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, ...)

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.

Value

a data frame containing the prior and posterior probabilities, and log(BF) for each effect.

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

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.

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).

References

  • 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

See also

weighted_posteriors for Bayesian parameter averaging.

Author

Mattan S. Ben-Shachar

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) 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
if (FALSE) { # BayesFactor # ------------------------------- library(BayesFactor) BF <- generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE) bayesfactor_inclusion(BF) # compare only matched models: bayesfactor_inclusion(BF, match_models = TRUE) }