LDA vs Predictor screening to determine which process parameters describe differences between runs

[#1]

Currently I'm investigating a unit in a chemical process. This unit runs for several months before it's cleaned. The performance differs between runs. My performance indicator for a run has 3 levels: Low, Med, High. There are about 30 process parameters (pressures, temperatures etc) identified as candidates that could describe/influence the performance indicator. These process parameters vary during a run. I try to identify which of these process parameters are the most influential and what there influences are (positive/negative).

I used LDA and predictor screening. The list of top 10 most influencing process parameters differ between the 2 methods. Because the methods are different I expected some differences, but not as extensive. My question: how to identify which of the 2 methods gives me the most reliable answer?

[#2 · Accepted solution]

I (forgive me I should have read closer) just noticed you only have 13 runs? So automatically you have far fewer runs than you have predictor variables (30). Typically tree and many linear modeling methods need a larger number of runs than parameters you are trying to estimate. So I'm not surprised you are seeing differing answers. LDA has some dimensionality reduction features. Quite frankly without JMP Pro (if you had you could use FDE or partial least squares or perhaps one of the penalized regression  methods). Good luck.