genRidge: Generalized Ridge Regression

In abnormally-distributed/Bayezilla: Bayesian Models With JAGS and Several Utilities for Data Exploration and Model Evaluation.

Description

The Bayesian implementation of the generalized ridge regression estimators discussed by Ishwaran & Rao (2014) and Yuzbacsi et al. (2017). This is similar to the adaptive Bayesian LASSO in that it utilizes coefficient-specific shrinkage parameters. Plug-in pseudovariances are used for the binomial and poisson likelihood functions.

Model Specification:



Plugin Pseudo-Variances:

Usage

genRidge(formula, data, family = "gaussian", lambda.prior = "dmouch",
  log_lik = FALSE, iter = 8000, warmup = 1000, adapt = 1000,
  chains = 4, thin = 1, method = "rjparallel", cl = makeCluster(2),
  ...)

Arguments

formula	the model formula
data	a data frame.
family	one of "gaussian", "binomial", or "poisson".
lambda.prior	either "dmouch" (the default) or "gamma"
log_lik	Should the log likelihood be monitored? The default is FALSE.
iter	How many post-warmup samples? Defaults to 8000.
warmup	How many warmup samples? Defaults to 1000.
adapt	How many adaptation steps? Defaults to 1000.
chains	How many chains? Defaults to 4.
thin	Thinning interval. Defaults to 1.
method	Defaults to "rjparallel". For an alternative parallel option, choose "parallel". Otherwise, "rjags" (single core run).
cl	Use parallel::makeCluster(# clusters) to specify clusters for the parallel methods. Defaults to two cores.
...	Other arguments to run.jags.

Value

a runjags object

References

Ishwaran, H. & Rao, J. (2014) Geometry and properties of generalized ridge regression in high dimensions. Contemporary Mathematics , 622. doi: 10.1090/conm/622

Yuzbacsi, B., Arashi, M., & Ahmed, S.E. (2017). Shrinkage Estimation Strategies in Generalized Ridge Regression Models Under Low/High-Dimension Regime (preprint). https://arxiv.org/abs/1707.02331v1

Examples

genRidge()

abnormally-distributed/Bayezilla documentation built on Oct. 31, 2019, 1:57 a.m.