Maximum Redundancy Analysis

Multivariate Analyses

Maximum Redundancy Analysis

In contrast to canonical redundancy analysis, which examines how well the original variables can be predicted from the canonical variables, maximum redundancy analysis finds the variables that can best predict the original variables.

Given two sets of variables, maximum redundancy analysis finds a linear combination from one set of variables that best predicts the variables in the opposite set. SAS/INSIGHT software normalizes the coefficients of the linear combinations so that each maximum redundancy variable has a variance of 1.

Maximum redundancy analysis continues by finding a second maximum redundancy variable from one set of variables, uncorrelated with the first one, that produces the second highest prediction power for the variables in the opposite set. The process of constructing maximum redundancy variables continues until the number of maximum redundancy variables equals the number of variables in the smaller group.

Either raw variances (Raw Variance) or standardized variances (Std Variance) can be used in the analysis. You specify the selection in the method options dialog as shown in Figure 40.3. By default, standardized variances are used.

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