nested mixed model?
Hi Mauro, I agree with both Thierry and Luciano, considering your nested design the random effects lie at the level of the individual. My only concern is that given the coding below, only the intercept varies and the slopes are set to be equal for the random variables. From what I have read (e.g. Schielzeth and Forstmeier 2008, Behavioral Ecology), not investigating the slopes of the random variables can lead to spurious results. I would also consider using Markov Chain Monte Carlo (MCMC) approximations (see Bolker et al. 2009, TREE). However, I don't know if MCMC estimations work with nested designs. Hence, perhaps the following might work? tooth.lmer<-lmer(response ~ species, data=tooth, random=~age_of_individual|individual/bone/tooth) tooth.pval<-pvals.fnc(tooth.lmer, nsim=1000, withMCMC=TRUE) tooth.pval$fixed tooth.pval$random Perhaps others have an opinion regarding variation of slopes and intercepts with mixed effects models? And the use of MCMC estimations with nested designs? All the best, Andrew Andrew Kosydar University of Washington Department of Biology 24 Kincaid Hall, Box 351800 Seattle, WA 98195 USA
On Wed, 3 Feb 2010, ONKELINX, Thierry wrote:
I aggree with Luciano. You should take the individual into account. The point is only relevant as a random effect if you have multiple records per point. I would use something like lme(response ~ species, data=tooth, random=~1|individual/bone/tooth) HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek team Biometrie & Kwaliteitszorg Gaverstraat 4 9500 Geraardsbergen Belgium Research Institute for Nature and Forest team Biometrics & Quality Assurance Gaverstraat 4 9500 Geraardsbergen Belgium tel. + 32 54/436 185 Thierry.Onkelinx at inbo.be www.inbo.be To call in the statistician after the experiment is done may be no more than asking him to perform a post-mortem examination: he may be able to say what the experiment died of. ~ Sir Ronald Aylmer Fisher The plural of anecdote is not data. ~ Roger Brinner The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data. ~ John Tukey -----Oorspronkelijk bericht----- Van: r-sig-ecology-bounces at r-project.org [mailto:r-sig-ecology-bounces at r-project.org] Namens Luciano Selzer Verzonden: woensdag 3 februari 2010 13:59 Aan: Mauricio Cifuentes CC: r-sig-ecology at r-project.org Onderwerp: Re: [R-sig-eco] nested mixed model? Hi, I'm by no means an expert, just an PhD student. But in my humble opinion shouldn't you consider the individual effect? I think that the microtexture could be influenced by this. Luciano 2010/2/3 Mauricio Cifuentes <mcifuent at gmail.com>
Hi everybody, I am trying to fit a model in R using the lme() function. I would like
to have your opinion about what I did and if there are better ways to resolve this analysis. First Let me explain you how look the data that
we are analyzing. We want to compare the tooth microtexture of four species of ungulates. For that we have taken pictures in eight different points within each tooth of one individual. We used as many teeth as were available for each individual taken in account their position and at the same time separating them by the place they were located (mandible: down tooth; maxilla: upper tooth). I am not an expert, but until here the model looks as nested design, please let me know if I am wrong. In summary we have the following hierarchy arrangement: Species (4 species) > bone(mandible or maxilla) > tooth > points within each tooth (8 points). I have fitted this model using: lme(response ~ species, data=tooth, random=~1|bone/tooth/points,na.action=na.omit) I will be really grateful if you can give me your opinion about that. Best wishes Mauro
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