bayesfactor | R Documentation |
This function compte the Bayes factors (BFs) that are appropriate to the
input. For vectors or single models, it will compute BFs for single parameters()
, or is hypothesis
is specified,
BFs for restricted models()
. For multiple models,
it will return the BF corresponding to comparison between models()
and if a model comparison is passed, it will
compute the inclusion BF()
.
For a complete overview of these functions, read the Bayes factor vignette.
bayesfactor(
...,
prior = NULL,
direction = "two-sided",
null = 0,
hypothesis = NULL,
effects = c("fixed", "random", "all"),
verbose = TRUE,
denominator = 1,
match_models = FALSE,
prior_odds = NULL
)
... |
A numeric vector, model object(s), or the output from
|
prior |
An object representing a prior distribution (see 'Details'). |
direction |
Test type (see 'Details'). One of |
null |
Value of the null, either a scalar (for point-null) or a range (for a interval-null). |
hypothesis |
A character vector specifying the restrictions as logical conditions (see examples below). |
effects |
Should results for fixed effects, random effects or both be returned? Only applies to mixed models. May be abbreviated. |
verbose |
Toggle off warnings. |
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
|
match_models |
See details. |
prior_odds |
Optional vector of prior odds for the models. See
|
Some type of Bayes factor, depending on the input. See bayesfactor_parameters()
, bayesfactor_models()
or bayesfactor_inclusion()
There is also a plot()
-method implemented in the see-package.
library(bayestestR)
prior <- distribution_normal(1000, mean = 0, sd = 1)
posterior <- distribution_normal(1000, mean = .5, sd = .3)
bayesfactor(posterior, prior = prior, verbose = FALSE)
# rstanarm models
# ---------------
model <- suppressWarnings(rstanarm::stan_lmer(extra ~ group + (1 | ID), data = sleep))
bayesfactor(model, verbose = FALSE)
# Frequentist models
# ---------------
m0 <- lm(extra ~ 1, data = sleep)
m1 <- lm(extra ~ group, data = sleep)
m2 <- lm(extra ~ group + ID, data = sleep)
comparison <- bayesfactor(m0, m1, m2)
comparison
bayesfactor(comparison)
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