Fit a Poisson cluster process

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

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.

Author

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

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
#> $par
#>         s2        rho 
#> 6.16109743 0.01136752 
#> 
#> $value
#> [1] 0.02734823
#> 
#> $counts
#> function gradient 
#>       77       NA 
#> 
#> $convergence
#> [1] 0
#> 
#> $message
#> NULL
#>