# Negative mse values

3 messages
Open this post in threaded view
|
Report Content as Inappropriate

## Negative mse values

 Hi  dear friends. I was trying to smooth a spatial point pattern with kernel function but I am a bit confused . I need some  explanation about negative MSE values obtained ( used for selecting optimum bandwidth)  by using mse2d function of "splancs " package. As the definition of mse function  for one dimension I know it might not take negative values. However I have no idea for the two dimensional definition of mse for a spatial point pattern. Could the mse values be negative in this case? I found an example taking minimum negative value of mse fort he optimum bandwidth which is the lowest negative value Thanks in advance. Res. Asst. Cenk Icoz Statistics Department ,Anadolu University, Turkey         [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
Open this post in threaded view
|
Report Content as Inappropriate

## Re: Negative mse values

 On 27/03/17 21:28, Cenk İÇÖZ via R-sig-Geo wrote: > Hi  dear friends. > > I was trying to smooth a spatial point pattern with kernel function > but I am a bit confused . > I need some explanation about negative MSE values obtained ( used > forselecting optimum bandwidth) by using mse2d function of "splancs " package. > As the definition of mse function for one dimension I know it might > not take negative values. > However I have no idea for the two dimensional definition of mse for > aspatial point pattern. Could the mse values be negative in this case? I > found an example taking minimum negative value of mse fort he optimum > bandwidth which is the lowest negative value The short answer is that the value returned by mse2d() is not actually the MSE but rather MSE minus a data dependent constant.  So this can "legitimately" be negative. The off-setting constant "does no harm" since interest lies in determining the optimum bandwidth, so it is the relative sizes of the values produced that are of interest. It has been suggested to me in the past (by Barry Rowlingson, one of the original authors of splancs) that the function bw.diggle() from the spatstat package may be more reliable than mse2d().  The former function uses a somewhat different calculation procedure, whence the results of the two functions are not exactly comparable. Note that bw.diggle() is expressed in terms of "sigma" whereas mse2d() is expressed in terms of "h" where sigma = h/2. So if mse2d() gives an "optimal" value of 3, one would *roughly* expect bw.diggle() to give an optimal value of 1.5. Note also that estimating an "optimal" bandwidth is a pretty inexact endeavour under the best of circumstances.  The smooth surface to be fitted is an ill-determined creature and the bandwidth that gives the best fit is even more ill-determined. cheers, Rolf Turner -- Technical Editor ANZJS Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276 _______________________________________________ R-sig-Geo mailing list [hidden email] https://stat.ethz.ch/mailman/listinfo/r-sig-geo