% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/rope_range.R
\name{rope_range}
\alias{rope_range}
\alias{rope_range.default}
\title{Find Default Equivalence (ROPE) Region Bounds}
\usage{
rope_range(x, ...)

\method{rope_range}{default}(x, verbose = TRUE, ...)
}
\arguments{
\item{x}{A \code{stanreg}, \code{brmsfit} or \code{BFBayesFactor} object.}

\item{...}{Currently not used.}

\item{verbose}{Toggle warnings.}
}
\description{
This function attempts at automatically finding suitable "default"
values for the Region Of Practical Equivalence (ROPE).
}
\details{
\cite{Kruschke (2018)} suggests that the region of practical
equivalence could be set, by default, to a range from \code{-0.1} to
\code{0.1} of a standardized parameter (negligible effect size
according to Cohen, 1988).

\itemize{
\item For \strong{linear models (lm)}, this can be generalised to
\ifelse{html}{\out{-0.1 * SD<sub>y</sub>, 0.1 *
    SD<sub>y</sub>}}{\eqn{[-0.1*SD_{y}, 0.1*SD_{y}]}}.

\if{html}{\out{<div class="sourceCode">}}\preformatted{\\item For **logistic models**, the parameters expressed in log odds
ratio can be converted to standardized difference through the formula
\ifelse{html}{\out{&pi;/&radic;(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.
}\if{html}{\out{</div>}}

}
}
\examples{
\dontrun{
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)
}
}

}
\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}.
}