Hi Kaspar and others, Sorry for responding so late to this. I am not sure if there exists any precedent for doing this but it seems problematic. We do not expect BM alone (or a variant thereof) to be a good model to account for variation within a species and I would strongly recommend against doing what you propose.
However, there have been several methods published directly relevant to this that are worth checking out. Someone else on the list may have more insight than me on this (and please correct me if i am wrong). There exists a PGLS-esque method which I believe is available only in matlab... Ives, A.R., Midford, P.E. & Garland, T. 2007. Within-species variation and measurement error in phylogenetic comparative methods. Syst. Biol. 56: 252270. And for a contrast-based approach, Felsenstein has developed machinery which has the advantage of simultaneously estimating the variances. It is coded up and availalbe in PHYLIP within the contrast program. Felsenstein, J. 2008. Comparative methods with sampling error and within-species variation: contrasts revisited and revised. Am. Nat. 171: 713725. There is also some discussion of this in: HADFIELD, J. D. and NAKAGAWA, S. (2010), General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. Journal of Evolutionary Biology, 23: 494508. Hope this is helpful. cheers matt On Tue, Mar 6, 2012 at 4:42 PM, Kaspar Delhey <kaspar.del...@monash.edu>wrote: > Hello, > > I was wondering whether I could get some input into the following analysis: > > I am interested to test for the association between one dependent variable > (continuous, normal) and one factor and a covariate while accounting for > phylogenetic relatedness. In my analysis I have 12 species and I have > measured the dependent variable and the covariate (which are both colour > descriptors) on several patches per species (between 1 and 7 patches per > species). The factor (the type of visual sensitivity) does not vary within > species. Thus the repeated observations per species do not correspond to > different individuals but are different patches of colour in the same > individual, measured on the same scale and units (hence directly > comparable). > > Does it make sense to make use of the whole dataset (i.e. without > computing species averages) by replacing each tip in the phylogeny by a > polytomy including all patches measured in that particular species? > > For example if I have this tree: > > (A, (B, C)) > > and I have measured two patches of colour in sp. A and three in spp B and > C I could replace the tips in this tree by: > > ((A1,A2),((B1,B2,B3),(C1,C2,**C3))) > > > In this way I could use a gls approach with nlme and for example corPagel > such as: > > model1<-gls(dep.var~factor+**covariate, correlation=Pagel, method="REML", > data=data) > > The results that I obtain from such a model make sense and qualitatively > agree with the results from a mixed model including species ID as a random > factor but ignoring phylogenetic relatedness between species. > > My question then is whether there is a flaw in this analysis and whether > it has been used before in other publications in order to be able to > include references to back it up. > > Thanks in advance for any help. > > best > > kaspar > > > > > -- > Kaspar Delhey > e-mail: kaspar.del...@monash.edu > https://sites.google.com/site/**kaspardelhey/<https://sites.google.com/site/kaspardelhey/> > Tel:+61-(0)3-99020377 > Bldg.18 School of Biological Sciences > Monash University > Clayton, 3800 Victoria > Australia > > ______________________________**_________________ > R-sig-phylo mailing list > R-sig-phylo@r-project.org > https://stat.ethz.ch/mailman/**listinfo/r-sig-phylo<https://stat.ethz.ch/mailman/listinfo/r-sig-phylo> > [[alternative HTML version deleted]]
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