GaussianPooling_RBesT

An R6 class representing a pooled Gaussian model using RBesT.

Super classes

Model -> Model_RBesT -> GaussianPooling_RBesT

Public fields

method

Method name

Methods

Public methods

• GaussianPooling_RBesT$new()

• GaussianPooling_RBesT$prior_to_RBesT()

• GaussianPooling_RBesT$posterior_to_RBesT()

• GaussianPooling_RBesT$vectorised_prior_components()

• GaussianPooling_RBesT$clone()

Inherited methods

• Model$calibrate_for_design()
• Model$check_data()
• Model$create()
• Model$empirical_bayes_update()
• Model$estimate_bayesian_operating_characteristics()
• Model$estimate_frequentist_operating_characteristics()
• Model$hypothesis_space_transformation()
• Model$inference()
• 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$summary_rows()
• Model$test_decision()
• Model_RBesT$credible_interval()
• Model_RBesT$posterior_cdf()
• Model_RBesT$posterior_mean()
• Model_RBesT$posterior_median()
• Model_RBesT$posterior_moments()
• Model_RBesT$posterior_pdf()
• Model_RBesT$posterior_variance()
• Model_RBesT$prior_cdf()
• Model_RBesT$prior_pdf()
• Model_RBesT$sample_posterior()
• Model_RBesT$sample_prior()
• Model_RBesT$vectorised_posterior_parameters()
• Model_RBesT$vectorised_replicate_inference()

---

GaussianPooling_RBesT$new()

Initializes the GaussianPooling_RBesT object.

Usage

GaussianPooling_RBesT$new(prior)

Arguments

prior

Prior information for the analysis.

Returns

A new GaussianPooling_RBesT object.

---

GaussianPooling_RBesT$prior_to_RBesT()

Converts the prior distribution to the RBesT format.

Usage

GaussianPooling_RBesT$prior_to_RBesT(...)

Arguments

...

Additional arguments.

Returns

None

---

GaussianPooling_RBesT$posterior_to_RBesT()

Converts the posterior distribution to the RBesT format.

Usage

GaussianPooling_RBesT$posterior_to_RBesT(target_data, ...)

Arguments

target_data

Target study data object.

...

Additional arguments.

Returns

None

---

GaussianPooling_RBesT$vectorised_prior_components()

Prior mixture components for each replicate.

Usage

GaussianPooling_RBesT$vectorised_prior_components(target_data, samples)

Arguments

target_data

Target study data.

samples

Data frame of generated replicates.

Returns

A list with weights, means and sds.

---

GaussianPooling_RBesT$clone()

The objects of this class are cloneable with this method.

Usage

GaussianPooling_RBesT$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.