Hello All,
I would also like to echo a thank you for the responses. They have been
helpful. As for your summary Luke, I'll have to let the experts comment on
that.
Ben
On Mon, Oct 27, 2014 at 1:49 PM, Luke Matthews <
lmatth...@activatenetworks.net> wrote:
> Hi all,
> Thanks Brian and Liam for these very cool responses. It does make sense
> that one should be able to iterate through the characters and add the
> likelihoods, which is elegant and seems appropriate. It seems to me what
> this process accomplishes is:
>
> 1. Completely couples the estimation of delta across the characters - thus
> we are now picking the value for delta that maximizes the likelihood across
> all characters not allowing any variation in delta across them.
>
> 2. Leaves completely uncoupled the transition rate estimates across the
> different characters. Thus, the different characters are freely estimating
> rates of transition from 2 to 3 gene copies, etc. such that the rate for
> one gene has no influence on the rate for another.
>
> Do I have that right? Depending on Ben's hypothesized processes those may
> be reasonable assumptions. I can also imagine processes where somewhat
> decoupling the delta estimation or somewhat coupling the rate estimations
> would be reasonable.
>
> Best
> Luke
>
> -----Original Message-----
> From: Liam J. Revell [mailto:liam.rev...@umb.edu]
> Sent: Monday, October 27, 2014 1:26 PM
> To: omeara.br...@gmail.com; Luke Matthews
> Cc: r-sig-phylo@r-project.org
> Subject: Re: [R-sig-phylo] fitDiscrete across multiple datasets
>
> Hi all.
>
> Here is a link to code that does what Brian suggests:
>
> http://blog.phytools.org/2014/10/optimizing-tree-transformations-for.html
>
> Note that (as noted in the blog post) I have arbitrarily used ace
> internally to compute the discrete character log-likelihood, because it is
> fast. You could instead change the code in minor ways and use fitDiscrete,
> which is slower but should be more robust.
>
> The demo is fairly explicit, but let us know if it works or if I have made
> any errors.
>
> All the best, Liam
>
> Liam J. Revell, Assistant Professor of Biology University of Massachusetts
> Boston
> web: http://faculty.umb.edu/liam.revell/
> email: liam.rev...@umb.edu
> blog: http://blog.phytools.org
>
> On 10/27/2014 7:54 AM, Brian O'Meara wrote:
> > You can calculate the likelihood of the data under a given
> transformation:
> > transform the tree with a delta of 0.3 or whatever, then calculate the
> > likelihood of the data under a Brownian motion model using this
> > transformed tree. This is the same likelihood as calculating the
> > likelihood of the data under a delta model directly (assuming the same
> > delta). However, what you can do is apply the same transformation to
> > all your datasets and add the likelihood. This becomes a new
> > likelihood function. You can then optimize this (optim, nloptr, etc.).
> > I can rig up a working example later tonight -- off to teach now.
> >
> > Brian
> >
> > _______________________________________
> > Brian O'Meara
> > Assistant Professor
> > Dept. of Ecology & Evolutionary Biology U. of Tennessee, Knoxville
> > http://www.brianomeara.info
> >
> > Postdoc collaborators wanted: http://nimbios.org/postdocs/
> > Calendar: http://www.brianomeara.info/calendars/omeara
> >
> > On Mon, Oct 27, 2014 at 9:34 AM, Luke Matthews <
> > lmatth...@activatenetworks.net> wrote:
> >
> >> 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
> >>
> >> [[alternative HTML version deleted]]
> >>
> >>
> >>
> >> ------------------------------
> >>
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> >>
> >>
> >> End of R-sig-phylo Digest, Vol 81, Issue 12
> >>
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--
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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