[R-sig-Geo] Why PCNM (MEM) approach cannot remove spatial autocorrelation in the residuals?

[Roger Bivand]

On Wed, 15 Nov 2017, niv de malach wrote:

> Hi,
> I am trying to include spatial eigenvectors ( in my regression using *dbmem
> *command from *adespatial* package) in order to account for spatial
> correlation. The problem is that even after including all the positive
> eigenvectors there is still a positive significant spatial autocorrelation
> in the residuals (based on Moran's I test). The magnitude of this problem
> is affected by the styles I use for the spatial weight (using the styles
> "U","W" "B" "C" of the function *nb2list* from *spdep*) but in all styles
> Moran's I is still significantly positive.

Maybe there is negative residual autocorrelation, and only choosing 
eigenvectors matching positive eigenvalues is enhancing pattern jumble 
(oversmoothing the model leaving jumble in the residuals)? Do you have an 
example you could share (link to offline downloadable code+data if need 
be)?

Roger

>
> Interestingly this problem doesn't occur when I use a SAR models (
> *errorsarlm* command from *spdep* package).
>
> So, does it make sense to use PCNM approach when it removes only a portion
> the spatial autocorrelation?
>
> Thanks
> Niv
>
> ᐧ
>
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-- 
Roger Bivand
Department of Economics, Norwegian School of Economics,
Helleveien 30, N-5045 Bergen, Norway.
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Editor-in-Chief of The R Journal, https://journal.r-project.org/index.html
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