PCA Singular Value Decomposition (SVD) approach is faster and is recommended (set 'use.svd = TRUE')
I want to analysis pca
pc <- pca.xyz(xyz[,sel.ins$xyz]) I am getting this error
NOTE: In input xyz (MxN), N > 3000 and M < N
Singular Value Decomposition (SVD) approach is faster
and is recommended (set 'use.svd = TRUE') I dont why I am getting this error
Comments (7)
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reporter ok.what is the suggestion to me ? how to use this suggestion as a code ?
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reporter pc <- pca.xyz(xyz[,sel.ins$xyz]) after this command,I am getting this warning
Error: cannot allocate vector of size 9.1 Gb
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Try
pc <- pca.xyz(xyz[, sel.ins$xyz], use.svd=TRUE)
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reporter I got this warning
pc <- pca.xyz(xyz[, sel.ins$xyz], use.svd=TRUE)
Warning message:
In pca.xyz(xyz[, sel.ins$xyz], use.svd = TRUE) : In input xyz (MxN), M < N:
Only 1015 eigenvalues and eigenvectors are returned! -
The warning is fine. Your number of data points is less than the dimension of the degree of freedom. In PCA, you always get the number of eigenvalues/eigenvectors equal to the smaller number of the two.
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Hi,
This is not an error, just a suggestion. You should get the result anyway, but if it takes too long, you may think of following the suggestion.