If this is obtained by numerical integration, you don't need all 3 decimals to agree. When uncertain, use OPTSEED to run contending solutions and see how different the parameter estimates are - often they are essentially the same in these situations.
John Woo posted on Wednesday, September 16, 2015 - 11:49 am
Hi Dr. Muthen, I am seeing a rather strange case of log likelihood replication. No matter whether start=100 or start=1000, I see this: -706.710 846194 93 -708.841 311214 64 -708.841 915107 54 -708.841 354559 73 -708.841 650371 14 -708.841 392418 28 -708.841 804561 59 -708.841 902278 21 -708.841 565819 65 -708.842 207896 25
I am running GMM, and the results associated with the second best LL (-708.841, which is replicated) is very reasonable. But the result associated with the best but non replicated LL (-706.841) has negative residual variances for my growth factors and is problematic. What should I make of this? Why is there always that gap between the best and the second-best no matter the number of random starts? Is there a case where using the second best but replicated LL is acceptable? Thank you in advance.
I have been running a LCGM on a dataset of N = 1612. So far, the best loglikehood replicated for classes 2 to 4. But for the 5th cIass solution, the best loglikelihood did not replicate even after several increase of the starting values. The latest was 1500 380, still I get same warning that the solution may not be trustworthy. Does it mean that the 4-class solution is sufficient and that the data do not support an extra class?