Assessing residual spatial autocorrelation in a Poisson or Negative Binomial model
On Thu, 26 Nov 2009, Luis Iv?n 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
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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