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Dear GKonyarov,
Thanks for the post. I have read it a couple of times and although you describe your models I wasn't sure what exactly question you are asking here? Apologies that in lockdown I have been a bit slow to look at the forums and sadly we have lost our colleague Harvey Goldstein whose ...

Hi Ashley,
Thanks for your question. I am a bit rusty on these sorts of models and taking a quick look at the equations window in MLwiN when you try and fit multivariate response models you are restricted to responses being normal, binomial or Poisson. I don't know if this has changed since the ...

Hi Ken,
Apologies that this one hasn't been answered after so long. Basically I think R2MLwiN has a very similar syntax to glmer and thus will likely fit the same models. They are using different algorithms so I am not sure which will be the quicker. I suspect estimates for 2 models would be ...

Hi Sun,
Not really as as you say you don't have normality so any use of the SEs would be very approximate. you might consider bootstrapping I guess but easiest with MCMC.
Best wishes,
Bill.

Hi Sun,
With regard MCMC my suggestion is to look at my MCMC manual where I cover how one works out confidence intervals for derived quantities - here I cover in section 4.7 the difference between 2 schools, 4.8 their ranks with CIs and most relevant for you the ICC/VPC in section 4.9. Here you can ...

Hi Rachael,
MCMC is fitting a Bayesian model so the concept of a classical P value is not valid for MCMC estimates - I think we do offer Bayesian P values for all parameters though which basically check what proportion of iterations are positive / negative for a chain.
Best wishes,
Bill.

That depends on which estimation method you are using but generally involves fitting the model with and without the random intercept and looking at some indicator of model fit - a LR test for ML methods or comparing DIC for MCMC. Suggest you look in the user guides for examples.
Bill.

Hi Johann,
Generally when we talk about cross-classified models we are assuming that each of these levels is a random source of variation and is treated as a set of RANDOM effects. It doesn't make a lot of sense to include fixed effects as well as random effects for the same terms. Generally fixed ...

Hi Rachael,
Just generate a level 1 residual (using the level 1 variance) and add it to the prediction and you should be sorted.
Bill.

Hi Rachael,
If you were hoping to get 95% intervals for individuals rather than for the average individual you will also need to have level 1 in your predictions I would expect.
Best wishes,
Bill.