Revision fe07bfa906d7e155439160caee538a3449cd3877 authored by Dominique Makowski on 08 April 2019, 08:42:41 UTC, committed by cran-robot on 08 April 2019, 08:42:41 UTC
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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/p_map.R
\title{Bayesian p-value based on the density at the Maximum A Priori (MAP)}
p_map(posterior, ...)

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

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

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

p_map(posterior = rnorm(1000, 0, 1))
p_map(posterior = rnorm(1000, 10, 1))
model <- rstanarm::stan_glm(mpg ~ wt + cyl, data = mtcars)

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

\href{}{Mill's talk}
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