rGaussPoisson(kappa, r, p2, win = owin(c(0,1),c(0,1)), ..., nsim=1, drop=TRUE)"owin"
or something acceptable to as.owin.
nsim=1 and drop=TRUE (the default), the
result will be a point pattern, rather than a list
containing a point pattern.
"ppp")
if nsim=1, or a list of point patterns if nsim > 1.Additionally, some intermediate results of the simulation are
returned as attributes of the point pattern.
See rNeymanScott.
win.
The process is constructed by first
generating a Poisson point process of parent points
with intensity kappa. Then each parent point is either retained
(with probability 1 - p2)
or replaced by a pair of points at a fixed distance r apart
(with probability p2). In the case of clusters of 2 points,
the line joining the two points has uniform random orientation.In this implementation, parent points are not restricted to lie in the window; the parent process is effectively the uniform Poisson process on the infinite plane.
rpoispp,
rThomas,
rMatClust,
rNeymanScott
pp <- rGaussPoisson(30, 0.07, 0.5)
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