Based on a discussion in another thread (http://openmx.psyc.virginia.edu/thread/505), I'm soliciting feature request details for a helper function that builds saturated and independence models.
Saturated and independence models are commonly used comparison models for many SEM fit indices. OpenMx will calculate the likelihoods for these models only in cases that they can be added with trivial impact on estimation time and without making assumptions regarding the user's model. As of OpenMx 1.1, we estimate these models only when covariance matrices are used as input in the ML and RAM objectives, because the final fitted values for each of these models can be determined directly from the data without invoking the estimation procedure. For other types of models, specifically raw data methods, these models must be manually specified.
I'm proposing a function that takes a dataset (and customization options) as input and returns a model as output that can be run and used as a saturated model for model comparison. It may look something like so:
imxSaturatedHelper(data, useVars=??, labelOption=??, ..., name=??) <\code> The resulting model would contain free parameters for all means, variances and covariances between all included variables. Questions for the userbase: -what options would you like to specify? -how should the function handle definition variables? -should Independence and Saturated models be separate functions with similar options, or one function with a 'type' option? -what am I forgetting?