The empirical robust mixture prior of Egidi et al. (2022) for a
binary endpoint, on the components of BinomialRMP: for each dataset, the
weight of the weak component is the smallest on the grid at which the
prior-predictive conflict p-value of the observed counts reaches
alpha_pc, the p-value being computed exactly from the components'
prior-predictive tables (see egidi_select_weak_weight_binomial()).
Super classes
Model -> MCMCModel -> BinomialLatticePrior -> BinomialRMP -> BinomialEgidiMixture
Public fields
alpha_pcConflict threshold.
pvalue_methodConflict p-value method.
weight_grid_stepResolution of the weight scan.
selectionThe selection of the current dataset.
quantile_summary_columnsColumns summarised by quantiles across replicates.
empirical_bayesThe prior depends on the target data.
empirical_bayes_from_sampleThe prior is a function of the replicate's sample alone.
methodMethod name.
Methods
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_mean()Model$posterior_moments()Model$posterior_quantile()Model$posterior_to_RBesT()Model$print_model_summary()Model$prior_to_RBesT()Model$simulation_for_given_treatment_effect()Model$test_decision()Model$vectorised_replicate_inference()MCMCModel$check_mcmc_config()MCMCModel$credible_interval()MCMCModel$draw_mcmc_prior()MCMCModel$inference()MCMCModel$posterior_cdf()MCMCModel$posterior_ess()MCMCModel$posterior_median()MCMCModel$posterior_pdf()MCMCModel$prepare_data()MCMCModel$prior_cdf()MCMCModel$prior_pdf()MCMCModel$sample_posterior()MCMCModel$sample_prior()MCMCModel$stan_sampler()MCMCModel$uses_quadrature()BinomialLatticePrior$prior_given_control_rate()BinomialLatticePrior$quadrature_prior()BinomialLatticePrior$source_counts()BinomialRMP$components()BinomialRMP$kernel_key()BinomialRMP$kernels()BinomialRMP$summary_rows()
BinomialEgidiMixture$quadrature_posterior()
The posterior on a grid, from the two components' kernels
and the weight, without forming the mixture kernel; see
binomial_rmp_posterior().
Returns
The list binomial_npp_posterior() returns.
BinomialEgidiMixture$prior_elir_ess()
ELIR effective sample size of the current prior. The prior changes between datasets only through its weight, so the unit-scale ELIR is computed at weights 0, 0.05, ..., 1, once per worker, and interpolated linearly.