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Re: st: RE: Multilevel models and multiple imputation


From   "Nailing Xia" <[email protected]>
To   [email protected]
Subject   Re: st: RE: Multilevel models and multiple imputation
Date   Tue, 11 Nov 2008 13:10:23 -0500

Thanks, Rodrigo, and Jay.

I tried using -mim, cat(combine)-, but Stata gives error message
"invalid category combine".

Although it is skewed, I intend to present the result to non-technical
audience, in which case the original paramter (instead of the log of
the paramter) is much intuitive to understand.

Any further suggestions?

Thank you for the help.

Nailing

On Tue, Nov 11, 2008 at 10:47 AM, Rodrigo Alfaro A. <[email protected]> wrote:
>
> ///
>
> Hi,
>
> You could use combine option in -mim-. You should add the jacobian by
> hand.
> For example suppose that you want to have the log(mean of y):
>
> mim, cat(combine) est(log(_b[_cons])) se(_se[_cons]/_b[_cons]): reg y
>
> HTH, Rodrigo.
>
>
> -----Mensaje original-----
> De: [email protected]
> [mailto:[email protected]] En nombre de Nailing Xia
> Enviado el: Martes, 11 de Noviembre de 2008 11:48 a.m.
> Para: [email protected]
> Asunto: st: Multilevel models and multiple imputation
>
> Hi,
>
> I have a question regarding the standard error of the random-effects
> parameter in multi-level models (-xtmixed).
>
> I am estimating a multilevel model using -xtmixed-, and the dataset I
> use is from multiple imputation procedure using -ice-. I use -mim- for
> estimation and the command is : mim: xtmixed irtm pexpect pbelmth
> mclsize mtexp if [parwgt] || schid: || childid:. The output gives the
> random-effects paramters in logrithm of the paramters:
> ------------------------------------------------------------------------
> ------
>        irtm |     Coef.  Std. Err.        [95% Conf. Int.]   MI.df
> -------------+----------------------------------------------------------
> -------------+------
>   /lns1_1_1 |   1.20731   .052497                    1.10429  1.31034
> 923.0
>   /lns2_1_1 |   2.42047   .009313                    2.40208  2.43886
> 164.9
>    /lnsig_e   |   2.21744    .00496                     2.2077
> 2.22718  597.9
> ------------------------------------------------------------------------
> ------
>
> I know I can get the paramter by using di exp(1.20731), but how can I
> get the corresponding standard errors?
>
> Thank you!
> Nailing Xia
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