Modeling and Multivariate Methods - SAS

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666 Statistical Details Appendix A The Factor Models Table A.10 Comparison of GLM and JMP Hypotheses (Continued) a3b3 -0.1111 -0.1111 -0.3333 -0.3333 -0.3333 -0.3333 -0.3333 -0.3333 a1b1c1 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a1b1c2 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a1b2c1 0.0556 -0.0278 0.1111 0 0.0833 0 0.0909 0 a1b2c2 0.0556 -0.0278 0.1111 0 0.0833 0 0.0909 0 a1b2c3 0.0556 -0.0278 0.1111 0 0.0833 0 0.0909 0 a1b3c1 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a1b3c2 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a1b4c1 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a1b4c2 0.0833 -0.0417 0.1111 0 0.125 0 0.1212 0 a2b1c1 -0.0833 0.1667 0 0.25 0 0.25 0 0.25 a2b1c2 -0.0833 0.1667 0 0.25 0 0.25 0 0.25 a2b2c1 -0.0833 0.1667 0 0.25 0 0.25 0 0.25 a2b2c2 -0.0833 0.1667 0 0.25 0 0.25 0 0.25 a3b1c1 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 a3b1c2 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 a3b2c1 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 a3b2c2 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 a3b3c1 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 a3b3c2 -0.0556 -0.0556 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 -0.1667 Ordinal Factors Factors marked with the ordinal modeling type are coded differently than nominal factors. The parameters estimates are interpreted differently, the tests are different, and the least squares means are different.
Appendix A Statistical Details 667 The Factor Models The theme for ordinal factors is that the first level of the factor is a control or baseline level, and the parameters measure the effect on the response as the ordinal factor is set to each succeeding level. The coding is appropriate for factors with levels representing various doses, where the first dose is zero: Table A.11 Ordinal Factors Term Coded Column A a2 a3 A1 0 0 control level, zero dose A2 1 0 low dose A3 1 1 higher dose From the perspective of the JMP parameterization, the tests for A are: Table A.12 Tests for A parameter GLM–IV test JMP test m 0 0 0 0 a13 2 1 1 0 a23 1 2 0 1 a1:b14 0 0 0 0 a1:b24 0.11111 0 0 0 a1:b34 0 0 0 0 a2:b12 0 0 0 0 a3:b13 0 0 0 0 a3:b23 0 0 0 0 a1b1:c12 0 0 0 0 a1b2:c13 0 0 0 0 a1b2:c23 0 0 0 0 a1b3:c12 0 0 0 0 a1b4:c12 0 0 0 0 a2b1:c13 0 0 0 0 a2b2:c12 0 0 0 0
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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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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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158 Fitting Multiple Response Model
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180 Fitting Generalized Linear Mode
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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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306 Fitting Dispersion Effects with
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318 Recursively Partitioning Data C
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354 Performing Time Series Analysis
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386 Performing Categorical Response
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Contents Introduction to Clustering
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462 Clustering Data Chapter 18 The
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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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494 Performing Discriminant Analysi
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544 Plotting Surfaces Chapter 23 Th
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556 Visualizing, Optimizing, and Si
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Page 652 and 653: Contents The Response Models . . .
Page 654 and 655: 654 Statistical Details Appendix A
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Modeling and Multivariate Methods - SAS

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