Limited number of principal components in PCA
Providing the data will help, but the first thing I noted is that you have more columns (variables) than rows (cases). PCA will return a maximum of (the number of columns) or (the number of rows-1) whichever is less. With 84 columns and 66 rows means you can get no more than 65 components. If the variables are highly correlated, you will get fewer components and that probably explains the reduction to 54. I would guess the variables are highly correlated and the first eigenvalue is very large. ---------------------------------------------- David L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352 -----Original Message----- From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org] On Behalf Of Joshua Wiley Sent: Friday, July 29, 2011 10:20 PM To: William Armstrong Cc: r-help at r-project.org Subject: Re: [R] Limited number of principal components in PCA Hi Billy, Can you provide your data? You could attach it as a text file or provide it by pasting the output of: dput(Q) into an email. It would help if we could reproduce what you are doing. You might also consider a list or forum that is more statistics oriented than Rhelp, as your questions are more related to the statistics than the software itself (but still, if you give us data, you will probably get farther). Cheers, Josh On Fri, Jul 29, 2011 at 11:33 AM, William Armstrong
<William.Armstrong at noaa.gov> wrote:
Hi all, I am attempting to run PCA on a matrix (nrow=66, ncol=84) using 'prcomp' (stats package). My data (referred to as 'Q' in the code below) are separate river streamflow gaging stations (columns) and peak instantaneous discharge (rows). I am attempting to use PCA to identify regions of that vary together. I am entering the following command: test_pca_Q<-prcomp(~.,data=Q,scale.=TRUE,retx=FALSE,na.action=na.omit) It is outputting 54 'standard deviation' numbers (which are the sqrt(eigenvalues) in respect to a certain PC, am I correct?), and 54 'rotation' numbers, which are the variable loadings with respect to a given PC. I have two questions: 1.) Why is it only outputting 54 PCs and standard deviations? If I have 84 variables isn't the maximum number of PCs I can create 84 as well? 2.) Can I now use the 'rotation' values to find clusters of gages that I acting together, or is there another step I must take? Thank you very much for your insight. Billy -- View this message in context: http://r.789695.n4.nabble.com/Limited-number-of-principal-components-in-PCA-tp3704956p3704956.html Sent from the R help mailing list archive at Nabble.com.
______________________________________________ R-help at 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.
Joshua Wiley Ph.D. Student, Health Psychology University of California, Los Angeles https://joshuawiley.com/ ______________________________________________ R-help at 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.