I'm still learning a bit about JMP, but I created a model where the output was oxygen level. My instrumentation gave me a precise number (down to hundreds of percent), but we determined that the accuracy is not appropriate at these levels, so I would like to recode the data for all values less than the set threshold. Most of the data actually falls below this threshold value.
How should I approach that so that I don't lose the continuous nature of this data? Is there a way that I can recode the column to show the values at < LOQ, but still have something that will be significant when I try to build a model from this?
You have data that can be handled in the Qualtiy and Reliability platform. Your < LOQ data is what's called 'left-censored'. For these results, your value of LOQ really means LOQ OR LESS. You need to add another column called "Censor" beside your results. This is an indicator column, place a 1 next to all LOQ values, and a 0 beside the remaining values that all represent actual precision measurements. See the link below for a 1-page summary to get a better idea.
Thank you. That's very helpful.
How do I then port that feature when building a model or trying to fit a model with these data?
Perform your modeling in the Analyze>Fit Model >Personality: Parametric Survival -- it will add a dialog box to indicate which column contains the censor indicator values. You can choose the distribution type as Normal (I think it defaults to Weibull) -- examine your residuals to see if your selected distribution is appropriate. If you are unfamiliar with these Survival distributions, I would get in touch with JMP Support.