Dear All
I am comparing kriging and IDW methods in mapping Forest Site Productivity using 10-fold cross validation.
Both kriging and IDW methods produced negative mean error. Now I want to use relative mean error for comparing these methods other than RMSE and mean absolute error. here is the results of kriging;
mean of response variable: 34.76982
mean error: -0.03613827
mean absolute error: 1.598008
RMSE: 2.053376
how can I calculate relative mean error?
I read somewhere we can use this function for calculating relative mean error:
OK_CV <- krige.cv(Site_Form ~1, ~X+Y, Data, model = model1.out, nfold=10)
# mean error, ideally 0:
ME_OK <- mean(OK_CV$observed - OK_CV$ var1.pred) ME_OK
### Mean Absolutely 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 the above function for relative mean error, the result will be negative!!!
How can I interpret the negative value?
I would be very grateful if anyone can help me to calculate relative mean error in R.
Regards
Jalil
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