Modeling and Multivariate Methods - SAS

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488 Analyzing Principal Components and Reducing Dimensionality Chapter 19 Platform Options Figure 19.5 Factor Analysis Report Note that the first factor loads on the Carbon Tetrachloride-Chloroform-Benzene-Hexane cluster and that the second factor loads on the Ether–1-octanol cluster. Rotation Methods Rotations are used to better align the directions of the factors with the original variables so that the factors may be more interpretable. You hope for clusters of variables that are highly correlated to define the rotated factors. After the initial extraction, the factors are uncorrelated with each other. If the factors are rotated by an orthogonal transformation, the rotated factors are also uncorrelated. If the factors are rotated by an oblique transformation, the rotated factors become correlated. Oblique rotations often produce more useful patterns
Chapter 19 Analyzing Principal Components and Reducing Dimensionality 489 Platform Options than do orthogonal rotations. However, a consequence of correlated factors is that there is no single unambiguous measure of the importance of a factor in explaining a variable. Available orthogonal rotation methods are: Varimax specifies the orthogonal varimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=1. Biquartimax specifies the orthogonal biquartimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=0.5. Equamax specifies the orthogonal equamax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=number of factors/2. Factorparsimax specifies the orthogonal factor parsimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=number of variables. Orthomax specifies the orthogonal orthomax rotation with orthomax weight p. The corresponding SAS Proc Factor option specification is either ROTATE=ORTHOMAX or ROTATE=ORTHOMAX(p), which specifies p as the orthomax weight or the GAMMA=value. The default p value is 1 unless specified otherwise in the GAMMA= option. For additional information on orthomax weight, see the SAS documentation, “Simplicity Functions for Rotations.” Parsimax specifies the orthogonal parsimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=[(nvar(nfact - 1))/(nvar + nfact - 2)] where nvar is the number of variables, and nfact is the number of factors. Quartimax specifies the orthogonal quartimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=ORTHOMAX with GAMMA=0. Available oblique rotation methods are: Biquartimin specifies the oblique biquartimin rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBLIMIN(.5) or ROTATE=OBLIMIN with TAU=.5. Covarimin specifies the oblique covarimin rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBLIMIN(1) or ROTATE=OBLIMIN with TAU=1. Obbiquartimax specifies the oblique biquartimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBBIQUARTIMAX. Obequamax specifies the oblique equamax rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBEQUAMAX. Obfactorparsimax specifies the oblique factor parsimax rotation. The corresponding SAS Proc Factor option specification is ROTATE=OBFACTORPARSIMAX. Oblimin specifies the oblique oblimin rotation with oblimin weight p. The corresponding SAS Proc Factor option specification is ROTATE=OBLIMIN, where the default p value is zero, unless specified otherwise in the TAU= option. ROTATE=OBLIMIN(p) specifies p as the oblimin weight or the TAU= value. For additional information on oblimin weight, see the SAS documentation, “Simplicity Functions for Rotations.”
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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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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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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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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 Launching the Platform. .
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296 Modeling Relationships With Gau
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306 Fitting Dispersion Effects with
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318 Recursively Partitioning Data C
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354 Performing Time Series Analysis
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Page 482 and 483: Contents Principal Components. . .
Page 484 and 485: 484 Analyzing Principal Components
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538 Plotting Surfaces Chapter 23 La
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544 Plotting Surfaces Chapter 23 Th
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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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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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