R: Principal Component Analysis

pca.tor {bio3d} | R Documentation

## Principal Component Analysis

### Description

Performs principal components analysis (PCA) on torsion angle `data`.

### Usage

    ## S3 method for class 'tor'
    pca(data, ...)

### Arguments

`data` | numeric matrix of torsion angles with a row per structure.
`...` | additional arguments passed to the method `pca.xyz`.

### Value

Returns a list with the following components:

`L` | eigenvalues.
`U` | eigenvectors (i.e. the variable loadings).
`z.u` | scores of the supplied `data` on the pcs.
`sdev` | the standard deviations of the pcs.
`mean` | the means that were subtracted.

### Author(s)

Barry Grant and Karim ElSawy

### References

Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.

### See Also

`torsion.xyz`, `plot.pca`, `plot.pca.loadings`, `pca.xyz`

### Examples

    ##-- PCA on torsion data for multiple PDBs
    attach(kinesin)

    gaps.pos <- gap.inspect(pdbs$xyz)
    tor <- t(apply( pdbs$xyz[, gaps.pos$f.inds], 1, torsion.xyz, atm.inc=1))
    pc.tor <- pca.tor(tor[,-c(1,233,234,235)])
    #plot(pc.tor)
    plot.pca.loadings(pc.tor)

    detach(kinesin)

    ## Not run:
    ##-- PCA on torsion data from an MD trajectory
    trj <- read.dcd( system.file("examples/hivp.dcd", package="bio3d") )
    tor <- t(apply(trj, 1, torsion.xyz, atm.inc=1))
    gaps <- gap.inspect(tor)
    pc.tor <- pca.tor(tor[,gaps$f.inds])
    plot.pca.loadings(pc.tor)

    ## End(Not run)

[Package bio3d version 2.3-4 Index]