https://github.com/cran/bayestestR
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Tip revision: 9985109256c08d654edb46adb9cb20c913fb1888 authored by Dominique Makowski on 29 May 2019, 14:10 UTC
version 0.2.0
Tip revision: 9985109
p_map.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_map.R
\name{p_map}
\alias{p_map}
\alias{p_map.numeric}
\alias{p_map.stanreg}
\alias{p_map.brmsfit}
\alias{p_map.BFBayesFactor}
\title{Bayesian p-value based on the density at the Maximum A Priori (MAP)}
\usage{
p_map(x, ...)

\method{p_map}{numeric}(x, precision = 2^10, ...)

\method{p_map}{stanreg}(x, precision = 2^10, effects = c("fixed",
  "random", "all"), parameters = NULL, ...)

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

\method{p_map}{BFBayesFactor}(x, precision = 2^10, ...)
}
\arguments{
\item{x}{Vector representing a posterior distribution. Can also be a
\code{stanreg} or \code{brmsfit} model.}

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

\item{precision}{Number of points for density estimation. See the \code{n}-parameter in \link[=density]{density}.}

\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 a Bayesian equivalent of the p-value, related to the odds that a parameter (described by its posterior distribution) has against the null hypothesis (\emph{h0}) using Mills' (2014, 2017) \emph{Objective Bayesian Hypothesis Testing} framework. It is mathematically based on the density at the Maximum A Priori (MAP) and corresponds to the density value at 0 divided by the density of the MAP estimate.
}
\examples{
library(bayestestR)

p_map(rnorm(1000, 0, 1))
p_map(rnorm(1000, 10, 1))

library(rstanarm)
model <- stan_glm(mpg ~ wt + gear, data = mtcars, chains = 2, iter = 200)
p_map(model)

\dontrun{
library(brms)
model <- brms::brm(mpg ~ wt + cyl, data = mtcars)
p_map(model)

library(BayesFactor)
bf <- ttestBF(x = rnorm(100, 1, 1))
p_map(bf)
}

}
\references{
\href{https://www.youtube.com/watch?v=Ip8Ci5KUVRc}{Mill's talk}
}
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