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Thanks Bill, when writing up my results one more question of interpreting three-level multilevel models came up. If we have, for example, a level-2 unit of teachers-within-schools (with a VPC of 6%) and a level-3 unit of schools (with a variance of 4%) in a model studying a binary pass indicator, as ...

One last thing about the specifics of this model - my model is a binary outcome multiple membership model, with level 1 units (tests) being examined (equally weighted) by two level two units (readers), which are strictly nested within level three units (centres). Effectively like the Three-level ...

Thanks Chris, I took a long time to experiment with variable names and data subsets to see where the issue was coming from. It seems that the issue came from having a factor variable with levels "I" and "P" that was not set as a factor beforehand. The error was recreated sometimes when running the ...

Hi Chris, after checking the data within MLwiN using debugmode the variables at line 37667 did not have the same value, being displayed as 13 and 14, however after clicking "show value labels" they both had the same value of 13.000. I believe I fixed the error by not setting the multiple membership ...

Hello,

On the topic of multiple membership binary outcome models, I am currently attempting to fit one. Upon running the runMLwiN command - after around a minute the following error is given.

"Error in foreign::read.dta(chainfile ) :
unable to open file: 'No such file or directory"

When using ...

Thank you Richard, the LEMMA course has been incredibly useful.

Thanks Chris, that's really helpful to know. How does one interpret the random effects coefficients in such a model? Does the interpretation vary depending on the link function?

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MLwiN (version ...

Thanks for your reply Chris. In your reply do you mean that only the specific combination of at least one Normal and one Binomial are estimable using MCMC? Attempting to use MCMC sampling in the Chapter 14 example obtains the following error:

> (mymodel6 <- runMLwiN(c(logit(binexam), logit(binlrt ...

Hello,

I am attempting to model two binary responses that represent two agents' decisions on the same task. Decisions are nested within agents which are nested within centres, are influenced by several covariates, and the marginal probabilities of the two decisions are identical except for when the ...