This class represents a model with a Gaussian prior derived from static borrowing, and a Gaussian likelihood. It inherits from the GaussianConjugate class.
Super classes
Model -> GaussianConjugate -> GaussianStaticBorrowing
Public fields
power_parameterThe power parameter for the model. A NULL value indicates no power parameter, whereas a non-zero value sets the prior variance based on the source's standard error and the power parameter.
prior_varThe variance of the prior. This is set based on the power parameter.
methodMethod name
Methods
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$test_decision()GaussianConjugate$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$prior_pdf()GaussianConjugate$prior_to_RBesT()GaussianConjugate$sample_posterior()GaussianConjugate$sample_prior()GaussianConjugate$vectorised_posterior_parameters()GaussianConjugate$vectorised_replicate_inference()
GaussianStaticBorrowing$new()
Usage
GaussianStaticBorrowing$new(prior)GaussianStaticBorrowing$vectorised_prior_variance()
Prior variance for each replicate
Static borrowing fixes the prior up front, so every replicate shares it.