mcmccontrol: Markov chain Monte Carlo (MCMC) parameters

In DDPstar: Density Regression via Dirichlet Process Mixtures of Normal Structured Additive Regression Models

Markov chain Monte Carlo (MCMC) parameters

Description

This function is used to set various parameters controlling the Markov chain Monte Carlo (MCMC) parameters.

Usage

mcmccontrol(nsave = 8000, nburn = 2000, nskip = 1)

Arguments

nsave	An integer giving the total number of scans to be saved (does not include the burn-in and thinning iterations).
nburn	An integer giving the number of burn-in scans.
nskip	An integer giving the thinning interval,

Details

The value returned by this function is used as a control argument of the DDPstar function.

Value

A list with components for each of the possible arguments.

See Also

DDPstar

Examples

library(DDPstar)
data(dde)
dde$GAD <- dde$GAD/7 # GAD in weeks

mcmc <- mcmccontrol(nburn = 20000, nsave = 15000, nskip = 1)

set.seed(10) # For reproducibility
fit_dde <- DDPstar(formula = GAD ~ f(DDE, bdeg = 3, nseg = 20, pord = 2, atau = 1, btau = 0.005), 
  data = dde, mcmc = mcmc, prior = list(a = 2, b = 0.5, aalpha = 2, balpha = 2, L = 20), 
  standardise = TRUE, compute.lpml = TRUE, compute.WAIC = TRUE, compute.DIC = TRUE)
summary(fit_dde)

DDPstar documentation built on April 3, 2025, 8:46 p.m.