Two summary statistics are provided, both of which are generalizations of the sample variance to multivariate data.
The first is the generalized variance of Wilks (1960), which provides a scalar
measure of multidimensional scatter.
For a random vector X, the generalized variance is defined as the determinant of the
sample variance-covariance matrix of X, i.e. |\text{Cov}(X)|.
Note that this can be 0 if there is linear dependence among the columns of X.
The second is the total variance.
For a random vector X, the total variance is the matrix trace of the sample
variance-covariance matrix of X, i.e. the sum of the sample variances of the columns
of X.
This can only be 0 if there is only a single observation.
When X has only one column, both of these measures are equivalent to the sample
variance of the column.
MultivariateTests.genvar
MultivariateTests.totalvar
Wilks, S.S. (1960). "Multidimensional Statistical Scatter." In Contributions to Probability and Statistics, I. Olkin et al., ed. Stanford University Press, Stanford, CA, pp. 486-503.