Modeling and Multivariate Methods - SAS

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514 Fitting Partial Least Squares Models Chapter 21 Model Fit Options Figure 21.8 Cross Validation Report Model Fit Report Displays detailed results for each fitted model. The fit uses either the optimum number of factors based on cross validation, or the specified number of factors if no cross validation methods was specified. The report title indicates whether NIPALS or SIMPLS was used and gives the number of extracted factors. The report includes the following summary information: X-Y Scores Plots Produces scatterplots of the X and Y scores for each extracted factor. Percent Variation Explained Shows the percent variation and cumulative percent variation explained for both X and Y. Results are given for each extracted factor. Model Coefficients for Centered and Scaled Data For each Y, shows the coefficients of the Xs for the model based on the centered and scaled data. Model Coefficients for Original Data For each Y, shows the coefficients of the Xs for the model based on the original data. Model Fit Options The Model Fit red triangle menu has the following options: Percent Variation Plots Plots the percent variation explained by each extracted factor for the Xs and Ys. Variable Importance Plot Plots the VIP values for each X variable (Figure 21.9). The VIP score is a measure of a variable’s importance in modeling both X and Y. If a variable has a small coefficient and a small VIP, then it is a candidate for deletion from the model (Wold,1995). A value of 0.8 is generally considered to be a small VIP (Eriksson et al, 2006) and a blue line is drawn on the plot at 0.8.
Chapter 21 Fitting Partial Least Squares Models 515 Model Fit Options Figure 21.9 Variable Importance Plot VIP vs Coefficients Plots Plots the VIP statistics against the model coefficients (Figure 21.10). Check boxes enable you to display only those points corresponding to your selected Ys. Additional labeling options are provided. There are plots for both the centered and scaled data and the original data. To the right of the plot for the centered and scaled data are two options that facilitate variable reduction and model building. Make Model Using VIP opens and populates a launch window with the appropriate responses entered as Ys and the variables whose VIPs exceed the specified threshold entered as Xs. Make Model Using Selection enables you to select Xs directly in the plot and then enters the Ys and only the selected Xs into a launch window. In JMP Pro, the model is built through the Fit Model specification window. In JMP, the model is specified in the PLS launch window.
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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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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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30 Learn about JMP Chapter 1 Additi
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Contents Launch the Fit Model Platf
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34 Introduction to the Fit Model Pl
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Contents Example Using Standard Lea
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56 Fitting Standard Least Squares M
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Contents Overview of Stepwise Regre
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136 Fitting Stepwise Regression Mod
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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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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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Page 482 and 483: Contents Principal Components. . .
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564 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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