EBEN (version 4.1)

EBelasticNet.Gaussian: 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 = "no",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

Epis

"yes" or "no" for including two-way interactions

verbose

0 or 1; 1: display message; 0 no message

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

Details

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

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 = "no",verbose = 0)
betas 			= Blup$weight
betas