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Find Default Equivalence (ROPE) Region Bounds

Source: R/rope_range.R
rope_range.Rd

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

Usage

rope_range(x, ...)

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

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 * SDy, 0.1 * SDy.

    \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

# \dontrun{
if (require("rstanarm")) {
  model <- suppressWarnings(stan_glm(
    mpg ~ wt + gear,
    data = mtcars,
    chains = 2,
    iter = 200,
    refresh = 0
  ))
  rope_range(model)

  model <- suppressWarnings(
    stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
  )
  rope_range(model)
}
#> [1] -0.1813799  0.1813799

if (require("brms")) {
  model <- brm(mpg ~ wt + cyl, data = mtcars)
  rope_range(model)
}
#> Compiling Stan program...
#> Start sampling
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 1).
#> Chain 1: 
#> Chain 1: Gradient evaluation took 1.3e-05 seconds
#> Chain 1: 1000 transitions using 10 leapfrog steps per transition would take 0.13 seconds.
#> Chain 1: Adjust your expectations accordingly!
#> Chain 1: 
#> Chain 1: 
#> Chain 1: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 1: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 1: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 1: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 1: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 1: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 1: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 1: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 1: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 1: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 1: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 1: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 1: 
#> Chain 1:  Elapsed Time: 0.033393 seconds (Warm-up)
#> Chain 1:                0.036619 seconds (Sampling)
#> Chain 1:                0.070012 seconds (Total)
#> Chain 1: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 2).
#> Chain 2: 
#> Chain 2: Gradient evaluation took 8e-06 seconds
#> Chain 2: 1000 transitions using 10 leapfrog steps per transition would take 0.08 seconds.
#> Chain 2: Adjust your expectations accordingly!
#> Chain 2: 
#> Chain 2: 
#> Chain 2: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 2: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 2: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 2: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 2: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 2: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 2: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 2: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 2: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 2: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 2: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 2: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 2: 
#> Chain 2:  Elapsed Time: 0.03324 seconds (Warm-up)
#> Chain 2:                0.032757 seconds (Sampling)
#> Chain 2:                0.065997 seconds (Total)
#> Chain 2: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 3).
#> Chain 3: 
#> Chain 3: Gradient evaluation took 9e-06 seconds
#> Chain 3: 1000 transitions using 10 leapfrog steps per transition would take 0.09 seconds.
#> Chain 3: Adjust your expectations accordingly!
#> Chain 3: 
#> Chain 3: 
#> Chain 3: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 3: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 3: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 3: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 3: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 3: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 3: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 3: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 3: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 3: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 3: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 3: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 3: 
#> Chain 3:  Elapsed Time: 0.033962 seconds (Warm-up)
#> Chain 3:                0.031519 seconds (Sampling)
#> Chain 3:                0.065481 seconds (Total)
#> Chain 3: 
#> 
#> SAMPLING FOR MODEL '2d19b3a372313df641edf05db5e9f303' NOW (CHAIN 4).
#> Chain 4: 
#> Chain 4: Gradient evaluation took 7e-06 seconds
#> Chain 4: 1000 transitions using 10 leapfrog steps per transition would take 0.07 seconds.
#> Chain 4: Adjust your expectations accordingly!
#> Chain 4: 
#> Chain 4: 
#> Chain 4: Iteration:    1 / 2000 [  0%]  (Warmup)
#> Chain 4: Iteration:  200 / 2000 [ 10%]  (Warmup)
#> Chain 4: Iteration:  400 / 2000 [ 20%]  (Warmup)
#> Chain 4: Iteration:  600 / 2000 [ 30%]  (Warmup)
#> Chain 4: Iteration:  800 / 2000 [ 40%]  (Warmup)
#> Chain 4: Iteration: 1000 / 2000 [ 50%]  (Warmup)
#> Chain 4: Iteration: 1001 / 2000 [ 50%]  (Sampling)
#> Chain 4: Iteration: 1200 / 2000 [ 60%]  (Sampling)
#> Chain 4: Iteration: 1400 / 2000 [ 70%]  (Sampling)
#> Chain 4: Iteration: 1600 / 2000 [ 80%]  (Sampling)
#> Chain 4: Iteration: 1800 / 2000 [ 90%]  (Sampling)
#> Chain 4: Iteration: 2000 / 2000 [100%]  (Sampling)
#> Chain 4: 
#> Chain 4:  Elapsed Time: 0.033346 seconds (Warm-up)
#> Chain 4:                0.029713 seconds (Sampling)
#> Chain 4:                0.063059 seconds (Total)
#> Chain 4: 
#> [1] -0.6026948  0.6026948

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)
}
#> [1] -0.6026948  0.6026948
# }