JMP 13 Preview: Improvements to the Analyze menu for a better user experience
Sep 8, 2016 1:25 PM
From time to time, the addition of new features requires a review of how capabilities are organized and presented in JMP. Are they located where it makes the most sense and where users would expect to find them? For example, in JMP 12 there was enough new material combined with existing functionality to warrant a Consumer Research submenu in the Analyze menu.
In JMP 13, users will also see some changes to the Analyze menu:
The old Modeling submenu has been replaced by two new submenus: Predictive Modeling and Specialized Modeling.
A Clustering submenu has been added so that you can quickly find your favorite clustering technique.
You can easily find the Fit Curve platform under Specialized Modeling in JMP 13.
The Predictive Modeling submenu is the new home for a variety of modeling platforms that emphasize prediction, such as Neural, Partition, Random Forest, and K-Nearest Neighbors. The Specialized Modeling menu is where you will find platforms like Fit Curve, Nonlinear, and Time Series.
Senior Research Statistician Developer Clay Barker, who wrote the Fit Curve and Normal Mixture platforms, was happy to see these platforms find a home in the reorganized menus. “They used to be buried inside other platforms (Fit Curve inside Nonlinear and Normal Mixtures inside k-Means), which made them harder to find. Now they will get more exposure, and hopefully more people will start using them,” Clay says.
The new Clustering submenu includes Normal Mixtures and Cluster Variables among other platforms.
The new Clustering submenu also contains a new platform: Latent Class Analysis or LCA for short. The addition of the generalized LCA for categorical clustering didn’t fit with the continuous response clustering methods that already existed. This was also the case with the new specialized implementation of LCA for text analytics in JMP Pro. Some of the output for the new LCA clustering includes Multidimensional Scaling (MDS) plots and “share” charts.
Multi-Dimensional Scaling plot allows you to see distance between clusters.
While JMP includes a number of good clustering methods, they were hard to find especially if you didn’t know where to look, so many users didn’t know about them. Some, like Normal Mixture clustering or Variable Clustering, were “platform personalities” or in a list of red-triangle menus. The new Clustering submenu groups new and existing clustering methods together.
The new share charts in the LCA report show the conditional probabilities given cluster membership for each cluster and each Y, plotted as a horizontal stacked bar chart.
The addition of LCA will enable users to do more with their text data. Chris Gotwalt, director of statistical R&D at JMP, enjoyed working on the sparse-matrix LCA, as he had never done anything like applying sparse methods to a clustering algorithm.
“The response so far has been positive on the new functionality as well as the new visuals. Having the other clustering methods more prominently featured in the submenu will hopefully lead to greater use,” Chris says.
Both Clay and Chris will be leading tutorials at Discovery Summit 2016, and it's not too late to sign up for these special sessions. In addition, they'll be presenting a paper titled "Visually Exploring Design of Experiments Models with the Generalized Regression Platform" at conference.
To learn more about what's coming in JMP 13, visit the preview page at our website.