Abstract class for all-subject modified-Poisson inference in KK designs

Abstract class for all-subject modified-Poisson inference in KK designs

Super classes

Inference -> InferenceAbstractKKMarginalIncid -> InferenceAbstractKKModifiedPoisson

Methods

Public methods

• InferenceAbstractKKModifiedPoisson$compute_estimate()

• InferenceAbstractKKModifiedPoisson$compute_estimate_with_bootstrap_weights()

• InferenceAbstractKKModifiedPoisson$compute_asymp_confidence_interval()

• InferenceAbstractKKModifiedPoisson$compute_asymp_two_sided_pval()

• InferenceAbstractKKModifiedPoisson$clone()

+ inherited public methods from InferenceAbstractKKMarginalIncid

• InferenceAbstractKKMarginalIncid$approximate_bayesian_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_jackknife_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_rand_bootstrap_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_randomization_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$approximate_subsampling_distribution_beta_hat_T()
• InferenceAbstractKKMarginalIncid$compute_bayesian_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_bayesian_bootstrap_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_bootstrap_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_gradient_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_gradient_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_jackknife_bias_estimate()
• InferenceAbstractKKMarginalIncid$compute_jackknife_estimate()
• InferenceAbstractKKMarginalIncid$compute_jackknife_std_error()
• InferenceAbstractKKMarginalIncid$compute_jackknife_wald_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_jackknife_wald_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_approx_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_approx_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_exact_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_exact_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bartlett_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_bootstrap_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_lik_ratio_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_m_out_of_n_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_m_out_of_n_bootstrap_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_param_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_param_bootstrap_estimate()
• InferenceAbstractKKMarginalIncid$compute_param_bootstrap_pval()
• InferenceAbstractKKMarginalIncid$compute_rand_bootstrap_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_rand_bootstrap_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_rand_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_rand_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_score_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_score_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_subsampling_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_subsampling_sensitivity()
• InferenceAbstractKKMarginalIncid$compute_subsampling_two_sided_pval()
• InferenceAbstractKKMarginalIncid$compute_wald_confidence_interval()
• InferenceAbstractKKMarginalIncid$compute_wald_two_sided_pval()
• InferenceAbstractKKMarginalIncid$get_information_preference()
• InferenceAbstractKKMarginalIncid$get_information_source_used()
• InferenceAbstractKKMarginalIncid$get_last_param_bootstrap_diagnostics()
• InferenceAbstractKKMarginalIncid$get_last_param_bootstrap_estimate_diagnostics()
• InferenceAbstractKKMarginalIncid$get_mod()
• InferenceAbstractKKMarginalIncid$get_summary()
• InferenceAbstractKKMarginalIncid$get_supported_bayesian_bootstrap_ci_types()
• InferenceAbstractKKMarginalIncid$get_supported_bayesian_bootstrap_pval_types()
• InferenceAbstractKKMarginalIncid$get_supported_bootstrap_ci_types()
• InferenceAbstractKKMarginalIncid$get_supported_bootstrap_pval_types()
• InferenceAbstractKKMarginalIncid$get_supported_information_preferences()
• InferenceAbstractKKMarginalIncid$get_supported_rand_bootstrap_ci_types()
• InferenceAbstractKKMarginalIncid$get_supported_rand_bootstrap_pval_types()
• InferenceAbstractKKMarginalIncid$get_supported_testing_types()
• InferenceAbstractKKMarginalIncid$get_testing_type()
• InferenceAbstractKKMarginalIncid$initialize()
• InferenceAbstractKKMarginalIncid$select_optimal_b_subsampling()
• InferenceAbstractKKMarginalIncid$select_optimal_m_out_of_n_bootstrap()
• InferenceAbstractKKMarginalIncid$set_custom_randomization_statistic_cpp()
• InferenceAbstractKKMarginalIncid$set_custom_randomization_statistic_function()
• InferenceAbstractKKMarginalIncid$set_information_preference()
• InferenceAbstractKKMarginalIncid$set_testing_type()
• InferenceAbstractKKMarginalIncid$supports_rand_pval_for_incidence()

+ inherited public methods from Inference

• Inference$capabilities()
• Inference$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$is_nonestimable()
• Inference$set_optimization_alg()
• Inference$set_seed()
• Inference$supports()

---

InferenceAbstractKKModifiedPoisson$compute_estimate()

Compute the KK marginal incidence treatment-effect estimate using the class-specific marginal risk-difference or risk-ratio estimator and cache it for related InferenceAbstractKKMarginalIncid methods.

Usage

InferenceAbstractKKModifiedPoisson$compute_estimate(estimate_only = FALSE)

Arguments

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKModifiedPoisson$compute_estimate_with_bootstrap_weights()

Recomputes the KK marginal incidence estimate under Bayesian-bootstrap weights.

Usage

InferenceAbstractKKModifiedPoisson$compute_estimate_with_bootstrap_weights(
  subject_or_block_weights,
  estimate_only = FALSE
)

Arguments

subject_or_block_weights

Numeric vector. Row weights for bootstrap.

estimate_only

Logical. If TRUE, skip variance component calculations.

---

InferenceAbstractKKModifiedPoisson$compute_asymp_confidence_interval()

Compute the KK marginal incidence asymptotic confidence interval using the cached marginal effect and design-aware standard error. See InferenceAsymp.

Usage

InferenceAbstractKKModifiedPoisson$compute_asymp_confidence_interval(
  alpha = 0.05
)

Arguments

alpha

Numeric. Significance level (default 0.05).

---

InferenceAbstractKKModifiedPoisson$compute_asymp_two_sided_pval()

Compute the KK marginal incidence asymptotic two-sided p-value using the cached marginal effect and design-aware standard error. See InferenceAsymp.

Usage

InferenceAbstractKKModifiedPoisson$compute_asymp_two_sided_pval(delta = 0)

Arguments

delta

Numeric. Null treatment effect value (default 0).

---

InferenceAbstractKKModifiedPoisson$clone()

The objects of this class are cloneable with this method.

Usage

InferenceAbstractKKModifiedPoisson$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.