Generate a random point pattern, a simulated realisation of the Gauss-Poisson Process.
rGaussPoisson(kappa, r, p2, win = owin(c(0,1),c(0,1)),
…, nsim=1, drop=TRUE)Intensity of the Poisson process of cluster centres. A single positive number, a function, or a pixel image.
Diameter of each cluster that consists of exactly 2 points.
Probability that a cluster contains exactly 2 points.
Window in which to simulate the pattern.
An object of class "owin"
or something acceptable to as.owin.
Ignored.
Number of simulated realisations to be generated.
Logical. If nsim=1 and drop=TRUE (the default), the
result will be a point pattern, rather than a list
containing a point pattern.
A point pattern (an object of class "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.
This algorithm generates a realisation of the Gauss-Poisson
point process inside the window 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.
# NOT RUN {
pp <- rGaussPoisson(30, 0.07, 0.5)
# }
Run the code above in your browser using DataLab