I have been using the spatstat package to determine if a point pattern is clustered, random or regular by comparing it to relative frequency distribution of nearest-neighbor distances generated under complete spatial randomness (CSR). Code is as follows (Y is a ppp object of x,y coordinates):
Y<-rpoint(60, 2, fmax=NULL, win=unit.square(),giveup=1000, verbose=FALSE,
nsim=1, drop=TRUE)
envelope(Y, Gest, nsim = 999, nrank = 25, global=FALSE, fix.n=TRUE)
However, I just realized that the random points have to be distributed at the intersections of a polygon network within the window; they cannot be compared against complete spatial randomness, since they are restricted to crossings of segments of a polygon network. Is there any way to simulate spatial randomness on a network pattern?
I managed to create random line segments and add points at each crossings:
l<-rpoisline(4, win=owin())
i<-selfcrossing.psp(l)
par(mfrow=c(1,2))
plot(l)
plot(i)
However, I am not sure how to integrate this with the envelope() function.
I also need the number of crossing to be constant. So is there anyway to specify the number of crossings in the rpoisline() function?