Boosting computation time of glmmPQL when specifying spatial, correlation structure
On 17/01/2010 11:00, r-sig-mixed-models-request at r-project.org wrote:
Send R-sig-mixed-models mailing list submissions to r-sig-mixed-models at r-project.org To subscribe or unsubscribe via the World Wide Web, visit
https://stat.ethz.ch/mailman/listinfo/r-sig-mixed-models or, via email, send a message with subject or body 'help' to r-sig-mixed-models-request at r-project.org I know how to speed up GAMM when specifying a spatial correlation structure by splitting up the dataset to compute the
You are not splitting up the data set. Instead you are imposing the correlation on a sub-block of data.
spatial correlation coefficients of corSpher. such (if dataG is my dataset) cutx<-cut(dataG$x,breaks=(4)) cuty<- cut(dataG$y, breaks=(4)) cutxy<- paste(cutx, cuty) and then gamm(Response~(var1)+s(var2),family=binomial, data=dataG,correlation=corSpher(form=~(x+y)|cutxy)). the cutxy doesn't seem to work with glmmPQL and with 1500 points, it takes ages... Does anyone know if there is a way to apply the same "trick" ?
The first thing to do is to use decent starting values for the range (and nugget???). See ?corSpher Alain
By the way (take a breath...), does the plotting of a spatial correlogram with residuals(model, type="pearson") from a glmmPQL model (where correlation structure was specified) makes sense to you ? I'm not sure if residuals of such model account for the stucture (and I can hear some of you, why don't you check this by yourself... yes , I will try !) Best regards and thanks for any hint Alex Alexandre Villers PhD. Candidate Team Agripop CEBC CNRS UPR 1934 79360 Beauvoir sur Niort Phone: +33 (0)549 099 613 __________ Information from ESET Mail Security, version of virus signature database 4777 (20100116) __________ The message was checked by ESET Mail Security. http://www.eset.com ------------------------------
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Dr. Alain F. Zuur First author of: 1. Analysing Ecological Data (2007). Zuur, AF, Ieno, EN and Smith, GM. Springer. 680 p. URL: www.springer.com/0-387-45967-7 2. Mixed effects models and extensions in ecology with R. (2009). Zuur, AF, Ieno, EN, Walker, N, Saveliev, AA, and Smith, GM. Springer. http://www.springer.com/life+sci/ecology/book/978-0-387-87457-9 3. A Beginner's Guide to R (2009). Zuur, AF, Ieno, EN, Meesters, EHWG. Springer http://www.springer.com/statistics/computational/book/978-0-387-93836-3 Other books: http://www.highstat.com/books.htm Statistical consultancy, courses, data analysis and software Highland Statistics Ltd. 6 Laverock road UK - AB41 6FN Newburgh Tel: 0044 1358 788177 Email: highstat at highstat.com URL: www.highstat.com URL: www.brodgar.com