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update Emilie
[valse.git]
/
pkg
/
R
/
computeGridLambda.R
diff --git
a/pkg/R/computeGridLambda.R
b/pkg/R/computeGridLambda.R
index
8449d10
..
3dae84c
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
#'
#'
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
@@
-11,21
+11,23
@@
#' @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 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
#'
#' @export
#'
#' @return the grid of regularization parameters
#'
#' @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 <- ncol(X)
m <- ncol(Y)
k <- length(piInit)
{
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, fast)
+ 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)
{
grid <- array(0, dim = c(p, m, k))
for (j in 1:p)
{