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Re: st: mi impute chained


From   "JVerkuilen (Gmail)" <jvverkuilen@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: mi impute chained
Date   Fri, 26 Oct 2012 08:20:35 -0400

As I said before, your model is way too complex to troubleshoot. Cut
down on the number of variables or simplify the imputation model and
build UP. There are simply too many failure points right now to be
able to diagnose anything. For instance I seem to recall you're using
a multinomial logit for a categorical variable. You may have problems
there. The censored parts may be trouble too. With imputation the goal
is to get a "good enough" representation of the dataset. So cut down
to a much simpler model. See if that runs. Then start adding
components and find out where it fails. Try this a few different ways.
If it always fails on the same variable that tells you what's wrong.
I'd also run descriptives and missing data patterns on all variables.



On Fri, Oct 26, 2012 at 3:44 AM, chong shiauyun <shiauyun416@hotmail.com> wrote:
> Hi,
>
> I am not sure if I should proceed to imputation or I need to change my dryrun model because in my dryrun, it seems to have some convergence problems. I am not sure how to check which sub-model that caused the convergence problem, even though I have specified -noisily-. The thing is that the iterations keep running something like:
> Iteration 611: log likelihood = -12162.404  (not concave)
> Iteration 612: log likelihood =   -12162.4  (not concave)
> Iteration 613: log likelihood = -12162.397  (not concave)
> Iteration 614: log likelihood = -12162.394  (not concave)
> Iteration 615: log likelihood =  -12162.39  (not concave)
> Iteration 616: log likelihood = -12162.387  (not concave)
> Iteration 617: log likelihood = -12162.384  (not concave)
> Iteration 618: log likelihood =  -12162.38  (not concave)
>
> Shiau
>
> ----------------------------------------
>> From: ymarchenko@stata.com
>> To: statalist@hsphsun2.harvard.edu
>> Subject: Re: st: mi impute chained
>> Date: Thu, 25 Oct 2012 07:21:35 -0500
>>
>> Chong Shiauyun <shiauyun416@hotmail.com> receives a "no observation" error
>> when he runs the following imputation model using -mi impute chained-:
>>
>> > . mi impute chained (reg) birthweight (ologit, augment) ednmatpat
>> > (logit, augment)sex (truncreg, ll(lVIQ) ul(uVIQ))verbiq,
>> > add(20) rseed(11349730) burnin(50) chainonly dryrun report
>>
>> Shiau probably meant to omit the -dryrun- option in the above since
>> -mi impute chained- does not perform any estimation when the -dryrun- option
>> is specified.
>>
>> The -mi impute chained- command starts off by fitting univariate models on the
>> observed data to obtain initial imputed values for each imputed variable. The
>> "no observation" error typically occurs when one of such models contains no
>> observations and is often caused by the existence of missing values in
>> variables other than the imputed variables. In Shiau's case, the offending
>> variables may be -lVIQ- and -uVIQ-.
>>
>> Shiau can use the -noisily- option of -mi impute chained- to identify the
>> particular conditional model for which there are no observations and run this
>> model manually on the observed data to determine the problem.
>>
>> Shiau can also send his data and do file to our technical support group at
>> tech-support@stata.com to help him identify the problem.
>>
>>
>> -- Yulia
>> ymarchenko@stata.com
>> *
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>
> *
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-- 
JVVerkuilen, PhD
jvverkuilen@gmail.com

"Out beyond ideas of wrong-doing and right-doing there is a field.
I'll meet you there. When the soul lies down in that grass the world
is too full to talk about." ---Rumi

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