# 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

 Approximate F test Perform multivariate statistical tests of the hypothesis that there is no canonical correlation between endog and exog. 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