# sexit_thresholds: Find Effect Size Thresholds In easystats/bayestestR: Understand and Describe Bayesian Models and Posterior Distributions

 sexit_thresholds R Documentation

## 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)
}

``````

easystats/bayestestR documentation built on Aug. 1, 2024, 9:41 a.m.