Year: 2019
Type: preprint
Abstract: Motivation: Elastic net regression is a form of penalized regression that lies between ridge and least absolute shrinkage and selection operator (LASSO) regression. The elastic net penalty is a powerful tool controlling the impact of correlated predictors and the overall complexity of generalized linear regression models. The elastic net penalty has two tuning parameters: $λ$ for the complexity and $α$ for the compromise between LASSO and ridge. The R package glmnet provides efficient tools for fitting elastic net models and selecting $λ$ for a given $α.$ However, glmnet does not simultaneously search the $λ - α$ space for the optional elastic net model. Results: We built the R package ensr, elastic net searcher. enser extends the functionality of glment to search the $λ - α$ space and identify an optimal $λ - α$ pair. Availability: ensr is available from the Comprehensive R Archive Network at https://cran.r-project.org/package=ensr
Source: arXiv (Cornell University)
Authors: DeWitt, Peter E., Bennett, Tellen D.
Institution: University of Colorado Denver
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2954774635