Description Usage Arguments Details Value Author(s) References See Also
View source: R/build.invhess.r
Computes the inverse Hessian matrix. The covariance matrix is computed as a pseudo-inverse derived from the eigenvalues and eigenvectors by a singular value decomposition (get.svd()
) of the Hessian matrix. Otherwise, if neither the Hessian matrix nor the eigenvalues need to be stored, the inverse Hessian can directly be computed from the contact, interaction and distance matrices.
1 2 3 | build.invhess(svd_obj, singularity = 6)
get.cov(cm, im, deltas)
|
svd_obj |
svd object computed by |
singularity |
number of eigenvalues equal/close to zero due to symmetries |
cm |
contact map for a protein |
im |
matrix of interaction strengths between the amino acids of the protein |
deltas |
difference matrices (x, y, z, squared) for all pairs of C_{α} atoms as derived from |
The calculation of the matrix omits by default the first six eigenvalues, because of translational and rotational symmetry in the model. The computation depends on the eigenvalues and -vectors. The number of eigenvalues to omit in the calculation can be specified by singularity
. If the number of eigenvalues equalling zero is unknown and should be determined, the parameter singularity
can be set to NULL
. The threshold for zero is set to 10^{-8}.
Return value is the covariance matrix (also called inverse Hessian matrix).
Franziska Hoffgaard
Hamacher (2006) Journal of Chemical Theory and Computation 2, 873–878.
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