simulateMPP {MarkedPointProcess}R Documentation

Simulation of marked point processes

Description

simulateMPP generates realisations of marked point processes

Usage

simulateMPP(coordmodel=c("given", "uniform", "Poisson"),
             coord=NULL, npoints=NULL, lambda=NULL,
             window=NULL,  edgecorrection=0.0,
             repetitions=1, coordrepet=1, model=NULL,
             register=0, method=NULL) 

Arguments

coordmodel if coordmodel="given" then coord are expected to be given and not simulated; if coordmodel="uniform" then npoints uniformly distributed points are created; if coordmodel="Poisson" then a conditional Poisson point process is simulated with intensity lambda
coord matrix with 2 columns; coordinates of the points; coord is given only if coordmodel="given"
npoints number of coordinates; npoints must be given if coordmodel="uniform".
lambda intensity of the Poisson process; lambda must be given if coordmodel="Poisson".
window = c(xlim, ylim). window must be given if coordmodel equals "uniform" or "Poisson".
edgecorrection double. If edgecorrection>0 then a Poisson process is simulated with intensity lambda in a frame of thickness edgecorrection around the window. If window is not given, the range of the x values and the range of the y values are taken to define the window. If lambda is not given, the intensity within the window is used instead.
repetitions integer; number of independent drawings of the marks for a given set of coordinates
coordrepet number of independent drawing of the coordinates; this parameter is ignored in case of coordmodel="given"
model list of lists; model for the marks; see Details and get.mpp.names.
register the register where intermediate results in the Gaussian random field simulation are stored, see GaussRF
method the method by which the Gaussian random field is simulated; if is.null(method) then the method is chosen automatically, see GaussRF

Details

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 left to the points, and at the transformed points the values of the random field 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

coordrepet=1
the function returns list(coord, data), data contains the independent drawing of the marks (as columns)

coordrepet>1
the function returns list( list(coord, data), ..., list(coord, data) )

Author(s)

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

See Also

get.mpp.names, rfm.test, simulateMPP, splitmodel, MarkedPointProcess

Examples

 xlim <- c(0, if (interactive()) 200 else 20)
 mpp <- simulateMPP(coordmodel="Poisson", lambda=1,
              window=c(xlim=xlim, ylim=c(20, 70)),
              repet=3, coordrepet=4,
              model=list(list(model="exp", var=1, scale=10),
                         "+",
                         list(model="nearest neighbour", p=1)))
  str(mpp)

[Package MarkedPointProcess version 0.2.9 Index]