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
Optional vector of prior odds for the models. See
Arguments passed to or from other methods.
a data frame containing the prior and posterior probabilities, and log(BF) for each effect.
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=FALSE (default), Inclusion BFs are computed by comparing all models
with a term against all models without that term. If
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.
Random effects in the
lmer style are converted to interaction terms:
(X|G) will become the terms
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).
Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. 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]. Retrieved from https://www.cogsci.nl/blog/interpreting-bayesian-repeated-measures-in-jasp
weighted_posteriors for Bayesian parameter averaging.
Mattan S. Ben-Shachar
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) bayesfactor_inclusion(BFmodels)#> Inclusion Bayes Factors (Model Averaged) #> #> P(prior) P(posterior) Inclusion BF #> Species 0.75 1.00 2.02e+55 #> Petal.Length 0.50 1.00 3.58e+26 #> Petal.Length:Species 0.25 0.04 0.113 #> #> * Compared among: all models #> * Priors odds: uniform-equal