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The tree in the All Models pane displays the hierarchical structure of the models you have built. Views and functionality within the browser differ according to which node is selected.
The following is an example of a model tree.

The elements of the tree consist of the following:
Global nodes — All one-stage model nodes are global models. For two-stage models, global models are fitted to response features of the local models. Each step down in the tree is a child node of the node above. Global models are child nodes of local nodes and so on.
You can rename all nodes, as in Windows Explorer, by clicking again or by pressing F2 when a node is selected.
There is a context menu available. When you right-click any node in the model tree, you can choose to delete or rename that node, or create a new child node.

The preceding example shows a more extensive model tree, with two two-stage models as child nodes of a single response model.
There can be many models within (or under, as child nodes in the tree) each two-stage global node, or any one-stage model node.
There can also be many different response nodes within a single test plan, and each project can contain several different test plans. However, there is only one project node in the tree at any time.
Note You can only have one project open at any one time; if you open another, you are prompted to save or discard your current project. |
You can add child nodes to all global models — several candidate models can be tried at each global node and the best selected. There is an example showing this at the end of the section on Icons: Blue Backgrounds and Purple Worlds and the process is illustrated in the Tutorial: Model Quickstart in the Getting Started documentation.
The icons are designed to give visual reminders of their function.
Test plan icons have a tiny representation of the test plan diagram. You can see the one-stage and two-stage icons in the following example.
The local model icon shows a curve over a house.
Global model icons show a curve over a globe. All one-stage models are global models and for two stage models, all nodes below the local node are global models.

The response node (empty until a two-stage model is calculated) has an icon that combines the local and global motifs — a curve over a house and a globe — to symbolize the two-stage process.
When a two-stage model has been calculated, the icon at the local node changes to show the combination of local and global motifs.

Icon changes convey information about modeling processes.
When a model is selected as the best model, its icon changes color and gains a blue background, like the BSPLINE1 model in the preceding example.
When the maximum likelihood estimate (MLE) is calculated and chosen as the best model, the associated model icon and all its child nodes (along with the plots of that model) become purple.
You can see this in the preceding example: the B Spline model and all its response features have purple curves, globes, and house, indicating that they are MLE models. The Poly3 model and its children have blue curves and globes and a red house, indicating that they are univariate models.
Observe the other difference between the B Spline and the Poly3 icons: the B Spline has a blue background. This indicates that this is selected as best model, and is used to calculate the two-stage model at the response node, so the response node is also purple. If an MLE model (with purple worlds) is selected as best model and is used to create the two-stage model, the response node always reflects this and is also purple.
Notice also that the response features all have blue backgrounds. This shows they are selected as best and are all being used to calculate the two-stage model. In this case they are all needed. That is, a B Spline model needs six response features, and a Poly3 model requires four. If more response features are added, however, some combination must be selected as best, and the response features not in use do not have a blue background. An example is shown in Tutorial: Model Quickstart in the Getting Started documentation.
In the following example you can see child nodes of a global model. You can try different models within a global model, and you must select one of the attempts as best. In this example you can see that Cubic is selected as best, because it has a blue background, so it is the model being used for the Blow_2 global model.

When a model is selected as best it is copied up a level in the tree together with the outliers for that model fit.
When a new global or local model is created the parent model and outliers are copied from the current level to the new child node. This gives a mechanism for copying outliers around the model tree.
A tree node is automatically selected as best if it is the only child, except two-stage models which are never automatically selected - you must use the Model Selection window.
If a best model node is changed the parent node loses best model status (but the automatic selection process will reselect that best model if it is the only child node).
Note Try the Quick Start tutorial in the Getting Started documentation to understand how to use the model tree. The last section, Creating Multiple Models to Compare, guides you through the process of creating a variety of models of different types and how to understand the information in the model tree. You need to complete the previous sections of the tutorial first, which guides you through setting up a single two-stage model to get started. |
![]() | Project Level: Startup View | Test Plans | ![]() |

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