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Principal components for allometric analysis

Bibliographic Data

ID8316734
AuthorsRoberts S Corruccini (Southern Illinois University Carbondale, corresponding author)
Year1983
Volume60
Issue4
Pages451-453
Publication date1983-04-01
Open AccessYes
TypeARTICLE
VenueAmerican Journal of Physical Anthropology (JOURNAL)
Journal identifiersISSN: 0002-9483 • E-ISSN: 1096-8644
PublisherWiley (PUBLISHER • GB)
DOI10.1002/ajpa.1330600406
PMID6846515
OpenAlexW2138396362
LanguageEN
Citations received8
References cited8

Logarithmic bivariate regression slopes and logarithmic principal component coefficient ratios are two methods for estimating allometry coefficients corresponding to a in the classic power formula Y = BX a . Both techniques depend on high correlation between variables. Interpretation is logically limited to the variables included in analysis. Principal components analysis depends also on relatively uniform intercorrelations; given this, it serves satisfactorily as a method for summarizing many bivariate combinations. Unmodified major principal component coefficients cannot represent scaling to body weight; rather, they represent scaling to a composite size vector which usually is highly correlated with body size or weight but has an unspecified allometry. Thus, the concepts of proportionality and of isometry must be kept distinct

Allometry · Biology · Bivariate analysis · Geometry · Linear regression · Logarithm · Mathematical analysis · Principal component analysis · Regression · Regression analysis · Scaling · Statistics · Animal Behavior and Reproduction · Animal Behavior and Welfare Studies · Ecology · Mathematics · Primate Behavior and Ecology

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  • Note

    Pierre Jolicoeur•Biometrics•1963

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Citations per year0.2
Citation span1985 - 2019 (35)
Highly citedNo

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