| pcp.sim {splancs} | R Documentation |
The function generates a Poisson cluster process for a given polygon within a larger bounding region and given process parameters
pcp.sim(rho, m, s2, region.poly, larger.region=NULL, vectorise.loop=TRUE)
rho |
intensity of the parent process |
m |
average number of offsprings per parent |
s2 |
variance of location of offsprings relative to their parent |
region.poly |
a polygon defining the region in which the process is to be generated |
larger.region |
a rectangle containing the region of interest given in the form (xl,xu,yl,yu), defaults to sbox() around region.poly |
vectorise.loop |
if TRUE, use new vectorised code, if FALSE, use loop as before |
The function generates the parents in the larger bounding region, generates their children also in the larger bounding region, and then returns those inside the given polygon.
A point object with the simulated pattern
Giovanni Petris <GPetris@uark.edu>, Roger.Bivand@nhh.no
Diggle, P. J. (1983) Statistical analysis of spatial point patterns, London: Academic Press, pp. 55-57 and 78-81; Bailey, T. C. and Gatrell, A. C. (1995) Interactive spatial data analysis, Harlow: Longman, pp. 106-109.
data(cardiff)
polymap(cardiff$poly)
pointmap(as.points(cardiff), add=TRUE)
title("Locations of homes of 168 juvenile offenders")
pcp.fit <- pcp(as.points(cardiff), cardiff$poly, h0=30, n.int=30)
pcp.fit
m <- npts(as.points(cardiff))/(areapl(cardiff$poly)*pcp.fit$par[2])
sims <- pcp.sim(pcp.fit$par[2], m, pcp.fit$par[1], cardiff$poly)
pointmap(as.points(sims), add=TRUE, col="red")