bayestestR: bayesfactor – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

bayesfactor

Bayes Factors (BF)

Description

This function compte the Bayes factors (BFs) that are appropriate to the input. For vectors or single models, it will compute BFs for single parameters, or is hypothesis is specified, BFs for restricted models. For multiple models, it will return the BF corresponding to comparison between models and if a model comparison is passed, it will compute the inclusion BF.

For a complete overview of these functions, read the Bayes factor vignette.

Usage

bayesfactor(
  ...,
  prior = NULL,
  direction = "two-sided",
  null = 0,
  hypothesis = NULL,
  effects = c("fixed", "random", "all"),
  verbose = TRUE,
  denominator = 1,
  match_models = FALSE,
  prior_odds = NULL
)

Arguments

`...`	A numeric vector, model object(s), or the output from `bayesfactor_models`.
`prior`	An object representing a prior distribution (see 'Details').
`direction`	Test type (see 'Details'). One of `0`, `"two-sided"` (default, two tailed), `-1`, `"left"` (left tailed) or `1`, `"right"` (right tailed).
`null`	Value of the null, either a scalar (for point-null) or a range (for a interval-null).
`hypothesis`	A character vector specifying the restrictions as logical conditions (see examples below).
`effects`	Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated.
`verbose`	Toggle off warnings.
`denominator`	Either an integer indicating which of the models to use as the denominator, or a model to be used as a denominator. Ignored for `BFBayesFactor`.
`match_models`	See details.
`prior_odds`	Optional vector of prior odds for the models. See `BayesFactor::priorOdds<-`.

Value

Some type of Bayes factor, depending on the input. See bayesfactor_parameters, bayesfactor_models or bayesfactor_inclusion

Note

There is also a plot()-method implemented in the see-package.

Examples

library(bayestestR)

if (require("logspline")) {
  prior <- distribution_normal(1000, mean = 0, sd = 1)
  posterior <- distribution_normal(1000, mean = .5, sd = .3)

  bayesfactor(posterior, prior = prior)
}
## Not run: 
# rstanarm models
# ---------------
if (require("rstanarm")) {
  model <- stan_lmer(extra ~ group + (1 | ID), data = sleep)
  bayesfactor(model)
}

## End(Not run)

if (require("logspline")) {
  # Frequentist models
  # ---------------
  m0 <- lm(extra ~ 1, data = sleep)
  m1 <- lm(extra ~ group, data = sleep)
  m2 <- lm(extra ~ group + ID, data = sleep)

  comparison <- bayesfactor(m0, m1, m2)
  comparison

  bayesfactor(comparison)
}

bayestestR

Understand and Describe Bayesian Models and Posterior Distributions

v0.10.0

GPL-3

Authors

Dominique Makowski [aut, cre] (<https://orcid.org/0000-0001-5375-9967>, @Dom_Makowski), Daniel Lüdecke [aut] (<https://orcid.org/0000-0002-8895-3206>, @strengejacke), Mattan S. Ben-Shachar [aut] (<https://orcid.org/0000-0002-4287-4801>, @mattansb), Indrajeet Patil [aut] (<https://orcid.org/0000-0003-1995-6531>, @patilindrajeets), Michael D. Wilson [aut] (<https://orcid.org/0000-0003-4143-7308>), Paul-Christian Bürkner [rev], Tristan Mahr [rev] (<https://orcid.org/0000-0002-8890-5116>), Henrik Singmann [ctb] (<https://orcid.org/0000-0002-4842-3657>), Quentin F. Gronau [ctb] (<https://orcid.org/0000-0001-5510-6943>), Sam Crawley [ctb] (<https://orcid.org/0000-0002-7847-0411>)

Initial release