Hello,
I'm trying to understand the support OpenMx has for nested models and especially cross-classified models. I've found some great examples (http://psychological-research.org/R/) for doing these models in a wide data format. This approach though is a bit cumbersome for larger and more complex models, especially cross-classified models.
I can't find any examples using a multiple group approach (e.g. Muthen, 1994). Can OpenMx use this approach? Are there examples?
Also, I've seen various discussions around the forums about developing a simplified syntax for nested models. Has any progress been made here? I'd be especially interested in seeing examples of cross-classified models if they exist.
Thank you,
Mark
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> I've seen various discussions around the forums about developing a simplified syntax for nested models.
I have a manuscript in preparation that I can email you and you can look at some models in the source distribution,
passing/xxm-1.R
passing/xxm-2.R
passing/xxm-3.R
passing/xxm-4.R
passing/lmer-1.R
passing/lmer-2.R
passing/Rampart1.R
passing/Autoregressive_Tree_Matrix.R
passing/Autoregressive_Tree_Path.R
passing/multilevelLatentRegression.R
passing/MultilevelUniRandomSlopeInt.R
nightly/univACErSEM.R
nightly/multilevelLatentRegression2.R
nightly/mplus-ex9.1.R
nightly/mplus-ex9.6.R
nightly/mplus-ex9.11.R
nightly/mplus-ex9.12.R
nightly/mplus-ex9.23.R
nightly/xxm-cfars.R
nightly/xxm-faces.R
nightly/xxm-hcfa.R
nightly/xxm-lgc.R
nightly/xxm-mlcfa.R
My email is jpritikin (at) pobox (dot) com
Let me know if you have any further questions.
The manuscript was published in Structural Equation Modeling, 24(5), 684-698. See Pritikin et al 2017
For example of a cross-classified model is xxm-4.R
OpenMx does not yet have an efficient strategy to evaluate cross-classified models. If your data is large, it's going to be really slow.