On Thu, May 22, 2008 at 5:55 PM, Caroline Lehmann <[EMAIL PROTECTED]> wrote: > Hello, I would suggest reading: Prior L. D., Brook B. W., Williams R. J., > Werner P. A., Bradshaw C. J. A. & Bowman D. M. J. S. (2006) Environmental and > allometric drivers of tree growth rates in a north Australian savanna. Forest > Ecology and Management 234, 164-80. > > In this paper tree growth was analysed and accounted for the repeated measure > of individuals using either glmm or lme (now the in lmer package). Models > were compared and ranked using AICc. I would suggest modifying this to BIC > since there are so many measurements. >
Is there theory to support this suggestion? I find choosing an *IC to be a confusing issue and would appreciate any pointers to theory, simulations, etc that may shed some light on the subject. best, Kingsford > Kind regards, Caroline > > > -----Original Message----- > From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Péter Sólymos > Sent: Friday, 23 May 2008 6:14 AM > To: r-sig-ecology@r-project.org > Subject: [R-sig-eco] Fwd: nlme model specification > > Dear List, > here is my response from today and yesterday to Matt's question, that was > missed. I sent my first message from an unsubscribed e-mail address. Sorry > for that. > Peter > > > Matt, > I am now absolutely confused. Am I right that you have 3 measurements per > individuals per year? In other words, you measured growth (in cm? > or what is growth) diameter and vine load. And I think you want to partial > out the variation. > If so, your problem became a multivariate problem, when you have 3 response > variables measured on same individuals, plus some grouping variables (inds, > yr). Than you can easily use multiple regression to partial the variation. I > have never saw a multivariate mixed model, so I think you don't have to try > too hard. > Let me know if I was able to understand it. > Yours, > Peter > ps: I don't know why my letter did not wet out to the list. > > > ---------- Forwarded message ---------- > From: Péter Sólymos <[EMAIL PROTECTED]> > Date: Wed, May 21, 2008 at 10:21 PM > Subject: Re: [R-sig-eco] nlme model specification > To: "Landis, R Matthew" <[EMAIL PROTECTED]>, "r-sig-ecology@r-project.org" > > > Dear Matthew, > > I think that your case is a bit different than you proposed, since, - if I am > right based on your letter - you have repeated measures for the same 300 > trees over 9 successive periods (resulting in 2700 measurements). So > observations are not only biased by some spatial or temporal non independence > (like in case of a wildlife survey), but essentially the subjects are the > same. I mean that observations are not really grouped in time. I would prefer > a model with fixed model term as you wrote, a random factor like ~1| > tree.individuals and an explicitly defined correlation structure with corAR1 > or corARMA (or you can define groups for individuals within correlation term). > > This can be done with gls in nlme package, or glmmPQL in MASS. > Probably there are options in lme4 but I haven't tried those. > > The problem becomes more complicated if the growth is not linear, but follows > an allometric relationship. In this case you should use nlme function. > Further, there might be problems with variance homogeneity, than you shoud > define a variance function, too. These are all covered in the P-B book as far > as I remember. > > Hope this helps, and sorry if I made some chaos instead of a clear-cut answer. > > Best, > > Peter > > -- > Peter Solymos, PhD > Institute for Biology > Faculty of Veterinary Science > Szent Istvan University, Hungary > http://www.univet.hu/users/psolymos/personal/ > > mefa R package > http://mefa.r-forge.r-project.org/ > > On Wed, May 21, 2008 at 7:54 PM, Landis, R Matthew <[EMAIL PROTECTED]> wrote: >> Greetings R-eco folks, >> >> I'm trying to analyze a dataset on tree growth rates to see which factors >> are important (and their relative importance too, if I can get that), and >> I'm having some trouble figuring out how to specify the model, despite >> having carefully read Pinheiro and Bates, the help files for nlme, Crawley's >> book on Statistics with S, MASS, and other books besides. >> >> The dataset consists of ~ 300 trees measured annually for 10 years. So, I >> have 9 pseudo-replicated intervals over which to assess growth (about 2700 >> rows in the dataset). There are 5 different explanatory factors, which are >> a combination of continuous variables and categorical factors. Some of >> these vary with time. In the end, I would like to get both coefficient >> estimates and partial R2 (or some other way of ranking them) for each >> factor. Unlike most time-series examples in the books, I am not interested >> in how growth varies with time, nor am I particular interested in >> interactions of explanatory factors with time. >> >> Based on this, I've convinced myself that I should specify the model as: >> >> fit <- lme(fixed = growth ~ (x1 + x2 + x3+ x4 + x5)^2, random = >> ~1|year, method = 'ML') >> >> Year is clearly a random effect, and is the grouping variable for the >> analysis. Each of the other coefficients is "inner" to this variable. I'm >> ignoring individual tree as a grouping factor, since I don't want to >> estimate separate coefficients for each tree. Does this sound like the >> correct way to do this? >> >> Thanks for any help. Apologies if this is more of a statistics question and >> less of an R question. >> >> Matt Landis >> >> **************************************************** >> R. Matthew Landis, Ph.D. >> Dept. Biology >> Middlebury College >> Middlebury, VT 05753 >> >> tel.: 802.443.3484 >> ************************************************** >> >> >> [[alternative HTML version deleted]] >> >> _______________________________________________ >> R-sig-ecology mailing list >> R-sig-ecology@r-project.org >> https://stat.ethz.ch/mailman/listinfo/r-sig-ecology >> > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > > _______________________________________________ > R-sig-ecology mailing list > R-sig-ecology@r-project.org > https://stat.ethz.ch/mailman/listinfo/r-sig-ecology > _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
