Hi, I have spatially autocorrelated data (with a binary response variable and continuous predictor variables). I believe I need to do an autologistic model, does anyone know a method for doing this in R? Many thanks C Bell
Hi Charlotte, I suggest you give a look ate Hmisc and Design packages. May be there you can find some solution. Good luck miltinho, brazil ...User R!... On Thu, Dec 18, 2008 at 10:14 AM, Charlotte Bell < charlotte.bell@sheffield.ac.uk> wrote:> Hi, > > I have spatially autocorrelated data (with a binary response variable and > continuous predictor variables). I believe I need to do an autologistic > model, does anyone know a method for doing this in R? > > Many thanks > > C Bell > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. >[[alternative HTML version deleted]]
Charlotte Bell <charlotte.bell <at> sheffield.ac.uk> writes:> > Hi, > > I have spatially autocorrelated data (with a binary response variable and > continuous predictor variables). I believe I need to do an autologistic > model, does anyone know a method for doing this in R?There are several approaches that you could try. One direct spatial approach is the off-CRAN Rcitrus package: http://www.leg.ufpr.br/Rcitrus/ which although the documentation is in Portuguese, should get you most of the way there. You could also look at geoRglm on CRAN, which handles a similar setting in a geostatistical way. You may also find it helpful to look at the handling of spatial autocorrelation in the nlme package in a GLMM context, using the CorSpatial approach. If you like, you could also look at a GAMM approach in mgcv. The glmmBUGS package can be used for preparing a GLMM for running in *BUGS if the spatial autocorrelation is expressed through a spatial weights matrix rather than as a function of distance. Hope this helps, Roger Bivand. PS. RSiteSearch on autologistic does find: http://finzi.psych.upenn.edu/R/Rhelp02a/archive/147538.html which is a posting by Elias Krainski on R-sig-geo, where a further link is given for a forthcoming stLattice package.> > Many thanks > > C Bell >
Curiosity and Google lead me to this paper which may be of interest: Assessing the validity of autologistic regression Purchase the full-text article References and further reading may be available for this article. To view references and further reading you must purchase this article. Carsten F. DormannCorresponding Author Contact Information, a, E-mail The Corresponding Author aDepartment of Computational Landscape Ecology, UFZ Centre for Environmental Research, Permoserstr. 15, 04318 Leipzig, Germany Received 11 July 2006; revised 30 April 2007; accepted 7 May 2007. Available online 20 June 2007. Abstract In autologistic regression models employed in the analysis of species? spatial distributions, an additional explanatory variable, the autocovariate, is used to correct the effect of spatial autocorrelation. The values of the autocovariate depend on the values of the response variable in the neighbourhood. While this approach has been widely used over the last ten years in biogeographical analyses, it has not been assessed for its validity and performance against artificial simulation data with known properties. I here present such an assessment, varying the range and strength of spatial autocorrelation in the data as well as the prevalence of the focal species. Autologistic regression models consistently underestimate the effect of the environmental variable in the model and give biased estimates compared to a non-spatial logistic regression. A comparison with other methods available for the correction of spatial autocorrelation shows that autologistic regression is more biased and less reliable and hence should be used only in concert with other reference methods. charlotte.bell wrote:> > Hi, > > I have spatially autocorrelated data (with a binary response variable and > continuous predictor variables). I believe I need to do an autologistic > model, does anyone know a method for doing this in R? > > Many thanks > > C Bell > > ______________________________________________ > R-help at r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > >-- View this message in context: http://www.nabble.com/autologistic-modelling-in-R-tp21072582p21108851.html Sent from the R help mailing list archive at Nabble.com.
David Katz <david <at> davidkatzconsulting.com> writes:> > > Curiosity and Google lead me to this paper which may be of interest: > > Assessing the validity of autologistic regression > Carsten F. Dormann > Abstract >...> the focal species. Autologistic regression models consistently underestimate > the effect of the environmental variable in the model and give biased > estimates compared to a non-spatial logistic regression. A comparison with > other methods available for the correction of spatial autocorrelation shows > that autologistic regression is more biased and less reliable and hence > should be used only in concert with other reference methods.Good point and reference. My understanding is that the term "autologistic" is used both of a GLMM with a spatially structured random effect, and of a GLM with the product of the response and a matrix of spatial weights included on the right hand side as an "autocovariate". I believe that the article finds that the use of such an "autocovariate" can lead to the problems described, for example when the "autocovariate" is strongly correlated with the other right hand side variables. A properly specified GLMM should not suffer from the same problems. Roger Bivand> > charlotte.bell wrote: > > > > Hi, > > > > I have spatially autocorrelated data (with a binary response variable and > > continuous predictor variables). I believe I need to do an autologistic > > model, does anyone know a method for doing this in R? > > > > Many thanks > > > > C Bell > >