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Cited by 91Evolution and Paleontology StudiesMorphological variations and asymmetryAnimal Behavior and Reproduction

Phylogenetic Regression for Binary Dependent Variables

Ives, Anthony R., Garland, Theodore·2014·Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology
DOI 10.1007/978-3-662-43550-2_9
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  • This is good news for those who want to apply models with Gaussian error structure to binomial or count data when models with other structures fail to reach convergence or produce nonsensical estimates.

    “This is good news also for those who want to apply models with Gaussian error structure to binomial or count data when models with other structures fail to reach convergence or produce nonsensical estimates (e.g., Ives & Garland, 2014; Plaschke et al., 2019).”

    From Violating the normality assumption may be the lesser of two evils (2021) · cites this paper as Ives & Garland, 2014

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References 55 · Cited by 91

Authors
Ives, A. R., & Garland, T.
Affiliations
  • University of Wisconsin–Madison (US)
  • University of California, Riverside (US)
Corresponding author
Ives, Anthony R.
ORCID
Venue
Modern Phylogenetic Comparative Methods and Their Application in Evolutionary Biology
Publisher
Springer Berlin Heidelberg
Published
2014
Pages
231-261
DOI
10.1007/978-3-662-43550-2_9
OpenAlex
W207291124
Cited by
91
References
55
Citations by year
2026: 6 · 2025: 4 · 2024: 6 · 2023: 4 · 2022: 11 · 2021: 6 · 2020: 9 · 2019: 8 · 2018: 18 · 2017: 7 · 2016: 8 · 2015: 3 · 2014: 1
FWCI
16.62
Type
book chapter
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
Springer eBooks (publishedVersion)
Topics
  • Physical Sciences › Earth and Planetary Sciences › Paleontology › Evolution and Paleontology Studies
  • Physical Sciences › Mathematics › Geometry and Topology › Morphological variations and asymmetry
  • Life Sciences › Agricultural and Biological Sciences › Ecology, Evolution, Behavior and Systematics › Animal Behavior and Reproduction
Keywords
phylogenetic regression, binary dependent variables, phylogenetic signal, MCMCglmm, type I error, logistic regression, group size, simulation study

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