rNeymanScott
Simulate Neyman-Scott Process
Generate a random point pattern, a realisation of the Neyman-Scott cluster process.
Usage
rNeymanScott(kappa, rmax, rcluster, win = owin(c(0,1),c(0,1)), ..., lmax=NULL)
Arguments
- kappa
- 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 toas.owin
. - ...
- Arguments passed to
rcluster
- lmax
- Optional. Upper bound on the values of
kappa
whenkappa
is a function or pixel image.
Details
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 kappa
. Here kappa
may be a single
positive number, a function kappa(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
.
These clusters are combined together to yield a single point pattern
which is then returned as the result of rNeymanScott
.
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.
If the return value of rcluster
is a point pattern (object of
class "ppp"
) then it may have marks. The result of
rNeymanScott
will then be a marked point pattern.
If required, the intermediate stages of the simulation (the parents
and the individual clusters) can also be extracted from
the return value of rNeymanScott
through the attributes "parents"
and "parentid"
.
The attribute "parents"
is the point pattern of parent points.
The attribute "parentid"
is an integer vector specifying
the parent for each of the points in the simulated pattern.
Value
- The simulated point pattern (an object of class
"ppp"
). Additionally, some intermediate results of the simulation are returned as attributes of this point pattern: see Details.
See Also
Examples
# each cluster consist of 10 points in a disc of radius 0.2
nclust <- function(x0, y0, radius, n) {
return(runifdisc(n, radius, centre=c(x0, y0)))
}
plot(rNeymanScott(10, 0.2, nclust, radius=0.2, n=5))
# multitype Neyman-Scott process (each cluster is a multitype process)
nclust2 <- function(x0, y0, radius, n, types=c("a", "b")) {
X <- runifdisc(n, radius, centre=c(x0, y0))
M <- sample(types, n, replace=TRUE)
marks(X) <- M
return(X)
}
plot(rNeymanScott(15,0.1,nclust2, radius=0.1, n=5))