Model_RBesT

An R6 class representing a Bayesian model using RBesT.

Super class

Model -> Model_RBesT

Public fields

posterior_summary

Summary of the posterior distribution

Methods

Public methods

• Model_RBesT$new()

• Model_RBesT$prior_pdf()

• Model_RBesT$prior_cdf()

• Model_RBesT$posterior_moments()

• Model_RBesT$posterior_mean()

• Model_RBesT$posterior_variance()

• Model_RBesT$posterior_median()

• Model_RBesT$posterior_pdf()

• Model_RBesT$posterior_cdf()

• Model_RBesT$sample_prior()

• Model_RBesT$sample_posterior()

• Model_RBesT$prior_to_RBesT()

• Model_RBesT$posterior_to_RBesT()

• Model_RBesT$credible_interval()

• Model_RBesT$vectorised_prior_components()

• Model_RBesT$vectorised_posterior_parameters()

• Model_RBesT$vectorised_replicate_inference()

• Model_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$new()

Initializes the Model_RBesT object

Usage

Model_RBesT$new(prior)

Arguments

prior

The prior information for the analysis.

---

Model_RBesT$prior_pdf()

Calculates the prior probability density function (PDF) for a given target treatment effect.

Usage

Model_RBesT$prior_pdf(target_treatment_effect)

Arguments

target_treatment_effect

The target treatment effect.

Returns

The prior PDF.

---

Model_RBesT$prior_cdf()

Calculates the prior cumulative distribution function (CDF) for a given target treatment effect.

Usage

Model_RBesT$prior_cdf(target_treatment_effect)

Arguments

target_treatment_effect

The target treatment effect.

Returns

The prior CDF.

---

Model_RBesT$posterior_moments()

Calculates the posterior moments based on the target data.

Usage

Model_RBesT$posterior_moments(target_data)

Arguments

target_data

The target data for the analysis.

Returns

None

---

Model_RBesT$posterior_mean()

Calculates the posterior mean.

Usage

Model_RBesT$posterior_mean()

Returns

The posterior mean.

---

Model_RBesT$posterior_variance()

Calculates the posterior variance.

Usage

Model_RBesT$posterior_variance()

Returns

The posterior variance.

---

Model_RBesT$posterior_median()

Calculates the posterior median.

Usage

Model_RBesT$posterior_median(...)

Arguments

...

Additional arguments

Returns

The posterior median.

---

Model_RBesT$posterior_pdf()

Calculates the posterior probability density function (PDF) for a given target treatment effect.

Usage

Model_RBesT$posterior_pdf(target_treatment_effect)

Arguments

target_treatment_effect

The target treatment effect.

Returns

The posterior PDF.

---

Model_RBesT$posterior_cdf()

Calculates the posterior cumulative distribution function (CDF) for a given target treatment effect.

Usage

Model_RBesT$posterior_cdf(target_treatment_effect)

Arguments

target_treatment_effect

The target treatment effect.

Returns

The posterior CDF.

---

Model_RBesT$sample_prior()

Samples from the prior distribution.

Usage

Model_RBesT$sample_prior(n_samples)

Arguments

n_samples

The number of samples to generate.

Returns

The samples from the prior distribution.

---

Model_RBesT$sample_posterior()

Samples from the posterior distribution.

Usage

Model_RBesT$sample_posterior(n_samples)

Arguments

n_samples

The number of samples to generate.

Returns

The samples from the posterior distribution.

---

Model_RBesT$prior_to_RBesT()

Converts the prior distribution to the RBesT format.

Usage

Model_RBesT$prior_to_RBesT(...)

Arguments

...

Additional arguments

Returns

None

---

Model_RBesT$posterior_to_RBesT()

Converts the posterior distribution to the RBesT format.

Usage

Model_RBesT$posterior_to_RBesT(target_data, ...)

Arguments

target_data

Target study data

...

Additional arguments

---

Model_RBesT$credible_interval()

Calculates the credible interval.

Usage

Model_RBesT$credible_interval(level = 0.95)

Arguments

level

Level of the credible interval.

Returns

A vector containing the lower and upper bounds of the credible interval.

---

Model_RBesT$vectorised_prior_components()

Prior mixture components for each replicate

Subclasses return the weights, means and sds of their prior, either as vectors shared by every replicate or as matrices with one row per replicate. Returning NULL disables the vectorised path.

Usage

Model_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, or NULL.

---

Model_RBesT$vectorised_posterior_parameters()

Posterior parameters reported by the vectorised path

Usage

Model_RBesT$vectorised_posterior_parameters(posterior)

Arguments

posterior

Posterior mixture returned by normal_mixture_posterior().

Returns

A data frame, or NULL.

---

Model_RBesT$vectorised_replicate_inference()

Run every replicate at once

Usage

Model_RBesT$vectorised_replicate_inference(
  target_data,
  samples,
  to_return,
  critical_value,
  theta_0,
  confidence_level,
  null_space
)

Arguments

target_data

Target study data.

samples

Data frame of generated replicates.

to_return

Character vector of requested outputs.

critical_value

Critical value for hypothesis testing.

theta_0

Null hypothesis value.

confidence_level

Confidence level for the credible interval.

null_space

The null space for hypothesis testing.

Returns

A list of simulation results, or NULL to use the replicate loop.

---

Model_RBesT$clone()

The objects of this class are cloneable with this method.

Usage

Model_RBesT$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.