Partial Least Squares VIP coefficients in JMP pro

[#1]

Hello,

I am struggling to understand the difference between NIPALS and SIMPLS, and why those two give me such a low VIP coefficients for my Y with same set of Xs (I have 1000 of them). I have 3 levels within X: I expected different correlation coefficients for all 3, but they are very closed to each other. Am I using PLS correctly? 

[#2]

Here you are predicting all Y variables at the same time so I believe the VIP is related to the importance of predicting any and all X and Y variables.  If you run the analysis three times with one Y variable each time you will see different VIP values for each X variable.

[#3]

Of course you will get different predictor variable results by using PLS in series, once for each response all by itself, compared to using PLS incorporating all the responses simultaneously. One of the unique and valuable characteristics of the PLS approach is to leverage correlation/covariance strutures among BOTH the x and y variables for those situations where using other regression based methods are problematic. One of the basic attractions of PLS is dimensionality reduction hence the idea of leveraging the latent structures that can be found using these methods. A great reference for PLS is:

https://www.sas.com/store/books/categories/usage-and-reference/discovering-partial-least-squares-wit...