assoc | R Documentation |
Produce an association plot indicating deviations from a specified independence model in a possibly high-dimensional contingency table.
## Default S3 method:
assoc(x, row_vars = NULL, col_vars = NULL, compress = TRUE,
xlim = NULL, ylim = NULL,
spacing = spacing_conditional(sp = 0), spacing_args = list(),
split_vertical = NULL, keep_aspect_ratio = FALSE,
xscale = 0.9, yspace = unit(0.5, "lines"), main = NULL, sub = NULL,
..., residuals_type = "Pearson", gp_axis = gpar(lty = 3))
## S3 method for class 'formula'
assoc(formula, data = NULL, ..., subset = NULL, na.action = NULL, main = NULL, sub = NULL)
x |
a contingency table in array form with optional category
labels specified in the |
row_vars |
a vector of integers giving the indices, or a character vector giving the names of the variables to be used for the rows of the association plot. |
col_vars |
a vector of integers giving the indices, or a character vector giving the names of the variables to be used for the columns of the association plot. |
compress |
logical; if |
xlim |
a |
ylim |
a |
spacing |
a spacing object, a spacing function, or a
corresponding generating function (see |
spacing_args |
list of arguments for the spacing-generating function, if
specified (see |
split_vertical |
vector of logicals of length |
keep_aspect_ratio |
logical indicating whether the aspect ratio should be fixed or not. |
residuals_type |
a character string indicating the type of residuals to be computed. Currently, only Pearson residuals are supported. |
xscale |
scale factor resizing the tile's width, thus adding additional space between the tiles. |
yspace |
object of class |
gp_axis |
object of class |
formula |
a formula object with possibly both left and right hand sides specifying the column and row variables of the flat table. |
data |
a data frame, list or environment containing the variables
to be cross-tabulated, or an object inheriting from class |
subset |
an optional vector specifying a subset of observations
to be used. Ignored if |
na.action |
an optional function which indicates what should happen when
the data contain |
main , sub |
either a logical, or a character string used for plotting
the main (sub) title. If logical and |
... |
other parameters passed to |
Association plots have been suggested by Cohen (1980) and extended by Friendly (1992) and provide a means for visualizing the residuals of an independence model for a contingency table.
assoc
is a generic function and currently has a default method and a
formula interface. Both are high-level interfaces to the
strucplot
function, and produce (extended) association
plots. Most of the functionality is described there, such as
specification of the independence model, labeling, legend, spacing,
shading, and other graphical parameters.
For a contingency table, the signed contribution
to Pearson's \chi^2
for cell \{ij\ldots k\}
is
d_{ij\ldots k} = \frac{(f_{ij\ldots k} - e_{ij\ldots k})}{
\sqrt{e_{ij\ldots k}}}
where f_{ij\ldots k}
and e_{ij\ldots
k}
are the observed and expected counts corresponding to the cell. In
the association plot, each cell is represented by a
rectangle that has (signed) height proportional to d_{ij\ldots
k}
and width proportional to
\sqrt{e_{ij\ldots k}}
,
so that the area of the box is proportional to the difference in
observed and expected frequencies. The rectangles in each row are
positioned relative to a baseline indicating independence
(d_{ij\ldots k} = 0
).
If the observed frequency of a cell is greater than the expected one,
the box rises above the baseline, and falls below otherwise.
Additionally, the residuals can be colored depending on a specified shading scheme (see Meyer et al., 2003). Package vcd offers a range of residual-based shadings (see the shadings help page). Some of them allow, e.g., the visualization of test statistics.
Unlike the assocplot
function in the
graphics package, this function allows the visualization of
contingency tables with more than two dimensions. Similar to the
construction of ‘flat’ tables (like objects of class "ftable"
or
"structable"
), the dimensions are folded into rows and columns.
The layout is very flexible: the specification of shading, labeling,
spacing, and legend is modularized (see strucplot
for
details).
The "structable"
visualized is returned invisibly.
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., Hornik, K. (2003), Visualizing independence using extended association plots. Proceedings of the 3rd International Workshop on Distributed Statistical Computing, K. Hornik, F. Leisch, A. Zeileis (eds.), ISSN 1609-395X. https://www.R-project.org/conferences/DSC-2003/Proceedings/
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.
\Sexpr[results=rd]{tools:::Rd_expr_doi("10.18637/jss.v017.i03")} and available as vignette ("strucplot", package = "vcd")
.
mosaic
,
strucplot
,
structable
data("HairEyeColor")
## Aggregate over sex:
(x <- margin.table(HairEyeColor, c(1, 2)))
## Ordinary assocplot:
assoc(x)
## and with residual-based shading (of independence)
assoc(x, main = "Relation between hair and eye color", shade = TRUE)
## Aggregate over Eye color:
(x <- margin.table(HairEyeColor, c(1, 3)))
chisq.test(x)
assoc(x, main = "Relation between hair color and sex", shade = TRUE)
# Visualize multi-way table
assoc(aperm(HairEyeColor), expected = ~ (Hair + Eye) * Sex,
labeling_args = list(just_labels = c(Eye = "left"),
offset_labels = c(right = -0.5),
offset_varnames = c(right = 1.2),
rot_labels = c(right = 0),
tl_varnames = c(Eye = TRUE))
)
assoc(aperm(UCBAdmissions), expected = ~ (Admit + Gender) * Dept, compress = FALSE,
labeling_args = list(abbreviate_labs = c(Gender = TRUE), rot_labels = 0)
)
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