adaplasso: Function for soft-thresholding

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

Description

Function for soft-thresholding

Usage

adaplasso(Xstd, Ystd, beta, lambda)

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
beta	p x 1 vector of parameters
lambda	tuning parameter(scalar)

Value

Objective function for adaplasso

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
#Defining beta
beta <- solve(crossprod(X)) %*% t(X) %*% Y
#Lambda value
lambda <- 2
#Objective function
obj <- adaplasso(Xstd, Ystd, beta, lambda)

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