R help - Nov 2011 - Imputing Missing Data: A Good Starting Point?

Hello

I am working on my first attempt to impute missing data of a data 
set with systematically incomplete answers (school performance 
tests). I was googling around for some information and found 
Amelia (Honaker et al., 2010) and the mi package (Yu-Sung et al., 
n.d.). However, since I am new to this field, I was wondering 
whether some experts could give a good recommendation of a 
starting point for me, that is a point that combines theory as 
well as practical examples. Of course, My primary interest is to 
complete the task in time (1 week), however, I want to acquire 
skills for a program that provides some future, and of course I 
want some background on what I am doing (and what not). Could you 
help with some hints, experiences, and recommendations?

Thank you.

Regards
*S*

-- 
Sascha Vieweg, saschaview at gmail.com

Hope this helps:
http://rss.acs.unt.edu/Rdoc/library/randomForest/html/rfImpute.html
Ken Hutchison

On Nov 1, 2554 BE, at 5:29 PM, Sascha Vieweg <saschaview at gmail.com>
wrote:
> Hello
> 
> I am working on my first attempt to impute missing data of a data set with
systematically incomplete answers (school performance tests). I was googling
around for some information and found Amelia (Honaker et al., 2010) and the mi
package (Yu-Sung et al., n.d.). However, since I am new to this field, I was
wondering whether some experts could give a good recommendation of a starting
point for me, that is a point that combines theory as well as practical
examples. Of course, My primary interest is to complete the task in time (1
week), however, I want to acquire skills for a program that provides some
future, and of course I want some background on what I am doing (and what not).
Could you help with some hints, experiences, and recommendations?
> 
> Thank you.
> 
> Regards
> *S*
> 
> -- 
> Sascha Vieweg, saschaview at gmail.com
> 
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