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Hello,

I am running a cross-classified binary logit model using the following code:
Output shows parameter estimates for the MCMC estimation, but under the IGLS output I see, "WARNING: IGLS algorithm failed to converge." How can one interpret the MCMC results given the IGLS algorithm failed to converge? Should one dismiss the MCMC results outright given the initialization values are in question? As a solution, should I use MQL1 instead of PQL2 (though I had chosen PQL2 given it is the most accurate of the four quasi-likelihood methods and my within higher level units are small (mean 2.6 for levels 2 and 4) and the response proportion is (relatively) extreme (prob=0.113)?

Thank you!
Kate

The model being fitted with IGLS will be different from the MCMC version, as IGLS always treats models as hierarchical whereas the MCMC model in this case is specified as cross-classified. This may be part of the reason why the IGLS version does not converge.

I would suggest performing the usual MCMC diagnostics to gauge the stability of the MCMC estimate chains, as well as running the model with a diverse set of starting values to check that the estimates always end up in the same place. You may have to run a longer burnin that usual if the starting estimates are further away from the correct values.

Thanks, Chris! I bump into this issue only after I add a covariate with a large number of missing observations that decreases my sample size to: level-1, n=1135; l-2, n=430; l-3, n=89; l-4, n=457. Before proceeding with the diagnostics that you helpfully suggest, I just wanted to be sure that this issue is not due to the reduced sample size. Thanks again, Kate

Have you tried simulating or imputing the missing observations so that the sample size does not decrease? If not this may be a way of telling if it is the reduction in sample size causing the problem.