Plots a mosaic on the current graphics device.
# S3 method for default 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 formula mosaicplot(formula, data = NULL, …, main = deparse(substitute(data)), subset, na.action = stats::na.omit)
- a contingency table in array form, with optional category
labels specified in the
dimnames(x)attribute. The table is best created by the
- character string for the mosaic title.
- 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
names(dimnames(X))(i.e., the name of the first and second variable in
- vector ordering of the variables, containing a permutation
of the integers
- 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.
- vector of split directions (
"v"for vertical and
"h"for horizontal) for each level of the mosaic, one direction for each dimension of the contingency table. The default consists of alternating directions, beginning with a vertical split.
- logical or (recycling) vector of colors for color
shading, used only when
NULL(default). By default, grey boxes are drawn.
color = TRUEuses a gamma-corrected grey palette.
color = FALSEgives empty boxes with no shading.
- 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
FALSE, and simple mosaics are created. Using
shade = TRUEcuts absolute values at 2 and 4.
- a list of vectors with the marginal totals to be fit in
the log-linear model. By default, an independence model is fitted.
loglinfor further information.
- The magnification to be used for axis annotation,
as a multiple of
- numeric; the style of axis labels, see
- colour of borders of cells: see
- a character string indicating the type of residual to be
represented. Must be one of
"pearson"(giving components of Pearson's \(\chi^2\)),
"deviance"(giving components of the likelihood ratio \(\chi^2\)), or
"FT"for the Freeman-Tukey residuals. The value of this argument can be abbreviated.
- a formula, such as
y ~ x.
- a data frame (or list), or a contingency table from which
the variables in
formulashould be taken.
- further arguments to be passed to or from methods.
- an optional vector specifying a subset of observations in the data frame to be used for plotting.
- a function which indicates what should happen
when the data contains variables to be cross-tabulated, and these
NAs. The default is to omit cases which have an
NAin any variable. Since the tabulation will omit all cases containing missing values, this will only be useful if the
na.actionfunction replaces missing values.
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
"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
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
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">https://CRAN.R-project.org/package=vcd
(Meyer, Zeileis and Hornik, 2005).
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, Wirtschaftsuniversit<U+00E4>t Wien, Research Report Series. http://epub.wu.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1
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)