Siny Tsang posted on Monday, April 03, 2017 - 9:01 am
I am trying to modify the Mplus code in Muthen & Asparouhov (2015) to fit some longitudinal BMI data, but I'm a bit confused with the Mplus codes provided in the Appendix.
The data is positively skewed, so we're looking to use DISTRIBUTION = SKEWT. With time varying covariates (i.e., age), I am assuming that we should use the codes in Tables B1 and B2 as an example. Instead of using the AT command to specify time-varying covariates, I see that the ON command is used. However, I don't quite understand why the regression paths are constrained to be equal for the different time points? Is there a way to get the equivalent of the slope estimates as in GMM with normal distribution?
Siny Tsang posted on Monday, April 03, 2017 - 10:03 am
Follow up to my previous message,
I am guessing that the estimated coefficient for the ON path is the same as the estimated means for the slope if we use the usually GMM script:
i s | w1 w2 w3 w4 AT age1 age2 age3 age4;
So what happened to the variances of S if we use the ON method like this?
i BY w1-w4@1; w1 ON age1; w2 ON age2; w3 ON age3; w4 ON age4;
Is this essentially a fixed slope within class (but vary between class)? If so, can we model a class-varying slope effect as well?