bayesfactor_inclusion: Inclusion Bayes Factors for testing predictors across...

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Inclusion Bayes Factors for testing predictors across Bayesian models

The ⁠bf_*⁠ function is an alias of the main function.

For more info, see the Bayes factors vignette.

Description

Inclusion Bayes Factors for testing predictors across Bayesian models

The ⁠bf_*⁠ function is an alias of the main function.

For more info, see the Bayes factors vignette.

Usage

bayesfactor_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)

bf_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)

Arguments

models	An object of class bayesfactor_models() or BFBayesFactor.
match_models	See details.
prior_odds	Optional vector of prior odds for the models. See ⁠BayesFactor::priorOdds<-⁠.
...	Arguments passed to or from other methods.

Details

Inclusion Bayes factors answer the question: Are the observed data more probable under models with a particular effect, than they are under models without that particular effect? In other words, on average - are models with effect X more likely to have produced the observed data than models without effect X?

Match Models

If match_models=FALSE (default), Inclusion BFs are computed by comparing all models with a term against all models without that term. If TRUE, comparison is restricted to models that (1) do not include any interactions with the term of interest; (2) for interaction terms, averaging is done only across models that contain the main effect terms from which the interaction term is comprised.

Additional methods

The resulting output is supported by the following methods:

• as.numeric(): Extract the (possibly log-)Bayes factor values.

See bayesfactor_methods.

Value

a data frame containing the prior and posterior probabilities, and log(BF) for each effect (Use as.numeric() to extract the non-log Bayes factors; see examples).

Interpreting Bayes Factors

A Bayes factor greater than 1 can be interpreted as evidence against the null, at which one convention is that a Bayes factor greater than 3 can be considered as "substantial" evidence against the null (and vice versa, a Bayes factor smaller than 1/3 indicates substantial evidence in favor of the null-model). See also effectsize::interpret_bf().

Note

Random effects in the lmer style are converted to interaction terms: i.e., (X|G) will become the terms 1:G and X:G.

Author(s)

Mattan S. Ben-Shachar

References

• Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.31234/osf.io/wgb64")}

• Clyde, M. A., Ghosh, J., & Littman, M. L. (2011). Bayesian adaptive sampling for variable selection and model averaging. Journal of Computational and Graphical Statistics, 20(1), 80-101.

• Mathot, S. (2017). Bayes like a Baws: Interpreting Bayesian Repeated Measures in JASP. Blog post.

See Also

weighted_posteriors() for Bayesian parameter averaging.

Other Bayes factors: bayesfactor_models(), bayesfactor_parameters(), bayesfactor_restricted()

Examples

library(bayestestR)

# Using bayesfactor_models:
# ------------------------------
mo0 <- lm(Sepal.Length ~ 1, data = iris)
mo1 <- lm(Sepal.Length ~ Species, data = iris)
mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)

BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0)
(bf_inc <- bayesfactor_inclusion(BFmodels))

as.numeric(bf_inc)


# BayesFactor
# -------------------------------
BF <- BayesFactor::generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE)
bayesfactor_inclusion(BF)

# compare only matched models:
bayesfactor_inclusion(BF, match_models = TRUE)

bayestestR documentation built on May 22, 2026, 1:06 a.m.