Description Usage Arguments Details Value Note Author(s) References See Also Examples

Principal components analysis is a eigenanalysis of a correlation or covariance matrix used to project a high-dimensional system to fewer dimensions.

1 2 3 4 5 6 7 |

`mat` |
a matrix or data.frame of interest, samples as rows, attributes as columns |

`cor` |
logical: whether to use a correlation matrix (if TRUE), or covariance matrix (if FALSE) |

`dim` |
the number of dimensions to return |

`object` |
an object of class ‘pca’ |

`x` |
an object of class ‘pca’ |

`labels` |
an (optional) vector of labels to identify points |

`digits` |
number of digits to report |

`cutoff` |
threshold to suppress printing small values |

`...` |
arguments to pass to function summary |

PCA is a common multivariate technique. The version here is simply
a wrapper for the `prcomp`

function to make its use and
plotting consistent with the other LabDSV functions.

an object of class "pca", a list with components:

`scores` |
a matrix of the coordinates of the samples in the reduced space |

`loadings` |
a matrix of the contributions of the variables to the axes of the reduced space. |

`sdev` |
a vector of standard deviations for each dimension |

The current version of pca is based on the `prcomp`

function, as opposed to the `princomp`

function. Nonetheless,
it maintains the more conventional labels "scores" and "loadings",
rather than x and rotation. prcomp is based on a
singular value decomposition algorithm, as has worked better in our
experience. In the rare cases where it fails, you may want to try
`princomp`

.

David W. Roberts [email protected]

http://ecology.msu.montana.edu/labdsv/R/labs/lab7/lab7.html

`princomp`

, `prcomp`

,
`pco`

, `nmds`

,
`fso`

, `cca`

1 2 3 4 5 6 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.