Edit

Context

With the Edit function, you can modify existing Classes to add or remove nodes as their members.
This is often very helpful for fine-tuning Classes after having performed Variable Clustering.

The following example is based on a typical driver analysis study of customer satisfaction (see Seminar: Key Drivers Analysis and Optimization).
One key element in this study's workflow is to group manifest variables into so-called clusters using BayesiaLab's Variable Clustering function.
In BayesiaLab, clusters are implemented as Classes, which means that a "node belonging to a cluster" translates into that node being a member of a Class representing the cluster.
In this workflow example, we reassign the node Wiper Performance (front/rear), which was originally assigned to the Class [Factor_9], to the Class [Factor_4].
We perform this reassignment based on our domain knowledge, i.e., "overrule" the automatic assignment produced by Variable Clustering.
Although the motivation is irrelevant to our example, we might think this particular node fits better into Class [Factor_4].

There are several things to note in this context:
- The new Class assignment does not change the structure of the network. Although the node Wiper Performance (front/rear) is now part of Class [Factor_4], its place in the network and its position on the screen remain the same.
- As the node colors had already been applied before the Class reassignment we just performed, the color of the node Wiper Performance (front/rear) remains unchanged.