GaussianGravestockEBPP class

This class inherits from GaussianEmpiricalBayesPP and implements the Gravestock's EBPP method.

Format

R6Class object.

Super classes

Model -> GaussianConjugate -> GaussianStaticBorrowing -> GaussianEmpiricalBayesPP -> GaussianGravestockEBPP

Public fields

method

Method name

Methods

Public methods

• GaussianGravestockEBPP$new()

• GaussianGravestockEBPP$power_parameter_estimation()

• GaussianGravestockEBPP$vectorised_power_parameter()

• GaussianGravestockEBPP$clone()

Inherited methods

• Model$calibrate_for_design()
• Model$check_data()
• Model$create()
• Model$estimate_bayesian_operating_characteristics()
• Model$estimate_frequentist_operating_characteristics()
• Model$hypothesis_space_transformation()
• Model$inference_cache_scope()
• Model$plot_pdfs()
• Model$plot_posterior_pdf()
• Model$plot_prior_pdf()
• Model$posterior_beta_mixture()
• Model$posterior_ess()
• Model$posterior_quantile()
• Model$print_model_summary()
• Model$prior_elir_ess()
• Model$simulation_for_given_treatment_effect()
• Model$test_decision()
• GaussianConjugate$credible_interval()
• GaussianConjugate$posterior_cdf()
• GaussianConjugate$posterior_mean()
• GaussianConjugate$posterior_median()
• GaussianConjugate$posterior_moments()
• GaussianConjugate$posterior_pdf()
• GaussianConjugate$posterior_to_RBesT()
• GaussianConjugate$posterior_variance()
• GaussianConjugate$prior_cdf()
• GaussianConjugate$prior_to_RBesT()
• GaussianConjugate$sample_posterior()
• GaussianConjugate$sample_prior()
• GaussianConjugate$vectorised_replicate_inference()
• GaussianStaticBorrowing$summary_rows()
• GaussianEmpiricalBayesPP$empirical_bayes_update()
• GaussianEmpiricalBayesPP$inference()
• GaussianEmpiricalBayesPP$plot_power_parameter_vs_drift()
• GaussianEmpiricalBayesPP$prior_pdf()
• GaussianEmpiricalBayesPP$vectorised_posterior_parameters()
• GaussianEmpiricalBayesPP$vectorised_prior_variance()

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GaussianGravestockEBPP$new()

Initialize the GaussianGravestockEBPP object.

Usage

GaussianGravestockEBPP$new(prior, null_space, theta_0)

Arguments

prior

The prior object.

null_space

Side of the null hypothesis space

theta_0

Boundary of the null hypothesis space

Returns

NULL

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GaussianGravestockEBPP$power_parameter_estimation()

Estimate the power parameter using the Gravestock's EBPP method.

Usage

GaussianGravestockEBPP$power_parameter_estimation(target_data)

Arguments

target_data

The target data.

source_treatment_effect_estimate

Treatment effect estimate in the source study

target_treatment_effect_estimate

Treatment effect estimate in the target study

Returns

The estimated power parameter.

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GaussianGravestockEBPP$vectorised_power_parameter()

Estimate the power parameter for every replicate at once.

Same closed form as power_parameter_estimation(), evaluated on vectors.

Usage

GaussianGravestockEBPP$vectorised_power_parameter(target_data, samples)

Arguments

target_data

The target data.

samples

Data frame of generated replicates.

Returns

A vector of power parameters.

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GaussianGravestockEBPP$clone()

The objects of this class are cloneable with this method.

Usage

GaussianGravestockEBPP$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.