1. Canonical Correlation Analysis (CCA)

2. CCA This is it! The mother of all linear statistical analysis When ? We want to find a structural relation between a set of independent variables and a set of dependent variables.

3. CCA When ? (part 2) 1. 1. To what extend can one set of two or more variables be predicted or “explained” by another set of two or more variables? 2. 2. What contribution does a single variable make to the explanatory power to the set of variables to which the variable belongs? 3. 3. What contribution does a single variable contribute to predicting or “explaining” the composite of the variables in the variable set to which the variable does not belong? 4. 4. What different dynamics are involved in the ability of one variable set to “explain” in different ways different portions of other variable set? 5. 5. What relative power do different canonical functions have to predict or explain relationships? 6. 6. How stable are canonical results across samples or sample subgroups? 7. 7. How closely do obtained canonical results conform to expected canonical results?

4. CCA Assumptions Linearity: if not, nonlinear canonical correlation analysis. Absence of multicollinearity: If not, Partial Least Squares (PLS) regression to reduce the space. Homoscedasticity: If not, data transformation. Normality: If not, re-sampling. A lot of data: Max(p, q)  20  nb of pairs. Absence of outliers.

5. CCA Toy example IVsDVs = X = X

6. CCA Z score transformation IV1DV2IV1DV2 = Z = Z

7. CCA Canonical Correlation Matrix

8. CCA Relations with other subspace methods

9. CCA Eigenvalues and eigenvectors decomposition R = PCA

10. CCA Eigenvalues and eigenvectors decomposition The roots of the eigenvalues are the canonical correlation values

11. CCA Significance test for the canonical correlation A significant output indicates that there is a variance share between IV and DV sets Procedure: We test for all the variables (m=1,…,min(p,q)) If significant, we removed the first variable (canonical correlate) and test for the remaining ones (m=2,…, min(p,q) Repeat

12. CCA Significance test for the canonical correlation Since all canonical variables are significant, we will keep them all.

13. CCA Canonical Coefficients Analogous to regression coefficients Eigenvectors Correlation matrix of the dependant variables BY=BY= Bx=Bx=

14. CCA Canonical Variates Analogous to regression coefficients

15. CCA Loading matrices Matrices of correlations between the variables and the canonical coefficients AxAx AyAy

16. CCA Loadings and canonical correlations for both canonical variate pairs Only coefficient higher than |0.3| are interpreted. LoadingCanonical correlation

17. CCA Proportion of variance extracted How much variance does each of the canonical variates extract form the variables on its own side of the equation? First Second First Second

18. CCA Redundancy How much variance the canonical variates form the IVs extract from the DVs, and vice versa. Eigenvalues rd y  x

19. CCA Redundancy How much variance the canonical variates form the IVs extract from the DVs, and vice versa. Summary The first canonical variate from IVs extract 40% of the variance in the y variable. The second canonical variate form IVs extract 30% of the variance in the y variable. Together they extract 70% of the variance in the DVs. The first canonical variate from DVs extract 49% of the variance in the x variable. The second canonical variate form DVs extract 24% of the variance in the x variable. Together they extract 73% of the variance in the IVs.

20. CCA Rotation A rotation does not influence the variance proportion or the redundancy. = Loading matrix =