Hi all
As part of a bigger project we are working with applications of mixture models. And we have found that no R package provides all the functionality we need. The only programs so far with all the functionality is LatentGold, but this is a propietary and have working with the license has been complicated
For these reasons we decided to find a way to work as many of these models as possible in R, and found that the most promising way is to develop a package around OpenMx for the types of models we need. So, now we are the process of developing the models, and then have the user friendly functions around it
I wanted to start this thread to ask questions as I find go on this process
So far I have developed Latent Class Analysis (LCA) models for continuous, binary, ordered and nominal indicators. These results are close to the results from LatentGold
The first question I have is about the optimizers, so far I have worked with the mxExpectationMixture() function, which uses the quasi-newton optimization routines (if I understand properly). But in LatentGold they use EM with Newton-Raphson [6] (page 56)
Could you help me with these?
- Do you think think this optmization methods would lead to substantial result differences?
- I saw in the mxComputeEM() example how to estimate a LCA with EM [7]. But I cant figure out how to add the mxComputeNewtonRaphson() to it, in a similar way as it is done for IRT models [8] mxComputeEM('expectation', 'scores', mxComputeNewtonRaphson())
Thank you