Hi you all,

My outcome variable is a semicontinuous variable measured over time with a bunch of zero values. Since it is continuous data I cannot use poison or other zero-inflated models. However, I am implementing a two-parts model. The first part is a logistic mixed effects model for modeling the zero currency. Then, the second part is a linear mixed effects model. Now, there are two main issues. First, the random effects of both models are correlated. Second, the distribution of the random effects is a mixture of normal distributions.

Before the mixture of normals was involved in the model, I was using SAS NLMIXED that uses a quasi-Newton optimization of the likelihood approximated by adaptive Gaussian quadrature. In some publications, this approach has been used since it is less complicated than EM - E step requires the expectation of nonlinear quantities with respect to nonstandard distribution and the M step cannot be express in a closed form - and is faster than using Bayes approach.

Now that I am considering the mixture of normals for the distribution of the random effects, I have troubles using the SAS NLMIXED since I do not have information about the membership to the normal distributions - something that is not a big concern when using EM.

Then, here I am looking for ideas!. Somebody suggested that a path model might be an option. So I was wondering if somebody has used OpenMx for doing two-parts models with mixed effects.

Thanks