Modeling and Multivariate Methods - SAS

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486 Analyzing Principal Components and Reducing Dimensionality Chapter 19 Platform Options Figure 19.3 3D Score Plot Switch among Principal Components, Rotated Components, Data Columns Select specific axis contents Cycle among all axis content possibilities The variables show as rays in the plot. These rays, called biplot rays, approximate the variables as a function of the principal components on the axes. If there are only two or three variables, the rays represent the variables exactly. The length of the ray corresponds to the eigenvalue or variance of the principal component. Factor Analysis performs factor-analytic style rotations of the principal components, or factor analysis. For more information, see “Factor Analysis” on page 487. Cluster Variables performs a cluster analysis on the columns, useful for grouping the columns. Save Principal Components saves the principal component to the data table, with a formula for computing the components. The formula can not evaluate rows with any missing values. Save Rotated Components saves the rotated components to the data table, with a formula for computing the components. This option appears after the Factor Analysis option is used. The formula can not evaluate rows with missing values.
Chapter 19 Analyzing Principal Components and Reducing Dimensionality 487 Platform Options Save Principal Components with Imputation imputes missing values, and saves the principal components to the data table. The column contains a formula for doing the imputation, and computing the principal components. This option is available only if there are missing values. Save Rotated Components with Imputation imputes missing values, and saves the rotated components to the data table. The column contains a formula for doing the imputation, and computing the rotated components. This option appears after the Factor Analysis option is used, and if there are missing values. Script contains options that are available to all platforms. See Using JMP. Factor Analysis The Factor Analysis option on the platform red-triangle menu performs rotations of the principal components, or factor analysis. When the option is selected, you are presented with the window shown in Figure 19.4. Figure 19.4 Factor Analysis Window Use this dialog to specify: 1. Factoring Method, either Principal Components or Maximum Likelihood. If you select the Maximum Likelihood method, a factor analytic model is estimated. The use of maximum likelihood allows for hypothesis testing for testing how many factors are significant. 2. Prior Communality, Principal Components (where the diagonals are 1) or Common Factor Analysis (where the diagonals are SMC, the squared multiple correlation between X i and the other p –1 variables). 3. The number of principal components or factors to rotate. 4. The rotation method. See “Rotation Methods” on page 488. After all selections are made, click OK. The report is shown in Figure 19.5.
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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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12 Examining the Residuals . . . .
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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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Contents Launch the Fit Model Platf
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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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232 Analyzing Screening Designs Cha
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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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306 Fitting Dispersion Effects with
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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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386 Performing Categorical Response
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538 Plotting Surfaces Chapter 23 La
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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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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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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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