Modeling and Multivariate Methods - SAS

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406 Performing Choice Modeling Chapter 16 Launch the Choice Platform and Select Data Sets Figure 16.2 Pizza Response Data Table Segment Notice that each choice value refers to an ID value in the Profile data table which has the attribute information. If data about the subject are to be used, a separate Subject data table is needed. This table includes a subject ID column and characteristics of the subject. In the pizza example, the only characteristic or attribute about the Subject is Gender. Subject data for the first four subjects are shown in Figure 16.3. Notice that the response choices and choice sets in the response data table use the ID names given in the profile data set. Similarly, the subject identifications in the response data table match those in the subject data table. Figure 16.3 Pizza Subject Data Table Segment Launch the Choice Platform and Select Data Sets Since the Choice platform can use several data tables, no initial assumption is made about using the current data table—as is the case with other JMP platforms. You must select the data table for each of the three choice data sources. You are prompted to select the profile data set and the response data set. If you want to model subject attributes, then a subject data set must also be selected. You can expand or collapse each section of the Choice dialog box, as needed. To illustrate the Choice platform, three data sets from the pizza example are used and are found in the sample data directory. The first data set is entered into the Profile Data section of the Choice Dialog Box, as shown in Figure 16.4.
Chapter 16 Performing Choice Modeling 407 Launch the Choice Platform and Select Data Sets • Select Analyze > Modeling > Choice to open the launch dialog box. You see three separate sections for each of the data sources. • Click on Select Data Table under Profile Data. A new dialog box opens, which prompts you to specify the data table for the profile data. You can select from any of the data sets already open in the current JMP session, or you can select Other. Selecting Other allows you to open a file that is not currently open. • Select Pizza Profiles.jmp. The columns from this table now populate the field under Select Columns in the Choice Dialog box. • Select ID for Profile ID under Pick Role Variables and Add Crust, Cheese, and Topping under Construct Model Effects. If the Profile ID column does not uniquely identify each row in the profile data table, you need to add Grouping columns until the combination of Grouping and Profile ID columns uniquely identify the row, or profile. For example, if Profile ID = 1 for Survey = A, and a different Profile ID = 1 for Survey = B, then Survey would be used as a Grouping column. In this simple experiment, all eight combinations of the three two-level factors were used. Figure 16.4 Profile Data Set Dialog Box The second data set is the Response Data containing the experimental results. For the pizza example, Choice1 and Choice2 are the profile ID choices given to a subject on each of four trials. The Choice column contains the chosen preference between Choice1 and Choice2. • Open the Response Data section of the dialog box. Click Select Data Table. When the Response Data Table dialog box opens, select Pizza Responses.jmp. • Select Choice for the Profile ID Chosen, and Choice1 and Choice2 for the Profile ID Choices.
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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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Contents Launch the Fit Model Platf
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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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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 Principal Components. . .
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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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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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