Notice: On April 23, 2014, Statalist moved from an email list to a forum, based at statalist.org.
[Date Prev][Date Next][Thread Prev][Thread Next][Date Index][Thread Index]
RE: st: mi impute chained
From
Richard Williams <[email protected]>
To
[email protected], <[email protected]>
Subject
RE: st: mi impute chained
Date
Fri, 02 Nov 2012 09:33:01 -0500
At 03:28 AM 11/2/2012, chong shiauyun wrote:
I used -force- in my MI model it resulted in a number of missing
values which can't be imputed. For example, I have 7000 missing data
on IQ and only 1800 are imputed.
Do you have hard missing or soft missing? From the manual:
hard missing and soft missing. A hard missing value is a value of .a,
.b, : : : , .z in m = 0 in an imputed variable. Hard missing values
are not replaced in m > 0. A soft missing value is a value of . in m
= 0 in an imputed variable. If an imputed variable contains soft
missing, then that value is eligible to be imputed, and perhaps is
imputed, in m > 0. Although you can use the terms hard missing and
soft missing for passive, regular, and unregistered variables, it has
no special significance in terms of how the missing values are treated.
I know this is because of the missingness of predictors in my model
but I don't understand why is happens because I have already
specified to use -mi impute chained- to impute other predictors as well.
is there any ways to overcome this problem?
This is how the IQ conditional model looks like:
truncreg totaliq birthweight i.smkpreg i.marist i.matdepr i.homeown3
i.alcpreg3 i.hhcrowd i.ednmatpat i.findiff i.ethnicity i.scmatpat3
i.mumhealth i.breastfed i.social verbiq perfiq mumiq tempcatwlc_i
tempcatclc_i sex mumage babygestation , ll(45) ul(151) noisily
Many thanks
Shiau
> Date: Wed, 31 Oct 2012 08:48:39 -0400
> Subject: Re: st: mi impute chained
> From: [email protected]
> To: [email protected]
>
> On Wed, Oct 31, 2012 at 4:54 AM, chong shiauyun
<[email protected]> wrote:
> > Hi,
> >
> > thanks for your advice.
> > I simplified my MI model by excluding some interactions and
reduced some of my variables. It works fine. However, I am concern
that I have to use the -force- option to make the model works. It
am quiet reluctant to drop all of the interactions seeing that it
may affect the relationship between the exposure and the outcome
which I am interested in.>
>
> I've used -force- and I think it works OK but check using
> -midiagplots-, which you can download.
>
> As to the interactions, if they're very collinear with the other
> variables you have in the model they're not adding anything. You can
> experiment with dropping or adding variables and keep checking with
> -midiagplots- to determine how things are working. Remember, the idea
> in MI is not to have a perfectly reconstructed dataset but to
> optimally preserve insofar as is possible the information you do have.
>
> *
> * For searches and help try:
> * http://www.stata.com/help.cgi?search
> * http://www.stata.com/support/faqs/resources/statalist-faq/
> * http://www.ats.ucla.edu/stat/stata/
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
*
* For searches and help try:
* http://www.stata.com/help.cgi?search
* http://www.stata.com/support/faqs/resources/statalist-faq/
* http://www.ats.ucla.edu/stat/stata/