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 |

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.

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

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