# slycat.cca¶

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. 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.