R: Extended Mosaic Plots

mosaic {vcd} | R Documentation

## Extended Mosaic Plots

### Description

Plots (extended) mosaic displays.

### Usage

    ## Default S3 method:
    mosaic(x, condvars = NULL,
      split_vertical = NULL, direction = NULL, spacing = NULL,
      spacing_args = list(), gp = NULL, expected = NULL, shade = NULL,
      highlighting = NULL, highlighting_fill = grey.colors, 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 class 'formula'
    mosaic(formula, data, highlighting = NULL,
      ..., main = NULL, sub = NULL, subset = NULL, na.action = NULL)

### Arguments

`x` | a contingency table in array form, with optional category labels specified in the `dimnames(x)` attribute, or an object of class `"structable"`.
`condvars` | vector of integers or character strings indicating conditioning variables, if any. The table will be permuted to order them first.
`formula` | 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.
`data` | either a data frame, or an object of class `"table"` or `"ftable"`.
`subset` | an optional vector specifying a subset of observations to be used.
`na.action` | a function which indicates what should happen when the data contain `NA`s. Ignored if `data` is a contingency table.
`zero_size` | size of the bullets used for zero entries (if 0, no bullets are drawn).
`zero_split` | 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.
`zero_shade` | 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.
`zero_gp` | object of class `"gpar"` used for zero bullets in case they are not shaded.
`split_vertical` | vector of logicals of length k, where k is the number of margins of `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`.
`direction` | 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 horizontally, whereas `"v"` indicates vertical split(s).
`spacing` | 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.
`spacing_args` | list of arguments for the generating function, if specified (see `strucplot` for more information).
`gp` | 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`.
`shade` | 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.
`expected` | 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`).
`highlighting` | character vector or integer specifying a variable to be highlighted in the cells.
`highlighting_fill` | color vector or palette function used for a highlighted variable, if any.
`highlighting_direction` | Either `"left"`, `"right"`, `"top"`, or `"bottom"` specifying the direction of highlighting in the cells.
`panel` | 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.
`main, sub` | 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`

### Details

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`.

### Value

The `"structable"` visualized is returned invisibly.

### Author(s)

David Meyer David.Meyer@R-project.org

### 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. (2006), The strucplot framework: Visualizing multi-way contingency tables with vcd. Journal of Statistical Software, 17(3), 1-48. URL http://www.jstatsoft.org/v17/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/.

### See Also

`assoc`, `strucplot`, `mosaicplot`, `structable`, `doubledecker`

### Examples

    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")

[Package vcd version 1.4-4 Index]