Hi Emmanuel,
I'm wondering why prop.clade always returns 100% for the first node?
For example:
library(ape)
a <- as.DNAbin(matrix('a',10,10)) # DNA data with no variation
rownames(a) <- paste('tip',1:10,sep="")
f <- function(x) nj(dist.dna(x[sample(nrow(x)), ]))
tr <- f(a)
o <- boot.phylo(tr, a, f
Joe,
I agree with what you wrote. To me, this makes even stronger the point
of looking at the distribution of pairwise distances before estimating
the tree. I'll modify boot.phylo() so that it randomizes rows by default.
Besides of this, Klaus Schliep and I are working on ways to improve
cod
Emmanuel wrote:
Is it a problem with ties or with identical sequences? I guess you can
solve the latter easily (eg, using the haplotype function in
pegas), and
this will solve the vast majority of ties. Other cases of ties will
certainly not result in such high bootstrap values (that's my
Hi Alastair,
Alastair Potts wrote on 08/05/2011 00:07:
Hi Emmanuel (Klaus and Joe),
The example data was meant to demonstrate that the tie-breaking in nj is
affecting the bootstrap results - or rather the lack of any way to deal
with tie breaking.
I've noticed that a bunch of identical sequen
Hi Emmanuel (Klaus and Joe),
The example data was meant to demonstrate that the tie-breaking in nj is
affecting the bootstrap results - or rather the lack of any way to deal
with tie breaking.
I've noticed that a bunch of identical sequences form a 'polytomy' in my
real dataset (but obviously
Hi Alastair, Klaus & Joe,
Before doing the tree, you should do some preliminary data explorations,
such as:
d <- dist.dna(a)
hist(d)
summary(d)
That'd show you any tree estimation procedure (not only NJ) has very
little meaning -- just like you do plot(x, y) before doing lm(y ~ x).
Best,
Hi Klaus and Joe,
Thanks very much for your responses.
From Klaus:
it is not that surprising. NJ normally does not produce poytomies,
just edge weights of length 0. How these are broken may depends from
the input order (from labels in the distance matrix like in this
implementation) or could be
Klaus Schliep wrote --
> it is not that surprising. NJ normally does not produce poytomies,
> just edge weights of length 0. How these are broken may depends from
> the input order (from labels in the distance matrix like in this
> implementation) or could be broken randomly. I added some code b
Hi Alastair,
it is not that surprising. NJ normally does not produce poytomies,
just edge weights of length 0. How these are broken may depends from
the input order (from labels in the distance matrix like in this
implementation) or could be broken randomly. I added some code below
to highlight i
Good day all,
I noticed something that I would consider an anomaly when analysing one
of my trees with NJ.
A 'polytomy' of samples contained many bootstrap values of 100 between
samples. I was looking at the total change in bootstrap values for all
nodes when I picked this up (as the signal in
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