Additional Example of the Latent Class Analysis Platform
In this example, you produce a plot to visualize the probabilities of cluster membership. You have survey data for car owners and car makes and you are interested in classifying car owners into three clusters. A ternary plot provides a good visualization when you have three clusters.
1. Select Help > Sample Data Folder and open Car Poll.jmp.
2. Select Analyze > Clustering > Latent Class Analysis.
3. Select all of the columns except age and click Y.
4. Click OK.
5. Click the red triangle next to Latent Class Model for 3 Clusters and select Color by Cluster.
6. Click the red triangle next to Latent Class Model for 3 Clusters and select Save Mixture Probabilities.
7. In the Car Poll data table window, select the LCA Cluster Probabilities column group from the column list.
8. Select Graph > Ternary Plot.
9. Click X, Plotting.
10. Click OK.
Figure 16.6 Ternary Plot of Cluster Membership Probabilities
In the ternary plot of cluster probabilities for each observation, most of the cluster membership probabilities fall near the vertices. This indicates that they have high values for one cluster and lower values for the other two. However, there are some points in the middle of the plot, indicating that these observations do not have high probabilities of cluster membership for any of the clusters. These observations might warrant closer inspection or they might indicate that more clusters are needed to better represent the data.
Note: Your results might be different because a random seed was not specified.