https://github.com/cran/bayestestR
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Tip revision: 68a979e69aa2a1e57017730e1397470d5614d216 authored by Dominique Makowski on 02 September 2021, 23:10 UTC
version 0.11.0
Tip revision: 68a979e
map_estimate.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/map_estimate.R
\name{map_estimate}
\alias{map_estimate}
\alias{map_estimate.numeric}
\alias{map_estimate.bayesQR}
\alias{map_estimate.stanreg}
\alias{map_estimate.brmsfit}
\alias{map_estimate.data.frame}
\alias{map_estimate.emmGrid}
\title{Maximum A Posteriori probability estimate (MAP)}
\usage{
map_estimate(x, precision = 2^10, method = "kernel", ...)

\method{map_estimate}{numeric}(x, precision = 2^10, method = "kernel", ...)

\method{map_estimate}{bayesQR}(x, precision = 2^10, method = "kernel", ...)

\method{map_estimate}{stanreg}(
  x,
  precision = 2^10,
  method = "kernel",
  effects = c("fixed", "random", "all"),
  component = c("location", "all", "conditional", "smooth_terms", "sigma",
    "distributional", "auxiliary"),
  parameters = NULL,
  ...
)

\method{map_estimate}{brmsfit}(
  x,
  precision = 2^10,
  method = "kernel",
  effects = c("fixed", "random", "all"),
  component = c("conditional", "zi", "zero_inflated", "all"),
  parameters = NULL,
  ...
)

\method{map_estimate}{data.frame}(x, precision = 2^10, method = "kernel", ...)

\method{map_estimate}{emmGrid}(x, precision = 2^10, method = "kernel", ...)
}
\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{precision}{Number of points of density data. See the \code{n} parameter in \code{density}.}

\item{method}{Density estimation method. Can be \code{"kernel"} (default), \code{"logspline"} or \code{"KernSmooth"}.}

\item{...}{Currently not used.}

\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{
A numeric value if \code{posterior} is a vector. If \code{posterior}
is a model-object, returns a data frame with following columns:
\itemize{
\item \code{Parameter} The model parameter(s), if \code{x} is a model-object. If \code{x} is a vector, this column is missing.
\item \code{MAP_Estimate} The MAP estimate for the posterior or each model parameter.
}
}
\description{
Find the \strong{Highest Maximum A Posteriori probability estimate (MAP)} of a posterior, i.e., the value associated with the highest probability density (the "peak" of the posterior distribution). In other words, it is an estimation of the \emph{mode} for continuous parameters. Note that this function relies on \link{estimate_density}, which by default uses a different smoothing bandwidth (\code{"SJ"}) compared to the legacy default implemented the base R \link{density} function (\code{"nrd0"}).
}
\examples{
\dontrun{
library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)

plot(density(posterior))
abline(v = map_estimate(posterior), col = "red")

library(rstanarm)
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)
map_estimate(model)

library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
map_estimate(model)
}

}
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