sqrtEnet: Square-Root Elastic Net
In abnormally-distributed/cvreg: Cross Validation and Robust Estimation Utilities
Description
Usage
Arguments
Value
References
Description
This fits an elastic net model to continuous responses using the square root method for selecting an optimal
penalty parameter. No cross validation is required. Data are automatically unit scaled and centered. Coefficients are
returned on the original scale of the inputs. Hence, it is not neccessary to center and/or standardize the inputs here.
Note that the returned coefficients have the L2 penalty relaxed after fitting per
Zou & Hastie (2005), rather than the naive estimates returned by glmnet.
Usage
1
Arguments
formula
a model formula
data
a data frame
alpha
a value between 0 and 1 for the mixing parameter. defaults to 0.5.
conf.level
the confidence level to use in setting the penalty. the default is 0.95.
Value
a model fit
References
Zou, H. & Trevor, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society, Series B, 67(2), 301-320.
Belloni A., Chernozhukov, V., & Wang, L. (2011) Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika, 98(4):791-806.
van de Geer S. (2016) The Square-Root Lasso. In: Estimation and Testing Under Sparsity. Lecture Notes in Mathematics, vol 2159. Springer, Cham
Raninen, E. & Ollila, E. (2017) Scaled and square-root elastic net. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 4336-4340. doi: 10.1109/ICASSP.2017.7952975
abnormally-distributed/cvreg documentation built on May 3, 2020, 3:45 p.m.
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Description Usage Arguments Value References
Description
This fits an elastic net model to continuous responses using the square root method for selecting an optimal penalty parameter. No cross validation is required. Data are automatically unit scaled and centered. Coefficients are returned on the original scale of the inputs. Hence, it is not neccessary to center and/or standardize the inputs here. Note that the returned coefficients have the L2 penalty relaxed after fitting per Zou & Hastie (2005), rather than the naive estimates returned by glmnet.
Usage
1 |
Arguments
formula |
a model formula |
data |
a data frame |
alpha |
a value between 0 and 1 for the mixing parameter. defaults to 0.5. |
conf.level |
the confidence level to use in setting the penalty. the default is 0.95. |
Value
a model fit
References
Zou, H. & Trevor, T. (2005). Regularization and Variable Selection via the Elastic Net. Journal of the Royal Statistical Society, Series B, 67(2), 301-320.
Belloni A., Chernozhukov, V., & Wang, L. (2011) Square-root lasso: pivotal recovery of sparse signals via conic programming. Biometrika, 98(4):791-806.
van de Geer S. (2016) The Square-Root Lasso. In: Estimation and Testing Under Sparsity. Lecture Notes in Mathematics, vol 2159. Springer, Cham
Raninen, E. & Ollila, E. (2017) Scaled and square-root elastic net. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), New Orleans, LA, 2017, pp. 4336-4340. doi: 10.1109/ICASSP.2017.7952975
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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