| bayesfactor_inclusion {bayestestR} | R Documentation |
Inclusion Bayes Factors for testing predictors across Bayesian models
Description
The bf_* function is an alias of the main function.
For more info, see the Bayes factors vignette.
Usage
bayesfactor_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)
bf_inclusion(models, match_models = FALSE, prior_odds = NULL, ...)
Arguments
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. |
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.
Value
a data frame containing the prior and posterior probabilities, and
log(BF) for each effect (Use as.numeric() to extract the non-log Bayes
factors; see examples).
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).
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.
Author(s)
Mattan S. Ben-Shachar
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.
See Also
weighted_posteriors() for Bayesian parameter averaging.
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)
(bf_inc <- bayesfactor_inclusion(BFmodels))
as.numeric(bf_inc)
# BayesFactor
# -------------------------------
BF <- BayesFactor::generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE)
bayesfactor_inclusion(BF)
# compare only matched models:
bayesfactor_inclusion(BF, match_models = TRUE)