title: sklearn PLSRegression - Variance of X explained by latent vectors
body: <p>I performed a partial least squares regression using Python's <a href="http://scikit-learn.org/stable/modules/generated/sklearn.cross_decomposition.PLSRegression.html#sklearn.cross_decomposition.PLSRegression" rel="nofollow noreferrer">sklearn.cross_decomposition.PLSRegression</a></p>
  
  <p>Is there a way to retrieve the fraction of explained variance for X, i.e. <strong>R<sup>2</sup>(X)</strong>, for each PLS component? I'm looking for something similar to the explvar() function from the R pls package. However, I'd also appreciate any suggestions on how to compute it myself.</p>
  
  <p>There is a similar <a href="https://stackoverflow.com/q/25266514/1276810">question</a> and there is one <a href="https://stackoverflow.com/a/45706516/1276810">answer</a> that explains how to get the variance of Y. I guess, that "variance in Y" is what was asked for in that case. That's why I opened a new question - hope that's O.K.</p>