| splitmodel {MarkedPointProcess} | R Documentation |
splitmodel splits a model given in form of a list
(the third variant of model definition for random fields, see
CovarianceFct) into a random field
part and a marked
point process part
splitmodel(model)
model |
The definition of a model is of the form
model = list(l.1, OP.1, l.2, OP.2, ..., l.n).
The lists l.i
are all either of the form l.i = list(model=,var=,kappas=,scale=)
or of the form l.i = list(model=,var=,kappas=,aniso=) in case
of random field parts, or of the form l.i =
list(model=,param=) in case of marked point process parts.
l.i$model is a string; var gives the variance;
scale is a scalar whereas aniso is a d x d matrix, which is multiplied from the right to the points, and
at the transformed points the values of the (isotropic) random field
(with scale 1) are
calculated. The dimension d of matrix must match the
number of rows of x. param is vector of real values
whose length depends on the specified model. The
models for the random field part
can be combined by OP.i="+" or OP.i="*", those for the
marked point process parts only by OP.i="+".
|
list(RF=RF, mpp=mpp) where RF is a usual model
definition for a random field. Further,
mpp=list(mpp.1,...,mpp.n),
where mpp.i=list(model=model,param=param,mnr=) and mnr
is the internal C code for model.
Martin Schlather, schlath@hsu-hh.de http://www.unibw-hamburg.de/WWEB/math/schlath/schlather.html
str(splitmodel(list(list(model="exp", var=5, scale=3))))
str(splitmodel(list(list(model="nearest neighbour", param=4))))
str(splitmodel(list(list(model="exp", var=5, scale=3),
"+",
list(model="nearest neighbour", param=4)
)))
str(splitmodel(list(list(model="exp", var=5, scale=3),
"*",
list(model="spherical", var=1, scale=2),
"+",
list(model="nearest neighbour", param=4),
"+",
list(model="random coin",
param=c(fct=1, scale=7, height=8))
)))