
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, ...)
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).
An object of class bayesfactor_models()
or BFBayesFactor
.
See details.
Optional vector of prior odds for the models. See
BayesFactor::priorOdds<-
.
Arguments passed to or from other methods.
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).
Mattan S. Ben-Shachar
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
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.
Hinne, M., Gronau, Q. F., van den Bergh, D., and Wagenmakers, E. (2019, March 25). A conceptual introduction to Bayesian Model Averaging. tools:::Rd_expr_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.
weighted_posteriors()
for Bayesian parameter averaging.
if (FALSE) { # require("BayesFactor")
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)
# \donttest{
# BayesFactor
# -------------------------------
BF <- BayesFactor::generalTestBF(len ~ supp * dose, ToothGrowth, progress = FALSE)
bayesfactor_inclusion(BF)
# compare only matched models:
bayesfactor_inclusion(BF, match_models = TRUE)
# }
}
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