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

Comparative Phylogenetics

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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 The original Neyman-Pearson paper Testing Statistical Hypotheses Text by Lehmann & Romano Permutation, Parametric and Bootstrap Tests of Hypotheses by Good Thanks to Peter on these; Bibsonomy collection 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: BM_loglik my_update LR_bootstrap LR_bootstrap_all 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 P values another commenting forum on open science Doing vs Discussing Science Cameron's research cycle Nature's position on pre-published blogging science