Modeling and Multivariate Methods - SAS

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408 Performing Choice Modeling Chapter 16 Launch the Choice Platform and Select Data Sets • The Subject ID is optional, but is needed if you later want to model subject effects or if you later want to cluster subjects into groups. • Freq and Weight are used to weight the analysis. For example, if you have summarized a set that had the same trial Freq is used for the count in that row of the response data table. • Select Subject for Subject ID to identify individual subjects for later analysis. If you are not interested in assessing subjects at this time, it is not necessary to enter the subject data set into the model. The completed dialog box, without the subject data set, is shown in Figure 16.5. Figure 16.5 Response Data Set Dialog Box
Chapter 16 Performing Choice Modeling 409 Launch the Choice Platform and Select Data Sets If you are scripting the Choice platform, you can also set the acceptable criterion for convergence when estimating the parameters by adding this command to the Choice() specification: Choice( ..., Convergence Criterion( fraction ), ... ) See the Object Scripting Index for an example. Choice Model Output Click on Run Model to obtain the results. These results are shown in Figure 16.6. • The resulting parameter estimates are sometimes referred to as part-worths. Each part-worth is the coefficient of utility associated with that attribute. By default, these estimates are based upon the Firth bias-corrected maximum likelihood estimators, and are, therefore, considered to be more accurate than MLEs without bias correction. • Comparison criteria are used to help determine the better-fitting model(s) when more than one model is investigated for your data. The model with the lower or lowest criterion value is believed to be the better or best model. Three criteria are shown in the Choice Model output and include AICc (corrected Akaike’s Information Criterion), -2*LogLikelihood, and -2*Firth Loglikelihood. The AICc formula is: AICc 2k( k+ 1) = – 2loglikelihood + 2k + ---------------------- n – k – 1 where k is the number of estimated parameters in the model and n is the number of observations in the dataset. Note that the -2*Firth Loglikelihood result is only included in the report when the Firth Bias-adjusted Estimates checkbox is checked in the launch window. (See Figure 16.5.) This option is checked by default. The decision to use or not use the Firth Bias-adjusted Estimates does not affect the AICc score or the -2*LogLikelihood results. • Likelihood ratio tests appear for each effect in the model. These results are obtained by default if the model is fit quickly (less than five seconds); otherwise, you can click on the Choice Model drop down menu and select Likelihood Ratio Tests. Figure 16.6 Choice Model Results with No Subject Data for Pizza Example Part-worths for crust, cheese, and topping
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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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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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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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100 Fitting Standard Least Squares
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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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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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356 Performing Time Series Analysis
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458 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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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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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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