Hi,
Do you have a software program for plug and ply to measure phylogenetic signal
in a phylogenetic tree? I have a very high signal in an extinct species that
is represented in a morphology matrix but not in the molecular sequence matrix,
yet it appears to have the highest phylogenetic signa
tion with body mass. In such a case, it is generally better to
>> do the log-log regression and compute residuals. Or, you can do what I
>> described previously (see Blomberg et al., 2003, pages 720-721).
>>
>> In some cases, you might have strong a priori knowledge or particul
or example, one might compute the ratio of forelimb
> length divided by hindlimb length of lizards for some purposes. In general,
> however, the regression approach is probably safest if you want to then
> analyze a trait that is no longer correlated with body size.
>
> Che
proach is probably safest if you want to then analyze
a trait that is no longer correlated with body size.
Cheers,
Ted
Original message
Date: Wed, 23 Mar 2011 02:13:36 +0200
From: Alberto Gallano
Subject: Re: [R-sig-phylo] How to detect phylogenetic signal
(lambda) in one
Thanks Alejandro,
yes, I see this difference. I think my question is: if the goal is to assess
phylogenetic signal in a trait, after accounting for interspecific
differences in body size, which of these two alternatives is preferable?
They both seem to calculate lambda after correcting for body si
Hi Alberto,
The results differ between the two approaches because you're actually
estimating two different things.
> gls(logY ~ logX, correlation=corPagel(1, tree), method="ML")
Will give you the estimate of lambda for the residuals of the fitted model.
while:
> fitContinuous(tree, log(Y/X),
Thanks Ted and Joe, that helps a lot with my understanding.
Given then that the variables should be on a log scale, as you suggest, is
there any reason to chose a regression model estimate of lambda:
gls(logY ~ logX, correlation=corPagel(1, tree), method="ML")
where X is a body size proxy (i.e.
Ted wrote:
> Following on that, various papers (I can't remember the references)
> have argued that imagining Brownian-like evolution of body size on a
> log scale seems reasonable. That is, it should be equally easy for an
> elephant's body size to evolve 10% as for a mouse's body size to
> evo
] How to detect phylogenetic signal
(lambda) in one unscaled trait?
To: Alberto Gallano
Cc: tgarl...@ucr.edu, r-sig-phylo@r-project.org
>
>Alberto Gallano wrote:
>
>> I think I was not clear with what I said about the log
transformation, and I
>> see now what you
Alberto Gallano wrote:
> I think I was not clear with what I said about the log transformation, and I
> see now what you mean about using log-log when using regression. Though It
> does seem to me that logging two variables in a ratio context:
>
> log(Y) / log(X)
>
> or
>
> log(Y / X)
>
> wou
;> brain mass, metabolic rate), we first computed size-corrected
>values in the
>>> following way.
>>>
>>> 1. log-transform the trait and body mass.
> >>
>>> 2. Use a phylogenetic regression method (e.g. independent
>contrasts, PGLS,
>>> maybe a
take the log of that quantity.
>>
>> 4. Compute the K statistic.
>>
>> Cheers,
>> Ted
>>
>>
>>
>> Original message
>>
>> Date: Tue, 22 Mar 2011 20:37:58 +0200
>> From: A
g exponent
> (i.e., the slope from #2), then take the log of that quantity.
>
> 4. Compute the K statistic.
>
> Cheers,
> Ted
>
>
>
> Original message ----
>
>Date: Tue, 22 Mar 2011 20:37:58 +0200
>From: Alberto Gallano
>Subject: [R-sig-phylo]
t: [R-sig-phylo] How to detect phylogenetic signal (lambda) in
one unscaled trait?
To: r-sig-phylo@r-project.org
>This is a repost of an earlier question, after my colleague helped
me with
>my English:
>
>
>To calculate signal in PGLS multiple regression
This is a repost of an earlier question, after my colleague helped me with
my English:
To calculate signal in PGLS multiple regression (with say two independent
variables) I can use the following model:
lambdaModel <- gls(Y ~ X + bodymass, correlation=corPagel(1, tree),
method="ML")
This will t
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