I have a question very specific for the function xvalid (package geoR) in R which is used in spatial statistics only, so I hope it's not too specific for someone to be able to answer. In any case, suggestions for alternative functions/packages are welcome too.
I would like to compute a variogram, fit it, and then perform cross-validation. Function xvalid seems to work pretty nice to do the cross-validation. It works when I set reestimate=TRUE (so it reestimates the variogram for every point removed from the dataset in cross-validation) and it also works when using a trend. However, it does not seem to work when combining these two...
Here is an example using the classical Meuse dataset:
library(geoR)
library(sp)
data(meuse) # import data
coordinates(meuse) = ~x+y # make spatialpointsdataframe
meuse@proj4string <- CRS("+init=epsg:28992") # add projection
meuse_geo <- as.geodata(meuse) # create object of class geodata for geoR compatibility
meuse_geo$data <- meuse@data # attach all data (incl. covariates) to meuse_geo
meuse_vario <- variog(geodata=meuse_geo, data=meuse_geo$data$lead, trend= ~meuse_geo$data$elev) # variogram
meuse_vfit <- variofit(meuse_vario, nugget=0.1, fix.nugget=T) # fit
# cross-validation works fine:
xvalid(geodata=meuse_geo, data=meuse_geo$data$lead, model=meuse_vfit, variog.obj = meuse_vario, reestimate=F)
# cross-validation does not work when reestimate = T:
xvalid(geodata=meuse_geo, data=meuse_geo$data$lead, model=meuse_vfit, variog.obj = meuse_vario, reestimate=T)
The error I get is:
Error in variog(coords = cv.coords, data = cv.data, uvec = variog.obj$uvec, : coords and trend have incompatible sizes
It seems to remove the point from the dataset during cross-validation, but it doesn't seem to remove the point from the covariates/trend data. Any ideas on solving this or using a different package?
Thanks a lot in advance!
