Principal Component Analysis

Usage

"pca"(data, ...)

Arguments

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

Description

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

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.

References

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

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)






##-- PCA on torsion data from an MD trajectory
trj <- read.dcd( system.file("examples/hivp.dcd", package="bio3d") )



 NATOM = 198 
 NFRAME= 117 
 ISTART= 0 
 last  = 117 
 nstep = 117 
 nfile = 117 
 NSAVE = 1 
 NDEGF = 0 
 version 24 
Reading (x100)
  |======================================================================| 100%



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)

See also

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

Author

Barry Grant and Karim ElSawy