clusterfit(X, clusters, lambda = NULL, startpar = NULL,
q = 1/4, p = 2, rmin = NULL, rmax = NULL, ...,
statistic = NULL, statargs = NULL)
"Thomas"
, "MatClust"
,
"Cauchy"
, "VarGamma"
and "LGCP"
."im"
) giving the
intensity values at all locations, a fitted point process model
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:
[object Object],[object Object]
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 $\mu$.
Waagepetersen, R. (2007).
An estimating function approach to inference for
inhomogeneous Neyman-Scott processes.
Biometrics 63 (2007) 252--258.
}
[object Object],[object Object]
kppm