# 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

array – See Parameters.

exog

array – See Parameters.

cancorr

array – The canonical correlation values

y_cancoeff

array – The canonical coeefficients for endog

x_cancoeff

array – The canonical coefficients for exog

References

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