mse2d | R Documentation |
Estimate the Mean Square Error for a Kernel Smoothing.
mse2d(pts,poly,nsmse, range)
pts |
A set of points. |
poly |
A polygon containng the points. |
nsmse |
Number of steps of |
range |
Maximum value of |
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 $mse
can be passed to kernel2d
as the optimum smoothing parameter.
Berman M. & Diggle P.J. (1989) Estimating Weighted Integrals of the Second-Order Intensity of a Spatial Point Pattern. J. R. Statist Soc B 51 81–92; Rowlingson, B. and Diggle, P. 1993 Splancs: spatial point pattern analysis code in S-Plus. Computers and Geosciences, 19, 627-655; the original sources can be accessed at: https://www.maths.lancs.ac.uk/~rowlings/Splancs/. See also Bivand, R. and Gebhardt, A. 2000 Implementing functions for spatial statistical analysis using the R language. Journal of Geographical Systems, 2, 307-317.
kernel2d
data(bodmin)
Mse2d <- mse2d(as.points(bodmin), bodmin$poly, nsmse=50, range=8)
plot(Mse2d$h[5:50],Mse2d$mse[5:50], type="l")
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