Performs principal components analysis (PCA) on biomolecular structure data.

pca(...)

## Arguments

... |
arguments passed to the methods `pca.xyz` ,
`pca.pdbs` , etc. Typically this includes either a numeric
matrix of Cartesian coordinates with a row per structure/frame (function
`pca.xyz()` ), or an object of class `pdbs` as obtained from
function `pdbaln` or `read.fasta.pdb` (function
`pca.pdbs()` ). |

## Details

Principal component analysis can be performed on any structure dataset of equal or unequal sequence composition to capture and characterize inter-conformer relationships.

This generic `pca`

function calls the corresponding methods function for actual calculation, which is determined by the class of the input argument `x`

. Use
`methods("pca")`

to list all the current methods for `pca`

generic. These will include:

`pca.xyz`

, which will be used when `x`

is a numeric matrix
containing Cartesian coordinates (e.g. trajectory data).

`pca.pdbs`

, which will perform PCA on the
Cartesian coordinates of a input `pdbs`

object (as obtained from
the ‘read.fasta.pdb’ or ‘pdbaln’ functions).

Currently, function `pca.tor`

should be called explicitly as there
are currently no defined ‘tor’ object classes.

See the documentation and examples for each individual function for
more details and worked examples.

## References

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

## Author

Barry Grant, Lars Skjaerven

## See also