Modeling and Multivariate Methods - SAS

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86 Fitting Standard Least Squares Models Chapter 3 Effect Screening Figure 3.24 Bayes Plot Specifications 8. Click Go to start the calculation of the posterior probabilities. Figure 3.25 Bayes Plot Report The Std Err Est value is set to 0 for a saturated model with no estimate of error. If there is an estimate of standard error (the root mean square error), this value is set to 1 because the estimates have already been transformed and scaled to unit variance. If you specify a different value, it should be done as a scale factor of the RMSE estimate.
Chapter 3 Fitting Standard Least Squares Models 87 Effect Screening The resulting posterior probabilities are listed and plotted with bars as shown in Figure 3.25. An overall posterior probability is also shown for the outcome that the sample is uncontaminated. Bayes Plot for Factor Activity JMP includes a script that helps you determine which factors are active in the design. 1. Open the Reactor.jmp sample data table. 1. Open the BayesPlotforFactors.jsl file in the Sample Scripts folder. 2. Select Edit > Run Script. 3. Select Y and click Y, Response. 4. Select F, Ct, A, T, and Cn and click X, Factor. 5. Click OK. Figure 3.26 Bayes Plot for Factor Activity In this case, we specified that the highest order interaction to consider is two. Therefore, all possible models that include (up to) second-order interactions are constructed. Based on the value assigned to Prior Probability (see the Controls section of the plot), a posterior probability is computed for each of the possible models. The probability for a factor is the sum of the probabilities for each of the models where it was involved. In this example, we see that Ct, T, and Cn are active and that A and F are not. These results match those of the Bayes Plot shown in Figure 3.25. Note: If the ridge parameter were zero (not allowed), all the models would be fit by least squares. As the ridge parameter gets large, the parameter estimates for any model shrink toward zero. Details on the ridge parameter (and why it cannot be zero) are explained in Box and Meyer (1993).
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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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Contents Launch the Fit Model Platf
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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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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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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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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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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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550 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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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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