quiry-based Middle School Lesson Plan:
> "Born to Run: Artificial Selection Lab"
> http://www.indiana.edu/~ensiweb/lessons/BornToRun.html
>
> From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org]
> on behalf of Tom Schoenemann [t...@indiana.edu]
&g
gt; "Born to Run: Artificial Selection Lab"
> http://www.indiana.edu/~ensiweb/lessons/BornToRun.html
>
> From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org]
> on behalf of Tom Schoenemann [t...@indiana.edu]
> Sent: Friday, July 26, 2013 3:21 PM
>
uly 26, 2013 3:21 PM
To: Tom Schoenemann
Cc: r-sig-phylo@r-project.org
Subject: Re: [R-sig-phylo] PGLS vs lm
OK, so I haven't gotten any responses that convince me that PGLS isn't
biologically suspect. At the risk of thinking out loud to myself here, I wonder
if my finding might ha
OK, so I haven't gotten any responses that convince me that PGLS isn't
biologically suspect. At the risk of thinking out loud to myself here, I wonder
if my finding might have to do with the method detecting phylogenetic signal in
the error (residuals?):
From:
Revell, L. J. (2010). Phylogenetic
Dear Santiago,
I agree that evolving traits might have all sorts of complicated relationships,
but that doesn't mean we shouldn't rule out simple relationships first. And
besides, the most basic question one can ask - really the first question to ask
- is whether there is any association at all
Thanks Liam,
A couple of questions:
How does one do a hypothesis test on a regression, controlling for phylogeny,
if not using PGLS as I am doing? I realize one could use independent
contrasts, though I was led to believe that is equivalent to a PGLS with lambda
= 1.
I take it from what y
Hi Tom.
Joe pointed out that if we assume that our variables are multivariate
normal, then a hypothesis test on the regression is the same as a test
that cov(x,y) is different from zero.
If you insist on using lambda, one logical extension to this might be to
jointly optimize lambda for x &
Hi all,
I'm still unsure of how I should interpret results given that using PGLS to
predict group size from brain size gives different significance levels and
lambda estimates than when I do the reverse (i.e., predict brain size from
group size). Biologically, I don't think this makes any sens
-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 bi
t.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 firs
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 =
link to code here: http://www.math.chalmers.se/~krzbar/GLSME/GLSME.R
On Fri, Jul 12, 2013 at 1:59 PM, Joe Felsenstein wrote:
>
> 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,
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 Theo
Thanks Liam,
OK, I'm starting to understand this better. But I'm not sure what now to do.
Given that the mathematics are such that a PGLS gives significance in one
direction, but not in another, what is the most convincing way to show that the
two variables really ARE associated (at some level
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 wro
ser=iSSbrhwJ
>
> Inquiry-based Middle School Lesson Plan:
> "Born to Run: Artificial Selection Lab"
> http://www.indiana.edu/~ensiweb/lessons/BornToRun.html
>
> From: r-sig-phylo-boun...@r-project.org [r-sig-phylo-boun...@r-project.org]
> on behalf of Tom Schoeneman
Thanks Joe. That's a very clear way of explaining why models that assume
a fixed & common correlation structure (OLS in lm, contrasts regression,
or gls::corBrownian) are 'symmetric' (i.e., the same P-value is obtained
by fitting y~x vs. x~y); whereas models that do not (e.g., corPagel) are
not
If the "regressions" are being done in a model which implies
that the two variables are multivariate normal, then we can
simply estimate the parameters of that joint distribution,
which are of course the two means and the three elements of the
covariance matrix.
If we then test whether Cov(X
Hi Tom.
This is actually not a property of GLS - but of using different
correlation structures when fitting y~x vs. x~y. When you set
correlation=corPagel(...,fixed=FALSE) (the default for corPagel), gls
will fit Pagel's lambda model to the residual error in y|x. The fitted
value of lambda wi
uly 11, 2013 11:19 AM
To: Emmanuel Paradis
Cc: r-sig-phylo@r-project.org
Subject: Re: [R-sig-phylo] PGLS vs lm
Thanks Emmanuel,
OK, so this makes sense in terms of the math involved. However, from a
practical, interpretive perspective, shouldn't I assume this to mean that we
actually cannot say
Thanks Emmanuel,
OK, so this makes sense in terms of the math involved. However, from a
practical, interpretive perspective, shouldn't I assume this to mean that we
actually cannot say (from this data) whether VarA and VarB ARE actually
associated with each other? In the real world, if VarA is
Hi Tom,
In an OLS regression, the residuals from both regressions (varA ~ varB
and varB ~ varA) are different but their distributions are (more or
less) symmetric. So, because the residuals are independent (ie, their
covariance is null), the residual standard error will be the same (or
very c
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