Internal base for user-defined bootstrap inference extensions

This class uses the same fit() result contract as InferenceCustomAsymp, but only promises estimate/bootstrap behavior.

Value

A two-sided p-value.

Super class

Inference -> InferenceCustomBoot

Methods

Public methods

• InferenceCustomBoot$compute_rand_two_sided_pval()

• InferenceCustomBoot$fit()

• InferenceCustomBoot$compute_estimate()

• InferenceCustomBoot$clone()

+ inherited public methods from Inference

• Inference$capabilities()
• Inference$compute_asymp_confidence_interval()
• Inference$compute_asymp_two_sided_pval()
• Inference$compute_exact_confidence_interval()
• Inference$compute_exact_two_sided_pval_for_treatment_effect()
• Inference$duplicate()
• Inference$get_analysis_data()
• Inference$get_covariates()
• Inference$get_design_object()
• Inference$get_model_formula()
• Inference$get_nonestimable_reason()
• Inference$get_nonestimable_stage()
• Inference$get_optimization_alg()
• Inference$get_response()
• Inference$get_response_type()
• Inference$get_treatment()
• Inference$initialize()
• Inference$is_nonestimable()
• Inference$set_optimization_alg()
• Inference$set_seed()
• Inference$supports()

---

InferenceCustomBoot$compute_rand_two_sided_pval()

Computes a randomization two-sided p-value. Delegates to the `RandomizationCI`-provided dispatch (Zhang incidence support, type/args_for_type) since `NonparametricBootstrap` pulls in both `RandomizationTest` and `RandomizationCI`, and the two provide conflicting `compute_rand_two_sided_pval` implementations.

Usage

InferenceCustomBoot$compute_rand_two_sided_pval(
  r = 501,
  delta = 0,
  transform_responses = "none",
  na.rm = TRUE,
  show_progress = TRUE,
  permutations = NULL,
  type = NULL,
  args_for_type = NULL,
  zero_one_logit_clamp = .Machine$double.eps
)

Arguments

r

Number of randomization vectors.

delta

Null treatment effect value.

transform_responses

Response transformation to apply during the test. For survival responses the default "log" multiplies the recorded times of the units treated under each reference allocation by \(e^\delta\), event and censoring times alike, with censoring indicators unchanged – the rank-based AFT residual construction (Tsiatis 1990; Wei, Ying and Lin 1990; Jin, Lin, Wei and Ying 2003); see compute_rand_confidence_interval() for the assumptions.

na.rm

Whether to remove non-finite simulated statistics.

show_progress

Whether to show progress.

permutations

Optional pre-generated assignment draws.

type

Optional incidence-specific exact randomization type.

args_for_type

Optional arguments keyed by type.

zero_one_logit_clamp

The clamping amount for exact 0 and 1 values when logging

---

InferenceCustomBoot$fit()

Calls the user-defined fit callback for this custom inference path; see InferenceCustomAsymp.

Usage

InferenceCustomBoot$fit(estimate_only = FALSE)

Arguments

estimate_only

If TRUE, skip variance calculations.

Returns

A list with fit results.

---

InferenceCustomBoot$compute_estimate()

Compute the treatment-effect estimate by delegating to the user-supplied custom asymptotic estimator. See InferenceCustomAsymp and InferenceAsymp.

Usage

InferenceCustomBoot$compute_estimate(estimate_only = FALSE)

Arguments

estimate_only

If TRUE, skip variance calculations.

Returns

The treatment estimate.

---

InferenceCustomBoot$clone()

The objects of this class are cloneable with this method.

Usage

InferenceCustomBoot$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.