print.CAvariants {CAvariants} | R Documentation |

## Main printing function for numerical summaries

### Description

This function prints the numerical output for any of the six variants of correspondence analysis called by `catype`

.

The input parameter is the name of the output of the main function `CAvariants`

.

### Usage

```
## S3 method for class 'CAvariants'
print(x, printdims = 2, ellcomp = TRUE, digits = 3,...)
```

### Arguments

`x` |
The name of the output object from the main function |

`printdims` |
The number of dimensions that are used for summarising the numerical output of the analysis. By default, |

`ellcomp` |
This parameter specifies whether the characteristics of the confidence ellipses (eccentricity, semi-axis, area, p-values)
are to be computed. By default, |

`digits` |
The number of decimal places used for displaying the numerical summaries of the analysis.
By default, |

`...` |
Further arguments passed to, or from, other functions. |

### Details

This function uses another function (called `printwithaxes`

) for specifying the number of
columns of a matrix to print.

### Value

The output returned depends on the type of correspondence analysis that is performed

`Xtable` |
The two-way contingency table. |

`Row weights: Imass` |
The row weight matrix. These weights depend on the type of analysis that is performed. |

`Column weights: Jmass` |
The column weight matrix. These weights are equal to the column marginal relative frequencies for all types of analysis performed. |

`Total inertia` |
The total inertia of the analysis performed. For example, for variants of non symmetrical correspondence analysis, the output produced includes the numerator of the Goodman-Kruskal tau index, its C-statistic and p-value. |

`Inertias` |
The inertia values, their percentage contribution to the total inertia and the cumulative percent inertias for the row and column variables. |

`Generalised correlation matrix` |
The matrix of generalised correlations when performing
an ordered correspondence analysis, |

`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 performing an ordered correspondence analysis. |

`Column principal polynomial coordinates` |
The column principal coordinates when performing a doubly ordered correspondence analysis. |

`Row standard polynomial coordinates` |
The row standard polynomial coordinates, when performing an ordered variant of correspondence analysis. |

`Column standard polynomial coordinates` |
The column standard polynomial coordinates, when performing an ordered variant of correspondence analysis. |

`Row distances from the origin of the plot` |
The squared Euclidean distance of the row categories from the origin of the plot. |

`Column distances from the origin of the plot` |
The squared Euclidean distance of the column categories from the origin of the plot. |

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

`Inner product` |
The inner product of the biplot coordinates for the two-dimensional plot. |

`eccentricity` |
Value of ellipse eccentricity, the distance between its center and either of its two foci, It can be thought of as a measure of how much the conic section deviates from being circular. |

`HL Axis 1` |
Value of ellipse semi-axis 1 for each row and column points. |

`HL Axis 2` |
Value of ellipse semi-axis 2 for each row and column points. |

`Area` |
Ellipse area for each row and column points. |

`pvalcol` |
P-value for each row and column points. |

### Author(s)

Rosaria Lombardo and Eric J. Beh

### References

Beh EJ and Lombardo R 2014 Correspondence Analysis: Theory, Practice and New Strategies. Wiley.

Lombardo R Beh EJ 2016 Variants of Simple Correspondence Analysis. The R Journal, 8 (2), 167–184.

### Examples

```
data(asbestos)
resasbestos <- CAvariants(asbestos, catype = "DOCA")
print(resasbestos)
```

*CAvariants*version 6.0 Index]