Debarchana Ghosh writes: >I'm doing exploratory point pattern data analysis with 285 points in a >irregular polygon. I'm really exploring and hence trying out all possible >clustering functions on a single data set. In the 'Kmeasure' function, one >of the arguments that the user needs to be supplied is the 'sigma', which is >standard deviation of the isotropic Gaussian smoothing kernel. I don't know >how to compute that or which function to use to compute that value.
The smoothing parameter is a matter of choice. Although there are some rules for automatically selecting a value of the smoothing parameter based on the data, even these automatic rules need human supervision. The best advice is simply to try different values of sigma, ranging from about 5% to 25% of the diameter of the observation window. A very blotchy image probably means that sigma was too small, while a completely flat image means that sigma was too large. The value of sigma reflects an implicit assumption about the `scale' of interesting features in the pattern. If you don't know what features you are looking for, try different scales. Adrian Baddeley _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
