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


From   Maria Fleischmann <maria.s.fleischmann@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   st: mi impute: ologit, mlogit, logit in one equation?
Date   Tue, 17 May 2011 15:43:11 +0200

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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