) / (n*list_EMG$pi^gamma)
-       }
-       grid = unique(grid)
-       grid
+  list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
+    X, Y, eps, fast)
+
+  grid <- array(0, dim = c(p, m, k))
+  for (j in 1:p)
+  {
+    for (mm in 1:m)
+      grid[j, mm, ] <- abs(list_EMG$S[j, mm, ])/(n * list_EMG$pi^gamma)
+  }
+  sort(unique(grid))
 }