% Generated by roxygen2: do not edit by hand

% Please edit documentation in R/postestimation.R

\name{bayes_R2.hsstan}

\alias{bayes_R2.hsstan}

\alias{bayes_R2}

\alias{loo_R2.hsstan}

\alias{loo_R2}

\title{Bayesian and LOO-adjusted R-squared}

\usage{

\method{bayes_R2}{hsstan}(object, prob = 0.95, summary = TRUE, ...)

\method{loo_R2}{hsstan}(object, prob = 0.95, summary = TRUE, ...)

}

\arguments{

\item{object}{An object of class \code{hsstan}.}

\item{prob}{Width of the posterior interval (0.95, by default). It is

ignored if \code{summary=FALSE}.}

\item{summary}{Whether a summary of the distribution of the R-squared

should be returned rather than the pointwise values (\code{TRUE} by

default).}

\item{...}{Currently ignored.}

}

\value{

The mean, standard deviation and posterior interval of R-squared if

\code{summary=TRUE}, or a vector of R-squared values with length equal to

the size of the posterior sample if \code{summary=FALSE}.

}

\description{

Compute the Bayesian and the LOO-adjusted R-squared from the posterior

samples. For Bayesian R-squared it uses the modelled residual variance

(rather than the variance of the posterior distribution of the residuals).

The LOO-adjusted R-squared uses Pareto smoothed importance sampling LOO

residuals and Bayesian bootstrap.

}

\examples{

\dontshow{utils::example("hsstan", echo=FALSE)}

\dontshow{oldopts <- options(mc.cores=2)}

# continued from ?hsstan

bayes_R2(hs.biom)

loo_R2(hs.biom)

\dontshow{options(oldopts)}

}

\references{

Andrew Gelman, Ben Goodrich, Jonah Gabry and Aki Vehtari (2019),

R-squared for Bayesian regression models,

\emph{The American Statistician}, 73 (3), 307-309.

\doi{10.1080/00031305.2018.1549100}

Aki Vehtari, Andrew Gelman, Ben Goodrich and Jonah Gabry (2019),

Bayesian R2 and LOO-R2.

\url{https://avehtari.github.io/bayes_R2/bayes_R2.html}

}