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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 = 3340 -...

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 po...

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 _1_D...

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 ...