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Currently, I am working on a count data set measuring events in a third-order administrative unit. The frequency of events varies for each third-order administrative unit to account for the variability, I am using a negative binomial regression model.

A crude graphical analysis of the residual of the regression indicates that no spatial autocorrelation exists. I would like to find a method to assess spatial autocorrelation in a negative binomial regression model, since a regular test of spatial autocorrelation are not feasible (ex. Global Moran's I).

Do you have any suggestions?

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  • $\begingroup$ IMO This is material for crossvalidated.com. $\endgroup$ Oct 18, 2012 at 13:52
  • $\begingroup$ What is a third-order administrative unit? Are they contiguous or do you have gaps? Why do you think that a graphical analysis of residuals vs. spatial lags is "crude"? $\endgroup$ Oct 18, 2012 at 15:09

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I suspect you can use moran.mc to do a monte-carlo permutation test. Basically, it computes a measure of spatial correlation for your residuals, then randomly reassigns your residuals to your regions and recomputes the measure. Do that 999 times, see where your data measure ranks with your MC measures. If its in the far positive tail, you can say you have significant residual autocorrelation.

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