www.cmm.bristol.ac.uk/forum

Unfortunately the only ways to avoid the single precision message would be one of the following: Reduce the precision in the output data files. Process the imputation output in Stata using the -RealcomImpute- ( http://missingdata.lshtm.ac.uk/index.php?option=com_content&view=article&id=50&am...

The iv.ado file is a direct translation of the MLwiN macro, which as far as I could see only allowed a one-to-one correspondence between the variables. If you are aware of a way of setting up more than one instrument per instrumental variable in the original MLwiN macro I would be interested in seei...

When Realcom runs it tries to write information about the model to a file in its current working directory, which in your case it is failing to do. If it is still generating the imputations that you want you can probably ignore this, however if you want to fix the error you should be able to do so b...

I passed on your question to Prof. Jones and his response was as follows: My understanding is that it is good practice to keep the lower order terms in the model; so that main effects A and B are included when the interaction A*B is included. The same applies to cross level interactions. There is a ...

Unfortunately MLwiN cannot fit these types of model. An example of software that can would be Mplus (http://www.statmodel.com/).

As your questions are fairly general, rather than being specifically related to Realcom, you might also want to ask them on the missing data discussion group (https://groups.google.com/forum/#!forum/missing-data) if you don't get any answers here.

When you run a model using Realcom it attempts to write some information to files in its current directory, which by default is under c:\program files\. Normal users cannot usually write to directories in this location so to enable it to work you will need to do one of the following: 1) Run with an ...

Currently MLwiN only allocates enough space for residual columns as the maximum number of variables allowed in the model, which in runmlwin we set based on the number of variables specified. As saving residuals needs a column for the residual and its standard error this means that if you have more t...

Unfortunately as you have seen MLwiN cannot currently fit negative binomial models using MCMC. As an alternative method for introducing over-dispersion you can however add a pseudo level with the identifier set to your level-1 identifier and then add a random effect at this level. As you are using a...

If you haven't already done so I would suggest that you look at module 13 of our on-line training materials (http://www.cmm.bris.ac.uk/lemma/) which covers multiple membership models in detail.