EBEN (version 5.1)

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 = FALSE,verbose = 0)

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

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

Author

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

Details

If Epis=TRUE, 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 = FALSE,verbose = 0)
betas 			= Blup$weight
betas