vargamma.estpcf(X, startpar=c(kappa=1,scale=1), nu = -1/4, lambda=NULL, q = 1/4, p = 2, rmin = NULL, rmax = NULL, ..., pcfargs = list())-1/2.
optim
to control the optimisation algorithm. See Details.
pcf.ppp
to control the smoothing in the estimation of the
pair correlation function.
"minconfit". There are methods for printing
and plotting this object. It contains the following main components: The argument X can be either
The algorithm fits the Neyman-Scott Cluster point process
with Variance Gamma kernel to X,
by finding the parameters of the model
which give the closest match between the
theoretical pair correlation function of the model
and the observed pair correlation function.
For a more detailed explanation of the Method of Minimum Contrast,
see mincontrast.
The Neyman-Scott cluster point process with Variance Gamma
kernel is described in Jalilian et al (2013).
It is a cluster process formed by taking a
pattern of parent points, generated according to a Poisson process
with intensity $kappa$, and around each parent point,
generating a random number of offspring points, such that the
number of offspring of each parent is a Poisson random variable with mean
$mu$, and the locations of the offspring points of one parent
have a common distribution described in Jalilian et al (2013).
The shape of the kernel is determined by the dimensionless
index nu. This is the parameter
$nu' = alpha/2 - 1$ appearing in
equation (12) on page 126 of Jalilian et al (2013).
In previous versions of spatstat instead of specifying nu
(called nu.ker at that time) the user could specify
nu.pcf which is the parameter $nu = alpha-1$
appearing in equation (13), page 127 of Jalilian et al (2013).
These are related by nu.pcf = 2 * nu.ker + 1
and nu.ker = (nu.pcf - 1)/2. This syntax is still supported but
not recommended for consistency across the package. In that case
exactly one of nu.ker or nu.pcf must be specified.
If the argument lambda is provided, then this is used
as the value of the point process intensity $lambda$.
Otherwise, if X is a
point pattern, then $lambda$
will be estimated from X.
If X is a summary statistic and lambda is missing,
then the intensity $lambda$ cannot be estimated, and
the parameter $mu$ will be returned as NA.
The remaining arguments rmin,rmax,q,p control the
method of minimum contrast; see mincontrast.
The corresponding model can be simulated using rVarGamma.
The parameter eta appearing in startpar is equivalent to the
scale parameter omega used in rVarGamma.
Homogeneous or inhomogeneous Neyman-Scott/VarGamma models can also be
fitted using the function kppm and the fitted models
can be simulated using simulate.kppm.
The optimisation algorithm can be controlled through the
additional arguments "..." which are passed to the
optimisation function optim. For example,
to constrain the parameter values to a certain range,
use the argument method="L-BFGS-B" to select an optimisation
algorithm that respects box constraints, and use the arguments
lower and upper to specify (vectors of) minimum and
maximum values for each parameter.
Waagepetersen, R. (2007) An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252--258.
kppm,
vargamma.estK,
lgcp.estpcf,
thomas.estpcf,
cauchy.estpcf,
mincontrast,
pcf,
pcfmodel. rVarGamma to simulate the model.
u <- vargamma.estpcf(redwood)
u
plot(u, legendpos="topright")
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