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Mplus Discussion > Missing Data Modeling >
 Jon Heron posted on Monday, December 08, 2014 - 6:25 am
Hi Bengt/Linda,

I'm attempting to derive the appropriate H0 model prior to fitting a 2-class GMM with two covariates.

Since the Bayes estimator does not work with latent class regression I have been forced to remove the line "c on x1 x2;" from my imputation. I'm left wondering how to preserve the covariate effect during the imputation stage, and have opted for

[x1 x2];

[x1 x2];

although this feels less than ideal. Do you perhaps have any other suggestions?

many thanks, Jon
 Bengt O. Muthen posted on Monday, December 08, 2014 - 4:07 pm
Is it the missing on x1, x2 that makes you want to do multiple imputation as a first step?
 Jon Heron posted on Tuesday, December 09, 2014 - 2:42 am
Not yet, but the next step is to simulate missingness in x1. I've adapted ex 12.2 and added a second class.

(Since I can't include a model missing statement for x1 as its independent I plan to introduce further missingness to my simulated data in another package.)

Once x1 has missingness I'm hoping to see gains over FIML. At the minute FIML is winning, perhaps because the way I have included my covariates is imperfect.
 Jon Heron posted on Tuesday, December 09, 2014 - 4:53 am
Clarification - the way I have included my covariates in the H0 imputation model
 Tihomir Asparouhov posted on Tuesday, December 09, 2014 - 6:43 pm

I would also include class specific variance covariance for x1 and x2 and also use x1 and x2 as predictors of the intercept and slope of the growth model.

I would first try a simulation with no covariates and make sure you can see benefits of the H0 imputation over FIML in these simpler settings.

 Jon Heron posted on Wednesday, December 10, 2014 - 12:57 am
Thanks Tihomir,
I tried to include co/var terms but was informed that this would require Metropolis Hastings. Does this indicate I may have done something wrong, or would you be happy to embrace MH?

cheers, Jon
 Jon Heron posted on Wednesday, December 10, 2014 - 1:15 am
Ignore me, I had been mentioning variances but not covariances. I can now run this without MH.

I guess I am now making distributional assumptions regarding X that would not be a requirement for my subsequent latent class regression. But those same assumptions will need to be made anyway once I've induced missingness in X.

best, Jon
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