This function prints summary results of any of the specified six variants of correspondence analysis.
The input parameter is the name of the output of the main function `CAvariants`

.

1 2 |

`object` |
The output of the main function |

`printdims` |
The dimension number in printing. By default, |

`digits` |
The minimum number of significant digits to be printed in values. By default, |

`...` |
Further arguments passed to or from other methods. |

The value of output returned depends on the kind of correspondence analysis performed.

`Inertias` |
The inertia value and its percentage, in the row and column space for each principal or polynomial axis when |

`Generalized correlation matrix` |
The generalized correlation matrix when |

`Row principal coordinates` |
The row principal coordinates when |

`Column principal coordinates` |
The column principal coordinates when |

`Row standard coordinates ` |
The row standard coordinates when |

`Column standard coordinates` |
The column standard coordinates when |

`Row principal polynomial coordinates` |
The row principal polynomial coordinates
when |

`Column principal polynomial coordinates` |
The column principal coordinates when |

`Row standard polynomial coordinates ` |
The row standard polynomial coordinates when |

`Column standard polynomial coordinates` |
The column standard polynomial coordinates when |

`Total inertia` |
The total inertia. For example in case of non symmetrical correspondence analysis, it gives the numerator of the Goodman-Kruskal tau index, the associated C-statistic and its p-value. |

`Polynomial components` |
The polynomial components of inertia and their p-values. The inertia of the column space is partitioned in terms
of polynomial components when |

`Inner product` |
The inner product of coordinates. |

Rosaria Lombardo and Eric J. Beh

Beh EJ and Lombardo R 2014 Correspondence Analysis: Theory, Practice and New Strategies. John Wiley & Sons. Lombardo R Beh EJ Variants of Simple Correspondence Analysis. The R Journal (accepted).

1 2 3 4 5 | ```
asbestos<-matrix(c(310, 36, 0, 0, 212, 158, 9, 0, 21, 35, 17, 4, 25,102,
49, 18, 7, 35, 51, 28), 4, 5, dimnames = list(c("none","grade1", "grade2", "grade3"),
c("0-9", "10-19", "20-29", "30-39", "40")))
risasbestos<-CAvariants(asbestos, catype = "DOCA", firstaxis = 1, lastaxis = 2)
summary(risasbestos)
``` |

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