# rcellnumber

##### Generate Random Numbers of Points for Cell Process

Generates random integers for the Baddeley-Silverman counterexample.

- Keywords
- datagen

##### Usage

`rcellnumber(n, N = 10, mu=1)`

##### Arguments

- n
Number of random integers to be generated.

- N
Distributional parameter: the largest possible value (when

`mu <= 1`

). An integer greater than 1.- mu
Mean of the distribution (equals the variance). Any positive real number.

##### Details

If `mu = 1`

(the default),
this function generates random integers which have mean and variance
equal to 1, but which do not have a Poisson distribution.
The random integers take the values \(0\), \(1\) and \(N\)
with probabilities \(1/N\), \((N-2)/(N-1)\) and \(1/(N(N-1))\)
respectively.
See Baddeley and Silverman (1984).

If `mu`

is another positive number, the random integers will
have mean and variance equal to `mu`

. They are obtained by
generating the
one-dimensional counterpart of the cell process and counting the
number of points in the interval from `0`

to `mu`

. The
maximum possible value of each random integer is `N * ceiling(mu)`

.

##### Value

An integer vector of length `n`

.

##### References

Baddeley, A.J. and Silverman, B.W. (1984)
A cautionary example on the use of second-order methods for analyzing
point patterns. *Biometrics* **40**, 1089-1094.

##### See Also

##### Examples

```
# NOT RUN {
rcellnumber(30, 3)
# }
```

*Documentation reproduced from package spatstat, version 1.52-1, License: GPL (>= 2)*