Modeling and Multivariate Methods - SAS

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556 Visualizing, Optimizing, and Simulating Response Surfaces Chapter 24 Introduction to Profiling Table 24.2 Where to Find JMP Profilers (Continued) Location Profiler Contour Profiler Surface Profiler Mixture Profiler Custom Profiler Fit Model: Generalized Linear Yes Yes Yes Nonlinear: Factors and Response Yes Yes Yes Nonlinear: Parameters and SSE Yes Yes Yes Neural Net Yes Yes Yes Gaussian Process Yes Yes Yes Custom Design Prediction Variance Yes Yes Life Distribution Yes Fit Life by X Yes Yes Choice Yes Note: In this chapter, we use the following terms interchangeably: • factor, input variable, X column, independent variable, setting • response, output variable, Y column, dependent variable, outcome The Profiler (with a capital P) is one of several profilers (lowercase p). Sometimes, to distinguish the Profiler from other profilers, we call it the Prediction Profiler. When the profiler is invoked as (main menu) a platform, rather than through a fitting platform, you provide columns with formulas as the Y, Prediction Formula columns. These formulas could have been saved from the fitting platforms. Figure 24.2 Profiler Launch Window
Chapter 24 Visualizing, Optimizing, and Simulating Response Surfaces 557 The Profiler The columns referenced in the formulas become the X columns (unless the column is also a Y). Y, Prediction Formula are the response columns containing formulas. Noise Factors are only used in special cases for modeling derivatives. Details are in “Noise Factors (Robust Engineering)” on page 615. Expand Intermediate Formulas tells JMP that if an ingredient column to a formula is a column that itself has a formula, to substitute the inner formula, as long as it refers to other columns. To prevent an ingredient column from expanding, use the Other column property with a name of "Expand Formula" and a value of 0. The Surface Plot platform is discussed in a separate chapter. The Surface Profiler is very similar to the Surface Plot platform, except Surface Plot has more modes of operation. Neither the Surface Plot platform nor the Surface Profiler have some of the capabilities common to other profilers. The Profiler The Profiler displays profile traces (see Figure 24.3) for each X variable. A profile trace is the predicted response as one variable is changed while the others are held constant at the current values. The Profiler recomputes the profiles and predicted responses (in real time) as you vary the value of an X variable. • The vertical dotted line for each X variable shows its current value or current setting. If the variable is nominal, the x-axis identifies categories. See “Interpreting the Profiles” on page 558, for more details. For each X variable, the value above the factor name is its current value. You change the current value by clicking in the graph or by dragging the dotted line where you want the new current value to be. • The horizontal dotted line shows the current predicted value of each Y variable for the current values of the X variables. • The black lines within the plots show how the predicted value changes when you change the current value of an individual X variable. In fitting platforms, the 95% confidence interval for the predicted values is shown by a dotted blue curve surrounding the prediction trace (for continuous variables) or the context of an error bar (for categorical variables). The Profiler is a way of changing one variable at a time and looking at the effect on the predicted response.
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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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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 Launch the Fit Model Platf
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Contents Example Using Standard Lea
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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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Contents Launch the Multivariate Pl
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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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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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