sieveplot {vcd} | R Documentation |
Visualization of two-way contingency tables: plots rectangles with surfaces proportional to the expected cell frequencies and filled with a number of squares equal to the expected frequencies. Thus, the densities visualize the deviations of the observed from the expected values.
sieveplot(x, reverse.y = TRUE, type = c("sieve", "expected"), main = NULL, values = c("none", "cells", "margins", "both"), frequencies = c("absolute", "relative"), sieve.colors = c("red","blue"), sieve.lty = c("longdash", "solid"), exp.color = "gray", exp.lty = "dotted", margin = 0.01, cex.main = 2, cex.lab = 1.5, xlab = names(dimnames(x))[2], ylab = names(dimnames(x))[1], ...) sieveplot(formula, data = NULL, ..., subset)
x |
a two-way contingency table, as generated by table . |
reverse.y |
if TRUE , the y axis is reversed (i.e., the
rectangles' positions correspond to the contingency table). |
type |
expected fills the rectangles according to the
expected frequencies. |
main |
user specified title. |
values |
optionally, the frequencies of the cells or
margins or of both can be plotted. |
frequencies |
chooses the type of these frequencies:
relative or absolute . |
sieve.colors, sieve.lty |
vectors with up to two color/line type entries: the first is used for negative and the second for positive deviations from the expected frequencies. |
exp.color, exp.lty |
color/line type entry for the expected values grid. |
margin |
lines of margin between the cell rectangles. |
cex.main |
font size of title. |
cex.lab |
font size of labels. |
xlab, ylab |
labels of x- and y-axis. |
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 observations to be used for plotting. |
... |
further graphics parameters (see par ). |
David Meyer
david.meyer@ci.tuwien.ac.at
H. Riedwyl & M. Schüpbach (1994), Parquet diagram to plot contingency tables. In F. Faulbaum (ed.), Softstat '93: Advances in Statistical Software, 293-299. Gustav Fischer, New York.
M. Friendly (2000), Visualizing Categorical Data, SAS Institute, Cary, NC.
data(HairEyeColor) ## aggregate over `sex': (tab <- margin.table(HairEyeColor, c(2,1))) ## plot expected values: sieveplot(tab, type = "expected", values = "both") ## plot sieve diagram: sieveplot(tab) ## an example for the formula interface: data(VisualAcuity) sieveplot(Freq ~ right + left, data = VisualAcuity, reverse.y = FALSE)