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arXiv · 1907.00914

ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters

Peter E. DeWitt·Tellen D. Bennett
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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

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Peter E. DeWitt, Tellen D. Bennett. 2019-07-01. ensr: R Package for Simultaneous Selection of Elastic Net Tuning Parameters. https://arxiv.org/abs/1907.00914

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