Dear list, I have a set of ~130 ground stations measurements and several grid covariates with which I am spatialising the ground point values. I am using external drift kriging: I noticed discontinuities in the predicted map when using a _localized_ prediction, hence restricting the neighbors count with the *nmax* parameter in the gstat function.
I am a bit surprised since I guessed that localising the prediction wouldn't change that much the final map: the more distant the ground point, the smaller the kriging weight associated with it. I attach a screenshot to visually understand the situation: on the left side is the KED prediction, on the right side the localized (nearest 50 points) KED prediction. I don't know if these evident discontinuities are a normal consequence of localized models, or if I am doing something wrong. This is an example of what I did, using the meuse dataset: ___________________________ library(gstat) library(sp) data(meuse) data(meuse.grid) coordinates(meuse) <- ~x+y coordinates(meuse.grid) <- ~x+y cvgm <- variogram(zinc~dist, data=meuse, width=100, cutoff=1000) fitted <- fit.variogram(cvgm, vgm(psill=1, model="Exp", range=100, nugget=1)) KED_fit <- gstat(id="KED_fit", formula=zinc~dist, data=meuse, model=fitted) KED10_fit <- gstat(id="KED10_fit", formula=zinc~dist, data=meuse, model=fitted, nmax=10) KED_pred <- predict.gstat(KED_fit, meuse.grid) KED10_pred <- predict.gstat(KED10_fit, meuse.grid) KED_pred$KED10_fit.pred <- KED10_pred$KED10_fit.pred gridded(KED_pred) <- TRUE spplot(KED_pred, zcol=c("KED_fit.pred", "KED10_fit.pred"), col.regions=colorRampPalette(brewer.pal(7, "BuPu")[-(1:1)])(21)) ___________________________ Thanks for any clarification in advance.
<<attachment: Screenshot from 2012-06-09 19_44_23.jpg>>
_______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo