EBEN (version 5.1)

EBlassoNEG.Gaussian: 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)

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

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

Author

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

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.

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)