Theirry, thank you for you informative reply. I have had a go at your suggestions but have been stumped: --On 07 May 2009 10:16 +0200 "ONKELINX, Thierry" <Thierry.ONKELINX at inbo.be> wrote:
Dear Kate, Adding SPECIES as a random effect indicates that you want to take the effect of SPECIES into account but not need to know the effect of the individual SPECIES. If you do want to know that effect then you have to add species to fixed effects. Examining the effect of A, B and C on species (as a fixed effect) requires interactions between them. The model then looks like (A + B + C) * SPECIES + Year + (1|SITE) + (1|YEAR) This will only work if you have sufficiend data.
I tried this approach with data I have that is SPECIES recorded as SITES over YEARS but when I tried A*SPECIES as a fixed factor I received this error message: "Error in mer_finalize(ans) : Downdated X'X is not positive definite, 88." I've searched for what this error means but I cannot understand it. This was written by Douglas Bates in response to [Re: [R] lme4, error in mer_finalize(ans)] posted 05 Dec 2008: "That, admittedly obscure, error message relates to the fixed-effects specification rt ~ length + length:pos being rank deficient. If you look at the summary of the linear model fit you will see that there are 3 coefficients that are not determined because of singularities. The lm function detects the singularities and fits a lower-rank model. The lmer function is not as sophisticated. It just detects the singularities and quits." I am unsure what this means or how it translates to my data. In my example, I have 78 "SPECIES" (factor, coded as numbers) and "A" is ordered data 0, 1, 2. The y variable is number/m. You wrote that this would only work is you had sufficient data - each species is not recorded each time, so is this reduced data the cause i.e. not enough observations for n?
Another option is to keep species as a random effect and add random slopes according to A, B and C. This will allow a different effect of A, B anc C for each species. The model would look like A + B + C + Year + (1|SITE) + (1|YEAR) + (A + B + C|SPECIES)
I have tried this way also but I am unsure of the output - it does not give species specific information and therefore I cannot work out which species is more affected by A, only if SPECIES as a whole are affected or not by each category of A. This is not useful to me as I would like to determine, given the random effects, if A 0, 1, or 2 affect which species in the data set. Any thoughts? Kate
HTH, Thierry ------------------------------------------------------------------------ ---- ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest Cel biometrie, methodologie en kwaliteitszorg / Section biometrics, methodology and 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-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] Namens CL Pressland Verzonden: woensdag 6 mei 2009 20:18 Aan: R Mixed Models Onderwerp: Re: [R-sig-ME] Modelling random effects with SITE, YEAR and SPECIES How can you work out how A, B or C affect SPECIES? By this I mean, could you find out how species n is affected by A, B and C in the correlation output? Or would you need to adjust the response to look at individual species separately? --On 29 April 2009 17:58 -0400 Ben Bolker <bolker at ufl.edu> wrote:
David R. wrote:
Hello all, First, sorry for the english and the basic questions. I'm using mixed models (lme4 package) to analyse variability in 13 SPECIES of birds observed during 15 YEARS across 5 SITES. All the SPECIES were observed in all the sites in most years. My fixed effects are A, B, C and Year. I'm interested in the stochastic effect of A, B and C on the dependent variable, but also in a possible linear trend of the dependent variable over time. My random effects are SPECIES, YEAR and SITE, to control for the effects of nonindependence. I have a model with SITE, YEAR and SPECIES as crossed random effects like A + B + C + Year + (1|SITE) + (1|YEAR) + (1|SPECIES). My questions are: 1) Is this model correct? It is correct to model YEAR both as random effect and fixed effect? Is there the possibility that the variance accounted for by the random effect could robbing year as a fixed effect of explanatory power?
Seems OK and sensible to me. I would guess that the linear trend and the random variation are sufficiently different patterns that they would not conflict too
badly,
but you could try the different nested models and see what happens ...
2) It is meaningful, instead, to model YEAR as repeated measure, if the experimental unit were species within sites?
"Modeling YEAR as a random effect" and "Modeling YEAR as a repeated measure" are, in my opinion, almost the same thing (but I'm ready to
be
corrected, as always). The only aspect of "repeated measures" that would be different would be if you wanted to fit an autoregressive
model
so that samples closer together in time were more correlated (which
you
can't do with lmer at this point). Ben Bolker
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---------------------- Kate Pressland Office D95 School of Biological Sciences University of Bristol Woodland Road Bristol, BS8 1UG Tel: 0117 9288918 (Internal 88918) Kate.Pressland at bristol.ac.uk www.bio.bris.ac.uk/people/staff.cfm?key=1137
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---------------------- Kate Pressland Office D95 School of Biological Sciences University of Bristol Woodland Road Bristol, BS8 1UG Tel: 0117 9288918 (Internal 88918) Kate.Pressland at bristol.ac.uk www.bio.bris.ac.uk/people/staff.cfm?key=1137