Yes, certainly, but if you want something like the K statistic of Blomberg
et al. (2003), which will let you compare with a lot of other traits in
their database, then you need to do the univariate calculations on
size-corrected data.  Also, if you are worried about saying "signal was
significant for this trait but not that trait," then you want to make sure
your power is comparable.  It is likely to be different if you use
different approaches for different traits.
Cheers,
Ted

On Fri, Jul 13, 2018 at 2:30 PM, Manabu Sakamoto <manabu.sakam...@gmail.com>
wrote:

> Following on from what Ted just said about size-correction - one can use a
> phylogenetic regression (GLS) with the trait of interest as the dependent
> variable and size as the independent variable, while simultaneously
> estimating lambda. The program BayesTraits can do this (
> http://www.evolution.rdg.ac.uk/BayesTraitsV3.0.1/BayesTraitsV3.0.1.html)
> and I think the pgls function in the caper R package should be also able to
> do this if I recall correctly.
>
> thanks,
> Manabu
>
> On Fri, 13 Jul 2018 at 20:43, Theodore Garland <theodore.garl...@ucr.edu>
> wrote:
>
> > I agree with everything that Liam wrote -- right on.
> >
> > Another point is that if you are looking at morphometric traits, then
> most
> > of them are probably highly positively correlated with body size.  In
> that
> > case, testing for phylogenetic signal in, say, wing length, is going to
> be
> > largely redundant with testing for signal in body mass.  Hence, for your
> > traits other than body size, you may want to analyze size-corrected
> values,
> > as described here:
> >
> > Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for
> > phylogenetic signal in comparative data: behavioral traits are more
> labile.
> > Evolution 57:717–745.
> >
> > Cheers,
> > Ted
> >
> >
> > Theodore Garland, Jr., Distinguished Professor
> >
> > Department of Evolution, Ecology, and Organismal Biology (EEOB)
> >
> > University of California, Riverside
> >
> > Riverside, CA 92521
> >
> > Office Phone:  (951) 827-3524
> >
> > Facsimile:  (951) 827-4286 (not confidential)
> >
> > Email:  tgarl...@ucr.edu
> >
> > http://www.biology.ucr.edu/people/faculty/Garland.html
> >
> > http://scholar.google.com/citations?hl=en&user=iSSbrhwAAAAJ
> >
> >
> > Director, UCR Institute for the Development of
> > <http://idea.ucr.edu/>Educational
> > Applications <http://idea.ucr.edu/>
> >
> >
> > Editor in Chief, *Physiological and Biochemical Zoology
> > <http://www.press.uchicago.edu/ucp/journals/journal/pbz.html>*
> >
> >
> > Fail Lab: Episode One
> >
> > *https://www.youtube.com/watch?v=c0msBWyTzU0
> > <https://www.youtube.com/watch?v=c0msBWyTzU0>*
> >
> > On Fri, Jul 13, 2018 at 12:29 PM, Liam J. Revell <liam.rev...@umb.edu>
> > wrote:
> >
> > > Dear Alyson.
> > >
> > > There is no general rule about this; however, my suggestion would be to
> > > use log-scaled values. This is because on a log-scale proportional
> > changes
> > > in the trait are equal, independent of the magnitude of the trait. That
> > is,
> > > a change of 1% in mass of whale is the same as a change in 1% in mass
> of
> > a
> > > mouse. If your analysis includes both mice and whales, then on the
> > original
> > > scale mice may appear to be changing very little in mass, while whales
> > > change a great deal - even if (relative to their sizes) both groups are
> > > changing just as much. On the other hand, if your analysis is of only
> > > whales or only mice it will make relatively little difference whether
> you
> > > use log-scaled data or the original values.
> > >
> > > I hope this is of some help. All the best, Liam
> > >
> > > Liam J. Revell, Associate Professor of Biology
> > > University of Massachusetts Boston
> > > web: http://faculty.umb.edu/liam.revell/
> > >
> > > On 7/13/2018 2:07 PM, Alyson Brokaw 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/>
> > >>
> > >>         [[alternative HTML version deleted]]
> > >>
> > >> _______________________________________________
> > >> R-sig-phylo mailing list - R-sig-phylo@r-project.org
> > >> https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
> > >> Searchable archive at http://www.mail-archive.com/r-
> > >> sig-ph...@r-project.org/
> > >>
> > >>
> > > _______________________________________________
> > > R-sig-phylo mailing list - R-sig-phylo@r-project.org
> > > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
> > > Searchable archive at http://www.mail-archive.com/r-
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> > >
> >
> >         [[alternative HTML version deleted]]
> >
> > _______________________________________________
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> > https://stat.ethz.ch/mailman/listinfo/r-sig-phylo
> > Searchable archive at
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> >
>
>
> --
> Manabu Sakamoto, PhD
> manabu.sakam...@gmail.com
>
>         [[alternative HTML version deleted]]
>
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>

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