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: |
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Returns: | Object that stores the computed eigenvalues, the proportion explained by each of them (per unit), transformed coordinates for feature and samples, biplot scores, sample constraints, etc. |
Return type: |
See also
Notes
The algorithm is based on [1], S 11.1, and is expected to
give the same results as rda(y, x)
in R’s package vegan.
The eigenvalues reported in vegan are re-normalized to
\(\sqrt{\frac{s}{n-1}}\) n is the number of samples,
and s is the original eigenvalues. Here we will only return
the original eigenvalues, as recommended in [1].
References
[1] | (1, 2, 3) Legendre P. and Legendre L. 1998. Numerical Ecology. Elsevier, Amsterdam. |