# assocplot: Association Plots

### Description

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

### Usage

 ```1 2``` ```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 `x`. `ylab` a label for the y axis. Defaults to the name (if any) of the column dimension in `x`.

### 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, 2005).

### References

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

`mosaicplot`, `chisq.test`.
 ```1 2 3 4``` ```## Aggregate over sex: x <- margin.table(HairEyeColor, c(1, 2)) x assocplot(x, main = "Relation between hair and eye color") ```