Modeling and Multivariate Methods - SAS

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428 Performing Choice Modeling Chapter 16 Special Data Rules Subject Data with Response Data If you have subject data in the Response data table, just select this table as the Select Data Table under the Subject Data. In this case, a Subject ID column does not need to be specified. In fact, it is not used. It is generally assumed that the subject data repeats consistently in multiple runs for each subject. Logistic Regression Ordinary logistic regression can be done with the Choice Modeling platform. Note: The Fit Y by X and Fit Model platforms are more convenient to use than the Choice Modeling platform for logistic regression modeling. This section is used only to demonstrate that the Choice Modeling platform can be used for logistic regression, if desired. If your data are already in the choice-model format, you might want to use the steps given below for logistic regression analysis. However, three steps are needed: • Create a trivial Profile data table with a row for each response level. • Put the explanatory variables into the Response data. • Specify the Response data table, again, for the Subject data table. An example of using the Choice Modeling platform for logistic regression follows: 1. Select Analyze > Modeling > Choice > Select Data Table > Other > OK. 2. Open the sample data set Lung Cancer Responses.jmp. Notice this data table has only one column (Lung Cancer) with two rows (Cancer and NoCancer). 3. Select Lung Cancer as the Profile ID and Add Lung Cancer as the model effect. The Profile Data dialog box is shown in Figure 16.34.
Chapter 16 Performing Choice Modeling 429 Special Data Rules Figure 16.34 Profile Data for Lung Cancer Example 4. Click on the disclosure icon for Response Data > Select Data Table > Other > OK. 5. Open the sample data set Lung Cancer Choice.jmp. 6. Select Lung Cancer for Profile ID Chosen, Choice1 and Choice2 for Profile ID Choices, and Count for Freq. The Response Data launch dialog box is shown in Figure 16.35. Figure 16.35 Response Data for Lung Cancer Example
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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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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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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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478 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 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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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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630 Comparing Model Performance Cha
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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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