Dear all,
We have got data (response and predictor variables) for each country of the world; I started by fitting standard GLM and tested for spatial correlation using variogram models (geoR) fitted to the residuals of the GLM. Spatial autocorrelation is significant. Therefore, I think about using general estimation equations (geeglm or geese in geepack) allowing for residual spatial correlation . Several questions: 1. Are GEE’s the right choice? 2. How can I specify a user-defined spatial (exponential) correlation structure in geeglm or geese? I did not really get this from the help-sites. I guess I need to set corstr = "userdefined" and zcor = Zcor, with Zcor being in some way the specified correlation matrix – but how? I earlier specified spatial exponential correlation structures in glmmPQL, but it seems that this does not work in the same way for geeglm or geese. Do I need to use the function genZcor? 3. Makes it sense to use continents to specify clusters? Or is the number of clusters (8) too low? I am grate-full for any comments! Thank you in advance, Swantje Swantje Löbel PhD student Department of Plant Ecology Evolutionary Biology Centre (EBC) Uppsala University Villavägen 14 SE-752 36 Uppsala Sweden Tel. +46 18 471 28 70 Fax +46 18 55 34 19 http://www.vaxtbio.uu.se/resfold/lobel.htm [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
