I initially created a post here, where I was having difficulties understanding the basics of LPA syntax in Stata. Following the post here, I was able to replicate Masyn's (2013) LPA using startvalues(randompr, draws(5) seed(15)). I applied the same starting values uniformly across my 6 classes with 4 different model restrictions. My BIC statistics results are as follows:
class | BIC class-invariant, diagonal | BIC class-varying, diagonal | BIC class-invariant, unrestricted | BIC class varying, unrestricted |
1 | 6536.391 | 6536.391 | 5726.355 | 5726.355 |
2 | 6044.384 | 5982.513 | DRE | 5648.8 |
3 | 5923.718 | 5917.452 | 5563.118 | 5620.018 |
4 | 5915.317 | 5820.818 | 5587.027 | 5741.81 |
5 | 5898.285 | 5838.543 | 5731.829 | 5756.148 |
6 | 5843.54 | 5817.436 | 5259.08 | 5927.461 |
Now, comment # 7 in the same post recommends using startvalues(randompr, draws(50) seed(15)) emopts(iterate(10)) as one hits 5+ latent classes. I applied this criteria uniformly across my 6 class models with 4 different restrictions. My results look as follows:
class | BIC class-invariant, diagonal | BIC class-varying, diagonal | BIC class-invariant, unrestricted | BIC class varying, unrestricted |
1 | 6536.391 | 6536.391 | 5726.355 | 5726.355 |
2 | 6044.384 | 5982.513 | 5677.851 | 5648.8 |
3 | 5923.718 | 5932.882 | 5698.263 | 5620.018 |
4 | 5915.317 | 5820.818 | 5715.92 | 5773.844 |
5 | 5898.285 | 5846.176 | 5750.381 | 5780.88 |
6 | 5843.54 | 5818.873 | 5772.309 | 5924.519 |
I'm leaning towards using Masyn's starting values, as in my first table. It just seems like a standard I can follow. But if anyone has some insights on this topic, I would be very grateful to discuss. Many thanks.
P.S. I am aware of the "gsem estimation options" document from the Stata manual. Unfortunately, I could not solve my problem after reading it.
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