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RE: st: using Stata's micombine command on a SAS-imputed dataset


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: using Stata's micombine command on a SAS-imputed dataset
Date   Mon, 11 Oct 2004 15:15:38 +0100

I don't think this is the only issue. There
is at least one more, that -mvis- sets 
a characteristic, which must be set otherwise. 

As mentioned more than once on this list recently, 
Patrick Royston, the author of these commands, 
is not a member of Statalist. That reduces (although 
not to zero) the chance of getting an authoritative
answer from a posting to Statalist. 

In this case, I forwarded the original question
to Patrick, who will no doubt reply in due course. 

Nick 
n.j.cox@durham.ac.uk 

Fred Wolfe

> >A colleague of mine imputed data using SAS's mi command. The 
> dataset, 
> >containing m=5 imputations, I translated into a .dta format using 
> >StatTransfer. I wondered if Stata's micombine command might 
> be able to get 
> >the regression coefficients. I created the two variables _i 
> and _j that 
> >normally would be present had we used Stata's mvis command 
> instead of 
> >SAS's mi command and ordered the dataset as mvis would have.
> >
> >I still get the following error message after using micombine.
> >
> >. micombine regress s_mh dum1grp dum2grp gesl lft cd4 vload, 
> impid (_j)
> >no row-identifier variable found - data may have incorrect format
> >r(198);
> >
> >What format does Stata expect? Any help to sort this out 
> will be greatly 
> >appreciated.
> 
> 
> Here is an example of the format from a data set of mine that worked 
> without problem.
> 
>         +---------------------+
>          |    _i   _j      sex |
>          |---------------------|
>       1. |     1    1   Female |
>       2. |     1    2   Female |
>       3. |     1    3   Female |
>       4. |     1    4   Female |
>       5. |     1    5   Female |
>          |---------------------|
>       6. |     2    1   Female |
>       7. |     2    2   Female |
>       8. |     2    3   Female |
>       9. |     2    4   Female |
>      10. |     2    5   Female |
>          |---------------------|
> 
> In the final data set there are 5 combined data sets (_j) and 
> (_j=1 to 5). 
> Within each data set, _i is the observation number.

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