Programmer Guide/Command Reference/EVAL/svd

Compute the variance, covariance or covariance-matrix.

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Usage 1

svd(0, A, trn)

A

the NxM matrix to be transformed

trn

transformation to be applied to A

trn	transformed matrix T (NxM)	description	trn(T)*T (MxM)
0	T[i,j] = A[i,j]	no transformation	"Streumatrix"
1	T[i,j] = A[i,j]-avr(A)	subtract matrix mean
2	T[i,j] = A[i,j]-avr(A[*,j])	subtract column mean (center columns)	covariance matrix
3	T[i,j] = (A[i,j]-avr(A[*,j]))/dev(A[*,j])	subtract column mean, devide by column deviation (center and standardize columns)	correlation matrix
4	T[i,j] = A[i,j]-(avr(A[i,*])+avr(A[*,j]))+avr(A)	subtract row and column mean, add matrix mean (center rows and columns)

Result 1

The transformed matrix T.

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Usage 2

svd(1, A, trn)

Result 2

Returns the matrix C=trn(T)*T. C is a square matrix with M rows and columns (MxM).

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Usage 3

svd(0, A, trn, U, S, V)

U

contains on return the NxM matrix U

S

contains on return the vector S with M elements; Note: the returned vector contains the diagonal elements of the matrix &Sigma, which are called the singular values.

V

contains on return the MxM matrix V

Result

Computes the SVD of the transformed input matrix T.

T = U * S * trn

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See also

var, corr, dist, haclust, modclust

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