standardLasso: Auxiliary function which returns the objective, penalty, and...

In smoothedLasso: A Framework to Smooth L1 Penalized Regression Operators using Nesterov Smoothing

Description

Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso.

Usage

standardLasso(X, y, lambda)

Arguments

X	The design matrix.
y	The response vector.
lambda	The Lasso regularization parameter.

Value

A list with six functions, precisely the objective u, penalty v, and dependence structure w, as well as their derivatives du, dv, and dw.

References

Tibshirani, R. (1996). Regression Shrinkage and Selection Via the Lasso. J Roy Stat Soc B Met, 58(1):267-288.

Hahn, G., Lutz, S., Laha, N., and Lange, C. (2020). A framework to efficiently smooth L1 penalties for linear regression. bioRxiv:2020.09.17.301788.

Examples

library(smoothedLasso)
n <- 100
p <- 500
betavector <- runif(p)
X <- matrix(runif(n*p),nrow=n,ncol=p)
y <- X %*% betavector
lambda <- 1
temp <- standardLasso(X,y,lambda)

smoothedLasso documentation built on March 21, 2021, 9:07 a.m.