statsmodels.multivariate.cancorr.CanCorr

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

Canonical correlation analysis using singluar 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.

endog

See Parameters.

Type:array
exog

See Parameters.

Type:array
cancorr

The canonical correlation values

Type:array
y_cancoeff

The canonical coeefficients for endog

Type:array
x_cancoeff

The canonical coefficients for exog

Type:array

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/

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.

Attributes

endog_names Names of endogenous variables
exog_names Names of exogenous variables