acc: Auto Cross Covariance (ACC) for Generating Scales-Based...

In nanxstats/Rcpi: Molecular Informatics Toolkit for Compound-Protein Interaction in Drug Discovery

Auto Cross Covariance (ACC) for Generating Scales-Based Descriptors of the Same Length

Description

Calculates auto covariance and auto cross covariance for generating scale-based descriptors of the same length.

Usage

acc(mat, lag)

Arguments

mat	A p * n matrix. Each row represents one scale (total p scales), each column represents one amino acid position (total n amino acids).
lag	The lag parameter. Must be less than the amino acids.

Value

A length lag * p^2 named vector, the element names are constructed by: the scales index (crossed scales index) and lag index.

Note

To see more details about auto cross covariance, check the references.

References

Wold, S., Jonsson, J., Sjörström, M., Sandberg, M., & Rännar, S. (1993). DNA and peptide sequences and chemical processes multivariately modelled by principal component analysis and partial least-squares projections to latent structures. Analytica chimica acta, 277(2), 239–253.

Sjöström, M., Rännar, S., & Wieslander, Å. (1995). Polypeptide sequence property relationships in Escherichia coli based on auto cross covariances. Chemometrics and intelligent laboratory systems, 29(2), 295–305.

See Also

See extractPCMScales for generalized scales-based descriptors. For more details, see extractPCMDescScales and extractPCMPropScales.

Examples

p = 8    # p is the scales number
n = 200  # n is the amino acid number
lag = 7  # lag parameter
mat = matrix(rnorm(p * n), nrow = p, ncol = n)
acc(mat, lag)

nanxstats/Rcpi documentation built on Sept. 24, 2024, 9:36 a.m.