ridgeSpike: Ridge Regression Stochastic Search Variable Selection...

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

Description

The Bayesian implementation of ridge regression combined with the Bernoulli-Normal mixture model for stochastic search variable selection. Plug-in pseudovariances are used for the binomial and poisson likelihood functions.

In a way this is comparable to the elastic net. The elastic net is a convex combination of L1 and L2 norm penalities, while this model is a combination of L2 and L0 penalities (albeit not convex, since the L0 norm is not convex).

Model Specification:



Plugin Pseudo-Variances:

Usage

ridgeSpike(formula, data, family = "gaussian", log_lik = FALSE,
  iter = 10000, warmup = 1000, adapt = 2000, chains = 4,
  thin = 1, method = "parallel", cl = makeCluster(2), ...)

Arguments

formula	the model formula
data	a data frame.
family	one of "gaussian", "binomial", or "poisson".
log_lik	Should the log likelihood be monitored? The default is FALSE.
iter	How many post-warmup samples? Defaults to 10000.
warmup	How many warmup samples? Defaults to 1000.
adapt	How many adaptation steps? Defaults to 2000.
chains	How many chains? Defaults to 4.
thin	Thinning interval. Defaults to 1.
method	Defaults to "parallel". For an alternative parallel option, choose "rjparallel" or. 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

Examples

ridgeSpike()

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