Do people ever use PLS on historical data for variable selection to find important variables as inputs for DOE?

Question

I've seen several JMP webinars where they use Bootstrap forest to find the VIP variables, typically followed by Elastic net or Stepwise regression to further whittle down terms that might be important for future DOEs?

I'm curious if anyone uses PLS regression in the process - maybe after Bootstrap forest, to further ID important or correlated variables before doing Elastic Net etc.., or does the fact that Elastic net keep/remove sets of correlated variables negate the need for this?

Just wondering if anyone uses this in their process? I've seen a few webinars where PLS is used to mine biological process data?

Ben_BarrIngh · Accepted Answer

Hi @mjz5448 ,

As with all statistical approaches the simplest answer is 'it depends'. You are right that the fact that Elastic Net already performs variable selection efficiently so you may not need/want to apply PLS on top of that - the other advantage is that the model coefficients in EN's are directly interpretable, so you can directly relate your predictors to your response variable. You also need to consider the purpose of PLS, which is primarily there to produce a predictive model, not an explanatory one (although you could do this, albeit with an added level of complexity due to the loading and linear combinations of all of the factors), which in the case of selecting DOE factors you want to try and use an approach better suited to explanatory v. predictive modelling.

Hope that helps, I'm sure others have some valuable discussion to add.

Thanks,

Ben

P_Bartell · Accepted Answer

Years ago when I worked at a large chemical company that was very good at coating materials on a substrate (think photographic film and paper) we used PLS to help inform designed experiments all the time for the following reasons:

1. Very small number of runs in manufacturing with lots of input factors. Think wide and short data sets. Tailor made for PLS.

2. Lots of multcollinearity between the factors.

3. True process understanding has been lost to the ages...some of these products we've been making for scores of years and the usual mantra in manufacturing is 'just make it like last time'...because we really don't know how it all works.

4. Penalized regression/tree based variable selection methods were not yet in popular use in software.

5. And the thing that motivates the whole endeavor is "The magic of physics, chemistry or biology didn't come together last night when we tried to make (fill in the blank). And we have no idea of root cause(s)."

So we used PLS to try and identify key factors, then go into pilot/small scale facilities to run designed experiments as a means of gaining process understanding so we could identify root causes of systemic failure modes. Two of the engineers I worked with had some sayings that resonated with me (as the statistician on many of these problem solving teams)..."Sometimes knowing and 'is not' is just as valuable as knowing and 'is'. And the other was, "Until you can turn a failure mode on and off...you don't know root cause."

In short it was the combination of PLS and DOE that got us what we were after. Hope this helps?