Dear Kaspar, Are your traits the same? Basically, are the seven color patches on the same individual all one trait, or are they seven traits? Do all species have the seven color patches? Or are the number of color patches variable? (this could also be a character).
I think before you attempt a comparative analysis, it is helpful to think about how you would set up a standard statistical analysis, for example some sort of ANOVA. I am not sure I understand your description clearly. It sort of sounds like you are considering the multiple color patches as repeated measures of color patch on the same individual, but then you want to include them all as separate traits in the analysis. You could consider them as repeated measurements of a single "color patch" trait, in which case you would include the individual and species as factors in your analysis. Or you could consider each patch a different trait, and then compare them across species, so that each color patch is a separate dependent variable (in which case "color patch 1" would be the same trait across individuals and species, etc.). Or you could take the mean of all color patches within an individual and use that in an analysis across individuals and species. Do you have any clues from development as to their identity? It seems like you want to consider them all to be replicates of the same measure. But then why measure so many? I assume there is variation among color patches within an individual? Ultimately you will have to decide if 3 color patches is one trait or if it is three traits. After you decide this, it will be much easier to design a comparative analysis. Good luck, Marguerite On Mar 6, 2012, at 2:42 PM, Kaspar Delhey 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/ > 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 _______________________________________________ R-sig-phylo mailing list R-sig-phylo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
