Plots a mosaic on the current graphics device.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 | ```
mosaicplot(x, ...)
## Default S3 method:
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 class 'formula'
mosaicplot(formula, data = NULL, ...,
main = deparse(substitute(data)), subset,
na.action = stats::na.omit)
``` |

`x` |
a contingency table in array form, with optional category
labels specified in the |

`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 |

`sort` |
vector ordering of the variables, containing a permutation
of the integers |

`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 ( |

`color` |
logical or (recycling) vector of colors for color
shading, used only when |

`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, |

`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 |

`cex.axis` |
The magnification to be used for axis annotation,
as a multiple of |

`las` |
numeric; the style of axis labels, see |

`border` |
colour of borders of cells: see |

`type` |
a character string indicating the type of residual to be
represented. Must be one of |

`formula` |
a formula, such as |

`data` |
a data frame (or list), or a contingency table from which
the variables in |

`...` |
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 |

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).

S-PLUS original by John Emerson [email protected].
Originally modified and enhanced for **R** by Kurt Hornik.

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

`assocplot`

,
`loglin`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 | ```
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)
``` |

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