Message/Author 

Tess Yanisch posted on Tuesday, December 13, 2016  12:04 pm



Hello. I'm trying to get graphs of estimated means in a latent class analysis. My output says they are not available: PLOT INFORMATION The following plots are available: Histograms of sample values Scatterplots (sample values) Sample proportions and estimated probabilities I do not know why; I've done this approach before with a different dataset and gotten them. Relevant syntax: ANALYSIS: Type= mixture ; Starts = 100 25; Plot: type=plot3; series is V5252 (1) V5253 (2) V5254 (3) V5255 (4) V5256 (5) [and so on to 17 items] ; Thank you! 


Perhaps your variables are categorical in which case estimated probabilities are provided instead of estimated means. 

Tess Yanisch posted on Wednesday, December 14, 2016  8:52 am



Yes, that's it! Thanks. Is there a way to get a graph with estimated probabilities like the first one shown here? http://www.ats.ucla.edu/stat/mplus/seminars/IntroMplus/lca.htm My variables are 15 scales of agreement, so while I could arguably treat them as continuous, I would rather not. 


Yes, that's the graph those estimated probabilities show. You can show them for the highest category or sums of some of the highest categories. 

Tess Yanisch posted on Wednesday, December 14, 2016  12:55 pm



Got it! I was trying to find it through the Scatterplot menus. Thank you very much! 

Tess Yanisch posted on Wednesday, December 14, 2016  1:07 pm



Another question, sorry. I have an odd thing happening, and I'm not sure if I'm doing something wrong methodologically or if I'm interpreting the graph incorrectly. Some of my items are on a 15 scale; others are actually 16. On the plot of estimated probability for 6, I see some classes with very high probabilities on items where 6 is not actually an option. In fact, the estimated probabilities on these items are higher than for some of the items where it is! Am I misunderstanding what "category 6" is? 


Please send the output and gh5 files to Support along with your license number so we can look at what you are seeing. 

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