[R] Multiple imputation using mice with "mean"
mothsailor at googlemail.com
Mon Sep 25 15:47:26 CEST 2006
You might find something useful at this web site:
On 25/09/06, Eleni Rapsomaniki <e.rapsomaniki at mail.cryst.bbk.ac.uk> wrote:
> I am trying to impute missing values for my data.frame. As I intend to use the
> complete data for prediction I am currently measuring the success of an
> imputation method by its resulting classification error in my training data.
> I have tried several approaches to replace missing values:
> - mean/median substitution
> - substitution by a value selected from the observed values of a variable
> - MLE in the mix package
> - all available methods for numerical data in the MICE package (ie. pmm, sample,
> mean and norm)
> I found that the least classification error results using mice with the "mean"
> option for numerical data. However, I am not sure how the "mean" multiple
> imputatation differs from the simple mean substitution. I tried to read some of
> the documentation supporting the R package, but couldn't find much theory about
> the "mean" imputation method.
> Are there any good papers to explain the background behind each imputation
> option in MICE?
> I would really appreciate any comments on the above, as my understanding of
> statistics is very limited.
> Many thanks
> Eleni Rapsomaniki
> Birkbeck College, UK
> R-help at stat.math.ethz.ch mailing list
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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