Hi Bryan,
On 07/05/2012 15:33, "Bryan Hanson" <han...@depauw.edu> wrote: >I don't know the answer, Jessica gave some insight. > >I avoid the biplot at all costs, because IMHO it violates one of the >tenets of good graphic design: It has two entirely different scales on >axes. These are maximally confusing to the end-user. So I never use it. I couldn't agree more :) >If it is gene expression data, have you looked in Bioconductor for >something that will help you? Maybe runPCA in package EMA? I may do, but you've answered my question and I've got a PCA plot that works. Many thanks, Chris >Bryan > >On May 7, 2012, at 9:57 AM, Christian Cole wrote: > >> Hi Bryan, >> >> >> Many thanks for the replies. >> >> The data is gene expression data for 36 samples over 11k genes. >> >> I see that I can plot PC1 vs PC2 by using $x, but compared to biplot() I >> can see that the range of values are different. For example, if I use >> plot() the PC1 scale ranges from -150 to 150 whereas in biplot() it >>scales >> from -0.4 to 0.4. Do you know what scaling biplot() uses? Does it even >> matter? >> Cheers, >> >> Chris >> >> >> On 07/05/2012 14:36, "Bryan Hanson" <han...@depauw.edu> wrote: >> >>> Christian, is that 36 samples x 11K variables? Sounds like it. Is >>>this >>> spectroscopic data? >>> >>> In any case, the scores are in the list element $x as follows: >>> >>> answer <- prcomp(your matrix) >>> >>> answer$x contains the scores, so if you want to plot the 1st 2 pcs, you >>> could do >>> >>> plot(answer$x[,1], answer$x[,2]) >>> >>> Because the columns of answer$x contain the scores of the PCs in order. >>> >>> [I see Jessica just answered...] >>> >>> If you want the loading plot, it's going to be interesting with all >>>those >>> variables, but this will do it: >>> >>> plot(1:11000, answer$rotation[,1], type = "l") # for the loadings of >>>the >>> 1st PC >>> >>> Depending upon what kind of data this is, the 1:11000 could be replaced >>> by something more sensible. If it is spectroscopic data, then replace >>>it >>> with your frequency values. >>> >>> By the way, plot(answer) will give you the scree plot to determine how >>> many PCs are worthy. >>> >>> Good luck. Bryan >>> >>> *********** >>> Bryan Hanson >>> Professor of Chemistry & Biochemistry >>> DePauw University >>> >>> On May 7, 2012, at 6:22 AM, Christian Cole wrote: >>> >>>> 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. >>> >>> >> >> >> The University of Dundee is a registered Scottish Charity, No: SC015096 >> > > 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.