rope_range {bayestestR} | R Documentation |

## Find Default Equivalence (ROPE) Region Bounds

### Description

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

### Usage

```
rope_range(x, ...)
## Default S3 method:
rope_range(x, verbose = TRUE, ...)
```

### Arguments

`x` |
A |

`...` |
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}.For

**logistic models**, the parameters expressed in log odds ratio can be converted to standardized difference through the formula π/√(3), resulting in a range of`-0.18`

to`0.18`

.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.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!For

**t-tests**, the standard deviation of the response is used, similarly to linear models (see above).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.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

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

*bayestestR*version 0.13.2 Index]