[R] Logistic regression with multiple imputation

[David Winsemius]

On Jun 30, 2010, at 1:14 AM, Daniel Chen wrote:

> Hi,
>
> I am a long time SPSS user but new to R, so please bear with me if my
> questions seem to be too basic for you guys.
>
> I am trying to figure out how to analyze survey data using logistic
> regression with multiple imputation.
>
> I have a survey data of about 200,000 cases and I am trying to  
> predict the
> odds ratio of a dependent variable using 6 categorical independent  
> variables
> (dummy-coded). Approximatively 10% of the cases (~20,000) have  
> missing data
> in one or more of the independent variables. The percentage of missing
> ranges from 0.01% to 10% for the independent variables.
>
> My current thinking is to conduct a logistic regression with multiple
> imputation, but I don't know how to do it in R. I searched the web but
> couldn't find instructions or examples on how to do this. Since SPSS  
> is
> hopeless with missing data, I have to learn to do this in R. I am  
> new to R,
> so I would really appreciate if someone can show me some examples or  
> tell me
> where to find resources.

The rms/Hmisc duo of packages has several functions supporting  
multiple imputation. aregImpute() is nicely integrated with his other  
utility functions and extensively documented in Harrell's excellent  
text: "Regression Modeling Strategies". He also provides quite a bit  
of free, online documentation at his Vanderbilt website. The help page  
for aregImpute is a small chapter in itself with multiple worked  
examples.

install.packages(c("rms", "Hmisc")
reauire(rms) # rms has dependecy of Hmisc which will load automagically
?aregImpute

-- 
David Winsemius
>
> Thank you!
>
> Daniel
>
> 	[[alternative HTML version deleted]]
>
> ______________________________________________
> R-help at r-project.org mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.

David Winsemius, MD
West Hartford, CT