assocplot {graphics} | R Documentation |

## Association Plots

### Description

Produce a Cohen-Friendly association plot indicating deviations from independence of rows and columns in a 2-dimensional contingency table.

### Usage

```
assocplot(x, col = c("black", "red"), space = 0.3,
main = NULL, xlab = NULL, ylab = NULL)
```

### Arguments

`x` |
a two-dimensional contingency table in matrix form. |

`col` |
a character vector of length two giving the colors used for drawing positive and negative Pearson residuals, respectively. |

`space` |
the amount of space (as a fraction of the average rectangle width and height) left between each rectangle. |

`main` |
overall title for the plot. |

`xlab` |
a label for the x axis. Defaults to the name (if any) of
the row dimension in |

`ylab` |
a label for the y axis. Defaults to the name (if any) of
the column dimension in |

### Details

For a two-way contingency table, the signed contribution to Pearson's
`\chi^2`

for cell `i, j`

is ```
d_{ij} = (f_{ij} -
e_{ij}) / \sqrt{e_{ij}}
```

,
where `f_{ij}`

and `e_{ij}`

are the observed and expected
counts corresponding to the cell. In the Cohen-Friendly association
plot, each cell is represented by a rectangle that has (signed) height
proportional to `d_{ij}`

and width proportional to
`\sqrt{e_{ij}}`

, so that the area of the box is
proportional to the difference in observed and expected frequencies.
The rectangles in each row are positioned relative to a baseline
indicating independence (`d_{ij} = 0`

). If the observed frequency
of a cell is greater than the expected one, the box rises above the
baseline and is shaded in the color specified by the first element of
`col`

, which defaults to black; otherwise, the box falls below
the baseline and is shaded in the color specified by the second
element of `col`

, which defaults to red.

A more flexible and extensible implementation of association plots
written in the grid graphics system is provided in the function
`assoc`

in the contributed package vcd
(Meyer, Zeileis and Hornik, 2006).

### References

Cohen, A. (1980),
On the graphical display of the significant components in a two-way
contingency table.
*Communications in Statistics—Theory and Methods*, **9**,
1025–1041.
doi:10.1080/03610928008827940.

Friendly, M. (1992),
Graphical methods for categorical data.
*SAS User Group International Conference Proceedings*, **17**,
190–200.
http://datavis.ca/papers/sugi/sugi17.pdf

Meyer, D., Zeileis, A., and Hornik, K. (2006)
The strucplot Framework: Visualizing Multi-Way Contingency Tables with
vcd.
*Journal of Statistical Software*, **17(3)**, 1–48.
doi:10.18637/jss.v017.i03.

### See Also

### Examples

```
## Aggregate over sex:
x <- marginSums(HairEyeColor, c(1, 2))
x
assocplot(x, main = "Relation between hair and eye color")
```

*graphics*version 4.4.1 Index]