MCMC convergence diagnostics — mcmc

Use diagnostics(), which reads a fit's draws provenance and returns the diagnostic that applies – chain mixing for MCMC draws, approximation reliability for deterministic fits. The name mcmc_diagnostics() described only one of the two branches it already routed between.

Usage

mcmc_diagnostics(
  fit,
  pars = NULL,
  measures = c("rhat", "ess_bulk", "ess_tail"),
  probs = c(0.05, 0.95)
)

Arguments

fit: A tulpa_fit (or subclass) carrying posterior $draws. Multiple chains are recognised from a 3D [iter, chain, param] draws array, a $chain_id row map, or an $n_chains count over chain-major rows.
pars: Optional character vector of parameter names to restrict to.
measures: Character vector selecting which diagnostics to compute, in output-column order. Available: "rhat", "rhat_bulk", "rhat_fold", "ess_bulk", "ess_tail", "ess_mean", "ess_sd", "mcse_mean", "mcse_sd", "ess_quantile", "mcse_quantile". Defaults to the core set c("rhat", "ess_bulk", "ess_tail"). Applies to chain fits; the approximation-reliability table has a fixed set of columns.
probs: Numeric probabilities for the quantile-based measures ("ess_quantile", "mcse_quantile"); each expands to one column named e.g. ess_q5, ess_q95. Default c(0.05, 0.95). Chain fits only.

Value

The value of diagnostics() for fit.