Modeling and Multivariate Methods - SAS

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208 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7 Logistic Fit Platform Options Two-level nominal effects are coded 1 and -1 for the first and second levels, so range odds ratios or their reciprocals would be of interest. Dose Response Example In the Dose Response.jmp sample data table, the dose varies between 1 and 12. 1. Open the Dose Response.jmp sample data table. 2. Select Analyze > Fit Model. 3. Select response and click Y. 4. Select dose and click Add. 5. Click OK. 6. From the red triangle next to Nominal Logistic Fit, select Odds Ratio. Figure 7.10 Odds Ratios The unit odds ratio for dose is 1.606 (which is exp(0.474)) and indicates that the odds of getting a Y =0 rather than Y = 1 improves by a factor of 1.606 for each increase of one unit of dose. The range odds ratio for dose is 183.8 (exp((12-1)*0.474)) and indicates that the odds improve by a factor of 183.8 as dose is varied between 1 and 12. Inverse Prediction For a two-level response, the Inverse Prediction command finds the x value that results in a specified probability. To see an example of inverse prediction: 1. Open the Ingots.jmp sample data table. 2. Select Analyze > Fit Y by X. 3. Select ready and click Y, Response. 4. Select heat and click X, Factor. 5. Select count and click Freq.
Chapter 7 Performing Logistic Regression on Nominal and Ordinal Responses 209 Logistic Fit Platform Options 6. Click OK. The cumulative logistic probability plot shows the result. Figure 7.11 Logistic Probability Plot Note that the fitted curve crosses the 0.9 probability level at a heat level of about 39.5, which is the inverse prediction. To be more precise and to get a fiducial confidence interval: 7. From the red triangle menu next to Logistic Fit, select Inverse Prediction. 8. For Probability, type 0.9. 9. Click OK. The predicted value (inverse prediction) for heat is 39.8775, as shown in Figure 7.12.
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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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34 Introduction to the Fit Model Pl
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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100 Fitting Standard Least Squares
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Contents Overview of Stepwise Regre
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136 Fitting Stepwise Regression Mod
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Contents Example of a Multiple Resp
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258 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 The Categorical Platform .
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386 Performing Categorical Response
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Contents Introduction to Choice Mod
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404 Performing Choice Modeling Chap
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Contents Launch the Multivariate Pl
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444 Correlations and Multivariate T
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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464 Clustering Data Chapter 18 Hier
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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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474 Clustering Data Chapter 18 Norm
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476 Clustering Data Chapter 18 Norm
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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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508 Fitting Partial Least Squares M
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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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550 Plotting Surfaces Chapter 23 Pl
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552 Plotting Surfaces Chapter 23 Ke
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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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