% 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.stanreg}
\alias{map_estimate.brmsfit}
\alias{map_estimate.data.frame}
\alias{map_estimate.get_predicted}
\title{Maximum A Posteriori probability estimate (MAP)}
\usage{
map_estimate(x, ...)

\method{map_estimate}{numeric}(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}{get_predicted}(
  x,
  precision = 2^10,
  method = "kernel",
  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{...}{Currently not used.}

\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{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.}
}
\value{
A numeric value if \code{x} is a vector. If \code{x} 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
\code{\link[=estimate_density]{estimate_density()}}, which by default uses a different smoothing bandwidth
(\code{"SJ"}) compared to the legacy default implemented the base R \code{\link[=density]{density()}}
function (\code{"nrd0"}).
}
\examples{
\dontshow{if (require("rstanarm") && require("brms")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}
\donttest{
library(bayestestR)

posterior <- rnorm(10000)
map_estimate(posterior)

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

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

model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
map_estimate(model)
}
\dontshow{\}) # examplesIf}
}