"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"), ...)
dimnames(x)
attribute.NULL
, no panel is drawn.NULL
, no panel is drawn.NULL
, no panel is drawn.main
is a logical and TRUE
, the
name of the object supplied as x
is used.sub
is a logical and TRUE
and main
is unspecified, the
name of the object supplied as x
is used."gpar"
containing the graphical
parameters used for the main (sub) title, if specified."unit"
of length 4, or
a numeric vector of length 4. The elements are recycled as needed.
The four components specify the top, right,
bottom, and left margin of the plot, respectively.
When a numeric vector is supplied, the numbers are interpreted as
"lines"
units. In addition, the unit or numeric vector
may have named arguments
(top, right, bottom, and left), in which
case the non-named arguments specify the default values (recycled as
needed), overloaded by the named arguments.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:
"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.
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.
URL http://www.jstatsoft.org/v17/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))
Run the code above in your browser using DataLab