vcd (version 0.9-9)

mosaic: Extended Mosaic Plots

Description

Plots (extended) mosaic displays.

Usage

## S3 method for class 'default':
mosaic(x, condvars = NULL,
  split_vertical = NULL, direction = NULL, spacing = NULL,
  spacing_args = list(), gp = NULL, expected = NULL, shade = NULL,
  highlighting = NULL, highlighting_fill = grey.colors, highlighting_direction = NULL,
  zero_size = 0.5, zero_split = FALSE, zero_shade = NULL,
  zero_gp = gpar(col = 0), main = NULL, sub = NULL, ...)
## S3 method for class 'formula':
mosaic(formula, data, highlighting = NULL,
  \dots, main = NULL, sub = NULL, subset = NULL, na.action = NULL)

Arguments

x
a contingency table in array form, with optional category labels specified in the dimnames(x) attribute, or an object of class "structable".
condvars
vector of integers or character strings indicating conditioning variables, if any. The table will be permuted to order them first.
formula
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. If any, a specified resp
data
either a data frame, or an object of class "table" or "ftable".
subset
an optional vector specifying a subset of observations to be used.
na.action
a function which indicates what should happen when the data contain NAs. Ignored if data is a contingency table.
zero_size
size of the bullets used for zero entries (if 0, no bullets are drawn).
zero_split
logical controlling whether zero cells should be further splitted. If FALSE and zero_shade is FALSE, only one bullet is drawn (centered) for unsplitted zero cells. If FALSE and zero_sha
zero_shade
logical controlling whether zero bullets should be shaded. The default is TRUE if shade is TRUE or expected is not null or gp is not null, and FALSE otherwise.
zero_gp
object of class "gpar" used for zero bullets in case they are not shaded.
split_vertical
vector of logicals of length $k$, where $k$ is the number of margins of x (default: FALSE). Values are recycled as needed. A TRUE component indicates that the tile(s) of the corresponding dimension s
direction
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
spacing object, spacing function, or corresponding generating function (see strucplot for more information). The default is spacing_equal if x has two dimensions,
spacing_args
list of arguments for the generating function, if specified (see strucplot for more information).
gp
object of class "gpar", shading function or a corresponding generating function (see details and shadings). Components of "gpar" objects are recycled as needed along t
shade
logical specifying whether gp should be used or not (see gp). If TRUE and expected is unspecified, a default model is fitted: if condvars (see
expected
optionally, an array of expected values of the same dimension as x, or alternatively the corresponding independence model specification as used by loglin or
highlighting
character vector or integer specifying a variable to be highlighted in the cells.
highlighting_fill
color vector or palette function used for a highlighted variable, if any.
highlighting_direction
Either "left", "right", "top", or "bottom" specifying the direction of highlighting in the cells.
main, sub
either a logical, or a character string used for plotting the main (sub) title. If logical and TRUE, the name of the data object is used.
...
Other arguments passed to strucplot

Value

  • The "structable" visualized is returned invisibly.

Details

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.

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.

See Also

assoc, strucplot, mosaicplot, structable, doubledecker

Examples

Run this code
data("Titanic")
mosaic(Titanic)

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

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

data("PreSex")
mosaic(PreSex, condvars = c(1,4))
mosaic(~ ExtramaritalSex + PremaritalSex | MaritalStatus + Gender,
       data = PreSex)

## Highlighting:
mosaic(Survived ~ ., data = Titanic)

data("Arthritis")
mosaic(Improved ~ Treatment | Sex, data = Arthritis, zero_size = 0)
mosaic(Improved ~ Treatment | Sex, data = Arthritis, zero_size = 0, highlighting_direction = "right")

Run the code above in your browser using DataCamp Workspace