Modeling and Multivariate Methods - SAS

424 Performing Choice Modeling Chapter 16
Segmentation
But, the disaggregation of Δ results in
Δ = Σ ij
Δ ij
= ΣH – 1 g ij
= 0
where i is the subject index, j is the choice response index for each subject,
Δij are the partial Newton-Raphson steps for each run, and
gij is the gradient of the log-likelihood by run.
The mean gradient step for each subject is then calculated as:
Δ i
=
Δ ij
Σ j
------
n i
where ni is the number of runs per subject.
These Δ i
are related to the force that subject i is applying to the parameters.
If groups of subjects have truly different preference structures, these forces are strong, and they can be used
to cluster the subjects.
The Δ i are the gradient forces that are saved.
A partial data table with these subject forces is shown in Figure 16.29.
Figure 16.29 Gradients by Subject for Pizza Data
You can cluster these values by clicking on the drop-down menu of Hierarchical Clustering in the new data
table and selecting Run Script. The resulting dendrogram of the clusters is shown in Figure 16.30.