Dear all,

I using the function krige.cv (in gstat library) to compare different
kriging methods (ordinary, external drift, universal, lognormal,...). To do
that I'm using among other the mean square normalized error
(mean(out$zscore^2)). I'm wondering if I can use this statistic to compare
results on a  logarithmic scale (lognormal kriging) and normal scale. To
compare the mean error (mean(out$residual)) I'm using backtransformed
values:

out$var1.pred.backtransform=exp(out$var1.pred+0.5*out$var1.var)-1
out$observed.backtransform=exp(out$observed)-1
out$residual.backtransform=out$observed.backtransform-out$var1.pred.backtransform
ME=mean(out$residual.backtransform)

To compare the mean square normalized error do I have to backtransform the
zscore value:

out$residual.backtransform=out$observed.backtransform-out$var1.pred.backtransform
out$var1.var.backtransform=??????????
out$zscore.backtransform=out$residual.backtransform/(sqrt(out$var1.var.backtransform))

if yes, is there a way to backtransform the prediction variance to the
normal scale?

Many thanks

Sam

	[[alternative HTML version deleted]]

