Plots (extended) mosaic displays.
# S3 method for 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 = rev(gray.colors(tail(dim(x), 1))),
highlighting_direction = NULL,
zero_size = 0.5, zero_split = FALSE, zero_shade = NULL,
zero_gp = gpar(col = 0), panel = NULL, main = NULL, sub = NULL, ...)
# S3 method for formula
mosaic(formula, data, highlighting = NULL,
..., main = NULL, sub = NULL, subset = NULL, na.action = NULL)
The "structable"
visualized is returned invisibly.
a contingency table in array form, with optional category
labels specified in the dimnames(x)
attribute, or an object
of class "structable"
.
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. If any, a specified response variable will be
highlighted in the cells.
either a data frame, or an object of class "table"
or "ftable"
.
an optional vector specifying a subset of observations to be used.
a function which indicates what should happen when
the data contain NA
s. Ignored if data
is a contingency table.
size of the bullets used for zero entries (if 0, no bullets are drawn).
logical controlling whether zero cells should be
further split. If FALSE
and zero_shade
is
FALSE
, only one bullet is drawn (centered) for unsplit
zero cells. If FALSE
and zero_shade
is
TRUE
, a bullet for each zero cell is drawn to allow, e.g.,
residual-based shadings to be effective also for zero cells.
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.
object of class "gpar"
used
for zero bullets in case they are not shaded.
vector of logicals of length x
(default: FALSE
).
Values are recycled as needed.
A TRUE
component indicates that the tile(s) of the
corresponding dimension should be split vertically, FALSE
means horizontal splits. Ignored if direction
is not NULL
.
character vector of length 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 horizontally, whereas
"v"
indicates vertical split(s).
spacing object, spacing function, or corresponding
generating function (see strucplot
for more
information).
The default is spacing_equal
if x
has two dimensions,
spacing_increase
for more dimensions, and
spacing_conditional
if conditioning variables are specified
using condvars
or the formula interface.
list of arguments for the generating function, if
specified (see strucplot
for more information).
object of class "gpar"
, shading function or a
corresponding generating function (see details and
shadings
). Components of "gpar"
objects are recycled as needed along the last splitting dimension.
Ignored if shade = FALSE
.
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
strucplot
) is specified, a
corresponding conditional independence model, and else the total
independence model.
optionally, an array of expected values of the same dimension
as x
, or alternatively the corresponding independence model specification
as used by loglin
or loglm
(see strucplot
).
character vector or integer specifying a variable to be highlighted in the cells.
color vector or palette function used for a highlighted variable, if any.
Either "left"
, "right"
,
"top"
, or "bottom"
specifying the direction of
highlighting in the cells.
Optional function with arguments: residuals
,
observed
, expected
, index
, gp
, and
name
called by the
struc_mosaic
workhorse for each tile that is drawn in the
mosaic. index
is an integer vector with the tile's
coordinates in the contingency table, gp
a gpar
object
for the tile, and name
a label to be assigned to the drawn
grid object.
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
David Meyer David.Meyer@R-project.org
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
.
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. (2006),
The strucplot framework: Visualizing multi-way contingency tables with
vcd.
Journal of Statistical Software, 17(3), 1-48.
tools:::Rd_expr_doi("10.18637/jss.v017.i03") and available as
vignette("strucplot", package = "vcd")
.
The home page of Michael Friendly (http://datavis.ca) provides information on various aspects of graphical methods for analyzing categorical data, including mosaic plots. In particular, there are many materials for his course “Visualizing Categorical Data with SAS and R” at http://datavis.ca/courses/VCD/.
assoc
,
strucplot
,
mosaicplot
,
structable
,
doubledecker
library(MASS)
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")
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