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