[R] Limited number of principal components in PCA

[William Armstrong]

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


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