% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_rope.R
\name{p_rope}
\alias{p_rope}
\alias{p_rope.numeric}
\alias{p_rope.data.frame}
\alias{p_rope.BFBayesFactor}
\alias{p_rope.stanreg}
\alias{p_rope.brmsfit}
\title{ROPE-based p-value}
\usage{
p_rope(x, ...)

\method{p_rope}{numeric}(x, range = "default", precision = 0.1, ...)

\method{p_rope}{data.frame}(x, range = "default", precision = 0.1, ...)

\method{p_rope}{BFBayesFactor}(x, range = "default", precision = 0.1,
  ...)

\method{p_rope}{stanreg}(x, range = "default", precision = 0.1,
  effects = c("fixed", "random", "all"), parameters = NULL, ...)

\method{p_rope}{brmsfit}(x, range = "default", precision = 0.1,
  effects = c("fixed", "random", "all"), component = c("conditional",
  "zi", "zero_inflated", "all"), parameters = NULL, ...)
}
\arguments{
\item{x}{Vector representing a posterior distribution. Can also be a
\code{stanreg} or \code{brmsfit} model.}

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

\item{range}{ROPE's lower and higher bounds. Should be a list of two values (e.g., \code{c(-0.1, 0.1)}) or \code{"default"}. If \code{"default"}, the range is set to \code{c(0.1, 0.1)} if input is a vector and \code{x +- 0.1*SD(response)} if a Bayesian model is provided.}

\item{precision}{The precision by which to explore the ROPE space (in percentage). Lower values increase the precision of the returned p value but can be quite computationaly costly.}

\item{effects}{Should results for fixed effects, random effects or both be returned?
Only applies to mixed models. May be abbreviated.}

\item{parameters}{Regular expression pattern that describes the parameters that
should be returned. Meta-parameters (like \code{lp__} or \code{prior_}) are
filtered by default, so only parameters that typically appear in the
\code{summary()} are returned. Use \code{parameters} to select specific parameters
for the output.}

\item{component}{Should results for all parameters, parameters for the conditional model
or the zero-inflated part of the model be returned? May be abbreviated. Only
applies to \pkg{brms}-models.}
}
\description{
Compute the ROPE-based p-value, an exploratory index representing the maximum percentage of \link[=hdi]{HDI} that does not contain (positive values) or is entirely contained (negative values) in the negligible values space defined by the \link[=rope]{ROPE}. It differs from the ROPE percentage, \emph{i.e.}, from the proportion of a given CI in the ROPE, as it represents the maximum CI to reach a ROPE proportion of 0\% (positive values) or 100\% (negative values). A ROPE-based \emph{p} of 97\% means that there is a probability of .97 that a parameter (described by its posterior distribution) is outside the ROPE. On the contrary, a ROPE-based p of -97\% means that there is a probability of .97 that the parameter is inside the ROPE.
}
\examples{
library(bayestestR)

# precision = 1 is used to speed up examples...

p_rope(
  x = rnorm(1000, mean = 1, sd = 1),
  range = c(-0.1, 0.1),
  precision = 1
)

df <- data.frame(replicate(4, rnorm(100)))
p_rope(df, precision = 1)

library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
p_rope(model, precision = 1)

 \dontrun{
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
p_rope(model)

library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
p_rope(bf)
}

}