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

Share:

Description

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

Usage

1
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.

Details

This function calculates the auto covariance and auto cross covariance for generating scale-based descriptors of the same length.

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 know more details about auto cross covariance, see the references.

Author(s)

Nan Xiao <http://nanx.me>

References

Wold, S., Jonsson, J., Sj\"orstr\"om, M., Sandberg, M., & R\"annar, 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.

Sj\"ostr\"om, M., R\"annar, S., & Wieslander, A. (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 extractScales for scales-based descriptors. For more details, see extractDescScales and extractProtFP.

Examples

1
2
3
4
5
p = 8    # p is the scales number
n = 200  # n is the amino acid number
lag = 7  # the lag paramter
mat = matrix(rnorm(p * n), nrow = p, ncol = n)
acc(mat, lag)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.