R Hackathon 1/Trait Evolution SG

• Participants: Harmon, Hipp, Hunt

Targets.

1. Compare various implementations of the same methods (ape, geiger, OUCH, Mesquite)
2. Improve functionality of character fitting in R
3. Identify gaps in current implementation

Accomplishments.

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 different likelihoods.
       • the Brownian motion model is relatively easy to fit, with results that seem to be compatible across packages
       • the Ornstein-Uhlenbeck model with one optimum is a more difficult model to fit. There is often a large, nearly flat ridge in the log-likelihood surface, and different optimization routines and settings may stop at different places along this ridge. This can yield rather different parameter estimates, although the log-likelihoods should not vary as much.
   • 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.
     • This also means that you get different ancestral state reconstructions from ape and mesquite
2. Improved functionality
   • geiger was modified to give more reliable results by a more thorough search of the likelihood surface (fitContinuous)
   • geiger can deal with a more general set of discrete character models (fitDiscrete)
   • geiger's tree transformations now work for nonultrametric trees
3. Identify gaps in current implementation
   • The main gap, from an end-user perspective, is obtaining estimates of ancestral character states in R
   • ape does this, but only for joint likelihoods, and the function sometimes has trouble finding the ML solution
   • There is no way to get marginal ancestral character states for discrete characters in r other than interfacing with Mesquite

To do

1. Implement "white noise" and Brownian motion with a trend models in geiger's fitContinuous
2. Investigate statistical properties of these methods
   • Which models can we tell apart?
   • How much data are needed?
   • Are parameter estimates biased?
   • Performance of different model selection criteria (LRT, information criteria)