diagnostics.exdqlmForecast: Diagnostics Method for 'exdqlmForecast' Objects

In exdqlm: Extended Dynamic Quantile Linear Models

Diagnostics Method for exdqlmForecast Objects

Description

Computes held-out forecast scores from one or two exdqlmForecast objects, typically returned by predict.exdqlmFit or exdqlmForecast with return.draws = TRUE. This method evaluates posterior predictive forecast draws against observations reserved outside the fitted sample.

Usage

## S3 method for class 'exdqlmForecast'
diagnostics(
  object,
  y,
  m2 = NULL,
  p0 = NULL,
  crps_probs = seq(0.01, 0.99, by = 0.01),
  crps_weights = NULL,
  ...
)

Arguments

object	An exdqlmForecast object returned by predict.exdqlmFit or exdqlmForecast with return.draws = TRUE.
y	Required numeric vector or time series of held-out observations. Its length must equal the forecast horizon.
m2	An optional second object of class "exdqlmForecast" to compare with object.
p0	Optional quantile level used for the check-loss calculation. When NULL, the value is taken from object$m1$p0. If m2 is supplied, its fitted quantile level must agree with the resolved value.
crps_probs	Optional numeric vector of quantile levels used to approximate CRPS through the integrated quantile-score identity. Values must be strictly between 0 and 1. Default is seq(0.01, 0.99, by = 0.01).
crps_weights	Optional non-negative numeric weights for crps_probs. When NULL, equal weights are used. When provided, weights are normalized to sum to 1.
...	Additional arguments (unused).

Details

The check loss is computed at the target quantile level p0 using the forecast quantile means ff stored in each forecast object. CRPS is computed from samp.fore using the same finite integrated quantile-score approximation used by exdqlmDiagnostics(). This function does not compute KL diagnostics because KL in exdqlm is defined for fitted one-step-ahead MAP standardized forecast errors, not for arbitrary held-out forecast draws.

Value

An object of class "exdqlmForecastDiagnostic" containing:

• y - Held-out observations used for scoring.

• p0 - Quantile level used for check loss.

• horizon - Forecast horizon.

• m1.check_loss - Mean target-quantile check loss for m1.

• m1.CRPS - Mean CRPS approximation for m1.

• m1.pointwise - Pointwise held-out scores for m1.

• crps.method, crps.probs, and crps.weights - CRPS approximation metadata.

If m2 is supplied, analogous m2.* fields are included.

Examples

data("scIVTmag", package = "exdqlm")
old = options(exdqlm.max_iter = 15L)
y = scIVTmag[1:65]
y_train = y[1:60]
y_holdout = y[61:65]
model = polytrendMod(1, stats::quantile(y_train, 0.85), 10)
M0 = exdqlmLDVB(y_train, p0 = 0.85, model, df = c(0.98), dim.df = c(1),
                 gam.init = -3.5, sig.init = 15,
                 n.samp = 20, tol = 0.2, verbose = FALSE)
fFF = model$FF[, 1, drop = FALSE]
fGG = model$GG
M0.forecast = predict(M0, start.t = 60, k = 5,
                             fFF = fFF, fGG = fGG,
                             return.draws = TRUE, n.samp = 20, seed = 123,
                             plot = FALSE)
score = diagnostics(M0.forecast, y = y_holdout)
score

exdqlm documentation built on July 10, 2026, 1:08 a.m.