Approximate Bayesian Regularization for Parsimonious Estimates.
The R package shrinkem allows approximate Bayesian regularization (e.g., ridge, lasso, horseshoe) using Gaussian approximations of the errors of the estimates of the key parameters.
Installation
The developmental version can be installed with
if (!requireNamespace("remotes")) {
install.packages("remotes")
}
remotes::install_github("jomulder/shrinkem")
Example analyses
estimates <- -5:5
covmatrix <- diag(11)
# Bayesian horseshoe where all beta's have the same global shrinkage (using default 'group' argument)
shrink1 <- shrinkem(estimates, covmatrix, type="horseshoe")
# posterior modes of middle three estimates are practically zero
print(shrink1)
# how traceplots
par(mfrow=c(3,4))
par(mar=c(2,2,2,2))
for(p in 1:ncol(shrink1$draws$beta)){plot(shrink1$draws$beta[,p],type="l",
main=colnames(shrink1$draws$beta)[p])}
# plot posterior densities
par(mfrow=c(11,1))
par(mar=c(1,2,1,2))
for(p in 1:ncol(shrink1$draws$beta)){plot(density(shrink1$draws$beta[,p]),xlim=c(-10,10),
main=colnames(shrink1$draws$beta)[p])}
# Bayesian horseshoe where first three and last three beta's have different global shrinkage
# parameter than other beta's (using the 'group' argument)
shrink2 <- shrinkem(estimates, covmatrix, type="horseshoe", group=c(rep(1,3),rep(2,5),rep(1,3)))
# posterior modes of middle five estimates are practically zero
print(shrink2)
# show traceplots
par(mfrow=c(3,4))
par(mar=c(2,2,2,2))
for(p in 1:ncol(shrink2$draws$beta)){plot(shrink2$draws$beta[,p],type="l",
main=colnames(shrink2$draws$beta)[p])}
# plot posterior densities
par(mfrow=c(11,1))
par(mar=c(1,2,1,2))
for(p in 1:ncol(shrink2$draws$beta)){plot(density(shrink2$draws$beta[,p]),xlim=c(-10,10),
main=colnames(shrink2$draws$beta)[p])}
Citing shrinkem
You can cite the package and the paper using the following reference
Karimova, D., Leenders, R., van Erp, S., and Mulder, J. (preprint). Honey, I shunk the irrelevant effects! Simple and Fast Approximate Bayesian Regularization.
Contributing and Contact Information
If you have suggestions, please get involved. You can contribute by opening an issue on GitHub, or sending a pull request with proposed features.
By participating in this project, you agree to abide by the Contributor Code of Conduct v2.0.