R: Mean Square Error for a Kernel Smoothing

mse2d {splancs} | R Documentation

## Mean Square Error for a Kernel Smoothing

### Description

Estimate the Mean Square Error for a Kernel Smoothing.

### Usage

    mse2d(pts,poly,nsmse, range)

### 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.

### 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.

### 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: http://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.

### See Also

`kernel2d`

### Examples

    data(bodmin)
    Mse2d <- mse2d(as.points(bodmin), bodmin$poly, nsmse=50, range=8)
    plot(Mse2d$h[5:50],Mse2d$mse[5:50], type="l")

[Package splancs version 2.01-40 Index]