Modeling and Multivariate Methods - SAS

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138 Fitting Stepwise Regression Models Chapter 4 The Stepwise Report Table 4.2 Descriptions of Stepwise Platform Options (Continued) Model Averaging Plot Criterion History Plot RSquare History Model Dialog Enables you to average the fits for a number of models, instead of picking a single best model. See “The Model Averaging Option” on page 151. Available only for continuous responses. Creates a plot of AICc and BIC versus the number of parameters. Creates a plot of training and validation R-square versus the number of parameters. Available only for continuous responses. Shows the completed launch window for the current analysis. Stepwise Regression Control Panel Use the Stepwise Regression Control panel to limit regressor effect probabilities, determine the method of selecting effects, begin or stop the selection process, and create a model. Figure 4.3 Stepwise Regression Control Panel
Chapter 4 Fitting Stepwise Regression Models 139 The Stepwise Report Table 4.3 Description of the Stepwise Regression Control Panel Stopping Rule • P-value Threshold uses p-values (significance levels) to enter and remove effects from the model. Two other options appear when P-value Threshold is chosen: Prob to Enter is the maximum p-value that an effect must have to be entered into the model during a forward step. Prob to Leave is the minimum p-value that an effect must have to be removed from the model during a backward step. • Minimum AICc uses the minimum corrected Akaike Information Criterion to choose the best model. • Minimum BIC uses the minimum Bayesian Information Criterion to choose the best model. • Max Validation RSquare uses the maximum R-square from the validation set to choose the best model. This is available only when a validation column is used, and the validation column has two or three distinct values. For more information about validation, see “Using Validation” on page 152. • Max K-Fold RSquare uses the maximum R-square from K-fold cross validation to choose the best model. This is available only when K-Fold cross validation is used. For more information about validation, see “Using Validation” on page 152. Direction Choose how effects enter and leave the model: • Forward brings in the regressor that most improves the fit, given that term is significant at the level specified by Prob to Enter. See “Forward Selection Example” on page 141. • Backward removes the regressor that affects the fit the least, given that term is not significant at the level specified in Prob to Leave. See “Backward Selection Example” on page 142. • Mixed alternates the forward and backward steps. It includes the most significant term that satisfies Prob to Enter and removes the least significant term satisfying Prob to Leave. It continues removing terms until the remaining terms are significant and then it changes to the forward direction.
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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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12 Examining the Residuals . . . .
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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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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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188 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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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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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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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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