Canonical correspondence analysis
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 and that the independent variables are consistent within the sample site and error-free.[1]
See also
- Canonical correlation analysis (CANCOR)
References
- ↑ McGarigal, K., S. Cushman, and S. Stafford (2000). Multivariate Statistics for Wildlife and Ecology Research. New York, New York, USA: Springer.