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 matrixand 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
[*] | 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/ |
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. |
endog_names | Names of endogenous variables |
exog_names | Names of exogenous variables |
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.multivariate.cancorr.CanCorr.html