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Hi Sian,
My predint.txt macro is going to be tricky to replicate for bigger datasets - basically the following is done:
1) The residual chains have been stored stacked up in c200 - there are 130 of them as there are 65 schools (intercepts and slopes)
2) The school ids go from 1 to 65 which is ...

Hi Matt,
I am still a little puzzled. If as you say there are 13 categories for each GOR then a nested 3 level model should have 12 level 3 with 12*13 categories = 156 level 2 units. The only way that categories should be 13 would be if you had a cross-classified model. Am happy to take a look if ...

Hi Mattoftheday,

your email puzzles me a little. When you say you have a structure of 12 > 13 > 84 do you in fact mean 12 GORS with 12*13 Categories within the 12 i.e. categories within GOR or do you mean you have a cross-classified structure with GOR crossed by Category ? Either way these sorts of ...

Hi Sian,
I think you are a little confused here. The DIC diagnostic is an information criterion that can be used to compare models in a similar way to the AIC and BIC in a non-Bayesian/MCMC setting. What it is not is equivalent to 'a two-tailed test at 5% significance level'. The closest thing in ...

Hi Jelle,
Yes this sounds appropriate for the analysis you intend to do. I would firstly look at each of the 2 ratings separately and fit a straightforward single response cross-classified model as in the chapter in my MCMC book. To fit a MV model you will need to follow the example in the MV ...

This shouldn't be too bad in MCMC. Basically set up your model as in the MCMC manual chapter for multinomial then add on the extra levels as you would anyway. Ignore any error messages from IGLS. Switch to MCMC and tick the cross classified box as in the cross classified chapter in the MCMC manual ...

Hi Thomasm,

If you wish to create imputed datasets to fill in what will ultimately be X variables in your model to fit you need to have them as response variables in the imputation model you first fit.

Say for example with the hungary dataset your final model is going to be a regression of biol ...

Hi Dave,
I took a look at your data and discovered that the IGLS algorithm is estimating a non-positive definite variance matrix at level 2. Apologies for the technical jargon but basically if you were to look at your
estimates for the between TherID variance matrix in the Estimate Tables window ...

Hi Michael,
Rather impressively you are the first person to spot this mistake - yes the elements given give 90% not 95% confidence intervals. If/When I get around to updating the manual next I'll try and amend this.
Thanks,
Bill.

Hi Karen,
I could take a look if you were happy to send on the worksheet. Note i and j might refer to the areas in the order the software receives them and not the labels they have.
Bill.