This function attempts at automatically finding suitable "default" values for the Region Of Practical Equivalence (ROPE).

## Arguments

- x
A

`stanreg`

,`brmsfit`

or`BFBayesFactor`

object.- ...
Currently not used.

- verbose
Toggle warnings.

## Details

Kruschke (2018) suggests that the region of practical
equivalence could be set, by default, to a range from `-0.1`

to
`0.1`

of a standardized parameter (negligible effect size
according to Cohen, 1988).

For

**linear models (lm)**, this can be generalised to -0.1 * SD_{y}, 0.1 * SD_{y}.`\item For **logistic models**, the parameters expressed in log odds ratio can be converted to standardized difference through the formula \ifelse{html}{\out{π/√(3)}}{\eqn{\pi/\sqrt{3}}}, resulting in a range of `-0.18` to `0.18`. \item For other models with **binary outcome**, it is strongly recommended to manually specify the rope argument. Currently, the same default is applied that for logistic models. \item For models from **count data**, the residual variance is used. This is a rather experimental threshold and is probably often similar to `-0.1, 0.1`, but should be used with care! \item For **t-tests**, the standard deviation of the response is used, similarly to linear models (see above). \item For **correlations**, `-0.05, 0.05` is used, i.e., half the value of a negligible correlation as suggested by Cohen's (1988) rules of thumb. \item For all other models, `-0.1, 0.1` is used to determine the ROPE limits, but it is strongly advised to specify it manually.`

## 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

```
if (FALSE) {
if (require("rstanarm")) {
model <- stan_glm(
mpg ~ wt + gear,
data = mtcars,
chains = 2,
iter = 200,
refresh = 0
)
rope_range(model)
model <- stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
rope_range(model)
}
if (require("brms")) {
model <- brm(mpg ~ wt + cyl, data = mtcars)
rope_range(model)
}
if (require("BayesFactor")) {
model <- ttestBF(mtcars[mtcars$vs == 1, "mpg"], mtcars[mtcars$vs == 0, "mpg"])
rope_range(model)
model <- lmBF(mpg ~ vs, data = mtcars)
rope_range(model)
}
}
```