Alyson-
Following off of what Liam said, one thing to consider is as most measures
of phylogenetic signal aren't relative to the units of the traits
considered, any transformation of the data should be about equally
interpretable. To take a spin with Liam's example, if , if the log-scale
trait had high phylogenetic signal, you would infer that large things
(Liam's whales) really are more likely to be related to other large things
(and the same for small things), in the case where you reduce variation
among the bigger things (whales), and treat the smaller things (mice) as if
they had more variance.
For example, you might think the large size of 'whales' in your dataset
reflects signal (evolutionary conservatism - simply that they are all big
partly because they share common ancestors that had a big size), but if the
sizes of large things (whales) vary much more than all the variation among
your small things ('mice'), a measurement of signal on a raw scale might
think that maybe that is not very good signal, as much more evolution
change had to occur along the branches linking large/'whale' species than
the branches linking related 'mice'. This is a pretty typical situation.
I suppose one might not want to log scale if they thought that there was
not much evolutionary difference among big things, but a lot among small
things, but this just reflected higher rates of change between small
things. So, to defend not log-scaling, you'd basically need to argue that
evolutionary size change *doesn't* scale with size (or doesn't scale
positively, at least), but evolution-scales-with-size seems like one of
those things we generally assume prima facie is true in biology, so I
suppose you'd need to have a pretty good explanation why that would be.
Uh, I hope that philosophizing made sense.
(And yeah, I'm sure someone in the peanut gallery will point out that our
choice of a log scaling is entirely arbitrary, because who really knows
what the proper scaling of biological data along size gradients are
anyway....)
-Dave Bapst
Geology & Geophysics, Texas A & M University
On Fri, Jul 13, 2018 at 1:07 PM, Alyson Brokaw <afbro...@gmail.com> wrote:
> Hello Everyone,
>
> I am working with a comparative dataset using bat morphometrics. As part of
> my analysis, I want to estimate the phylogenetic signal of my variables. I
> understand how to do this using R. My question is more specifically about
> what kind of data I should be using when calculating the estimates.
>
> For the purposes of my other analyses (linear regressions), I have
> log-transformed my data to meet assumptions for normality. When estimating
> phylogenetic signal, should I use my non-transformed, raw variables or the
> transformed variables? I get slightly different outputs if I run both on
> the same measure. My intuition is that using the raw values is more
> interpretable, but figured I would ask some people with more experience.
>
> Thank you for your time.
>
> -Alyson
> ____________________________________________________________
>
> Alyson Brokaw
> M.S. Candidate: Biology, Humboldt State University
> Cornell University '11, Ecology and Evolutionary Biology
>
> LinkedIn Profile <http://www.linkedin.com/pub/alyson-brokaw/3a/704/820>
> Follow my research journey here! <http://afbrokaw.wordpress.com/>
>
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--
David W. Bapst, PhD
Asst Research Professor, Geology & Geophysics, Texas A & M University
Postdoc, Ecology & Evolutionary Biology, Univ of Tenn Knoxville
https://github.com/dwbapst/paleotree
Google Calendar: https://goo.gl/EpiM4J
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