[R] Logistic regression with multiple imputation

[Jeremy Miles]

Hi Daniel

First, newer versions of SPSS have dramatically improved their ability
to do stuff with missing data - I believe it's an additional module,
and in SPSS-world, each additional module = $$$.

Analyzing missing data is a 3 step process.  First, you impute,
creating multiple datasets, then you analyze each dataset in the
conventional way, then you combine the results.   There are two (that
I know of) packages for imputaton - these are mi and mice.  rseek.org
will find them for you.

Hope that helps,

Jeremy




On 29 June 2010 22:14, Daniel Chen <news at pushih.com> 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.
>
> Thank you!
>
> Daniel
>
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>
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-- 
Jeremy Miles
Psychology Research Methods Wiki: www.researchmethodsinpsychology.com