Table 2.
Convergence in the simulation study with ML estimation.
| Pop | Requested | N Incomplete | % Incomplete | Complete | Warnings | % Warnings | Warning types* |
|---|---|---|---|---|---|---|---|
| 1, 50:5 | 5000 | 0 | 0 | 5000 | 0 | 0 | – |
| 2, 50:10 | 5000 | 0 | 0 | 5000 | 0 | 0 | – |
| 3, 25:5 | 5000 | 0 | 0 | 5000 | 2 | 0.04 | 2*1 |
| 4, 25:10 | 5000 | 0 | 0 | 5000 | 0 | 0 | – |
| 5, 10:5 | 5000 | 3 | 0.06 | 4997 | 2077 | 41.565 | 165*1, 1912*2, 1*4 + 3 |
| 6, 10:10 | 5000 | 0 | 0 | 5000 | 1734 | 34.68 | 14*1, 1720*2 |
| 7, 5:5 | 5000 | 1596 | 31.92 | 3404 | 3404 | 100 | 3404*2 |
| 8, 5:10 | 5000 | 543 | 10.86 | 4457 | 4457 | 100 | 4457*2 |
Warning types.
1. Warning: The MLR standard errors could not be computed. The MLF standard errors were computed instead. The MLR condition number is −0.463D-03. Problem involving parameter 17. This may be due to near of the random effect variance/covariance or incomplete convergence singularity.
2. The standard errors of the model parameter estimates may not be trustworthy for some parameters due to a non-positive definite first-order derivative product matrix. This may be due to the starting values but may also be an indication of model non-identification. The condition number is 0.820D-11. Problem involving parameter 20. The non-identification is most likely due to having more parameters than the number of clusters. reduce the number of parameters.
3. The model estimation did not terminate normally due to an ill-conditioned fisher information matrix. Change your model and/or starting values. The model estimation did not terminate normally due to a non-positive definite fisher information matrix. This may be due to the starting values but may also be an indication of model non-identification. The condition number is 0.371D-15. The standard errors of the model parameter estimates could not be computed. This is often due to the starting values but may also be an indication of model non-identification. Change your model and/or starting values. Problem involving parameter 20.
4. One or more parameters were fixed to avoid singularity of the information matrix. The singularity is most likely distribution of the categorical variables in the model. Model is not identified, or because of empty cells in the joint because the following parameters were fixed: 21.