vcd (version 1.4-4)

cotab_panel: Panel-generating Functions for Contingency Table Coplots

Description

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

Usage

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, …)

Arguments

x

a contingency tables in array form.

condvars

margin name(s) of the conditioning variables.

ylim

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

test

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

level,n,h,c,l,lty,interpolate

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

type

character indicating which type of plot should be produced.

legend

logical. Should a legend be produced in each panel?

…

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

Details

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).

References

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. URL http://www.jstatsoft.org/v17/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

Examples

# NOT RUN {
data("UCBAdmissions")

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)
# }