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Apr 28, 2011 1:56 AM
(1018 views)

Hello all!

I am trying to model the number of error occurrence to two input values. My table contains:

pressure: analog

radius: analog

error: ordinal (0/1)

and some index for type, date, run of experiment.

There is many lines with same pressure/radius combinations that may have an error value of 0 or 1. The table contains roughly 200.000 entries.

Which functionality can I use for getting an answer to which pressure/radius delivers the least errors? Basically I would want to plot a response surface in the end.

One of my problems seems to be that I simply do not know the correct wording in english (I am native german speaker). also my jmp runs in german and I cannot find a switch to english. Interestingly my help file is in english!?

Can someone please give me a pointer?

Thanks a lot!

I am trying to model the number of error occurrence to two input values. My table contains:

pressure: analog

radius: analog

error: ordinal (0/1)

and some index for type, date, run of experiment.

There is many lines with same pressure/radius combinations that may have an error value of 0 or 1. The table contains roughly 200.000 entries.

Which functionality can I use for getting an answer to which pressure/radius delivers the least errors? Basically I would want to plot a response surface in the end.

One of my problems seems to be that I simply do not know the correct wording in english (I am native german speaker). also my jmp runs in german and I cannot find a switch to english. Interestingly my help file is in english!?

Can someone please give me a pointer?

Thanks a lot!

3 REPLIES

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I think the first thing to do a table summary on the pressure, radius and sum up the errors.

Next create a new column that is the sum(errors) divided by the Nrows (assuming there are no missing values for errors). Now you have a percent column that you can plot as the response for your two factors.

Next create a new column that is the sum(errors) divided by the Nrows (assuming there are no missing values for errors). Now you have a percent column that you can plot as the response for your two factors.

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You can try a logistic model using the Fit Model Platform. Before you start, change error column to nominal. Set error as Y, pressure and radius (and the combination pressure*radius if relevant) as effects and run the model.

Then you can use the prediction profiler to observe what levels that minimize the error probability. There are even some built-in optimization algorithms that can be invoked under the profiler red triangle (I have not tried them though).

Then you can use the prediction profiler to observe what levels that minimize the error probability. There are even some built-in optimization algorithms that can be invoked under the profiler red triangle (I have not tried them though).

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Thanks for the pointers, I did both.

summary

The summary is easy going. It basically yields an error rate percentage. I plotted those as contour diagram and got what I was looking for. The response surface diagram works as well. The alternative I see there is the Pareto Plot (pressure and radius are properties of discrete exchangeable machine components). They all yield matching results.

Percentages are around 0.12% which is quite high for my application.

logistic model

input is the original data table, error is nominal.

There I see the possibility to get information to which pressure/radius combination could be optimal even if it has not been among the test candidates.

Here I got one question: How do you enter pressure*radius into the model dialog? All I see is single column entries!? How do I enter that?

The result I got using only radius and pressure by themselves yields two graphs with vertical lines at the pressure and radius levels where I have test candidates. There are 3 and 5 levels of pressure and radius respectively. There I must admit I fail to interpret the graph. What does it mean to have those points in vertical lines?

Thanks a lot for the input!

-Jean-

summary

The summary is easy going. It basically yields an error rate percentage. I plotted those as contour diagram and got what I was looking for. The response surface diagram works as well. The alternative I see there is the Pareto Plot (pressure and radius are properties of discrete exchangeable machine components). They all yield matching results.

Percentages are around 0.12% which is quite high for my application.

logistic model

input is the original data table, error is nominal.

There I see the possibility to get information to which pressure/radius combination could be optimal even if it has not been among the test candidates.

Here I got one question: How do you enter pressure*radius into the model dialog? All I see is single column entries!? How do I enter that?

The result I got using only radius and pressure by themselves yields two graphs with vertical lines at the pressure and radius levels where I have test candidates. There are 3 and 5 levels of pressure and radius respectively. There I must admit I fail to interpret the graph. What does it mean to have those points in vertical lines?

Thanks a lot for the input!

-Jean-