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Hi George,

I just manage to get s.e. just by copying or replacing original vars with a set of vars_1 and vars_2 which the stata recognises. The commands are like these (response 0: reference; response 1: oc; response 2: ic):
predict ocxb, outcome(FP1) xb
predict ocse, outcome(FP1) stdp
predict ...

Hi George,

Thank you for your kind reply. I have calculated the probabilities for each response of the multinominal model by hand. But I need 95% CI of prediction, and hence have to calculate standard error of prediction by hand as well (I am not sure the stata command predict se, stdp can work in ...

Hi,

I find runmlwin is not compatible with command "margins".
So, I fit a multinominal model and then calculate probabilities by hand using the expression like:
"Pr(y = 1) = " exp([FP1]cons_1)/ ///
(1 + exp([FP1]cons_1) + exp([FP2]cons_2) + exp([FP3]cons_3))"
But I am not sure the correct way to ...

Thank you, George. Your reply is very helpful!
best wishes,
Rodrigo

Hi,

I fit multilevel nominal models using mcmc by running runmlwin in Stata. In their results tables, I found a pattern that the p values of coefficients tend to be smaller than expected. For example, there is one coefficient like this:
Mean S.D. EES P 95% cred. Interval
-.2965969 .1812569 548 0 ...

Dear George,

Thank you very much for your help. I will look through these iterations as you suggested.

Best wishes,

Rodrigo

Hi Chris,

Thank you very much for your reply.
I can confirm that there is no such problem if I use melogit command in Stata. The problem of Stata is the low-efficient maximum likelihood estimation, which can take dozens of days to estimate random slopes. MCMC in MLwin would be faster than ML. I ...

Hi folks,

I met some problem when I used runmlwin with multiply imputed data (done by stata mi command).
My command is like this, a very simple one:
sort commid idind2 wave
mi est, cmdok noi sav(mlw, replace): runmlwin phi cons, level3(commid: cons) level2(idind2: cons) level1(wave) discrete ...