bayesfactor_inclusion {bayestestR}  R Documentation 
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 (Use as.numeric()
to extract the nonlog Bayes
factors; see examples).
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).
Random effects in the lmer
style are converted to interaction terms:
i.e., (XG)
will become the terms 1:G
and X:G
.
Mattan S. BenShachar
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.
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)
(bf_inc < bayesfactor_inclusion(BFmodels))
as.numeric(bf_inc)
## 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)