pairs.table | R Documentation |

Produces a matrix of strucplot displays.

## S3 method for class 'table' pairs(x, upper_panel = pairs_mosaic, upper_panel_args = list(), lower_panel = pairs_mosaic, lower_panel_args = list(), diag_panel = pairs_diagonal_mosaic, diag_panel_args = list(), main = NULL, sub = NULL, main_gp = gpar(fontsize = 20), sub_gp = gpar(fontsize = 15), space = 0.3, newpage = TRUE, pop = TRUE, return_grob = FALSE, margins = unit(1, "lines"), ...)

`x` |
a contingency table in array form, with optional category
labels specified in the |

`upper_panel` |
function for the upper triangle of the matrix, or
corresponding generating function. If |

`upper_panel_args` |
list of arguments for the generating function, if specified. |

`lower_panel` |
function for the lower triangle of the matrix, or
corresponding generating function. If |

`lower_panel_args` |
list of arguments for the panel-generating function, if specified. |

`diag_panel` |
function for the diagonal of the matrix, or
corresponding generating function. If |

`diag_panel_args` |
list of arguments for the generating function, if specified. |

`main` |
either a logical, or a character string used for plotting
the main title. If |

`sub` |
a character string used for plotting the subtitle.
If |

`main_gp, sub_gp` |
object of class |

`space` |
double specifying the distance between the cells. |

`newpage` |
logical controlling whether a new grid page should be created. |

`pop` |
logical indicating whether all viewports should be popped after the plot has been drawn. |

`return_grob` |
logical. Should a snapshot of the display be returned as a grid grob? |

`margins` |
either an object of class |

`...` |
For convenience, list of arguments for the panel-generating functions of upper and lower panels, if specified. |

This is a `pairs`

method for objects inheriting
from class `"table"`

or `"structable"`

.
It plots a matrix of pairwise mosaic plots.

Four independence types are distinguished: `"pairwise"`

,
`"total"`

, `"conditional"`

and `"joint"`

.
The pairwise mosaic matrix shows bivariate marginal relations,
collapsed over all other variables.
The total independence mosaic matrix shows mosaic plots for mutual
independence, i.e., for marginal and conditional independence among
all pairs of variables.
The conditional independence mosaic matrix shows mosaic plots for
conditional independence for each pair of variables, given all other variables.
The joint independence mosaic matrix shows mosaic plots for joint
independence of all pairs of variables from the others.

This method uses panel functions called for each cell of the
matrix which can be different for upper matrix, lower matrix, and
diagonal cells. Correspondingly, for each panel parameter `foo`
(= upper, lower, or diag), `pairs.table`

takes
two arguments: `foo_panel` and `foo_panel_args`, which can
be used to specify the parameters as follows:

Passing a suitable panel function to

`foo_panel`which subsequently is called for each cell with the corresponding coordinates.Passing a corresponding

*generating function*(of class`"panel_generator"`

) to`foo_panel`, along with parameters passed to`foo_panel_args`, that generates such a function.

Hence, the second approach is equivalent to the first if
`foo_panel(foo_panel_args)` is passed to
`foo_panel`.

David Meyer David.Meyer@R-project.org

Cohen, A. (1980),
On the graphical display of the significant components in a two-way
contingency table.
*Communications in Statistics—Theory and Methods*, **A9**,
1025–1041.

Friendly, M. (1992),
Graphical methods for categorical data.
*SAS User Group International Conference Proceedings*, **17**,
190–200.
http://datavis.ca/papers/sugi/sugi17.pdf

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.
doi: 10.18637/jss.v017.i03 and available as
`vignette("strucplot")`

.

`pairs_mosaic`

,
`pairs_assoc`

,
`pairs_sieve`

,
`pairs_diagonal_text`

,
`pairs_diagonal_mosaic`

,
`pairs_text`

,
`pairs_barplot`

,
`assoc`

,
`sieve`

,
`mosaic`

data("UCBAdmissions") data("PreSex") data(HairEyeColor) hec = structable(Eye ~ Sex + Hair, data = HairEyeColor) pairs(PreSex) pairs(UCBAdmissions) pairs(UCBAdmissions, upper_panel_args = list(shade = TRUE)) pairs(UCBAdmissions, lower_panel = pairs_mosaic(type = "conditional")) pairs(UCBAdmissions, diag_panel = pairs_text) pairs(UCBAdmissions, upper_panel = pairs_assoc, shade = TRUE) pairs(hec, highlighting = 2, diag_panel_args = list(fill = grey.colors)) pairs(hec, highlighting = 2, diag_panel = pairs_diagonal_mosaic, diag_panel_args = list(fill = grey.colors, alternate_labels =TRUE))

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