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

First axis of Constrained Correspondence Analysis using weighted averaging.

1 | ```
CCAwa1(Y, X)
``` |

`Y` |
Data matrix. |

`X` |
Model matrix of constraints. |

This is a poor algorithm to perform CCA, and is included only to show how simple it is. Constrained Correspondence Analysis was originally introduced in ecology as a result of this algorithm (ter Braak 1986). Palmer (1993) gives a very lucid explanation of the algorithm and its two kind of row scores, known as Weighted Averages (WA) scores and Linear Combination (LC) scores. The function only finds one axis. It would be possible to find later axes by orthogonalizing against previous axes, but if several axes are needed, it is better to use other algorithms from the beginning.

`eig` |
Eigenvalue of the first axis. |

`w` |
WA scores for rows of the first axis. |

`u` |
LC scores for rows of the first axis. |

`v` |
Column scores of the first axis. |

Jari Oksanen.

Palmer, M. W. (1993) Putting things in even better order: The
advantages of canonical correspondence analysis. *Ecology*
74, 2215–2230.

ter Braak, C. J. F. (1986) Canonical correspondence analysis: a
new eigenvector technique of multivariate direct gradient analysis.
*Ecology* 67, 1167–1179.

`CA`

and `CAeig`

are better algorihtms for
CA. `PCApot1`

is a similar poor algorithm for PCA.

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