A LinearTime Algorithm for Gaussian and NonGaussian Trait Evolution Models
We developed a lineartime algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylog... Full description
Journal Title:  Systematic biology 2014, Vol.63 (3), p.397408 
Main Author:  Ho, Lam si Tung 
Other Authors:  Ané, Cécile 
Format:  Electronic Article 
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English 
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Quelle:  Alma/SFX Local Collection 
Publisher:  England: Oxford University Press 
ID:  ISSN: 10635157 
Link:  https://www.ncbi.nlm.nih.gov/pubmed/24500037 
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title:  A LinearTime Algorithm for Gaussian and NonGaussian Trait Evolution Models 
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ispartof:  Systematic biology, 2014, Vol.63 (3), p.397408 
description:  We developed a lineartime algorithm applicable to a large class of trait evolution models, for efficient likelihood calculations and parameter inference on very large trees. Our algorithm solves the traditional computational burden associated with two key terms, namely the determinant of the phylogenetic covariance matrix V and quadratic products involving the inverse of V. Applications include Gaussian models such as Brownian motionderived models like Pagel's lambda, kappa, delta, and the earlyburst model; OrnsteinUhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as nonGaussian models such as phylogenetic logistic regression, phylogenetic Poisson regression, and phylogenetic generalized linear mixed models. Outside of phylogenetic regression, our algorithm also applies to phylogenetic principal component analysis, phylogenetic discriminant analysis or phylogenetic prediction. The computational gain opens up new avenues for complex models or extensive resampling procedures on very large trees. We identify the class of models that our algorithm can handle as all models whose covariance matrix has a 3point structure. We further show that this structure uniquely identifies a rooted tree whose branch lengths parametrize the trait covariance matrix, which acts as a similarity matrix. The new algorithm is implemented in the R package phylolm, including functions for phylogenetic linear regression and phylogenetic logistic regression. 
language:  eng 
source:  Alma/SFX Local Collection 
identifier:  ISSN: 10635157 
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