November 2017: note from the author:
This add-in doesn't do a good job of handling Fit Group windows. This type of output used to only appear when using stepwise regression but now JMP uses them for standard least squares when multiple responses are present. In this instance the add-in will probably only detect the first response. For those of you who have the intellectual capacity to model multiple responses simultaneously I will provide an update to the add-in - but will probably wait and combine it with support for version 14 of JMP.
Latest Update (May 2017)
In the original release of this add-in, models were discovered based on open windows. Now,. if there are no model windows open, the add-in will scan scripts attached to the current data table. Any scripts that have been saved from the Fit Model platform will be used to generate summary statistics and diagnostic information:
In addition the following enhancements have been implemented :-
- Residual plots now have a smoother curve to help identify curvature
- It is now possible to plot residuals versus variables not included in the model
- The partition platform is used to model residual variation to help indentify latent variables
- The add-in can now perform a check to verify that you have the latest version
Details of this update are included in this blog post.
Detailed Description (based on prior version)
This add-in aggregates various model diagnostic information into a single tabbed window. The add-in also explores various transformation options that can be applied to the response(s).
The add-in automatically discovers model windows (least square models generated using Fit Model or via Stepwise), and generates the following information:
Models
- List of models grouped by response, with summary fit statistics for each model
Terms
- List of terms for the selected model
Effects
- Half normal probability plot of effects. To decrease clutter, abbreviations are used on the plot with a legend showing the associated factor names
Residuals
- Normal probability plot of residuals
- Residuals versus response
- Residuals by run order
- Cook's Distance
- Residuals versus each predictor
- Residual versus non-model variables
Transformations
- Box-Cox transformation test with user-friendly labelling for the lambda parameter
- Model comparison statistics - various transformations are performed and comparison statistics generated, included a measure of complexity
- Links from the model comparison tabulation allow these new models to be quickly launched and reviewed
Example Output
Video Demonstration
