How to interpret the number of anomaly counts?

[MengLu-flw]

Hi :^)

I really like the ‘gimble query’ function, which gives a very clean summary of the ‘gimble optimize’ outputs.

However, I was wondering how I shall interpret the number of anomaly counts (e.g., does a large number of anomaly counts mean that a model fits badly, despite it shows a high lnCL value compared to the other models?)

Detailed descriptions:
I fitted my data to all five available models in gIMble (DIV, IM_AB, IM_BA, MIG_AB, MIG_BA), and ran them with the code like:
gimble optimize -g CRS2 -e 39 [I change this random seed number for each run] -i 10000

For all IM models (regardless of which dataset I used), they tend to give high anomaly counts (around 5000 anomalies out of 10000) – I feel that cannot be a good thing (?)

Is there any way that I could improve this abnormal behaviour?

Very looking forward to your insightful advice, and many thanks in advance!

Gratefully,
Meng