Hi Hadley , I really apreciate the suggestions you gave, It was helpful , but I still didnt quite get it all. and I really want to do a good job , so any comments would sure come helpful, please understand me .
hadley wrote: > > You've asked the same question on stackoverflow.com and received the > same answer. This is rude because it duplicates effort. If you > urgently need a response to a question, perhaps you should consider > paying for it. > > Hadley > > On Sun, Nov 22, 2009 at 12:04 PM, masterinex <xevilgan...@hotmail.com> > wrote: >> >> so under which cases is it better to standardize the data matrix first >> ? >> also is PCA generally used to predict the response variable , should I >> keep that variable in my data matrix ? >> >> >> Uwe Ligges-3 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 >>> >>> ______________________________________________ >>> 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. >>> >>> >> >> -- >> View this message in context: >> http://old.nabble.com/how-to-tell-if-its-better-to-standardize-your-data-matrix-first-when-you-do-principal-tp26462070p26466400.html >> Sent from the R help mailing list archive at Nabble.com. >> >> ______________________________________________ >> 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. >> > > > > -- > http://had.co.nz/ > > ______________________________________________ > 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. > > -- View this message in context: http://old.nabble.com/how-to-tell-if-its-better-to-standardize-your-data-matrix-first-when-you-do-principal-tp26462070p26471673.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ 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.
