Hi Sidzabda, Adjusting df for non-sphericity is generally not discussed in modern mixed effects modeling because likelihood-based estimators are far more flexible in structuring covariance matrices than the classical method of moments estimators based on expected mean squares. However, there are still many unanswered questions and controversies relating to the appropriate df to use for testing. Some issues are discussed here:
https://stat.ethz.ch/pipermail/r-help/2006-May/094765.html That said, the good news is (and I'll try to be a little cautious since it's a messy issue) when models are carefully developed, there are tests available in nlme that are generally well received by reviewers for applied journals. I don't know enough about your data to recommend specific models and tests, but I can recommend that you look into the correlation structures available in nlme. Essentially these allow you to model the autocorrelation through the error covariance matrices so you are left with (reasonably) independent pieces of information for testing and building confidence intervals. A possibility for your data would be to add the argument correlation=corAR1(form=~Year) to a gls (no random effects) or lme (mixed effects) call. Chapter 5 of Pinheiro and Bates 2000 will give you insight into how the correlation arguments work, while sections 2.3 and 2.4 will help you understand some of the issues concerning inferences. If you have further questions I recommend the r-sig-mixed-models list. hoping it helps, Kingsford Jones On Fri, Jul 10, 2009 at 9:27 AM, Djibril Dayamba<djibril.daya...@ess.slu.se> wrote: > Hello, > I would appreciate if somebody could help me clear my mind about the below > issues. > I have a factorial experiment to study the effects of Grazing and Fire on > Forest biomass production. The experimental unit (to which the treatment > combinations are applied) are PLOTs. The measures were made repeatedly for 13 > years. > I am planning to use the linear mixed effect model function lme in R for > this. I know that in software like SPSS, using Repeated Measure analysis of > variance for studies like mine, sometimes (case of non-sphericity), one needs > to adjust for the degree of freedom (DF) used to test the significance of the > "within subject factor" (Time i.e., Year in my case). > My question is: > How does this work with lme in R? Isn't it enough for me to specify in my > model, Year в plotID as a random factor to account for the temporal > autocorrelation? Or what else should I do to ensure that I have correct > results from the summary function applied to my model (correct t- and > p-values)? Thanks in advance. > > With regards, > > Sidzabda Djibril Dayamba, > Swedish University of Agricultural Sciences > Faculty of Forest SCience > Southern Swedish Forest Research Centre > Tropical Silviculture and Seed Laboratory > PO Box 101 > SE - 230 53 Alnarp, > Sweden > Tel: +46 76 83 515 70 (Mobile) > +46 40 41 53 95 (Office) > > > [[alternative HTML version deleted]] > > > ______________________________________________ > 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. > > ______________________________________________ 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.
