# 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 (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. 10.1080/03610928008827940.

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. (2006) The strucplot Framework: Visualizing Multi-Way Contingency Tables with vcd. Journal of Statistical Software, 17(3), 1--48. 10.18637/jss.v017.i03.

mosaicplot, chisq.test.
library(graphics) # NOT RUN { ## Aggregate over sex: x <- margin.table(HairEyeColor, c(1, 2)) x assocplot(x, main = "Relation between hair and eye color") # }