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. ______________________________________________ [email protected] 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.
