"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))
"assoc"(formula, data = NULL, ..., subset = NULL, na.action = NULL, main = NULL, sub = NULL)dimnames(x) attribute, or an object
inheriting from the "ftable" class (such as
"structable" objects).FALSE, the space between the rows
(columns) are chosen such that the total heights (widths) of
the rows (columns) are all equal. If TRUE, the space between
rows and columns is fixed and hence the plot is more
“compressed”.xlim
correspond to the columns of the association plot, the rows describe
the column ranges (minimums in the first row, maximums in the second
row). If xlim is NULL, the ranges are determined from
the residuals according to compress (if TRUE: widest
range from each column, if FALSE: from the whole association
plot matrix).ylim
correspond to the rows of the association plot, the rows describe
the column ranges (minimums in the first row, maximums in the second
row). If ylim is NULL, the ranges are determined from
the residuals according to compress (if TRUE: widest
range from each row, if FALSE: from the whole association
plot matrix).strucplot for
more information). The default is the spacing-generating function
spacing_conditional that is (by default) called with the
argument list spacing_args (see spacings for more details).strucplot for more information).x (default: FALSE).
Values are recycled as needed.
A TRUE component indicates that the corresponding dimension
is folded into the columns, FALSE folds the dimension into the
rows."unit" specifying additional
space separating the rows."gpar" specifying the visual
aspects of the tiles' baseline.table.data is a contingency table.NAs. Ignored if data is a contingency table.TRUE, the
name of the data object is used.strucplot"structable" visualized is returned invisibly.
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 $f_{ij\ldotsk}$ and $e_{ij\ldotsk}$ 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 $d_{ij\ldotsk}$ and width proportional to $sqrt(e_{ij...k})$, 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 ($d_{ij\ldotsk} = 0$). 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).
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. http://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.
URL http://www.jstatsoft.org/v17/i03/ and available as
vignette("strucplot").
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 = c(Gender = TRUE), rot_labels = 0)
)
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