Modeling and Multivariate Methods - SAS

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214 Performing Logistic Regression on Nominal and Ordinal Responses Chapter 7 Validation Confusion Matrix Profiler A confusion matrix is a two-way classification of the actual response levels and the predicted response levels. For a good model, predicted response levels should be the same as the actual response levels. The confusion matrix gives a way of assessing how the predicted responses align with the actual responses. Brings up the prediction profiler, showing the fitted values for a specified response probability as the values of the factors in the model are changed. This feature is available for both nominal and ordinal responses. For detailed information about profiling features, refer to the “Visualizing, Optimizing, and Simulating Response Surfaces” chapter on page 553. Validation Validation is the process of using part of a data set to estimate model parameters, and using the other part to assess the predictive ability of the model. • The training set is the part that estimates model parameters. • The validation set is the part that assesses or validates the predictive ability of the model. • The test set is a final, independent assessment of the model’s predictive ability. The test set is available only when using a validation column. The training, validation, and test sets are created by subsetting the original data into parts. This is done through the use of a validation column in the Fit Model launch window. The validation column’s values determine how the data is split, and what method is used for validation: • If the column has two distinct values, then training and validation sets are created. • If the column has three distinct values, then training, validation, and test sets are created. • If the column has more than three distinct values, or only one, then no validation is performed. When validation is used, model fit statistics are given for the training, validation, and test sets. Example of a Nominal Logistic Model A market research study was undertaken to evaluate preference for a brand of detergent (Ries and Smith 1963). The results are in the Detergent.jmp sample data table. The model is defined by the following: • the response variable, brand with values m and x • an effect called softness (water softness) with values soft, medium, and hard • an effect called previous use with values yes and no • an effect called temperature with values high and low • a count variable, count, which gives the frequency counts for each combination of effect categories.
Chapter 7 Performing Logistic Regression on Nominal and Ordinal Responses 215 Example of a Nominal Logistic Model The study begins by specifying the full three-factor factorial model as shown by the Fit Model dialog in Figure 7.16. To specify a factorial model, highlight the three main effects in the column selector list. Then select Full Factorial from the Macros popup menu. Figure 7.16 A Three-Factor Factorial Model with Nominal Response Validation is available only in JMP Pro. The tables in Figure 7.17 show the three-factor model as a whole to be significant (Prob>ChiSq = 0.0006) in the Whole Model table. The Effect Likelihood Ratio Tests table shows that the effects that include softness do not contribute significantly to the model fit.
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INDIRECT OR CONSEQUENTIAL DAMAGES O
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6 Emphasis Options for Standard Lea
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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Contents Launch the Fit Model Platf
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158 Fitting Multiple Response Model
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264 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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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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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 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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642 References Draper, N. and Smith
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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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