[R-sig-Geo] Assessing residual spatial autocorrelation in a Poisson or Negative Binomial model

[Marcelino de la Cruz]

Hi,

You may find useful this:

Dormann et al. 2007. Methods to account for 
spatial autocorrelation in the analysis
of species distributional data: a review. Ecography 30: 609-628,

And the suplementary material with several examples worked in R.

HTH,

Marcelino

At 11:46 26/11/2009, Karen Lamb wrote:
>Hi,
>
>I am currently trying to determine a way of 
>assessing whether or not there is spatial 
>autocorrelation present in my model residuals 
>and was hoping someone could help me with this.
>
>I have information on counts in over six 
>thousand areas, with around half of the areas 
>found to have  a count of zero. I decided to fit 
>a Zero-Inflated Poisson model and a Negative 
>Binomial as the data is greatly overdispersed. 
>However, neither of these approaches take into 
>account the likelihood that there is spatial 
>autocorrelation present in the data set.
>
>I have been searching for the last two weeks to 
>find appropriate methods to fit a spatial glm 
>model. However, as I am new to spatial 
>statistical methodology I am finding it 
>difficult to decide how best to do this. It am 
>not sure that any of the existing R functions 
>are particularly suitable to my use. I am not 
>interested in prediction as I have data on a 
>population. I am interested in assessing the 
>coefficients of variables and whether or not the 
>variables are significant in determining 
>outcome. I have noticed that a lot of analyses 
>use a Bayesian approach which may be the way forward.
>
>My question, however, relates to the glm models 
>I have fitted. I have included variables which 
>may explain some of the spatial correlations 
>such as urban/rural classification. I would like 
>to see if any residual spatial autocorrelation 
>remains in the model but cannot find a way of 
>doing this. On searching the R-sig-Geo archives 
>the Morans Test or Morans I are mentioned. 
>However, I noticed someone had queried using the 
>moran test in R for residuals from a logistic 
>regression and had been told that lm.morantest() 
>is available for linear regression but there is 
>not an alternative for the glm. Has anyone got 
>any suggestions for how to check my residuals? 
>Are there particular plots that can be assessed?
>
>Thanks for your assistance.
>
>Cheers,
>Karen
>
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________________________________

Marcelino de la Cruz Rot

Departamento de  Biología Vegetal
E.U.T.I. Agrícola
Universidad Politécnica de Madrid
28040-Madrid
Tel.: 91 336 54 35
Fax: 91 336 56 56
marcelino.delacruz at upm.es
_________________________________