skbio.stats.ordination.
rda
(y, x, scale_Y=False, scaling=1)[source]¶Compute redundancy analysis, a type of canonical analysis.
State: Experimental as of 0.4.0.
It is related to PCA and multiple regression because the explained variables y are fitted to the explanatory variables x and PCA is then performed on the fitted values. A similar process is performed on the residuals.
RDA should be chosen if the studied gradient is small, and CCA when it’s large, so that the contingency table is sparse.
Parameters: | y : pd.DataFrame
x : pd.DataFrame
scale_Y : bool, optional
scaling : int
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Returns: | OrdinationResults
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See also
Notes
The algorithm is based on [R86], S 11.1, and is expected to
give the same results as rda(y, x)
in R’s package vegan.
References
[R86] | (1, 2, 3) Legendre P. and Legendre L. 1998. Numerical Ecology. Elsevier, Amsterdam. |