strucplot(x, residuals = NULL, expected = NULL,
condvars = NULL, shade = NULL, type = c("observed", "expected"),
residuals_type = c("Pearson", "deviance", "FT"), split_vertical = TRUE,
spacing = spacing_equal, spacing_args = list(),
gp = NULL, gp_args = list(),
labeling = labeling_text, labeling_args = list(),
panel = struc_mosaic, panel_args = list(),
legend = NULL, legend_args = list(),
main = NULL, sub = NULL, margins = rep.int(2.5, 4),
legend_width = unit(0.15, "npc"), title_gp = gpar(fontsize = 20),
newpage = TRUE, pop = TRUE, keep_aspect_ratio = TRUE)
dimnames
attribute.x
(see details).x
, or alternatively the corresponding independence model specification
as used by loglin
or
gp
). If TRUE
and expected
is unspecified,
a default model is fitted: if condvars
is specified, a
corresponding conditional independenc"pearson"
(giving components of Pearson's
chi-squared), "deviance"
(giving components of the likelihood
ratx
(values are recycled as needed).
A TRUE
component indicates that the tile(s) of the
corresponding dimension should be split vertically, F
spacings
)."gpar"
, shading function or a
corresponding generating function (see details and
shadings
).
Ignored if shade = FALSE
.labelings
.struc_mosaic
) and association plots
(
legends
), or a logical.
If legend
is NULL
or TRUE
and gp
is a
functmain
is a logical and TRUE
, the
name of the object supplied as x
is used."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
m"unit"
of length
1, or a numeric value interpreted as proportion of the available
horizontal space, specifying the width of the legend (if any)."gpar"
containing the graphical
parameters used for the main and sub titles."structable"
corresponding to the
plot.assoc
and mosaic
---generates conditioning
plots of contingency tables. First, it sets up a set of viewports for
main- and subtitles, legend, and the actual plot region. Then,
residuals are computed as needed from observed and expected
frequencies, where the expected frequencies are optionally computed
for a specified independence model. Finally, the specified functions
for spacing, gp, main plot, legend, and labeling are called to produce
the plot. The function invisibly returns the "structable"
object
visualized. Most elements of the plot, such as the panel function, the spacing
between the tiles, the shading of the tiles, the labeling, and the
legend, are modularized in functions and specified as parameters. For
each element foo (= spacing
, labeling
, panel
,
or legend
), strucplot
takes two arguments:
foo and foo_args, which can be used to specify the
parameters in the following alternative ways:
strucplot
to compute shadings, labelings,
etc."panel_generator"
. If type = "expected"
, the expected values are passed to the
observed
argument of the panel function, and the observed
values to the expected
argument.
Although the gp
argument is typically used for shading, it can
be used for arbitrary modifications of the tiles' graphics parameters
(e.g., for highlighting particular cells, etc.).
assoc
,
mosaic
,
struc_assoc
,
struc_mosaic
,
structable
,
doubledecker
,
labelings
,
shadings
,
legends
,
spacings
strucplot(Titanic)
strucplot(Titanic, panel = struc_assoc)
strucplot(Titanic, spacing = spacing_increase,
spacing_args = list(start = 0.5, rate = 1.5))
strucplot(Titanic, spacing = spacing_increase(start = 0.5, rate = 1.5))
## modify a tile's color
strucplot(Titanic, pop = FALSE)
grid.edit("rect..Class.1st..Sex.Male..Age.Adult..Survived.Yes",
gp = gpar(fill = "red"))
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