Limited number of principal components in PCA
At 19:07 04/08/2011, William Armstrong wrote:
David and Josh,
Thank you for the suggestions. I have attached a file ('q_values.txt') that
contains the values of the 'Q' variable.
David -- I am attempting an 'S' mode PCA, where the columns are actually the
cases (different stream gaging stations) and the rows are the variables (the
maximum flow at each station for a given year). I think the format you are
referring to is 'R' mode, but I was under the impression that R (the
program, not the PCA mode) could handle the analyses in either format. Am I
mistaken?
My first eigenvalue is:
unrotated_pca_q$sdev[1]^2
[1] 17.77812 Does that value seem large enough to explain the reduction in principal components from 65 to 54?
try doing table(complete.cases(q_values)) or whatever you are calling q_values.txt Does that help? Moral: when R does not do what you thought you told it to do it may still have done what you told it to do.
Also, the loadings on the first PC are not particularly high:
> max(abs(unrotated_pca_q$rotation[1:84]))
[1] 0.1794776 Does that suggest that maybe the data are not very highly correlated? Thank you both very much for your help. Billy http://r.789695.n4.nabble.com/file/n3719440/q_values.txt q_values.txt -- View this message in context: http://r.789695.n4.nabble.com/Limited-number-of-principal-components-in-PCA-tp3704956p3719440.html Sent from the R help mailing list archive at Nabble.com.
Michael Dewey info at aghmed.fsnet.co.uk http://www.aghmed.fsnet.co.uk/home.html