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Residual diagnostics on the training design

residuals.mixgpd_fit.Rd

residuals.mixgpd_fit() computes residual diagnostics for conditional fits on the original training data.

Usage

# S3 method for class 'mixgpd_fit'
residuals(
  object,
  type = c("raw", "pit"),
  fitted_type = c("mean", "median"),
  pit = c("plugin", "bayes_mean", "bayes_draw"),
  pit_seed = NULL,
  ...
)

Arguments

object

A fitted object of class "mixgpd_fit" (must have covariates).

type

Residual type:

  • "raw": observed minus fitted values

  • "pit": probability integral transform residuals (see pit argument)

fitted_type

For type = "raw", use fitted means or medians.

pit

PIT mode for type = "pit":

  • "plugin": plug-in PIT using the posterior mean CDF.

  • "bayes_mean": Bayesian PIT using draw-wise CDFs averaged over draws.

  • "bayes_draw": Bayesian PIT using a single draw-wise CDF per observation.

Bayesian PIT modes drop invalid posterior draws using the same validation rules as prediction and attach diagnostics via attr(res, "pit_diagnostics").

pit_seed

Optional integer seed for reproducible bayes_draw sampling.

...

Unused.

Value

Numeric vector of residuals with length equal to the training sample size. PIT variants attach diagnostic metadata as attributes.

Details

Raw residuals are based on posterior predictive fitted means or medians. PIT residuals instead assess calibration through the posterior predictive CDF. The plug-in PIT uses a posterior mean CDF, while the Bayesian PIT variants work draw by draw.

This method is not available for unconditional models because no training design matrix is stored for observation-specific fitted values.

See also

fitted.mixgpd_fit, predict.mixgpd_fit, plot.mixgpd_fitted.

Examples

if (FALSE) { # \dontrun{
y <- abs(stats::rnorm(50)) + 0.1
X <- data.frame(x1 = stats::rnorm(50), x2 = stats::runif(50))
bundle <- build_nimble_bundle(y = y, X = X, backend = "sb", kernel = "lognormal",
                             GPD = FALSE, components = 4,
                             mcmc = list(niter = 200, nburnin = 50, thin = 1, nchains = 1))
fit <- run_mcmc_bundle_manual(bundle)
pit_plugin <- residuals(fit, type = "pit", pit = "plugin")
pit_bayes_mean <- residuals(fit, type = "pit", pit = "bayes_mean", pit_seed = 1L)
pit_bayes_draw <- residuals(fit, type = "pit", pit = "bayes_draw", pit_seed = 1L)
attr(pit_bayes_draw, "pit_diagnostics")
} # }