Abstract mixin: Zhang combined randomisation CI for quantile regression

Provides compute_rand_confidence_interval() via Zhang's combined test-inversion method for both Bernoulli (\(m = 0\), all subjects in the reservoir) and KK matching-on-the-fly designs (\(m > 0\)).

Super classes

Inference -> InferenceRand -> InferenceRandCI -> InferenceNonParamBootstrap -> InferenceRandBootstrap -> InferenceRandBootstrapCI -> InferenceBayesianBootstrap -> InferenceJackknife -> InferenceAsymp -> InferenceMLEorKMSummaryTable -> InferenceAsympLik -> InferenceKKPassThroughCompoundNoParamBootstrap -> InferenceAbstractQuantileRandCI

Methods

Public methods

• InferenceAbstractQuantileRandCI$new()

• InferenceAbstractQuantileRandCI$clone()

+ inherited public methods from InferenceKKPassThroughCompoundNoParamBootstrap

• InferenceKKPassThroughCompoundNoParamBootstrap$approximate_bootstrap_distribution_beta_hat_T()
• InferenceKKPassThroughCompoundNoParamBootstrap$compute_estimate_with_bootstrap_weights()

+ inherited public methods from InferenceAsympLik

• InferenceAsympLik$compute_asymp_confidence_interval()
• InferenceAsympLik$compute_asymp_two_sided_pval()
• InferenceAsympLik$compute_gradient_confidence_interval()
• InferenceAsympLik$compute_gradient_two_sided_pval()
• InferenceAsympLik$compute_lik_ratio_bartlett_approx_confidence_interval()
• InferenceAsympLik$compute_lik_ratio_bartlett_approx_two_sided_pval()
• InferenceAsympLik$compute_lik_ratio_bartlett_confidence_interval()
• InferenceAsympLik$compute_lik_ratio_bartlett_exact_confidence_interval()
• InferenceAsympLik$compute_lik_ratio_bartlett_exact_two_sided_pval()
• InferenceAsympLik$compute_lik_ratio_bartlett_two_sided_pval()
• InferenceAsympLik$compute_lik_ratio_confidence_interval()
• InferenceAsympLik$compute_lik_ratio_two_sided_pval()
• InferenceAsympLik$compute_score_confidence_interval()
• InferenceAsympLik$compute_score_two_sided_pval()
• InferenceAsympLik$get_information_preference()
• InferenceAsympLik$get_information_source_used()
• InferenceAsympLik$get_supported_information_preferences()
• InferenceAsympLik$get_supported_testing_types()
• InferenceAsympLik$get_testing_type()
• InferenceAsympLik$set_information_preference()
• InferenceAsympLik$set_testing_type()

+ inherited public methods from InferenceMLEorKMSummaryTable

• InferenceMLEorKMSummaryTable$compute_estimate()

+ inherited public methods from InferenceAsymp

• InferenceAsymp$compute_wald_confidence_interval()
• InferenceAsymp$compute_wald_two_sided_pval()
• InferenceAsymp$get_mod()
• InferenceAsymp$get_summary()

+ inherited public methods from InferenceJackknife

• InferenceJackknife$approximate_jackknife_distribution_beta_hat_T()
• InferenceJackknife$compute_jackknife_bias_estimate()
• InferenceJackknife$compute_jackknife_estimate()
• InferenceJackknife$compute_jackknife_std_error()
• InferenceJackknife$compute_jackknife_wald_confidence_interval()
• InferenceJackknife$compute_jackknife_wald_two_sided_pval()

+ inherited public methods from InferenceBayesianBootstrap

• InferenceBayesianBootstrap$approximate_bayesian_bootstrap_distribution_beta_hat_T()
• InferenceBayesianBootstrap$compute_bayesian_bootstrap_confidence_interval()
• InferenceBayesianBootstrap$compute_bayesian_bootstrap_two_sided_pval()
• InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_ci_types()
• InferenceBayesianBootstrap$get_supported_bayesian_bootstrap_pval_types()

+ inherited public methods from InferenceRandBootstrapCI

• InferenceRandBootstrapCI$compute_rand_bootstrap_confidence_interval()
• InferenceRandBootstrapCI$get_supported_rand_bootstrap_ci_types()

+ inherited public methods from InferenceRandBootstrap

• InferenceRandBootstrap$approximate_rand_bootstrap_distribution_beta_hat_T()
• InferenceRandBootstrap$compute_rand_bootstrap_two_sided_pval()
• InferenceRandBootstrap$get_supported_rand_bootstrap_pval_types()

+ inherited public methods from InferenceNonParamBootstrap

• InferenceNonParamBootstrap$approximate_m_out_of_n_bootstrap_distribution_beta_hat_T()
• InferenceNonParamBootstrap$approximate_subsampling_distribution_beta_hat_T()
• InferenceNonParamBootstrap$compute_bootstrap_confidence_interval()
• InferenceNonParamBootstrap$compute_bootstrap_two_sided_pval()
• InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_confidence_interval()
• InferenceNonParamBootstrap$compute_m_out_of_n_bootstrap_two_sided_pval()
• InferenceNonParamBootstrap$compute_subsampling_confidence_interval()
• InferenceNonParamBootstrap$compute_subsampling_sensitivity()
• InferenceNonParamBootstrap$compute_subsampling_two_sided_pval()
• InferenceNonParamBootstrap$get_supported_bootstrap_ci_types()
• InferenceNonParamBootstrap$get_supported_bootstrap_pval_types()
• InferenceNonParamBootstrap$select_optimal_b_subsampling()
• InferenceNonParamBootstrap$select_optimal_m_out_of_n_bootstrap()

+ inherited public methods from InferenceRandCI

• InferenceRandCI$compute_rand_confidence_interval()
• InferenceRandCI$compute_rand_two_sided_pval()

+ inherited public methods from InferenceRand

• InferenceRand$approximate_randomization_distribution_beta_hat_T()
• InferenceRand$set_custom_randomization_statistic_cpp()
• InferenceRand$set_custom_randomization_statistic_function()
• InferenceRand$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()

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InferenceAbstractQuantileRandCI$new()

Initialize the inference object.

Usage

InferenceAbstractQuantileRandCI$new(
  des_obj,
  model_formula = NULL,
  verbose = FALSE,
  smart_cold_start_default = NULL
)

Arguments

des_obj

A DesignSeqOneByOne object.

model_formula

Optional formula for covariate adjustment.

verbose

Whether to print messages.

smart_cold_start_default

Whether to use smart cold start values.

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InferenceAbstractQuantileRandCI$clone()

The objects of this class are cloneable with this method.

Usage

InferenceAbstractQuantileRandCI$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.