You can use distance based weight matrices as well. Graph based weight  
matrices are only one type of weights. If you can approximate your  
plots as points as you seem to think, then l suggest you look at

?knn
?knn2nb

in spdep. You could also look at spatstat package. As Roger says, the  
correct use of weights really depends on the assumptions and structure  
of the spatial process.

Nikhil Kaza
Asst. Professor,
City and Regional Planning
University of North Carolina

nikhil.list@gmail.com

On Aug 23, 2010, at 8:59 AM, Breitbach, Nils wrote:

> Dear Roger, dear Community,
>
> thanks for the detailed answer to my question(s)! It helped a lot,  
> but on the other hand cause some more uncertainties for me.
>
> Why do some functions for the Moran's I test require detailed  
> knoledge of creating neighbours and weighting them like the  
> moran.test() function while others do not like the Moran.I()  
> function? The moran.test() function from the spdep package requires  
> a listw object, where you have to create a weighted neighbourhood  
> matrix with all the preliminary considerations, while the Moran.I()  
> function sipmly works with a ad hoc created distance matrix (e.g.  
> done with the dist() function) as it was done here: http://www.ats.ucla.edu/stat/r/faq/morans_i.htm 
>  (even though Moran.I expects "a matrix of weights" looking closer  
> to the help files for the function). Can the distances itself be  
> considered as weights? The method with the Moran.I() function seems  
> much simpler and requires a lot less knoledge and preliminary  
> considerations ...
>
> Thanks for help in this difficult field!
> Regards,
>
> Nils
>
>
> ________________________________________
> Von: Roger Bivand [Roger.Bivand@nhh.no]
> Gesendet: Montag, 23. August 2010 08:15
> Bis: Breitbach, Nils
> Cc: r-sig-geo@stat.math.ethz.ch
> Betreff: Re: [R-sig-Geo] Choosing the right nb2listw-style
>
> On Sun, 22 Aug 2010, Breitbach, Nils wrote:
>
>> Dear Community,
>>
>> to be able to evaluate the spatial autocorrelation within may data  
>> I am
>> forced with the question of how to correctly choose the neighbours  
>> for
>> my data. My study plots (points) are not evenly distributed over my
>> study area (approx. 30 x 30 km in size) and also the land-use type is
>> not evenly distributed over the study area and therefore I want to
>> evaluate the spatial autocorrelation for this data set. To finally
>> calculate the Moran's I or plot variograms/correlograms I now need to
>> calculate the neighbourhood relationships of my study plots. For the
>> given characteristic of my data (especially their non-even  
>> distribution)
>> I am now somewhat uncertain about the right style (W, B, C, U or S)  
>> of
>> the nb2listw() object that suits best for my kind of data.
>>
>> Can anyone recommend the "right" style for my kind of data?
>
> Typically, varying sub-discipline communities have different prefered
> flavours, both of the neighbour list object, for using general  
> weights (or
> not) - including inverse distance weighting, and for using
> row-standardisation (W), raw (binary or general - B), or  
> standardised raw
> (C - sum to n, U - sum to 1). There isn't a tradition dor using  
> variance
> stabilising (S) although there probably should be. It seems sensible  
> to
> see what others in your field use, and choose among those. The same  
> for
> schemes for finding the neighbours to start with. Using an approach  
> which
> is unusual in your field will attract referees' attention to your  
> choice -
> they will want to know why you are doing something different. Since  
> you
> are in ecology, look at papers using weights there, and unless you  
> can see
> that the modal scheme is suboptimal for you, go with the stream.
>
> Note however that some of the graph-based neighbour schemes advanced  
> early
> on by Sokal in ecology are little used, and probably deserve more
> exposure, especially when the distances between observations differ  
> a lot
> - leading to observations in dense parts of the study area having many
> neighbours in schemes using a distance threshold. Try to think about  
> the
> plausibility of the science in the implied spatial process - could
> observations realistically influence each other at that distance? It  
> may
> not matter if the weights are only "mopping up" unwanted spatial
> autocorrelation, but if the dependencies have a substantive
> interpretation, it isn't wise to imply mutual dependence that isn't
> scientifically plausible (think of natural boundaries that organisms
> cannot "cross" as well as distances). But no, no "right" scheme as  
> such -
> it's up to you! Pay attention to the inhomogeneity of your setting  
> too, as
> it may induce apparent dependency if not modelled.
>
> Hope this helps,
>
> Roger
>
>>
>> Thanks for help!
>> Regards,
>>
>> Nils
>>
>> _________________________________________________________
>>
>> Nils Breitbach, Dipl.-Biol.
>> Institut für Zoologie, Abt. 5: Ökologie
>> J.-J.-Becher-Weg 13
>> Johannes Gutenberg-Universität
>> 55128 Mainz
>> Germany
>>
>> phone: +49 6131 39-22718
>> fax:   +49 6131 39-23731
>> WWW: www.community-ecology.uni-mainz.de/126_ENG_HTML.php
>> _________________________________________________________ x k,
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo@stat.math.ethz.ch
>> https://stat.ethz.ch/mailman/listinfo/r-sig-geo
>>
>
> --
> Roger Bivand
> Economic Geography Section, Department of Economics, Norwegian  
> School of
> Economics and Business Administration, Helleveien 30, N-5045 Bergen,
> Norway. voice: +47 55 95 93 55; fax +47 55 95 95 43
> e-mail: Roger.Bivand@nhh.no
>
> _______________________________________________
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> R-sig-Geo@stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/r-sig-geo


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