BayesTools_model_tables: Create BayesTools model tables

Description

Creates model summary based on a model objects or provides estimates table for a runjags fit.

Usage

model_summary_table(
  model,
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE,
  formula_prefix = TRUE,
  remove_parameters = NULL
)
runjags_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  conditional = FALSE,
  probs = c(0.025, 0.5, 0.975),
  remove_spike_0 = TRUE,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE,
  remove_inclusion = FALSE,
  remove_parameters = NULL,
  remove_formulas = NULL,
  keep_parameters = NULL,
  keep_formulas = NULL,
  return_samples = FALSE,
  transform_scaled = FALSE,
  remove_diagnostics = FALSE,
  diagnostic_columns = getOption("BayesTools.JAGS_estimates_diagnostic_columns", if
    (remove_diagnostics) "none" else "all")
)
runjags_inference_table(
  fit,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE,
  logBF = FALSE,
  BF01 = FALSE,
  BF_diagnostics = FALSE,
  BF_diagnostic_columns = getOption("BayesTools.JAGS_BF_diagnostic_columns", if
    (BF_diagnostics) "all" else "none")
)
JAGS_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  conditional = FALSE,
  probs = c(0.025, 0.5, 0.975),
  remove_spike_0 = TRUE,
  transform_factors = FALSE,
  transform_orthonormal = FALSE,
  formula_prefix = TRUE,
  remove_inclusion = FALSE,
  remove_parameters = NULL,
  remove_formulas = NULL,
  keep_parameters = NULL,
  keep_formulas = NULL,
  return_samples = FALSE,
  transform_scaled = FALSE,
  remove_diagnostics = FALSE,
  diagnostic_columns = getOption("BayesTools.JAGS_estimates_diagnostic_columns", if
    (remove_diagnostics) "none" else "all")
)
JAGS_inference_table(
  fit,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  formula_prefix = TRUE,
  logBF = FALSE,
  BF01 = FALSE,
  BF_diagnostics = FALSE,
  BF_diagnostic_columns = getOption("BayesTools.JAGS_BF_diagnostic_columns", if
    (BF_diagnostics) "all" else "none")
)
JAGS_summary_table(
  model,
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_spike_0 = TRUE,
  short_name = FALSE,
  formula_prefix = TRUE,
  remove_parameters = NULL
)
model_summary_empty_table(
  model_description = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)
runjags_estimates_empty_table(
  probs = c(0.025, 0.5, 0.975),
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_diagnostics = FALSE,
  diagnostic_columns = getOption("BayesTools.JAGS_estimates_diagnostic_columns", if
    (remove_diagnostics) "none" else "all")
)
runjags_inference_empty_table(
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  logBF = FALSE,
  BF01 = FALSE,
  BF_diagnostics = FALSE,
  BF_diagnostic_columns = getOption("BayesTools.JAGS_BF_diagnostic_columns", if
    (BF_diagnostics) "all" else "none")
)
JAGS_estimates_empty_table(
  probs = c(0.025, 0.5, 0.975),
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  remove_diagnostics = FALSE,
  diagnostic_columns = getOption("BayesTools.JAGS_estimates_diagnostic_columns", if
    (remove_diagnostics) "none" else "all")
)
JAGS_inference_empty_table(
  title = NULL,
  footnotes = NULL,
  warnings = NULL,
  logBF = FALSE,
  BF01 = FALSE,
  BF_diagnostics = FALSE,
  BF_diagnostic_columns = getOption("BayesTools.JAGS_BF_diagnostic_columns", if
    (BF_diagnostics) "all" else "none")
)
stan_estimates_table(
  fit,
  transformations = NULL,
  title = NULL,
  footnotes = NULL,
  warnings = NULL
)

Value

model_summary_table returns a table with overview of the fitted model, runjags_estimates_table returns a table with MCMC estimates, and runjags_estimates_empty_table

returns an empty estimates table. All of the tables are objects of class 'BayesTools_table'.

Arguments

model: model object 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, and model inclusion Bayes factor inclusion_BF
model_description: named list with additional description to be added to the table
title: title to be added to the table
footnotes: footnotes to be added to the table
warnings: warnings to be added to the 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.
formula_prefix: whether the parameter prefix from formula should be printed. Defaults to TRUE.
remove_parameters: parameters to be removed from the summary. Can be NULL (default, no removal), a character vector of parameter names to remove, or TRUE to remove all parameters that are not part of any formula.
fit: runjags model fit
transformations: named list of transformations to be applied to specific parameters
conditional: summarizes estimates conditional on being included in the model for spike and slab priors. Defaults to FALSE.
probs: quantiles for parameter estimates
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
remove_inclusion: whether estimates of the inclusion probabilities should be excluded from the summary table. Defaults to FALSE.
remove_formulas: character vector of formula names whose parameters should be removed from the summary. Defaults to NULL.
keep_parameters: character vector of parameter names to keep. All other parameters will be removed unless they belong to formulas specified in keep_formulas. Defaults to NULL.
keep_formulas: character vector of formula names whose parameters should be kept. All other parameters will be removed unless they are specified in keep_parameters. Defaults to NULL.
return_samples: whether to return the transoformed and formated samples instead of the table. Defaults to FALSE.
transform_scaled: whether coefficients from standardized continuous predictors should be transformed back to the original scale. Defaults to FALSE.
remove_diagnostics: whether to exclude MCMC diagnostics (MCMC error, ESS, R-hat) from the output table. Defaults to FALSE. Setting to TRUE will exclude diagnostics columns regardless of the conditional setting.
diagnostic_columns: MCMC diagnostic columns to display in JAGS estimates tables. Can be "all", "none", TRUE, FALSE, or a character vector containing any subset of "MCMC_error", "MCMC_SD_error", "ESS", and "R_hat". Defaults to the BayesTools.JAGS_estimates_diagnostic_columns option, or all diagnostics unless remove_diagnostics = TRUE.
logBF: whether the Bayes factor should be on log scale
BF01: whether the Bayes factor should be inverted
BF_diagnostics: whether to add MCMC diagnostics for Bayes factors computed from model indicator frequencies. The Bayes factor error is reported as a relative Monte Carlo standard error percentage. Defaults to FALSE.
BF_diagnostic_columns: MCMC diagnostic columns to display in JAGS inclusion Bayes factor tables. Can be "all", "none", TRUE, FALSE, or a character vector containing any subset of "ESS", "MCMC_error", and "BF_error_percent". Defaults to the BayesTools.JAGS_BF_diagnostic_columns option, or all diagnostics when BF_diagnostics = TRUE and none otherwise.

Details

For product-space JAGS inclusion Bayes factors, posterior inclusion probabilities of exactly 0 or 1 cannot produce a finite point estimate of the model odds ratio. In that case, inclusion BFs marked with "<" or ">" are finite-sample bounds: posterior inclusion probabilities of 0 or 1 were replaced by 1/S or (S - 1)/S, where S is the number of posterior samples. This is a reporting convention, not an unbiased finite Bayes-factor estimate. If the prior inclusion probability is exactly 0 or 1, the inclusion Bayes factor is undefined and reported as NA, because the corresponding inclusion/exclusion comparison was not tested.

Description

Usage

Value

Arguments

Details

See Also