% 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.get_predicted}
\alias{p_significance.data.frame}
\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}{get_predicted}(
  x,
  threshold = "default",
  use_iterations = FALSE,
  verbose = TRUE,
  ...
)

\method{p_significance}{data.frame}(x, threshold = "default", rvar_col = NULL, ...)

\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, which can have following possible values:
\itemize{
\item \code{"default"}, in which case 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 a single numeric value (e.g., 0.1), which is used as range around zero
(i.e. the threshold range is set to -0.1 and 0.1, i.e. reflects a symmetric
interval)
\item a numeric vector of length two (e.g., \code{c(-0.2, 0.1)}), useful for
asymmetric intervals
\item a list of numeric vectors, where each vector corresponds to a parameter
\item a list of \emph{named} numeric vectors, where names correspond to parameter
names. In this case, all parameters that have no matching name in \code{threshold}
will be set to \code{"default"}.
}}

\item{use_iterations}{Logical, if \code{TRUE} and \code{x} is a \code{get_predicted} object,
(returned by \code{\link[insight:get_predicted]{insight::get_predicted()}}), the function is applied to the
iterations instead of the predictions. This only applies to models that return
iterations for predicted values (e.g., \code{brmsfit} models).}

\item{verbose}{Toggle off warnings.}

\item{rvar_col}{A single character - the name of an \code{rvar} column in the data
frame to be processed. See example in \code{\link[=p_direction]{p_direction()}}.}

\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.}
}
\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{
\dontshow{if (require("rstanarm")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
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)
\donttest{
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
  data = mtcars,
  chains = 2, refresh = 0
)
p_significance(model)
# multiple thresholds - asymmetric, symmetric, default
p_significance(model, threshold = list(c(-10, 5), 0.2, "default"))
# named thresholds
p_significance(model, threshold = list(wt = 0.2, `(Intercept)` = c(-10, 5)))
}
\dontshow{\}) # examplesIf}
}