Hi, Thanks for the inputs. I talked to my coworker, who has been the one doing the analysis. Perhaps I wasn't making myself clear about the “differences in spatial scales”. Here is what he says:
"The truth is that measuring scales (i.e all area related variable are measured in m2) and spatial definition of initial cartography are homogeneous among extracted variables. But all variables (ie. sum of the total rocky bottom in the surrounding area) are computed for each different integration areas (buffer) (i.e in an area of 40squaremeters around the sample, in an area of 80m2, …). The question is then if we can build a model that includes variables measured at different buffers (for example a model that includes 3 variables: 1.- the amount of rocky bottom in an area of 80m2 ; 2- the amount of sandy bottom in an area of 200m2; and the mean depth calculated in a surrounding area of 50m2) considering that each variable may be expressing different ecological processes. I believe that if there is not an ecological constrain in the interpretation of the variables (and their ecological effect over the specie), including them in a model is correct, unless there is not a mathematical constrain." Also, about the spatial correlation I thought from what I've read so far that I had to build the model and then check if there was spatial correlation in the residuals since they are supposed to be i.i.d. And if it turns out that they are then I have to do something about it like gamm, gee, sar, car, etc. Cheers, Lucia -- View this message in context: http://r.789695.n4.nabble.com/offset-in-gam-and-spatial-scale-of-variables-tp2222483p2224528.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
