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.
References
- *
- †
http://www.csun.edu/~ata20315/psy524/docs/Psy524%20Lecture%208%20CC.pdf
- ‡
http://www.mathematica-journal.com/2014/06/canonical-correlation-analysis/
- Attributes
- endogarray
See Parameters.
- exogarray
See Parameters.
- cancorrarray
The canonical correlation values
- y_cancoeff: array
The canonical coeefficients for endog
- x_cancoeff: array
The canonical coefficients for exog
Methods
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.