Comprehensive diagnostics for an exact Gaussian-process model

Combines exact leave-one-out predictive diagnostics, uncertainty-calibration summaries, and numerical-conditioning diagnostics. The components are kept separate so predictive misfit is not automatically attributed to numerical instability, and numerical instability is not mistaken for model misfit.

Usage

gp_diagnostics(
  model,
  interval_level = 0.95,
  condition_tolerance = sqrt(.Machine$double.eps)
)

Arguments

model

A fitted gaussianprocesses_model.

interval_level

Probability level for LOO prediction intervals.

condition_tolerance

Positive reciprocal-condition threshold used by the numerical diagnostic summary.

Value

An object of class gaussianprocesses_diagnostics.

Stability

Stable: from version 1.0.0 this interface changes incompatibly only in a major release, after a deprecation period. Results and options that concern an experimental model class, kernel, or argument follow that interface's tier. See gaussianprocesses-package for the policy. Its numerical component comes from the experimental gp_numerical_diagnostics().

Examples

x <- seq(-2, 2, length.out = 20)
model <- fit_gp(
  x,
  sin(2 * x),
  kernel = rbf_kernel(length_scale = 0.6),
  noise_variance = 0.01
)

gp_diagnostics(model)
#> Gaussian-process diagnostics
#>   LOO empirical coverage: 1 (nominal 0.95)
#>   mean standardized residual: -6.9552e-18
#>   SD standardized residual: 0.15687
#>   numerical jitter: 0
#>   reciprocal condition number: 0.00082526