One of the novel features of OpenMx is that models can contain other models as shown in Figure 3. This allows one to think very naturally about how dependency is structured in an SEM context. For instance, a model hierarchy can be built that expresses dependency in a genetic SEM analysis: An ACE model is built that contains matrices common to all groups and then two submodels are constructed, one for the monozygotic twin pairs and one for the dizygotic twin pairs. This approach partitions the problem into submodels that follow the logical group structure in the data. A Mixture distribution analysis can be set up as a model tree where the submodels are the elements of the mixture and the top level model expresses the overall likelihood calculation for the mixture.
