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May 3, 2019 11:40 AM
(699 views)

I ran a DOE to find out which manufacturing factors affect the fatigue life of a material (as well as other properties).

Since the response variable of interest is survival data with censored data, I am using the Parametric Survival platform in JMP 14 to analyze the data. The Parametric Survival platform doesn’t support the Assess Variable Importance option or a profiler as far as I can figure out.

I would like to determine which factors have the biggest influence on life and the settings that maximize life. I’m at a loss as to how do this without the Assess Variable Importance option or the profiler. Can anyone recommend the approach I should take to do this?

Thanks,

Larry

4 REPLIES 4

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Would it be sufficient to assess the effect sizes visually in the profiler?

-- Cameron Willden

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I think I’m starting to see why JMP removed Assess Variable Importance and the Profiler from the Parametric Survival platform. In this platform you are not predicting a response so much as you are simply fitting a non-linear function, specifically a probability distribution. For similar reasons the profiler doesn’t make sense either.

At this point I have a probability distribution that is a function of ‘n’ of my factors. What I need to do now is pick a measure of reliability for my problem and maximize it. For example, if my goal is to maximize the cycles before 5% of my samples fail, then I need maximize the inverse cumulative distribution function for p = 0.05.

Now I just need to figure out how to do that in JMP.

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Use the Quantile Profiler. Set the Desirability Function to Maximize (default). Set the Probability to 0.05 and lock it. Then maximize the desirability.

Learn it once, use it forever!

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Re: How to use Parametric Survival platform or alternate technique to assess variable importance and optimal settings?

The Quantile Profiler is a good solution if you have JMP Pro. I'll probably have to export the distribution equations and use an outside program.

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