Oh and i came across this:
Negative values in VC matrix may indicate:
A not positive definite input covariance matrix may signal a perfect linear dependency of one variable on another. In those cases, sequential analysis of the covariance matrix, adding one variable at a time and computing the determinant, should help to isolate the problem. Starting Values- The model-implied matrix Sigma is computed from the model's parameter estimates. Especially before iterations begin, those estimates may be such that Sigma is not positive definite. Then it is up to the researcher to supply likely starting values. Missing Data-Large amounts of missing data can lead to a covariance or correlation matrix not positive definite. My Variable is a Constant! Sometimes, either through an error reading data or through the process of deleting cases that include missing data, it happens that some variable in a data set takes on only a single value. In other words, one of the variables is actually a constant. This variable will then have zero variance, and the covariance matrix will be not positive definite. Simple tabulation of the data will provide a forewarning of this. If this is the problem, either the researcher must choose a different missing-data strategy, or else the variable must be deleted.
