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**d73e4a2afcfbd6402c11716877e8f7466f309ef4**authored by Dominique Makowski on**22 October 2020, 13:40 UTC**, committed by cran-robot on**22 October 2020, 13:40 UTC****1 parent**1b89ec8

point_estimate.Rd

```
% 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.stanreg}
\alias{point_estimate.brmsfit}
\alias{point_estimate.BFBayesFactor}
\title{Point-estimates of posterior distributions}
\usage{
point_estimate(x, centrality = "all", dispersion = FALSE, ...)
\method{point_estimate}{stanreg}(
x,
centrality = "all",
dispersion = FALSE,
effects = c("fixed", "random", "all"),
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, ...)
}
\arguments{
\item{x}{Vector representing a posterior distribution, or a data frame of such
vectors. Can also be a Bayesian model (\code{stanreg}, \code{brmsfit},
\code{MCMCglmm}, \code{mcmc} or \code{bcplm}) or a \code{BayesFactor} model.}
\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"} 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).}
\item{...}{Additional arguments to be passed to or from methods.}
\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 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{
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"))
\dontrun{
# rstanarm models
# -----------------------------------------------
library(rstanarm)
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
# -----------------------------------------------
library(emmeans)
point_estimate(emtrends(model, ~1, "wt"), centrality = c("median", "MAP"))
# brms models
# -----------------------------------------------
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
point_estimate(model, centrality = "all", dispersion = TRUE)
point_estimate(model, centrality = c("median", "MAP"))
# BayesFactor objects
# -----------------------------------------------
library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
point_estimate(bf, centrality = "all", dispersion = TRUE)
point_estimate(bf, centrality = c("median", "MAP"))
}
}
\references{
\href{https://easystats.github.io/bayestestR/articles/indicesEstimationComparison.html}{Vignette In-Depth 1: Comparison of Point-Estimates}
}
```

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