# rNeymanScott

##### Simulate Neyman-Scott Process

Generate a random point pattern using the Neyman-Scott cluster process.

- Keywords
- spatial

##### Usage

`rNeymanScott(lambda, rmax, rcluster, win = owin(c(0,1),c(0,1)), ..., lmax=NULL)`

##### Arguments

- 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.

##### 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 `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.

##### Value

- The simulated point pattern (an object of class
`"ppp"`

).

##### See Also

##### Examples

```
nclust <- function(x0, y0, radius, n) {
return(runifdisc(n, radius, x0, y0))
}
X <- rNeymanScott(10, 0.2, nclust, radius=0.2, n=5)
```

*Documentation reproduced from package spatstat, version 1.6-11, License: GPL version 2 or newer*