Hi John,
Have you seen the zoom() function? If that's not what you want, try:
data(chiroptera)
plot(chiroptera)
drop.tip(chiroptera, identify(chiroptera, tips = TRUE)$tips)
If your tree is smaller, you can do:
drop.tip(tr, interactive = TRUE)
bind.tree(), extract.clade() and root() also have
Heja John,
I used Treegraph (http://treegraph.bioinfweb.info/). It's a free tool,
small programm and it's relatively easy and intuitve to work on a
graphical basis. You can collapse, delete, add, copy etc nodes and
branches.
good luck,
cheers
M.
Am 22.03.11, schrieb John Cumbers
Here is the Email:
R phylo mailing list mailing list r-sig-phylo@r-project.org
Cheers
Alex
__
Alejandro Gonzalez Voyer
Post-doc
Estación Biológica de Doñana (CSIC)
Avenida Américo Vespucio s/n
41092 Sevilla
Spain
Tel: +34- 954 466700, ext 1749
E-mail:
Dan, Robert, others-
(i) Can you use a Likelihood ratio test to test whether a birth death model
fits your data better than a pure birth model? (likelihood scores are
obtained from LASER).
Yes, this would be valid, as the models are nested.
If I might ask, what biological inference would
This can also be done using the package diversitree, with the
function make.bd.split and variants thereof. You will probably
have to write a function to iteratively partition the tree
into all possible splits.
Hi Dan,
How would this approach deal with the problem of multiple comparisons, i.e.,
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
Hi Alberto,
I'll jump in here. Aside from anything you would do with Pagel's
lambda, Grafen's rho, or an OU or ACDC transform, it is useful to have a value
for the K statistic, as presented here:
Blomberg, S. P., T. Garland, Jr., and A. R. Ives. 2003. Testing for
phylogenetic signal
Thanks Ted,
Do I understand correctly then that the K statistic cannot be calculated in
a regression model? In other words, the trait needs to be scaled first and
then to proceed as you outlined?
Also, I am confused by the logarithmic transformation of traits in PGLS and
when calculating K. This
Hi Liam,
I've played around with your code and I see what you mean...
tiny.edge definitely influences whether it is significant or not. And
I re-ran my data playing with the edge length and that, too, is very
strongly influenced by the value of the edge length. And that makes me
very
Hi Daniel,
Offline, I'd like to see your actual tree and tip data.
Cheers,
Ted
Original message
Date: Tue, 22 Mar 2011 15:17:02 -0500
From: dwe...@life.illinois.edu
Subject: Re: [R-sig-phylo] Help Interpreting Phylogenetic ANOVA
Results
To: Liam J. Revell
Dan, All-
If I understand correctly, that's a separate issue than what Ted is
getting at; I think he's worried about the effect of data dredging by
running all possible models (as Burnham and Anderson warn against) and
not mis-parametrization of the simple model. Data-dredging will bias
us toward
Hello everyone,
first of all, I would like to apologize if this question is too basic. I've
been trying to study comparative methods and their implementation in R for
several months now, but haven't taken any formal courses on it, so maybe I'm
missing something extremely obvious. I also
Hi Dave-
If I understand correctly, that's a separate issue than what Ted is
getting at; I think he's worried about the effect of data dredging by
running all possible models (as Burnham and Anderson warn against) and
not mis-parametrization of the simple model.
I don't think I'd call this
Thanks again Ted,
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)
would influence phylogenetic
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
evolve
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 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
Dear All,
Does anyone have a working version of the tRate package for R (Moore Donogue,
2009) I could test?
I am also interested if anyone has used it and compared results with MEDUSA and
LASER.
Cheers,
Markos Alexandrou
Environment Centre Wales
School of Biological Sciences
Bangor
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