splitmodel {MarkedPointProcess}R Documentation

Split betweem marked point processes and random fields

Description

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

Usage

splitmodel(model)

Arguments

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="+".

Value

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.

Author(s)

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

See Also

simulateMPP

Examples

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))
                    )))

[Package MarkedPointProcess version 0.2.9 Index]