mosaicplot {graphics} | R Documentation

## Mosaic Plots

### Description

Plots a mosaic on the current graphics device.

### Usage

    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"),
               type = c("pearson", "deviance", "FT"), ...)

    ## S3 method for class 'formula':
    mosaicplot(formula, data = NULL, ...,
               main = deparse(substitute(data)), subset,
               na.action = stats::na.omit)

### Arguments

`x` | a contingency table in array form, with optional category labels specified in the `dimnames(x)` attribute. The table is best created by the `table()` command.
`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 `names(dimnames(X))` (i.e., the name of the first and second variable in `X`).
`sort` | vector ordering of the variables, containing a permutation of the integers `1:length(dim(x))` (the default).
`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 (`"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.
`color` | logical or (recycling) vector of colors for color shading, used only when `shade` is `FALSE`, or `NULL` (default). By default, grey boxes are drawn. `color=TRUE` uses a gamma-corrected grey palette. `color=FALSE` gives empty boxes with no shading.
`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, `shade` is `FALSE`, and simple mosaics are created. Using `shade = TRUE` cuts absolute values at 2 and 4.
`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 `loglin` for further information.
`cex.axis` | The magnification to be used for axis annotation, as a multiple of `par("cex")`.
`las` | numeric; the style of axis labels, see `par`.
`type` | a character string indicating the type of residual to be represented. Must be one of `"pearson"` (giving components of Pearson's chi-squared), `"deviance"` (giving components of the likelihood ratio chi-squared), or `"FT"` for the Freeman-Tukey residuals. The value of this argument can be abbreviated.
`formula` | a formula, such as `y ~ x`.
`data` | a data frame (or list), or a contingency table from which the variables in `formula` should be taken.
`...` | 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 `NA`s. The default is to omit cases which have an `NA` in any variable. Since the tabulation will omit all cases containing missing values, this will only be useful if the `na.action` function replaces missing values.

### Details

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.) Negative residuals are drawn in shaded of red and with broken outlines; positive ones are drawn in blue with solid outlines.

For the formula method, if `data` is an object inheriting from classes `"table"` or `"ftable"`, or an array with more than 2 dimensions, it is taken as a contingency table, and hence all entries should be nonnegative. 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 of this 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).

### Author(s)

S-PLUS original by John Emerson emerson@stat.yale.edu. Originally modified and enhanced for R by Kurt Hornik.

### 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.

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-wien.ac.at/dyn/openURL?id=oai:epub.wu-wien.ac.at:epub-wu-01_8a1

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

`assocplot`, `loglin`.

### Examples

    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 crosstabulation 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)

[Package graphics version 2.3.1 Index]