The modal assurance criterion (MAC) in general measures the degree of proportion between two (modal) vectors, in the form of a correlation coefficient of a least squares ratio estimate. The MAC principle can be extended in several ways, thus increasing its field of applications. The partial MAC (PMAC) correlates parts of (modal) vectors. The spatial MAC (SMAC) allows to compare different vector spaces. Furthermore this paper suggests a way of calculating the MAC sensitivities to model changes. All those extensions are illustrated by their possible uses in correlating measured dynamic data with (finite element) matrix models and in the area of model updating. Those applications might be helpful tools to indicate regions of poor measurement-model correlation, to complete measured vectors, to judge approximate eigenvalue solvers, or to improve model updating procedures.

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