[R-sig-Geo] Anselin Local Moran's I with R

[David Romero]

Hello Roger,

Thank you for aclarations. Obviously, climatic data are dependent of
elevation. I wished to calculate local Moran as an exploratory method
to identifie outliers before computing semivariograms. Could you
suggest a better method?

Thank you,

David

2014-09-09 8:24 GMT-05:00 Roger Bivand <Roger.Bivand at nhh.no>:
> On Tue, 9 Sep 2014, David Romero wrote:
>
>> Hello,
>>
>> Could somebody help me with the method to compute the Cluster and Outlier
>> Analysis using spdep and obtain Local I Index, Z-scores and cluster type
>> like with the Arcgis tool.
>
>
> Preferably not. The Arcgis tool answers lots of the wrong questions wrongly.
> If you take multiple comparisons seriously - see ?p.adjust - and treat
> permutations of all values except i to permutation bootstrap with reserve
> (use localmoran.sad() or localmoran.exact() instead), you'll realise that
> the z-scores are deceptive, and the "cluster" types (HH/LL, LH/HL)
> wrongheaded if you colour only "significant" ones - they are only
> significant in most cases before correcting for multiple comparisons.
> Further, if the mean model is mis-specified (yi - \bar{y} uses \bar{y} alone
> as the mean model), there may well not be any autocorrelation anyway, just
> omitted spatially patterned covariates.
>
>> My data are points with temperature value.
>
>
> In your case temperature should surely be related to elevation, and very
> likely to urban density before trying to measure local residual
> autocorrelation. Just because they do this in Arcgis, it doesn't mean that
> it is an appropriate procedure.
>
> Hope this clarifies,
>
> Roger
>
>>
>> Thank you,
>>
>> David Romero
>> phD student
>> Instituto de Geografía
>> UNAM
>>
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>
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>
>>
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> --
> Roger Bivand
> Department of Economics, Norwegian School of Economics,
> Helleveien 30, N-5045 Bergen, Norway.
> voice: +47 55 95 93 55; fax +47 55 95 91 00
> e-mail: Roger.Bivand at nhh.no