Modeling and Multivariate Methods - SAS

Chapter 5 Fitting Multiple Response Models 161
Response Specification
Table 5.4 Descriptions of the Choose Response Options
Repeated Measures
Sum
Identity
Contrast
Polynomial
Helmert
Profile
Mean
Compound
Custom
Constructs and runs both Sum and Contrast responses.
Sum of the responses that gives a single value.
Uses each separate response, the identity matrix.
Compares each response and the first response.
Constructs a matrix of orthogonal polynomials.
Compares each response with the combined responses listed below it.
Compares each response with the following response.
Compares each response with the mean of the others.
Creates and runs several response functions that are appropriate if the
responses are compounded from two effects.
Uses any custom M matrix that you enter.
The most typical response designs are Repeated Measures and Identity for multivariate regression. There
is little difference in the tests given by the Contrast, Helmert, Profile, and Mean options, since they span
the same space. However, the tests and details in the Least Squares means and Parameter Estimates tables for
them show correspondingly different highlights.
The Repeated Measures and the Compound options display dialogs to specify response effect names.
They then fit several response functions without waiting for further user input. Otherwise, selections
expand the control panel and give you more opportunities to refine the specification.
Custom Test Option
Set up custom tests of effect levels using the Custom Test option.
Note: For instructions on how to create custom tests, see “Custom Test” on page 68 in the “Fitting
Standard Least Squares Models” chapter.
The menu icon beside each effect name gives you the commands shown here, to request additional
information about the multivariate fit:
Table 5.5 Description of the Custom Test Options
Test Details Displays the eigenvalues and eigenvectors of the E – 1 H matrix used to
construct multivariate test statistics.
Centroid Plot
Plots the centroids (multivariate least squares means) on the first two
canonical variables formed from the test space.