In AnderWilson/BayesPen: Bayesian Penalized Credible Regions

Overview

The BayesPen package implements variable and confounder selection via penalized credible regrions. The methods are detailed in the following papers

Bondell HD, Reich BJ. 2012. Consistent high-dimensional Bayesian variable selection via penalized credible regions. J. Am. Stat. Assoc. 107: 1610–1624.

Wilson A, Reich BJ. 2014. Confounder selection via penalized credible regions. Biometrics 70: 852–861.

The citation for this package is

Ander Wilson, Howard D. Bondell and Brian J. Reich (2015). BayesPen: Bayesian Penalized Credible Regions. R package version 1.2.

Installation

The BayesPen package can be installed using devtools.

library(devtools)
install_github(repo="BayesPen", username="AnderWilson")
library(BayesPen)

An Example

First load the R package.

library(BayesPen)

Variable Selection via Penalized Credible Regions

Simulate data.

set.seed(1234)
dat <- SimExample(500,model="BR1")
X <- dat$X
y <- dat$y

Fit the full model assuming flat priors on beta

fit1 <- lm(y~X-1)
betahat <- coef(fit1)
cov <- vcov(fit1)

Find solution path

fit.BayesPen <- BayesPen(beta=betahat, beta_cov=cov)

Refit the model.

refit <- BayesPen.refit(y,X,fit.BayesPen)

Plot the solution path.

BayesPen.plot(refit)

Confounder Selection via Penalized Credible Regions

set.seed(1234)
dat <- SimExample(500,model="WPD2")
X <- dat$X
U <- dat$U
W <- cbind(X,U)
y <- dat$y

Fit the full outcome model assuming flat priors on beta.

fit1 <- lm(y~W-1)
betahat <- coef(fit1)
cov <- vcov(fit1)

Fit the full exposure model assuming flat priors on beta.

fit2 <- lm(X~U-1)
gammahat <- coef(fit2)

Find solution path.

fit.BayesPen <- BayesPen(beta=betahat, beta_cov=cov, confounder.weights=c(0,gammahat), force=1)

Refit the outcome model.

refit <- BayesPen.refit(y,W,fit.BayesPen)

Plot the solution path.

BayesPen.plot(refit)

AnderWilson/BayesPen documentation built on May 5, 2019, 4:56 a.m.