BayesTools_ensemble_tables: Create BayesTools ensemble summary tables

In BayesTools: Tools for Bayesian Analyses

Create BayesTools ensemble summary tables

Description

Creates estimate summaries based on posterior distributions created by mix_posteriors, inference summaries based on inference created by ensemble_inference, or ensemble summary/diagnostics based on a list of models_inference models (or marginal_inference in case of marginal_estimates_table).

Usage

ensemble_estimates_table(
  samples,
  parameters,
  probs = c(0.025, 0.95),
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE
)

ensemble_inference_table(
  inference,
  parameters,
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

ensemble_summary_table(
  models,
  parameters,
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE
)

ensemble_diagnostics_table(
  models,
  parameters,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE
)

ensemble_estimates_empty_table(
  probs = c(0.025, 0.95),
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

ensemble_inference_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

ensemble_summary_empty_table(title = NULL, footnotes = NULL, warnings = NULL)

ensemble_diagnostics_empty_table(
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

marginal_estimates_table(
  samples,
  inference,
  parameters,
  probs = c(0.025, 0.95),
  logBF = FALSE,
  BF01 = FALSE,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE
)

Arguments

samples	posterior samples created by mix_posteriors
parameters	character vector of parameters (or a named list with of character vectors for summary and diagnostics tables) specifying the parameters (and their grouping) for the summary table
probs	quantiles for parameter estimates
title	title to be added to the table
footnotes	footnotes to be added to the table
warnings	warnings to be added to the table
transform_factors	whether factors with orthonormal/meandif prior distribution should be transformed to differences from the grand mean
transform_orthonormal	(to be depreciated) whether factors with orthonormal prior distributions should be transformed to differences from the grand mean
formula_prefix	whether the parameter prefix from formula should be printed. Defaults to TRUE.
inference	model inference created by ensemble_inference
logBF	whether the Bayes factor should be on log scale
BF01	whether the Bayes factor should be inverted
models	list of models_inference model objects, each of which containing a list of priors and inference object, The inference must be a named list with information about the model: model number m_number, marginal likelihood marglik, prior and posterior probability prior_prob and post_prob, inclusion Bayes factor inclusion_BF, and fit summary generated by runjags_estimates_table for the diagnostics table
remove_spike_0	whether prior distributions equal to spike at 0 should be removed from the prior_list
short_name	whether the prior distribution names should be shortened. Defaults to FALSE.

Value

ensemble_estimates_table returns a table with the model-averaged estimates, ensemble_inference_table returns a table with the prior and posterior probabilities and inclusion Bayes factors, ensemble_summary_table returns a table with overview of the models included in the ensemble, and ensemble_diagnostics_table returns an overview of the MCMC diagnostics for the models included in the ensemble. All of the tables are objects of class 'BayesTools_table'.

See Also

ensemble_inference mix_posteriors BayesTools_model_tables

BayesTools documentation built on July 26, 2023, 5:37 p.m.