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

[Roger Bivand]

On Thu, 26 Nov 2009, Luis Iván Ortiz Valencia wrote:

> Hi Karen
>
> I am interested in this points too. What variable are you modeling? Counts
> or incidence? did you standardized by population areas?
>
> R has lot of spatial models for spatial models. see at
> http://r-spatial.sourceforge.net/

Rather:

http://cran.r-project.org/view=Spatial

please, the sourceforge site is more for development, and is linked from 
the task view on your nearest CRAN mirror.

While lm.morantest() can be used on glm output objects, no work has been 
done to establish whether this is a sensible idea. It remains problematic 
to simulate spatially dependent discrete variables. However, it is 
possible that if you ignore the "test" of doubtful substance, you could 
track how Moran's I moves when adding variables in an exploratory way. Try 
with a smaller dataset first.

Hope this helps,

Roger

>
> hope this help
>
> ivan
>
> 2009/11/26 Karen Lamb <k.lamb at sphsu.mrc.ac.uk>
>
>> 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
>>
>> _______________________________________________
>> R-sig-Geo mailing list
>> R-sig-Geo at 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 at nhh.no