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!?
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.
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).
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?