--- a
+++ b/man/bayes_R2.hsstan.Rd
@@ -0,0 +1,56 @@
+% 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}
+}