cv.gamma: Cross-Validation to choose the optimal gamma from a sequence...

In Saptati-Datta/AdapLasso: Computes Adaptive Lasso Using Lars Algorithm

Description

Cross-Validation to choose the optimal gamma from a sequence of gamma values for a particular sequence of lambdas

Usage

cv.gamma(
  X,
  Y,
  tuning_seq = NULL,
  len_tuning = 60,
  gamma_seq = NULL,
  n_gamma = 60,
  k = 5,
  id_fold = NULL,
  eps = 0.001
)

Arguments

X	n x p design matrix of inputs
Y	n x 1 vector of outputs
tuning_seq	(optional)sequence of tuning parameters
len_tuning	length of desired tuning parameter sequence
gamma_seq	(optional)sequence of gamma values(used in determining weights)
n_gamma	length of the desired sequence of gamma values
k	number of folds for k-fold cross-validation, default is 5
id_fold	(optional) vector of length n specifying the folds assignment (from 1 to max(folds_ids)), if supplied the value of k is ignored
eps	precision level for convergence assessment, default 0.001

Value

cv	a n_gamma x len_tuning matrix giving CV(lambda, gamma) for each pair of (lambda, gamma)
gamma_min	optimal gamma
lambda_min	selected lambda based on minimal rule

Examples

X <- matrix(rnorm(500), 50, 10)
Y <- rnorm(50)
fit_cv_gamma <- cv.gamma(X , Y)

Saptati-Datta/AdapLasso documentation built on Dec. 18, 2021, 12:57 p.m.