% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/r2_nakagawa.R
\name{r2_nakagawa}
\alias{r2_nakagawa}
\title{Nakagawa's R2 for mixed models}
\usage{
r2_nakagawa(
  model,
  by_group = FALSE,
  tolerance = 1e-05,
  ci = NULL,
  iterations = 100,
  ...
)
}
\arguments{
\item{model}{A mixed effects model.}

\item{by_group}{Logical, if \code{TRUE}, returns the explained variance
at different levels (if there are multiple levels). This is essentially
similar to the variance reduction approach by \emph{Hox (2010), pp. 69-78}.}

\item{tolerance}{Tolerance for singularity check of random effects, to decide
whether to compute random effect variances for the conditional r-squared
or not. Indicates up to which value the convergence result is accepted. When
\code{r2_nakagawa()} returns a warning, stating that random effect variances
can't be computed (and thus, the conditional r-squared is \code{NA}),
decrease the tolerance-level. See also \code{\link[=check_singularity]{check_singularity()}}.}

\item{ci}{Confidence resp. credible interval level. For \code{icc()} and \code{r2()},
confidence intervals are based on bootstrapped samples from the ICC resp.
R2 value. See \code{iterations}.}

\item{iterations}{Number of bootstrap-replicates when computing confidence
intervals for the ICC or R2.}

\item{...}{Arguments passed down to \code{brms::posterior_predict()}.}
}
\value{
A list with the conditional and marginal R2 values.
}
\description{
Compute the \emph{marginal} and \emph{conditional} r-squared value for
mixed effects models with complex random effects structures.
}
\details{
Marginal and conditional r-squared values for mixed models are calculated
based on \emph{Nakagawa et al. (2017)}. For more details on the computation of
the variances, see \code{?insight::get_variance}. The random effect variances are
actually the mean random effect variances, thus the r-squared value is also
appropriate for mixed models with random slopes or nested random effects
(see \emph{Johnson, 2014}).
\itemize{
\item \strong{Conditional R2}: takes both the fixed and random effects into account.
\item \strong{Marginal R2}: considers only the variance of the fixed effects.
}

The contribution of random effects can be deduced by subtracting the
marginal R2 from the conditional R2 or by computing the \code{\link[=icc]{icc()}}.
}
\examples{
if (require("lme4")) {
  model <- lmer(Sepal.Length ~ Petal.Length + (1 | Species), data = iris)
  r2_nakagawa(model)
  r2_nakagawa(model, by_group = TRUE)
}
}
\references{
\itemize{
\item Hox, J. J. (2010). Multilevel analysis: techniques and applications
(2nd ed). New York: Routledge.
\item Johnson, P. C. D. (2014). Extension of Nakagawa and Schielzeth’s R2 GLMM
to random slopes models. Methods in Ecology and Evolution, 5(9), 944–946.
\doi{10.1111/2041-210X.12225}
\item Nakagawa, S., and Schielzeth, H. (2013). A general and simple method for
obtaining R2 from generalized linear mixed-effects models. Methods in
Ecology and Evolution, 4(2), 133–142. \doi{10.1111/j.2041-210x.2012.00261.x}
\item Nakagawa, S., Johnson, P. C. D., and Schielzeth, H. (2017). The
coefficient of determination R2 and intra-class correlation coefficient from
generalized linear mixed-effects models revisited and expanded. Journal of
The Royal Society Interface, 14(134), 20170213. \doi{10.1098/rsif.2017.0213}
}
}
