Modeling and Multivariate Methods - SAS

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498 Performing Discriminant Analysis Chapter 20 Commands and Options On the right of the graph, the report shows the category with the highest prediction probability, and, if they exist, other categories that have a predicted probability of over 0.1. An asterisk(*) indicates which points are misclassified. In Figure 20.6, the option Show Interesting Rows Only option is set so that only those rows that have fitted probabilities between 0.05 and 0.95 or are misclassified are shown. Figure 20.6 Show Interesting Rows Only The Counts report appears at the bottom of Figure 20.6. The counts are zero off the diagonal if everything is classified correctly. Commands and Options The following commands are available from the platform popup menu. Stepwise Variable Selection returns to the stepwise control panel. Discriminant Method chooses the discriminant method. Details are shown in the section “Discriminant Method” on page 494. Linear, Common Covariance assumes a common covariance. Quadratic, Different Covariances Regularized, Compromise Method which estimates different covariances. a compromise method.
Chapter 20 Performing Discriminant Analysis 499 Commands and Options Score Data shows or hides the listing of the scores by row in the Discriminant Scores portion of the report. Score Options deal with the scoring of the observations and includes the following: Show Interesting Rows Only shows rows that are misclassified and those where p>.05 and p Color or Mark by Column and selecting the X variable. Show Canonical Details shows or hides the Canonical details. Details for the Iris data set are shown in Figure 20.7. The matrices at the bottom of the report are opened by clicking on the disclosure icon beside their name and closed by clicking on the name of the matrix.
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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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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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446 Correlations and Multivariate T
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Page 482 and 483: Contents Principal Components. . .
Page 484 and 485: 484 Analyzing Principal Components
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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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