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Phylogenetic ANOVA
by
Collyer, Michael L.
, Adams, Dean C.
in
Agglomeration
/ Comparative studies
/ Computer simulation
/ Macroevolution, morphological evolution, multivariate data
/ ORIGINAL ARTICLE
/ Permutations
/ Phenotypes
/ phylogenetic comparative methods
/ Phylogenetics
/ Phylogeny
/ Regression analysis
/ Regression models
/ Statistical analysis
/ Statistical methods
/ Statistics
/ Variance analysis
2018
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Phylogenetic ANOVA
by
Collyer, Michael L.
, Adams, Dean C.
in
Agglomeration
/ Comparative studies
/ Computer simulation
/ Macroevolution, morphological evolution, multivariate data
/ ORIGINAL ARTICLE
/ Permutations
/ Phenotypes
/ phylogenetic comparative methods
/ Phylogenetics
/ Phylogeny
/ Regression analysis
/ Regression models
/ Statistical analysis
/ Statistical methods
/ Statistics
/ Variance analysis
2018
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Do you wish to request the book?
Phylogenetic ANOVA
by
Collyer, Michael L.
, Adams, Dean C.
in
Agglomeration
/ Comparative studies
/ Computer simulation
/ Macroevolution, morphological evolution, multivariate data
/ ORIGINAL ARTICLE
/ Permutations
/ Phenotypes
/ phylogenetic comparative methods
/ Phylogenetics
/ Phylogeny
/ Regression analysis
/ Regression models
/ Statistical analysis
/ Statistical methods
/ Statistics
/ Variance analysis
2018
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Journal Article
Phylogenetic ANOVA
2018
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Overview
Phylogenetic regression is frequently used in macroevolutionary studies, and its statistical properties have been thoroughly investigated. By contrast, phylogenetic ANOVA has received relatively less attention, and the conditions leading to incorrect statistical and biological inferences when comparing multivariate phenotypes among groups remain underexplored. Here, we propose a refined method of randomizing residuals in a permutation procedure (RRPP) for evaluating phenotypic differences among groups while conditioning the data on the phylogeny. We show that RRPP displays appropriate statistical properties for both phylogenetic ANOVA and regression models, and for univariate and multivariate datasets. For ANOVA, we find that RRPP exhibits higher statistical power than methods utilizing phylogenetic simulation. Additionally, we investigate how group dispersion across the phylogeny affects inferences, and reveal that highly aggregated groups generate strong and significant correlations with the phylogeny, which reduce statistical power and subsequently affect biological interpretations. We discuss the broader implications of this phylogenetic group aggregation, and its relation to challenges encountered with other comparative methods where one or a few transitions in discrete traits are observed on the phylogeny. Finally, we recommend that phylogenetic comparative studies of continuous trait data use RRPP for assessing the significance of indicator variables as sources of trait variation.
Publisher
Wiley,Oxford University Press
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