Abstract
In the previous chapter, Bray-Curtis ordination was explained, and more recently developed multivariate techniques were mentioned. Principal component analysis (PCA), correspondence analysis (CA), discriminant analysis (DA) and non-metric multidimensional scaling (NMDS) can be used to analyse data without explanatory variables, whereas canonical correspondence analysis (CCA) and redundancy analysis (RDA) use both response and explanatory variables. In this chapter, we present PCA and RDA, and in the next chapter CA and CCA are discussed.
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© 2007 Springer Science + Business Media, LLC
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(2007). Principal component analysis and redundancy analysis. In: Analysing Ecological Data. Statistics for Biology and Health. Springer, New York, NY. https://doi.org/10.1007/978-0-387-45972-1_12
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DOI: https://doi.org/10.1007/978-0-387-45972-1_12
Publisher Name: Springer, New York, NY
Print ISBN: 978-0-387-45967-7
Online ISBN: 978-0-387-45972-1
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