Create BayesTools ensemble summary tables

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).

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'.

Arguments

Value

See also