gemmr.estimators.SVDCCA

class gemmr.estimators.SVDCCA(n_components=1, covariance='empirical', scale=False, std_ddof=1, cov_out_of_bounds='nan', normalize_weights=True, calc_loadings=False)

Canonical Correlation Analysis estimator based on singular value decomposition.

Parameters:

  • n_components (int >= 1) – number of between-set components to estimate

  • covariance (str) – must be ‘empirical’

  • scale (bool) – whether to divide each feature by its standard deviation before fitting

  • std_ddof (int >= 0) – when calculating standard deviations and covariances, they are normalized by 1 / n-std_ddof

  • cov_out_of_bounds (str) – if fitting results in a canonical correlation > 1, which indicates some problem, potentially that too few samples were used raise an error if cov_out_of_bounds=='raise', set association strengths and weight vectors to np.nan if cov_out_of_bounds=='nan', or ignore the problem if cov_out_of_bounds == 'ignore'

  • normalize_weights (bool (default True)) – If normalize_weights == False weights are calculated as in Härdle and Simar (2015). In this case they are not normalized (i.e. || w ||_2 != 1). Set normalize_weights to True to get normalized weights.

corrs_

contains the canonical correlations. This is the quantity that’s maximized by CCA

Type:

np.ndarray (n_components,)

assocs_

Identical to corrs_. assocs_ is the common identifier used in in SVDPLS, SVDCCA, NIPALSPLS and NIPALSCCA for the association strength that is optimized by each particular method

Type:

np.ndarray (n_components,)

References

Härdle and Simar, Applied Multivariate Statistical Analysis, Chapter 16, Springer (2015)

__init__(n_components=1, covariance='empirical', scale=False, std_ddof=1, cov_out_of_bounds='nan', normalize_weights=True, calc_loadings=False)

Methods

__init__([n_components, covariance, scale, ...])

fit(X, Y[, copy, x_align_ref])

Fit the estimator.

fit_transform(X, Y, **fit_params)

Fit the estimator and return the resulting scores

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

get_x_loadings(X)

get_y_loadings(Y)

score(X, Y[, ftr])

Returns the pearson correlation of the ftr-th canonical variates (scores).

set_fit_request(*[, copy, x_align_ref])

Request metadata passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_score_request(*[, ftr])

Request metadata passed to the score method.

set_transform_request(*[, copy])

Request metadata passed to the transform method.

transform(X[, Y, copy])

Apply the previously fitted estimator to new data.