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Re: st: mi impute: ologit, mlogit, logit in one equation?

From   Richard Goldstein <>
Subject   Re: st: mi impute: ologit, mlogit, logit in one equation?
Date   Tue, 17 May 2011 09:49:28 -0400


since you have already imputed the data, why not use -mi import- and
then you can use -mi estimate- (or any other built-in tool)?


On 5/17/11 9:43 AM, Maria Fleischmann wrote:
> Dear statalister,
> I am currently trying to impute multiple individual-level variables
> from a clustered dataset.
> I did so with stata command ice, but I would rather like to use mi
> impute due to the mi estimate command.
> My equation in ice (without taking into account the layered structure
> of the data) was the following, where I impute variabels by ologit,
> mlogit, logistic or linear regression, all delineated in one command:
> ice var0 var1 o.var2 o.var3 var4 m.var5 i.var6 var7 i.var8, m(5)
> saving(20110511_ice_1, replace)
> Now I would like to transfer this to a mi impute command, but this
> raises the question whether I can use mlogit, ologit, linear
> regression in the same equation if the missing structure in my data is
> not monotonic?
> I think the mi impute command should look somehow like this:
> mi set mlong
> mi set M=5
> mi register imputed var1 var2 var3 var5
> mi register regular var0 var4 var6 var7 var8
> mi impute (regress) var1 (ologit) var2 var3 (mlogit) var5 = var0 var4
> i.var6 var7 i.var8, replace
> This however does not work, because the data is not monotonic?
> So I tried to do the imputation separate for all the different
> regression types, which also does not work, because the missing
> structure is still not monotonic and I have more variables for one
> type of regression, e.g. ologit for var2 and var3.
> But also when doing the imputation separate for each variable (which I
> find highly inconvenient especially due to the large number of
> variables I want to impute), stata reports problems because I am
> imputing variables with variables that also have missing values. Do I
> just have to force stata to impute or is there a more elegant way of
> solving my problem (which I hope and think there is)?
> These are all problems I encountered besides the fact that I also
> would like to account for the clustering of the data.
> Thanks you for your help!
> Maria
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