#601

R/rope_range.R

@@ -3,74 +3,63 @@
 #' @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 `-0.1` to
-#'   `0.1` of a standardized parameter (negligible effect size
-#'   according to Cohen, 1988).
+#' @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_).
 #'
-#'   \itemize{
-#'     \item For **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}]}}.
+#' - For **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 **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`.
+#' - 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.
+#' - 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!
+#' - 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).
+#' - 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.
+#' - 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.
-#'   }
+#' - For all other models, `-0.1, 0.1` is used to determine the ROPE limits,
+#'   but it is strongly advised to specify it manually.
 #'
 #' @param x A `stanreg`, `brmsfit` or `BFBayesFactor` object.
 #' @param verbose Toggle warnings.
 #' @inheritParams rope
 #'
-#' @examples
+#' @examplesIf require("rstanarm") && require("brms") && require("BayesFactor")
 #' \donttest{
-#' 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)
-#' }
-#'
-#' 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)
-#' }
+#' model <- suppressWarnings(rstanarm::stan_glm(
+#'   mpg ~ wt + gear,
+#'   data = mtcars,
+#'   chains = 2,
+#'   iter = 200,
+#'   refresh = 0
+#' ))
+#' rope_range(model)
+#'
+#' model <- suppressWarnings(
+#'   rstanarm::stan_glm(vs ~ mpg, data = mtcars, family = "binomial", refresh = 0)
+#' )
+#' rope_range(model)
+#'
+#' model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
+#' rope_range(model)
+#'
+#' model <- BayesFactor::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