rVarGamma.Rd
\name{rVarGamma}
\alias{rVarGamma}
\title{Simulate Neyman-Scott Point Process with Variance Gamma cluster kernel}
\description{
Generate a random point pattern, a simulated realisation of the
Neyman-Scott process with Variance Gamma (Bessel) cluster kernel.
}
\usage{
rVarGamma(kappa, nu.ker, omega, mu, win = owin(), eps = 0.001)
}
\arguments{
\item{kappa}{
Intensity of the Poisson process of cluster centres.
A single positive number, a function, or a pixel image.
}
\item{nu.ker}{
Shape parameter for the cluster kernel. A number greater than -1.
}
\item{omega}{
Scale parameter for cluster kernel. Determines the size of clusters.
A positive number in the same units as the spatial coordinates.
}
\item{mu}{
Mean number of points per cluster (a single positive number)
or reference intensity for the cluster points (a function or
a pixel image).
}
\item{win}{
Window in which to simulate the pattern.
An object of class \code{"owin"}
or something acceptable to \code{\link{as.owin}}.
}
\item{eps}{
Threshold below which the values of the cluster kernel
will be treated as zero for simulation purposes.
}
}
\value{
The simulated point pattern (an object of class \code{"ppp"}).
Additionally, some intermediate results of the simulation are
returned as attributes of this point pattern.
See \code{\link{rNeymanScott}}.
}
\details{
This algorithm generates a realisation of the Neyman-Scott process
with Variance Gamma (Bessel) cluster kernel, inside the window \code{win}.
The process is constructed by first
generating a Poisson point process of ``parent'' points
with intensity \code{kappa}. Then each parent point is
replaced by a random cluster of points, the number of points in each
cluster being random with a Poisson (\code{mu}) distribution,
and the points being placed independently and uniformly
according to a Variance Gamma kernel.
In this implementation, parent points are not restricted to lie in the
window; the parent process is effectively the uniform
Poisson process on the infinite plane.
This model can be fitted to data by the method of minimum contrast,
using \code{\link{cauchy.estK}}, \code{\link{cauchy.estpcf}}
or \code{\link{kppm}}.
The algorithm can also generate spatially inhomogeneous versions of
the cluster process:
\itemize{
\item The parent points can be spatially inhomogeneous.
If the argument \code{kappa} is a \code{function(x,y)}
or a pixel image (object of class \code{"im"}), then it is taken
as specifying the intensity function of an inhomogeneous Poisson
process that generates the parent points.
\item The offspring points can be inhomogeneous. If the
argument \code{mu} is a \code{function(x,y)}
or a pixel image (object of class \code{"im"}), then it is
interpreted as the reference density for offspring points,
in the sense of Waagepetersen (2006).
}
When the parents are homogeneous (\code{kappa} is a single number)
and the offspring are inhomogeneous (\code{mu} is a
function or pixel image), the model can be fitted to data
using \code{\link{kppm}}, or using \code{\link{cauchy.estK}}
or \code{\link{cauchy.estpcf}}
applied to the inhomogeneous \eqn{K} function.
}
\seealso{
\code{\link{rpoispp}},
\code{\link{rNeymanScott}},
\code{\link{cauchy.estK}},
\code{\link{cauchy.estpcf}},
\code{\link{kppm}}.
}
\examples{
# homogeneous
X <- rVarGamma(30, 2, 0.02, 5)
# inhomogeneous
Z <- as.im(function(x,y){ exp(2 - 3 * x) }, W= owin())
Y <- rVarGamma(30, 2, 0.02, Z)
}
\references{
Jalilian, A., Guan, Y. and Waagepetersen, R. (2011)
Decomposition of variance for spatial Cox processes.
Manuscript submitted for publication.
Waagepetersen, R. (2007)
An estimating function approach to inference for
inhomogeneous Neyman-Scott processes.
\emph{Biometrics} \bold{63}, 252--258.
}
\author{Abdollah Jalilian and Rasmus Waagepetersen.
Adapted for \pkg{spatstat} by Adrian Baddeley
\email{Adrian.Baddeley@csiro.au}
\url{http://www.maths.uwa.edu.au/~adrian/}
}
\keyword{spatial}
\keyword{datagen}