Dear Greg.
I've never tried this package; however, it's not difficult to either
substitute the set of values that are the branch lengths of a "phylo"
object with a different set of values, or to multiple them by a
different set.
For example, if you have the set of rates for your trait in a vector
ordered by the edge lengths of the tree, you could just take your
original tree (phy), substitute the edge-specific rates (sig2), then
plot your tree as follows:
phy.sig2<-phy
phy.sig2$edge.length<-sig2
phytools::plotTree(phy.sig2)
Alternatively, if you want to show the amount of evolution that's
expected to occur on each edge, that would be equivalent to the product
of the edge length and it's edge-specific rate.
phy.sig2<-phy
phy.sig2$edge.length<-phy$edge.length*sig2
phytools::plotTree(phy.sig2)
I hope I'm understanding the problem correctly.
All the best, Liam
Liam J. Revell
University of Massachusetts Boston [Assoc. Prof.]
Universidad Católica de la Ssma Concepción [Adj. Res.]
Web & phytools:
http://faculty.umb.edu/liam.revell/, http://www.phytools.org,
http://blog.phytools.org
On 6/8/2021 4:09 PM, Gregory Mutumi wrote:
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BayesPCM9.Log.txt
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BayesPCM9.Output.trees
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BayesPCM9.Schedule.txt
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BayesPCM9.Stones.txt
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BayesPCM9.VarRates.txt
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Dear All
Is there a way in BTprocessR or BTRTools (or any other package) to plot a
tree in such a way that the branch lengths are scaled by rate. Colour
gradients are a bit difficult to compare across many structures. I am
looking for a plot where faster rates make branches longer and slower rates
produce shorter branches.
The plotshifts function in BTRTools plots the tree with color gradient
depicting the rates. I am thinking maybe there is an option I am missing
out that simply does the trick to plot branches scaled by rate?
Please also help me with how to make the legend not plot over the scale. I
am not getting how to command the legend positions, I get errors if I try
the available option, maybe I am missing out something.
My code goes like this:
btresults9 <- rjpp(rjlog = "BayesPCM9.VarRates.txt", rjtrees =
"BayesPCM9.Output.trees",tree = my.tree, burnin = 0, thinning = 1, verbose
= TRUE)
plotShifts(btresults9, plot.options = list(layout = c("e")), tips = T)
I have also attached the files here
Thank you.
Regards,
Greg.
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