assocplot          package:graphics          R Documentation(latin1)

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 (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, 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.
     <URL: 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, 
     Wirtschaftsuniversitaet Wien, Research Report Series. <URL:
     http://epub.wu-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1>

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")

