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| Latent class analysis with random effect |
 
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Hello,
I ran a LCA with 4 classes and 12 binary indicators (u1-u12) (n=1264; with missing data). The model fits the data well except for the presence of residual variance between 2 indicators, namelly between level 2 of u1 and level 2 of u2. I can see this by looking at the bivariate model fit information.
I want to correct this using a LCA with random effect. I am using ex 7.16 as a guide to write my model.
1) How do I know if the conditional independence is violated in one, two, three or all four classes? If I can't find out, do I just write my model like this:
MODEL:
%overall%
f by u2-u3;
2) If I have residual variance between another set of indicators, say u3 and u4, how do I model it? I could not find an exemple for this.
3) Do I have to use numerical integration here or can I just leave it at type = mixture? I read chapter 13 re: numerical integration but (sorry if this is basic) I do not understand what you mean by "...posterior distribution does not have a CLOSED FORM".
Thank you for your precious help.
LL |
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1. If you specify the residual variance for one class, the program will find the class for which that fits best. Given that each residual covariance is one dimension of integration, I would do it one at a time as shown in Example 7.16.
2. You would specify another factor.
3. Yes, numerical integration is required when the outcomes are categorical. |
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Thanks. I'll try modeling the covariances one at a time then.
LL |
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