statsmodels.multivariate.cancorr.CanCorr#

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

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.

Parameters:

endogarray_like: The endogenous (left-hand-side) variables.
exogarray_like: The exogenous (right-hand-side) variables.
tolerancefloat: Eigenvalue tolerance, values smaller than which are considered 0.
missingstr: Available options are ‘none’, ‘drop’, and ‘raise’. If ‘none’, no nan checking is done. If ‘drop’, any observations with nans are dropped. If ‘raise’, an error is raised. Default is ‘none’.
hasconstNone or bool: Indicates whether the RHS includes a user-supplied constant. If True, a constant is assumed. If False, no constant is checked for. If None, the code checks for a constant.
**kwargs: Extra arguments that are used to set model properties when using the formula interface.

Attributes:

Methods

`corr_test`()	Approximate F test
`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

References

Methods

`corr_test`()	Approximate F test
`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