On Nov 22, 2009, at 10:22 AM, Uwe Ligges wrote: > masterinex wrote: >> Hi guys , Im trying to do principal component analysis in R . There is 2 >> ways of doing >> it , I believe. One is doing principal component analysis right away the >> other way is standardizing the matrix first using s = scale(m)and then >> apply principal >> component analysis. How do I tell what result is better ? What values in >> particular should i >> look at . I already managed to find the eigenvalues and eigenvectors , the >> proportion of variance for each eigenvector using both methods. > > Generally, it is better to standardize. But in some cases, e.g. for the same > units in your variables indicating also the importance, it might make sense > not to do so. > You should think about the analysis, you cannot know which result is `better' > unless you know an interpretation. > > > >> I noticed that the proportion of the variance for the first pca without >> standardizing had a larger value . Is there a meaning to it ? Isnt this >> always the case? >> At last , if I am supposed to predict a variable ie weight should I drop >> the variable ie weight from my data matrix when I do principal component >> analysis ? > > > This sounds a bit like homework. If that is the case, please ask your teacher > rather than this list. > Anyway, it does not make sense to predict weight using a linear combination > (principle component) that contains weight, does it? > > Uwe Ligges
It's likely to have been homework: A quick search on "masterinex" "xevilgang79" reveal which university this undergraduate student is at. It also produces a phone number, which can be used to lookup an address, and a cell phone number. MK ______________________________________________ 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.
