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I've checked the StatJR complete case template against the R procedure and they produce identical datasets.

I've managed to successfully fit the MOI (with missing values removed) using the StatJR 'nlevelmod' template and both the eStat and JAGS estimation engines. I was getting poor mixing with the eStat engine on some of the variance parameters (ESS on some of these were in the hundreds rather than the ...

Hi Chris,

I've copied and pasted several chunks of code below where I can see errors. The two school-level variables I'm attempting to include are school average KS1 and KS2 scores (for 150 schools) which are normally distributed, albeit left-skewed. There are approximately 4 schools with missing ...

That's definitely helped thanks. I've now uninstalled, deleted all the files and reinstalled StatJR, along with the missing data templates from a fresh download. This seems to have fixed the first issue regarding the binary invalid value issue and the template now runs a four-level imputation of my ...

The code is identical, but when I select the template (1LevelMVMixedResponsecc.py) in the TREE interface, the response type for each response variable has binomial rather than the binary found on the n-level imputation template. This might not matter, but that's the only difference I can find so far.

Hi Chris,

Unfortunately, I still get the same error message.

Hi Chris,

Thanks for the very quick reply. Yes all the variables with missing data are specified as responses in the imputation model. My MOI takes a subset of these variables. Here is the command I used, and a screen shot with the error messages is attached:

Command: RunStatJR(template ...

Hi,

I've been trying to use the nlevel imputation template to impute missing data for a four level model with 1775 respondents and I'm running into a few problems. My data has been processed in R and saved as a STATA file and contains a mixture of user collected test score data, combined with ...