LDA vs Predictor screening to determine which process parameters describe differences between runsCurrently 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?
AnalyticsRe: LDA vs Predictor screening to determine which process parameters describe differences between runsVery hard to say without the data, or a reasonable sample thereof. Given that 'process parameters vary during a run', what does this variation actually look like within a run, and how many runs do you have data for? How many times do each level of the performance indicator occur?
Re: LDA vs Predictor screening to determine which process parameters describe differences between runsI have 13 runs. Each level occurs approx 4 times. Flows change depending on required throughput, pressures can change because of ambient conditions or long time fouling
Re: LDA vs Predictor screening to determine which process parameters describe differences between runsLet me start with one basic question. What are your criteria for '...most reliable answer.'? Are you conducting an investigation where you are seeking an exploratory 'answer' or a predictive 'answer'? The data analytics methods I'd recommend will in large measure be dictated by your answer.
Re: LDA vs Predictor screening to determine which process parameters describe differences between runsSo the predictor variables vary over time (during a run)? You might try Functional Data Explorer in JMP Pro to obtain the functional principle components, and then use them as predictors instead of the original measurements.
Re: LDA vs Predictor screening to determine which process parameters describe differences between runsI want to identify which process variables explain why a run has a low or high performance. Next step is to see if I can set these variables to a bandwidth (or compensate with other variables) to maintain high runs on a consistent basis. From an engineering perspective, a number of candidate variables have been identified. The analysis is meant to find the most influential variables. (like a Root Cause Analysis)
I hope this answers your question. I wanted to prevent mislabling my question as exploratory or predictive
