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A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH-DIMENSIONAL MULTIVARIATE DATA

Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is in... Full description

Journal Title: Evolution 2014-09, Vol.68 (9), p.2675-2688
Main Author: Adams, Dean C
Format: Electronic Article Electronic Article
Language: English
Subjects:
Publisher: United States: Blackwell Publishing Ltd
ID: ISSN: 0014-3820
Link: https://www.ncbi.nlm.nih.gov/pubmed/24899536
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title: A METHOD FOR ASSESSING PHYLOGENETIC LEAST SQUARES MODELS FOR SHAPE AND OTHER HIGH-DIMENSIONAL MULTIVARIATE DATA
format: Article
creator:
  • Adams, Dean C
subjects:
  • Analysis
  • Analysis of variance
  • Animals
  • Biological Evolution
  • Biological taxonomies
  • Causal covariation
  • Classification - methods
  • Covariance matrices
  • Datasets
  • Evolution
  • Geometric morphometrics
  • Head - anatomy & histology
  • Least squares
  • Least-Squares Analysis
  • macroevolution
  • macroevolution, morphological evolution
  • Models, Statistical
  • morphological evolution
  • Morphometrics (Biology)
  • Multivariate Analysis
  • Phenotypic traits
  • phylogenetic comparative method
  • Phylogenetics
  • Phylogeny
  • Research
  • Statistics
  • Urodela - anatomy & histology
  • Urodela - classification
  • Usage
ispartof: Evolution, 2014-09, Vol.68 (9), p.2675-2688
description: Studies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high-dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high-dimensional datasets.
language: eng
source:
identifier: ISSN: 0014-3820
fulltext: no_fulltext
issn:
  • 0014-3820
  • 1558-5646
url: Link


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descriptionStudies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high-dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high-dimensional datasets.
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subjectAnalysis ; Analysis of variance ; Animals ; Biological Evolution ; Biological taxonomies ; Causal covariation ; Classification - methods ; Covariance matrices ; Datasets ; Evolution ; Geometric morphometrics ; Head - anatomy & histology ; Least squares ; Least-Squares Analysis ; macroevolution ; macroevolution, morphological evolution ; Models, Statistical ; morphological evolution ; Morphometrics (Biology) ; Multivariate Analysis ; Phenotypic traits ; phylogenetic comparative method ; Phylogenetics ; Phylogeny ; Research ; Statistics ; Urodela - anatomy & histology ; Urodela - classification ; Usage
ispartofEvolution, 2014-09, Vol.68 (9), p.2675-2688
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abstractStudies of evolutionary correlations commonly use phylogenetic regression (i.e., independent contrasts and phylogenetic generalized least squares) to assess trait covariation in a phylogenetic context. However, while this approach is appropriate for evaluating trends in one or a few traits, it is incapable of assessing patterns in highly multivariate data, as the large number of variables relative to sample size prohibits parametric test statistics from being computed. This poses serious limitations for comparative biologists, who must either simplify how they quantify phenotypic traits, or alter the biological hypotheses they wish to examine. In this article, I propose a new statistical procedure for performing ANOVA and regression models in a phylogenetic context that can accommodate high-dimensional datasets. The approach is derived from the statistical equivalency between parametric methods using covariance matrices and methods based on distance matrices. Using simulations under Brownian motion, I show that the method displays appropriate Type I error rates and statistical power, whereas standard parametric procedures have decreasing power as data dimensionality increases. As such, the new procedure provides a useful means of assessing trait covariation across a set of taxa related by a phylogeny, enabling macroevolutionary biologists to test hypotheses of adaptation, and phenotypic change in high-dimensional datasets.
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