Modeling and Multivariate Methods - SAS

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212 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7 Logistic Fit Platform Options ROC Curve • columns called Prob[j] for j < r with a formula for the fit to level j • a column called Most Likely responsename that picks the most likely level of each row based on the computed probabilities. For an ordinal response model with r levels, JMP creates • a column called Linear that contains the formula for a linear combination of the regressors without an intercept term • columns called Cum[j], each with a formula for the cumulative probability that the response is less than or equal to level j, for levels j =1,2,...r -1. There is no Cum[ j =1,2,...r -1] that is 1 for all rows • columns called Prob[ j =1,2,...r -1], for 1 < j < r, each with the formula for the probability that the response is level j. Prob[j] is the difference between Cum[j] and Cum[j –1]. Prob[1] is Cum[1], and Prob[r] is 1–Cum[r –1]. • a column called Most Likely responsename that picks the most likely level of each row based on the computed probabilities. Save Quantiles creates columns in the current data table named OrdQ.05, OrdQ.50, and OrdQ.95 that fit the quantiles for these three probabilities. Save Expected Value creates a column in the current data table called Ord Expected that is the linear combination of the response values with the fitted response probabilities for each row and gives the expected value. Receiver Operating Characteristic (ROC) curves measure the sorting efficiency of the model’s fitted probabilities to sort the response levels. ROC curves can also aid in setting criterion points in diagnostic tests. The higher the curve from the diagonal, the better the fit. An introduction to ROC curves is found in the Basic Analysis and Graphing book. If the logistic fit has more than two response levels, it produces a generalized ROC curve (identical to the one in the Partition platform). In such a plot, there is a curve for each response level, which is the ROC curve of that level versus all other levels. Details on these ROC curves are found in “Graphs for Goodness of Fit” on page 336 in the “Recursively Partitioning Data” chapter. Example of an ROC Curve 1. Open the Ingots.jmp sample data table. 2. Select Analyze > Fit Model. 3. Select ready and click Y. 4. Select heat and soak and click Add. 5. Select count and click Freq. 6. Click Run. 7. From the red triangle next to Nominal Logistic Fit, select ROC Curve. 8. Select 1 as the positive level and click OK.
Chapter 7 Performing Logistic Regression on Nominal and Ordinal Responses 213 Logistic Fit Platform Options Figure 7.14 ROC Curve Lift Curve Produces a lift curve for the model. A lift curve shows the same information as an ROC curve, but in a way to dramatize the richness of the ordering at the beginning. The Y-axis shows the ratio of how rich that portion of the population is in the chosen response level compared to the rate of that response level as a whole. See “Lift Curves” on page 339 in the “Recursively Partitioning Data” chapter for more details about lift curves. Figure 7.15 shows the lift curve for the same model specified for the ROC curve (Figure 7.14). Figure 7.15 Lift Curve
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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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158 Fitting Multiple Response Model
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160 Fitting Multiple Response Model
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262 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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