Modeling and Multivariate Methods - SAS

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340 Recursively Partitioning Data Chapter 13 Missing Values 5 5 times the richness of the general population base rate 0.10 1 Sorted from highest predicted rate to lowest predicted rate In problem situations where the response rate for a category is very low anyway (for example, a direct mail response rate), the lift curve explains things with more detail than the ROC curve. Figure 13.15 Lift Curve Missing Values The Partition platform has methods for handling missing values in both Y and X variables. Missing Responses The handling of missing values for responses depends on if the response is categorical or continuous.
Chapter 13 Recursively Partitioning Data 341 Example Categorical If the Missing Value Categories checkbox (see Figure 13.2) is not selected, missing rows are not included in the analysis. If the Missing Value Categories checkbox is selected, the missing rows are entered into the analysis as another level of the variable. Continuous Rows with missing values are not included in the analysis. Missing Predictors The handling of missing values for predictors depends on if it is categorical or continuous. Categorical If the variable is used as a splitting variable, and if the Missing Value Categories checkbox (see Figure 13.2) is not selected, then each missing row gets randomly assigned to one of the two sides of the split. When this happens using the Decision Tree method, the Imputes message appears showing how many times this has happened. See Figure 13.16. If the Missing Value Categories checkbox is selected, the missing rows are entered into the analysis as another level of the variable. Continuous If the variable is used as a splitting variable, then each missing row gets randomly assigned to one of the two sides of the split. When this happens using the Decision Tree method, the Imputes message appears showing how many times this has happened. See Figure 13.16. Figure 13.16 Impute Message Ten observations were missing and randomly assigned to a split Example The examples in this section use the Boston Housing.jmp data table. Suppose you are interested in creating a model to predict the median home value as a function of several demographic characteristics. The Partition platform can be used to quickly assess if there is any relationship between the response and the potential predictor variables. Build a tree using all three partitioning methods, and compare the results.
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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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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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390 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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Contents Example of Model Compariso
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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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