agreementplot {vcd}R Documentation

Bangdiwala's Observer Agreement Chart

Description

Representation of a k by k confusion matrix, where the observed and expected diagonal elements are represented by superposed black and white rectangles, respectively. The function also computes a statistic measuring the strength of agreement (relation of respective area sums).

Usage

## Default S3 method:
agreementplot(x, reverse_y = TRUE, main = NULL,
              weights = c(1, 1 - 1/(ncol(x) - 1)^2), margins = par("mar"),
              newpage = TRUE, pop = TRUE, xlab = names(dimnames(x))[2],
              ylab = names(dimnames(x))[1],
              xlab_rot = 0, xlab_just = "center",
              ylab_rot = 90, ylab_just = "center", ...)
## S3 method for class 'formula':
agreementplot(formula, data = NULL, ..., subset)

Arguments

x a confusion matrix, i.e., a table with equal-sized dimensions.
reverse_y if TRUE, the y axis is reversed (i.e., the rectangles' positions correspond to the contingency table).
main user-specified main title.
weights vector of weights for successive larger observed areas, used in the agreement strength statistic, and also for the shading. The first element should be 1.
margins vector of margins (see par).
newpage logical; if TRUE, the plot is drawn on a new page.
pop logical; if TRUE, all newly generated viewports are popped after plotting.
xlab, ylab labels of x- and y-axis.
xlab_rot, ylab_rot rotation angle for the category labels.
xlab_just, ylab_just justification for the category labels.
formula a formula, such as y ~ x. For details, see xtabs.
data a data frame (or list), or a contingency table from which the variables in formula should be taken.
subset an optional vector specifying a subset of the rows in the data frame to be used for plotting.
... further graphics parameters (see par).

Details

Weights can be specified to allow for partial agreement, taking into account contributions from off-diagonal cells. A weight vector of length 1 means strict agreement only, each additional element increases the maximum number of disagreement steps.

Value

Invisibly returned, a list with components

Bangdiwala the unweighted agreement strength statistic.
Bangdiwala_Weighted the weighted statistic.
weights the weigtht vector used.

Author(s)

David Meyer David.Meyer@R-project.org

References

Michael Friendly (2000), Visualizing Categorical Data. SAS Institute, Cary, NC.

Examples

data("SexualFun")
agreementplot(t(SexualFun))

data("MSPatients")
## best visualized using a resized device, e.g. using:
## get(getOption("device"))(width = 12)
pushViewport(viewport(layout = grid.layout(ncol = 2)))
pushViewport(viewport(layout.pos.col = 1))
agreementplot(t(MSPatients[,,1]), main = "Winnipeg Patients",
              newpage = FALSE)
popViewport()
pushViewport(viewport(layout.pos.col = 2))
agreementplot(t(MSPatients[,,2]), main = "New Orleans Patients",
              newpage = FALSE)
popViewport(2)
dev.off()

[Package vcd version 1.2-4 Index]