Estimate the Mean Square Error for a Kernel Smoothing.
Usage
mse2d(pts,poly,nsmse, range)
Value
A list with two components, $h and $mse. These vectors store
corresponding values of the mean square error at values of the kernel
smoothing parameter, h.
The value of h corresponding to the minimum value of $mse
can be passed to kernel2d as the optimum smoothing parameter.
Arguments
pts
A set of points.
poly
A polygon containng the points.
nsmse
Number of steps of h at which to calculate the mean square error.
range
Maximum value of h for calculating the mean square error.
References
Berman M. & Diggle P.J. (1989) Estimating Weighted Integrals of the
Second-Order Intensity of a Spatial Point Pattern.
J. R. Statist Soc B 51 81--92;
Rowlingson, B. and Diggle, P. 1993 Splancs: spatial point pattern analysis
code in S-Plus. Computers and Geosciences, 19, 627-655;
the original sources can be accessed at:
https://www.maths.lancs.ac.uk/~rowlings/Splancs/. See also Bivand, R. and
Gebhardt, A. 2000 Implementing functions for spatial statistical analysis
using the R language. Journal of Geographical Systems, 2, 307-317.