This function attempts at automatically finding suitable "default" values for the Region Of Practical Equivalence (ROPE).
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
to0.18
.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
# \donttest{
model <- suppressWarnings(rstanarm::stan_glm(
mpg ~ wt + gear,
data = mtcars,
chains = 2,
iter = 200,
refresh = 0
))
rope_range(model)
#> [1] -0.6026948 0.6026948
model <- suppressWarnings(
rstanarm::stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
)
rope_range(model)
#> [1] -0.1813799 0.1813799
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
#> Compiling Stan program...
#> Start sampling
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 1).
#> Chain 1:
#> Chain 1: Gradient evaluation took 8e-06 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1:
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#> Chain 1:
#> Chain 1: Elapsed Time: 0.024 seconds (Warm-up)
#> Chain 1: 0.021 seconds (Sampling)
#> Chain 1: 0.045 seconds (Total)
#> Chain 1:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 2).
#> Chain 2:
#> Chain 2: Gradient evaluation took 3e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 2: Adjust your expectations accordingly!
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#> Chain 2:
#> Chain 2: Elapsed Time: 0.023 seconds (Warm-up)
#> Chain 2: 0.019 seconds (Sampling)
#> Chain 2: 0.042 seconds (Total)
#> Chain 2:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 3).
#> Chain 3:
#> Chain 3: Gradient evaluation took 3e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
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#> Chain 3: Elapsed Time: 0.022 seconds (Warm-up)
#> Chain 3: 0.021 seconds (Sampling)
#> Chain 3: 0.043 seconds (Total)
#> Chain 3:
#>
#> SAMPLING FOR MODEL 'anon_model' NOW (CHAIN 4).
#> Chain 4:
#> Chain 4: Gradient evaluation took 3e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.03 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4:
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#> Chain 4: 0.04 seconds (Total)
#> Chain 4:
rope_range(model)
#> [1] -0.6026948 0.6026948
model <- BayesFactor::ttestBF(mtcars[mtcars$vs == 1, "mpg"], mtcars[mtcars$vs == 0, "mpg"])
rope_range(model)
#> [1] -0.6026948 0.6026948
model <- lmBF(mpg ~ vs, data = mtcars)
rope_range(model)
#> [1] -0.6026948 0.6026948
# }