standardLasso {smoothedLasso} | R Documentation |
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the Lasso.
standardLasso(X, y, lambda)
X |
The design matrix. |
y |
The response vector. |
lambda |
The Lasso regularization parameter. |
A list with six functions, precisely the objective u
, penalty v
, and dependence structure w
, as well as their derivatives du
, dv
, and dw
.
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.
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)