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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_significance.R
\title{Practical Significance (ps)}
p_significance(x, ...)

\method{p_significance}{numeric}(x, threshold = "default", ...)

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

\method{p_significance}{MCMCglmm}(x, threshold = "default", ...)

\method{p_significance}{emmGrid}(x, threshold = "default", ...)

\method{p_significance}{stanreg}(x, threshold = "default",
  effects = c("fixed", "random", "all"), parameters = NULL,
  verbose = TRUE, ...)
\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{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.}
Values between 0.5 and 1 corresponding to the probability of practical significance (ps).
Compute the probability of \strong{Practical Significance} (\strong{\emph{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.

# Simulate a posterior distribution of mean 1 and SD 1
# ----------------------------------------------------
posterior <- rnorm(1000, mean = 1, sd = 1)

# Simulate a dataframe of posterior distributions
# -----------------------------------------------
df <- data.frame(replicate(4, rnorm(100)))

# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl,
  data = mtcars,
  chains = 2, refresh = 0
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