sexit_thresholds: Find Effect Size Thresholds

In bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

Find Effect Size Thresholds

Description

This function attempts at automatically finding suitable default values for a "significant" (i.e., non-negligible) and "large" effect. This is to be used with care, and the chosen threshold should always be explicitly reported and justified. See the detail section in sexit() for more information.

Usage

sexit_thresholds(x, ...)

Arguments

x	Vector representing a posterior distribution. Can also be a stanreg or brmsfit model.
...	Currently not used.

References

Kruschke, J. K. (2018). Rejecting or accepting parameter values in Bayesian estimation. Advances in Methods and Practices in Psychological Science, 1(2), 270-280. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1177/2515245918771304")}.

Examples

sexit_thresholds(rnorm(1000))

if (require("rstanarm")) {
  model <- suppressWarnings(stan_glm(
    mpg ~ wt + gear,
    data = mtcars,
    chains = 2,
    iter = 200,
    refresh = 0
  ))
  sexit_thresholds(model)

  model <- suppressWarnings(
    stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
  )
  sexit_thresholds(model)
}

if (require("brms")) {
  model <- brm(mpg ~ wt + cyl, data = mtcars)
  sexit_thresholds(model)
}

if (require("BayesFactor")) {
  bf <- ttestBF(x = rnorm(100, 1, 1))
  sexit_thresholds(bf)
}

bayestestR documentation built on Oct. 17, 2024, 5:07 p.m.