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 <[EMAIL PROTECTED]> 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 > > [EMAIL PROTECTED] > > > > ______________________________________________ > > R-help@stat.math.ethz.ch mailing list > > https://stat.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! > > http://www.R-project.org/posting-guide.html > > > > ______________________________________________ > R-help@stat.math.ethz.ch mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html > ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
