Modeling and Multivariate Methods - SAS

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244 Performing Nonlinear Regression Chapter 9 Example of Nonlinear Fitting Figure 9.2 Nonlinear Fit Report The Plot report contains an overlaid plot of the fitted model of each formulation. There is also a separate plot for each formulation. The plot of the fitted curves suggests that formulation B might be different, because the test B curve starts to rise sooner than the others. This rise is a consequence of the inflection point parameter. 8. Select Compare Parameter Estimates from the Logistic 4P red triangle menu. A portion of the Parameter Comparison report is shown in Figure 9.3. Figure 9.3 Parameter Comparison Report
Chapter 9 Performing Nonlinear Regression 245 Example of Nonlinear Fitting Notice that the Inflection Point parameter for the test B formulation is significantly lower than the average inflection point. This agrees with what we noticed in the plots of the fitted models. Launch the Nonlinear Platform To launch the Nonlinear platform, select Analyze > Modeling > Nonlinear. The launch window is shown in Figure 9.4. Figure 9.4 Nonlinear Launch Window Begin fitting a nonlinear model in one of two ways: • Provide the Y and X variables and fit one of the built-in nonlinear models. This launches the Fit Curve personality of the Nonlinear platform. See “Nonlinear Fitting with Fit Curve” on page 246. • If you want to fit a model that is not in Fit Curve, you must first create a formula column that defines the model. This formula must include parameters to be estimated and the initial values of the parameters. Any nonlinear model can be fit using this method. For more information about this process, see “Fit a Custom Model” on page 256. You can also create the formula column using the Model Library. The Model Library creates the formula column and parameters, and helps you choose initial values, but only for models that are in Model Library. See “Use the Model Library” on page 264.
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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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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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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 of a Multiple Resp
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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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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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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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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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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