slycat.cca¶
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slycat.cca.
cca
(X, Y, scale_inputs=True, force_positive=None, significant_digits=None)[source]¶ Compute Canonical Correlation Analysis (CCA).
Parameters: - X (numpy.ndarray) – \(M \times I\) matrix containing \(M\) observations and \(I\) input features.
- Y (numpy.ndarray) – \(M \times O\) matrix containing \(M\) observations and \(O\) output features.
- scale_inputs (bool, optional) – Scale input and output features to unit variance.
- force_positive (integer, optional) – If specified, flip signs in the x, y, x_loadings, and y_loadings output values so that the values in row \(n\) of y_loadings are all positive.
- significant_digits (integer, optional) – Optionally specify the number of significant digits used to compute the X and Y ranks.
Returns: - x (numpy.ndarray) – \(M \times C\) matrix containing input metavariable values for \(M\) observations and \(C\) CCA components.
- y (numpy.ndarray) – \(M \times C\) matrix containing output metavariable values for \(M\) observations and \(C\) CCA components.
- x_loadings (numpy.ndarray) – \(I \times C\) matrix containing weights for \(I\) input variables and \(C\) CCA components.
- y_loadings (numpy.ndarray) – \(O \times C\) matrix containing weights for \(O\) output variables and \(C\) CCA components.
- r2 (numpy.ndarray) – length-\(C\) vector containing \(r^2\) values for \(C\) CCA components.
- wilks (numpy.ndarray) – length-\(C\) vector containing the likelihood-ratio for \(C\) CCA components.