G-Computation Risk-Ratio Inference for Binary Responses

Fits a logistic working model, $\mathrm{logit}\,\Pr(Y_i=1\mid x_i) = x_i^\top\hat\beta$, for an incidence outcome using treatment and, optionally, all recorded covariates, then estimates the marginal (standardized) risk ratio $\mathrm{RR} = \overline{\mathrm{risk}}_1 / \overline{\mathrm{risk}}_0$ by G-computation: setting every subject's treatment indicator to 1 (respectively 0) while holding their other observed covariates fixed, averaging the model-implied risk over the empirical covariate distribution under each counterfactual, and taking the ratio — see gcomp_logistic_point_estimate_cpp for the exact standardization formula (mean1/mean0). Bootstrap/jackknife inference on this estimand is generally done on the log risk-ratio scale internally (see $compute_bootstrap_confidence_interval(), $compute_bayesian_bootstrap_confidence_interval(), and the jackknife-Wald methods, whose "basic"/"wald" interval types route through log-scale-specific helpers for this estimand), then back-transformed, since ratio estimators are typically closer to normally distributed on the log scale. Wald inference uses the fitted logistic model's sandwich covariance and the delta method on the log risk-ratio scale. This standard error conditions on the observed covariates used for standardization: it quantifies uncertainty in the fitted coefficients for the sample-conditional standardized risk ratio, and does not add sampling variability from a target population's covariate distribution. Bootstrap, randomization, and jackknife methods remain available. likelihood_tier = "none" means that this class does not provide likelihood-ratio or score tests; it does not preclude its Wald inference.

Uses the shared nonparametric bootstrap distribution contract; see InferenceNonParamBootstrap.

Computes a bootstrap confidence interval for the treatment effect.

Computes a bootstrap two-sided p-value for the treatment effect.

Computes a Bayesian-bootstrap two-sided p-value for the treatment effect.

Computes a Bayesian-bootstrap confidence interval for the treatment effect.

Computes a jackknife-Wald two-sided p-value for the treatment effect.

Computes a jackknife-Wald confidence interval for the treatment effect.

Computes a PRW subsampling two-sided p-value for the treatment effect.

Computes a PRW subsampling confidence interval for the treatment effect.

Computes an m-out-of-n bootstrap two-sided p-value for the treatment effect.

Computes an m-out-of-n bootstrap confidence interval for the treatment effect.

Value

A numeric vector of bootstrap estimates.

Super class

Inference -> InferenceIncidGCompRiskRatio

Methods

Public methods

InferenceIncidGCompRiskRatio$approximate_bootstrap_distribution_beta_hat_T()
InferenceIncidGCompRiskRatio$compute_bootstrap_confidence_interval()
InferenceIncidGCompRiskRatio$compute_bootstrap_two_sided_pval()
InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_two_sided_pval()
InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_confidence_interval()
InferenceIncidGCompRiskRatio$compute_jackknife_wald_two_sided_pval()
InferenceIncidGCompRiskRatio$compute_jackknife_wald_confidence_interval()
InferenceIncidGCompRiskRatio$compute_subsampling_two_sided_pval()
InferenceIncidGCompRiskRatio$compute_subsampling_confidence_interval()
InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_two_sided_pval()
InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_confidence_interval()
InferenceIncidGCompRiskRatio$clone()

+ inherited public methods from Inference

`InferenceIncidGCompRiskRatio$approximate_bootstrap_distribution_beta_hat_T()`

Usage

InferenceIncidGCompRiskRatio$approximate_bootstrap_distribution_beta_hat_T(
  B = 501,
  show_progress = TRUE,
  debug = FALSE,
  bootstrap_type = NULL
)

Arguments

B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
debug: Whether to return diagnostics.
bootstrap_type: Optional resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.

`InferenceIncidGCompRiskRatio$compute_bootstrap_confidence_interval()`

Usage

InferenceIncidGCompRiskRatio$compute_bootstrap_confidence_interval(
  alpha = 0.05,
  B = 501,
  type = NULL,
  na.rm = TRUE,
  show_progress = TRUE,
  min_number_usable_samples = 5L
)

Arguments

alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.

`InferenceIncidGCompRiskRatio$compute_bootstrap_two_sided_pval()`

Usage

InferenceIncidGCompRiskRatio$compute_bootstrap_two_sided_pval(
  delta = NULL,
  B = 501,
  type = "symmetric",
  na.rm = FALSE,
  show_progress = TRUE,
  min_number_usable_samples = 5L
)

Arguments

delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.

`InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_two_sided_pval()`

Usage

InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_two_sided_pval(
  delta = NULL,
  B = 501,
  type = NULL,
  na.rm = FALSE,
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  weighting_unit_type = NULL
)

Arguments

delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
weighting_unit_type: Optional resampling unit override.
weighting_unit_type: Optional resampling unit override.

`InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_confidence_interval()`

Usage

InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_confidence_interval(
  alpha = 0.05,
  B = 501,
  type = NULL,
  na.rm = TRUE,
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  weighting_unit_type = NULL
)

Arguments

alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
na.rm: Whether to remove non-finite bootstrap replicates.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
weighting_unit_type: Optional resampling unit override.
weighting_unit_type: Optional resampling unit override.

`InferenceIncidGCompRiskRatio$compute_jackknife_wald_two_sided_pval()`

Usage

InferenceIncidGCompRiskRatio$compute_jackknife_wald_two_sided_pval(
  delta = NULL,
  unit = "auto"
)

Arguments

delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
unit: Deletion unit. Default "auto".
unit: Deletion unit. Default "auto".

