[R] Normalization and missing values

[Adaikalavan Ramasamy]

What the best missing value imputation ? It depends on how the values
were generated (e.g. missing at random, informative missing ) and what
type of data (e.g. counts, continuous).

If you are interested in this you could either :

1) take the dataset of complete cases and impute missing values
according to the pattern of missing-ness you see on the whole data. Then
apply different types of imputation techniques and see which one has the
best results.

2) Or look for studies that have evaluated different techniques in your
_field_ and apply the best one.

Regards, Adai



On Wed, 2005-04-13 at 13:36 -0500, WeiWei Shi wrote:
> the way of scaling, IMHO, really depends on the distribution of each
> column in your original files. if each column in your data follows a
> normal distrbution, then a standard "normalization" will fit your
> requirement.
> 
> My previous research in microarray data shows me a simple "linear
> standardization" might be good enough for some purpose.
> 
> If your columns differ in magnitude, then some data transformation
> like (log) might be needed first.
> 
> Ed
> 
> 
> On 4/13/05, Achim Zeileis <Achim.Zeileis at wu-wien.ac.at> wrote:
> > On Wed, 13 Apr 2005 14:33:25 -0300 (ADT) Rolf Turner wrote:
> > 
> > >
> > > Bert Gunter wrote:
> > >
> > > > You can't expect statistical procedures to rescue you from poor
> > > > data.
> > >
> > >       That should ***definitely*** go into the fortune package
> > >       data base!!!
> > 
> > :-) added for the next release.
> > Z
> > 
> > >                               cheers,
> > >
> > >                                       Rolf Turner
> > >                                       rolf at math.unb.ca
> > >
> > > ______________________________________________
> > > R-help at stat.math.ethz.ch mailing list
> > > https://stat.ethz.ch/mailman/listinfo/r-help
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> > >
> > 
> > ______________________________________________
> > R-help at stat.math.ethz.ch mailing list
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> 
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