bayesfactor {bayestestR} R Documentation

## Bayes Factors (BF)

### Description

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)
}
## Not run:
# rstanarm models
# ---------------
if (require("rstanarm")) {
model <- stan_lmer(extra ~ group + (1 | ID), data = sleep)
bayesfactor(model)
}

## End(Not run)

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)
}
```

[Package bayestestR version 0.10.0 Index]