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1) Go to Model -> Weights. If you only have one level you should see just one pair of drop-down boxes. In the one on the left choose your weights variable and in the one on the right any free column (i.e. the default that's already there is probably fine). I'll confess to not being entirely sure ...

Yes, for binary (and other discrete response) models MLwiN uses a different estimation procedure to gllamm in Stata: MLwiN uses quasi-likelihood estimation whereas gllamm uses maximum likelihood (via quadrature). Quasi-likelihood estimation is known to give results for higher level variances (level ...

VPCs are beautifully simple for 2 level models, but become a very complicated topic when you have more levels than that. There are many different versions depending on what you actually want from the VPC.

One approach I've seen is just to take

variance at a particular level / total variance

so ...

One possibility is that the level 2 variance could be negative, or could need to go negative during estimation before finally emerging as positive. The default in MLwiN is that when a variance goes negative, it's reset to 0 for the next iteration. You can change this by clicking the Estimation ...

You can find out how long it takes a model to run by selecting Options -> Smileys. With this turned on, when the model has run a box pops up that tells you how long it took. However it seems to only give you hours, minutes and seconds, so if you're interested in fractions of a second then bad luck!

When you ask for levels to be treated as cross classified, this will apply to all levels; there is no way to specify that the cross classification should apply to just certain pairs of levels. However, MLwiN will of course look at your actual data, and if there is no cross classification present in ...

Glad to hear it worked, and thanks for passing on the info about what it uses for the actual estimates - useful to know!

Defining a variable as a level 2 variable is very simple in MLwiN: you just have to make sure that the variable takes on the same value for all the level 1 units that belong to the same level 2 unit. When you enter the variable into a model, MLwiN will look at it, and if it has this property, it ...

When MLwiN uses MCMC estimation, for each MCMC iteration it stores the estimate at that iteration of each parameter. This is how it is able to plot out the trajectories for each parameter. So in your case, it will need to store (20 [coefficients of explanatory variables] + 1 [coefficient of cons ...

It sounds like you might have overparameterised your model. Are you including time as a set of dummy variables, and have you allowed each of those dummy variables to be random at level 2? If so, then for each individual you have the same number of random effects at level 2 as you have observations ...