Shige Song posted on Saturday, March 10, 2007 - 12:08 am
Dear Linda and Bengt,
Here is something that has been bothering for a while: I want to do a growth mixture model to identify latent sub-groups. Some of the covariates I choose to predict latent class membership have significant amount of missing values. From what I have read, I can 1) give the covariates some distributional assumptions and make them as part of the model or, 2) I can do a multiple imputation.
In case of growth mixture model, is multiple imputation still a viable option? I mean, will the latent class group membership parameters be combined in the same way as other parameters using Rubin's rule? Does Mplus automatically use these combined class membership parameters to classify individuals into latent classes?
In short, what is the optimal method to handle missing covariate values in a growth mixture model?
We think that multiple imputation is a viable option for growth mixture modeling. One issue is that when you analyze the imputed data sets, give good starting values so that you do not run into label switching.
Shige Song posted on Saturday, March 10, 2007 - 4:37 pm
Do you have examples showing how to do growth mixture modeling using imputed data sets?
No. You just use the IMPUTATION option of the data command instead of a single data set. There is nothing else different from any other growth mixture model other than using starting values to avoid label switching.
I’m running growth mixture models with 4 binary covariates and one binary distal outcome. The growth outcome variables are ordinal (3 categories). All variables have some missing cases (1% - 30%). I created 5 imputed datasets by using ICE in STATA and then used “type=imputation” in Mplus. The outputs looked good.
But the output did not print both the results of probability scale of distal outcome in each class and the latent class odds ratio results. I want to know how the classes are related to distal outcome. How can I get these results? (If I don’t use “type=imputation” option, these outputs were always printed.)
I appreciate your help!
Ashley Hum posted on Tuesday, May 27, 2014 - 4:22 pm
Hello, I'm doing a GMM with individually varying times of observation and using 100 imputed datasets. I see from posts that I should use starting values (SVs) to avoid label switching. 1) Does this still apply?
2) If I obtain SVs by using 1 dataset and running a 1-group GMM with the svalues command, how do I use these values (pasted below) to specify SVs for later models? How do I identify SVs for different classes or do I set SVs for the general model?
2)You get SVALUES from runs for each number of classes. The SVALUES contain values for both the Overall and class-specific parts of the model.
Ashley Hum posted on Tuesday, May 27, 2014 - 7:04 pm
Thank you for your quick response.
1)Just to clarify, I should run a 2-class GMM on one of the imputed datasets and get svalues and then use these values for the analysis across the 100 datasets? And then continue this procedure for more classes, i.e., run a 3-class on 1 dataset and then svalues from that to run across all datasets?
2) Sorry for the likely simple question, but do I copy all values from the svalues output onto the next analyses input or just a portion? Besides copying these, are there other things required to use these values as starting values?
2) Copy all values. I think that's it, but the program will tell you. Use Starts=0.
Ashley Hum posted on Saturday, June 07, 2014 - 1:49 pm
Hello again, Thanks for your help with my previous question. As mentioned with my previous question, I'm doing a GMM with individually varying times of observation and using 100 imputed datasets.
I tried to use Aux (R) to use covariates to predict class membership, but I received an error message that indicated that aux (r) was not allowed with type=imputation. This is likely a simple solution, but what is the appropriate syntax for using covariates to predict class membership?
See Appendix 1 of our 3-step paper on our website, showing how Auxiliary R3STEP can be done manually:
Asparouhov & Muthén (2013). Auxiliary Variables in Mixture Modeling: 3-Step Approaches Using Mplus. Accepted for publication in Structural Equation Modeling. An earlier version of this paper is posted as web note 15. Appendices with Mplus scripts are available here.