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My interpretation of this is that he is suggesting that you duplicate each row of your dataset 1000 times (presumably with https://www.stata.com/help.cgi?expand ) so that you have a copy for each MCMC iteration. You would then combine this with the parameter estimates for the corresponding iteration...

You could manually create the data required for this plot by generating a chain of predictions and then calculating the mean and credible intervals from this. An example of this would be as follows: library(R2MLwiN) library(coda) data(tutorial, package = "R2MLwiN") (model1 <- runMLwiN(normexam ~ 1 +...

I suspect that the command is complaining that your 'CNTSCHID' column is a different length to the others. All the input columns involved in this command need to be of the same length. c1500 is included in the command as this is temporary output column where it will put the short version of the ID c...

Have you tried running this with fewer responses? You don't say how many rows you have in your data, or number of categories in each response but it may be that there isn't enough data to support this model.

As you are fitting a multivariate model there will already be a level-1 identifier included for the response indicator. When setting up the model you would therefore need to specify three levels, with patient at level-2 and clinic at level-3. MLwiN checks that the identifier variable you set at leve...

Bill gives some guidance on slide 57 of a workshop presentation that he gave some years ago, available at: https://www.ukdataservice.ac.uk/media/307220/presentation4.pdf The MRC Biostatistics unit, who originally developed the BUGS software, also give some suggestions: https://www.mrc-bsu.cam.ac.uk/...

You can obtain the MCMC chains as a dataset with the following command: mcmcsum, getchains Once you have this you can use the standard Stata data manipulation commands to process it, i.e. gen , sort and centile . You ought to get the same results as before as this should be equivalent to what runmlw...

The way that the credible intervals for this would be calculated is as follows: Fetch the MCMC chain for the parameter Exponentiate the values to convert to odds-ratios Sort the chain Extract the 2.5% and 97.5% quantiles I suppose that if the chain contained a few extreme values then this could resu...

That does sound a bit strange. Have you tried plotting the chains? What do the results look like it you don't convert them to odds-ratios?

Sorry, there is no change here, and this is not currently in our in our future plans.