bayesfactor_inclusion {bayestestR}R Documentation

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.


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

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



An object of class bayesfactor_models() or BFBayesFactor.


See details.


Optional vector of prior odds for the models. See ⁠BayesFactor::priorOdds<-⁠.


Arguments passed to or from other methods.


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 containe the main effect terms from which the interaction term is comprised.


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) (Wetzels et al. 2011).


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


Mattan S. Ben-Shachar


See Also

weighted_posteriors() for Bayesian parameter averaging.



# 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))


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

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

[Package bayestestR version 0.13.2 Index]