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

rope_range(x, ...) # S3 method for default rope_range(x, verbose = TRUE, ...)

x | A |
---|---|

... | Currently not used. |

verbose | Toggle warnings. |

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.

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 .

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) } }