R/bayesfactor_inclusion.R
bayesfactor_inclusion.Rd
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, ...)
models  An object of class 

match_models  See details. 
prior_odds  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 \(X\)?
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.
Random effects in the lmer
style are converted to interaction terms:
i.e., (XG)
will become the terms 1:G
and X:G
.
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 nullmodel) (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), 80101.
Mathot, S. (2017). Bayes like a Baws: Interpreting Bayesian Repeated Measures in JASP Blog post.
weighted_posteriors()
for Bayesian parameter averaging.
Mattan S. BenShachar
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: uniformequalif (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) }