statsmodels.multivariate.cancorr.CanCorr

class statsmodels.multivariate.cancorr.CanCorr(endog, exog, tolerance=1e-08, missing='none', hasconst=None, **kwargs)

Canonical correlation analysis using singular value decomposition

For matrices exog=x and endog=y, find projections x_cancoef and y_cancoef such that:

x1 = x * x_cancoef, x1’ * x1 is identity matrix y1 = y * y_cancoef, y1’ * y1 is identity matrix

and the correlation between x1 and y1 is maximized.

References

*

http://numerical.recipes/whp/notes/CanonCorrBySVD.pdf

†

http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf

‡

http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/

Attributes

endogndarray

See Parameters.

exogndarray

See Parameters.

cancorrndarray

The canonical correlation values

y_cancoeffndarray

The canonical coefficients for endog

x_cancoeffndarray

The canonical coefficients for exog

Methods

corr_test()	Approximate F test Perform multivariate statistical tests of the hypothesis that there is no canonical correlation between endog and exog.
fit()	Fit a model to data.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
predict(params[, exog])	After a model has been fit predict returns the fitted values.

Methods

corr_test()	Approximate F test Perform multivariate statistical tests of the hypothesis that there is no canonical correlation between endog and exog.
fit()	Fit a model to data.
from_formula(formula, data[, subset, drop_cols])	Create a Model from a formula and dataframe.
predict(params[, exog])	After a model has been fit predict returns the fitted values.

Properties

endog_names	Names of endogenous variables.
exog_names	Names of exogenous variables.