Description Usage Arguments Value References Examples
View source: R/smoothedLasso.r
Auxiliary function which returns the objective, penalty, and dependence structure among regression coefficients of the elastic net.
1 | 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.
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