ca {ca} | R Documentation |

Computation of simple correspondence analysis.

```
ca(obj, ...)
## S3 method for class 'matrix'
ca(obj, nd = NA, suprow = NA, supcol = NA,
subsetrow = NA, subsetcol = NA, ...)
## S3 method for class 'data.frame'
ca(obj, ...)
## S3 method for class 'table'
ca(obj, ...)
## S3 method for class 'xtabs'
ca(obj, ...)
## S3 method for class 'formula'
ca(formula, data, ...)
```

`obj` , `formula` |
The function is generic, accepting various forms of the principal argument
for specifying a two-way frequency table. Currently accepted forms are matrices, data frames
(coerced to frequency tables), objects of class |

`nd` |
Number of dimensions to be included in the output; if NA the maximum possible dimensions are included. |

`suprow` |
Indices of supplementary rows. |

`supcol` |
Indices of supplementary columns. |

`subsetrow` |
Row indices of subset. |

`subsetcol` |
Column indices of subset. |

`data` |
A data frame against which to preferentially resolve variables in the |

`...` |
Other arguments passed to the |

The function `ca`

computes a simple correspondence analysis based on the
singular value decomposition.

The options `suprow`

and `supcol`

allow supplementary (passive) rows and columns to be specified.
Using the options `subsetrow`

and/or `subsetcol`

result in a subset CA being performed.

`sv` |
Singular values |

`nd` |
Dimenson of the solution |

`rownames` |
Row names |

`rowmass` |
Row masses |

`rowdist` |
Row chi-square distances to centroid |

`rowinertia` |
Row inertias |

`rowcoord` |
Row standard coordinates |

`rowsup` |
Indices of row supplementary points |

`colnames` |
Column names |

`colmass` |
Column masses |

`coldist` |
Column chi-square distances to centroid |

`colinertia` |
Column inertias |

`colcoord` |
Column standard coordinates |

`colsup` |
Indices of column supplementary points |

`N` |
The frequency table |

Nenadic, O. and Greenacre, M. (2007). Correspondence analysis in R, with two- and three-dimensional graphics: The ca package. *Journal of Statistical Software*, **20 (3)**, http://www.jstatsoft.org/v20/i03/

Greenacre, M. (2007). *Correspondence Analysis in Practice*. Second Edition. London: Chapman & Hall / CRC.
Blasius, J. and Greenacre, M. J. (1994), Computation of correspondence analysis,
in *Correspondence Analysis in the Social Sciences*, pp. 53-75, London: Academic Press.

Greenacre, M.J. and Pardo, R. (2006), Subset correspondence analysis: visualizing relationships among a selected set of response categories from a questionnaire survey. *Sociological Methods and Research*, **35**, pp. 193-218.

`svd`

, `plot.ca`

, `plot3d.ca`

, `summary.ca`

, `print.ca`

```
data("author")
ca(author)
plot(ca(author))
# table method
haireye <- margin.table(HairEyeColor, 1:2)
haireye.ca <- ca(haireye)
haireye.ca
plot(haireye.ca)
# some plot options
plot(haireye.ca, lines=TRUE)
plot(haireye.ca, arrows=c(TRUE, FALSE))
```

[Package *ca* version 0.71.1 Index]