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 R¨¦publique¡¡|¡¡75011 Paris¡¡| France
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