KrigingNeighbourhood: Create a parameter set of local for neighbourhood specification.

Description

Create a parameter set describing a kriging neighbourhood (local or global) for cokriging and cokriging based simulation. This heavily relies on the definitions of gstat::gstat(). All parameters are optional, as their default amounts to a global neihghbourhood.

Usage

KrigingNeighbourhood(
  nmax = Inf,
  nmin = 0,
  omax = 0,
  maxdist = Inf,
  force = FALSE,
  anisotropy = NULL,
  ...
)

Arguments

nmax

maximum number of data points per cokriging system

nmin

minimum number of data points per cokriging system

omax

maximum number of data points per cokriging system per quadrant/octant

maxdist

maximum radius of the search neighborhood

force

logical; if less than nmin points are found inside maxdist radius, keep searching.

anisotropy

currently ignored; in the future, argument to specify anisotropic search areas.

...

further arguments, currently ignored

Value

an S3-list of class "gmKrigingNeighbourhood" containing the six elements given as arguments to the function. This is just a compact way to provide further functions such as predict.gmSpatialModel() with appropriate triggers for choosing a prediction method or another, in this case for triggering cokriging (if alone) or eventually sequential simulation (see SequentialSimulation()).

Examples

Run this code

# NOT RUN {
data("jura", package="gstat")
X = jura.pred[,1:2]
summary(X)
Zc = jura.pred[,7:10]
ng_global = KrigingNeighbourhood()
ng_local = KrigingNeighbourhood(maxdist=1, nmin=4, 
                                omax=5, force=TRUE)
ng_local
ng_global
make.gmCompositionalGaussianSpatialModel(data = Zc, coords = X,
                                         V = "alr", ng = ng_local)
# }

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