sexit_thresholds: Find Effect Size Thresholds

Description Usage Arguments References Examples

View source: R/sexit_thresholds.R

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

1

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. doi: 10.1177/2515245918771304.

Examples

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sexit_thresholds(rnorm(1000))
## Not run: 
if (require("rstanarm")) {
  model <- stan_glm(
    mpg ~ wt + gear,
    data = mtcars,
    chains = 2,
    iter = 200,
    refresh = 0
  )
  sexit_thresholds(model)

  model <- 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)
}

## End(Not run)

DominiqueMakowski/bayestestR documentation built on July 27, 2021, 4:12 p.m.