ENetXplorer: an R package for the quantitative exploration of elastic net families for generalized linear models.

ABSTRACT:

Background

Regularized generalized linear models (GLMs) are popular regression methods in bioinformatics, particularly useful in scenarios with fewer observations than parameters/features or when many of the features are correlated. In both ridge and lasso regularization, feature shrinkage is controlled by a penalty parameter λ. The elastic net introduces a mixing parameter α to tune the shrinkage continuously from ridge to lasso. Selecting α objectively and determining which features contributed significantly to prediction after model fitting remain a practical challenge given the paucity of available software to evaluate performance and statistical significance.

Results

eNetXplorer builds on top of glmnet to address the above issues for linear (Gaussian), binomial (logisti

SUBMITTER: Candia J 

PROVIDER: S-EPMC6469092 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature