R: Mosaic Plots

mosaicplot {graphics}

R Documentation

Mosaic Plots

Description

Plots a mosaic on the current graphics device.

Usage

mosaicplot(x, ...)

## Default S3 method:
mosaicplot(x, main = deparse(substitute(x)), 
           sub = NULL, xlab = NULL, ylab = NULL,
           sort = NULL, off = NULL, dir = NULL,
           color = NULL, shade = FALSE, margin = NULL,
           cex.axis = 0.66, las = par("las"), border = NULL,
           type = c("pearson", "deviance", "FT"), ...)

## S3 method for class 'formula'
mosaicplot(formula, data = NULL, ...,
           main = deparse(substitute(data)), subset,
           na.action = stats::na.omit)

Arguments

`x`	a contingency table in array form, with optional category labels specified in the `dimnames(x)` attribute. The table is best created by the `table()` command.
`main`	character string for the mosaic title.
`sub`	character string for the mosaic sub-title (at bottom).
`xlab,ylab`	x- and y-axis labels used for the plot; by default, the first and second element of `names(dimnames(X))` (i.e., the name of the first and second variable in `X`).
`sort`	vector ordering of the variables, containing a permutation of the integers `1:length(dim(x))` (the default).
`off`	vector of offsets to determine percentage spacing at each level of the mosaic (appropriate values are between 0 and 20, and the default is 20 times the number of splits for 2-dimensional tables, and 10 otherwise. Rescaled to maximally 50, and recycled if necessary.
`dir`	vector of split directions (`"v"` for vertical and `"h"` for horizontal) for each level of the mosaic, one direction for each dimension of the contingency table. The default consists of alternating directions, beginning with a vertical split.
`color`	logical or (recycling) vector of colors for color shading, used only when `shade` is `FALSE`, or `NULL` (default). By default, grey boxes are drawn. `color=TRUE` uses a gamma-corrected grey palette. `color=FALSE` gives empty boxes with no shading.
`shade`	a logical indicating whether to produce extended mosaic plots, or a numeric vector of at most 5 distinct positive numbers giving the absolute values of the cut points for the residuals. By default, `shade` is `FALSE`, and simple mosaics are created. Using `shade = TRUE` cuts absolute values at 2 and 4.
`margin`	a list of vectors with the marginal totals to be fit in the log-linear model. By default, an independence model is fitted. See `loglin` for further information.
`cex.axis`	The magnification to be used for axis annotation, as a multiple of `par("cex")`.
`las`	numeric; the style of axis labels, see `par`.
`border`	colour of borders of cells: see `polygon`.
`type`	a character string indicating the type of residual to be represented. Must be one of `"pearson"` (giving components of Pearson's chi-squared), `"deviance"` (giving components of the likelihood ratio chi-squared), or `"FT"` for the Freeman-Tukey residuals. The value of this argument can be abbreviated.
`formula`	a formula, such as `y ~ x`.
`data`	a data frame (or list), or a contingency table from which the variables in `formula` should be taken.
`...`	further arguments to be passed to or from methods.
`subset`	an optional vector specifying a subset of observations in the data frame to be used for plotting.
`na.action`	a function which indicates what should happen when the data contains variables to be cross-tabulated, and these variables contain `NA`s. The default is to omit cases which have an `NA` in any variable. Since the tabulation will omit all cases containing missing values, this will only be useful if the `na.action` function replaces missing values.

Details

This is a generic function. It currently has a default method (mosaicplot.default) and a formula interface (mosaicplot.formula).

Extended mosaic displays visualize standardized residuals of a loglinear model for the table by color and outline of the mosaic's tiles. (Standardized residuals are often referred to a standard normal distribution.) Cells representing negative residuals are drawn in shaded of red and with broken borders; positive ones are drawn in blue with solid borders.

For the formula method, if data is an object inheriting from class "table" or class "ftable" or an array with more than 2 dimensions, it is taken as a contingency table, and hence all entries should be non-negative. In this case the left-hand side of formula should be empty and the variables on the right-hand side should be taken from the names of the dimnames attribute of the contingency table. A marginal table of these variables is computed, and a mosaic plot of that table is produced.

Otherwise, data should be a data frame or matrix, list or environment containing the variables to be cross-tabulated. In this case, after possibly selecting a subset of the data as specified by the subset argument, a contingency table is computed from the variables given in formula, and a mosaic is produced from this.

See Emerson (1998) for more information and a case study with television viewer data from Nielsen Media Research.

Missing values are not supported except via an na.action function when data contains variables to be cross-tabulated.

A more flexible and extensible implementation of mosaic plots written in the grid graphics system is provided in the function mosaic in the contributed package vcd (Meyer, Zeileis and Hornik, 2005).

Author(s)

S-PLUS original by John Emerson john.emerson@yale.edu. Originally modified and enhanced for R by Kurt Hornik.

References

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

Emerson, J. W. (1998) Mosaic displays in S-PLUS: A general implementation and a case study. Statistical Computing and Graphics Newsletter (ASA), 9, 1, 17–23.

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

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, Wirtschaftsuniversität Wien, Research Report Series. http://epub.wu.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1

The home page of Michael Friendly (http://www.math.yorku.ca/SCS/friendly.html) provides information on various aspects of graphical methods for analyzing categorical data, including mosaic plots.

Examples

require(stats)
mosaicplot(Titanic, main = "Survival on the Titanic", color = TRUE)
## Formula interface for tabulated data:
mosaicplot(~ Sex + Age + Survived, data = Titanic, color = TRUE)

mosaicplot(HairEyeColor, shade = TRUE)
## Independence model of hair and eye color and sex.  Indicates that
## there are more blue eyed blonde females than expected in the case 
## of independence and too few brown eyed blonde females.
## The corresponding model is:
fm <- loglin(HairEyeColor, list(1, 2, 3))
pchisq(fm$pearson, fm$df, lower.tail = FALSE)

mosaicplot(HairEyeColor, shade = TRUE, margin = list(1:2, 3))
## Model of joint independence of sex from hair and eye color.  Males
## are underrepresented among people with brown hair and eyes, and are
## overrepresented among people with brown hair and blue eyes.
## The corresponding model is:
fm <- loglin(HairEyeColor, list(1:2, 3))
pchisq(fm$pearson, fm$df, lower.tail = FALSE)

## Formula interface for raw data: visualize cross-tabulation of numbers
## of gears and carburettors in Motor Trend car data.
mosaicplot(~ gear + carb, data = mtcars, color = TRUE, las = 1)
# color recycling
mosaicplot(~ gear + carb, data = mtcars, color = 2:3, las = 1)

[Package graphics version 2.15.1 Index]