User:Carl Boettiger/Notebook/Comparative Phylogenetics/2010/06/04

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 * style="background-color: #EEE"|[[Image:owwnotebook_icon.png|128px]] Comparative Phylogenetics
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Comparative Phylogenetics Talk
Practice talk in Wainwright lab meeting next Weds. 12 minutes.


 * Introduce question: Inferring the number of regimes
 * Context relative to existing methods
 * Statistical challenges -- model-dependent phylogenetic signal problem
 * Labrid example

Frameworks for approaching regimes question

 * wrightscape
 * phylogenetically corrected clustering


 * speed/feasibility concerns, rate limiting steps.
 * model realism / representation concerns
 * statistical concerns

Returning to Wrightscape approach
Haven't worked on the full problem of inferring multiple peaks directly from comparative data using the joint probability calculation since the beginning of this notebook, so about time to return to the direct problem and compare with the more heuristic approach of "phylogenetically corrected clustering" and also synthesize the work on uncertainty which has been a dominate theme thus far (add category label for this group!)

The direct calculation is still going by the name wrightscape, and the project (without its svn history unforunately) has been migrated to git and pushed to github. So thus far the current code for this project is independent of that in Comparative-Phylogenetics project repository. This is the home of the Wrightscape repository.

Since it's been a while since I've worked on this project, I begin by refreshing myself as to the organizational structure of the code. The callgraph provides a good summary:



Generated with valgrind and visualized in kcachegrind by: make debug valgrind --tool=callgrind ./jointprob kcachegrind callgrind.out.*

kcachegrind then displays the call graph, (and other information), which can save as png.

We see the code compare a likelihood calculation using the linear solutions compared with that of the matrix approach for the sample data (which includes both trivially small tree example and the bimac dataset from the Butler & King (2004) paper) for BM fit, OU fit, and the matrix approach OU fit. The matrix approach clearly takes the dominate amount of time, though results agree closely with the exact linear solutions in both examples:

analytic bm llik = -8.648713 analytic ou llik = -8.570838 matrix llik = -8.582334 analytic bm llik = -65.106558 analytic ou llik = -61.906121 matrix llik = -61.927070

So what is the matrix approach implement exactly? Desending the call graph further, we see it is mostly computing the transition matrix that sits at each node (which it can do directly using the analytic OU solution). Then the matrix solution to the joint probability is specified by:

So what generality is gained by this approach? While the matrix formulation is general, this requires the direct calculation applied to each node. Should be able to simultaneously solve everything in a particular regime directly, if there is a meaningful way to MCMC over larger regime partitions rather than over node assignments.

Evolution meeting

 * Aaron King's student, Clayton Cressler talk
 * Liam Revell talk
 * Luke Harmon talks
 * Michael Alfaro talk


 * Hilmar Lapp, Rod Page
 * My talk
 * Search all talks


 * Doesn't look like Andrew Hipp is listed. See my  mattice entry from April.


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