Modeling and Multivariate Methods - SAS

Chapter 5
Fitting Multiple Response Models
Using the Fit Model Platform
Multivariate models fit several responses (Y variables) to a set of effects. The functions across the Y variables
can be tested with appropriate response designs.
In addition to creating standard MANOVA (Multivariate Analysis of Variance) models, you can use the
following techniques:
• Repeated measures analysis when repeated measurements are taken on each subject and you want to
analyze effects both between subjects and within subjects across the measurements. This multivariate
approach is especially important when the correlation structure across the measurements is arbitrary.
• Canonical correlation to find the linear combination of the X and Y variables that has the highest
correlation.
• Discriminant analysis to find distance formulas between points and the multivariate means of various
groups so that points can be classified into the groups that they are most likely to be in. A more
complete implementation of discriminant analysis is in the Discriminant platform.
Multivariate fitting has a different flavor from univariate fitting. The multivariate fit begins with a
rudimentary preliminary analysis that shows parameter estimates and least squares means. You can then
specify a response design across the Y variables and multivariate tests are performed.