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

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

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

\item{verbose}{Toggle warnings.}
This function attempts at automatically finding suitable "default"
  values for the Region Of Practical Equivalence (ROPE).
\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).

    \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}]}}.

    \item For \strong{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 \code{-0.18} to \code{0.18}.

    \item For other models with \strong{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 \strong{count data}, the residual variance is used.
    This is a rather experimental threshold and is probably often similar to
    \code{-0.1, 0.1}, but should be used with care!

    \item For \strong{t-tests}, the standard deviation of the response is
    used, similarly to linear models (see above).

    \item For \strong{correlations}, \code{-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, \code{-0.1, 0.1} is used to determine the
    ROPE limits, but it is strongly advised to specify it manually.
if (require("rstanarm")) {
  model <- stan_glm(
    mpg ~ wt + gear,
    data = mtcars,
    chains = 2,
    iter = 200,
    refresh = 0

  model <- stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)

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

if (require("BayesFactor")) {
  model <- ttestBF(mtcars[mtcars$vs == 1, "mpg"], mtcars[mtcars$vs == 0, "mpg"])

  model <- lmBF(mpg ~ vs, data = mtcars)

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}.
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