Extended Mosaic Plots

Plots (extended) mosaic displays.

## S3 method for class 'default':
mosaic(x, condvars = NULL,
  split_vertical = FALSE, direction = NULL, spacing = NULL,
  spacing_args = list(), zero_size = 0.5, ...)
## S3 method for class 'formula':
mosaic(formula, data, \dots, main = NULL)
a contingency table in array form, with optional category labels specified in the dimnames(x) attribute.
vector of integers or character strings indicating conditioning variables, if any. The table will be permuted to order them first.
a formula specifying the variables used to create a contingency table from data. For convenience, conditioning formulas can be specified; the conditioning variables will then be used first for splitting. Formulas for mosaic dis
either a data frame, or an object of class "table" or "ftable".
size of the bullets used for zero entries (if 0, no bullets are drawn).
vector of logicals of length $k$, where $k$ is the number of margins of x (values are recycled as needed). A TRUE component indicates that the tile(s) of the corresponding dimension should be split vertically,
character vector of length $k$, where $k$ is the number of margins of x (values are recycled as needed). For each component, a value of "h" indicates that the tile(s) of the corresponding dimension should be split hor
spacing object, spacing function, or corresponding generating function (see strucplot for more information). The default is spacing_equal if x has two dimensions,
list of arguments for the generating function, if specified (see strucplot for more information).
either a logical, or a character string used for plotting the main title. If main is a logical and TRUE, the name of the data object is used.
Other arguments passed to strucplot

Mosaic displays have been suggested in the statistical literature by Hartigan and Kleiner (1984) and have been extended by Friendly (1994). mosaicplot is a base graphics implementation and mosaic is a much more flexible and extensible grid implementation.

mosaic is a generic function which currently has a default method and a formula interface. Both are high-level interfaces to the strucplot function, and produce (extended) mosaic displays. Most of the functionality is described there, such as specification of the independence model, labeling, legend, spacing, shading, and other graphical parameters.

A mosaic plot is an area proportional visualization of a (possibly higher-dimensional) table of expected frequencies. It is composed of tiles (corresponding to the cells) created by recursive vertical and horizontal splits of a square. The area of each tile is proportional to the corresponding cell entry, given the dimensions of previous splits.

An extended mosaic plot, in addition, visualizes the fit of a particular log-linear model. Typically, this is done by residual-based shadings where color and/or outline of the tiles visualize sign, size and possibly significance of the corresponding residual. The layout is very flexible: the specification of shading, labeling, spacing, and legend is modularized (see strucplot for details).

In contrast to the mosaicplot function in graphics, the splits start with the horizontal direction by default to match the printed output of structable.


  • The "structable" visualized is returned invisibly.


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 ( provides information on various aspects of graphical methods for analyzing categorical data, including mosaic plots.

See Also

assoc, strucplot, mosaicplot, structable, doubledecker

  • mosaic
  • mosaic.default
  • mosaic.formula

## Formula interface for tabulated data plus shading and legend:
mosaic(~ Sex + Age + Survived, data = Titanic,
  main = "Survival on the Titanic", shade = TRUE, legend = TRUE)

mosaic(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).

mosaic(HairEyeColor, shade = TRUE, expected = 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.
mosaic(~ gear + carb, data = mtcars, shade = TRUE)

mosaic(PreSex, condvars = c(1,4))
mosaic(~ ExtramaritalSex + PremaritalSex | MaritalStatus + Gender,
       data = PreSex)
Documentation reproduced from package vcd, version 0.9-0, License: GPL

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