Modeling and Multivariate Methods - SAS

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590 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 Mixture Profiler Figure 24.39 Factor Settings factor settings As shown in Figure 24.39, the optimal factor settings can be read from the Current X boxes. The factor values above hold for the current settings of x4, x5 and Type. Select Factor Settings > Remember Settings from the Mixture Profiler pop-up menu to save the current settings. The settings are appended to the bottom of the report window and appear as shown below. Figure 24.40 Remembered Settings With the current settings saved, you can now change the values of x4, x5 and Type to see what happens to the feasible region. You can compare the factor settings and response values for each level of Type by referring to the Remembered Settings report.
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 591 Surface Profiler Surface Profiler The Surface Profiler shows a three-dimensional surface plot of the response surface. The functionality of Surface Profiler is the same as the Surface Plot platform, but with fewer options. Details of Surface Plots are found in the “Plotting Surfaces” chapter on page 535. The Custom Profiler The Custom Profiler allows you to optimize factor settings computationally, without graphical output. This is used for large problems that would have too many graphs to visualize well. It has many fields in common with other profilers. The Benchmark field represents the value of the prediction formula based on current settings. Click Reset Benchmark to update the results. The Optimization outline node allows you to specify the formula to be optimized and specifications about the optimization iterations. Click the Optimize button to optimize based on current settings. Figure 24.41 Custom Profiler Custom Profiler Options Factor Settings is a submenu identical to the one covered on “Factor Settings” on page 565. Log Iterations outputs iterations to a table. Alter Linear Constraints allows you to add, change, or delete linear constraints. The constraints are incorporated into the operation of Custom Profiler. See “Linear Constraints” on page 575. Save Linear Constraints allows you to save existing linear constraints to a Table Property/Script called Constraint. See“Linear Constraints” on page 575. Simulator launches the Simulator. See “The Simulator” on page 592.
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Version 10 Modeling and Multivariat
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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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Contents Launch the Fit Model Platf
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56 Fitting Standard Least Squares M
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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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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Contents Launch the Multivariate Pl
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Contents Introduction to Clustering
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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 Surface Plots . . . . . .
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538 Plotting Surfaces Chapter 23 La
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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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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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