Hi All,
I am going to compare a few spatial interpolation techniques including kriging with an external drift (KED) and ordinary co-kriging (OCK) (such as those defined in: Goovaerts, 1997. Geostatistics for Natural Resources Evaluation.) to interpolate marine sediment data (mud content in this case) using bathymetry as a secondary variable. However, it seems that the ordinary cokriging in gstat as shown in demo(cokriging) is different from the OCK we planned to use. Is it possible to do such OCK in gstat? Any comments and example? Thanks. As to KED, I tried > vgm1 <- variogram(sqrt(mud)~bathy, data.file.dev) > model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1)) > # plot(vgm1, model.1) > coordinates(data.file.pred) = ~LON+LAT > mud.ok <- krige(sqrt(mud)~bathy, data.file.dev, data.file.pred, model = model.1) [using universal kriging] > vgm1 <- variogram(sqrt(mud)~LON+LAT, data.file.dev) > model.1 <- fit.variogram(vgm1,vgm(1,"Sph",5,1)) > # plot(vgm1, model.1) > coordinates(data.file.pred) = ~LON+LAT > mud.ok <- krige(sqrt(mud)~LON+LAT, data.file.dev, data.file.pred, model = model.1) [using universal kriging] Both of them are UK. But the first one seems regression kriging. Is it identical to KED in this case? If not, any comments and examples of KED are appreciated. Cheers, Jin -------------------------------------------- Jin Li, PhD Spatial Modeller/ Computational Statistician Marine & Coastal Environment Geoscience Australia Ph: 61 (02) 6249 9899 Fax: 61 (02) 6249 9956 email: [EMAIL PROTECTED] <mailto:[EMAIL PROTECTED]> -------------------------------------------- [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
