EBlassoNEG.Gaussian: The EBlasso Algorithm for Gaussian Model with...

In EBEN: Empirical Bayesian Elastic Net

The EBlasso Algorithm for Gaussian Model with Normal-Exponential-Gamma (NEG) Prior Distribution

Description

General linear regression, normal-exponential-gamma (NEG) hierarchical prior for regression coefficients

Usage

EBlassoNEG.Gaussian(BASIS, Target, a_gamma, b_gamma, Epis, verbose, group)

Arguments

BASIS	sample matrix; rows correspond to samples, columns correspond to features
Target	Response each individual
a_gamma	Hyperparameters control degree of shrinkage; can be obtained via Cross Validation
b_gamma	Hyperparameters control degree of shrinkage; can be obtained via Cross Validation
Epis	TRUE or FALSE for including two-way interactions
verbose	from 0 to 5; larger verbose displays more messages
group	0 or 1; 0: No group effect; 1 two-way interaction grouped. Only valid when Epis = TRUE

Details

If Epis=TURE, the program adds two-way interaction K*(K-1)/2 more columns to BASIS
for memory efficient, the function pass n_effect to C. n_effect > n_true effects, which is
a rough guess on how many variables will be selected by the function
by providing a relative 'small' n_effect, the function will not allocate
a large trunck of memory during computation.

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
residVar	residual variance
a_gamma	the hyperparameter; same as input
b_gamma	the hyperparameter; same as input

Author(s)

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

References

Cai, X., Huang, A., and Xu, S. (2011). Fast empirical Bayesian LASSO for multiple quantitative trait locus mapping. BMC Bioinformatics 12, 211.

Examples

library(EBEN)
data(BASIS)
data(y)
n = 50;
k = 100;
BASIS = BASIS[1:n,1:k];
y  = y[1:n];
output = EBlassoNEG.Gaussian(BASIS, y, a_gamma = 0.1, b_gamma = 0.1)

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