spineplot: Spine Plots and Spinograms

Description Usage Arguments Details Value Author(s) References See Also Examples

Description

Spine plots are a special cases of mosaic plots, and can be seen as a generalization of stacked (or highlighted) bar plots. Analogously, spinograms are an extension of histograms.

Usage

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 spineplot(x, ...) ## Default S3 method: spineplot(x, y = NULL, breaks = NULL, tol.ylab = 0.05, off = NULL, ylevels = NULL, col = NULL, main = "", xlab = NULL, ylab = NULL, xaxlabels = NULL, yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), axes = TRUE, ...) ## S3 method for class 'formula' spineplot(formula, data = NULL, breaks = NULL, tol.ylab = 0.05, off = NULL, ylevels = NULL, col = NULL, main = "", xlab = NULL, ylab = NULL, xaxlabels = NULL, yaxlabels = NULL, xlim = NULL, ylim = c(0, 1), axes = TRUE, ..., subset = NULL)

Arguments

 x an object, the default method expects either a single variable (interpreted to be the explanatory variable) or a 2-way table. See details. y a "factor" interpreted to be the dependent variable formula a "formula" of type y ~ x with a single dependent "factor" and a single explanatory variable. data an optional data frame. breaks if the explanatory variable is numeric, this controls how it is discretized. breaks is passed to hist and can be a list of arguments. tol.ylab convenience tolerance parameter for y-axis annotation. If the distance between two labels drops under this threshold, they are plotted equidistantly. off vertical offset between the bars (in per cent). It is fixed to 0 for spinograms and defaults to 2 for spine plots. ylevels a character or numeric vector specifying in which order the levels of the dependent variable should be plotted. col a vector of fill colors of the same length as levels(y). The default is to call gray.colors. main, xlab, ylab character strings for annotation xaxlabels, yaxlabels character vectors for annotation of x and y axis. Default to levels(y) and levels(x), respectively for the spine plot. For xaxlabels in the spinogram, the breaks are used. xlim, ylim the range of x and y values with sensible defaults. axes logical. If FALSE all axes (including those giving level names) are suppressed. ... additional arguments passed to rect. subset an optional vector specifying a subset of observations to be used for plotting.

Details

spineplot creates either a spinogram or a spine plot. It can be called via spineplot(x, y) or spineplot(y ~ x) where y is interpreted to be the dependent variable (and has to be categorical) and x the explanatory variable. x can be either categorical (then a spine plot is created) or numerical (then a spinogram is plotted). Additionally, spineplot can also be called with only a single argument which then has to be a 2-way table, interpreted to correspond to table(x, y).

Both, spine plots and spinograms, are essentially mosaic plots with special formatting of spacing and shading. Conceptually, they plot P(y | x) against P(x). For the spine plot (where both x and y are categorical), both quantities are approximated by the corresponding empirical relative frequencies. For the spinogram (where x is numerical), x is first discretized (by calling hist with breaks argument) and then empirical relative frequencies are taken.

Thus, spine plots can also be seen as a generalization of stacked bar plots where not the heights but the widths of the bars corresponds to the relative frequencies of x. The heights of the bars then correspond to the conditional relative frequencies of y in every x group. Analogously, spinograms extend stacked histograms.

Value

The table visualized is returned invisibly.

Author(s)

Achim Zeileis Achim.Zeileis@R-project.org

References

Friendly, M. (1994), Mosaic displays for multi-way contingency tables. Journal of the American Statistical Association, 89, 190–200.

Hartigan, J.A., and Kleiner, B. (1984), A mosaic of television ratings. The American Statistician, 38, 32–35.

Hofmann, H., Theus, M. (2005), Interactive graphics for visualizing conditional distributions, Unpublished Manuscript.

Hummel, J. (1996), Linked bar charts: Analysing categorical data graphically. Computational Statistics, 11, 23–33.