Modeling and Multivariate Methods - SAS

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500 Performing Discriminant Analysis Chapter 20 Commands and Options Figure 20.7 Canonical Details Save Canonical Scores creates columns in the data table holding the canonical score for each observation. The new columns are named Canon[ ]. Save to New Data Table available only through scripting, saves the group means and the biplot rays on the canonical variables, together with the canonical scores to a new data table. Canonical 3D Plot is available only when there are four or more groups (levels). An example of this plot is shown in Figure 20.8. It shows a three-dimensional version of the Canonical Plot and respects other Canonical Options.
Chapter 20 Performing Discriminant Analysis 501 Commands and Options Figure 20.8 Canonical 3D Plot The example in Figure 20.8 is displayed by: 1. Open cereal.jmp. 2. Click on Analyze > Multivariate Methods > Discriminant. 3. Specify Calories, Protein, Fat, and Sodium as Y, Covariates. 4. Specify Manufacturer as X, Categories and click OK. 5. Click on the red triangle in the Discriminant Analysis title bar and select Canonical 3D Plot. 6. Click inside the plot and rotate the plot until you see the desired effect. Specify Priors lets you specify the prior probabilities for each level of the X variable: Equal Probabilities assigns an equal probability to each level. Proportional to Occurrence assigns probabilities to each level according to their frequency in the data. Other brings up a dialog to allow custom specification of the priors, shown in Figure 20.9. By default, each level is assigned equal probabilities.
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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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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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