rNeymanScott {spatstat} | R Documentation |
Generate a random point pattern using the Neyman-Scott cluster process.
rNeymanScott(lambda, rmax, rcluster, win = owin(c(0,1),c(0,1)), ..., lmax=NULL)
lambda |
Intensity of the Poisson process of cluster centres. A single positive number, a function, or a pixel image. |
rmax |
Maximum radius of a random cluster. |
rcluster |
A function which generates random clusters. |
win |
Window in which to simulate the pattern.
An object of class "owin"
or something acceptable to as.owin .
|
... |
Arguments passed to rcluster
|
lmax |
Optional. Upper bound on the values of lambda
when lambda is a function or pixel image.
|
This algorithm generates a realisation of the
general Neyman-Scott process, with the cluster mechanism
given by the function rcluster
.
The clusters must have a finite maximum possible radius rmax
.
First, the algorithm
generates a Poisson point process of ``parent'' points
with intensity lambda
. Here lambda
may be a single
positive number, a function lambda(x, y)
, or a pixel image
object of class "im"
(see im.object
).
See rpoispp
for details.
Second, each parent point is
replaced by a random cluster of points, created by calling the
function rcluster
.
The function rcluster
should expect to be called as
rcluster(xp[i],yp[i],...)
for each parent point at a location
(xp[i],yp[i])
. The return value of rcluster
should be a list with elements
x,y
which are vectors of equal length giving the absolute
x and y
coordinates of the points in the cluster.
The simulated point pattern (an object of class "ppp"
).
Adrian Baddeley adrian@maths.uwa.edu.au http://www.maths.uwa.edu.au/~adrian/ and Rolf Turner rolf@math.unb.ca http://www.math.unb.ca/~rolf
nclust <- function(x0, y0, radius, n) { return(runifdisc(n, radius, x0, y0)) } X <- rNeymanScott(10, 0.2, nclust, radius=0.2, n=5)