User:Carl Boettiger/Notebook/Comparative Phylogenetics/2010/03/20

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Research

  • Bootstrap the fitted models (rather than allow the parameters to be refit, simply evaluate the likelihood of the model under each simulated dataset)
  • Directly evaluate Neyman-Pearson lemma for each pairwise model comparison.
  • Also analyze the bootstrapped likelihoods directly (and without refitting) for each model.

To Do

  • Add a summary function for the likelihood_ratio_bootstrap.R library
  • final wrapper function for infer_niches.R partitioning library
  • More error handling and documentation
  • Example of partitioning Anoles then bootstrapping
  • Bootstrapping likelihoods directly

References on thinking about bootstrapping model comparisons

ouch2ape.R function needs debugging! Works only some of the time. Seems to error on converting back from an ape2ouch conversion.

Computing

  • Adding documentation and testing the new bootstrapping functions.
  • New functions make up the bootstrapping likelihood ratio suite:
  1. BM_loglik
  2. my_update
  3. LR_bootstrap
  4. LR_bootstrap_all
  5. plot.LR_boots

The function should be easy to call on a list of ouch models.

 plot(LR_bootstrap_all(model_list))

Inline documentation provides more details. Functions in this library are contained in the likelihood_ratio_bootstrap.R file.

General Notes

Break Reading