"Observed" correlation matrix with missing data--is it computed?

FIML is a raw data method. It does not create a covariance matrix from the raw data at any point. It estimates an expected covariance matrix that can be accessed in several ways. One way is as follows:


yourModelFit <- mxRun(yourModel)
yourModelFit$fitfunction$info$expCov


This gives you the model-implied expected covariance matrix. Perhaps the closest thing to what you're after is the expected covariance matrix for a raw data saturated model. In OpenMx 2.0 and the Beta you can get saturated and independence models like this:


yourRefModels <- mxRefModels(yourModelFit, run=TRUE)
#yourRefModels is a list with two elements. The first one is the saturated model.
yourRefModels[[1]]$fitfunction$info$expCov


That would give you the saturated model expected covariance matrix as fit by FIML. This is the absolutely best fitting covariance matrix that takes into account the missing data via FIML.

Hope this helps!