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

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

Arguments

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 https://CRAN.R-project.org/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, Wirtschaftsuniversit<U+00E4>t Wien, Research Report Series. http://epub.wu.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1

Examples

Run this code

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