Description Usage Arguments Details Value Author(s) References Examples

View source: R/418-extractPCMFAScales.R

Generalized Scales-Based Descriptors derived by Factor Analysis

1 2 | ```
extractPCMFAScales(x, propmat, factors, scores = "regression", lag,
scale = TRUE, silent = TRUE)
``` |

`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 |

`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
( |

`silent` |
Logical. Whether we print the SS loadings,
proportion of variance and the cumulative proportion of
the selected factors or not.
Default is |

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

A length `lag * p^2`

named vector,
`p`

is the number of scales (factors) selected.

Nan Xiao <https://nanx.me>

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.

1 2 3 4 | ```
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)
``` |

Rcpi documentation built on May 20, 2017, 10:25 p.m.

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