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I checked this with George and the behaviour is expected and is covered in Snijders and Bosker . George gives the following explanation for the simplest multilevel case (two-level variance components model): The intercept is interpreted as the population average of the cluster-specific population me...

The loading procedure should be the same for both models, I think that you just don't see the loading message on the four level model because the data finishes loading before the message is displayed. The question is therefore why the data takes so much longer to load for the five level model. My su...

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...