R: Generate a Poisson Cluster Process

pcp.sim {splancs} | R Documentation

## Generate a Poisson Cluster Process

### Description

The function generates a Poisson cluster process for a given polygon within a larger bounding region and given process parameters

### Usage

    pcp.sim(rho, m, s2, region.poly, larger.region=NULL, vectorise.loop=TRUE)

### Arguments

`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

### Details

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.

### Value

A point object with the simulated pattern

### Author(s)

Giovanni Petris <GPetris@uark.edu>, Roger.Bivand@nhh.no

### References

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.

### See Also

`pcp`, `Kenv.pcp`, `khat`

### Examples

    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")

[Package splancs version 2.01-40 Index]