# assocplot

0th

Percentile

##### Association Plots

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

Keywords
hplot
##### 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 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">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

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