Canonical correspondence analysis

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In applied statistics, canonical correspondence analysis (CCA) is a multivariate constrained ordination technique that extracts major gradients among combinations of explanatory variables in a dataset. 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.[1]

See also[edit]


  1. ^ McGarigal, K., S. Cushman, and S. Stafford (2000). Multivariate Statistics for Wildlife and Ecology Research. New York, New York, USA: Springer.