Modeling and Multivariate Methods - SAS

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160 Fitting Multiple Response Models Chapter 5 Response Specification Table 5.2 Description of the Manova Fit Options (Continued) Model Dialog Script Shows the completed launch window for the current analysis. Contains options that are available to all platforms. See Using JMP. Response Specification Specify the response designs for various tests using the Response Specification panel. Figure 5.3 Response Specification Panel Table 5.3 Description of the Response Specification Panel Choose Response Univariate Tests Also Test Each Column Separately Also Provides choices for the M matrix. See “Choose Response Options” on page 160. Obtains adjusted and unadjusted univariate repeated measures tests and multivariate tests. Use in repeated measures models. Obtains univariate ANOVA tests and multivariate tests on each response. The following buttons only appear once you have chosen a response option: Run Help Orthogonalize Delete Last Column Performs the analysis and shows the multivariate estimates and tests. See “Multivariate Tests” on page 165. Takes you to the help for the Response Specification panel. Orthonormalizes the matrix. Orthonormalization is done after the column contrasts (sum to zero) for all response types except Sum. Reduces the dimensionality of the transformation. Choose Response Options The response design forms the M matrix. The columns of an M matrix define a set of transformation variables for the multivariate analysis. The Choose Response button contains the options for the M matrix.
Chapter 5 Fitting Multiple Response Models 161 Response Specification Table 5.4 Descriptions of the Choose Response Options Repeated Measures Sum Identity Contrast Polynomial Helmert Profile Mean Compound Custom Constructs and runs both Sum and Contrast responses. Sum of the responses that gives a single value. Uses each separate response, the identity matrix. Compares each response and the first response. Constructs a matrix of orthogonal polynomials. Compares each response with the combined responses listed below it. Compares each response with the following response. Compares each response with the mean of the others. Creates and runs several response functions that are appropriate if the responses are compounded from two effects. Uses any custom M matrix that you enter. The most typical response designs are Repeated Measures and Identity for multivariate regression. There is little difference in the tests given by the Contrast, Helmert, Profile, and Mean options, since they span the same space. However, the tests and details in the Least Squares means and Parameter Estimates tables for them show correspondingly different highlights. The Repeated Measures and the Compound options display dialogs to specify response effect names. They then fit several response functions without waiting for further user input. Otherwise, selections expand the control panel and give you more opportunities to refine the specification. Custom Test Option Set up custom tests of effect levels using the Custom Test option. Note: For instructions on how to create custom tests, see “Custom Test” on page 68 in the “Fitting Standard Least Squares Models” chapter. The menu icon beside each effect name gives you the commands shown here, to request additional information about the multivariate fit: Table 5.5 Description of the Custom Test Options Test Details Displays the eigenvalues and eigenvectors of the E – 1 H matrix used to construct multivariate test statistics. Centroid Plot Plots the centroids (multivariate least squares means) on the first two canonical variables formed from the test space.
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Version 10 Modeling and Multivariat
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6 Emphasis Options for Standard Lea
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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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Contents Launch the Fit Model Platf
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100 Fitting Standard Least Squares
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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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470 Clustering Data Chapter 18 K-Me
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Contents Principal Components. . .
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Contents Introduction . . . . . . .
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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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556 Visualizing, Optimizing, and Si
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640 References Becker, R.A. and Cle
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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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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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