`pca.pdbs.Rd`

Performs principal components analysis (PCA) on an ensemble of PDB structures.

# S3 method for pdbs pca(pdbs, core.find = FALSE, fit = FALSE, ...)

pdbs | an object of class |
---|---|

core.find | logical, if TRUE core.find() function will be called to find core positions and coordinates of PDB structures will be fitted based on cores. |

fit | logical, if TRUE coordinates of PDB structures will be fitted based on all CA atoms. |

... | additional arguments passed to the method |

The function `pca.pdbs`

is a wrapper for the function
`pca.xyz`

, wherein more details of the PCA procedure
are documented.

Returns a list with the following components:

eigenvalues.

eigenvectors (i.e. the variable loadings).

scores of the supplied `data`

on the pcs.

the standard deviations of the pcs.

the means that were subtracted.

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

Barry Grant, Lars Skjaerven and Xin-Qiu Yao

attach(transducin) #-- Do PCA ignoring gap containing positions pc.xray <- pca(pdbs) # Plot results (conformer plots & scree plot) plot(pc.xray, col=annotation[, "color"])detach(transducin)