Hi Moshood, I think you can do this way #### Ordinary kriging to create kriging prediction orkrig <- krige(yield ~ 1, canmod.sp, newdata = grid, model=exp.mod,nmax=20)
OrdKrigecv <- Krige.cv(yield ~ 1, canmod.sp, model=exp.mod,nmax=20) RMSE.ok <- sqrt(sum(OrdKrigecv $residual^2)/length(OrdKrigecv $residual)) ## Inverse Distance Weighting (IDW) Interpolation method maxdist=16.5 idw1cv <- krige.cv(yield~1, canmod.sp, nmax=20,idp=1)) RMSE.id <- sqrt(sum(idw1cv $residual^2)/length(idw1cv $residual)) -----Messaggio originale----- Da: r-sig-geo-boun...@r-project.org [mailto:r-sig-geo-boun...@r-project.org] Per conto di Moshood Agba Bakare Inviato: mercoledì 21 maggio 2014 21:16 A: r-sig-geo@r-project.org Oggetto: [R-sig-Geo] gstat cross validation for accuracy ordinary kriging vs IDW Hi all, I have been having a couple of challenge with my analysis. I have irregularly space spatial yield monitor data over four years. Pooling this data together is not feasible because of misalignment. That is, the coordinates of data point varies from one year to the other. I created a common regular interpolation grid for each year with the same grid size of 10 x 10 m. I am able to get interpolated value for each point using ordinary kriging and inverse distance weighting method (IDW). Please how I cross validate this two interpolation methods to know which one give me the best estimate. The problem I notice is that there is no observe value in each interpolation point to assess the prediction accuracy of these methods. Please what do I do? see my script below. I correlated the interpolated values from the two methods. They are highly correlated (r=0.98). How do I know which method gave good prediction? grid <- expand.grid(easting=seq(from = 299678, to = 301299, by=10), northing=seq(from = 5737278, to = 5738129, by=10)) ## convert the grid to SpatialPixel class to indicate gridded spatial data coordinates(grid)<-~easting+northing proj4string(grid)<-CRS("+proj=utm +zone=12 +ellps=WGS84 +datum=WGS84 +units=m +no_defs +towgs84=0,0,0") gridded(grid)<- TRUE #### Ordinary kriging to create kriging prediction orkrig <- krige(yield ~ 1, canmod.sp, newdata = grid, model=exp.mod,nmax=20) ## Inverse Distance Weighting (IDW) Interpolation method maxdist=16.5 idw1 = idw(yield~1, canmod.sp, newdata=grid,nmax=20,idp=1) Thanks while looking forward to reading from you. [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo