Our World Statistics Day conversations have been a great reminder of how much statistics can inform our lives. Do you have an example of how statistics has made a difference in your life? Share your story with the Community!
Choose Language Hide Translation Bar

Prediction Profiler for Generalized Linear Models with Confidence Limits Formula

When I am using the Fit Model platform with Standard Least Squares, I can effectively save the prediction formula and standard errors to a separate column. When I do this for multiple responses, I can effectively use the Graph -> Profiler menu to overlay those models. This is very helpful, as each response may have a different set of meaningful predictors in it. Here is a reference to such a thread on the topic:




However, if I have a generalized linear model (GLM) such as a logistic regression, I have no such luck. Within the single model for fitting the GLM Personality, the results in that platform have confidence intervals on them. However, I am only able to export the prediction formula. If my most 2 important responses contains one normal response with Standard Least Squares, and one binomial response with a GLM logistic regression model, I cannot create a joint Prediction Profiler with effective predictions and confidence intervals. I am optimistic this can be done because we already have evidence that the confidence intervals can be created for GLMs. You just have to work out what the StdErr Pred Formula column would look like.


I brought this up as a ticket with JMP as well. The following series of responses from JMP support certainly brought attention to the issue, but perhaps if others find value in this enhancement, then it is more likely to be completed.


"Unfortunately, at the moment, I do not see a way to get a confidence interval around the predicted probability for the binary response in the combined standalone profiler."


"I see what you mean about the inability to save Standard Error Formulas for GLM's or for Least Squares models containing nested terms. I am not aware of any particular reason why this is the case. I will pass these items on to JMP R&D for their consideration in a future version of JMP."


Tracking Number: 7611866199

Defect ID: S1161499

1 Comment
Level V

Thank you for bringing this up. Having both continuous and binary responses is a very typical scenario for us, as well, and I miss the confidence intervals for the logistic model every time.