SRMR with missing data

model@output
 
The output has a lot of slots, including those for matrices, algebras, likelihoods, hessians and other fun convergence and computational issues. Expected covariances and means are in the algebras slot, and can be referenced either like this (the right way):
 
<code>
mxEval(covariance, model)
mxEval(means, model)
 
Or like this (the inefficient but works if you're just manually viewing them way):
<code>
model@output$algebras$'Your Model Name'.covariance
model@output$algebras$'Your Model Name'.mean
 
One caveat for the expected covariance and means matrices is that they are the expected matrices for the last row of the data. When you don't use definition variables, every row of the data has the same expected covariance and means matrices, so that's fine. When you have definition variables, then every value of the definition variable creates a different expected covariance matrix, so what is returned is one of the many expected covariance matrices that were used.
 
As far as the observed covariance goes, there isn't a closed-form way to find this matrix. This is why we use things like full information methods. Unless you're absolutely sure that missingness is completely at random (i.e., you're using a planned missingness design which is responsible for all of the missing data), you'll have to estimate the saturated model directly.