@PaigeMiller & @SteveDenham , Instead of dividing the variables into 2 groups like this, why not specify all of the variables both as predictors and responses? When X=Y, PLS on complete data reduces to PCA in the sense that you get the same scores and loadings from each matrix and they match PC scores and loadings. So I have been using the missing=EM syntax, with incomplete data tables and a high number of iterations, to obtain EM-PCA single imputations. You don't even need to make copies of your variables as PROC PLS does not complain when the same variable names appear on the left and right sides of the model statement.
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