set_options: Set up Bayesian model priors and settings

In bsreg: Bayesian Spatial Regression Models

Set up Bayesian model priors and settings

Description

Set up Bayesian model priors and settings

Usage

set_options(
  type = c("Independent", "Conjugate", "Shrinkage", "Horseshoe"),
  NG = set_NG(),
  SNG = set_SNG(),
  HS = set_HS(),
  SAR = set_SAR(),
  SLX = set_SLX(),
  SEM = set_SEM(),
  SV = set_SV(),
  ...
)

Arguments

type	Character scalar with the prior type for the nested linear model.
NG	Settings for the Normal-Gamma prior (independent or conjugate). See set_NG.
SNG	Settings for the Normal-Gamma shrinkage prior (Polson and Scott, 2010). See set_NG.
HS	Settings for the Horseshoe shrinkage prior (Makalic and Schmidt, 2015). See set_NG.
SAR	Settings for the spatial autoregressive setup. See set_SAR.
SLX	Settings for the spatially lagged explanatory setup. See set_SAR. Note that settings for the spatial term 'theta' are provided to NG instead.
SEM	Settings for the spatial error setup. See set_SAR.
SV	Settings for the stochastic volatility setup. See set_SV.
...	Used to provide custom prior elements.

Value

Returns a list with priors and settings for a Bayesian model.

Examples

set_options("Shrinkage", SNG = set_SNG(lambda_a = 1, lambda_b = 1))

bsreg documentation built on March 18, 2022, 5:36 p.m.