EBlassoNEG.Binomial {EBEN}

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

Description

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

Usage

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

Arguments

BASIS

sample matrix; rows correspond to samples, columns correspond to features

Target

Class label of each individual, TAKES VALUES OF 0 OR 1

a_gamma

Hyperparameters control degree of shrinkage; can be obtained via Cross Validation; a_gamma>=-1

b_gamma

Hyperparameters control degree of shrinkage; can be obtained via Cross Validation; b_gamma>0

Epis

TRUE or FALSE for including two-way interactions

verbose

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

group

0 or 1; 0: No group effect; 1 two-way interaction grouped. Only valid when Epis = TRUE

Details

If Epis=TRUE, the program adds two-way interaction 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

logLikelihood

log likelihood with the final regression coefficients

WaldScore

Wald Score

Intercept

Intercept

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

Huang, A., Xu, S., and Cai, X.(2012). Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC Genetics. Submitted

Examples

library(EBEN)
data(BASISbinomial)
data(yBinomial)
#reduce sample size to speed up the running time
n = 50;
k = 100;
BASIS = BASISbinomial[1:n,1:k];
y  = yBinomial[1:n];
output = EBlassoNEG.Binomial(BASIS,y,0.1,0.1,Epis = FALSE)