elasticNet {smoothedLasso} | R Documentation |
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the elastic net.
elasticNet(X, y, alpha)
X |
The design matrix. |
y |
The response vector. |
alpha |
The regularization parameter of the elastic net. |
A list with six functions, precisely the objective u
, penalty v
, and dependence structure w
, as well as their derivatives du
, dv
, and dw
.
Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. J Roy Stat Soc B Met, 67(2):301-320.
Friedman, J., Hastie, T., Tibshirani, R., Narasimhan, B., Tay, K., Simon, N., and Qian, J. (2020). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. R-package version 4.0.
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
alpha <- 0.5
temp <- elasticNet(X,y,alpha)