I am trying to weigh up if kriging (or cokriging) the residual error from my model is a valid approach. My first step was to compute whether the error is spatially autocorrelated. I have done this using Moran's I statistic but I'm having trouble interpreting the results. Below are 5 examples (my actual data is the ERROR example), the text is the output from the ape package in R.
I interpret that as the p-value of RANDOM is >0.05 then the data is not autocorrelated. Reading the help file with ape it states that if the observed values are significantly greater than the expected value then your data is positively spatially autocorrelated (e.g. E vs W and X coord). I would have expected the observed value for CHESS to be negative. How should I interpret the results from ERROR? The p-value is
I interpret that as the p-value of RANDOM is >0.05 then the data is not autocorrelated. Reading the help file with ape it states that if the observed values are significantly greater than the expected value then your data is positively spatially autocorrelated (e.g. E vs W and X coord). I would have expected the observed value for CHESS to be negative. How should I interpret the results from ERROR? The p-value is