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RE: st: Re: Trying to do some multiple imputation


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: Re: Trying to do some multiple imputation
Date   Tue, 24 Jan 2006 21:48:58 -0000

As I indicated, I cannot myself advise on this.
This is open to anyone who cares to answer. 

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

Mosi A.Ifatunji
 
> Nick,
> 
> I'm not exactly sure if this accomplishes what I am interested in
> accomplishing. I am sure that you're telling me the right 
> answer, but I may
> not be asking the right question, LOL (i.e., I may not be 
> communicating the
> question correctly). Let me give it another go:
> 
> So, to start at square one; I have a dataset named das1995r. 
> In that dataset
> there is an income variable, 'income.' There are a lot of 
> values missing for
> 'income.' I am assuming that these values are missing at 
> random (MAR) and
> can be predicted by the respondents race ('black'), sex ('male'), age
> ('age2') and level of education ('educate').
> 
> I would like to end up with a variable in my original dataset 
> (das1995r)
> with a new variable for income. That is, the old variable 
> will still be
> there ('income') but there would also be a new variable at 
> the end of the
> dataset (das1995r) that represents old values for 'income' 
> with new values
> for 'income' where there were once missing values. I would like this
> variable to be called, 'imp_income.'
> 
> In order to accomplish this, I have been given the following syntax:
> 
> ===
> 
> clear
> 
> cd "/Users/Ifatunji/Documents/IFATUNJI
> Docs/University/Academic/Graduate/Data/DAS 1995/"
> 
> use das1995r
> 
> forv i = 1(1)5 {
> preserve
> uvis regress income black male age2 educate, gen(income`i') 
> seed(123695`i')
> replace income = income`i'
> save das`i', replace
> restore
> }
> 
> /* forv i = 1(1)5 {
> use das`i', clear
> tab income, miss
> } */
> 
> miset using das
> 
> mifit, indiv: regress income black male age2 educate
> 
> ===
> 
> The problem is that when this syntax is finished running, I 
> find myself in a
> new dataset (i.e., not das1995r, but some other dataset) that seems to
> represent several datasets in one. In this new dataset, the 'income'
> variable has almost no missing values (which is fine).
> 
> My problem is that I cannot get a representation of this new 'income'
> variable with no missing values from this new dataset, back 
> to the original
> dataset, das1995r. Ultimately, when this new 'income' 
> variable gets back to
> the original dataset, I would like it to have one observation 
> per case and
> be called 'imp_income.'
> 
> Any thoughts you might have would be very welcome,
 
On 1/24/06 12:17 PM, "Nick Cox" <n.j.cox@durham.ac.uk> wrote:
 
> > I forward below some comments from Patrick Royston
> > who wrote what is now -mice- (but is not a member
> > of Statalist). They may be redundant given
> > other postings. I am not familiar with the details
> > of -mice- and cannot advise myself.
> > 
> > Nick 
> > n.j.cox@durham.ac.uk
> > 
> > --------------------------------------------
> > 
> > The commands -misplit- and -mijoin- have a specialised purpose and
> > are not often used.
> > 
> > If you wish to combine the imputed dataset with the 
> original data you
> > only need load the imputed dataset and append the original one.
> > 
> > For the example given by Mosi,
> > 
> > use imp, clear
> > append using <original data file name>
> > 
> > Note that with this approach, the imputation indicator _j 
> and the observation
> > indicator _i will be missing for the original dataset but 
> of course present
> > for the imputed dataset.
> > 
> > Then you can do what you wish with the combined dataset. 
> -micombine- will
> > still work correctly, it will just ignore observations in 
> which _j is missing.
> > 
> > Preferably, you should now be using -ice-, not -mvis- which 
> is out of date.
> > See the Stata Journal 5(4) update.

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