build.invhess: Compute the Covariance Matrix / Inverse Hessian Matrix
In BioPhysConnectoR: BioPhysConnectoR

Description Usage Arguments Details Value Author(s) References See Also

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.

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build.invhess(svd_obj, singularity = 6)

get.cov(cm, im, deltas)

`svd_obj`	svd object computed by `get.svd()` containing the eigenvector matrices, the eigenvalues and the index vector
`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 `build.contacts()`

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.

build.hess, get.svd

BioPhysConnectoR documentation built on May 30, 2017, 6:46 a.m.