Modeling and Multivariate Methods - SAS

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398 Performing Categorical Response Analysis Chapter 15 Categorical Reports A common application of this analysis is when the responses represent adverse events (side effects) from a drug. The computations represent the conditional probability of one side effect given the presence of another side effect. Rater Agreement The Rater Agreement analysis answers the questions of how closely raters agree with one another and if the lack of agreement is symmetrical. As an example, open Attribute Gauge.jmp. The Attribute Chart script runs the Variability Chart platform, which has a test for agreement among raters. Launch the Categorical platform and designate the three raters (A, B, and C) as Rater Agreement responses in the launch dialog. In the resulting report, you have a similar test for agreement that is augmented by a symmetry test that the lack of agreement is symmetric. Save Tables The Save Tables menu on the platform red-triangle menu has the following options: Save Frequencies saves the Frequency report to a new data table, without the marginal totals. Save Share of Responses totals. saves the Share of Responses report to a new data table, without the marginal Save Rate Per Case saves the Rate Per Case report to a new data table, without the marginal totals. Save Transposed Frequencies marginal totals. saves the Transposed Freq Chart report to a new data table, without the Save Transposed Share of Responses new data table saves a transposed version of the Share of Responses report to a Save Transposed Rate Per Case table. saves a transposed version of the Rate Per Case report to a new data
Chapter 15 Performing Categorical Response Analysis 399 Categorical Reports Save Test Rates saves the results of the Test Each Response option to a new data table. Save Test Homogeneity saves the results of the Test Response Homogeneity option to a new data table. This option is available only when using Aligned Responses.
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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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30 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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56 Fitting Standard Least Squares M
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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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Contents Introduction to Partitioni
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318 Recursively Partitioning Data C
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448 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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470 Clustering Data Chapter 18 K-Me
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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 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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Contents Introduction to Profiling
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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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