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 .

#> [1] 0.05 0.30if (FALSE) { 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) } }