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

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

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 `x`

.

ylab

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

.

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, 2005).

Cohen, A. (1980),
On the graphical display of the significant components in a two-way
contingency table.
*Communications in Statistics---Theory and Methods*, **A9**,
1025--1041.

Friendly, M. (1992),
Graphical methods for categorical data.
*SAS User Group International Conference Proceedings*, **17**,
190--200.
http://www.math.yorku.ca/SCS/sugi/sugi17-paper.html

Meyer, D., Zeileis, A., and Hornik, K. (2005)
The strucplot framework: Visualizing multi-way contingency tables with vcd.
*Report 22*, Department of Statistics and Mathematics,
Wirtschaftsuniversität Wien, Research Report Series.
http://epub.wu.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1

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

Run the code above in your browser using DataLab