This program performs a global principal component analysis (PCA). It gives the eigenvalues of the covariance matrix and depending on the -W flag eigenvectors, projections... of the input time series.

Everything not being a valid option will be interpreted as a potential datafile name. Given no datafile at all, means read stdin. Also - means stdin

Possible options are:

Option | Description | Default |
---|---|---|

-l# | number of data to be used | whole file |

-x# | number of lines to be ignored | 0 |

-c# | columns to be read | 1 |

-m#,# | no. of input columns,embedding dimension | 2,1 |

-d# | delay | 1 |

-W# | 0: Just write the eigenvalues1: Write the eigenvectors. The columns of the output matrix are the
eigenvectors.2: Transformation of the time series onto the eigenvector basis. The
number of components printed is determined by the -q flag.3: Project the time series onto the first -q eigenvectors (global noise reduction).
| 0 |

-q# | depending on -W:-W2: Number of components written-W3: Projection dimension
| full dimension |

-o[#] | output file name | -o without argument means 'datafile'.pca (or stdin.pca if data were read from stdin) without -o means stdout |

-V# | verbosity level 0: only panic messages 1: add input/output messages | 1 |

-h | show these options | none |

View the C-sources.

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