convergence.dalmatian: Convergence

In sjbonner/dalmatian: Automating the Fitting of Double Linear Mixed Models in 'JAGS' and 'nimble'

Description

Compute convergence diagnostics for a dalmatian object.

Usage

## S3 method for class 'dalmatian'
convergence(
  object,
  pars = NULL,
  nstart = start(object$coda),
  nend = end(object$coda),
  nthin = coda::thin(object$coda),
  raftery = NULL,
  ...
)

Arguments

object	Object of class dalmatian created by dalmatian().
pars	List of parameters to assess. If NULL (default) then diagnostics are computed for the fixed effects and random effects standard deviations in the mean, dispersion, and joint components.
nstart	Start point for computing summary statistics (relative to true start of chain).
nend	End point for computing summary statistics (relative to true start of chain).
nthin	Thinning factor for computing summary statsitics (relative to full chain and not previously thinned output).
raftery	List of arguments to be passed to raftery.diag(). Any values not provided will be set to their defaults (see help(raftery.diag()) for details).
...	Ignored

Value

List containing Gelman-Rubin and Raftery convergence diagnostics and effective sample sizes for the selected parameters. This information is used to diagnose convergence of the MCMC sampling algorithms.

sjbonner/dalmatian documentation built on Nov. 4, 2021, 2:32 a.m.