pcp: Fit a Poisson cluster process

In splancs: Spatial and Space-Time Point Pattern Analysis

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

Author(s)

Giovanni Petris <[e-mail]>, [e-mail]

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

splancs documentation built on Aug. 21, 2023, 9:08 a.m.