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Tip revision: **68a979e69aa2a1e57017730e1397470d5614d216** authored by ** Dominique Makowski ** on **02 September 2021, 23:10:30 UTC**

**version 0.11.0**

Tip revision: **68a979e**

p_significance.Rd

```
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_significance.R
\name{p_significance}
\alias{p_significance}
\alias{p_significance.numeric}
\alias{p_significance.emmGrid}
\alias{p_significance.stanreg}
\alias{p_significance.brmsfit}
\title{Practical Significance (ps)}
\usage{
p_significance(x, ...)
\method{p_significance}{numeric}(x, threshold = "default", ...)
\method{p_significance}{emmGrid}(x, threshold = "default", ...)
\method{p_significance}{stanreg}(
x,
threshold = "default",
effects = c("fixed", "random", "all"),
component = c("location", "all", "conditional", "smooth_terms", "sigma",
"distributional", "auxiliary"),
parameters = NULL,
verbose = TRUE,
...
)
\method{p_significance}{brmsfit}(
x,
threshold = "default",
effects = c("fixed", "random", "all"),
component = c("conditional", "zi", "zero_inflated", "all"),
parameters = NULL,
verbose = TRUE,
...
)
}
\arguments{
\item{x}{Vector representing a posterior distribution. Can also be a \code{stanreg} or \code{brmsfit} model.}
\item{...}{Currently not used.}
\item{threshold}{The threshold value that separates significant from negligible effect. If \code{"default"}, the range is set to \code{0.1} if input is a vector, and based on \code{\link[=rope_range]{rope_range()}} if a Bayesian model is provided.}
\item{effects}{Should results for fixed effects, random effects or both be
returned? Only applies to mixed models. May be abbreviated.}
\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.}
\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{verbose}{Toggle off warnings.}
}
\value{
Values between 0 and 1 corresponding to the probability of practical significance (ps).
}
\description{
Compute the probability of \strong{Practical Significance} (\emph{\strong{ps}}), which can be conceptualized as a unidirectional equivalence test. It returns the probability that effect is above a given threshold corresponding to a negligible effect in the median's direction. Mathematically, it is defined as the proportion of the posterior distribution of the median sign above the threshold.
}
\details{
\code{p_significance()} returns the proportion of a probability
distribution (\code{x}) that is outside a certain range (the negligible
effect, or ROPE, see argument \code{threshold}). If there are values of the
distribution both below and above the ROPE, \code{p_significance()} returns
the higher probability of a value being outside the ROPE. Typically, this
value should be larger than 0.5 to indicate practical significance. However,
if the range of the negligible effect is rather large compared to the
range of the probability distribution \code{x}, \code{p_significance()}
will be less than 0.5, which indicates no clear practical significance.
}
\note{
There is also a \href{https://easystats.github.io/see/articles/bayestestR.html}{\code{plot()}-method} implemented in the \href{https://easystats.github.io/see/}{\pkg{see}-package}.
}
\examples{
library(bayestestR)
# Simulate a posterior distribution of mean 1 and SD 1
# ----------------------------------------------------
posterior <- rnorm(1000, mean = 1, sd = 1)
p_significance(posterior)
# Simulate a dataframe of posterior distributions
# -----------------------------------------------
df <- data.frame(replicate(4, rnorm(100)))
p_significance(df)
\dontrun{
# rstanarm models
# -----------------------------------------------
if (require("rstanarm")) {
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
data = mtcars,
chains = 2, refresh = 0
)
p_significance(model)
}
}
}
```