I'm running GMM for a symptom variable for six assessments. First is immediately following treatment, 2nd is at an expected high point for maximum symptoms, 3rd is after symptoms are reduced, and just prior to next treatment. The second set of three assessments repeats this pattern. I think a piecewise model will fit this data best with two quadratic curves like ^ ^ . I confirmed this pattern fit the data best with a pw latent growth model using isq (compared to simple linear, LQ, & LQC), and have gotten GMM solutions with the pw model. I have individually varying times of assessement available. Is it possible to estimate a piecewise model like this also using IVT of assessment for LG and GMM models? Thanks! Bruce
Thanks, Linda! I know how to do that, but I'm puzzled about how to set up a piecewise model to identify two series, while also using the AT command for the varying times. Right now, my code for a pw latent growth model without the AT command is:
See Example 6.12 which uses the AT option. See the TSCORES option and the data set which shows how the TSCORE variables are scored. To use AT with a piecewise model, you would have to generalize the time scores from the piecewise model to the TSCORE variables.
Jon Heron posted on Monday, March 17, 2014 - 8:06 am
I banged my head against this for ages and finally found a solution. This was for LGM but I expect it extends to GMM readily enough. There's a trick to it.
Hi Linda & Jon - If I may take advantage of your suggestion, Jon ... I tried the model with the improved syntax you provided, and it worked (!) but the model did not fit as well conceptually as the regular pw model with "averaged" assessment times. :-( Oh, well. But, what do you think about using a factor approach to allow the program to provide the means across time? I tried:
Can you obtain your frown-model by fitting a high-order polynomial?
I'm also wondering whether you might benefit from splitting your data into more time points - I recently managed to fit a 4 time point "AT-model" as a 50 time point model with no age variability. There you have a simple way of building in some of the additional information on actual age.