Modeling and Multivariate Methods - SAS

Chapter 20 Performing Discriminant Analysis 497
Discriminating Groups
Click Apply This Model to estimate the model. After estimation and scoring are done, two reports are
produced: a Canonical Plot (Figure 20.5), and a Scoring Report.
Canonical Plot
The Canonical Plot shows the points and multivariate means in the two dimensions that best separate the
groups.
Figure 20.5 Canonical Plot
• Each row in the data set is a point, controlled by the Canonical Options > Show Points option.
• Each multivariate mean is a labeled circle. The size of the circle corresponds to a 95% confidence limit
for the mean. Groups that are significantly different tend to have non-intersecting circles. This is
controlled by the Canonical Options > Show Means CL Ellipses option.
• The directions of the variables in the canonical space is shown by labeled rays emanating from the grand
mean. This is controlled by the Canonical Options > Show Biplot Rays option. You can drag the
center of the biplot rays to other places in the graph.
• The option Show Normal 50% Contours shows areas that contain roughly 50% of the points for that
group if the assumptions are correct. Under linear discriminant analysis, they are all the same size and
shape.
In order to have the points color-coded like the centroid circles, use the Color Points option or button.
This is equivalent to Rows > Color or Mark by Column, coloring by the classification column.
The canonical plot can also be referred to as a biplot when both the points and the variable direction rays are
shown together, as in Figure 20.5. It is identical to the Centroid plot produced in the Manova personality of
the Fit Model platform.
Discriminant Scores
The scores report shows how well each point is classified. The first five columns of the report represent the
actual (observed) data values, showing row numbers, the actual classification, the distance to the mean of
that classification, and the associated probability. JMP graphs -Log(Prob) to show the loss in log-likelihood
when a point is predicted poorly. When the red bar is large, the point is being poorly predicted.