Modeling and Multivariate Methods - SAS

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422 Performing Choice Modeling Chapter 16 One-Table Analysis Figure 16.26 Choice Dialog Box for Pizza Data One-Table Analysis A new dialog box appears asking if this is a one-table analysis with all of the data in the Profile Table. • Click Yes to fit the model, as shown in Figure 16.27. Figure 16.27 Choice Model for Pizza Data One-Table Analysis
Chapter 16 Performing Choice Modeling 423 Segmentation • Select Profiler from the drop-down menu to obtain the results shown in Figure 16.28. Notice that the parameter estimates and the likelihood ratio test results are identical to the results obtained for the Choice Model with only two tables, shown in Figure 16.6 and Figure 16.7. Figure 16.28 Prediction Profiler for Pizza Data One-Table Analysis Segmentation Market researchers sometimes want to analyze the preference structure for each subject separately in order to see whether there are groups of subjects that behave differently. However, there are usually not enough data to do this with ordinary estimates. If there are sufficient data, you can specify “By groups” in the Response Data or you could introduce a Subject identifier as a subject-side model term. This approach, however, is costly if the number of subjects is large. Other segmentation techniques discussed in the literature include Bayesian and mixture methods. You can also use JMP to segment by clustering subjects using response data. For example, after running the model using the Pizza Profiles.jmp, Pizza Responses.jmp, and the optional Pizza Subjects.jmp data sets, click on the drop-down menu for the Choice Model platform and select Save Gradients by Subject. A new data table is created containing the average Hessian-scaled gradient on each parameter, and there is one row for each subject. Note: This feature is regarded as an experimental method, since, in practice, little research has been conducted on its effectiveness. These gradient values are the subject-aggregated Newton-Raphson steps from the optimization used to produce the estimates. At the estimates, the total gradient is zero, and Δ = H – 1 g = 0 where g is the total gradient of the log-likelihood evaluated at the MLE, and H – 1 is the inverse Hessian function or the inverse of the negative of the second partial derivative of the log-likelihood.
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Version 10 Modeling and Multivariat
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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8 Models with Crossed, Interaction,
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10 8 Analyzing Screening Designs Us
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12 Examining the Residuals . . . .
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14 Response Frequencies . . . . . .
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16 19 Analyzing Principal Component
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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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24 Learn about JMP Chapter 1 Book C
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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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200 Performing Logistic Regression
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Contents Overview of the Screening
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232 Analyzing Screening Designs Cha
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Contents Introduction to the Nonlin
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244 Performing Nonlinear Regression
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Contents Overview of Neural Network
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280 Creating Neural Networks Chapte
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Contents Launching the Platform. .
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296 Modeling Relationships With Gau
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Contents Overview of the Loglinear
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306 Fitting Dispersion Effects with
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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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Contents Launch the Platform . . .
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354 Performing Time Series Analysis
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472 Clustering Data Chapter 18 K-Me
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478 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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Contents Introduction . . . . . . .
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Contents Introduction to Item Respo
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Contents Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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646 References Matsumoto, M. and Ni
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648 References Rawlings, J.O. (1988
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650 References Umetrics (1995), Mul
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654 Statistical Details Appendix A
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690 Index Birth Death Subset.jmp 46
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692 Index Ellipses Transparency 451
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694 Index test for curvature 46 Kru
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696 Index Customizing 267 Nonlinear
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698 Index reliability analysis 453-
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700 Index Stable 363 Stable Inverti
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702 Index
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Modeling and Multivariate Methods - SAS

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