EBelasticNet.Gaussian: The EB Elastic Net Algorithm for Gaussian Model

In EBEN: Empirical Bayesian Elastic Net

The EB Elastic Net Algorithm for Gaussian Model

Description

General linear regression, normal-Gamma (NG) hierarchical prior for regression coefficients

Usage

EBelasticNet.Gaussian(BASIS, Target, lambda, alpha,Epis = FALSE,verbose = 0)

Arguments

BASIS	sample matrix; rows correspond to samples, columns correspond to features
Target	Response each individual
lambda	Hyperparameter controls degree of shrinkage; can be obtained via Cross Validation; lambda>0
alpha	Hyperparameter controls degree of shrinkage; can be obtained via Cross Validation; 0<alpha<1
Epis	TRUE or FALSE for including two-way interactions
verbose	0 or 1; 1: display message; 0 no message

Details

If Epis=TRUE, the program adds two-way interaction of K*(K-1)/2 more columns to BASIS

Value

weight	the none-zero regression coefficients: col1,col2 are the indices of the bases(main if equal); col3: coefficent value; col4: posterior variance; col5: t-value; col6: p-value
WaldScore	Wald Score
Intercept	Intercept
lambda	the hyperparameter; same as input lambda
alpha	the hyperparameter; same as input alpha

Author(s)

Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL

References

Huang, A., Xu, S., and Cai, X. (2014). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. Heredity 10.1038/hdy.2014.79

Examples

library(EBEN)
data(BASIS)
data(y)
n = 50;
k = 100;
BASIS = BASIS[1:n,1:k];
y  = y[1:n];
Blup = EBelasticNet.Gaussian(BASIS, y,lambda = 0.0072,alpha = 0.95, Epis = FALSE,verbose = 0)
betas 			= Blup$weight
betas

EBEN documentation built on May 31, 2023, 8:43 p.m.