Modeling and Multivariate Methods - SAS

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496 Performing Discriminant Analysis Chapter 20 Discriminating Groups Figure 20.3 Stepwise Control Panel Entered checkboxes show which columns are currently in the model. You can manually click columns in or out of the model. Lock checkboxes are used when you want to force a column to stay in its current state regardless of any stepping by the buttons. Step Forward adds the most significant column not already in the model. Step Backward removes the least significant column already in the model. Enter All enters all the columns into the model. Remove All removes all the columns from the model. Apply This Model is used when you are finished deciding the columns to include in the analysis, and want to proceed to estimation and scoring. Figure 20.4 shows three forward steps, which add all the columns to the model except Sepal length. Figure 20.4 Stepped Model
Chapter 20 Performing Discriminant Analysis 497 Discriminating Groups Click Apply This Model to estimate the model. After estimation and scoring are done, two reports are produced: a Canonical Plot (Figure 20.5), and a Scoring Report. Canonical Plot The Canonical Plot shows the points and multivariate means in the two dimensions that best separate the groups. Figure 20.5 Canonical Plot • Each row in the data set is a point, controlled by the Canonical Options > Show Points option. • Each multivariate mean is a labeled circle. The size of the circle corresponds to a 95% confidence limit for the mean. Groups that are significantly different tend to have non-intersecting circles. This is controlled by the Canonical Options > Show Means CL Ellipses option. • The directions of the variables in the canonical space is shown by labeled rays emanating from the grand mean. This is controlled by the Canonical Options > Show Biplot Rays option. You can drag the center of the biplot rays to other places in the graph. • The option Show Normal 50% Contours shows areas that contain roughly 50% of the points for that group if the assumptions are correct. Under linear discriminant analysis, they are all the same size and shape. In order to have the points color-coded like the centroid circles, use the Color Points option or button. This is equivalent to Rows > Color or Mark by Column, coloring by the classification column. The canonical plot can also be referred to as a biplot when both the points and the variable direction rays are shown together, as in Figure 20.5. It is identical to the Centroid plot produced in the Manova personality of the Fit Model platform. Discriminant Scores The scores report shows how well each point is classified. The first five columns of the report represent the actual (observed) data values, showing row numbers, the actual classification, the distance to the mean of that classification, and the associated probability. JMP graphs -Log(Prob) to show the loss in log-likelihood when a point is predicted poorly. When the red bar is large, the point is being poorly predicted.
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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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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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Page 482 and 483: Contents Principal Components. . .
Page 484 and 485: 484 Analyzing Principal Components
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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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Contents Introduction to Profiling
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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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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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