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
- 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
- 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
TRUEcomponent 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
strucplotfor more information). The default is
xhas two dimensions,
- list of arguments for the generating function, if
strucplotfor more information).
- either a logical, or a character string used for plotting
the main title. If
mainis a logical and
TRUE, the name of the
dataobject is used.
- Other arguments passed to
Mosaic displays have been suggested in the statistical literature
by Hartigan and Kleiner (1984) and have been extended by Friendly
mosaicplot is a base graphics
mosaic is a much more flexible and extensible
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
"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
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)