And i always forget the question.. I haven't understood biplots a 100%, but from what i gleaned this scaling is done so it looks better/is easier to read, while the scaling retains certain properties of the biplot (something about projecting).
If you want to use the data for anything else, i wouldn't use that scaling, just use what the prcomp() or princomp() function returns to you. Am 07.05.2012 um 16:11 schrieb Jessica Streicher: > Biplot, depending on what parameters you give it, scales the data in a > certain way. > > See > http://stat.ethz.ch/R-manual/R-patched/library/stats/html/biplot.princomp.html > > scale > The variables are scaled by lambda ^ scale and the observations are scaled by > lambda ^ (1-scale) where lambda are the singular values as computed by > princomp. Normally 0 <= scale <= 1, and a warning will be issued if the > specified scale is outside this range. > > > > Am 07.05.2012 um 16:01 schrieb Christian Cole: > >> Hi Jessica, >> >> Yes, that does help. It confirms my digging around in the prcomp object. >> >> I was plotting $x, but wasn't sure whether this was appropriate. Mainly >> because the data ranges are different in $x than when plotted by biplot() >> - as I mentioned my reply to Bryan. Do you know if this difference is data >> range matters? >> Many thanks, >> >> Chris >> >> >> >> On 07/05/2012 14:24, "Jessica Streicher" <j.streic...@micromata.de> wrote: >> >>> That depends on what you want to plot there. Basically, you could just >>> use plot() with pcaResult$x. You might need to define which PCs you want >>> to plot there though. >>> >>> pcaResult<-prcomp(iris[,1:4]) >>> plot(pcaResult$x) # gives the first 2 PCs >>> plot(pcaResult$x[,2:3]) #gives the second vs the 3rd PC >>> >>> or if you want to see more you can use pairs() >>> >>> pairs(pcaResult$x) >>> >>> if you want things colored, theres the col parameter that works for both >>> functions: >>> >>> pairs(pcaResult$x,col=iris[,5]) >>> >>> Does this help? >>> >>> Am 07.05.2012 um 12:22 schrieb Christian Cole: >>> >>>> I have a decent sized matrix (36 x 11,000) that I have preformed a PCA >>>> on >>>> with prcomp(), but due to the large number of variables I can't plot the >>>> result with biplot(). How else can I plot the PCA output? >>>> >>>> I tried posting this before, but got no responses so I'm trying again. >>>> Surely this is a common problem, but I can't find a solution with >>>> google? >>>> >>>> >>>> The University of Dundee is a registered Scottish Charity, No: SC015096 >>>> >>>> ______________________________________________ >>>> 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. >>> >>> ______________________________________________ >>> 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. >>> >> >> >> The University of Dundee is a registered Scottish Charity, No: SC015096 >> > > > [[alternative HTML version deleted]] > > ______________________________________________ > 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. ______________________________________________ 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.