new_dScoreTest: Constructor for the debiased score test

In dScoreTest: Debiased Score Tests for Goodness of Fit and Model Comparison

Constructor for the debiased score test

Description

Internal worker that builds a dScoreTest object from a fixed three-way (or two-way) sample split. Called by dScoreTest. Splits, hunting algorithm, and predict semantics are taken as fully resolved arguments — no defaults are inferred from the data.

Usage

new_dScoreTest(
  y,
  X,
  idx.hunt,
  idx.debias,
  idx.test,
  score_fun,
  weight_fun,
  fit_method,
  wls_method,
  hunt.style = "optimal",
  hunt.method = "customized",
  debias.method = "standard",
  debias_fun = debias_standard,
  fit_hunt_method = NULL,
  wls_hunt_method = NULL,
  X.cols.hunt = 1:ncol(X),
  binary.y = FALSE,
  trim.outlier.hunt = TRUE,
  predict_fun = stats::predict,
  predict_fun_hunt = stats::predict,
  arg.fit_method = NULL,
  arg.wls_method = NULL,
  arg.fit_hunt_method = NULL,
  arg.wls_hunt_method = NULL
)

Arguments

y	Numeric response vector of length n.
X	Numeric covariate matrix of dimension n x p.
idx.hunt	Integer indices into 1:n for the hunting subsample.
idx.debias	Integer indices into 1:n for the debiasing subsample (refitting the null model and projecting the hunted direction).
idx.test	Integer indices into 1:n for the test subsample on which the test statistic is evaluated. May coincide with idx.debias (two-way split) or be disjoint (three-way split).
score_fun	Function with signature score_fun(fit, y, X) returning a vector of scores l'(\hat{f}(x_i), y_i).
weight_fun	Function with signature weight_fun(fit, X) returning the weight \mathbb{E}[l''(\hat{f}(x_i), y_i) | x_i] for each row of X.
fit_method	Function with signature fit_method(y, X, ...) returning a fitted null model. The returned object must support predict_fun(fit, X).
wls_method	Function with signature wls_method(y, X, w, ...) that fits the null model by weighted least squares. The returned object must support predict_fun(fit, X).
hunt.style	One of "optimal" (default), "wls", or "vanilla". Selects which hunt_* routine is used.
hunt.method	String for the hunting method.
debias.method	String for the debiasing method, recorded in the returned object's Call.
debias_fun	Function performing the debiasing, with the same signature as debias_standard (the default). Must return a list with an element h, the debiased hunted function.
fit_hunt_method	Required when hunt.style = "vanilla"; ignored otherwise. Function with signature fit_hunt_method(y, X, ...) returning a fitted alternative model that supports predict_fun_hunt(g, X).
wls_hunt_method	Required when hunt.style %in% c("optimal", "wls"); ignored otherwise. Function with signature wls_hunt_method(y, X, w, ...) returning a fitted alternative model that supports predict_fun_hunt(g, X).
X.cols.hunt	Integer or name vector selecting which columns of X drive the hunt. Default: all columns.
binary.y	Logical. When TRUE, the optimal hunter computes \mathrm{Var}(l' | x) from predict_fun(fit, X, type='response') assuming a Bernoulli response. Only consulted by hunt.style = "optimal".
trim.outlier.hunt	Logical. Passed to the chosen hunt_* routine as trim.outlier. If TRUE (default), extreme values in the hunted function will be removed using Tukey's IQR rule.
predict_fun	Function with signature predict_fun(fit, X) returning predictions from a fitted null model. Default stats::predict.
predict_fun_hunt	Function with signature predict_fun_hunt(fit, X) returning predictions from a model fitted under alternative. Default stats::predict.
arg.fit_method, arg.wls_method, arg.fit_hunt_method, arg.wls_hunt_method	Named lists of additional arguments forwarded to the corresponding fitter via do.call. Default NULL.

Value

A list of class "dScoreTest" with elements:

t.stat

Debiased test statistic \sqrt{n_{\mathrm{test}}}\,\bar{L}/\hat{\sigma}_L.

p.val

One-sided p-value (right tail of the standard normal).

resids

Score residuals on the test subsample.

h

Orthogonalized hunted direction on the test subsample.

h.raw

Hunted direction before the outer debias projection.

hunted_fun

The debiased hunted function \hat{h} - \hat{m}_{\hat{h}}, a function that can be applied to X.

Data

List with X, y, and the three index vectors.

Call

Named list of methods, hunt.style, hunt.method, debias.method, both predict functions, and the four arg.* lists.

See Also

dScoreTest, hunt_optimal, hunt_wls, hunt_vanilla

dScoreTest documentation built on Sept. 3, 2026, 1:06 a.m.