It's a shame there aren't awards for great threads, because this is one!
The minor twist I would throw in is that it's difficult to make universal
generalizations about the quality of ancestral state estimation. If one is
interested in the ancestral state value at node N, it might be reasonably
e in another R package. Off the top
> of my head, I recall that Nick Matzke had a similar, 'chainsaw'
> function, which you can find here and appears not to call dist.nodes:
>
> https://stat.ethz.ch/pipermail/r-sig-phylo/2011-July/001483.html
>
> Again, maybe someone on the list knows o
Hi all,
I have been hitting intermittent problems using trees generated by
ape::rphylo. Here is a reproducible example.
library(ape)
sessionInfo()
# Simulate a tree with e.g. 5 species
nspecies = 5
set.seed(123465)
tr_wFossils = rphylo(n=nspecies, birth=0.3,
Hi all,
In FigTree, there is an option (Left menu-Trees-Order nodes-increasing,
or decreasing) to plot trees and order them such that the higher nodes/tips
in the tree plot at the top or bottom of the plot.
Does anything like this exist for plotting trees in R? Or do I have to
hunt-n-peck and
of Biology
University of Massachusetts Boston
web: http://faculty.umb.edu/liam.revell/
email: liam.rev...@umb.edu
blog: http://blog.phytools.org
On 4/26/2015 6:37 PM, Nick Matzke wrote:
Hi all,
In FigTree, there is an option (Left menu-Trees-Order nodes-increasing,
or decreasing) to plot
I have written several custom mutations of various data-reading
functions to get around some of the common limitations and to read
e.g. ambiguous characters in morphology datasets.
But wouldn't the best solution in the long run be to implement the
equivalent of readseq and/or the Nexus Class
All sites are informative under likelihood, but I assume you
mean parsimony-informative, in which case all you have to do
is count which sites are either (a) uniform or (b) uniform
except for differences found only in a single species.
Probably easiest if you convert the read.nexus.data
Thanks...here's the code. However, it looks like the output
I am processing does not actually come with post-order
labeling.
I.e., in the 2nd plot below:
node 1 should be labeled 1
node 10 should be labeled 2
node 2 should be labeled 3
node 14 should be labeled 4
node 3 should be labeled 5
Somewhere I wrote a function that samples at a series of
user-set timepoints and counts the # of lineages crossing
each timepoint -- this is pretty flexible, allows for
increases/decreases in diversity etc., let me know if the
other options aren't working for you I can dig it up...
On
On 7/14/11 2:45 AM, ppi...@uniroma3.it wrote:
Thankyou NIck,
now...I have an error when running it:
Oops. Apologies, this stuff is in-house code, I haven't
organized it. Add these to the text file...
===
get_daughters - function(nodenum, t)
{
daughter_edgenums =
Oops, left that out. Here it is as text and an attached file.
You will have to do
library(ape)
and maybe
library(phangorn)
...ahead of time (hopefully not others...)
chainsaw - function(tr, timepoint=10)
{
# Take a tree and saw it off evenly across a certain
I wrote a function called chainsaw to do something like
this, it saws off all the branches at a particular time
point, then you just have to drop.tip on branch tips older
than your time period. Would that be helpful?
On 7/12/11 3:19 PM, ppi...@uniroma3.it wrote:
Hi all,
someone knows a
Here's chainsaw(). It also requires sourcing a few other
functions. Please cite me if you use it :-).
=
chainsaw - function(tr, timepoint=10)
{
# Take a tree and saw it off evenly across a certain timepoint.
# This removes any tips above the
After a fair amount of annoyment involving in shifting back
and forth between BioPython and R, I also think it would be
useful to have BioPython-like sequence management
capabilities in R. It would even be good to be able to do
some things like access NCBI genbank records and download
them,
The APE command NJ (neighbor-joining) will form a tree from
a distance matrix, so that's one option. You could do it
and then see if you get the same topology from NJ as from
your topology tree. The branch lengths will reflect
whatever distances were calculated from the data (which
might be
and the like for no
apparent reason (not specifically associated with this, though), so it
might not even be an APE issue.
Cheers!
Nick
Nick Matzke wrote on 22/03/2011 12:30:
Hi all,
This isn't crucial to my work at the moment since I am not using the PIC
option of ace to do ancestral
, the width of its CIs vary as
you might expect, so I was just surprised when PIC GLS
didn't exhibit the same behavior.
Cheers,
Nick
On 3/24/11 12:21 AM, Nick Matzke wrote:
On Wed, Mar 23, 2011 at 10:24 PM, Emmanuel Paradis
emmanuel.para...@ird.fr wrote:
Hi Nick,
With method = pic, the CIs
Hi all,
It seems to be a popular week for questions!
I am running fitContinuous on a variety of continuous trait
data. I am noticing that when the traits are in units where
the max is less than 1 (these are not ratio data, though),
many of the various models produce log-likelihoods that are
Doh! Really should have remembered that,
likelihoods-can-be-greater-than-1 is likelihood 101...
I am still a little puzzled by the dramatically different
results between rescaling and not, will try to post an
example in a sec...
On 3/7/11 12:37 PM, Nick Matzke wrote:
Hi all,
It seems
flexibility.
~Dan
On Mar 7, 2011, at 4:04 PM, Nick Matzke wrote:
Doh! Really should have remembered that,
likelihoods-can-be-greater-than-1 is likelihood 101...
I am still a little puzzled by the dramatically different
results between rescaling and not, will try to post an
example in a sec
You can call any command-line thing from R with system().
Typically I use R to write the control file (e.g. for r8s),
then do something like...
cmdstr = paste(program_name, -options, control_file,
output.log, sep= )
system(cmdstr)
Cheers!
Nick
On 2/22/11 5:42 AM, Scott Chamberlain wrote:
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