Generate a realisation of the cell process on a linear network.
Either a linear network (object of class
Intensity of the process (expected number of points per unit length),
Optional. Random number generator for the number of points in each cell.
Additional arguments to
Logical value indicating whether to save information about cell membership.
This function generates simulated realisations of a cell point process on a network, as described in Baddeley et al (2017). This is the analogue on a linear network of the two-dimensional cell point process of Baddeley and Silverman (1988).
L should be a tessellation on a linear network.
L is a linear network, it is converted to a
tessellation by treating each network segment as a tile in the tessellation.
The cell process generates a point process by generating independent point processes inside each tile of the tessellation. Within each tile, given the number of random points in the tile, the points are independent and uniformly distributed within the tile.
By default (when
rnumgen is not given), the number of points in
a tile of length
t is a random variable with
mean and variance equal to
lambda * t, generated by calling
rnumgen is given, it should be a function with
rnumgen(n, mu, ...) where
n is the number of
random integers to be generated,
mu is the mean value of the
... are additional arguments, if needed.
It will be called in the form
rnumgen(1, lambda * t, ...)
to determine the number of random points falling in each tile of
Point pattern on a linear network (object of class
saveid=TRUE, the result has an attribute
which is a factor specifying the cell that contains each point.
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.
Baddeley, A., Nair, G., Rakshit, S. and McSwiggan, G. (2017) ‘Stationary’ point processes are uncommon on linear networks. STAT 6, 68–78.
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