pca.tor {bio3d}R Documentation

Principal Component Analysis

Description

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

Usage

pca.tor(data, subset = rep(TRUE, nrow(as.matrix(data))))

Arguments

data numeric matrix of torsion angles with a row per structure.
subset an optional vector of numeric indices that selects a subset of rows (e.g. experimental structures vs molecular dynamics trajectory structures) from the full data matrix. Note: the full data is projected onto this subspace.

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 
data(kinesin)
attach(kinesin)

gaps <- gap.inspect(pdbs)
tgap.xyz <- atom2xyz(gaps$t.inds)
fgap.xyz <- atom2xyz(gaps$f.inds)
tor <- t(apply( pdbs$xyz[,fgap.xyz], 1, torsion.xyz, atm.inc=1))
pc.tor <- pca.tor(tor[,-c(1,219,220)])
#plot(pc.tor)
plot.pca.loadings(pc.tor)

## 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 1.0-6 Index]