Modeling and Multivariate Methods - SAS

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490 Analyzing Principal Components and Reducing Dimensionality Chapter 19 Platform Options Obparsimax specifies the oblique parsimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBPARSIMAX. Obquartimax specifies the oblique quartimax rotation. This is equivalent to the QUARTIMIN method. The corresponding SAS Proc Factor option specification is ROTATE=OBQUARTIMAX. Obvarimax specifies the oblique varimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBVARIMAX. Quartimin specifies the oblique quartimin rotation. It is equivalent to the oblique quartimax method. The corresponding SAS Proc Factor option specification is ROTATE=OBLIMIN(0) or ROTATE=OBLIMIN with TAU=0. Promax specifies oblique promax rotation. The corresponding SAS Proc Factor option specification is ROTATE=PROMAX.
Chapter 20 Performing Discriminant Analysis Using the Discriminant Platform Discriminant Analysis seeks to find a way to predict a classification (X) variable (nominal or ordinal) based on known continuous responses (Y). It can be regarded as inverse prediction from a multivariate analysis of variance (MANOVA). In fact, the Manova personality of Fit Model also provides some discriminant features. Features include: • A stepwise selection dialog to help choose variables that discriminate well. • Choice among Linear, Quadratic, or Regularized-Parameter analyses. • The discriminant scores, showing which groups each point is near. • A misclassification summary. • Plots of the points and means in a canonical space, which is the space that shows the best separation of means. • Options to save prediction distances and probabilities to the data table.
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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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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 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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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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Page 482 and 483: Contents Principal Components. . .
Page 484 and 485: 484 Analyzing Principal Components
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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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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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