Print a one-arm fitted model

print.mixgpd_fit() gives a compact header for a fitted one-arm model. It is meant as a quick identity check rather than a full posterior summary.

Usage

# S3 method for class 'mixgpd_fit'
print(x, ...)

Arguments

x

A fitted object of class "mixgpd_fit".

...

Unused.

Value

x invisibly.

Details

The fitted object represents posterior draws from a bulk mixture model, or from its spliced bulk-tail extension when GPD = TRUE. For the bulk part, the predictive law has the mixture form $$f(y \mid x) = \sum_{k=1}^{K} w_k(x) f_k(y \mid x, \theta_k).$$ When a GPD tail is active, exceedances above the threshold are instead routed through the generalized Pareto tail attached to the same bulk mixture.

The print method reports only the model identity and basic metadata. Use summary() for parameter-level posterior summaries, predict() for predictive functionals, and plot() for chain diagnostics.

See also

summary.mixgpd_fit, params, predict.mixgpd_fit.

Examples

# \donttest{
y <- abs(stats::rnorm(25)) + 0.1
bundle <- build_nimble_bundle(y = y, backend = "sb", kernel = "normal",
                             GPD = TRUE, components = 3,
                             mcmc = list(niter = 100, nburnin = 50, thin = 1, nchains = 1))
fit <- run_mcmc_bundle_manual(bundle)
#> [mixgpd] Validating configuration
#> [mixgpd] Checking build/compile cache
#> [mixgpd] Building model and MCMC configuration
#> [mixgpd] Compiling NIMBLE model
#> [mixgpd] Initializing chains
#> [mixgpd] Running MCMC
#> [mixgpd] Finalizing WAIC and diagnostics
#> [mixgpd] Assembling fit object
print(fit)
#> MixGPD fit | backend: Stick-Breaking Process | kernel: Normal Distribution | GPD tail: TRUE
#> n = 25 | components = 3 | epsilon = 0.025
#> MCMC: niter=100, nburnin=50, thin=1, nchains=1 
#> Fit
#> Use summary() for posterior summaries; plot() for diagnostics; predict() for predictions.
# }