Mean / Individual Confidence Interval in Mixed Models for new data points

[#1]

Hi everyone,

I fit a mixed model with one random effect in JMP 17. I would like to get confidence intervals for new predictions, i.e. data points which where not used in the model fitting. However, if I go to the red triangle in the fit model platform and use "Save Columns -> Mean/Individual Confidence Interval", it just gives values for the data points used in the model fitting. Is there some way in JMP or JSL to get confidence intervals for new data points? 

If I use models for predictions I am also interested in the uncertainty of this prediction. 

I would appreciate any help!

Best,

Anja

[#2]

Just saw your post. That is indeed an improvement. Thanks for the great work!

[#3]

The paper you referenced was correct when it was written; the DFs being used for Individual Confidence Intervals (ie Prediction Intervals) when a mixed model was fit were originally defaulting to the K-R DFs for a usual Confidence Interval. This was corrected (using the paper you referenced as a primary reference) as of JMP & JMP Pro 16. The Individual CI option was not available in the Mixed personality (the JMP Pro personality for fitting mixed models) prior to that version, though it was available if you fit a simple mixed model using REML under the Standard Least Squares personality. I can request that the Documentation team update the doc with more details, or at least point to that article for the math. 

As for needing to specify a random effect level despite it not being used in the calculation of an Individual CI, that is a result of the typical behavior of omitting a row from all calculations if an X (whether fixed or random) has a missing value. With a random effect level specified with the Y value missing, the row isn't used to fit the model, but it is still in the data for all other calculations.

[#4]

Thanks @eclaassen for the background and context...and the improvement! It’s sometimes hard to keep up with what’s new in the latest version, especially with what may be considered “minor” (but significant in my world!) updates like this. Indeed, it would be helpful to have some detail and/reference in the documentation.

[#5]

@MRB3855  I know that PI's calculated in JMP used to be way too conservative for certain designs. But it appears this has been improved in recent versions. But like you say information regarding the calculation method is missing.

Great article by the way.