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Hi again,
So it is probably important to pay attention to my comment about the challenges with multiple membership models in using VPC but I assume here you mean a standard multilevel model. I think your logic of if the total VPC for levels 2 and 3 is 10% with 4% at level 3 then clearly 40% of the ...

Two time points at level 1 should be no problem however you may consider constructing the change response post-pre and thus reducing your 3 levels to 2 levels. You could still use the pre response column as a predictor if you wanted to look at the impact of pre-value on size of change. Hope that ...

Hi Katekeri,

I think this might be a slight misunderstanding. Essentially if I consider a 2 response case - say I am looking for predictors for weight and height and to make life simple then I have age as a predictor then the correct thing would be to have both the intercept and age as separate ...

Not for fixing the issue of multiple comparisons as bootstrapping will not help with that.

I would imagine if you don't want to do Bonferroni then you could try something like Benjamini & Hockberg's FDR which is less conservative (false discovery rate) and I believe could be used for most models. Don't think there is an estimation procedure related approach like tweaking IGLS here.

Hi,
I think the fact level 2 is nested within level 3 is fine here as for MCMC nested is simply a special case of the more general cross classified model - just need to make sure that your IDs are unique i.e. if you have a reader 1 that this is the same person throughout and if say you have a ...

Hi John,
Thanks for the clarification. I'd therefore call the measures age 4 and age 5 rather than baseline and follow up. Then 3-levels might make more sense and you could include age as a predictor to control for age related differences perhaps with age related interactions which would pretty ...

Hi John,
Presumably when you talk about a quadratic relationship you are talking globally as having only 2 measures per child would not allow anything more than a linear relationship at the child level. When someone uses terms like baseline and follow-up it suggests something happens in between ...

Hi Cesarroga,
I can recommend my paper on this https://rss.onlinelibrary.wiley.com/doi ... 04.00365.x which looks at partitioning variation in a logistic model with overdispersion and has a similar example of counts for different categories.
Best wishes,
Bill.

Hi Cesarroga,
You should be able to code logistic regression examples where the denominator is not 1 so that you can then model the 6 proportions as 6 couplets (y,n) one per row in the dataset and nest these within small areas. In MLwiN you would have as you say the same levels as level 1 and level ...