# bayesfactor: Bayes Factors (BF) In DominiqueMakowski/bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

## 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30``` ```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) } ```

DominiqueMakowski/bayestestR documentation built on July 27, 2021, 4:12 p.m.