HI Ben,
I too am curious if anyone has an R answer to this question you pose. One
non-R way I can think of is to put the data into MrBayes, which has a number of
different models available. You could probably effectively test some models
not baked in MrBayes by systematically manipulating the branchlengths you
submit to it. MrBayes provides various options for how tightly coupled the
parameter estimates should be across the different genes. It would seem
something similar might exist within R but I'm not aware of any.
Best
Luke
Luke J. Matthews | Senior Scientific Director | Activate Networks, Inc.
------------------------------
Message: 2
Date: Fri, 24 Oct 2014 16:23:58 -0400
From: Benjamin Furman <benjamin.ls.fur...@gmail.com>
To: r-sig-phylo@r-project.org
Subject: [R-sig-phylo] fitDiscrete across multiple datasets
Message-ID:
<CACyKK5XATvW36iW_MfAR5x7LZzZjG+AAN+rtY2V7jO5VPBY5=g...@mail.gmail.com>
Content-Type: text/plain; charset="UTF-8"
Hello Everyone,
I have a tree and discrete data (number of gene copies, for many genes) and
would like to use the fitDiscrete function in geiger, or something similar.
However, I would like to estimate the parameters given all of the datasets, not
just with the data for each gene. For instance, if I was using the "delta"
model to vary rates across the tree, I would like this delta value to reflect
some sort of summary value across all datasets. Does anyone have an idea as to
how this could be accomplished or perhaps point me in the right direction?
Thank you for any guidance,
Ben
--
Benjamin Furman, B.Sc. Specialization
Ph.D. Candidate, Evans Lab <http://benevanslab.wordpress.com>
McMaster University
Twitter: @Xen_Ben
Email: benjamin.ls.fur...@gmail.com, furma...@mcmaster.ca
website: http://benjaminfurman.wordpress.com
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