select the best ncomp and alpha with cross-validation, then use them to fit the complete training set, and predict on the test set. scale = TRUE
1 2 | enspls.od.core(x.sample, y.sample, x.remain, y.remain, maxcomp, cvfolds,
alpha)
|
the error vector between predicted y and real y
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