ensemble_estimates_table(
samples,
parameters,
probs = c(0.025, 0.95),
title = NULL,
footnotes = NULL,
warnings = NULL
)
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
)

## 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 |

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