Principal components analysis

Syms, C. (2008) Principal components analysis. In: Jorgensen, Sven Erik, and Fath, Brian D., (eds.) Encyclopedia of Ecology. Elsevier, Oxford, pp. 2940-2949.

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Principal components analysis (PCA) is a multivariate ordination technique used to display patterns in multivariate data. It aims to graphically display the relative positions of data points in fewer dimensions while retaining as much information as possible, and explore relationships between dependent variables. It is a hypothesis-generating technique that is intended to describe patterns in a data table, rather than test formal statistical hypotheses. PCA assumes linear responses of variables, and works best over short ecological gradients, with few zeroes in the data. It has a range of applications other than data display including multiple regression, and variable reduction.

Item ID: 28020
Item Type: Book Chapter (Reference)
ISBN: 978-0-08-045405-4
Keywords: biostatistics, principal components, analysis
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Date Deposited: 11 Jul 2013 05:51
FoR Codes: 05 ENVIRONMENTAL SCIENCES > 0501 Ecological Applications > 050102 Ecosystem Function @ 50%
05 ENVIRONMENTAL SCIENCES > 0502 Environmental Science and Management > 050205 Environmental Management @ 50%
SEO Codes: 96 ENVIRONMENT > 9609 Land and Water Management > 960903 Coastal and Estuarine Water Management @ 50%
96 ENVIRONMENT > 9605 Ecosystem Assessment and Management > 960507 Ecosystem Assessment and Management of Marine Environments @ 50%
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