% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/point_estimate.R
\name{point_estimate}
\alias{point_estimate}
\alias{point_estimate.numeric}
\alias{point_estimate.stanreg}
\alias{point_estimate.brmsfit}
\alias{point_estimate.BFBayesFactor}
\alias{point_estimate.get_predicted}
\title{Point-estimates of posterior distributions}
\usage{
point_estimate(x, ...)

\method{point_estimate}{numeric}(x, centrality = "all", dispersion = FALSE, threshold = 0.1, ...)

\method{point_estimate}{stanreg}(
  x,
  centrality = "all",
  dispersion = FALSE,
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

\method{point_estimate}{brmsfit}(
  x,
  centrality = "all",
  dispersion = FALSE,
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  ...
)

\method{point_estimate}{BFBayesFactor}(x, centrality = "all", dispersion = FALSE, ...)

\method{point_estimate}{get_predicted}(
  x,
  centrality = "all",
  dispersion = FALSE,
  use_iterations = FALSE,
  verbose = TRUE,
  ...
)
}
\arguments{
\item{x}{Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model. \strong{bayestestR} supports a wide range
of models (see, for example, \code{methods("hdi")}) and not all of those are
documented in the 'Usage' section, because methods for other classes mostly
resemble the arguments of the \code{.numeric} or \code{.data.frame}methods.}

\item{...}{Additional arguments to be passed to or from methods.}

\item{centrality}{The point-estimates (centrality indices) to compute. Character
(vector) or list with one or more of these options: \code{"median"}, \code{"mean"}, \code{"MAP"}
(see \code{\link[=map_estimate]{map_estimate()}}), \code{"trimmed"} (which is just \code{mean(x, trim = threshold)}),
\code{"mode"} or \code{"all"}.}

\item{dispersion}{Logical, if \code{TRUE}, computes indices of dispersion related
to the estimate(s) (\code{SD} and \code{MAD} for \code{mean} and \code{median}, respectively).
Dispersion is not available for \code{"MAP"} or \code{"mode"} centrality indices.}

\item{threshold}{For \code{centrality = "trimmed"} (i.e. trimmed mean), indicates
the fraction (0 to 0.5) of observations to be trimmed from each end of the
vector before the mean is computed.}

\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{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.}
}
\description{
Compute various point-estimates, such as the mean, the median or the MAP, to describe posterior distributions.
}
\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") && require("emmeans") && require("brms") && require("BayesFactor")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
library(bayestestR)

point_estimate(rnorm(1000))
point_estimate(rnorm(1000), centrality = "all", dispersion = TRUE)
point_estimate(rnorm(1000), centrality = c("median", "MAP"))

df <- data.frame(replicate(4, rnorm(100)))
point_estimate(df, centrality = "all", dispersion = TRUE)
point_estimate(df, centrality = c("median", "MAP"))
\donttest{
# rstanarm models
# -----------------------------------------------
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))


# emmeans estimates
# -----------------------------------------------
point_estimate(
  emmeans::emtrends(model, ~1, "wt", data = mtcars),
  centrality = c("median", "MAP")
)

# brms models
# -----------------------------------------------
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))

# BayesFactor objects
# -----------------------------------------------
bf <- BayesFactor::ttestBF(x = rnorm(100, 1, 1))
point_estimate(bf, centrality = "all", dispersion = TRUE)
point_estimate(bf, centrality = c("median", "MAP"))
}
\dontshow{\}) # examplesIf}
}
\references{
Makowski, D., Ben-Shachar, M. S., Chen, S. H. A., and Lüdecke, D.
(2019). \emph{Indices of Effect Existence and Significance in the Bayesian Framework}.
Frontiers in Psychology 2019;10:2767. \doi{10.3389/fpsyg.2019.02767}
}