EBelasticNet.Binomial {EBEN} | R Documentation |
Generalized linear regression, normal-Gxponential (NG) hierarchical prior for regression coefficients
EBelasticNet.Binomial(BASIS, Target, lambda, alpha,Epis = "no",verbose = 0)
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 |
"yes" or "no" for including two-way interactions |
verbose |
0 or 1; 1: display message; 0 no message |
If Epis="yes", the program adds two-way interaction of K*(K-1)/2 more columns to BASIS
weight |
the none-zero regression coefficients: |
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 |
Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL
Huang A, Xu S, Cai X: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics 2013, 14(1):5.
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 = "no",verbose = 5)