Hi all, A question for the more experienced geostats users....
I have a data set containing 2-3 variables relating to submerged plant characteristics inferred from acoustic survey. The distribution of the % cover variable is bounded (0-100) and highly left skewed (many 0's). The transect spacing is quite even, and I can't seem to notice much difference between a run of ordinary kriging and a variant of RK using a zeroinflated glm of the %cover residuals. None of the other co-variates show much correlation with the data (i.e. bottom depth, x and y). Is this a possible reason why OK and RK seem to give more or less the same predictions?
my second question relates to transformation of the target variable...in this case zero inflated distributions are difficult to transform. Is it really a requirement of kriging that the data be transformed? or just that it will generally perform better with a target variable with a distribution close to normal?
Thanks, Dave _______________________________________________ R-sig-Geo mailing list R-sig-Geo@stat.math.ethz.ch https://stat.ethz.ch/mailman/listinfo/r-sig-geo
