swh:1:snp:2c68a6c5a8af2f06ac2c0225927f25b54fd1f9d0
Tip revision: 645c10f110efae44c5ac60025664cd27f2b46a87 authored by Dominique Makowski on 26 March 2020, 05:10:08 UTC
version 0.5.3
version 0.5.3
Tip revision: 645c10f
rope_range.R
#' @title Find Default Equivalence (ROPE) Region Bounds
#'
#' @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}]}}.
#' \item For \strong{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 \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 \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.
#' }
#'
#' @param x A \code{stanreg}, \code{brmsfit} or \code{BFBayesFactor} object.
#' @inheritParams rope
#'
#' @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")
#' rope_range(model)
#' }
#'
#' if (require("brms")) {
#' model <- brm(mpg ~ wt + cyl, data = mtcars)
#' rope_range(model)
#' }
#'
#' if (require("BayesFactor")) {
#' bf <- ttestBF(x = rnorm(100, 1, 1))
#' rope_range(bf)
#' }
#' }
#' @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}.
#'
#' @importFrom insight get_response model_info is_multivariate
#' @importFrom stats sd
#' @export
rope_range <- function(x, ...) {
UseMethod("rope_range")
}
#' @export
rope_range.brmsfit <- function(x, ...) {
response <- insight::get_response(x)
information <- insight::model_info(x)
if (insight::is_multivariate(x)) {
mapply(function(i, j) .rope_range(i, j), x, information, response)
} else {
.rope_range(x, information, response)
}
}
#' @export
rope_range.stanreg <- rope_range.brmsfit
#' @export
rope_range.BFBayesFactor <- rope_range.brmsfit
#' @export
rope_range.lm <- rope_range.brmsfit
#' @export
rope_range.merMod <- rope_range.brmsfit
#' @export
rope_range.default <- function(x, ...) {
c(-.1, .1)
}
.rope_range <- function(x, information, response) {
negligible_value <- tryCatch(
{
# Linear Models
if (information$is_linear) {
0.1 * stats::sd(response)
# General Linear Models
} else if (information$is_binomial) {
0.1 * pi / sqrt(3)
# T-tests
} else if (information$is_ttest) {
if ("BFBayesFactor" %in% class(x)) {
0.1 * stats::sd(x@data[, 1])
} else {
warning("Could not estimate a good default ROPE range. Using 'c(-0.1, 0.1)'.", call. = FALSE)
0.1
}
# Correlations
} else if (information$is_correlation) {
# https://github.com/easystats/bayestestR/issues/121
0.05
# Default
} else {
0.1
}
},
error = function(e) {
warning("Could not estimate a good default ROPE range. Using 'c(-0.1, 0.1)'.", call. = FALSE)
0.1
}
)
c(-1, 1) * negligible_value
}