`InferenceIncidGCompRiskRatio$compute_jackknife_wald_confidence_interval()`

Usage

InferenceIncidGCompRiskRatio$compute_jackknife_wald_confidence_interval(
  alpha = 0.05,
  unit = "auto"
)

Arguments

alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
unit: Deletion unit. Default "auto".
unit: Deletion unit. Default "auto".

`InferenceIncidGCompRiskRatio$compute_subsampling_two_sided_pval()`

Usage

InferenceIncidGCompRiskRatio$compute_subsampling_two_sided_pval(
  delta = NULL,
  B = 501,
  b = NULL,
  type = "centered",
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  subsampling_type = NULL
)

Arguments

delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
b: Subsample size. See InferenceNonParamBootstrap$compute_subsampling_two_sided_pval.
b: Subsample size. See InferenceNonParamBootstrap$compute_subsampling_confidence_interval.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
subsampling_type: Optional empirical-resampling scheme.
subsampling_type: Optional empirical-resampling scheme.

`InferenceIncidGCompRiskRatio$compute_subsampling_confidence_interval()`

Usage

InferenceIncidGCompRiskRatio$compute_subsampling_confidence_interval(
  alpha = 0.05,
  B = 501,
  b = NULL,
  type = "basic",
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  subsampling_type = NULL
)

Arguments

alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
b: Subsample size. See InferenceNonParamBootstrap$compute_subsampling_two_sided_pval.
b: Subsample size. See InferenceNonParamBootstrap$compute_subsampling_confidence_interval.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
subsampling_type: Optional empirical-resampling scheme.
subsampling_type: Optional empirical-resampling scheme.

`InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_two_sided_pval()`

Usage

InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_two_sided_pval(
  delta = NULL,
  B = 501,
  m = NULL,
  type = "centered",
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  bootstrap_type = NULL
)

Arguments

delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: The null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
delta: Null treatment effect. Defaults to 0 for RD and 1 for RR.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
m: Resample size. See InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_two_sided_pval.
m: Resample size. See InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_confidence_interval.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
bootstrap_type: Optional resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.

`InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_confidence_interval()`

Usage

InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_confidence_interval(
  alpha = 0.05,
  B = 501,
  m = NULL,
  type = "basic",
  show_progress = TRUE,
  min_number_usable_samples = 5L,
  bootstrap_type = NULL
)

Arguments

alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
alpha: Significance level. Default 0.05.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of Bayesian-bootstrap samples.
B: Number of subsamples.
B: Number of subsamples.
B: Number of resamples.
B: Number of resamples.
m: Resample size. See InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_two_sided_pval.
m: Resample size. See InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_confidence_interval.
type: Bootstrap CI type. See InferenceNonParamBootstrap$compute_bootstrap_confidence_interval.
type: Bootstrap p-value type. See InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval.
type: Bayesian-bootstrap p-value type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval.
type: Bayesian-bootstrap CI type. See InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval.
type: P-value type.
type: Confidence-interval type.
type: P-value type.
type: Confidence-interval type.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
show_progress: Whether to show a progress bar.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite bootstrap samples required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite subsampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
min_number_usable_samples: Minimum number of finite resampled estimates required.
bootstrap_type: Optional resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.
bootstrap_type: Optional empirical-resampling scheme.

`InferenceIncidGCompRiskRatio$clone()`

The objects of this class are cloneable with this method.

Usage

InferenceIncidGCompRiskRatio$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

# \donttest{
seq_des = DesignSeqOneByOneBernoulli$new(n = 10, response_type = 'incidence')
for (i in 1:10) {
  seq_des$add_one_subject_to_experiment_and_assign(data.frame(x1 = rnorm(1)))
}
seq_des$add_all_subject_responses(rbinom(10, 1, 0.5))
inf = InferenceIncidGCompRiskRatio$new(seq_des)
inf$compute_estimate()
#> [1] NA
# }

Value

See also

Super class

Methods

Public methods

InferenceIncidGCompRiskRatio$approximate_bootstrap_distribution_beta_hat_T()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_bootstrap_confidence_interval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_bootstrap_two_sided_pval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_two_sided_pval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_confidence_interval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_jackknife_wald_two_sided_pval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_jackknife_wald_confidence_interval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_subsampling_two_sided_pval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_subsampling_confidence_interval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_two_sided_pval()

Usage

Arguments

InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_confidence_interval()

Usage

Arguments

InferenceIncidGCompRiskRatio$clone()

Usage

Arguments

Examples

`InferenceIncidGCompRiskRatio$approximate_bootstrap_distribution_beta_hat_T()`

`InferenceIncidGCompRiskRatio$compute_bootstrap_confidence_interval()`

`InferenceIncidGCompRiskRatio$compute_bootstrap_two_sided_pval()`

`InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_two_sided_pval()`

`InferenceIncidGCompRiskRatio$compute_bayesian_bootstrap_confidence_interval()`

`InferenceIncidGCompRiskRatio$compute_jackknife_wald_two_sided_pval()`

`InferenceIncidGCompRiskRatio$compute_jackknife_wald_confidence_interval()`

`InferenceIncidGCompRiskRatio$compute_subsampling_two_sided_pval()`

`InferenceIncidGCompRiskRatio$compute_subsampling_confidence_interval()`

`InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_two_sided_pval()`

`InferenceIncidGCompRiskRatio$compute_m_out_of_n_bootstrap_confidence_interval()`

`InferenceIncidGCompRiskRatio$clone()`