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, ...)
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. |
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.
a data frame containing the prior and posterior probabilities, and log(BF) for each effect.
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
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.
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) ## Not run: # BayesFactor # ------------------------------- library(BayesFactor) BF <- generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE) bayesfactor_inclusion(BF) # compare only matched models: bayesfactor_inclusion(BF, match_models = TRUE) ## End(Not run)
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