Xu, Man posted on Tuesday, August 05, 2008 - 11:06 am
sorry if I multi posted this. I just experimented with the auxiliary function for the Mplus 5.1. The results are quite similary to FIML. I am not quite sure if the auxiliary vaiables are considered in relation to the clustering effect for data that has clustering structure. I used complex design function to account for the cluster effect. Can I have more information regarding this function please? Thanks!
Yes, Type=Complex is in operation when you use aux(m).
Xu, Man posted on Thursday, August 07, 2008 - 9:26 am
Thanks! I didn't totally understand the technical appendix for this function. But if I understood correctly, this function implemented method from Graham(2003), right? My confusion is that this paper didn't specify how this works for multilevel data. How Mplus takes into account of it when Type=Complex is in operation when you use aux(m)?
Graham, J. W. 2003 Adding missing-data-relevant variables to FIML-based structural equation models Structural Equation Modeling 10, 1 page 80-100
Aux(m) still uses maximum-likelihood estimation, just with an extended set of variables. Type=Complex adjusts for complex sample features just like with other ML estimation - so there is no extra difficulty when aux(m) is added. Type=twolevel is another matter.
Xu, Man posted on Thursday, August 07, 2008 - 10:40 am
I see. Thanks for the reply! How it is another matter when Type=twolevel? (so sorry if my question is a bit "idiotic...")
I've estimated a SEM and didn't have problems with fit, SE etc. Now I've added auxiliary variables (aux (m)) and got the following message:
"THE LATENT VARIABLE COVARIANCE MATRIX (PSI) IS NOT POSITIVE DEFINITE. THIS COULD INDICATE A NEGATIVE VARIANCE/RESIDUAL VARIANCE FOR A LATENT VARIABLE, A CORRELATION GREATER OR EQUAL TO ONE BETWEEN TWO LATENT VARIABLES, OR A LINEAR DEPENDENCY AMONG MORE THAN TWO LATENT VARIABLES. CHECK THE TECH4 OUTPUT FOR MORE INFORMATION. PROBLEM INVOLVING VARIABLE ."
(the variable RESID is specified in order to allow an additional path from the residuum of variable d on F2 over and above the loading of F1 - on which d loads, too). The correlation of d and RESID is .906 - which is no surprise given the model.
I'm confused because the model as above worked fine without auxiliary variables.
Carolyn CL posted on Thursday, December 13, 2012 - 9:12 am
Hello Dr. Muthen,
I was wondering if there is a way to make the AUXILIARY = (m)x; function work when some of the dependent variables are categorical?
If I treat my variables as continuous and run the (m)x function and compare this model to a saturated correlate model, the results tend to be quite similar in terms of fit indices (CFI, RMSEA), effect sizes and significance. However I am not comfortable treating these categorical variables as continuous because of their non-normal distributions.
Hi, i'm new to MPlus and wanted to confirm the following. I have a repeated measure design with 4 times points (steps per day at the end of CR, 3 mo after CR, 6 mo after CR, 9 mo after CR). In some preliminary analyses, I found that Diag_Rec (categorical), BMI, and frst_ev (categorical) were related to "missingness" at follow-up assessments. So, I just want to be sure that these variables are included in the model, so do I use the auxiliary function to do so in the analysis below? If not, do I have to use multiple imputation instead? Thanks! chris
VARIABLE: NAMES ARE id Diag_Rec BMI frst_ev t2_steps t3_steps t4_steps t5_steps;
I assume these 3 variables don't have a substantive role in your growth model - if they do - just included them in the model. So if not, either approach you mention is fine in principle. You have to specify Auxiliary Missing, not just Auxiliary (see UG). But I think we have not yet developed Aux Missing for Mixtures yet, so that won't work. Multiple imputation is possible, although you assume a 2-class model so regular unrestricted (1-class) imputation is a bit off, but probably better than not using those variables.
I have data from 2326 subjects. For the model that I'm interested in, I use 7 items. There are 8 subjects with complete missings on these 7 items.
If I estimate my model without the AUXILIARY (M) command, Mplus warns me there are 8 cases with complete missings and tells me N = 2318.
However, if I estimate my model with the AUXILIARY (M) command with my data, Mplus warns me "1 case with complete missing data" and tells me N = 2325.
This doesn't make sense to me - there are indeed 8 subjects with complete missing data on those 7 items that constitute my model; however, all 8 participants have non-missing data on the auxiliary variables.
Carolyn CL posted on Tuesday, March 19, 2013 - 12:11 pm
Hello Dr. Muthen,
I am running a saturated correlate structural equation model with socio-economic status as the auxiliary variable and dummies representing poverty trajectories (3 dummies, 4th reference category excluded) as independent variables predicting weight status (3 categories: normal, overweight, obese).
When I run a basic model (N = 1230):
Weight ON d_low d_inc d_dec;
The model runs fine.
When I add the auxiliary variable (N = 2120):
Weight ON d_low d_inc d_dec;
SES WITH Weight d_low d_inc d_dec;
The coefficients of the dummy variables are comparable, but the standard errors are inflated, and one sig. effect becomes ns.
When I run the full model (with additional independent and dependent variables) including the auxiliary variable SES, the model fails to converge. Increasing the number of iterations does not solve the problem. I can however run the model without the auxiliary and the model converges, but I obviously lose part of my sample.
Any idea why the full model with the auxiliary will not converge?
I would have no idea. You would need to send the outputs and your license number to email@example.com for further information.
Carolyn CL posted on Thursday, March 21, 2013 - 8:46 am
I found the problem - the model was by default allowing the dummy variables to correlate with each other once I added the auxiliary variable. Restricting the correlations to 0 allows the model to converge and provides the expected results.