Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within‐species variation

Summary Over the past several years, phylogenetic comparative studies have increasingly approached trait evolution in a multivariate context, with a number of taxa that continues to rise dramatically. Recent methods for phylogenetic comparative studies have provided ways to incorporate measurement e...

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Bibliographic Details
Published in:Methods in ecology and evolution Vol. 8; no. 1; pp. 22 - 27
Main Authors: Goolsby, Eric W., Bruggeman, Jorn, Ané, Cécile, Fitzjohn, Richard
Format: Journal Article
Language:English
Published: London John Wiley & Sons, Inc 01.01.2017
Subjects:
Algorithms
Brownian motion
Comparative studies
Computational efficiency
Computer applications
Covariance
fast methods
linear‐time algorithm
Missing data
Multivariate analysis
multivariate Ornstein‐Uhlenbeck
Parameter estimation
phylogenetic comparative method
phylogenetic generalized least squares
phylogenetic imputation
Phylogenetics
Phylogeny
Researchers
Species
ISSN:2041-210X, 2041-210X
Online Access:Get full text
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Summary:Summary Over the past several years, phylogenetic comparative studies have increasingly approached trait evolution in a multivariate context, with a number of taxa that continues to rise dramatically. Recent methods for phylogenetic comparative studies have provided ways to incorporate measurement error and to address computational challenges. However, missing data remain a particularly common problem, in which data are unavailable for some but not all traits of interest for a given species (or individual), leaving researchers with the choice between omitting observations or utilizing imputation‐based approaches. Here, we introduce an r implementation of PhyloPars, a tool for phylogenetic imputation of missing data and estimation of trait covariance across species (phylogenetic covariance) and within species (phenotypic covariance). Rphylopars provides expanded capabilities over the original PhyloPars interface including a fast linear‐time algorithm, thus allowing for extremely large data sets (which were previously computationally infeasible) to be analysed in seconds or minutes rather than hours. In addition to providing fast and computationally efficient implementations, we introduce in Rphylopars methods to estimate macroevolutionary parameters under alternative evolutionary models (e.g. Early‐Burst, multivariate Ornstein‐Uhlenbeck). By providing fast and computationally efficient methods with flexible options for various phylogenetic comparative approaches, Rphylopars expands the possibilities for researchers to analyse large and complex data with missing observations, within‐species variation and deviations from Brownian motion.
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ISSN:2041-210X
2041-210X
DOI:10.1111/2041-210X.12612