jiho wrote: > Thank you very much for this reference. However the problem it is > dealing with is not really similar to the one I target. In this paper > the authors assess the differences in positions of neurones in a 2D > plane between three groups of patients, with replicates in each group. > So the data of interest are the coordinates. > In my case, the positions of sampling stations are fixed (and on a > grid if that helps [1]) and I want to assess the differences in > abundances of two groups at these positions. So the data of interest > are the abundances (normalized to remove the effect of total > population sizes), and more specifically, the way the abundances are > distributed on these points. Maybe the subject of this email is not > correctly stated then. I am not a native english speaker and when it > comes to technical terms, it is even worse. >
"Spatial Point Pattern Analysis" only refers to cases where the locations of the points are 'interesting', which usually means they are generated by a stochastic process - like tree locations in a natural forest rather than rows of trees in a plantation. Analysis of data that comes from spatial locations that are 'uninteresting' are another branch of statistics altogether. It will probably end up being generalised linear modelling with spatially-correlated errors, and how you deal with the correlations is the interesting part. See if you can write down a model for your data and include a smoothly-varying spatial error term.... Then maybe we can find some R code to solve it. I don't think we'll find it in Spatstat, which I think is still exclusively spatial point pattern analysis. Have a look at geoRglm maybe... Barry _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
