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
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David Winsemius, MD
West Hartford, CT
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