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, martin.schlather@math.uni-goettingen.de http://www.stochastik.math.uni-goettingen.de/institute
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)) )))