R Hackathon 1/Trait Evolution SG

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Revision as of 16:31, 24 January 2008 by Lukeh@uidaho.edu (talk)
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  • Participants: Harmon, Hipp, Hunt


  1. Compare various implementations of the same methods (ape, geiger, OUCH, Mesquite)
  2. Improve functionality of character fitting in r
  3. Investigate statistical power of discriminating among various models
  4. Identify gaps in current implementation


  1. Evaluated the results of continuous character analyses in different packages
    • Packages are mostly consistent
    • Discrepancies come from two sources:
      • Different approaches (e.g. marginal versus joint likelihood)
      • Difficulties in finding the ML solution
    • For continuous characters:
      • geiger and OUCH tend to return the same parameter estimates
      • But they return different likelihoods
    • For discrete characters
      • geiger and mesquite are consistent, returning the same parameter estimates and likelihoods
      • geiger and ape are different
      • ape is reporting the joint likelihoods for ancestral states. This uses the single set of ancestral states that together result in the highest likelihood on the whole tree.
      • mesquite and geiger use marginal likelihoods for ancestral states. This represents the likelihood averaged over all possible ancestral character state values.
  2. Improve functionality or, at least, interpretability of output
    • GEIGER was modified to give more reliable results by a more thorough search of the likelihood surface
    • Some ape functions seem (to us) unreliable for large trees


    • Clarified why one sometimes gets different results from different programs
    • Modified GEIGER package so that results for fitting models of character evolution are more robust
    • Modified functions to rescale trees and variance-covariance matrices for non-ultrametric trees