Correspondence Analysis (CA) via ExPosition.

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`DATA` |
original data to perform a CA on. |

`DESIGN` |
a design matrix to indicate if rows belong to groups. |

`make_design_nominal` |
a boolean. If TRUE (default), DESIGN is a vector that indicates groups (and will be dummy-coded). If FALSE, DESIGN is a dummy-coded matrix. |

`masses` |
a diagonal matrix or column-vector of masses for the row items. |

`weights` |
a diagonal matrix or column-vector of weights for the column it |

`hellinger` |
a boolean. If FALSE (default), Chi-square distance will be used. If TRUE, Hellinger distance will be used. |

`symmetric` |
a boolean. If TRUE (default) symmetric factor scores for rows and columns are computed. If FALSE, the simplex (column-based) will be returned. |

`graphs` |
a boolean. If TRUE (default), graphs and plots are provided (via |

`k` |
number of components to return. |

`epCA`

performs correspondence analysis. Essentially, a PCA for qualitative data (frequencies, proportions). If you decide to use Hellinger distance, it is best to set `symmetric`

to FALSE.

See `coreCA`

for details on what is returned.

Derek Beaton

Abdi, H., and Williams, L.J. (2010). Principal component analysis. *Wiley Interdisciplinary Reviews: Computational Statistics*, 2, 433-459.

Abdi, H., and Williams, L.J. (2010). Correspondence analysis. In N.J. Salkind, D.M., Dougherty, & B. Frey (Eds.): *Encyclopedia of Research Design*. Thousand Oaks (CA): Sage. pp. 267-278.

Abdi, H. (2007). Singular Value Decomposition (SVD) and Generalized Singular Value Decomposition (GSVD). In N.J. Salkind (Ed.): *Encyclopedia of Measurement and Statistics*.Thousand Oaks (CA): Sage. pp. 907-912.

Greenacre, M. J. (2007). Correspondence Analysis in Practice. *Chapman and Hall*.

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