Fitted values on the training design

fitted.mixgpd_fit() is a thin training-data wrapper around predict.mixgpd_fit for conditional models.

Usage

# S3 method for class 'mixgpd_fit'
fitted(
  object,
  type = c("mean", "median", "quantile"),
  p = 0.5,
  level = 0.95,
  interval = "credible",
  seed = 1,
  ...
)

Arguments

object

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

type

Which fitted functional to return:

• "mean": posterior predictive mean

• "median": posterior predictive median

• "quantile": posterior predictive quantile at level p

p

Quantile level used when type = "quantile".

level

Credible level for confidence intervals (default 0.95 for 95 percent credible intervals).

interval

Character or NULL; type of credible interval:

• NULL: no interval

• "credible" (default): equal-tailed quantile intervals

• "hpd": highest posterior density intervals

seed

Random seed used for deterministic fitted values.

...

Unused.

Value

A data frame with columns for fitted values, optional intervals, and residuals computed on the training sample.

Details

The method returns posterior predictive fitted values on the observed design matrix. It is available only when the fitted model stored covariates.

See also

predict.mixgpd_fit, residuals.mixgpd_fit, plot.mixgpd_fitted.

Examples

if (FALSE) { # \dontrun{
# Conditional model (with covariates X)
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 = "normal",
                             GPD = TRUE, components = 6,
                             mcmc = list(niter = 200, nburnin = 50, thin = 1, nchains = 1))
fit <- run_mcmc_bundle_manual(bundle)
fitted(fit)
fitted(fit, level = 0.90)
fitted(fit, interval = "hpd")  # HPD intervals
fitted(fit, interval = NULL)   # No intervals
} # }