Hello all,
I'm currently trying to measure a parameter over a large number of
large trees (1700 tips), and part of this calculation requires
knowing the tip taxa descended from each node (I must compare the
difference in tip values among taxa descended from a node). Because I
must do this many
Marguerite and others-
I had missed that Scales et al paper. Thank for pointing it out. I
have also had to deal with variables that were bounded by 0 and 1
previously, and the use of the logit is something I'll have to try
when I go back to those analyses.
Although I am interested in
Hi Dave.
It seems like one way to get these values faster would be to count the
number of descendants as the tree is read in to R. This is possible
because when the ) character is reached by the Newick parser, all
descendant nodes (and tips) have already been created in memory.
This was
Klaus-
Oh, that worked rather splendid! Thanks for letting me know.
system.time(desc-Descendants(res_tree,edge_end))
user system elapsed
1.560.001.56
-Dave
On Sun, Mar 6, 2011 at 3:22 PM, Klaus Schliep klaus.schl...@gmail.com wrote:
Hi David,
you can supply Descendents (from
Hi David,
I am not sure what you mean. If you add a tiny value to all of your data, then
all you've done is shifted the mean ever so slightly. You then log-transform to
put the variables on a scale unbounded by 0, and then you measure phylogenetic
signal. The BM process models evolutionary
Hi David and others,
prop.part() with a single tree does what you want:
set.seed(123)
res_tree - rtree(1700)
system.time(desc2 - prop.part(res_tree))
user system elapsed
0.050 0.000 0.053
edge_end - unique(res_tree$edge[,1])
system.time(desc1 - Descendants(res_tree,edge_end))
