On Sun, Dec 30, 2012 at 1:55 PM, Suzana Stutz <[email protected]> wrote: > Dear list members. > > I've been trying to apply GLM for spatial analysis of a marine animal's > distribution (as the dependant variable) related to many environmental > variables (as the independant ones). But some of them are correlated, for > example, the depth increases as the distance to coast increases, and I > must test both. Does it disagree with the assumption of independence of the > data? If yes, how can I fix it in order to keep the two variables in the > model?
Correlation in your explanatory variables is a classical problem of variable selection, and there are methods for that. The bigger problem with doing a GLM with spatial data is that the data points are going to have spatial autocorrelation... Two measurements of 10 fish per square meter at depth 50m taken two metres apart are less likely to be independent than two measurements of 10 fish per square meter at depth 50m taken twenty kilometres apart. In the first case, the second measurement doesn't much extra information - you already knew that this location had 10 fish/m^2 - but in the second case the second measurement is much more useful. There were 10 fish/m^2 even though the locations were far apart! It might be due to the 50m depth! You can do a GLM for starters, but its important to check the independence assumption of the GLM otherwise you could be getting significant effects when none are really there. Barry _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
