Bayenet

Bayesian Quantile Elastic Net for Genetic Study

As heavy-tailed error distribution and outliers in the response variable widely exist, models which are robust to data contamination are highly demanded. Here, We develop a novel robust Bayesian variable selection method with elastic net penalty for quantile regression in genetic analysis. In particular, the spike-and-slab priors have been incorporated to impose sparsity. An efficient Gibbs sampler has been developed to facilitate computation. The algorithms of the proposed and alternative methods are efficiently implemented in 'C++'.

How to install

• To install from github, run these two lines of code in R

install.packages("devtools")
devtools::install_github("xilustat/Bayenet")

Examples

Example.1 (default method: Bayesian quantile elastic net with spike-and-slab priors)

library(Bayenet)
data(dat)

max.steps=10000
fit= Bayenet(X, Y, clin, max.steps, penalty="elastic net")
## coefficients of parameters
fit$coefficient
## Estimated values of main G effects 
fit$coefficient$G
## Estimated values of clincal effects 
 fit$coefficient$clin
    
 ## Prediction   
 test=sample((1:nrow(X)), floor(nrow(X)/5))
 fit=Bayenet(X[-test,], Y[-test], clin[-test,], max.steps=10000, penalty="elastic net")  
 predict.Bayenet(fit, X[test,], clin[test,], Y[test,])

Example.2 (alternative: Bayesian quantile lasso)

fit= Bayenet(X, Y, clin, max.steps, sparse="False", penalty="lasso")

Methods

This package provides implementation for methods proposed in

• Lu, X. and Wu, C. (2024). Bayesian quantile elastic net with spike-and-slab priors.