clusterfit  R Documentation 
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="NelderMead", verbose=FALSE, pspace=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. 
pspace 
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 Kfunction of the model to the
empirical Kfunction 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
Kfunction, 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 nonnull) 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 individuallynamed arguments, or as entries in the list
ctrl
. The individuallynamed 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
.
.
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 NeymanScott processes. Biometrics 63 (2007) 252–258.
kppm
fit < clusterfit(redwood, "Thomas") fit if(interactive()){ plot(fit) } K < Kest(redwood) fit2 < clusterfit(K, "MatClust")
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