SVD on a matix
the variance is the eigen values of the correlation matrix of yoru matrix X.cor <- cor(X) X.e <- eigen(X.cor) X.e$values# Eigenvalues of cor(X) = variances you're asking about
kayj wrote:
Hi All, I performed an svd on a matrix X and saved the first three column of the left singular matrix U. ( I assume that they correspond to the projection of the matrix on the first three eigen vectors that corresponds to the first three largest eigenvalues). I would like to know how much variance is explained by the first eigenvectors? how can I find that. Thanks for your help
----- Yasir H. Kaheil Catchment Research Facility The University of Western Ontario
View this message in context: http://www.nabble.com/SVD-on-a-matix-tp17441337p17455129.html Sent from the R help mailing list archive at Nabble.com.