rho_priors: Specify prior for the spatial autoregressive parameter and sampling settings

Description

Specify prior for the spatial autoregressive parameter and sampling settings

Usage

rho_priors(
  rho_a_prior = 1,
  rho_b_prior = 1,
  rho_min = 0,
  rho_max = 1,
  init_rho_scale = 1,
  griddy_n = 60,
  use_griddy_gibbs = TRUE,
  mh_tune_low = 0.4,
  mh_tune_high = 0.6,
  mh_tune_scale = 0.1
)

Arguments

rho_a_prior: Single number. Prior hyperparameter for the four-parameter beta distribution betapdf. Defaults to 1.
rho_b_prior: Single number. Prior hyperparameter for the four-parameter beta distribution betapdf. Defaults to 1.
rho_min: Minimum value for \(\rho\) (default: 0)
rho_max: Maximum value for \(\rho\) (default: 1)
init_rho_scale: For Metropolis-Hastings step the initial candidate variance (default: 1)
griddy_n: single integer number. Sets how fine the grid approximation is. Default value is 60.
use_griddy_gibbs: Binary value. Should griddy-Gibbs be used for \(\rho\) estimation? use_griddy_gibbs=TRUE does not work if row_standardized_prior = FALSE is specified in the \(W\) prior specification. if TRUE: griddy-Gibbs step for sampling \(\rho\); if FALSE: tuned random-walk Metropolis-Hastings step
mh_tune_low: Lower bound of acceptance rate for Metropolis-Hastings tuning (used if use_griddy_gibbs==FALSE)
mh_tune_high: Upper bound of acceptance rate for Metropolis-Hastings tuning (used if use_griddy_gibbs==FALSE)
mh_tune_scale: Scaling factor for Metropolis-Hastings tuning (used if use_griddy_gibbs==FALSE)