Comparing abundances at fixed locations in space - Syrjala test
On 2008-February-11 , at 10:19 , Barry Rowlingson wrote:
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.
Thanks for clarifying these terms. Indeed I am _not_ after spatial point pattern techniques. I changed the subject accordingly.
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...
Thank you for the pointer. The vignette of geoRglm seems promising, though much is about prediction from a given model while I am most interested in which terms are in the model, i.e. which variables have a notable influence on the repartition of the organisms. My scenario seems simpler than those presented however, since the data are standardized by the sampling effort, meaning that the same Poisson law applies to all points. A continuous variable than would represent the spatiality in this dataset could simply be the distance from the lower-left corner of the sampling grid for example, or the distance from the island around which the sampling grid is designed (such a distance would have a biological meaning since we expect the abundances to be inversely proportional to it). Is that something that could fit your definition of a "smoothly-varying spatial error term" or am I completely mistaken? Your answer and the vignette of geoRglm highlight how little I know about all this (I am just a young biologist after all) and how much reading I need to do. The page of geoRglm has a nice list of publications: http://www.daimi.au.dk/~olefc/geoRglm/Intro/books.html Could you (or someone else) direct me towards the best introductory text(s) on this matter please? Thank you very much for your help. Jean-Olivier Irisson --- UMR 5244 CNRS-EPHE-UPVD, 52 av Paul Alduy, 66860 Perpignan Cedex, France +336 21 05 19 90 http://jo.irisson.free.fr/work/