
56 Appendix A2 Exported Function Summary
Return value
See IEigen.
Description
IEigenSym computes the eigenvalues of a symmetric matrix, and optionally the
(normalized) eigenvectors.
IGenEigVecSym
int IGenEigVecSym(MATRIX mA, MATRIX mB, VECTOR vEval,
VECTOR vSubd, int cA);
mA[cA][cA] in: symmetric matrix.
out: the rows contain the normalized eigenvectors
(sorted according to eigenvals, largest first)
mB[cA][cA] in: symmetric pd. matrix.
out: work
vEval[cA] out: ordered eigenvalues (smallest first)
vSubd[cA] out: index of ordered eigenvalues
cA in: dimension of matrix;
Return value
0,1,2: see IEigen; -1: Choleski decomposition failed.
Description
Solves the general eigenproblem Ax = λBx,whereA and B are symmetric, B
also positive definite.
IGetAcf
int IGetAcf(VECTOR vX, int cT, int cLag, VECTOR vAcf, bool bCov);
vX[cT] in: variable of which to compute correlogram
cT in: number of observations
cLag in: required no of correlation coeffs
vAcf[cLag] out: correlation coeffs 1. ..cLag (0. if failed); unlike
acf(), the autocorrelation at lag 0 (which is 1)
is not included.
bCov in: FALSE: autocorrelation, else autocovariances
Return value
IGetAcf uses the full sample means (the standard textbook correlogram).
IGetAcf skips over missing values, in contrast to MatAcf. Also see under acf
and DrawCorrelogram.
IInvert, IInvDet
int IInvert(MATRIX mA, int cA);
int IInvDet(MATRIX mA, int cA, double *pdLogDet, int *piSignDet);
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