bio3d (version 2.4-4)

rmsip: Root Mean Square Inner Product

Description

Calculate the RMSIP between two mode subspaces.

Usage

rmsip(...)
# S3 method for enma
rmsip(enma, ncore=NULL, subset=10, ...)
# S3 method for default
rmsip(modes.a, modes.b, subset=10,
                        row.name="a", col.name="b", ...)

Value

Returns an rmsip object with the following components:

overlap

a numeric matrix containing pairwise (squared) dot products between the modes.

rmsip

a numeric RMSIP value.

For function rmsip.enma a numeric matrix containing all pairwise RMSIP values of the modes stored in the enma object.

Arguments

enma

an object of class "enma" obtained from function nma.pdbs.

ncore

number of CPU cores used to do the calculation. ncore>1 requires package ‘parallel’ installed.

subset

the number of modes to consider.

modes.a

an object of class "pca" or "nma" as obtained from functions pca.xyz or nma.

modes.b

an object of class "pca" or "nma" as obtained from functions pca.xyz or nma.

row.name

prefix name for the rows.

col.name

prefix name for the columns.

...

arguments passed to associated functions.

Author

Lars Skjaerven

Details

RMSIP is a measure for the similarity between two set of modes obtained from principal component or normal modes analysis.

References

Skjaerven, L. et al. (2014) BMC Bioinformatics 15, 399. Grant, B.J. et al. (2006) Bioinformatics 22, 2695--2696. Amadei, A. et al. (1999) Proteins 36, 19--424.

See Also

pca, nma, overlap.

Other similarity measures: sip, covsoverlap, bhattacharyya.

Examples

if (FALSE) {
# Load data for HIV example
trj <- read.dcd(system.file("examples/hivp.dcd", package="bio3d"))
pdb <- read.pdb(system.file("examples/hivp.pdb", package="bio3d"))

# Do PCA on simulation data
xyz.md <- fit.xyz(pdb$xyz, trj, fixed.inds=1:ncol(trj))
pc.sim <- pca.xyz(xyz.md)

# NMA 
modes <- nma(pdb)

# Calculate the RMSIP between the MD-PCs and the NMA-MODEs
r <- rmsip(modes, pc.sim, subset=10, row.name="NMA", col.name="PCA")

# Plot pairwise overlap values
plot(r, xlab="NMA", ylab="PCA")
}