Modeling and Multivariate Methods - SAS

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410 Performing Choice Modeling Chapter 16 Launch the Choice Platform and Select Data Sets The profiler option is particularly valuable in understanding the model. It shows the value of the linear model, or the utility, as you change each factor, one at a time. • You specify the profiler option by selecting Profiler in the platform menu. The Prediction Profiler is shown in Figure 16.7. • In this example involving only main effects, the factor showing the greatest difference is Cheese, favoring Mozzarella. • If there were interactions in the model, more exploration of the profiler would be needed in order to understand the response surface. Figure 16.7 Prediction Profiler with No Subject Data for Pizza Example Subject Effects If you want to include subject effects in the model, you need to open the Subject data table section of the Choice dialog box. Suppose you are interested in Gender main effects. • Open the Subject Data section of the launch dialog box. Click Select Data Table, and when the Subject Data Table opens, select Pizza Subjects.jmp. • Specify Subject as Subject ID, and add Gender under Construct Model Effects. The Subject data dialog box section for the pizza example is shown in Figure 16.8. • Click on Run Model.
Chapter 16 Performing Choice Modeling 411 Launch the Choice Platform and Select Data Sets Figure 16.8 Choice Model Subject Data Dialog Box for Pizza Example Figure 16.9 shows the parameter estimates and the likelihood ratio tests for the Choice Model with subject effects included. Strong interactions are seen between Gender and Crust and between Gender and Topping. When the Crust and Topping factors are assessed for the entire population, the effects are not significant. However, the effects of Crust and Topping are strong when they are evaluated between Gender groups. Figure 16.9 Choice Model Results with Subject Effects for Pizza Example Response-only effects Subject-effect by Response-effect interaction Cheese is significant as main effect Crust and Topping are only significant when interacted with Gender
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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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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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Contents Example of a Multiple Resp
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158 Fitting Multiple Response Model
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Contents Overview of Generalized Li
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180 Fitting Generalized Linear Mode
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Contents Introduction to Logistic M
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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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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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470 Clustering Data Chapter 18 K-Me
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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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480 Clustering Data Chapter 18 Self
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Contents Principal Components. . .
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484 Analyzing Principal Components
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Contents Introduction . . . . . . .
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494 Performing Discriminant Analysi
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Contents Overview of the Partial Le
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Contents Introduction to Item Respo
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526 Scoring Tests Using Item Respon
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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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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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Contents Introduction to Profiling
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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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642 References Draper, N. and Smith
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644 References Hooper, J. H. and Am
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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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Contents The Response Models . . .
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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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