View source: R/EBlassoNEG.Binomial.R
EBlassoNEG.Binomial | R Documentation |
Generalized linear regression, normal-exponential-gamma (NEG) hierarchical prior for regression coefficients
EBlassoNEG.Binomial(BASIS, Target, a_gamma, b_gamma, Epis,verbose,group)
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 |
If Epis=TRUE, the program adds two-way interaction K*(K-1)/2 more columns to BASIS
weight |
the none-zero regression coefficients: |
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 |
Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL
Huang, A., Xu, S., and Cai, X.(2012). Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC Genetics. Submitted
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)
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