Deployable learned-ensemble GOF test via parametric bootstrap

Turns a pre-trained ensemble meta into a deployable goodness-of-fit test for any fitted model: it scores the model, then calibrates the p-value by a per-dataset parametric bootstrap from the fitted model (so no knowledge of the truth or the data-generating design is required). Validity comes from the bootstrap, independent of how meta was trained.

Usage

deploy.gof(object, meta, B = 99, feature_fn = gof.features)

Arguments

object

A fitted binary logistic glm.

meta

A pre-trained scorer: either a function f(features) returning a scalar misfit score, or an object with a predict method consuming a one-row feature matrix.

B

Number of parametric-bootstrap resamples (default 99).

feature_fn

Function mapping a fitted glm to its feature vector (default gof.features).

Value

A one-row data.frame with the score, B, and the bootstrap p_value.

See also

gof.features, cdef.gof.