How to interpolate point value using kriging method with R and GRASS 6
Of all the methods we tried this is the most easy and (I suppose) exact too:
You have to have in your library the packages "gstat" and "spgrass6", you can download this last one directly from R using the command "install.packages". In GRASS we have a vector file named "giaciture_cat_clean3" and we want to do a prediction on this data... these are the commmands:
enter in R from GRASS and digit:
> library(spgrass6) #get vector points as SpatialPointsDataFrame: > giaciture <- readVECT6("giaciture_cat_clean3", ignore.stderr=TRUE) #shows the class of "giaciture" (SpatialPointsDataFrame): > class(giaciture) #get region from GRASS to R: > G <- gmeta6()
now if you want you can continue to work in R from GRASS or not...
#create a grid from the region settings of GRASS: > grd <- gmeta2grd(ignore.stderr=TRUE) #create a SpatialGridDataFrame: > mask_SG <- SpatialGridDataFrame(grd,data=list(k=rep(1,G$cols*G$rows)),\ proj4string=CRS(G$proj4)) > class(mask_SG) > library(gstat) #create variogram, and "IMMERSIONE" here is the our variable, the variable on #which we have to do the prediction, ~ 1 select the type of kriging, this #is the ordinary one: > cvgm <- variogram(IMMERSIONE~1,locations=giaciture,width=400,cutoff=4000) #choose the model to fit variogram (here is exponential) and give the #estimated parameters of the variogram (partial sill, range and nugget): > efitted <- fit.variogram(cvgm,vgm(psill=5000,model="Exp",range=1500,nugget=8000)) # make the kriging prediction: >OK_pred <- krige(IMMERSIONE~ 1,locations=giaciture,newdata=mask_SG,model=efitted) >names(OK_pred) #show the name of variable kriged #write a raster file and save it in GRASS, now you can open it from there: >writeRast6sp(OK_pred,"OK_pred",zcol="var1.pred",NODATA=-9999)
that's all!
special thanks to Roger Bivand, even ready to lend a hand!