rcellnumber.Rd
\name{rcellnumber}
\alias{rcellnumber}
\title{
Generate Random Numbers of Points for Cell Process
}
\description{
Generates random integers for the Baddeley-Silverman counterexample.
}
\usage{
rcellnumber(n, N = 10, mu=1)
}
\arguments{
\item{n}{
Number of random integers to be generated.
}
\item{N}{
Distributional parameter: the largest possible value
(when \code{mu <= 1}).
An integer greater than 1.
}
\item{mu}{
Mean of the distribution (equals the variance).
Any positive real number.
}
}
\details{
If \code{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 \eqn{0}, \eqn{1} and \eqn{N}
with probabilities \eqn{1/N}, \eqn{(N-2)/(N-1)} and \eqn{1/(N(N-1))}
respectively.
See Baddeley and Silverman (1984).
If \code{mu} is another positive number, the random integers will
have mean and variance equal to \code{mu}. They are obtained by
generating the
one-dimensional counterpart of the cell process and counting the
number of points in the interval from \code{0} to \code{mu}. The
maximum possible value of each random integer is \code{N * ceiling(mu)}.
}
\value{
An integer vector of length \code{n}.
}
\references{
Baddeley, A.J. and Silverman, B.W. (1984)
A cautionary example on the use of second-order methods for analyzing
point patterns. \emph{Biometrics} \bold{40}, 1089-1094.
}
\author{
\spatstatAuthors.
}
\seealso{
\code{\link{rcell}}
}
\examples{
rcellnumber(30, 3)
}
\keyword{datagen}