Find Effect Size Thresholds
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.
sexit_thresholds(x, ...)
x |
Vector representing a posterior distribution. Can also be a |
... |
Currently not used. |
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.
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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.