This function plots univariate and bivariate frequency tables of class
“freqtab”.
# S3 method for freqtab
plot(
x,
y = NULL,
xcol = 1,
ycol,
pch = 16,
ylty = 1,
xlab = names(dimnames(x))[1],
addlegend = !missing(y),
legendtext,
...
)# S3 method for freqtab
points(x, xcol = 1, pch = 16, ds = 50, dm = 100, ...)
univariate or bivariate score distribution of class
“freqtab”.
either an object of class “freqtab”, where
frequencies will be extracted, or a vector or matrix of frequencies, to be
added to the plot of x. See below for details.
colors used in plotting x and y.
plotting symbol used to plot bivariate points.
line type used to plot frequencies in y.
label for the x axis.
logical indicating whether or not a legend should be added.
character vector of text to be passed to the legend
argument of the legend function, defaulting to column names used in
y.
further arguments passed to or from other methods, such as
graphical parameters besides col, type, and pch.
integers for the scaling and center of the RGB density values,
with defaults of 50 and 100. These are used to convert the observed counts
in x to the [0, 255] range of RGB values.
The univariate option produces a single line plot of type =
"h". Frequencies from y are then superimposed. The bivariate option
produces a scatterplot with a marginal frequency plot for each distribution.
For the points method, a scatterplot for x is added to the current
opened plot.
For the plot method, when x is univariate, i.e, having 2 columns, a
frequency plot is created for x. When x is bivariate, e.g.,
coming from a single group equating design or one form of a nonequivalent
groups design, a scatterplot is produced with frequency plots for the
marginal distributions.
y is used to superimpose lines, e.g., smoothed frequencies, over the
(marginal) frequencies of x.
Colors must be specified using xcol and ycol. When ycol
is missing, a vector of colors is created using rainbow(ncol(y)).
# NOT RUN {
x <- freqtab(KBneat$x, scales = list(0:36, 0:12))
plot(x)
xs <- loglinear(x, degrees = c(4, 1),
stepup = TRUE, showWarnings = FALSE)
plot(x, xs, lwd = 2)
# }
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