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, 0.01))
#> [1] "moderate evidence in favour of"  "anecdotal evidence in favour of"
#> [3] "very strong evidence against"   
#> (Rules: jeffreys1961)
#>