% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/check_overdispersion.R
\name{check_overdispersion}
\alias{check_overdispersion}
\title{Check overdispersion of GL(M)M's}
\usage{
check_overdispersion(x, ...)
}
\arguments{
\item{x}{Fitted model of class \code{merMod}, \code{glmmTMB}, \code{glm},
or \code{glm.nb} (package \pkg{MASS}).}

\item{...}{Currently not used.}
}
\value{
A list with results from the overdispersion test, like chi-squared
statistics, p-value or dispersion ratio.
}
\description{
\code{check_overdispersion()} checks generalized linear (mixed)
models for overdispersion.
}
\details{
Overdispersion occurs when the observed variance is higher than the
variance of a theoretical model. For Poisson models, variance increases
with the mean and, therefore, variance usually (roughly) equals the mean
value. If the variance is much higher, the data are "overdispersed".

\subsection{Interpretation of the Dispersion Ratio}{
If the dispersion ratio is close to one, a Poisson model fits well to the
data. Dispersion ratios larger than one indicate overdispersion, thus a
negative binomial model or similar might fit better to the data. A p-value <
.05 indicates overdispersion.
}

\subsection{Overdispersion in Poisson Models}{
For Poisson models, the overdispersion test is based on the code from
\cite{Gelman and Hill (2007), page 115}.
}

\subsection{Overdispersion in Mixed Models}{
For \code{merMod}- and \code{glmmTMB}-objects, \code{check_overdispersion()}
is based on the code in the
\href{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html}{GLMM FAQ},
section \emph{How can I deal with overdispersion in GLMMs?}. Note that this
function only returns an \emph{approximate} estimate of an overdispersion
parameter, and is probably inaccurate for zero-inflated mixed models (fitted
with \code{glmmTMB}).
}

\subsection{How to fix Overdispersion}{
Overdispersion can be fixed by either modeling the dispersion parameter, or
by choosing a different distributional family (like Quasi-Poisson, or
negative binomial, see \cite{Gelman and Hill (2007), pages 115-116}).
}
}
\examples{
\dontshow{if (getRversion() >= "4.0.0" && require("glmmTMB", quietly = TRUE)) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf}

library(glmmTMB)
data(Salamanders)
m <- glm(count ~ spp + mined, family = poisson, data = Salamanders)
check_overdispersion(m)

m <- glmmTMB(
  count ~ mined + spp + (1 | site),
  family = poisson,
  data = Salamanders
)
check_overdispersion(m)
\dontshow{\}) # examplesIf}
}
\references{
\itemize{
\item Bolker B et al. (2017):
\href{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html}{GLMM FAQ.}
\item Gelman, A., and Hill, J. (2007). Data analysis using regression and
multilevel/hierarchical models. Cambridge; New York: Cambridge University
Press.
}
}
