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After trying everything (empty model, different dependent variables, varying level numbers, new imputed datasets, etc. etc.) I decided to replicate your example using the online bang dataset and the same convergence error occurred. I will now update the MLwiN 2.30 (Stata version is 12.1).

Ok, thank you once again.

I will first start to try with the IGLS listwise deletion startvalues (cfr. your example). That's something new.
If this doesn't work, I'll try the accumulate-approach following your suggestion.

This is what I further tried to do:

- update the runmlwin-procedure: does not make any difference.
- surpressing the errors on convergence by using the errorok -option and storing the results with the mi saving . This seemed to work but at a giving moment it errored again saying no imputations to ...

The list says

e(converged) = 1

. And I am using the 2.30 version. Do you think an update would help? MCMC models on a regular dataset tend to converge though. I'll browse further to find a solution.

Ok, the next problem I am running into is that the MCMC estimation procedure does not converge.

model did not converge on m=1
r(498);

Unfortunately this message is not really helpful. I get all estimates in mlwin, but the error pops up when the estimates are returned to stata. It does not ...

Thank you. I solved it by shortening the stored e(b_mi) matrix (i.e. losing the second OD parameter) and using the initsb option. I left out the initsv as it was a bit complicated to edit the e(V_mi) matrix. I learnt from the other forum posts that I should have an eye on the convergence diagnostics ...

Thank you very much for this feedback. I could have found this in a response to a similar question: https://www.cmm.bristol.ac.uk/forum/viewtopic.php?t=1103. Sorry for that.

However, I am running into a next problem now: stata complains that the row vector b of initial values in initsb() is of ...

I managed to solve above-mentioned problem. It had something to do with the auxiliary variables used in the imputation procedure and selections I make in the imputed datasets. Apparently, it had nothing to do with the -runmlwin- command. So far I succeeded to estimate a IGLS model using the imputed ...

The problem lies with the imputation procedure I guess. I now switched to the -ice- command using all variables of my model for imputation. Apparently there are still missings left in the imputed datasets (M=1,...). This is why runmlwin complains on the casewise deletion. I'll try to figure out why ...

Dear,
I would like to follow up on this old forum topic as I am currently struggling with some problems using the runmlwin command in the context of multiple imputation.

It concerns following situation:
I created 5 imputed datasets using the –mi impute chained- command in stata and registered all ...