cotab_panel: Panel-generating Functions for Contingency Table Coplots

cotab_panelR Documentation

Panel-generating Functions for Contingency Table Coplots


Panel-generating functions visualizing contingency tables that can be passed to cotabplot.


cotab_mosaic(x = NULL, condvars = NULL, ...)
cotab_assoc(x = NULL, condvars = NULL, ylim = NULL, ...)
cotab_sieve(x = NULL, condvars = NULL, ...)
cotab_loddsratio(x = NULL, condvars = NULL, ...)
cotab_agreementplot(x = NULL, condvars = NULL, ...)
cotab_fourfold(x = NULL, condvars = NULL, ...)
cotab_coindep(x, condvars,
  test = c("doublemax", "maxchisq", "sumchisq"),
  level = NULL, n = 1000, interpolate = c(2, 4),
  h = NULL, c = NULL, l = NULL, lty = 1,
  type = c("mosaic", "assoc"), legend = FALSE, ylim = NULL, ...)



a contingency tables in array form.


margin name(s) of the conditioning variables.


y-axis limits for assoc plot. By default this is computed from x.


character indicating which type of statistic should be used for assessing conditional independence.


variables controlling the HCL shading of the residuals, see shadings for more details.


character indicating which type of plot should be produced.


logical. Should a legend be produced in each panel?


further arguments passed to the plotting function (such as mosaic or assoc or sieve respectively).


These functions of class "panel_generator" are panel-generating functions for use with cotabplot, i.e., they return functions with the interface

panel(x, condlevels)

required for cotabplot. The functions produced by cotab_mosaic, cotab_assoc and cotab_sieve essentially only call co_table to produce the conditioned table and then call mosaic, assoc or sieve respectively with the arguments specified.

The function cotab_coindep is similar but additionally chooses an appropriate residual-based shading visualizing the associated conditional independence model. The conditional independence test is carried out via coindep_test and the shading is set up via shading_hcl.

A description of the underlying ideas is given in Zeileis, Meyer, Hornik (2005).


Achim Zeileis


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. doi: 10.18637/jss.v017.i03 and available as vignette("strucplot").

Zeileis, A., Meyer, D., Hornik K. (2007), Residual-based shadings for visualizing (conditional) independence, Journal of Computational and Graphical Statistics, 16, 507–525.

See Also

cotabplot, mosaic, assoc, sieve, co_table, coindep_test, shading_hcl



cotabplot(~ Admit + Gender | Dept, data = UCBAdmissions)
cotabplot(~ Admit + Gender | Dept, data = UCBAdmissions, panel = cotab_assoc)
cotabplot(~ Admit + Gender | Dept, data = UCBAdmissions, panel = cotab_fourfold)

ucb <- cotab_coindep(UCBAdmissions, condvars = "Dept", type = "assoc",
                     n = 5000, margins = c(3, 1, 1, 3))
cotabplot(~ Admit + Gender | Dept, data = UCBAdmissions, panel = ucb)

vcd documentation built on June 9, 2022, 9:07 a.m.