Modeling and Multivariate Methods - SAS

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98 Fitting Standard Least Squares Models Chapter 3 Factor Profiling 6. Click Run. 7. From the red triangle menu next to Response Y, select Factor Profiling > Cube Plots. Figure 3.35 Cube Plots To change the layout so that the factors are mapped to different cube coordinates, click one of the factor names in the first cube and drag it to the desired axis. For example, in Figure 3.35, if you click T and drag it over Ct, then T and Ct (and their corresponding coordinates) exchange places. When there is more than one response, the multiple responses are shown stacked at each vertex. Box Cox Y Transformations Sometimes a transformation on the response fits the model better than the original response. A commonly used transformation raises the response to some power. Box and Cox (1964) formalized and described this family of power transformations. The formula for the transformation is constructed so that it provides a continuous definition and the error sums of squares are comparable. y λ – 1 Y ( λ) ---------------- if λ ≠ 0 = λ – 1 λy· y· ln ( y) if λ = 0 where y· is the geometric mean The plot shown here illustrates the effect of this family of power transformations on Y.
Chapter 3 Fitting Standard Least Squares Models 99 Factor Profiling Figure 3.36 Power Transformations on Y The Box-Cox Y Transformation option fits transformations from λ = –2 to 2 in increments of 0.2. This transformation also plots the sum of squares error (SSE) across the λ power. Example of Box-Cox Y Transformation 1. Open the Reactor.jmp sample data table. 2. Select Analyze > Fit Model. 3. Select Y and click Y. 4. Make sure that the Degree box has a 2 in it. 5. Select F, Ct, A, T, and Cn and click Macros > Factorial to Degree. 6. Click Run. 7. From the red triangle menu next to Response Y, select Factor Profiling > Box Cox Y Transformation. Figure 3.37 Box Cox Y Transformation Figure 3.37 shows the best fit when λ is between 1.0 and 1.5. The best transformation is found on the plot by finding the lowest point on the curve. Save Best Transformation Creates a new column in the data table and saves the formula for the best transformation.
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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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18 Interpreting the Profiles . . .
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20 Discriminant Analysis . . . . .
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Contents Book Conventions. . . . .
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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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Contents Launch the Fit Model Platf
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Contents Example of a Multiple Resp
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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 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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480 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 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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546 Plotting Surfaces Chapter 23 Th
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548 Plotting Surfaces Chapter 23 Pl
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550 Plotting Surfaces Chapter 23 Pl
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552 Plotting Surfaces Chapter 23 Ke
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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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