Dear all,



I have a question regarding the spdep package:



Assume that each person within my dataset is characterized by a continuous variable y. In order to test for spatial autocorrelation in y I calculated a Moran's I statistic. The problem is that y is also likely to be influenced by a series of other variables x that are unique to each person in my dataset. To test to which extent the autocorrelation effect is present even when accounting for the effect of x, I first did a regression y = f(x) + Epsilon and then used the regression residuals Epsilon to determine a Moran's I statistic using the lm.morantest function. One of the reviewers of my paper now states that this two step approach (first running a regression and then using the residuals to determine Moran's I) could be inefficient. S/he asks me to correct for the potential impact of x on y in one step when calculating Moran's I.



Would anyone happen to know any other way of doing what I'd like to do that only requires one step?

Or could you please provide me with a source I could quote that states that this two-step approach is fine from a statistical perspective?



Thanks very much for your help in advance,



Regards,



Michael





Michael Haenlein
Professor of Marketing
ESCP Europe - The School of Management for Europe
79, Avenue de la République　|　75011 Paris　| France



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