assocplot | R Documentation |

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

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

`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 |

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

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).

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.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.1080/03610928008827940")}.

Friendly, M. (1992),
Graphical methods for categorical data.
*SAS User Group International Conference Proceedings*, **17**,
190–200.
http://datavis.ca/papers/sugi/sugi17.pdf

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.
\Sexpr[results=rd,stage=build]{tools:::Rd_expr_doi("10.18637/jss.v017.i03")}.

`mosaicplot`

, `chisq.test`

.

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

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