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

From   Richard Goldstein <>
To   Maria Fleischmann <>
Subject   Re: st: mi impute: ologit, mlogit, logit in one equation?
Date   Tue, 17 May 2011 10:50:01 -0400


as far as I know, the only way to do this is to do a separate _mi
impute- for each variable to be imputed and then use -mi add- (or -mi
append- depending on your specific situation)


On 5/17/11 10:42 AM, Maria Fleischmann wrote:
> Thanks, Richard, for this suggestion. I will consider it!
> But just out of interest: is it possible to impute missing values with
> a non-monotone missing structure if the variables should be imputed
> with different regression equations (mlogit, logit, reg)?
> Which should be something like:
> mi impute (regress) var1 (ologit) var2 var3 (mlogit) var5 = var0 var4
> i.var6 var7 i.var8, replace
> On 17 May 2011 15:49, Richard Goldstein <> wrote:
>> Maria,
>> 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)?
>> Rich
>> 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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