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Hi,
So no the data is 2-levels it is just that for fitting both multivariate response models and multinomial models MLwiN expands out the data to create a dummy level of categories/responses below level 1. The reason you are getting an error message is that when you use year within individual MLwiN ...

Sounds good and great that you get similar results to lme4. I think as long as you ensure you have unique ids everything will be fine i.e. not using the Execid number for different indiviudals in different Industries. I would then take a look at how multiple-membership works in MLwiN as you will ...

Hi Lmcrace,
Yes it sounds like you may have cross classifications and multiple memberships. My only question would be whether these different 'effects' are all identifiable and how big your data is. For example I can think in education of a dataset with kids nested within classes and teachers but if ...

Dear Tomay,
I would suggest that you take a look through the manual about multilevel multivariate responses.
1. When you add predictor variables you can as you correctly point out add them as common coefficient which (at least for IGLS) will give you the same effect for A and B.
2. Yes just make ...

Hi Sabine,
I am afraid outside of MCMC you are stuck with the approximate Wald test (which essentially assumes normality) of equivalently the P values spat out by the Store model window which does likewise.
Best wishes,
Bill.

Hi Sabine,
Not sure I follow your question - the interval and tests window will give you a P value if we are still talking about the same model but you need to tell it what Wald test to perform (there is something about this window in the help system and an example in the binary response chapter of ...

Hi Sabine,
The estimates from MQL / PQL are quasi-likelihood so you cannot do a standard likelihood-based test to get a P value. You can do an approximate Wald test via the intervals and tests window but bear in mind this assumes normality for the variables so is only an approximation.
Hope that ...

If you leave it unconstrained then it is simply pointing to the maximum (quasi)likelihood solution being 0 for the variance i.e. there is probably no influence of the hierarchical structure. You could try fitting the model using MCMC to see what estimates that gives,
Bill.

Dear KW844529,
What you are doing doesn't make sense at all as in the logistic regression model, value normally used for the level 1 variance stored in that column is in fact the scaling factor for over/underdispersion and set at 1 for a standard logistic regression with binomial variation. It ...

The effective sample size and Raftery Lewis are completely different diagnostics and in fact a low Raftery Lewis number is good as the diagnostic is a minimum number to run for. In contrast a high ESS is good as it gives an estimate of the equivalent number of independent estimates for the parameter ...