Modeling and Multivariate Methods - SAS

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562 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 The Profiler Figure 24.7 Continuous Factor Settings Dialog For continuous variables, you can specify Current Value is the value used to calculate displayed values in the profiler, equivalent to the red vertical line in the graph. Minimum Setting Maximum Value is the minimum value of the factor’s axis. is the maximum value of the factor’s axis. Number of Plotted Points specifies the number of points used in plotting the factor’s prediction traces. Show is a checkbox that allows you to show or hide the factor in the profiler. Lock Factor Setting locks the value of the factor at its current setting. Profiler Options The popup menu on the Profiler title bar has the following options: Profiler shows or hides the Profiler. Contour Profiler Custom Profiler Mixture Profiler Surface Profiler shows or hides the Contour Profiler. shows or hides the Custom Profiler. shows or hides the Mixture Profiler. shows or hides the Surface Profiler. Save As Flash (SWF) allows you to save the Profiler (with reduced functionality) as an Adobe Flash file, which can be imported into presentation and web applications. An HTML page can be saved for viewing the Profiler in a browser. The Save as Flash (SWF) command is not available for categorical responses. For more information about this option, go to http://www.jmp.com/support/swfhelp/. The Profiler will accept any JMP function, but the Flash Profiler only accepts the following functions: Add, Subtract, Multiply, Divide, Minus, Power, Root, Sqrt, Abs, Floor, Ceiling, Min, Max, Equal, Not Equal, Greater, Less, GreaterorEqual, LessorEqual, Or, And, Not, Exp, Log, Log10, Sine, Cosine, Tangent, SinH, CosH, TanH, ArcSine, ArcCosine, ArcTangent, ArcSineH, ArcCosH, ArcTanH, Squish, If, Match, Choose.
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 563 The Profiler Note: Some platforms create column formulas that are not supported by the Save As Flash option. Show Formulas opens a JSL window showing all formulas being profiled. Formulas for OPTMODEL Creates code for the OPTMODEL SAS procedure. Hold down CTRL and SHIFT and then select Formulas for OPTMODEL from the red triangle menu. Script contains options that are available to all platforms. See Using JMP. The popup menu on the Prediction Profiler title bar has the following options: Prop of Error Bars appears under certain situations. See “Propagation of Error Bars” on page 571. Confidence Intervals shows or hides the confidence intervals. The intervals are drawn by bars for categorical factors, and curves for continuous factors. These are available only when the profiler is used inside certain fitting platforms. Sensitivity Indicator shows or hides a purple triangle whose height and direction correspond to the value of the partial derivative of the profile function at its current value. This is useful in large profiles to be able to quickly spot the sensitive cells. Figure 24.8 Sensitivity Indicators Desirability Functions shows or hides the desirability functions, as illustrated by Figure 24.17 and Figure 24.18. Desirability is discussed in “Desirability Profiling and Optimization” on page 567. Maximize Desirability Maximization Options sets the current factor values to maximize the desirability functions. allows you to refine the optimization settings through a dialog.
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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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26 Learn about JMP Chapter 1 Book C
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28 Learn about JMP Chapter 1 Additi
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Contents Example Using Standard Lea
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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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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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508 Fitting Partial Least Squares M
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510 Fitting Partial Least Squares M
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612 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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