View source: R/bayesfactor_models.R
bayesfactor_models  R Documentation 
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)
## Default S3 method:
bayesfactor_models(..., denominator = 1, verbose = TRUE)
## S3 method for class 'bayesfactor_models'
update(object, subset = NULL, reference = NULL, ...)
## S3 method for class 'bayesfactor_models'
as.matrix(x, ...)
... 
Fitted models (see details), all fit on the same data, or a single

denominator 
Either an integer indicating which of the models to use as
the denominator, or a model to be used as a denominator. Ignored for

verbose 
Toggle off warnings. 
object , x 
A 
subset 
Vector of model indices to keep or remove. 
reference 
Index of model to reference to, or 
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 followup 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.
A data frame containing the models' formulas (reconstructed fixed and
random effects) and their log(BF)
s (Use as.numeric()
to extract the
nonlog Bayes factors; see examples), that prints nicely.
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).
There is also a plot()
method
implemented in the seepackage.
Mattan S. BenShachar
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), 773795.
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), 779804.
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. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/1745691611406923")}
# With lm objects:
# 
lm1 < lm(mpg ~ 1, data = mtcars)
lm2 < lm(mpg ~ hp, data = mtcars)
lm3 < lm(mpg ~ hp + drat, data = mtcars)
lm4 < lm(mpg ~ hp * drat, data = mtcars)
(BFM < bayesfactor_models(lm1, lm2, lm3, lm4, denominator = 1))
# bayesfactor_models(lm2, lm3, lm4, denominator = lm1) # same result
# bayesfactor_models(lm1, lm2, lm3, lm4, denominator = lm1) # same result
update(BFM, reference = "bottom")
as.matrix(BFM)
as.numeric(BFM)
lm2b < lm(sqrt(mpg) ~ hp, data = mtcars)
# Set check_response = TRUE for transformed responses
bayesfactor_models(lm2b, denominator = lm2, check_response = TRUE)
# With lmerMod objects:
# 
lmer1 < lme4::lmer(Sepal.Length ~ Petal.Length + (1  Species), data = iris)
lmer2 < lme4::lmer(Sepal.Length ~ Petal.Length + (Petal.Length  Species), data = iris)
lmer3 < lme4::lmer(
Sepal.Length ~ Petal.Length + (Petal.Length  Species) + (1  Petal.Width),
data = iris
)
bayesfactor_models(lmer1, lmer2, lmer3,
denominator = 1,
estimator = "REML"
)
# rstanarm models
# 
# (note that a unique diagnostic_file MUST be specified in order to work)
stan_m0 < suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ 1,
data = iris,
family = gaussian(),
diagnostic_file = file.path(tempdir(), "df0.csv")
))
stan_m1 < suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ Species,
data = iris,
family = gaussian(),
diagnostic_file = file.path(tempdir(), "df1.csv")
))
stan_m2 < suppressWarnings(rstanarm::stan_glm(Sepal.Length ~ Species + Petal.Length,
data = iris,
family = gaussian(),
diagnostic_file = file.path(tempdir(), "df2.csv")
))
bayesfactor_models(stan_m1, stan_m2, denominator = stan_m0, verbose = FALSE)
# brms models
# 
# (note the save_pars MUST be set to save_pars(all = TRUE) in order to work)
brm1 < brms::brm(Sepal.Length ~ 1, data = iris, save_pars = save_pars(all = TRUE))
brm2 < brms::brm(Sepal.Length ~ Species, data = iris, save_pars = save_pars(all = TRUE))
brm3 < brms::brm(
Sepal.Length ~ Species + Petal.Length,
data = iris,
save_pars = save_pars(all = TRUE)
)
bayesfactor_models(brm1, brm2, brm3, denominator = 1, verbose = FALSE)
# BayesFactor
# 
data(puzzles)
BF < BayesFactor::anovaBF(RT ~ shape * color + ID,
data = puzzles,
whichRandom = "ID", progress = FALSE
)
BF
bayesfactor_models(BF) # basically the same
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