I have conducted GMM and would like to essentially create a "SERIES = t1pts (s) t2pts (s) t3pts (s) t4pts (s); TYPE = PLOT3;" by hand in excel/ppt. Using Tech7 I am able to get sample means for each class at each timepoint for the figure but I haven't been able to sort out how to obtain the estimated means. Also, in a perfect world, I'd like to create error bars around each mean. Is there a command to give me these numbers that I have missed? I sincerely appreciate your help!
The values that are plotted are taken from the RESIDUAL option of the OUTPUT command. Alternatively, you can Save Graph Data while you are viewing plots in Mplus. I can't think of any way to get confidence intervals around the growth curve without computing them by hand.
Drs. Muthén, I am running a GMM on a randomized preventive intervention sample and am using the Muthén, Brown, Masyn, et. al. (2002) article as a guide (very helpful, especially with the additional examples posted on this website-thank you). In Figure 2 of this article you display graphs showing the simultaneous trajectories of the intervention and control groups; however, I do not understand how these graphs were obtained. Is/are additional syntax command(s) needed to produce these graphs, or must another program be used?
Figure 2 can be produced with the Mplus PLOT command. Use the Adjusted means menu option to choose the 2 x values for the tx dummy variable covariate to plot 2 curves for each mixture class in one and the same figure.
Kelvin Choi posted on Friday, December 03, 2010 - 7:10 am
Hi, I am running a GMM with a dichotomous variable, and I want to plot the trajectories of the classes I estimated. I used plot function and specified the series as
Series = MD12_5(0) MD13(0.5) MD13_5(1) MD14(1.5) MD14_5(2) MD15(2.5) MD15_5(3);
Should I look at the estimated probability for the trajectories? I thought I would be looking at estimated means... Please let me know what graph I should look at.
You should look at estimated probabilities. Continuous variables have means. Categorical variables have probabilities.
mari posted on Thursday, April 28, 2011 - 12:23 pm
Hello, I am running GMM of smoking with 4 time points. The smoking variable is ordinal with 7 categories. I found that some articles present "estimated mean trajectories" even though the outcome variable is ordinal (e.g., Feldman, Masyn, and Conger, 2009).
I want to draw the trajectories by hand. The following is the excerpt of my output.
Means I -2.780 0.122 -22.716 0.000 S 0.497 0.027 18.406 0.000
Thank you so much for your reply. I cannot use the PLOT command because I use "type=imputation" for multiply imputed 20 data sets.
Also, I want to present two graphs, one for estimated mean trajectories (line plot) and the other for estimated probabilities conditioned on class (stacked column graph in excel).
In the article (Feldman, Masyn, and Conger, 2009), the estimated Y* plot is not a probability scale because Y-axis ranges from -6 to 6. Also, in the other article (Muthén, Brown, Masyn, et. al. (2002), figure 1 is estimated mean trajectories with 1 to 6 on Y-axis. Both articles are using ordinal outcome variables. I wonder how those figures are made because my variables are also ordinal with 7 categories.
I know that categorical variables need to use probabilities, but if y-axis is "estimated mean y*" (not estimated probability), isn't it correct to use the values that I calculated above?
Thank you for your quick answer! Now I want to compute the estimated probabilities conditioned on class by hand. For unconditional models, I got the probabilities from the "residual" command. But after adding covariates, the results are no longer available. I have no idea how these are computed.
When the outputs are as follows, would you please show me how to compute the estimated probablies of the treatment group? I want probabilities of 7 categories.
S ON Treatment 1.226 Means I 0.162 Intercepts S -0.154
Estimated means for the latent variables I 0.162 S 0.081 Treatment 1.344
Adding covariates, you do the same as when you have Treatment - work with Tech4.
Note, that we are talking about getting the y* means for plotting. If you want the probabilities, things get more difficult because you have to do numerical integration over the growth factor distribution. That is not something one does by hand.
This suggests a need for direct effects between the outcome and the covariates. See the following paper on the website:
Muthén, B. (2004). Latent variable analysis: Growth mixture modeling and related techniques for longitudinal data. In D. Kaplan (ed.), Handbook of quantitative methodology for the social sciences (pp. 345-368).