Interpret Bayes Factor (BF)

interpret_bf(
  bf,
  rules = "jeffreys1961",
  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).

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) #>