www.cmm.bristol.ac.uk/forum

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...

If you are using MLwiN then the residuals returned from the Model>Residuals menu are already shrunken (see chapter 3 of the MLwiN User's Guide ). If you need an unshrunken version then the REFLATE command (see the MLwiN on-line help) is provided to calculate this, and more details of the formula use...

As you state the 'prior variance matrix is not positive definite' message usually occurs when the starting values for the covariance matrix, used as the prior for MCMC, is invalid. Normally this matrix is obtained from the IGLS estimates, however you can specify it manually via the startval option. ...

Although you have checked the sorting, this does look like the sort of behaviour you would get if the data was not sorted by the identifiers defining the unit hierarchy. Can you confirm that the data is sorted by all of these ('n', 'wave_num' and 'wave')? You could also try recoding 'n' to go from 1...

For MCMC models the parameter chains are stored in a chains slot of the returned object, in your case this would be modelstim@chains . You can use the functions provided in the coda package to manipulate these, for example: library(coda) summary(modelstim@chains) will list summary statistics for eac...

I asked George if he could think of an explanation for this and he suggested running the model with PQL2 instead of MQL1, as MQL1 variances are biased downwards.

You should be able to get this to work if you remove the predictors from the level-1 specification, i.e.: generate id = _n runmlwin depression cons age10, level2(mcsid: cons) level1(id:) discrete(distribution(binomial) link(logit) denominator(cons)) nopause You can find examples of this and other mo...

Thanks for letting us know about this, the error appears to be due to a bug. I have made a fix in the development version of the package which once the r-forge mirror ( https://github.com/r-forge/r2mlwin/tree/master/R2MLwiN ) has synchronised you should be able to install with the commands: library(...

The p-values reported here are one-tailed bayesian p-values, and essentially give the proportion of the chain that crosses zero (or one if the variable is exponentiated and it is aware of this). You can find the code for this in runmlwin_mcmcdiag.ado , and is as follows: local compval 0 if "`ef...