# mosaicplot

##### Mosaic Plots

Plots a mosaic on the current graphics device.

- Keywords
- hplot

##### Usage

`mosaicplot(x, …)`# S3 method for default
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 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 \(\chi^2\)),`"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 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).

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

##### See Also

##### Examples

`library(graphics)`

```
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)
```

*Documentation reproduced from package graphics, version 3.4.0, License: Part of R 3.4.0*