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

[Karen Lamb]

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