vcd (version 0.1-3.5)

# mosaicplot: Mosaic Plots

## Description

Plots a mosaic on the current graphics device.

## Usage

## S3 method for class 'default':
mosaicplot(x, main = deparse(substitute(x)), sub = NULL, xlab = NULL,
ylab = NULL, sort = NULL, off = NULL, dir = NULL,
color = FALSE, shade = !(is.null(residuals) && is.null(margin)), margin = NULL,
cex.axis = 0.66, las = par("las"), clegend = TRUE,
type = c("pearson", "deviance", "FT"), residuals = NULL, ...)
## S3 method for class 'formula':
mosaicplot(formula, data = NULL, \dots,
main = deparse(substitute(data)), 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.
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 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
color
logical or (recycling) vector of colors for color shading, used only when shade is FALSE. The default color=FALSE gives empty boxes with no shading.
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.
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. If no marginal model and no residuals are specified,
clegend
logical. Should a color legend be plotted? (only needed if shade is TRUE).
margin
a list of vectors with the marginal totals to be fit in the log-linear model, or a formula as used in loglm. By default, an independence model is fitted. See loglin and
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
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.
residuals
explicit argument for the residuals to be used in extended mosaic plots.

## 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.

## 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://www.math.yorku.ca/SCS/friendly.html) provides information on various aspects of graphical methods for analyzing categorical data, including mosaic plots.

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)
mosaicplot(~ gear + carb, data = mtcars, color = 2:3)# color recycling