interpret_bf: Interpret Bayes Factor (BF)

In effectsize: Indices of Effect Size

Interpret Bayes Factor (BF)

Description

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)
interpret_bf(c(5, 2, 0.01))

effectsize documentation built on July 3, 2024, 9:07 a.m.