Dear All I am comparing kriging and IDWmethods in mapping Forest Site Productivity using10-fold cross validation.
Bothkriging and IDW methods produced negative mean error. Now I want to use relativemean error for comparing these methods other than RMSE and mean absolute error. here is the results of kriging; mean ofresponse variable: 34.76982 meanerror: -0.03613827 meanabsolute error: 1.598008 RMSE:2.053376 how can Icalculate relative mean error? I readsomewhere we can use this function for calculating relative mean errorhighlighted with red color: OK_CV<- krige.cv(Site_Form ~1, ~X+Y, Data, model = model1.out, nfold=10) # meanerror, ideally 0: ME_OK<- mean(OK_CV$observed - OK_CV$ var1.pred) ME_OK ### MeanAbsolutely Error MAE_OK<- mean(abs(OK_CV$residual)) MAE_OK ###Relative Mean Error MEr_OK<- (ME_OK/mean(Data$Site_Form))*100 MEr_OK ### RMSE RMSE_OK<- sqrt(mean(OK_CV$residual^2)) RMSE_OK ###Relative RMSE RMSEr_OK<- (RMSE_OK/mean(Data$Site_Form))*100 RMSEr_OK if I use theabove function for relative mean error, the result will be negative!!! I wouldbe very grateful if anyone can help me to calculate relativemean error in R. Regards SJA [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo