Maybe what we need here is an approach based on
simultaneous equations (aka structural equation models), but I'm not
aware whether this exists in a phylogenetic framework.
Exactly! And it will need to incorporate measurement error in all variables
as well as, eventually, uncertainly in the phylogenetic topology and branch
lengths.
Good luck,
Ted
From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org] on
behalf of Emmanuel Paradis [emmanuel.para...@ird.fr]
Sent: Sunday, July 14, 2013 3:18 AM
To: Joe Felsenstein
Cc: r-sig-phylo@r-project.org
Subject: Re: [R-sig-phylo] PGLS vs lm
Hi all,
I would like to react a bit on this issue.
Probably one problem is that the distinction correlation vs.
regression is not the same for independent data and for phylogenetic data.
Consider the case of independent observations first. Suppose we are
interested in the relationship y = b x + a, where x is an environmental
variable, say latitude. We can get estimates of b and a by moving to 10
well-chosen locations, sampling 10 observations of y (they are
independent) and analyse the 100 data points with OLS. Here we cannot
say anything about the correlation between x and y because we controlled
the distribution of x. In practice, even if x is not controlled, this
approach is still valid as long as the observations are independent.
With phylogenetic data, x is not controlled if it is measured on the
species -- in other words it's an evolving trait (or intrinsic
variable). x may be controlled if it is measured outside the species
(extrinsic variable) such as latitude. So the case of using regression
or correlation is not the same than above. Combining intrinsic and
extinsic variables has generated a lot of debate in the literature.
I don't think it's a problem of using a method and not another, but
rather to use a method keeping in mind what it does (and its
assumptions). Apparently, Hansen and Bartoszek consider a range of
models including regression models where, by contrast to GLS, the
evolution of the predictors is modelled explicitly.
If we want to progress in our knowledge on how evolution works, I think
we have to not limit ourselves to assess whether there is a
relationship, but to test more complex models. The case presented by Tom
is particularly relevant here (at least to me): testing whether group
size affects brain size or the opposite (or both) is an important
question. There's been also a lot of debate whether comparative data can
answer this question. Maybe what we need here is an approach based on
simultaneous equations (aka structural equation models), but I'm not
aware whether this exists in a phylogenetic framework. The approach by
Hansen and Bartoszek could be a step in this direction.
Best,
Emmanuel
Le 13/07/2013 02:59, Joe Felsenstein a écrit :
Tom Schoenemann asked me:
With respect to your crankiness, is this the paper by Hansen that you are
referring to?:
Bartoszek, K., Pienaar, J., Mostad, P., Andersson, S., Hansen, T. F.
(2012). A phylogenetic comparative method for studying multivariate
adaptation. Journal of Theoretical Biology, 314(0), 204-215.
I wrote Bartoszek to see if I could get his R code to try the method
mentioned in there. If I can figure out how to apply it to my data, that
will be great. I agree that it is clearly a mistake to assume one variable
is responding evolutionarily only to the current value of the other
(predictor variables).
I'm glad to hear that *somebody* here thinks it is a mistake (because it
really is). I keep mentioning it here, and Hansen has published extensively
on it, but everyone keeps saying Well, my friend used it, and he got tenure,
so it must be OK.
The paper I saw was this one:
Hansen, Thomas F Bartoszek, Krzysztof (2012). Interpreting the evolutionary
regression: The interplay between observational and biological errors in
phylogenetic comparative studies. Systematic Biology 61 (3): 413-425. ISSN
1063-5157.
J.F.
Joe Felsenstein j...@gs.washington.edu
Department of Genome Sciences and Department of Biology,
University of Washington, Box 355065, Seattle, WA 98195-5065 USA
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