Interpret Bayes Factor (BF)

## Usage

```
interpret_bf(
bf,
rules = "jeffreys1961",
log = FALSE,
include_value = FALSE,
protect_ratio = TRUE,
exact = TRUE
)
```

## Arguments

- bf
Value or vector of Bayes factor (BF) values.

- rules
Can be

`"jeffreys1961"`

(default),`"raftery1995"`

or custom set of`rules()`

(for the*absolute magnitude*of evidence).- log
Is the

`bf`

value`log(bf)`

?- include_value
Include the value in the output.

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

- 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").

## Rules

Rules apply to BF as ratios, so BF of 10 is as extreme as a BF of 0.1 (1/10).

Jeffreys (1961) (

`"jeffreys1961"`

; default)**BF = 1**- No evidence**1 < BF <= 3**- Anecdotal**3 < BF <= 10**- Moderate**10 < BF <= 30**- Strong**30 < BF <= 100**- Very strong**BF > 100**- Extreme.

Raftery (1995) (

`"raftery1995"`

)**BF = 1**- No evidence**1 < BF <= 3**- Weak**3 < BF <= 20**- Positive**20 < BF <= 150**- Strong**BF > 150**- Very strong

## References

Jeffreys, H. (1961), Theory of Probability, 3rd ed., Oxford University Press, Oxford.

Raftery, A. E. (1995). Bayesian model selection in social research. Sociological methodology, 25, 111-164.

Jarosz, A. F., & Wiley, J. (2014). What are the odds? A practical guide to computing and reporting Bayes factors. The Journal of Problem Solving, 7(1), 2.

## Examples

```
interpret_bf(1)
#> [1] "no evidence against or in favour of"
#> (Rules: jeffreys1961)
#>
interpret_bf(c(5, 2, 0.01))
#> [1] "moderate evidence in favour of" "anecdotal evidence in favour of"
#> [3] "very strong evidence against"
#> (Rules: jeffreys1961)
#>
```