Fit Cluster or Cox Point Process Model via Minimum Contrast
Fit a homogeneous or inhomogeneous cluster process or Cox point process model to a point pattern by the Method of Minimum Contrast.
clusterfit(X, clusters, lambda = NULL, startpar = NULL, ...,
q = 1/4, p = 2, rmin = NULL, rmax = NULL,
ctrl=list(q=q, p=p, rmin=rmin, rmax=rmax),
statistic = NULL, statargs = NULL, algorithm="Nelder-Mead",
verbose=FALSE, pint=NULL)X |
Data to which the cluster or Cox model will be fitted. Either a point pattern or a summary statistic. See Details. |
clusters |
Character string determining the cluster or Cox model.
Partially matched.
Options are |
lambda |
Optional. An estimate of the intensity of the point process.
Either a single numeric specifying a constant intensity,
a pixel image (object of class |
startpar |
Vector of initial values of the parameters of the
point process mode. If |
q,p |
Optional. Exponents for the contrast criterion.
See |
rmin, rmax |
Optional. The interval of r values for the contrast criterion.
See |
ctrl |
Optional. Named list containing values of the parameters
|
... |
Additional arguments passed to |
statistic |
Optional. Name of the summary statistic to be used
for minimum contrast estimation: either |
statargs |
Optional list of arguments to be used when calculating
the |
algorithm |
Character string determining the mathematical optimisation algorithm
to be used by |
verbose |
Logical value indicating whether to print detailed progress reports for debugging purposes. |
pint |
For internal use by package code only. |
This function fits the clustering parameters of a cluster or Cox point
process model by the Method of Minimum Contrast, that is, by
matching the theoretical K-function of the model to the
empirical K-function of the data, as explained in
mincontrast.
If statistic="pcf" (or X appears to be an
estimated pair correlation function) then instead of using the
K-function, the algorithm will use the pair correlation
function.
If X is a point pattern of class "ppp" an estimate of
the summary statistic specfied by statistic (defaults to
"K") is first computed before minimum contrast estimation is
carried out as described above. In this case the argument
statargs can be used for controlling the summary statistic
estimation. The precise algorithm for computing the summary statistic
depends on whether the intensity specification (lambda) is:
If lambda is NUll or a single numeric the pattern is
considered homogeneous and either Kest or
pcf is invoked. In this case lambda is
not used for anything when estimating the summary statistic.
If lambda is a pixel image (object of class "im"),
a fitted point process model (object of class "ppm" or
"kppm") or a function(x,y) the pattern is considered
inhomogeneous. In this case either Kinhom or
pcfinhom is invoked with lambda as an
argument.
After the clustering parameters of the model have been estimated by
minimum contrast lambda (if non-null) is used to compute the
additional model parameter μ.
The algorithm parameters q,p,rmax,rmin are described in the
help for mincontrast. They may be provided either
as individually-named arguments, or as entries in the list
ctrl. The individually-named arguments q,p,rmax,rmin
override the entries in the list ctrl.
An object of class "minconfit". There are methods for printing
and plotting this object. See mincontrast.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au, Rolf Turner r.turner@auckland.ac.nz and Ege Rubak rubak@math.aau.dk.
Diggle, P.J. and Gratton, R.J. (1984) Monte Carlo methods of inference for implicit statistical models. Journal of the Royal Statistical Society, series B 46, 193 – 212.
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 (2007) 252–258.
fit <- clusterfit(redwood, "Thomas")
fit
if(interactive()){
plot(fit)
}
K <- Kest(redwood)
fit2 <- clusterfit(K, "MatClust")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.