skbio.stats.ordination.
ca
(X, scaling=1)[source]¶Compute correspondence analysis, a multivariate statistical
State: Experimental as of 0.4.0. technique for ordination.
In general, rows in the data table will correspond to samples and columns to features, but the method is symmetric. In order to measure the correspondence between rows and columns, the \(\chi^2\) distance is used, and those distances are preserved in the transformed space. The \(\chi^2\) distance doesn’t take double zeros into account, and so it is expected to produce better ordination that PCA when the data has lots of zero values.
It is related to Principal Component Analysis (PCA) but it should be preferred in the case of steep or long gradients, that is, when there are many zeros in the input data matrix.
Parameters: | X : pd.DataFrame
scaling : {1, 2}
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Returns: | OrdinationResults
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Raises: | NotImplementedError
ValueError
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See also
Notes
The algorithm is based on [R87], S 9.4.1., and is expected to give the same
results as cca(X)
in R’s package vegan.
References
[R87] | (1, 2) Legendre P. and Legendre L. 1998. Numerical Ecology. Elsevier, Amsterdam. |