before I know the scale() function, I just do it by coding it myself. But probably you could find some cool stuffs in dprep library. I've never tried it anyway.
for missing values, it is way more complex and also depends on the methodology you are going to use. some methods are more tolerant to missing values but others aren't. So the short answer is that there is no BEST way. On 4/13/05, Chris Bergstresser <[EMAIL PROTECTED]> wrote: > Hi all -- > > I've got a large dataset which consists of a bunch of different > scales, and I'm preparing to perform a cluster analysis. I need to > normalize the data so I can calculate the difference matrix. > First, I didn't see a function in R which does normalization -- did > I miss it? What's the best way to do it? > Second, what's the best way to deal with missing values? Obviously, > I could just set them to 0 (the mean of the normalized scales), but I'm > not sure that's the best way. > > -- Chris > > ______________________________________________ > 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 > -- WenSui Liu, MS MA Senior Decision Support Analyst Division of Health Policy and Clinical Effectiveness Cincinnati Children Hospital Medical Center ______________________________________________ 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
