The goal of dlbayes is to implement the Dirichlet-Laplace shrinkage prior in Bayesian linear regression and variable selection, featuring:
- utility functions to implement Dirichlet-Laplace priors;
- scalability in Bayesian linear regression;
- variable selection based on penalized credible regions.
You can install the released version of dlbayes from CRAN with:
install.packages("dlbayes") rho=0.5
p=1000
n=100
# set up correlation matrix
m<-matrix(NA,p,p)
for(i in 1:p){
for(j in i:p){
m[i,j]=rho^(j-i)
}
}
m[lower.tri(m)]<-t(m)[lower.tri(m)]
# generate x
library("mvtnorm")
x=rmvnorm(n,mean=rep(0,p),sigma=m)
# generate beta
beta=c(rep(0,10),runif(n=5,min=-1,max=1),rep(0,20),runif(n=5,min=-1,max=1),rep(0,p-40))
# generate y
y=x%*%beta+rnorm(n)
#tuning hyperparameter [1/max(n,p),1/2]
hyper=dlhyper(x,y)
# MCMC sampling
dlresult=dl(x,y,hyper=hyper)
# visualization of Dirichlet-Lapace priors
# set "plt=TRUE" to make plots
# theta=dlprior(hyper=1/2,p=10000000,plt=TRUE,min=-5,max=5,sigma=1)
# summary of posterior samples
da=dlanalysis(dlresult,alpha=0.05)
da$betamean
da$betamedian
da$LeftCI
da$RightCI
# variable selection
betaresult=dlvs(dlresult)
# indices of selected variables
num=which(betaresult!=0)
# coefficients of selected variable
coef=betaresult[num]
Bhattacharya, A., Pati, D., Pillai, N. S., and Dunson, D. B. (2015). "Dirichlet–Laplace priors for optimal shrinkage." Journal of the American Statistical Association, 110(512): 1479–1490.
Bhattacharya, A., Chakraborty, A., and Mallick, B. K. (2016). "Fast sampling with Gaussian scale-mixture priors in high-dimensional regression." Biometrika, 103(4): 985–991.
Bondell, H. D. and Reich, B. J. (2012). "Consistent high-dimensional Bayesian variable selection via penalized credible regions." Journal of the American Statistical Association, 107(500): 1610–1624.