Description
Implementation of the Empirical Bayes Method.
Description
Implements a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response. The method was first presented in Martin, Ryan and Mess, Raymond and Walker, Stephen G (2017) <doi:10.3150/15-BEJ797>. More details focused on the prediction problem are given in Martin, Ryan and Tang, Yiqi (2019) <arXiv:1903.00961>.
README.md
ebreg
The goal of ebreg is to implement a Bayesian-like approach to the high-dimensional sparse linear regression problem based on an empirical or data-dependent prior distribution, which can be used for estimation/inference on the model parameters, variable selection, and prediction of a future response.
Installation
You can install the released version of ebreg from CRAN with:
install.packages("ebreg")
And the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("antang93/Empirical-Bayes")
Example
This is a basic example which shows you how to solve a common problem:
library(ebreg)
#> Loading required package: lars
#> Loaded lars 1.2
## basic example code
n <- 70
p <- 100
beta <- rep(1, 5)
s0 <- length(beta)
sig2 <- 1
d <- 1
log.f <- function(x) -x * (log(1) + 0.05 * log(p)) + log(x <= n)
X <- matrix(rnorm(n * p), nrow=n, ncol=p)
X.new <- matrix(rnorm(p), nrow=1, ncol=p)
y <- as.numeric(X[, 1:s0] %*% beta[1:s0]) + sqrt(sig2) * rnorm(n)
res<-ebreg(y, X, X.new, TRUE, alpha=.99, gam=.005, NULL, FALSE, igpar=c(0.01, 4), log.f, M=5000, TRUE, FALSE, .95)
