Hi, I'm having trouble subsetting columns. In the code below, I'm looking to create multiple new data tables (and perform some analyses with them- though I deleted that part of the script) from my current table. My current table is formatted with one "X" column (column 1) and several "Y" columns (columns 2 through ncols(dt). I'm having an issue where the columns() function does not recognize icol as a variable representing a number (I assume it's looking for a column named icol). Is there a fix for this? dt=current data table();
For( icol = 2, icol <= ncols(dt) , icol++,
dt << Subset( All rows, columns( 1, icol ))
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I'm looking to do a destructive degradation analysis. The test was designed with storage temperature as the accelerating ageing variable and items were pulled and tested (once each- it was a destructive test) at different times. I don't work with JMP's reliability analyses too often, but I have the basics well enough that I could do the analysis if this was the extent of the test. However, in this case, I also have an additional variable (that is not an accelerating ageing variable)- it's the temperature at which the item was actually tested; there are 3 levels of this variable, and based on visually looking at the data, I would lean towards treating is as categorical. I could just fit 3 separate models- 1 for each test temperature; however, it seems like there should be a way to combine the data and fit a single model (which I would expect to be the better way to handle the data). Can anyone offer any insight on how I might be able to do this in JMP 13?
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Melissa Jablonski, Statistician, US Army ARDEC Douglas Ray, PhD, Statistician, US Army ARDEC Edward Cooke, Chemist, US Army ARDEC This presentation will focus on a deterministic computer experiment containing seven mixture type variables. Although the Army application for this data cannot be shared in a public setting, a bread baking analogue will be shared to make the data more relatable as the process for designing the experiment, fitting models to each of the response variables, visualizing the results, and narrowing down the design space is presented interactively with JMP. Specific topics that will be covered include using mixture constraints within the Space-Filling Design platform, fitting Gaussian process models, visualizing and interpreting the results (including using the Prediction Profiler, ternary surface plots and data filtering), and finding an optimum subset of the design space satisfying certain response criteria where the design of a follow-up physical experiment should focus.
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