Create reports of Bayes factors for model comparison.

## Arguments

- x
Object of class

`bayesfactor_inclusion`

.- interpretation
Effect size interpretation set of rules (see interpret_bf).

- exact
Should very large or very small values be reported with a scientific format (e.g., 4.24e5), or as truncated values (as "> 1000" and "< 1/1000").

- protect_ratio
Should values smaller than 1 be represented as ratios?

- ...
Arguments passed to or from other methods.

## Value

An object of class `report()`

.

## See also

Specific components of reports (especially for stats models):

Other types of reports:

Methods:

Template file for supporting new models:

## Examples

```
library(bayestestR)
# Bayes factor - models
mo0 <- lm(Sepal.Length ~ 1, data = iris)
mo1 <- lm(Sepal.Length ~ Species, data = iris)
mo2 <- lm(Sepal.Length ~ Species + Petal.Length, data = iris)
mo3 <- lm(Sepal.Length ~ Species * Petal.Length, data = iris)
BFmodels <- bayesfactor_models(mo1, mo2, mo3, denominator = mo0)
r <- report(BFmodels)
r
#> Bayes factors were computed using the BIC approximation, by which BF10 =
#> exp((BIC0 - BIC1)/2). Compared to the (Intercept only) model (the least
#> supported model), we found extreme evidence (BF = 1.70e+29) in favour of the
#> Species model; extreme evidence (BF = 5.84e+55) in favour of the Species +
#> Petal.Length model (the most supported model); extreme evidence (BF = 2.20e+54)
#> in favour of the Species * Petal.Length model.
# Bayes factor - inclusion
inc_bf <- bayesfactor_inclusion(BFmodels, prior_odds = c(1, 2, 3), match_models = TRUE)
r <- report(inc_bf)
r
#> Bayesian model averaging (BMA) was used to obtain the average evidence for each
#> predictor. Since each model has a prior probability (here we used subjective
#> prior odds of 1, 2, 3), it is possible to sum the prior probability of all
#> models that include a predictor of interest (the prior inclusion probability),
#> and of all models that do not include that predictor (the prior exclusion
#> probability). After the data are observed, we can similarly consider the sums
#> of the posterior models' probabilities to obtain the posterior inclusion
#> probability and the posterior exclusion probability. The change from prior to
#> posterior inclusion odds is the Inclusion Bayes factor. For each predictor,
#> averaging was done only across models that did not include any interactions
#> with that predictor; additionally, for each interaction predictor, averaging
#> was done only across models that contained the main effect from which the
#> interaction predictor was comprised. This was done to prevent Inclusion Bayes
#> factors from being contaminated with non-relevant evidence (see Mathot, 2017).
#> We found extreme evidence (BF = 3.90e+55) in favour of including Species, with
#> models including Species having an overall posterior probability of 95%;
#> extreme evidence (BF = 6.89e+26) in favour of including Petal.Length, with
#> models including Petal.Length having an overall posterior probability of 95%;
#> strong evidence (BF = 1/26.52) against including Petal.Length:Species, with
#> models including Petal.Length:Species having an overall posterior probability
#> of 5%.
as.data.frame(r)
#> Terms | Pr(prior) | Pr(posterior) | Inclusion BF
#> ---------------------------------------------------------------
#> Species | 0.43 | 0.95 | 128.00
#> Petal.Length | 0.29 | 0.95 | 61.80
#> Petal.Length:Species | 0.43 | 0.05 | 1/-3.05e-01
#>
#> Across matched models only.
#> With custom prior odds of [1, 2, 3].
```