Fits the Matern Cluster point process to a point pattern dataset by the Method of Minimum Contrast.
matclust.estK(X, startpar=c(kappa=1,scale=1), lambda=NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...)Data to which the Matern Cluster model will be fitted. Either a point pattern or a summary statistic. See Details.
Vector of starting values for the parameters of the Matern Cluster process.
Optional. An estimate of the intensity of the point process.
Optional. Exponents for the contrast criterion.
Optional. The interval of \(r\) values for the contrast criterion.
Optional arguments passed to optim
to control the optimisation algorithm. See Details.
An object of class "minconfit". There are methods for printing
and plotting this object. It contains the following main components:
Vector of fitted parameter values.
Function value table (object of class "fv")
containing the observed values of the summary statistic
(observed) and the theoretical values of the summary
statistic computed from the fitted model parameters.
This algorithm fits the Matern Cluster point process model to a point pattern dataset by the Method of Minimum Contrast, using the \(K\) function.
The argument X can be either
An object of class "ppp"
representing a point pattern dataset.
The \(K\) function of the point pattern will be computed
using Kest, and the method of minimum contrast
will be applied to this.
An object of class "fv" containing
the values of a summary statistic, computed for a point pattern
dataset. The summary statistic should be the \(K\) function,
and this object should have been obtained by a call to
Kest or one of its relatives.
The algorithm fits the Matern Cluster point process to X,
by finding the parameters of the Matern Cluster model
which give the closest match between the
theoretical \(K\) function of the Matern Cluster process
and the observed \(K\) function.
For a more detailed explanation of the Method of Minimum Contrast,
see mincontrast.
The Matern Cluster point process is described in Moller and Waagepetersen
(2003, p. 62). 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
are independent and uniformly distributed inside a circle of radius
\(R\) centred on the parent point, where \(R\) is equal to
the parameter scale. The named vector of stating values can use
either R or scale as the name of the second component,
but the latter is recommended for consistency with other cluster models.
The theoretical \(K\)-function of the Matern Cluster process is
$$
K(r) = \pi r^2 + \frac 1 \kappa h(\frac{r}{2R})
$$
where the radius R is the parameter scale and
$$
h(z) = 2 + \frac 1 \pi [ ( 8 z^2 - 4 ) \mbox{arccos}(z)
- 2 \mbox{arcsin}(z)
+ 4 z \sqrt{(1 - z^2)^3}
- 6 z \sqrt{1 - z^2}
]
$$
for \(z <= 1\), and \(h(z) = 1\) for \(z > 1\).
The theoretical intensity
of the Matern Cluster process
is \(\lambda = \kappa \mu\).
In this algorithm, the Method of Minimum Contrast is first used to find optimal values of the parameters \(\kappa\) and \(R\). Then the remaining parameter \(\mu\) is inferred from the estimated intensity \(\lambda\).
If the argument lambda is provided, then this is used
as the value of \(\lambda\). 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 Matern Cluster process can be simulated, using
rMatClust.
Homogeneous or inhomogeneous Matern Cluster models can also be
fitted using the function 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.
Moller, J. and Waagepetersen, R. (2003). Statistical Inference and Simulation for Spatial Point Processes. Chapman and Hall/CRC, Boca Raton.
Waagepetersen, R. (2007) An estimating function approach to inference for inhomogeneous Neyman-Scott processes. Biometrics 63, 252--258.
kppm,
lgcp.estK,
thomas.estK,
mincontrast,
Kest,
rMatClust to simulate the fitted model.
# NOT RUN {
data(redwood)
u <- matclust.estK(redwood, c(kappa=10, scale=0.1))
u
plot(u)
# }
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