Hi Edzer, I would say the spatial structure is regarded not significant when c0/c0+c1 is very much greater than 75%. In my case I used even distance intervals and calculated c0/c0+c1 for log(OLSENP) greater than 85%. I knew this index sometimes is very fragile, very much depending on how we fit the model.
However when I zoomed in by using variable distance intervals (boundaries=c(100,200,300,400,600,900,1000,1500,2000))and maxdist=2000 meters, I found a pretty good model-fitted experimental variogram. But the local OK interpolation using such a fitted model did not make sense when compared the predictions to the observations as in most areas values of OLSENP were severely underestimated. You may have seen my code with which I have tried the nested models, but unfortunately no luck either. I maybe think the parameters for local ordinary kriging are not optimized, but I have tried lots of sets of nmin, nmax and maxdist and did see the hopeful end. The journal editor insists in OK being better than IDW. I need to collect my evidence to defend my IDW choice. That is my intention raised such a question in our forum here. Cheers Yong -----Original Message----- From: Edzer Pebesma [mailto:edzer.pebe...@uni-muenster.de] Sent: Monday, 9 February 2009 9:41 AM To: Yong Li Cc: r-sig-geo@stat.math.ethz.ch Subject: Re: [R-sig-Geo] Interpolcation option: IDW or OK? Yong Li wrote: > Hi ALL, > > I have been with the attached dataset and R code to use OK to > interpolate soil OLSEN P spatial distribution for a couple of weeks. So > far I have not found a satisfactory solution using OK or local OK or > block OK, compared to IDW method. However theoretically OK is always > better than IDW as also a journal editor advised me in my submitted > manuscript. > "always better" is quite a strong statement. It does have a minimum variance property, but only under a number of assumptions that need to hold. IDW has not a naturally quantified variance, but has e.g. the nice property that the interpolated values do stay within the data range, which is not true for OK. > Normally if we do not find a significant spatial structure for a soil > variable, we may choose IDW or other methods. How is your guys' opinion > on this or may you help me to find a better solution using OK with my > dataset? > Why would IDW be useful when no significant spatial structure is present? Why not use the global mean as predictor? Other questions are whether variables exist out there that have no spatial structure, and also what significance means in your comment. Should we conclude that spatial correlation is zero when it is not significant? I would say no. > I appreciate any help. > And I'm looking for more opinions--anything! > Regards > > Yong Li > ------------------------------------------------------------------------ > > _______________________________________________ > R-sig-Geo mailing list > R-sig-Geo@stat.math.ethz.ch > https://stat.ethz.ch/mailman/listinfo/r-sig-geo -- Edzer Pebesma Institute for Geoinformatics (ifgi), University of Münster Weseler Straße 253, 48151 Münster, Germany. Phone: +49 251 8333081, Fax: +49 251 8339763 http://ifgi.uni-muenster.de/ http://www.springer.com/978-0-387-78170-9 e.pebe...@wwu.de _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
