The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
A Linear-Time Algorithm for Gaussian and Non-Gaussian Trait Evolution Models
Abstract
We developed a linear-time 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 motion-derived models like Pagel's lambda, kappa, delta, and the early-burst model; Ornstein-Uhlenbeck models to account for natural selection with possibly varying selection parameters along the tree; as well as non-Gaussian 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 3-point 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.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
Cressie, N. (1992) · Terra Nova · doi:10.1111/j.1365-3121.1992.tb00605.x · Cited by 8,823
Solving Least Squares Problems
Lawson, C. L., & Hanson, R. J. (1995) · doi:10.1137/1.9781611971217 · Cited by 7,103
Cressie, N. A. C. (1993) · Wiley Series in Probability and Statistics · doi:10.1002/9781119115151 · Cited by 6,507
Inferring the historical patterns of biological evolution
Pagel, M. (1999) · Nature · doi:10.1038/44766 · Cited by 5,200
MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmmR Package
Hadfield, J. D. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i02 · Cited by 4,799
TESTING FOR PHYLOGENETIC SIGNAL IN COMPARATIVE DATA: BEHAVIORAL TRAITS ARE MORE LABILE
Blomberg, S. P., Garland, T., & Ives, A. R. (2003) · Evolution · doi:10.1111/j.0014-3820.2003.tb00285.x · Cited by 4,678
Maximum likelihood estimation from incomplete data via the EM algorithm
Dempster, A. P. (1977) · Journal of the Royal Statistical Society Series A (Statistics in Society) · Cited by 4,543
The global diversity of birds in space and time
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K., & Mooers, A. O. (2012) · Nature · doi:10.1038/nature11631 · Cited by 3,934
Grafen, A. (1989) · Philosophical Transactions of the Royal Society of London. B, Biological Sciences · doi:10.1098/rstb.1989.0106 · Cited by 2,142
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Nayfach, S., Shi, Z. J., Seshadri, R., Pollard, K. S., & Kyrpides, N. C. (2019) · Nature · doi:10.1038/s41586-019-1058-x · Cited by 737
Evolutionary constraint and innovation across hundreds of placental mammals
Christmas, M. J., Kaplow, I. M., Genereux, D. P., Dong, M. X., Hughes, G. M., Li, X., Sullivan, P. F., Hindle, A. G., Andrews, G., Armstrong, J. C., et al. (2023) · Science · doi:10.1126/science.abn3943 · Cited by 292
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Suzuki, T. A., Fitzstevens, J. L., Schmidt, V. T., Enav, H., Huus, K. E., Mbong Ngwese, M., Grießhammer, A., Pfleiderer, A., Adegbite, B. R., Zinsou, J. F., et al. (2022) · Science · doi:10.1126/science.abm7759 · Cited by 210
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Atwood, T. B., Valentine, S. A., Hammill, E., McCauley, D. J., Madin, E. M. P., Beard, K. H., & Pearse, W. D. (2020) · Science Advances · doi:10.1126/sciadv.abb8458 · Cited by 191
Metadata
- Authors
- Tung Ho, L. S., & Ané, C.
- Venue
- Systematic Biology
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-02-04
- Volume
- 63
- Issue
- 3
- Pages
- 397-408
- ISSN
- 1063-5157 (ISSN-L)
- 1076-836X
- OpenAlex
- W2167241647
- Cited by
- 1,085
- References
- 53
- FWCI
- 31.16
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
