lambdahat: Kernel Density Estimation of Intensity Function

In spatialkernel: Non-Parametric Estimation of Spatial Segregation in a Multivariate Point Process

Description

Kernel density estimation of the intensity function of a two-dimensional point process.

Usage

lambdahat(pts, h, gpts = NULL, poly = NULL, edge = TRUE)

Arguments

pts	matrix containing the x,y-coordinates of the data point locations.
h	numeric value of the bandwidth used in the kernel smoothing.
gpts	matrix containing the x,y-coordinates of point locations at which to calculate the intensity function, usually a fine grid points within poly, default NULL to estimate intensity function at data locations.
poly	matrix containing the x,y-coordinates of the vertices of the polygon boundary in an anticlockwise order.
edge	logical, with default TRUE to do edge-correction.

Details

Kernel smoothing methods are widely used to estimate the intensity of a spatial point process. One problem which arises is the need to handle edge effects. Several methods of edge-correction have been proposed. The adjustment factor proposed in Berman and Diggle (1989) is a double integration int_AK[(x-x_0)/h]/h^2, where A is a polygonal area, K is the smoothing kernel and h is the bandwidth used for the smoothing. Zheng, P. et\ al (2004) proposed an algorithm for fast calculate of Berman and Diggle's adjustment factor.

When gpts is NULL, lambdahat uses a leave-one-out estimator for the intensity at each of the data points, as been suggested in Baddeley et al (2000). This leave-one-out estimate at each of the data points then can be used in the inhomogeneous K function estimation kinhat when the true intensity function is unknown.

The default kernel is the Gaussian. The kernel function is selected by calling setkernel.

Value

A list with components

lambda

numeric vector of the estimated intensity function.

...

copy of the arguments pts, gpts, h, poly, edge.

Note

In principle, the double adaptive double integration algorithm of Zheng, P. et\ al (2004) can be applied to other kernel functions. Furthermore, the area at the present is enclosed by a simple polygon which could be generalized into a complex area with polygonal holes inside. For instance, a large lake lays within the land area of study.

Other source codes used in the implementation of the double integration algorithm include

• Laurie, D.P. (1982) adaptive cubature code in Fortran;

• Shewchuk, J.R. triangulation code in C;

• Alan Murta's polygon intersection code in C (Project: Generic Polygon Clipper).

References

1. M. Berman and P. Diggle (1989) Estimating weighted integrals of the second-order intensity of a spatial point process, J. R. Stat. Soc. B, 51, 81–92.

2. P. Zheng, P.A. Durr and P.J. Diggle (2004) Edge–correction for Spatial Kernel Smoothing — When Is It Necessary? Proceedings of the GisVet Conference 2004, University of Guelph, Ontario, Canada, June 2004.

3. Baddeley, A. J. and Møller, J. and Waagepetersen R. (2000) Non and semi-parametric estimation of interaction in inhomogeneous point patterns, Statistica Neerlandica, 54, 3, 329–350.

4. Laurie, D.P. (1982). Algorithm 584 CUBTRI: Adaptive Cubature over a Triangle. ACM–Trans. Math. Software, 8, 210–218.

5. Jonathan R. Shewchuk, Triangle, a Two-Dimensional Quality Mesh Generator and Delaunay Triangulator at http://www-2.cs.cmu.edu/~quake/triangle.html.

6. Alan Murta, General Polygon Clipper at http://www.cs.man.ac.uk/~toby/alan/software/#gpc.

7. NAG's Numerical Library. Chapter 11: Quadrature, NAG's Fortran 90 Library. http://www.nag.co.uk/numeric/fn/manual/html/c11_fn03.html

See Also

setkernel, kinhat, density

spatialkernel documentation built on May 2, 2019, 2:26 p.m.