Latent structureMultivariate Analysis
Redundancy Analysis
Redundancy Analysis (RDA) is a multivariate technique developed by van den Wollenberg (1977) that combines multiple regression and principal component analysis. RDA finds linear combinations of predictor variables that best predict variation in response variables, making it ideal for understanding how sets of predictors collectively explain multivariate outcomes.
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Sources
- van den Wollenberg, A. L. (1977). Redundancy analysis: An alternative for canonical correlation analysis. Psychometrika, 42(2), 207-219. DOI: 10.1007/BF02294050 ↗
- Legendre, P., & Legendre, L. (1998). Numerical Ecology (2nd ed.). Elsevier. ISBN: 9780444892546
- Knudsen, S., Andersen, T., & Hansen, J. (2007). Redundancy analysis of multivariate data using PLS. Chemometrics and Intelligent Laboratory Systems, 87(2), 264-272. DOI: 10.1016/j.chemolab.2007.02.005 ↗