extractPCMFAScales {Rcpi} | R Documentation

## Generalized Scales-Based Descriptors derived by Factor Analysis

### Description

Generalized Scales-Based Descriptors derived by Factor Analysis

### Usage

    extractPCMFAScales(x, propmat, factors, scores = "regression", lag,
      scale = TRUE, silent = TRUE)

### Arguments

`x` | A character vector, as the input protein sequence.
`propmat` | A matrix containing the properties for the amino acids. Each row represent one amino acid type, each column represents one property. Note that the one-letter row names must be provided for we need them to seek the properties for each AA type.
`factors` | Integer. The number of factors to be fitted. Must be no greater than the number of AA properties provided.
`scores` | Type of scores to produce. The default is `"regression"`, which gives Thompson's scores, `"Bartlett"` given Bartlett's weighted least-squares scores.
`lag` | The lag parameter. Must be less than the amino acids number in the protein sequence.
`scale` | Logical. Should we auto-scale the property matrix (`propmat`) before doing Factor Analysis? Default is `TRUE`.
`silent` | Logical. Whether we print the SS loadings, proportion of variance and the cumulative proportion of the selected factors or not. Default is `TRUE`.

### Details

This function calculates the generalized scales-based descriptors derived by Factor Analysis (FA). Users could provide customized amino acid property matrices.

### Value

A length `lag * p^2` named vector, `p` is the number of scales (factors) selected.

### Author(s)

Nan Xiao <https://nanx.me>

### References

Atchley, W. R., Zhao, J., Fernandes, A. D., & Druke, T. (2005). Solving the protein sequence metric problem. Proceedings of the National Academy of Sciences of the United States of America, 102(18), 6395-6400.

### Examples

    x = readFASTA(system.file('protseq/P00750.fasta', package = 'Rcpi'))[[1]]
    data(AATopo)
    tprops = AATopo[, c(37:41, 43:47)]  # select a set of topological descriptors
    fa = extractPCMFAScales(x, propmat = tprops, factors = 5, lag = 7, silent = FALSE)

[Package Rcpi version 1.30.0 Index]