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

rope_range(x, ...)

## Arguments

x A stanreg, brmsfit or BFBayesFactor object. Currently not used.

## 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 * SDy, 0.1 * SDy.

• 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

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")
rope_range(model)
}

if (require("brms")) {
model <- brm(mpg ~ wt + cyl, data = mtcars)
rope_range(model)
}

if (require("BayesFactor")) {
bf <- ttestBF(x = rnorm(100, 1, 1))
rope_range(bf)
}
}