clusterfit(X, clusters, lambda = NULL, startpar = NULL, q = 1/4, p = 2, rmin = NULL, rmax = NULL, ..., statistic = NULL, statargs = NULL, algorithm="Nelder-Mead")
"Thomas"
, "MatClust"
,
"Cauchy"
, "VarGamma"
and "LGCP"
.
"im"
) giving the
intensity values at all locations, a fitted point process model
(object of class "ppm"
or "kppm"
)
or a function(x,y)
which
can be evaluated to give the intensity value at any location.
X
is a point pattern sensible defaults
are used. Otherwise rather arbitrary values are used.
mincontrast.
"K"
or "pcf"
.
statistic
. See Details.
"minconfit"
. There are methods for printing
and plotting this object. See mincontrast
.
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:
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 $\mu$.
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.
kppm
fit <- clusterfit(redwood, "Thomas")
fit
if(interactive()){
plot(fit)
}
Run the code above in your browser using DataLab