assocplot {graphics} | R Documentation

## 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

`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 of the row variable in `x` if non-`NULL`.
`ylab` | a label for the y axis. Defaults to the column names of the column variable in `x` if non-`NULL`.

### 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.

### 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

### See Also

`mosaicplot`; `chisq.test`.

### Examples

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

[Package graphics version 2.1.1 Index]