This function computes or extracts Bayes factors from fitted models.
The bf_* function is an alias of the main function.
bayesfactor_models(..., denominator = 1, verbose = TRUE)bf_models(..., denominator = 1, verbose = TRUE)
# S3 method for default
bayesfactor_models(..., denominator = 1, verbose = TRUE)
# S3 method for bayesfactor_models
update(object, subset = NULL, reference = NULL, ...)
# S3 method for bayesfactor_models
as.matrix(x, ...)
A data frame containing the models' formulas (reconstructed fixed and
random effects) and their log(BF)s (Use as.numeric() to extract the
non-log Bayes factors; see examples), that prints nicely.
Fitted models (see details), all fit on the same data, or a single
BFBayesFactor object (see 'Details'). Ignored in as.matrix(),
update(). If the following named arguments are present, they are passed
to insight::get_loglikelihood() (see details):
estimator (defaults to "ML")
check_response (defaults to FALSE)
Either an integer indicating which of the models to use as
the denominator, or a model to be used as a denominator. Ignored for
BFBayesFactor.
Toggle off warnings.
A bayesfactor_models() object.
Vector of model indices to keep or remove.
Index of model to reference to, or "top" to
reference to the best model, or "bottom" to reference to the worst
model.
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
If the passed models are supported by insight the DV of all models will
be tested for equality (else this is assumed to be true), and the models'
terms will be extracted (allowing for follow-up analysis with bayesfactor_inclusion).
For brmsfit or stanreg models, Bayes factors are computed using the bridgesampling package.
brmsfit models must have been fitted with save_pars = save_pars(all = TRUE).
stanreg models must have been fitted with a defined diagnostic_file.
For BFBayesFactor, bayesfactor_models() is mostly a wraparound BayesFactor::extractBF().
For all other model types, Bayes factors are computed using the BIC approximation. Note that BICs are extracted from using insight::get_loglikelihood, see documentation there for options for dealing with transformed responses and REML estimation.
In order to correctly and precisely estimate Bayes factors, a rule of thumb
are the 4 P's: Proper Priors and Plentiful
Posteriors. How many? The number of posterior samples needed for
testing is substantially larger than for estimation (the default of 4000
samples may not be enough in many cases). A conservative rule of thumb is to
obtain 10 times more samples than would be required for estimation
(Gronau, Singmann, & Wagenmakers, 2017). If less than 40,000 samples
are detected, bayesfactor_models() gives a warning.
See also the Bayes factors vignette.
Gronau, Q. F., Singmann, H., & Wagenmakers, E. J. (2017). Bridgesampling: An R package for estimating normalizing constants. arXiv preprint arXiv:1710.08162.
Kass, R. E., and Raftery, A. E. (1995). Bayes Factors. Journal of the American Statistical Association, 90(430), 773-795.
Robert, C. P. (2016). The expected demise of the Bayes factor. Journal of Mathematical Psychology, 72, 33–37.
Wagenmakers, E. J. (2007). A practical solution to the pervasive problems of p values. Psychonomic bulletin & review, 14(5), 779-804.
Wetzels, R., Matzke, D., Lee, M. D., Rouder, J. N., Iverson, G. J., and Wagenmakers, E.-J. (2011). Statistical Evidence in Experimental Psychology: An Empirical Comparison Using 855 t Tests. Perspectives on Psychological Science, 6(3), 291–298. tools:::Rd_expr_doi("10.1177/1745691611406923")