Principal Component Analysis
Performs principal components analysis (PCA) on torsion angle data.
## S3 method for class 'tor' pca(data, ...)
data |
numeric matrix of torsion angles with a row per structure. |
... |
additional arguments passed to the method |
Returns a list with the following components:
L |
eigenvalues. |
U |
eigenvectors (i.e. the variable loadings). |
z.u |
scores of the supplied |
sdev |
the standard deviations of the pcs. |
mean |
the means that were subtracted. |
Barry Grant and Karim ElSawy
Grant, B.J. et al. (2006) Bioinformatics 22, 2695–2696.
##-- 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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.