Thank you Duncan Murdoch <murd...@stats.uwo.ca> napsal dne 19.08.2009 14:49:52:
> On 19/08/2009 8:31 AM, Petr PIKAL wrote: > > Dear all > > <snip> > > I would say the answer depends on the meaning of the variables. In the > unusual case that they are measured in dimensionless units, it might > make sense not to scale. But if you are using arbitrary units of > measurement, do you want your answer to depend on them? For example, if > you change from Kg to mg, the numbers will become much larger, the > variable will contribute much more variance, and it will become a more > important part of the largest principal component. Is that sensible? Basically variables are in percentages (all between 0 and 6%) except dus which is present or not present (for the purpose of prcomp transformed to 0/1 by as.numeric:). The only variable which is not such is iep which is basically in range 5-8. So ranges of all variables are quite similar. What surprises me is that biplot without scaling I can interpret by used variables while biplot with scaling is totally different and those two pictures does not match at all. This is what surprised me as I would expected just a small difference between results from those two settings as all numbers are quite comparable and does not differ much. Best regards Petr > > Duncan Murdoch ______________________________________________ R-help@r-project.org 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.
