# New Features for Mx 1.16

#### Testing identification of models with option check

Anywhere after the covariance or mean statement, the line

Option check

will request, after optimization, the computation of the covariance
matrix of
the parameters by numerical methods. Although not strictly accurate, this
usually gives a fair impression of model identification through the ratio of
the largest to smallest eigenvalues of the covariance matrix. Examination of
the eigenvectors is used to detect which two parameters are most likely to be
causing the underidentification problem. Note that spurious messages are
possible, and that parameters identified in one observed covariance matrix may
not be identified in another, even though the model and design are exactly the
same.

**Mike Neale, neale@gems.vcu.edu, Medical College of
VA**