Hello everyone!
I am working on a org psych paper that is currently under review. The model is a quite complex multilevel mediation SEM, featuring four latent variables (only one is exogenous/independent, the others are endogeneous mediators/outcomes). I also include the measurement models for each variable (i.e., the respective items as manifest variables). Until now I have done the estimations in a 2-level model using lavaan. For the review I am trying to add a third level to control for further nestedness.
Thank you for providing and maintaining OpenMx, I am grateful there is a stable R package for this task! Thank you also for providing so much guidance on this forum that was of great help so far!
I am building up the model step-wise, both with regards to the levels as well as the variables I consider. The results generally fit the ones I get with lavaan. Unfortunately though, I am often running into problems with non-convex Hessian matrices and I'm missing an intuition to trouble shoot. For example, fixing the regression coefficient between both variables on both levels of a two-level two-latent-variable model solved the problem. This was surprising as the model converged in lavaan even when both parameters were free.
Coming from lavaan, I'm not used to the level of detail OpenMx allows. As I want to be respectful with your time, I want to make sure I've done my homework before I hit you with different models and dozens of lines of code for each. (I also imagine others might run in similar problems with different models so more general heuristics might be helpful.)
- Are there any major resources i might have overlooked to troubleshoot this problem?
- Are there any general requirements related to the Hessian I have to consider and that I might miss? (Too many/few specified paths?)
- Do you have helpful heuristics I can play around with? (e.g., with regard to starting values?)
(I've checked the example models here, https://openmx.ssri.psu.edu/node/4480, e.g., ex3, lmer-1, xxm-3, ex936. Another thread comparing lavaan and OpenMx here was also very helpful.)
Otherwise, I'm happy to share my code and walk with you step-wise through the models.
(As the paper is still under review I'm hesitating to share the full data set. Any best practices how do deal with this? Is a partial set of the data enough or should I generate random data from the observed covariance matrix - which would kill the multilevel structure though?)