EBelasticNet.Binomial {EBEN}

The EB Elastic Net Algorithm for Binomial Model with Normal-Gamma(NG) Prior Distribution

Description

Generalized linear regression, normal-Gxponential (NG) hierarchical prior for regression coefficients

Usage

EBelasticNet.Binomial(BASIS, Target, lambda, alpha,Epis = FALSE,verbose = 0)

Arguments

BASIS

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

Target

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

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

Details

If Epis=TRUE, the program adds two-way interaction of 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 from the final regression coefficients

WaldScore

Wald Score

Intercept

Intercept

lambda

the hyperparameter; same as input lambda

alpha

the hyperparameter; same as input alpha

Author(s)

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

References

Huang A, Xu S, Cai X: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics 2013, 14(1):5.

Examples

library(EBEN)
data(BASISbinomial)
data(yBinomial)
#reduce sample size to speed up the running time
n = 50;
k = 100;
N = length(yBinomial);
set  = sample(N,n);
BASIS = BASISbinomial[set,1:k];
y  = yBinomial[set];
output = EBelasticNet.Binomial(BASIS, y,lambda = 0.1,alpha = 0.5, Epis = FALSE,verbose = 5)