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\name{anova.lppm}
\alias{anova.lppm}
\title{ANOVA for Fitted Point Process Models on Linear Network}
\description{
Performs analysis of deviance for two or more fitted point process
models on a linear network.
}
\usage{
\method{anova}{lppm}(object, \dots, test=NULL)
}
\arguments{
\item{object}{A fitted point process model on a linear network
(object of class \code{"lppm"}).
}
\item{\dots}{
One or more fitted point process models on the same
linear network.
}
\item{test}{
Character string, partially matching one of
\code{"Chisq"}, \code{"F"} or \code{"Cp"}.
}
}
\value{
An object of class \code{"anova"}, or \code{NULL}.
}
\details{
This is a method for \code{\link{anova}} for
fitted point process models on a linear network
(objects of class \code{"lppm"},
usually generated by the model-fitting function \code{\link{lppm}}).
If the fitted models are all Poisson point processes,
then this function performs an Analysis of Deviance of
the fitted models. The output shows the deviance differences
(i.e. 2 times log likelihood ratio),
the difference in degrees of freedom, and (if \code{test="Chi"})
the two-sided p-values for the chi-squared tests. Their interpretation
is very similar to that in \code{\link{anova.glm}}.
If some of the fitted models are \emph{not} Poisson point processes,
then the deviance difference is replaced by the
adjusted composite likelihood ratio (Pace et al, 2011;
Baddeley et al, 2014).
}
\section{Errors and warnings}{
\describe{
\item{models not nested:}{
There may be an error message that the models are not \dQuote{nested}.
For an Analysis of Deviance the models must be nested, i.e. one model
must be a special case of the other. For example the point process
model with formula \code{~x} is a special case of the model with
formula \code{~x+y}, so these models are nested. However
the two point process
models with formulae \code{~x} and \code{~y} are not nested.
If you get this error message and you believe that the models should
be nested, the problem may be the inability of \R to recognise that
the two formulae are nested. Try modifying the formulae to make
their relationship more obvious.
}
\item{different sizes of dataset:}{
There may be an error message from \code{anova.glmlist} that
\dQuote{models were not all fitted to the same size of dataset}.
This generally occurs when the point process models
are fitted on different linear networks.
}
}
}
\seealso{
\code{\link{lppm}}
}
\examples{
X <- runiflpp(10, simplenet)
mod0 <- lppm(X ~1)
modx <- lppm(X ~x)
anova(mod0, modx, test="Chi")
}
\author{\adrian
}
\references{
Ang, Q.W. (2010)
\emph{Statistical methodology for events on a network}.
Master's thesis, School of Mathematics and Statistics, University of
Western Australia.
Ang, Q.W., Baddeley, A. and Nair, G. (2012)
Geometrically corrected second-order analysis of
events on a linear network, with applications to
ecology and criminology.
\emph{Scandinavian Journal of Statistics} \bold{39}, 591--617.
Baddeley, A., Turner, R. and Rubak, E. (2015)
Adjusted composite likelihood ratio test for Gibbs point processes.
\emph{Journal of Statistical Computation and Simulation}
\bold{86} (5) 922--941.
DOI: 10.1080/00949655.2015.1044530.
McSwiggan, G., Nair, M.G. and Baddeley, A. (2012)
Fitting Poisson point process models to events
on a linear network. Manuscript in preparation.
Pace, L., Salvan, A. and Sartori, N. (2011)
Adjusting composite likelihood ratio statistics.
\emph{Statistica Sinica} \bold{21}, 129--148.
}
\keyword{spatial}
\keyword{models}
\keyword{methods}
\concept{Model selection}
\concept{Linear network}
|
