Hello, yes, I know it is suppose to do it, but I could not find how, because it gives me an error... for example:
require(gstat); require(lattice) data(meuse) coordinates(meuse) = ~x + y data(meuse.grid) gridded(meuse.grid) = ~x + y meuse.g <- gstat(id = "zn", formula = log(zinc) ~ 1, data = meuse) meuse.g <- gstat(meuse.g, "cu", log(copper) ~ 1, meuse) meuse.g <- gstat(meuse.g, model = vgm(1, "Sph", 900, 1), fill.all = T) x <- variogram(meuse.g, cutoff = 1000) meuse.fit = fit.lmc(x, meuse.g) plot(x, model = meuse.fit) z <- predict(meuse.fit, newdata = meuse.grid) spplot(z) #map gstat.cv(meuse.g) #does not work... gstat.cv(meuse.g, remove.all=T) #either gstat.cv(meuse.g, all.residuals=T) #either gstat.cv(object=meuse.g, formula = log(zinc) ~ 1, data = meuse, model = vgm(1, "Sph", 900, 1), nmax=40, verbose=F) #either :-( # # Intrinsic Correlation found. Good. # [using ordinary cokriging] # "chfactor.c", line 130: singular matrix in function LDLfactor() # Error in predict.gstat(object, newdata = data[sel, ], ...) : # LDLfactor Maybe an example on the help file would be nice (eheheh).. I What am I missing? Thank you very much in advance, Marta _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
