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Would you be able to elaborate on the error that this is causing? The data should be loaded into MLwiN in both cases, however it may be that the data for the 5 level model is large enough that MLwiN has time to report the loading progress. Does the model fail to estimate, or are there problems when ...

Can you provide more details on what you are trying to do, as well as the steps that you have taken so far?

Are you able to provide a list of all of the inputs that you entered into MLPowSim so that we can attempt to replicate this?

I would recommend that you read through chapter 14 of the MLwiN user's guide which provides some examples of these type of model. If you have further questions then let us know.

When the model has finished running in MLwiN the various resulting outputs are saved to data files and then read into Stata. If you wanted to speed up this process you could look into whether you could reduce the amount of data that needs to be transferred (e.g. by not returning residuals or having ...

That would be my interpretation of George's suggest as well.

Thank you very much for reporting this problem. It should be fixed in the development version of the package, available from https://github.com/r-forge/r2mlwin/tree/master/R2MLwiN . We are hoping to do a new CRAN release that contains this and some other fixes soon, however we still have to do a few...

I asked George about this and his reply was as follows: I guess you could apply ideas from fixed versus random effects to panel data which are motivated by endogeneity concerns. So you could enter region as fixed-effects dummy variables instead of as random effects. The effects of the firm covariate...

I asked George about this and he suggested that the weighting options in MLwiN might do what you want. Unfortunately we don't provide much documentation for this, but you can see how to specify them by looking at the options in the runmlwin help file, i.e.

The reason for this is that your have chosen Binomial responses. This response type has a fixed level-1 variance (level-2 in this case as everything is shifted up a level for multivariate responses), which therefore cannot be estimated. If you had fitted a univariate Binomial response you would find...