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Hi, I am trying to model trajectories in aggression for a sample of over 1500 students. I assessed aggression 4 times over the course of a school year. Given that I had missing data both in my outcome and covariates, I decided to do multiple imputation. I followed the input model in you web training slides and example 12.13. In running some preliminary analysis with Mplus I got two warning messages I am concerned about: The first one is: *** WARNING in PLOT command No PLOT options are available for Monte Carlo analysis. Request for all PLOT options will be ignored. The second is: THE CHISQUARE COULD NOT BE COMPUTED. THIS MAY BE DUE TO AN INSUFFICIENT NUMBER OF IMPUTATIONS OR A LARGE AMOUNT OF MISSING DATA. Any guidance as to how to get my data plotted and how to get fit indices would be greatly appreciated. Thank you, Ana 


If you need to use TYPE=IMPUTATION, you would need to plot outside of Mplus. The chisquare is computed in a special way for TYPE=IMPUTATION. It sounds like something about your data make this computation impossible. 


Hello I have a question about a LGM I'm trying to run with MIdata. The LGM is based on data from three waves and the variables are continuous. The model also includes a binary outcome that is regressed on the intercept and the slope. I also want to include four additional covariates to the model which causes some trouble since this reduces the sample size by approximately half. I have therefore imputed data for my four covariates (but not for the other variables in the model). In the first step of the analysis I run the model without including the covariates, which should produce identical results regardless if the analysis is based on one dataset or on 100. When I specify the outcome as continuous the results are the same, but when I specify it as categorical the the effects of I and S differs completely (changes from positive and significant to a minimal negative effect and a pvalue of .99). Since the outcome is binary I want present ORs for the effects of I and S. I have used MLR as estimator in both analyses Do you have any explanation for why this happens and any suggestions how to resolve the problem. Best regards Daniel 


I don't understand what you are doing. Can you send the two outputs  continuous versus categorical and your license number to support@statmodel.com. 

Lina Homman posted on Friday, August 02, 2013  7:37 am



Dear Dr Muthen and Muthen, I am having a problem with my plots when running a LCGA. I am running a LCGA with 3 time points, outcome variables are continuous. I add covariates and direct effects from covariates to outcome variables. The model runs fine and the plots look good, but reduces my sample size. I decided to use MI for the covariates (I have tried to add them into the analysis but this does not work). The analysis runs fine but the plots do not. The issues with the plots is that the estimated means looks normal but all the sample means are consistently zero. Do you have any idea where this is going wrong? Many thanks Syntax: Usevariables= x1 x2 x3 y1y9; Classes = c (4); Cluster=y9; Data imputation: Impute= y1 (c) y2 (c) y3 (c) y4 y5 y6 (c) y7 (c) y8; ndatasets=50; Analysis: estimator=ml; type=mixture complex; Starts = 500 40; Stiterations = 40; Process=8; Model: %overall% i s  x1@0 x2@1.5 x3*; is@0; c#1c#3 on y1 y2 y3 y4 y5 y6 y7 y8; x1 on y1 y3 y4 y5; x2 on y1 y3 y5; x3 on y1 y3 y6 y5; Output: SAMPSTAT STANDARDIZED tech1 tech8 tech11 tech14; Plot: Series=x1x3 (s); Type=plot3; 


If you are not using Version 7.11, I suggest that as a first step. If you have the same problem with Version 7.11, please send the relevant files and your license number to support@statmodel.com. 

Lina Homman posted on Friday, August 02, 2013  9:09 am



Many thanks for this Linda, I have version 6. Which add ons would I need as to run the above model? Many thanks Lina 


You would need the Base plus Mixture or Base plus Combination AddOn. 

Lina Homman posted on Friday, August 02, 2013  9:45 am



great, many thanks 

Lina Homman posted on Friday, August 02, 2013  9:53 am



Linda, I tested the Demo version 7.11 but it does still not work. The output looks fine it is just the plots which do not, the sample mean. The estimated mean is what I expect it to be. Do you have any other ideas on how to deal with this? I cannot send the data files I am afraid due to data protection and confidentiality of the data. Thanks for your help Lina 


Please send the input and output files to support@statmodel.com. We will not be able to look into this until later in August when the programmer who handles this returns from vacation. 

Ashley Hum posted on Friday, June 06, 2014  5:04 pm



Hello, Hello, I'm doing a GMM with individually varying times of observation and using 100 imputed datasets. I would like to plot both the singlegroup and multiclass growth models, however, in an earlier post you said that for TYPE=IMPUTATION, you would need to plot outside of Mplus. How could I get the information needed (e.g, means) to plot outside of Mplus? Thank you, Ashley 


Hello, Is there a way to request a LoMendellRubin LRT in GMM with imputed data? I am getting an error stating that tech11 is being ignored because the data are imputed. Thank you! 


This is not available for imputed data. 

DMello posted on Sunday, November 16, 2014  11:47 am



Hello! After following the guidance of GCM using multiple imputation, my model ran successfully. However, the N was less than it should be if the data was imputed. In other words, were the parameters estimated using the imputed datasets? Here is my code: TITLE: ... DATA: ... VARIABLE: NAMES = a1ct1a1ct6 mincome mbod mlotr; USEVARIABLES = a1ct1a1ct6 mincome mbod mlotr mincomeMbod mincomeMlotr mbodMlotr mincomeMbodMlotr; MISSING = ALL (999); DATA IMPUTATION: IMPUTE = a1ct1a1ct6 mincome mbod mlotr; NDATASETS = 20; SAVE = A1cImp*.dat; ANALYSIS: ESTIMATOR = ML; DEFINE: mincomeMbod = mincome*mbod; mincomeMlotr = mincome*mlotr; mbodMlotr = mbod*mlotr; mincomeMbodMlotr = mincome*mbod*mlotr; MODEL: i s  a1ct1@0 a1ct2@1 a1ct3@2 a1ct4@3 a1ct5@4 a1ct6@5; i s ON mincome mbod mlotr; i s ON mincomeMbod; i s ON mincomeMlotr; i s ON mbodMlotr; i s ON mincomeMbodMlotr; OUTPUT: TECH1 TECH8; 


Please send the full output and license number to Support, pointing to the differences in sample size that you refer to. 

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