adaplassostd_lambda: Fits adaptive adaplasso based on standardized data

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

Description

Fits adaptive adaplasso based on standardized data

Usage

adaplassostd_lambda(Xstd, Ystd, lambda, beta_init = NULL, eps = 0.001)

Arguments

Xstd	n x p design matrix X scaled according to LARS algorithm and centered to mean 0
Ystd	n x 1 centered output vector
lambda	tuning parameter(scalar)
beta_init	p x 1, optional starting point for coordinate descent algorithm
eps	precision level for convergence assessment, default 0.001

Value

beta	vector of parameters
obj_min	optimal value of the objective function

Examples

X <- matrix(rnorm(500), 50, 10)
Y <- rnorm(50)
gamma <- 2
#Standardizing X and Y
std <- standardize(X , Y , gamma)
#Deriving weighted and centered design matrix
Xstd <- std$Xstd
#Deriving centered Y
Ystd <- std$Ystd
# tuning parameter
lambda  <- 0.1
fit <- adaplassostd_lambda(Xstd, Ystd, lambda)

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