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.

## Usage

```
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
)
```

## Arguments

- ...
A numeric vector, model object(s), or the output from

`bayesfactor_models`

.- prior
An object representing a prior distribution (see 'Details').

- direction
Test type (see 'Details'). One of

`0`

,`"two-sided"`

(default, two tailed),`-1`

,`"left"`

(left tailed) or`1`

,`"right"`

(right tailed).- 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

`BFBayesFactor`

.- match_models
See details.

- prior_odds
Optional vector of prior odds for the models. See

`BayesFactor::priorOdds<-`

.

## Value

Some type of Bayes factor, depending on the input. See `bayesfactor_parameters()`

, `bayesfactor_models()`

or `bayesfactor_inclusion()`

## Note

There is also a `plot()`

-method implemented in the see-package.

## Examples

```
library(bayestestR)
if (require("logspline")) {
prior <- distribution_normal(1000, mean = 0, sd = 1)
posterior <- distribution_normal(1000, mean = .5, sd = .3)
bayesfactor(posterior, prior = prior, verbose = FALSE)
}
#> Loading required package: logspline
#> Bayes Factor (Savage-Dickey density ratio)
#>
#> BF
#> ----
#> 1.21
#>
#> * Evidence Against The Null: 0
#>
# \dontrun{
# rstanarm models
# ---------------
if (require("rstanarm")) {
model <- stan_lmer(extra ~ group + (1 | ID), data = sleep)
bayesfactor(model, verbose = FALSE)
}
#> Loading required package: rstanarm
#> Loading required package: Rcpp
#> This is rstanarm version 2.21.3
#> - See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
#> - Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
#> - For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 4.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.43 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
#> Chain 1:
#> Chain 1: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 1: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 1: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 1: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 1: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 1:
#> Chain 1: Elapsed Time: 0.267143 seconds (Warm-up)
#> Chain 1: 0.22404 seconds (Sampling)
#> Chain 1: 0.491183 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 1.9e-05 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.19 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2:
#> Chain 2:
#> Chain 2: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 2: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 2: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 2: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 2: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 2:
#> Chain 2: Elapsed Time: 0.245042 seconds (Warm-up)
#> Chain 2: 0.200461 seconds (Sampling)
#> Chain 2: 0.445503 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 1.8e-05 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.18 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3:
#> Chain 3:
#> Chain 3: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 3: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 3: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 3: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 3: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 3:
#> Chain 3: Elapsed Time: 0.23583 seconds (Warm-up)
#> Chain 3: 0.241212 seconds (Sampling)
#> Chain 3: 0.477042 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'continuous' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 1.7e-05 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.17 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
#> Chain 4:
#> Chain 4: Iteration: 1 / 2000 [ 0%] (Warmup)
#> Chain 4: Iteration: 200 / 2000 [ 10%] (Warmup)
#> Chain 4: Iteration: 400 / 2000 [ 20%] (Warmup)
#> Chain 4: Iteration: 600 / 2000 [ 30%] (Warmup)
#> Chain 4: Iteration: 800 / 2000 [ 40%] (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%] (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%] (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%] (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%] (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%] (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%] (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%] (Sampling)
#> Chain 4:
#> Chain 4: Elapsed Time: 0.249065 seconds (Warm-up)
#> Chain 4: 0.227155 seconds (Sampling)
#> Chain 4: 0.47622 seconds (Total)
#> Chain 4:
#> Bayes Factor (Savage-Dickey density ratio)
#>
#> Parameter | BF
#> -------------------
#> (Intercept) | 0.214
#> group2 | 3.24
#>
#> * Evidence Against The Null: 0
#>
# }
if (require("logspline")) {
# 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)
}
#> Inclusion Bayes Factors (Model Averaged)
#>
#> P(prior) P(posterior) Inclusion BF
#> group 0.67 1.00 5.61e+03
#> ID 0.33 1.00 9.77e+03
#>
#> * Compared among: all models
#> * Priors odds: uniform-equal
```