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Plot prediction results

plot.mixgpd_predict.Rd

Generates type-specific visualizations for prediction objects returned by predict.mixgpd_fit(). Each prediction type produces a tailored plot:

  • quantile: Quantile indices vs estimates with credible intervals

  • sample: Histogram of samples with density overlay

  • mean: Histogram density with posterior mean vertical line and CI bounds

  • density: Density values vs evaluation points

  • survival: Survival function (decreasing y values)

Usage

# S3 method for class 'mixgpd_predict'
plot(x, y = NULL, ...)

Arguments

x

A prediction object returned by predict.mixgpd_fit().

y

Ignored; included for S3 compatibility.

...

Additional arguments passed to ggplot2 functions.

Value

Invisibly returns the ggplot object.

Details

The plotting method is tied to the predictive functional stored in the input object. Quantile and mean outputs display posterior point summaries and intervals, density and survival outputs show evaluated functions on the supplied grid, and posterior samples are visualized as empirical predictive draws.

In every case the plot reflects the quantity requested from predict.mixgpd_fit() after integrating over the retained posterior draws. It is therefore distinct from parameter-level summaries and from chain diagnostics.

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)

# Quantile prediction with plot
pred_q <- predict(fit, type = "quantile", index = c(0.25, 0.5, 0.75))
plot(pred_q)

# Sample prediction with plot
pred_s <- predict(fit, type = "sample", nsim = 500)
plot(pred_s)

# Mean prediction with plot
pred_m <- predict(fit, type = "mean", nsim_mean = 300)
plot(pred_m)
} # }