HSDC: Horseshoe with unpenalized design covariates

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 Carvalho et al. (2010), but with the allowance for a set of covariates that are not penalized. For example, you may wish to include variables such as age and gender in all models so that the coefficients for the other variables are penalized while controlling for these. This is a common need in research.

This tends to run very quickly even for larger data sets or larger numbers of predictors and in my experience is faster and more stable (at least on the tested data sets!) than the same model implemetned in Stan.

Model Specification:

Plugin Pseudo-Variances:

Usage

HSDC(formula, design.formula, data, log_lik = FALSE, iter = 4000,
  warmup = 3000, adapt = 3000, chains = 4, thin = 2,
  method = "rjparallel", cl = makeCluster(2), ...)

Arguments

formula	the model formula
design.formula	formula for the design covariates.
data	a data frame.
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.
design.formula	formula for the design covariates.

Value

an rjags object

References

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

Examples

HSDC()

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