Robust penalized (adaptive) elastic net S and M estimators for
linear regression. The methods are proposed in
Cohen Freue, G. V., Kepplinger, D., Salibián-Barrera, M., and Smucler, E.
(2019) < https://projecteuclid.org/euclid.aoas/1574910036>.
The package implements the extensions and algorithms described in
Kepplinger, D. (2020)
This R package implements the Penalized Elastic Net S-Estimator (PENSE) and MM-estimator (PENSEM) for linear regression.
The main functions in the package are
pense()… to compute a robust elastic net S-estimator for linear regression
pensem()… to compute a robust elastic net MM-estimator either directly from the data matrix or from an S-estimator previously computed with
Both of these functions perform k-fold cross-validation to choose the optimal penalty level
lambda, but the optimal balance between the L1 and the L2 penalties (the
alpha parameter) needs
to be pre-specified by the user.
The default breakdown point is set to 25%. If the user needs an estimator with a higher breakdown
delta argument in the
initest_options() can be set to the
desired breakdown point (.e.g,
delta = 0.5).
The package also exports an efficient classical elastic net algorithm available via the functions
elnet_cv() which chooses an optimal penalty parameter based on cross-validation.
The elastic net solution is computed either by the augmented LARS algorithm
en_options_aug_lars()) or via the Dual Augmented Lagrangian algorithm (Tomioka, et al. 2011)
en_options_dal() which is much faster in case of a large number of predictors
(> 500) and a small number of observations (< 200).
To install the latest release from CRAN, run the following R code in the R console:
The most recent stable version as well as the developing version might not yet be available on CRAN. These can be directly installed from github using the devtools package:
# Install the most recent stable version:install_github("dakep/pense-rpkg")# Install the (unstable) develop version:install_github("dakep/pense-rpkg", ref = "develop")
Tomioka, R., Suzuki, T., and Sugiyama, M. (2011). Super-linear convergence of dual augmented lagrangian algorithm for sparsity regularized estimation. The Journal of Machine Learning Research, 12:1537–1586.
pensem. Note: The lambda values in this release are not the same as in previous releases!
pensemobjects if computed from a fitted
pense_options()for the initial estimator. Remove
initest_options()and instead add them to
objis of class