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Plot MCMC diagnostics for a MixGPD fit (ggmcmc backend)

plot.mixgpd_fit.Rd

Uses ggmcmc to produce standard MCMC diagnostic plots. Works with 1+ chains.

Usage

# S3 method for class 'mixgpd_fit'
plot(x, family = "auto", params = NULL, nLags = 50, ...)

Arguments

x

A fitted object of class "mixgpd_fit".

family

Character vector of plot names (ggmcmc plot types) or a single one. Use "auto" (or "all") to include all plots supported for the available number of chains/parameters. Supported types:

  • "histogram": posterior histograms

  • "density": posterior density curves

  • "traceplot": MCMC trace plots

  • "running": running mean plots

  • "compare_partial": partial chain comparisons

  • "autocorrelation": autocorrelation plots

  • "crosscorrelation": cross-correlation matrix

  • "Rhat": Gelman–Rubin R-hat (2+ chains)

  • "grb": Gelman–Rubin–Brooks (2+ chains)

  • "effective": effective sample size

  • "geweke": Geweke diagnostic

  • "caterpillar": caterpillar/forest plots

  • "pairs": pairwise scatter plots (2+ params)

params

Optional parameter selector:

  • character vector of parameter patterns (exact names or partial matches)

  • a single regex string (e.g. "alpha|threshold|tail_")

  • NULL (default): plots all monitored parameters

nLags

Number of lags for autocorrelation (ggmcmc).

...

Passed through to the underlying ggmcmc plotting functions when applicable.

Value

Invisibly returns a named list of ggplot objects.

Details

The supported plots diagnose posterior simulation quality rather than data fit. Depending on the selected family, they show chain traces, marginal posterior densities, autocorrelation, cross-correlation, running means, or Gelman-style convergence summaries for the monitored parameters.

These graphics should be read before interpreting posterior summaries or treatment-effect results. Poor mixing or strong autocorrelation in the MCMC output can invalidate downstream summaries even when the fitted model itself is correctly specified.

Examples

if (FALSE) { # \dontrun{
y <- abs(stats::rnorm(50)) + 0.1
bundle <- build_nimble_bundle(y = y, backend = "sb", kernel = "normal",
                             GPD = TRUE, components = 6,
                             mcmc = list(niter = 200, nburnin = 50, thin = 1, nchains = 1))
fit <- run_mcmc_bundle_manual(bundle)
plot(fit, family = c("traceplot", "density"))
} # }