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CCA was developed in 1986 by Cajo ter Braak and implemented in the program CANOCO, an extension of DECORANA. To date, CCA is one of the most popular multivariate methods in ecology, despite the availability of contemporary alternatives. CCA was originally derived and implemented using an algorithm
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The requirements of a CCA are that the samples are random and independent. Also, the data are categorical and that the independent variables are consistent within the sample site and error-free. The original publication states the need for equal species tolerances, equal species maxima, and
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technique that determines axes from the response data as a unimodal combination of measured predictors. CCA is commonly used in
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89:"Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis"
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in order to extract gradients that drive the composition of ecological communities. CCA extends
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of weighted averaging, though
Legendre & Legendre (1998) derived an alternative algorithm.
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equispaced or uniformly distributed species optima and site scores.
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with regression, in order to incorporate predictor variables.
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Multivariate
Statistics for Wildlife and Ecology Research
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233:McGarigal, K., S. Cushman, and S. Stafford (2000).
128:"History of Canonical Correspondence Analysis"
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132:Visualization and Verbalization of Data
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237:. New York, New York, USA: Springer.
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267:. You can help Knowledge (XXG) by
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18:canonical correspondence analysis
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34:Correspondence Analysis (CA)
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16:In multivariate analysis,
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263:-related article is a
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314:Dimension reduction
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Text is available under the Creative Commons Attribution-ShareAlike License. Additional terms may apply.
