% 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, thus, 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{
if (require("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)
}
}
\references{
\itemize{
 \item Bolker B et al. (2017): \href{http://bbolker.github.io/mixedmodels-misc/glmmFAQ.html}{GLMM FAQ.}
 \item Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.
 }
}
