Empirical Bayesian Elastic Net.
Empirical Bayesian Elastic Net (EBEN) for Generalized Linear Models
We provide extremely efficient procedures for fitting the empirical Bayesian methods with lasso and elastic net hierarchical priors for linear regression (gaussian), and logistic regression (binomial) models. EBEN is a sister package to EBglmnet (available in CRAN). Both packages share key features include:
- sparse variable selection and effect estimation via generalized linear regression models;
- high dimensionality with p>>n; and
- significance test (with output of
p-value) for nonzero effects; and - closed-form solution for Bayesian variance estimation in an iterative cooridinate descent algorithm estimating the Bayesian means.
The implementation enables extremely efficient computation comparable with that of glmnet package.
When you need EBEN
While EBglmnet offers generic functions for a broad range of use cases, EBEN takes care of the following special cases:
- two-way interaction terms (
epistasis) are included withepis = TRUE: for input independent parameterXwith n x p dimension, the functions will evaluate p(p-1)/2 additional parameters; - group Empirical Bayesian Lasso are avaiable with
group = TRUE: the penalty parameter for the group of p(p-1)/2 parameters are weighted with group size in comparing with the group origin p variables.
Further readings
Details may be found in Huang A. and Liu D (2016), Huang A., Xu S., and Cai X. (2015), Huang A. (2014), Huang A., Xu S., and Cai X. (2013), and Cai X., Huang A., and Xu S., (2011).
Version notes
Version 5.1 is a major release with several new features, including:
- group Empirical Bayesian Lasso (EBlasso) and built-in two-way interaction support moved to
EBENpackage. - BLAS/Lapack routines are updated according to R-API change.
References
Huang A., Liu D., (2016)
EBglmnet: a comprehensive R package for sparse generalized linear regression models
Bioinformatics, Volume 37, Issue 11, Pages 1627–1629
Huang A., Xu S., and Cai X. (2015).
Empirical Bayesian elastic net for multiple quantitative trait locus mapping.
Heredity, Vol. 114(1), 107-115.
Huang A. (2014)
Sparse Model Learning for Inferring Genotype and Phenotype Associations.
Ph.D Dissertation, University of Miami, Coral Gables, FL, USA.
Huang A., Xu S., and Cai X. (2013).
Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping.
BMC Genetics, 14(1),5.
Cai X., Huang A., and Xu S., (2011).
Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping.
BMC Bioinformatics, 12(1),211.