Modeling and Multivariate Methods - SAS

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158 Fitting Multiple Response Models Chapter 5 Example of a Multiple Response Model Figure 5.2 Manova Report Window The initial results might not be very interesting in themselves, because no response design has been specified yet. After you specify a response design, the multivariate platform displays tables of multivariate estimates and tests. For details about specifying a response design, see “Response Specification” on page 160.
Chapter 5 Fitting Multiple Response Models 159 The Manova Report The Manova Report The Manova report window contains the following elements: Table 5.1 Description of the Manova Report Window Manova Fit red triangle menu Response Specification Parameter Estimates Least Squares Means Partial Correlation Overall E&H Matrices Contains save options. See “The Manova Fit Options” on page 159. Enables you to specify the response designs for various tests. See “Response Specification” on page 160. Contains the parameter estimates for each response variable (without details like standard errors or t-tests). There is a column for each response variable. Reports the overall least squares means of all of the response columns, least squares means of each nominal level, and least squares means plots of the means. Shows the covariance matrix and the partial correlation matrix of residuals from the initial fit, adjusted for the X effects. Shows the E and H matrices: • The elements of the E matrix are the cross products of the residuals. • The H matrices correspond to hypothesis sums of squares and cross products. There is an H matrix for the whole model and for each effect in the model. Diagonal elements of the E and H matrices correspond to the hypothesis (numerator) and error (denominator) sum of squares for the univariate F tests. New E and H matrices for any given response design are formed from these initial matrices, and the multivariate test statistics are computed from them. The Manova Fit Options Table 5.2 describes the options in the red triangle menu next to Manova Fit. Table 5.2 Description of the Manova Fit Options Save Discrim Save Predicted Save Residuals Performs a discriminant analysis and saves the results to the data table. For more details, see “Discriminant Analysis” on page 173. Saves the predicted responses to the data table. Saves the residuals to the data table.
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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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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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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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208 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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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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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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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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