mosaicplot {base}

R Documentation

Mosaic Plots

Description

Plots a mosaic on the current graphics device.

Usage

## Default S3 method:
mosaicplot(x, main = NULL, xlab = NULL, ylab = NULL,
                   sort = NULL, off = NULL, dir = NULL,
                   color = FALSE, shade = FALSE, margin = NULL,
                   type = c("pearson", "deviance", "FT"), ...)
## S3 method for class 'formula'
mosaicplot(formula, data = NULL, ..., subset)

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.
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 10 at each level). There should be one offset for each dimension of the contingency table.
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	(TRUE or vector of integer colors) for color shading or (FALSE, the default) for empty boxes with no shading. Ignored if shade is not FALSE.
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.
type	a character string indicating the type of residual to be represented. Must be one of "pearson" (giving components of Pearson's \chi^2), "deviance" (giving components of the likelihood ratio \chi^2), 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 to be used for plotting.

Details

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

Extended mosaic displays show the standardized residuals of a loglinear model of the counts from by the color and outline of the mosaic's tiles. (Standardized residuals are often referred to a standard normal distribution.) Negative residuals are drawn in shaded of red and with broken outlines; positive ones are drawn in blue with solid outlines.

For the formula method, if data is an object inheriting from classes "table" or "ftable", or an array with more than 2 dimensions, it is taken as a contingency table, and hence all entries should be nonnegative. 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 of this 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.

Author(s)

S-PLUS original by John Emerson emerson@stat.yale.edu. Modified and enhanced for R by KH.

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.

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

See Also

assocplot, loglin.

Examples

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

data(HairEyeColor)
mosaicplot(HairEyeColor, shade = TRUE)
## Independence model of hair and eye color and sex.  Indicates that
## there are significantly more blue eyed blond females than expected
## in the case of independence (and too few brown eyed blond females).
mosaicplot(HairEyeColor, shade = TRUE, margin = list(c(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, but not
## ``significantly''.

## Formula interface for raw data: visualize crosstabulation of numbers
## of gears and carburettors in Motor Trend car data.
data(mtcars)
mosaicplot(~ gear + carb, data = mtcars, color = TRUE)

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