You might find something useful at this web site: http://www.multiple-imputation.com/
On 25/09/06, Eleni Rapsomaniki <[EMAIL PROTECTED]> wrote: > > Hi > > 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 > > ______________________________________________ > [email protected] 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 Barron Said Business School University of Oxford Park End Street Oxford OX1 1HP ______________________________________________ [email protected] 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.
