Re: [R] Logistic regression with multiple imputation

From: Simon Blomberg <s.blomberg1_at_uq.edu.au>
Date: Wed, 30 Jun 2010 15:36:30 +1000

mitools is useful too, and I can vouch for mice. mice is easy to use, and easy to write new imputation methods too. So it is also very flexible.

Simon.

On 30/06/10 15:31, Jeremy Miles wrote:
> 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
>>
>> [[alternative HTML version deleted]]
>>
>> ______________________________________________
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>>
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>>
>>
>
>
>

-- 
Simon Blomberg, BSc (Hons), PhD, MAppStat.
Lecturer and Consultant Statistician
School of Biological Sciences
The University of Queensland
St. Lucia Queensland 4072
Australia
T: +61 7 3365 2506
email: S.Blomberg1_at_uq.edu.au
http://www.uq.edu.au/~uqsblomb/

Policies:
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2.  Your deadline is your problem

Statistics is the grammar of science - Karl Pearson.

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Received on Wed 30 Jun 2010 - 05:38:26 GMT

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