I've written a simple latent class model (simplified the OpenMx example code for the growth mixture model). I've run it with 1,000 random starts using the approach shown in the docs and I get the maximum likelihood estimates. When I use the same starting values that were used for the smallest -2LL (which has a status code of 0), I get the same estimates but when I request confidence interals, the upper and lower bounds equal the estimates. Usually, when I run various OpenMx models, I have few problems estimating the CI's. I have had this problem (lower bound = upper bound = estimate) before, but I have no idea why this is happening. Here are the first two lines of the CI's:

lbound estimate ubound

mean_tbut_1 15.0001004 15.000000 15.0001004

mean_sch_1 17.5494846 17.549385 17.5494847

You can see that the upper and lower bounds are about equal and differ only slightly from the estimate. The estimates appear very reasonable given the data. Does any one have an idea of why this is happening?

Here are the corresponding estimates and standard errors:

8 mean_tbut_1 Class1.M 1 m_tbut_1 14.9999999 0.004127186

9 mean_sch_1 Class1.M 1 m_sch_1 17.5493846 1.290374369

and the same for the other class:

17 mean_tbut_2 Class2.M 1 m_tbut_2 7.1495123 0.298924928

18 mean_sch_2 Class2.M 1 m_sch_2 11.6191305 0.534036366

The SE is small for mean_tbut_1 but this is the exception. Some of the variance and covariance parameters have NaN.