Modeling and Multivariate Methods - SAS

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264 Performing Nonlinear Regression Chapter 9 Fit a Custom Model Save Std Error of Individual saves the standard error for an individual prediction. This is the standard error for predicting an individual Y value for a given X value. The formula is of the form Sqrt(VecQuadratic(matrix1,vector1)+mse). matrix1 is the covariance matrix associated with the parameter estimates, vector1 is a composition of the partial derivatives of the model with respect to each parameter, and mse is the estimate of error variance. Save Residual Formula saves the formula for computing the residuals. Save Pred Confid Limit Formula saves the formula to calculate the confidence interval for a model prediction. This is a confidence interval for the average Y for a given X. Save Indiv Confid Limit Formula saves the formula to calculate the confidence interval for an individual prediction. This is a confidence interval for an individual Y for a given X. Save Inverse Prediction Formula saves formulas for the inverse of the model, the standard error of an inverse prediction, and the standard error of an individual inverse prediction. Save Specific Solving Formula is equivalent to Save Inverse Prediction Formula in simple cases. However, this command allows the formula to be a function of several variables and allows expressions to be substituted. This feature only works for solving easily invertible operators and functions that occur just once in the formula. After selecting this command, a dialog appears that enables you to select the variable to solve for. You can also edit the names of the columns in the resulting table. You can also substitute values for the names in the dialog. In these cases, the formula is solved for those values. Note: The standard errors, confidence intervals, and hypothesis tests are correct only if least squares estimation is done, or if maximum likelihood estimation is used with a proper negative log likelihood. Show Prediction Expression Shows the prediction model or the loss function at the top of the report. Use the Model Library The Model Library can assist you in creating the formula column with parameters and initial values. Click the Model Library button on the Nonlinear launch window to open the library (Figure 9.14).
Chapter 9 Performing Nonlinear Regression 265 Fit a Custom Model Figure 9.14 Nonlinear Model Library Dialog Select a model in the list to see its formula in the Formula box. Click Show Graph to show a 2-D theoretical curve for one-parameter models and a 3-D surface plot for two-parameter models. No graph is available for models with more than two explanatory (X) variables. On the graph window, change the default initial values of parameters using the slider, or clicking and typing values in directly. See Figure 9.15. Figure 9.15 Example Graph in Model Library
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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 Using Standard Lea
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56 Fitting Standard Least Squares M
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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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Page 214 and 215: 214 Performing Logistic Regression
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314 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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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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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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