splancs (version 2.01-16)

pcp: Fit a Poisson cluster process

Description

The function fits a Poisson cluster process to point data for a given enclosing polygon and fit parameters

Usage

pcp(point.data, poly.data, h0=NULL, expo=0.25, n.int=20)

Arguments

point.data

a points object

poly.data

a polygon enclosing the study region

h0

upper bound of integration in the criterion function

expo

exponent in the criterion function

n.int

number of intervals used to approximate the integral in the criterion function with a sum

Value

• The function returns an object as returned by optim, including:
• par
  The best set of parameters s2 and rho found
• value
  The value of the fit corresponding to `par'
• convergence
  `0' indicates successful convergence

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

optim, pcp.sim, 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