HSplus: Horseshoe+

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

Description

This is the horseshoe+ model described by Bhadra et al (2017). This is essentially the horseshoe homologue of the extended LASSO. The specification is identical to the horseshoe except an additional local shrinkage parameter is added to the model. Note that in the model I use somewhat different names for the paramters. This is because a key parameter in the model is often called "tau" in the literature (often geared towards Stan), but JAGS uses the precision parameterization of the normal distribution. The precision is denoted as "tau", so this warranted some new notation. See the model specification below.

Model Specification:

Plugin Pseudo-Variances:

Usage

HSplus(formula, data, family = "gaussian", log_lik = FALSE,
  iter = 4000, warmup = 3000, adapt = 3000, chains = 4, thin = 2,
  method = "rjparallel", 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 "rjparallel". For an alternative parallel option, choose "parallel" 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

an rjags object

References

Bhadra, Anindya; Datta, Jyotishka; Polson, Nicholas G.; Willard, Brandon. The Horseshoe+ Estimator of Ultra-Sparse Signals. Bayesian Anal. 12 (2017), no. 4, 1105–1131. doi:10.1214/16-BA1028. https://projecteuclid.org/euclid.ba/1474572263

Carvalho, C. M., Polson, N. G., and Scott, J. G. (2010). The horseshoe estimator for sparse signals. Biometrika, 97(2):465–480.

Examples

HSplus()

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