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.
The BayesPen package can be installed using devtools.
library(devtools) install_github(repo="BayesPen", username="AnderWilson") library(BayesPen)
First load the R package.
library(BayesPen)
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)
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)
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