Generate a realisation of the Switzer-type point process on a linear network.
rSwitzerlpp(L, lambdacut, rintens = rexp, ...,
cuts=c("points", "lines"))Point pattern on a linear network (object of class "lpp")
with an attribute "breaks" containing the breakpoints (if
cuts="points") or the random lines (if cuts="lines").
Linear network (object of class "linnet").
Intensity of Poisson process of breakpoints.
Optional. Random variable generator used to generate the random intensity in each component.
Additional arguments to rintens.
String (partially matched) specifying the type of random cuts to be generated.
Adrian Baddeley Adrian.Baddeley@curtin.edu.au.
This function generates simulated realisations of the Switzer-type point process on a network, as described in Baddeley et al (2017).
The linear network is first divided into pieces by a random mechanism:
if cuts="points",
a Poisson process of breakpoints with intensity lambdacut
is generated on the network, and these breakpoints separate the
network into connected pieces.
if cuts="lines", a Poisson line process in the plane
with intensity lambdacut is generated; these lines divide
space into tiles; the network is divided into subsets associated
with the tiles. Each subset may not be a connected sub-network.
In each piece of the network, a random intensity is generated
using the random variable generator rintens (the default is
a negative exponential random variable with rate 1). Given the
intensity value, a Poisson process is generated with the specified
intensity.
The intensity of the final process is determined by the mean
of the values generated by rintens. If rintens=rexp (the
default), then the parameter rate specifies the inverse of the
intensity.
Baddeley, A., Nair, G., Rakshit, S. and McSwiggan, G. (2017) ‘Stationary’ point processes are uncommon on linear networks. STAT 6, 68--78.
rcelllpp
plot(rSwitzerlpp(domain(spiders), 0.01, rate=100))
plot(rSwitzerlpp(domain(spiders), 0.0005, rate=100, cuts="l"))
Run the code above in your browser using DataLab