`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., `(X|G)`

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 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-equalif (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) }