| simulateMPP {MarkedPointProcess} | R Documentation |
simulateMPP generates realisations of marked point processes
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)
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
|
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="+".
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) )
Martin Schlather, schlath@hsu-hh.de http://www.unibw-hamburg.de/WWEB/math/schlath/schlather.html
get.mpp.names,
rfm.test, simulateMPP,
splitmodel, MarkedPointProcess
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)