Constructor for the debiased score test

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",
  fit_alt_method = NULL,
  wls_alt_method = NULL,
  X.cols.hunt = 1:ncol(X),
  binary.y = FALSE,
  trim.outlier.hunt = TRUE,
  predict_fun = stats::predict,
  predict_fun_alt = stats::predict,
  arg.fit_method = NULL,
  arg.wls_method = NULL,
  arg.fit_alt_method = NULL,
  arg.wls_alt_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.
fit_alt_method: Required when hunt.style = "vanilla"; ignored otherwise. Function with signature fit_alt_method(y, X, ...) returning a fitted alternative model that supports predict_fun_alt(g, X).
wls_alt_method: Required when hunt.style %in% c("optimal", "wls"); ignored otherwise. Function with signature wls_alt_method(y, X, w, ...) returning a fitted alternative model that supports predict_fun_alt(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 null-model fits. Default stats::predict.
predict_fun_alt: Function with signature predict_fun_alt(fit, X) returning predictions from alt-model fits. Default stats::predict.
arg.fit_method, arg.wls_method, arg.fit_alt_method, arg.wls_alt_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: Orthogonalised hunted direction on the test subsample.
h.raw: Hunted direction before the outer debias projection.
hunted_fun: The hunted 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, both predict functions, and the four arg.* lists.

Usage

Arguments

Value

See also