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").

## Details

Argument names can be partially matched.

## 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))
#> [1] "moderate evidence in favour of"  "anecdotal evidence in favour of"
#> (Rules: jeffreys1961)
#>