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I tried to use mi est with both xtmixed and runmlwin to fit an unconditional model, but the results are very different for the random part. See below.

mi est: xtmixed POST PRE||_all:R.PID||SCHID:,mle var

Multiple-imputation estimates Imputations = 5
Mixed-effects ML regression Number of obs ...

one more question on the model.....when I used the command below to fit the model on 5 imputed data sets, the final output gives some warning (although I still have the results): number of groups varies among imputations./number of observations per group varies among imputations. I checked that the ...

It makes sense. Thank both of you for all your help! You guys are awesome!

Hi Chris,

You are right. The problem is solved once I use a positive-definite matrix for the random part. One question I have (hopefully this is my last one) is whether starting values affect estimates in any important ways. I know they affect how fast the model would converge. I used some random ...

Hi,

Sorry for sending a flurry of messages. I tried two things. One is fit two separate naive 2-level models and use their estimates as starting value. I still got the same error message. Below is the output from the two models.
. runmlwin _1_POST cons _1_PRE _1_DEXP2 _1_DEXP3, level2(PID: cons ...

Yes, the starting values of the random part estimates for the second level of my model are zero as well. Do you have any suggestions on what starting values I should use? Right now it's the estimates from a naive 3-level model.
Also, regarding the output of the model, it also produces cov(cons\DEXP2 ...

Hi Chris,

Thank you very much for the advice! The problem is solved! But when I add random predictors at level2 (see syntax below). MLwiN gives me another error message:error while obeying batch file C:\Users\SHUANG~1\AppData\Local\Temp\ST_0000000a.tmp at line number 291: MCMC 0 500 1 5.8 50 10 1 1 ...

Hi,

I have five imputed data sets and am using mi est together with runmlwin to run cross-classified model on imputed data sets and combine results using Rubin's rules (code below). But the mi est doesn't seem to work with mcmc estimation and I got this error message "model did not converge on m=1 ...