This function is an interface for the user to a general SVD or related decomposition. It provides direct access to svd and eigen. Future decompositions will be available.
pickSVD(datain, is.mds = FALSE, decomp.approach = "svd", k = 0)A list with the following items:
Left singular vectors (rows)
Right singular vectors (columns)
Singular values
Explained variance per component
a data matrix to decompose.
a boolean. TRUE for a MDS decomposition.
a string. 'svd' for singular value decomposition, 'eigen' for an eigendecomposition. All approaches provide identical output. Some approaches are (in some cases) faster than others.
numeric. The number of components to return.
Derek Beaton