Modeling and Multivariate Methods - SAS

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174 Fitting Multiple Response Models Chapter 5 Discriminant Analysis For a classification variable with k levels, JMP adds k distance columns, k classification probability columns, the predicted classification column, and two columns of other computational information to the current data table. Example of the Save Discrim Option Examine Fisher’s Iris data as found in Mardia, Kent, and Bibby (1979). There are k = 3 levels of species and four measures on each sample. 1. Open the Iris.jmp sample data table. 2. Select Analyze > Fit Model. 3. Select Sepal length, Sepal width, Petal length, and Petal width and click Y. 4. Select Species and click Add. 5. Next to Personality, select Manova. 6. Click Run. 7. From the red triangle menu next to Manova Fit, select Save Discrim. The following columns are added to the Iris.jmp sample data table: SqDist[0] SqDist[setosa] SqDist[versicolor] SqDist[virginica] Prob[0] Prob[setosa] Prob[versicolor] Prob[virginica] Pred Species Quadratic form needed in the Mahalanobis distance calculations. Mahalanobis distance of the observation from the Setosa centroid. Mahalanobis distance of the observation from the Versicolor centroid. Mahalanobis distance of the observation from the Virginica centroid. Sum of the negative exponentials of the Mahalanobis distances, used below. Probability of being in the Setosa category. Probability of being in the Versicolor category. Probability of being in the Virginica category. Species that is most likely from the probabilities. Now you can use the new columns in the data table with other JMP platforms to summarize the discriminant analysis with reports and graphs. For example: 1. From the updated Iris.jmp data table (that contains the new columns) select Analyze > Fit Y by X. 2. Select Species and click Y, Response. 3. Select Pred Species and click X, Factor. 4. Click OK.
Chapter 5 Fitting Multiple Response Models 175 Discriminant Analysis The Contingency Table summarizes the discriminant classifications. Three misclassifications are identified. Figure 5.13 Contingency Table of Predicted and Actual Species
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Version 10 Modeling and Multivariat
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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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Contents Launch the Fit Model Platf
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Contents Example Using Standard Lea
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224 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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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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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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