Hi,
This may reflect my lack of understanding of PLS, but the explained_variance attribute gives the percent variance explained by each component for X and for Y. Is there a way to find the percent covariance between X and Y explained by each component?
Thanks.
hi @user2842,
The easiest way would be to simply calculate the sample covariance(object$variates$X[,1], object$variates$Y[,1]) for example for component 1,=.
To get the total covariance explained: Rd(X, Y) = Rd(X, object$variates$Y), and Rd(Y,X) = Rd(Y, object$variates$X) but that assumes you have calculated all possible dimensions. At this stage I can’t really comment on whether this is possible. Let me know if you find a better answer.
what function is Rd?
Rd is not a specific function within the mixOmics package. The formula in the above excerpt describes a measure (“redundancy”) of the explained variance of a given component.
If you are wanting to examine the source code used to calculate the explained variance, look at the explained_variance() function - found here.
Dear all,
I am very interesting in the concept of ‘explained_covariance’, so please let me know if there is any update on that topic!
Moreover, I don’t really understand why ‘explained_variance’ seems quite simple to compute, but not ‘explained_covariance’… 
Best,
Emile
