pca.tor: Principal Component Analysis

In bio3d: Biological Structure Analysis

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)

bio3d documentation built on Oct. 30, 2024, 1:08 a.m.