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Adjustments for CRAN upload
[valse.git]
/
pkg
/
R
/
computeGridLambda.R
diff --git
a/pkg/R/computeGridLambda.R
b/pkg/R/computeGridLambda.R
index
ac0788a
..
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
#'
#'
#' Construct the data-driven grid for the regularization parameters used for the Lasso estimator
#'
@@
-12,11
+12,13
@@
#' @param mini minimum number of iterations in EM algorithm
#' @param maxi maximum number of iterations in EM algorithm
#' @param eps threshold to stop EM algorithm
#' @param mini minimum number of iterations in EM algorithm
#' @param maxi maximum number of iterations in 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
#'
#' @export
-computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
+computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mini,
maxi, eps, fast)
{
n <- nrow(X)
maxi, eps, fast)
{
n <- nrow(X)
@@
-24,7
+26,7
@@
computeGridLambda <- function(phiInit, rhoInit, piInit, gamInit, X, Y, gamma, mi
m <- ncol(Y)
k <- length(piInit)
m <- ncol(Y)
k <- length(piInit)
- list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
+ list_EMG <- EMGLLF(phiInit, rhoInit, piInit, gamInit, mini, maxi, gamma, lambda = 0,
X, Y, eps, fast)
grid <- array(0, dim = c(p, m, k))
X, Y, eps, fast)
grid <- array(0, dim = c(p, m, k))